Life or Cell Death: Deciphering c-Myc Regulated Gene Networks in Two Distinct Tissues

The c-myc oncogene transcribes a transcriptional regulator involved in a wide range of biological processes, such as cell-cycle regulation, cell growth, terminal differentiation and apoptosis. It is known to be deregulated in a large number of human cancers, and provides an obvious target for therapeutic study.

A switchable model for cancer growth has been created allowing controlled activation of the c-Myc oncoprotein, which is sufficient and necessary to bring about a cancer-like phenotype. A cDNA transgene encoding a chimeric protein of c-Myc fused at the carboxy-terminal domain to the hormone binding domain of a 4-hydroxytamoxifen-binding mutant estrogen receptor (c-MycER^TAM) is incorporated into the genome at the developmental level. The fusion protein product is continuously synthesised within the cells, but remains inactive due to steric hindrance by Heat Shock protein 90. Activation of the protein can be achieved through administration of the specific ligand 4-hydroxytamoxifen (4-OHT) (see below).

c-MycER^TAM is specifically targetted to the suprabasal keratinocytes of skin epidermal tissue by putting the c-mycER^TAM gene under transcriptional control of the involucrin promoter, or is targetted to pancreatic beta-cells in the islets of Langerhans by putting the c-mycER^TAM gene under transcriptional control of the insulin promoter. In this way, c-MycER^TAM is targetted to specific cell types. Activation of c-MycER^TAM in the skin results in an increase in proliferative activity, and production of tumour-like papilomas. In stark contrast to this, activation of c-MycER^TAM in the pancreatic beta-cells results in a massive apoptotic response rather than proliferation, leading to islet involution and onset of diabetes. This indicates that the beta-cells are only mildly buffered against cell death compared to the suprabasal cells.

Microarray analysis is a high density aproach to genomic analysis, allowing measurement of the relative expression levels of tens of thousands of genes simultaneously. This provides a 'snapshot' of the transcriptome - all transcribed genes. Affymetrix microarray Genechips are made up of thousands of oligonucleotide sequences, bound to a glass or quartz support using a photolithographic process. Each of these oligonucleotide sequences is designed to match a specific mRNA sequence. Fluorescently labelled cRNA is created from RNA samples using in vitro transcription. These cRNA samples are bound to the chip, where the level of expression of a particular gene can be seen by the fluorescence level of the corresponding microarray probe.

 

Laser capture microdissection (LCM) is a novel method of microscopy that allows isolation of pure homogenous cell colonies from surrounding tissue. LCM will be used to isolate pancreatic islets from exocrine tissue to prevent contamination of RNA samples with changes in gene expression from non-c-MycER^TAM cells. In this way, we can be sure that any changes seen in gene expression are due to c-MycER^TAM.

The main experiment will consist of 4 microarray time courses, allowing comparison of gene expression changes across 3 main parameters:

1. Tissue type (skin vs. pancreas)

2. Treatment state (treated with 4-OHT vs. untreated)

3. Time course (4 hours, 8 hours, 16 hours and 32 hours after initial administration of 4-OHT)

 

This multi-parameter analysis provides an interesting problem for data analysis. Microarray analyses generally focus on a single parameter change, allowing use of ANOVA methods to calculate which genes have expression values that vary significantly due to this parameter. In this case, we require a method of analysing how gene expression changes based on three parameters simultaneously. Glimmer (working title) is a package written in R for Bioconductor that uses linear modelling techniques to estimate statistically changing genes based on multiple parameters simultaneously. These methods will be used in the multi-variate analysis of the gene expression data.

Using these techniques, I hope to find candidate c-Myc responsive genes that may explain the different phenotypes seen in skin and pancreas tissue. In this way, we hope to be able to further decode the role that c-Myc plays in tissue development, understand further how it is able to act as its own tumour suppressor, and find genetic markers that may indicate when this in-built tumour suppressor function fails.

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