Regulation of p53: a collaboration between Mdm2 and MdmX

p53 plays an important role in the regulation of the cell cycle, DNA repair, and apoptosis and is an attractive cancer therapeutic target. Mdm2 and Mdmx are recognized as the main p53 negative regulators. Although it is still unknown why Mdm2 and Mdmx both are required for p53 degradation, a model has been proposed whereby these two proteins function independent of one another; Mdm2 acts as an E3 ubiquitin ligase that catalyzes the ubiquitination of p53 for degradation, whereas Mdmx inhibits p53 by binding to and masking the transcriptional activation domain of p53, without causing its degradation. However, Mdm2 and Mdmx have been shown to function collaboratively. In fact, recent studies have pointed to a more important role for an Mdm2/Mdmx co-regulatory mechanism for p53 regulation than previously thought. In this review, we summarize current progress in the field about the functional and physical interactions between Mdm2 and Mdmx, their individual and collaborative roles in controlling p53, and inhibitors that target Mdm2 and Mdmx as a novel class of anticancer therapeutics

Inactivation of the MDM2 RING domain enhances p53 transcriptional activity in mice

The MDM2 RING domain harbors E3 ubiquitin ligase activity critical for regulating the degradation of tumor suppressor p53, which controls many cellular pathways. The MDM2 RING domain also is required for an interaction with MDMX. Mice containing a substitution in the MDM2 RING domain, MDM2C462A, disrupting MDM2 E3 function and the MDMX interaction, die during early embryogenesis that can be rescued by p53 deletion. To investigate whether MDM2C462A, which retains p53 binding, has p53-suppressing activity, we generated Mdm2C462A/C462A;p53ER/- mice, in which we replaced the endogenous p53 alleles with an inducible p53ER/- allele, and compared survival with that of similarly generated Mdm2-/-;p53ER/- mice. Adult Mdm2-null mice died ~7 days after tamoxifen-induced p53 activation, indicating that in the absence of MDM2, MDMX cannot suppress p53. Surprisingly, Mdm2C462A/C462A;p53ER/- mice died ~5 days after tamoxifen injection, suggesting that p53 activity is higher in the presence of MDM2C462A than in the absence of MDM2. Indeed, in MDM2C462A-expressing mouse tissues and embryonic fibroblasts, p53 exhibited higher transcriptional activity than in those expressing no MDM2 or no MDM2 and MDMX. This observation indicated that MDM2C462A not only is unable to suppress p53 but may have gained the ability to enhance p53 activity. We also found that p53 acetylation, a measure of p53 transcriptional activity, was higher in the presence of MDM2C462A than in the absence of MDM2. These results reveal an unexpected role of MDM2C462A in enhancing p53 activity and suggest the possibility that compounds targeting MDM2 RING domain function could produce even more robust p53 activation

GTP-ViT: Efficient Vision Transformers via Graph-based Token Propagation

Vision Transformers (ViTs) have revolutionized the field of computer vision, yet their deployments on resource-constrained devices remain challenging due to high computational demands. To expedite pre-trained ViTs, token pruning and token merging approaches have been developed, which aim at reducing the number of tokens involved in the computation. However, these methods still have some limitations, such as image information loss from pruned tokens and inefficiency in the token-matching process. In this paper, we introduce a novel Graph-based Token Propagation (GTP) method to resolve the challenge of balancing model efficiency and information preservation for efficient ViTs. Inspired by graph summarization algorithms, GTP meticulously propagates less significant tokens' information to spatially and semantically connected tokens that are of greater importance. Consequently, the remaining few tokens serve as a summarization of the entire token graph, allowing the method to reduce computational complexity while preserving essential information of eliminated tokens. Combined with an innovative token selection strategy, GTP can efficiently identify image tokens to be propagated. Extensive experiments have validated GTP's effectiveness, demonstrating both efficiency and performance improvements. Specifically, GTP decreases the computational complexity of both DeiT-S and DeiT-B by up to 26% with only a minimal 0.3% accuracy drop on ImageNet-1K without finetuning, and remarkably surpasses the state-of-the-art token merging method on various backbones at an even faster inference speed. The source code is available at https://github.com/Ackesnal/GTP-ViT.Comment: Accepted to WACV2024 (Oral