Biomedical subjects

Daniel Brewer

Publications and source records attributed to Daniel Brewer.

3 recordsLinked to original sources

PubMed2006

Time- and concentration-dependent changes in gene expression induced by benzo(a)pyrene in two human cell lines, MCF-7 and HepG2.

BACKGROUND: The multi-step process of carcinogenesis can be more fully understood by characterizing gene expression changes induced in cells by carcinogens. In this study, expression microarrays were used to monitor the activity of 18,224 cDNA clones in MCF-7 and HepG2 cells exposed to the carcinogen benzo(a)pyrene (BaP) or its non-carcinogenic isomer benzo(e)pyrene (BeP). Time and concentration gene expression effects of BaP exposure have been assessed and linked to other measures of cellular stress to aid in the identification of novel genes/pathways involved in the cellular response to genotoxic carcinogens. RESULTS: BaP (0.25-5.0 muM; 6-48 h exposure) modulated 202 clones in MCF-7 cells and 127 in HepG2 cells, including 27 that were altered in both. In contrast, BeP did not induce consistent gene expression changes at the same concentrations. Significant time- and concentration-dependent responses to BaP were seen in both cell lines. Expression changes observed in both cell lines included genes involved in xenobiotic metabolism (e.g., CYP1B1, NQO1, MGST1, AKR1C1, AKR1C3,CPM), cell cycle regulation (e.g., CDKN1A), apoptosis/anti-apoptosis (e.g., BAX, IER3), chromatin assembly (e.g., histone genes), and oxidative stress response (e.g., TXNRD1). RTqPCR was used to validate microarray data. Phenotypic anchoring of the expression data to DNA adduct levels detected by 32P-postlabelling, cell cycle data and p53 protein expression identified a number of genes that are linked to these biological outcomes, thereby strengthening the identification of target genes. The overall response to BaP consisted of up-regulation of tumour suppressor genes and down-regulation of oncogenes promoting cell cycle arrest and apoptosis. Anti-apoptotic signalling that may increase cell survival and promote tumourigenesis was also evident. CONCLUSION: This study has further characterised the gene expression response of human cells after genotoxic insult, induced after exposure to concentrations of BaP that result in minimal cytotoxicity. We have demonstrated that investigating the time and concentration effect of a carcinogen on gene expression related to other biological end-points gives greater insight into cellular responses to such compounds and strengthens the identification of target genes.

Benzo(a)pyrene↗

PubMed2006

Correction of scaling mismatches in oligonucleotide microarray data.

BACKGROUND: Gene expression microarray data is notoriously subject to high signal variability. Moreover, unavoidable variation in the concentration of transcripts applied to microarrays may result in poor scaling of the summarized data which can hamper analytical interpretations. This is especially relevant in a systems biology context, where systematic biases in the signals of particular genes can have severe effects on subsequent analyses. Conventionally it would be necessary to replace the mismatched arrays, but individual time points cannot be rerun and inserted because of experimental variability. It would therefore be necessary to repeat the whole time series experiment, which is both impractical and expensive. RESULTS: We explain how scaling mismatches occur in data summarized by the popular MAS5 (GCOS; Affymetrix) algorithm, and propose a simple recursive algorithm to correct them. Its principle is to identify a set of constant genes and to use this set to rescale the microarray signals. We study the properties of the algorithm using artificially generated data and apply it to experimental data. We show that the set of constant genes it generates can be used to rescale data from other experiments, provided that the underlying system is similar to the original. We also demonstrate, using a simple example, that the method can successfully correct existing imbalances in the data. CONCLUSION: The set of constant genes obtained for a given experiment can be applied to other experiments, provided the systems studied are sufficiently similar. This type of rescaling is especially relevant in systems biology applications using microarray data.

Algorithms↗

PubMed2006

Ranked prediction of p53 targets using hidden variable dynamic modeling.

Full exploitation of microarray data requires hidden information that cannot be extracted using current analysis methodologies. We present a new approach, hidden variable dynamic modeling (HVDM), which derives the hidden profile of a transcription factor from time series microarray data, and generates a ranked list of predicted targets. We applied HVDM to the p53 network, validating predictions experimentally using small interfering RNA. HVDM can be applied in many systems biology contexts to predict regulation of gene activity quantitatively.

Cell Line, Tumor↗