Development of phenotypic and transcriptional biomarkers to evaluate relative activity of potentially estrogenic chemicals in ovariectomized mice.

BACKGROUND: Concerns regarding potential endocrine-disrupting chemicals (EDCs) have led to a need for methods to evaluate candidate estrogenic chemicals. Our previous evaluations of two such EDCs revealed a response similar to that of estradiol (E2) at 2 hr, but a less robust response at 24 hr, similar to the short-acting estrogen estriol (E3). OBJECTIVES: Microarray analysis using tools to recognize patterns of response have been utilized in the cancer field to develop biomarker panels of transcripts for diagnosis and selection of treatments most likely to be effective. Biological effects elicited by long- versus short-acting estrogens greatly affect the risks associated with exposures; therefore, we sought to develop tools to predict the ability of chemicals to maintain estrogenic responses. METHODS: We used biological end points in uterine tissue and a signature pattern-recognizing tool that identified coexpressed transcripts to develop and test a panel of transcripts in order to classify potentially estrogenic compounds using an in vivo system. The end points used are relevant to uterine tissue, but the resulting classification of the compounds is important for other sensitive tissues and species. RESULTS: We evaluated biological and transcriptional end points with proven short- and long-acting estrogens and verified the use of our approach using a phytoestrogen. With our model, we were able to classify the diarylheptanoid D3 as a short-acting estrogen. CONCLUSIONS: We have developed a panel of transcripts as biomarkers which, together with biological end points, might be used to screen and evaluate potentially estrogenic chemicals and infer mode of activity.

Transcriptional responses to loss of RNase H2 in Saccharomyces cerevisiae.

We report here the transcriptional responses in Saccharomyces cerevisiae to deletion of the RNH201 gene encoding the catalytic subunit of RNase H2. Deleting RNH201 alters RNA expression of 349 genes by ≥1.5-fold (q-value <0.01), of which 123 are upregulated and 226 are downregulated. Differentially expressed genes (DEGs) include those involved in stress responses and genome maintenance, consistent with a role for RNase H2 in removing ribonucleotides incorporated into DNA during replication. Upregulated genes include several that encode subunits of RNA polymerases I and III, and genes involved in ribosomal RNA processing, ribosomal biogenesis and tRNA modification and processing, supporting a role for RNase H2 in resolving R-loops formed during transcription of rRNA and tRNA genes. A role in R-loop resolution is further suggested by a higher average GC-content proximal to the transcription start site of downregulated as compared to upregulated genes. Several DEGs are involved in telomere maintenance, supporting a role for RNase H2 in resolving RNA-DNA hybrids formed at telomeres. A large number of DEGs encode nucleases, helicases and genes involved in response to dsRNA viruses, observations that could be relevant to the nucleic acid species that elicit an innate immune response in RNase H2-defective humans.

The impact of classification of interest on predictive toxicogenomics.

The era of toxicogenomics has introduced a new way of monitoring the effect of environmental stressors and toxicants on biological systems via quantification of changes in gene expression. Because the liver is one of the major organs for synthesis and secretion of substances which metabolize endogenous and exogenous materials, there has been a great deal of interest in elucidating predictive and mechanistic genomic markers of hepatotoxicity. This mini-review will bring context to a limited number of toxicogenomics studies which used genomics to evaluate the transcriptional changes in blood and liver in response to acetaminophen (APAP) or other liver toxicants, but differed according to the classification of interest (COI), i.e., the partitioning of the samples a priori according to a common toxicological characteristic. The toxicogenomics studies highlighted are characterized by a classification of either no/low vs. high APAP dose exposure, none vs. observed necrosis, and severity of necrosis. The overlap or lack thereof between the gene classifiers and the modulated biological processes that are elucidated will be discussed to enhance the understanding of the effect of the particular COI model and experimental design used for prediction.

Principal component analysis-based filtering improves detection for Affymetrix gene expression arrays.

