Mutant p53 protects triple-negative breast adenocarcinomas from ferroptosis in vivo

The TP53 tumor suppressor gene is mutated early in most of the patients with triple-negative breast cancer (TNBC). The most frequent TP53 alterations are missense mutations that contribute to tumor aggressiveness. Here, we used an autochthonous somatic TNBC mouse model, in which mutant p53 can be toggled on and off genetically while leaving the tumor microenvironment intact and wild-type for p53 to identify physiological dependencies on mutant p53. In TNBCs that develop in this model, deletion of two different hotspot p53R172H and p53R245W mutants triggers ferroptosis in vivo, a cell death mechanism involving iron-dependent lipid peroxidation. Mutant p53 protects cells from ferroptosis inducers, and ferroptosis inhibitors reverse the effects of mutant p53 loss in vivo. Single-cell transcriptomic data revealed that mutant p53 protects cells from undergoing ferroptosis through NRF2-dependent regulation of Mgst3 and Prdx6, which encode two glutathione-dependent peroxidases that detoxify lipid peroxides. Thus, mutant p53 protects TNBCs from ferroptotic death.

Ultrasensitive Flexible κ-Phase Ga2O3 Solar-Blind Photodetector

Flexible Ga₂O₃ photodetectors have attracted considerable interest owing to their potential use in the development of implantable, foldable, and wearable optoelectronics. In particular, β-phase Ga₂O₃ has been most widely investigated due to the highest thermodynamic stability. However, high-quality β-phase Ga₂O₃ relies on the ultrahigh crystallization temperature (usually ≥750 °C), beyond the thermal tolerance of most flexible substrates. In this work, we epitaxially grow a high-quality metastable κ-phase Ga₂O₃ (002) thin film on a flexible mica (001) substrate under 680 °C and develop a flexible κ-Ga₂O₃ thin film photodetector with ultrahigh performance. Epitaxial κ-Ga₂O₃ and the mica substrate are maintained to be thermally stable up to 750 °C, suggesting their potential for harsh environment applications. The responsivity, on/off ratio, detectivity, and external quantum efficiency of the fabricated photodetector are 703 A/W, 1.66 × 10⁷, 4.08 × 10¹⁴ Jones, and 3.49 × 10⁵ %, respectively, for 250 nm incident light and a 20 V bias voltage. These values are record-high values reported to date for flexible Ga₂O₃ photodetectors. Furthermore, the flexible photodetector shows robust flexibility for bending radii of 1, 2, and 3 cm. More importantly, it shows strong mechanical stability against 10,000 bending test cycles. These results reveal the significance of high-quality κ-phase Ga₂O₃ grown heteroepitaxially on a flexible mica substrate, especially its potential for use in future flexible solar-blind detection systems.

CTD: An information-theoretic algorithm to interpret sets of metabolomic and transcriptomic perturbations in the context of graphical models

We consider the following general family of algorithmic problems that arises in transcriptomics, metabolomics and other fields: given a weighted graph G and a subset of its nodes S, find subsets of S that show significant connectedness within G. A specific solution to this problem may be defined by devising a scoring function, the Maximum Clique problem being a classic example, where S includes all nodes in G and where the score is defined by the size of the largest subset of S fully connected within G. Major practical obstacles for the plethora of algorithms addressing this type of problem include computational efficiency and, particularly for more complex scores which take edge weights into account, the computational cost of permutation testing, a statistical procedure required to obtain a bound on the p-value for a connectedness score. To address these problems, we developed CTD, "Connect the Dots", a fast algorithm based on data compression that detects highly connected subsets within S. CTD provides information-theoretic upper bounds on p-values when S contains a small fraction of nodes in G without requiring computationally costly permutation testing. We apply the CTD algorithm to interpret multi-metabolite perturbations due to inborn errors of metabolism and multi-transcript perturbations associated with breast cancer in the context of disease-specific Gaussian Markov Random Field networks learned directly from respective molecular profiling data.