The most frequently mutated tumor suppressor gene in human cancers is TP53, which encodes transcription factor p53. Induced by various types of cellular stress, p53 transactivates a broad range of genes involved in numerous anti-cancer programs including cell cycle arrest, DNA repair, senescence, and apoptosis. However, targeted induction of p53 by small molecule inhibitors of a key upstream negative regulator MDM2 leads in most cancer types to a reversible proliferation block, while the therapeutically desirable elimination of cancer cells by apoptosis is limited at best. Recently, we identified a novel regulatory mechanism within the p53 network (Andrysik et al., Nature Communications, 2022) responsible for restraining the apoptotic response upon p53 activation. In this scenario, p53 activity maintains eukaryotic translation initiation factor eIF2a dephosphorylated and fully functional. Severing such negative feedback loop pharmacologically by eIF2a inhibitors leads to rapid onset of apoptosis in cancer cells in vitro and stalled tumor growth and animal survival in vivo.
Rationale
Vast majority of targets for p53-driven transcriptional activation are protein-coding genes. Gene mRNAs are translated into proteins with variable efficiency, strongly affected by the condition of eIF2a. However, our preliminary data suggest yet another layer of gene-specific regulatory events for the p53 target genes: protein (de-)stabilization if eIF2a inhibition occurs (Fig. 1). Numerous genes including CDKN1A/p21, MDM2, and BAX show increased transcription and translation rates in cells treated with combination of MDM2 and eIF2a inhibitors compared to cultures exposed to MDM2 inhibitors alone. However, protein levels display an opposite trend implying possible changes in protein stability. Therefore, we propose to analyze the proteome changes upon translation inhibition and how p53 target gene protein stability impacts the outcome of p53 activation. In preparation for such experiments, we will survey the current literature for candidate mechanisms likely impacting the p53 network on the proteome level.
Specific Aims
Aim 1. To define the fraction of protein-coding genes among p53 transcriptional targets.
Aim 2. To characterize main mechanisms regulating the p53 network on the proteome level.