Gene expression array technology has reached the stage of being routinely used to study clinical samples in search of diagnostic and prognostic biomarkers. Due to the nature of array experiments, which examine the expression of tens of thousands of genes simultaneously, the number of null hypotheses is large. Hence, multiple testing correction is often necessary to control the number of false positives. However, multiple testing correction can lead to low statistical power in detecting genes that are truly differentially expressed. Filtering out non-informative genes allows for reduction in the number of null hypotheses. While several filtering methods have been suggested, the appropriate way to perform filtering is still debatable. We propose a new filtering strategy for Affymetrix GeneChips®, based on principal component analysis of probe-level gene expression data. Using a wholly defined spike-in data set and one from a diabetes study, we show that filtering by the proportion of variation accounted for by the first principal component (PVAC) provides increased sensitivity in detecting truly differentially expressed genes while controlling false discoveries. We demonstrate that PVAC exhibits equal or better performance than several widely used filtering methods. Furthermore, a data-driven approach that guides the selection of the filtering threshold value is also proposed.

Microarray analysis of ethanol-induced changes in gene expression.

DNA microarray studies offer a robust method for nonbiased analysis of whole genome messenger ribonucleic acid expression patterns. A growing number of studies have applied this experimental approach to studies on ethanol either in cell culture of animal models of ethanol exposure or self-administration. Expression profiling has identified novel gene networks responding to ethanol or differing across animal strains with differing responses to ethanol. Recent studies have shown benefit for meta-analysis of microarray data across different laboratories. Gene network analysis offers unique opportunities for understanding the molecular mechanisms of ethanol responses, toxicity and addiction. Eventually, such work may generate novel targets for future pharmacotherapy. To fully capitalize on the prom ise alluded to above, particularly in regard to meta-analysis of microarray data, it is critical that high quality standards are followed in the generation and analysis of microarray studies. This chapter will discuss experience of our laboratory in performing and analyzing microarray studies on ethanol, focusing discussion mainly on short oligonucleotide microarrays (Affymetrix). However, the general principals of technique and analysis that are discussed have broad applicability to other types of microarray platforms and experimental designs.

DNA microarray and proteomic strategies for understanding alcohol action.

This article summarizes the proceedings of a symposium presented at the 2005 annual meeting of the Research Society on Alcoholism in Santa Barbara, California. The organizer was James M. Sikela, and he and Michael F. Miles were chairs. The presentations were (1) Genomewide Surveys of Gene Copy Number Variation in Human and Mouse: Implications for the Genetics of Alcohol Action, by James M. Sikela; (2) Regional Differences in the Regulation of Brain Gene Expression: Relevance to the Detection of Genes Associated with Alcohol-Related Traits, by Robert Hitzemann; (3) Identification of Ethanol Quantitative Trait Loci Candidate Genes by Expression Profiling in Inbred Long Sleep/Inbred Short Sleep Congenic Mice, by Robnet T. Kerns; and (4) Quantitative Proteomic Analysis of AC7-Modified Mice, by Kathleen J. Grant.

SScore: an R package for detecting differential gene expression without gene expression summaries.

SUMMARY: SScore is an R package that facilitates the comparison of gene expression between Affymetrix GeneChips using the S-score algorithm. The S-score algorithm uses probe level data directly to assess differences in gene expression, without requiring a preliminary separate step of probe set expression summary estimation. Therefore, the algorithm avoids introduction of error associated with the expression summary estimation process and has been demonstrated to improve the accuracy of identifying differentially expressed genes. The S-score produces accurate results even when few or no replicates are available. AVAILABILITY: The R package SScore is available from Bioconductor at http://www.bioconductor.org

Identification of DHBcAg as a potent carrier protein comparable to KLH for augmenting MUC1 antigenicity.

MUC1 is expressed at the cell surface of epithelial cancers. We have shown previously that MUC1 conjugated to keyhole limpet hemocyanin (KLH) plus the saponin immunological adjuvant QS-21 induces consistent high titer IgM and IgG antibodies in patients after treatment of their primary or metastatic cancers. KLH however is poorly soluble and heterogeneous making it difficult to work with, and we hypothesize that changing carrier proteins mid-way through a vaccination schedule would further increase antibody titers. Consequently, there is need for an alternative potent carrier protein. Duck Hepatitis B core antigen (DHBcAg) has a molecular weight of approximately 25kDa and is easily purified as a single band, but it self aggregates into particles of approximately 6.4x10(6)Da. Consequently, it is highly immunogenic, easy to work with and amenable to chemical and genetic conjugation to antigens such as MUC1. We compare here in mice the immunogenicity of MUC1 chemically conjugated to KLH or DHBcAg and MUC1-DHBcAg recombinant protein after an initial series of three vaccinations and then after an additional series of three vaccinations with the same or opposite carrier, all mixed with the saponin immunological adjuvant GPI-0100. High titer IgG antibodies were observed in all groups after the initial three vaccinations: MUC1-DHBcAg median ELISA titer 1/51200, RecMUC1-DHBcAg 1/25600 and MUC1-KLH 1/12800. This increased to 1/6553600 after the second set of three immunizations when the carrier remained the same in all three groups, but titers were significantly lower when the carriers were changed for the final three immunizations. These data demonstrate that DHBcAg is an excellent carrier protein and that changing carrier proteins does not further augment immunogenicity.

