703,875 articles

The Hippo signaling pathway and its two downstream effectors, the YAP and TAZ transcriptional coactivators, are drivers of tumor growth in experimental models. Studying mouse models, we show that YAP and TAZ can also exert a tumor-suppressive function. We found that normal hepatocytes surrounding liver tumors displayed activation of YAP and TAZ and that deletion of Yap and Taz in these peritumoral hepatocytes accelerated tumor growth. Conversely, experimental hyperactivation of YAP in peritumoral hepatocytes triggered regression of primary liver tumors and melanoma-derived liver metastases. Furthermore, whereas tumor cells growing in wild-type livers required YAP and TAZ for their survival, those surrounded by Yap- and Taz-deficient hepatocytes were not dependent on YAP and TAZ. Tumor cell survival thus depends on the relative activity of YAP and TAZ in tumor cells and their surrounding tissue, suggesting that YAP and TAZ act through a mechanism of cell competition to eliminate tumor cells.

Intelligent machines using machine learning algorithms are ubiquitous, ranging from simple data analysis and pattern recognition tools to complex systems that achieve superhuman performance on various tasks. Ensuring that they do not exhibit undesirable behavior—that they do not, for example, cause harm to humans—is therefore a pressing problem. We propose a general and flexible framework for designing machine learning algorithms. This framework simplifies the problem of specifying and regulating undesirable behavior. To show the viability of this framework, we used it to create machine learning algorithms that precluded the dangerous behavior caused by standard machine learning algorithms in our experiments. Our framework for designing machine learning algorithms simplifies the safe and responsible application of machine learning.

Strange or bad metallic transport, defined by incompatibility with the conventional quasiparticle picture, is a theme common to many strongly correlated materials, including high-temperature superconductors. The Hubbard model represents a minimal starting point for modeling strongly correlated systems. Here we demonstrate strange metallic transport in the doped two-dimensional Hubbard model using determinantal quantum Monte Carlo calculations. Over a wide range of doping, we observe resistivities exceeding the Mott-Ioffe-Regel limit with linear temperature dependence. The temperatures of our calculations extend to as low as 1/40 of the noninteracting bandwidth, placing our findings in the degenerate regime relevant to experimental observations of strange metallicity. Our results provide a foundation for connecting theories of strange metals to models of strongly correlated materials.

The tumor suppressor folliculin (FLCN) enables nutrient-dependent activation of the mechanistic target of rapamycin complex 1 (mTORC1) protein kinase via its guanosine triphosphatase (GTPase) activating protein (GAP) activity toward the GTPase RagC. Concomitant with mTORC1 inactivation by starvation, FLCN relocalizes from the cytosol to lysosomes. To determine the lysosomal function of FLCN, we reconstituted the human lysosomal FLCN complex (LFC) containing FLCN, its partner FLCN-interacting protein 2 (FNIP2), and the RagAGDP:RagCGTP GTPases as they exist in the starved state with their lysosomal anchor Ragulator complex and determined its cryo–electron microscopy structure to 3.6 angstroms. The RagC-GAP activity of FLCN was inhibited within the LFC, owing to displacement of a catalytically required arginine in FLCN from the RagC nucleotide. Disassembly of the LFC and release of the RagC-GAP activity of FLCN enabled mTORC1-dependent regulation of the master regulator of lysosomal biogenesis, transcription factor E3, implicating the LFC as a checkpoint in mTORC1 signaling.