Dataset Information

Cross-species global and subset gene expression profiling identifies genes involved in prostate cancer response to selenium.

ABSTRACT:

Background

Gene expression technologies have the ability to generate vast amounts of data, yet there often resides only limited resources for subsequent validation studies. This necessitates the ability to perform sorting and prioritization of the output data. Previously described methodologies have used functional pathways or transcriptional regulatory grouping to sort genes for further study. In this paper we demonstrate a comparative genomics based method to leverage data from animal models to prioritize genes for validation. This approach allows one to develop a disease-based focus for the prioritization of gene data, a process that is essential for systems that lack significant functional pathway data yet have defined animal models. This method is made possible through the use of highly controlled spotted cDNA slide production and the use of comparative bioinformatics databases without the use of cross-species slide hybridizations.

Results

Using gene expression profiling we have demonstrated a similar whole transcriptome gene expression patterns in prostate cancer cells from human and rat prostate cancer cell lines both at baseline expression levels and after treatment with physiologic concentrations of the proposed chemopreventive agent Selenium. Using both the human PC3 and rat PAII prostate cancer cell lines have gone on to identify a subset of one hundred and fifty-four genes that demonstrate a similar level of differential expression to Selenium treatment in both species. Further analysis and data mining for two genes, the Insulin like Growth Factor Binding protein 3, and Retinoic X Receptor alpha, demonstrates an association with prostate cancer, functional pathway links, and protein-protein interactions that make these genes prime candidates for explaining the mechanism of Selenium's chemopreventive effect in prostate cancer. These genes are subsequently validated by western blots showing Selenium based induction and using tissue microarrays to demonstrate a significant association between downregulated protein expression and tumorigenesis, a process that is the reverse of what is seen in the presence of Selenium.

Conclusions

Thus the outlined process demonstrates similar baseline and selenium induced gene expression profiles between rat and human prostate cancers, and provides a method for identifying testable functional pathways for the action of Selenium's chemopreventive properties in prostate cancer.

SUBMITTER: Schlicht M

PROVIDER: S-EPMC516028 | biostudies-literature | 2004 Aug

REPOSITORIES: biostudies-literature

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Publications

Cross-species global and subset gene expression profiling identifies genes involved in prostate cancer response to selenium.

Schlicht Michael M Matysiak Brian B Brodzeller Tracy T Wen Xinyu X Liu Hang H Zhou Guohui G Dhir Rajiv R Hessner Martin J MJ Tonellato Peter P Suckow Mark M Pollard Morris M Datta Milton W MW

BMC genomics 20040820 1

<h4>Background</h4>Gene expression technologies have the ability to generate vast amounts of data, yet there often resides only limited resources for subsequent validation studies. This necessitates the ability to perform sorting and prioritization of the output data. Previously described methodologies have used functional pathways or transcriptional regulatory grouping to sort genes for further study. In this paper we demonstrate a comparative genomics based method to leverage data from animal ...[more]

PMID: 15318950

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Project description:BackgroundSerum Response Factor (SRF) is a transcription factor that is required for the expression of many genes including immediate early genes, cytoskeletal genes, and muscle-specific genes. SRF is activated in response to extra-cellular signals by its association with a diverse set of co-activators in different cell types. In the case of the ubiquitously expressed immediate early genes, the two sets of SRF binding proteins that regulate its activity are the TCF family of proteins that include Elk1, SAP1 and SAP2 and the myocardin-related MKL family of proteins that include MKL1 and MKL2 (also known as MAL, MRTF-A and -B and BSAC). In response to serum or growth factors these two classes of co-activators are activated by different upstream signal transduction pathways. However, it is not clear how they differentially activate SRF target genes.ResultsIn order to identify the serum-inducible SRF target genes that are specifically dependent on the MKL pathway, we have performed microarray experiments using a cell line that expresses dominant negative MKL1. This approach was used to identify SRF target genes whose activation is MKL-dependent. Twenty-eight of 150 serum-inducible genes were found to be MKL-dependent. The promoters of the serum-inducible genes were analyzed for SRF binding sites and other common regulatory elements. Putative SRF binding sites were found at a higher rate than in a mouse promoter database but were only identified in 12% of the serum-inducible promoters analyzed. Additional partial matches to the consensus SRF binding site were found at a higher than expected rate in the MKL-dependent gene promoters. The analysis for other common regulatory elements is discussed.ConclusionsThese results suggest that a subset of immediate early and SRF target genes are activated by the Rho-MKL pathway. MKL may also contribute to the induction of other SRF target genes however its role is not essential, possibly due to other activation mechanisms such as MAPK phosphorylation of TCFs.

Project description:Maize is a heavy consumer of fertilizer nitrogen (N) which not only results in the high cost of cultivation but may also lead to environmental pollution. Therefore, there is a need to develop N-use efficient genotypes, a prerequisite for which is a greater understanding of N-deficiency stress adaptation. In this study, comparative transcriptome analysis was performed using leaf and root tissues from contrasting inbred lines, viz., DMI 56 (tolerant to N stress) and DMI 81 (susceptible to N stress) to delineate the differentially expressed genes (DEGs) under low-N stress. The contrasting lines were grown hydroponically in modified Hoagland solution having either sufficient- or deficient-N, followed by high-throughput RNA-sequencing. A total of 8 sequencing libraries were prepared and 88-97% of the sequenced raw reads were mapped to the reference B73 maize genome. Genes with a p value ≤ 0.05 and fold change of ≥ 2.0 or ≤ - 2 were considered as DEGs in various combinations performed between susceptible and tolerant genotypes. DEGs were further classified into different functional categories and pathways according to their putative functions. Gene Ontology based annotation of these DEGs identified three different functional categories: biological processes, molecular function, and cellular component. The KEGG and Mapman based analysis revealed that most of the DEGs fall into various metabolic pathways, biosynthesis of secondary metabolites, signal transduction, amino acid metabolism, N-assimilation and metabolism, and starch metabolism. Some of the key genes involved in N uptake (high-affinity nitrate transporter 2.2 and 2.5), N assimilation and metabolism (glutamine synthetase, asparagine synthetase), redox homeostasis (SOD, POX), and transcription factors (MYB36, AP2-EREBP) were found to be highly expressed in the tolerant genotype compared to susceptible one. The candidate genes identified in the present study might be playing a pivotal role in low-N stress adaptation in maize and hence could be useful in augmenting further research on N metabolism and development of N-deficiency tolerant maize cultivars.

Dataset Information

Cross-species global and subset gene expression profiling identifies genes involved in prostate cancer response to selenium.

Background

Results

Conclusions

Publications

Cross-species global and subset gene expression profiling identifies genes involved in prostate cancer response to selenium.

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