p53-dependent and -independent mechanisms of p53-targeting small molecules

Tumor suppressor p53 (Tp53) is mutated in around half of human cancers, while in wild type p53 cells its activity is continuously inhibited by MDM2 through proteasome degradation resulting in the loss of its function. Currently, cancer treatments with small molecules based on reactivation of wild type p53 and restoration of mutant p53 have moved to clinical trials and exhibited promising anti-cancer effects. Our lab previously found a small molecule RITA which reactivates p53 and has strong anti-cancer effect without affecting normal cells. However, small molecules always have multiple targets and those should be validated for either predicting potential side effects or evaluating their efficacy in different types of cancers. In this thesis, we addressed a p53-independent mechanism of RITA along with two other anti-cancer compounds Aminoflavone and Oncrasin-1. Using thermal proteome profiling (TPP) approach, we found that transcription machinery is commonly inhibited by these three compounds in a reactive oxidative species (ROS)-dependent manner. Global transcription inhibition results in massive downregulation of the majority of oncogenes as well as genes that are involved in homologous recombination (HR). By taking advantage of that, we performed combination treatments of these three compounds with PARP-1 inhibitors Olaparib and talazoparib. The combination treatments displayed clear synergistic anti-cancer effects in several cancer lines as well as in primary ovarian and breast cancer patient samples. Moreover, we found that mRNA translation is also inhibited by RITA through activation of eIF2α phosphorylation, in a p53-independent manner. Complementary to these findings, we discovered a potent downregulation of MDM2 by RITA. Using different approaches, we confirmed that MDM2 is not inhibited by RITA through proteasome degradation, autophagy or microRNAs-mediated translation inhibition. In addition, the inhibition of MDM2 is not the cause of cell death since both MDM2 overexpression and MDM2 KO could not rescue RITA killing effect. We conclude that, RITA dramatically inhibits RNA processing in cancer cells, leading to inhibition of transcription and translation, resulting in cell death. Reactivation of p53 also has dark sides which are related to p53-mediated growth arrest or apoptosis in normal tissues. We investigated the mechanism of action of the well-known p53 inhibitor PFT-α and found that PFT-α cannot prevent p53 activation-induced growth repression in several cancer cell lines but can attenuate post-translational modifications (PTMs) of p53 and by that differentially inhibit p53 target genes. Although we found that PFT-α exhibits strong intracellular antioxidant activity through activation of AHR/NRF2 pathway, we cannot link the antioxidant activity to its capacity to attenuate PTMs of p53. Worth to note, both PFT-α and NAC can promote primary fibroblasts growth per se. Therefore, PFT-α rescued Nutlin-3-induced growth repression in primary fibroblasts. Our findings suggest that caution needs to be taken when using PFT-α to study p53 signaling cascade, since it is not a pan-p53 inhibitor as it is described. The phenomenon we observed with PFT-α in primary fibroblasts also indicates the clinical potential of combining p53 reactivators with PFT-α in cancer therapies

Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information Networks

Heterogeneous information networks (HINs) are ubiquitous in real-world applications. In the meantime, network embedding has emerged as a convenient tool to mine and learn from networked data. As a result, it is of interest to develop HIN embedding methods. However, the heterogeneity in HINs introduces not only rich information but also potentially incompatible semantics, which poses special challenges to embedding learning in HINs. With the intention to preserve the rich yet potentially incompatible information in HIN embedding, we propose to study the problem of comprehensive transcription of heterogeneous information networks. The comprehensive transcription of HINs also provides an easy-to-use approach to unleash the power of HINs, since it requires no additional supervision, expertise, or feature engineering. To cope with the challenges in the comprehensive transcription of HINs, we propose the HEER algorithm, which embeds HINs via edge representations that are further coupled with properly-learned heterogeneous metrics. To corroborate the efficacy of HEER, we conducted experiments on two large-scale real-words datasets with an edge reconstruction task and multiple case studies. Experiment results demonstrate the effectiveness of the proposed HEER model and the utility of edge representations and heterogeneous metrics. The code and data are available at https://github.com/GentleZhu/HEER.Comment: 10 pages. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, London, United Kingdom, ACM, 201

室内植物表型平台及性状鉴定研究进展和展望

Plant phenomics is under rapid development in recent years, a research field that is progressing towards integration, scalability, multi-perceptivity and high-throughput analysis. Through combining remote sensing, Internet of Things (IoT), robotics, computer vision, and artificial intelligence techniques such as machine learning and deep learning, relevant research methodologies, biological applications and theoretical foundation of this research domain have been advancing speedily in recent years. This article first introduces the current trends of plant phenomics and its related progress in China and worldwide. Then, it focuses on discussing the characteristics of indoor phenotyping and phenotypic traits that are suitable for indoor experiments, including yield, quality, and stress related traits such as drought, cold and heat resistance, salt stress, heavy metals, and pests. By connecting key phenotypic traits with important biological questions in yield production, crop quality and Stress-related tolerance, we associated indoor phenotyping hardware with relevant biological applications and their plant model systems, for which a range of indoor phenotyping devices and platforms are listed and categorised according to their throughput, sensor integration, platform size, and applications. Additionally, this article introduces existing data management solutions and analysis software packages that are representative for phenotypic analysis. For example, ISA-Tab and MIAPPE ontology standards for capturing metadata in plant phenotyping experiments, PHIS and CropSight for managing complicated datasets, and Python or MATLAB programming languages for automated image analysis based on libraries such as OpenCV, Scikit-Image, MATLAB Image Processing Toolbox. Finally, due to the importance of extracting meaningful information from big phenotyping datasets, this article pays extra attention to the future development of plant phenomics in China, with suggestions and recommendations for the integration of multi-scale phenotyping data to increase confidence in research outcomes, the cultivation of cross-disciplinary researchers to lead the next-generation plant research, as well as the collaboration between academia and industry to enable world-leading research activities in the near future