Ethanol-responsive brain region expression networks: implications for behavioral responses to acute ethanol in DBA/2J versus C57BL/6J mice.

Activation of the mesolimbic dopamine reward pathway by acute ethanol produces reinforcement and changes in gene expression that appear to be crucial to the molecular basis for adaptive behaviors and addiction. The inbred mouse strains DBA/2J and C57BL/6J exhibit contrasting acute behavioral responses to ethanol. We used oligonucleotide microarrays and bioinformatics methods to characterize patterns of gene expression in three brain regions of the mesolimbic reward pathway of these strains. Expression profiling included examination of both differences in gene expression 4 h after saline injection or acute ethanol (2 g/kg). Using a rigorous stepwise method for microarray analysis, we identified 788 genes differentially expressed in control DBA/2J versus C57BL/6J mice and 307 ethanol-regulated genes in the nucleus accumbens, prefrontal cortex, and ventral tegmental area. There were strikingly divergent patterns of ethanol-responsive gene expression in the two strains. Ethanol-responsive genes also showed clustering at discrete chromosomal regions, suggesting local chromatin effects in regulation. Ethanol-regulated genes were generally related to neuroplasticity, but regulation of discrete functional groups and pathways was brain region specific: glucocorticoid signaling, neurogenesis, and myelination in the prefrontal cortex; neuropeptide signaling and developmental genes, including factor Bdnf, in the nucleus accumbens; and retinoic acid signaling in the ventral tegmental area. Bioinformatics analysis identified several potential candidate genes for quantitative trait loci linked to ethanol behaviors, further supporting a role for expression profiling in identifying genes for complex traits. Brain region-specific changes in signaling and neuronal plasticity may be critical components in development of lasting ethanol behavioral phenotypes such as dependence, sensitization, and craving.

Alcohol effects on central nervous system gene expression in genetic animal models.

This article summarizes the proceedings of a symposium presented at the 2004 annual meeting of the Research Society on Alcoholism in Vancouver, British Columbia, Canada. The organizers and chairs were William J. McBride and Michael F. Miles. The presentations were (1) Molecular Triangulation on Gene Expression Patterns in Behavioral Responses to Acute Ethanol, by Robnet T. Kerns; (2) Gene Expression in Limbic Regions After Ethanol Self-Infusion Into the Posterior Ventral Tegmental Area, by Zachary A. Rodd; (3) Microarray Analysis of CNS Limbic Regions of Inbred Alcohol-Preferring and -Nonpreferring rats and Effects of Alcohol Drinking, by Wendy N. Strother and Howard J. Edenberg; and (4) Microarray Analysis of Mouse Lines Selected for Chronic Ethanol Withdrawal Severity: The Convergence of Basal, Ethanol Regulated, and Proximity to Ethanol Quantitative Trait Loci to Identify Candidate Genes, by Joel G. Hashimoto and Kristine M. Wiren.

Application of the S-score algorithm for analysis of oligonucleotide microarrays.

In the past several years, oligonucleotide microarrays have emerged as a widely used tool for the simultaneous, non-biased measurement of expression levels for thousands of genes. Several challenges exist in successfully utilizing this biotechnology; principal among these is analysis of microarray data. An experiment to measure differential gene expression can consist of a dozen microarrays, each consisting of over a hundred thousand data points. Previously, we have described the use of a novel algorithm for analyzing oligonucleotide microarrays and assessing changes in gene expression. This algorithm describes changes in expression in terms of the statistical significance (S-score) of change, which combines signals detected by multiple probe pairs according to an error model characteristic of oligonucleotide arrays. Software is available that simplifies the use of the application of this algorithm so that it may be applied to improving the analysis of oligonucleotide microarray data. The application of this method to problems of the central nervous system is discussed.