Category: Fellows Posters
Purpose: p53 is a transcription factor and tumor suppressor that arrests cell growth and induces apoptosis in response to cellular distress. Acetylation of DNA binding domain lysines, K120 and K164, facilitates p53 apoptosis and cell cycle arrest. R248W mutation reduces G2-M checkpoint suppression and has a tumorigenesis gain of function. These are sites of somatic missense mutations in cancer. We applied yeast phenomics to investigate gene interaction networks that buffer or promote p53-induced cell arrest in yeast, as a global functional readout of differential p53 activity and to discover genes and biological processes as potential targets for lethal gene-drug therapy.
Methods: Yeast alleles of tetracycline-inducible human p53, including WT, K120R + K164R (2KR) and R248W (RW), were integrated into the
Saccharomyces cerevisiae genome. Transformants were identified by doxycycline-induced growth inhibition, and p53 expression was verified by western blotting. p53 alleles were introduced into the genomic collection of yeast knockout and knockdown (YKO/KD) mutants by the synthetic genetic array method (SGA), deriving collections of ~5000 haploid double mutants. Phenomic analysis, meaning genome-wide measurement of p53 gene-gene interactions, was performed by quantitative high-throughput cell array phenotyping (Q-HTCP), a growth curve technology yielding cell proliferation data to quantify genetic interactions). The Warburg phenomenon was modeled, using respiratory (HLEG) and glycolytic (HLD media) media (ethanol and glycerol or dextrose alternated as the carbon source). Gene interaction was measured by differential growth that was induced by p53 expression, comparing each YKO/KD mutant to the parental reference strain. Thus, interactions were quantified as the influence of each YKO/KD allele on the growth phenotype induced by p53. Gene interactions for WT and 2KR p53 were analyzed by clustering and gene ontology (GO) analysis to help identify cellular functions that buffer or promote p53 functions in yeast. Additionally, all gene interactions were reviewed manually, focusing on those evolutionarily conserved in humans, to fully characterize the yeast p53 gene interaction network and its potential relevance in human cells. IRB approval was not required.
Results: p53 expression inhibited growth on HLEG, with that of 2KR being greater than R248W and R248W greater than WT. However, on HLD, only 2KR induced growth inhibition, indicating interdependence of p53 function and respiratory metabolism. In HLD media, p53 expression was similar, by western blotting, for the wild type, 2KR, and R248W alleles. Phenomic data was acquired to contrast the WT and 2KR gene interaction networks under respiratory conditions, and clustering of gene interactions revealed sets of genes sharing similar patterns of deletion enhancement and suppression of p53 growth inhibitory effects interactions for WT and 2KR. GO Term Finder (GTF) analysis of the gene interaction clusters associated the respective gene interaction profiles with enrichment of genes having GO-annotated biological functions. For example, 1) 2KR-specific deletion enhancing genes, ySCL1/hPSMA6 and yPRE1/hPSMB2, implicated subunits of the 20s proteasome, suggesting increased dependence on proteasomal degradation to buffer the growth inhibitory effects of p53-2KR; 2) 2KR-specific deletion suppressing genes, yLSM1/hLSM1, yDSC1/hDCPS, ySTO1/hNCBP1, implicated RNA cap binding and mRNA degradation, suggesting these functions promote growth arrest of p53-2KR; 3) yCHD1/hCHD1/hCHD2, a chromatin-remodeling protein, was 2KR-specific deletion suppressing and WT-specific deletion enhancing, potentially reporting on differential transcriptional activity of the 2KR mutation.
Conclusion: A phenomic model of p53 was developed to predict conserved biological pathways that influence cell proliferation in the context of cancer-associated p53 mutations, also incorporating the Warburg phenomenon, i.e., the influence of glycolytic vs. respiratory metabolism. GO analysis of gene interaction clusters involving WT and 2KR p53 predicted genes and cellular functions buffering or promoting 2KR and/or WT p53 expression. Most p53-interacting genes were not GO-enriched, and are currently being manually annotated. The model should generate testable new hypotheses about functional differences between p53 mutations and identify putative targets for killing cancer cells by p53 mutant-specific, and thus patient-specific strategies.