Science
7 Oct 2022
Volume 378 | Issue 6615
Research - Research Articles
1.Lysosomal enzyme trafficking factor LYSET enables nutritional usage of extracellular proteins
下载原文
First Author:
Catarina Pechicha①
Corresponding Authors:
Johannes Zuber②, Wihelm Palm③
Affiliations:
Cell Signaling and Metabolism, German Cancer Research Center (DKFZ), Heidelberg, Germany. ①③
Faculty of Biosciences, University of Heidelberg, Heidelberg, Germany①
Research Institute of Molecular Pathology (IMP), Vienna BioCenter (VBC), Vienna, Austria.②
Medical University of Vienna, VBC, Vienna, Austria.②
Abstract:
Mammalian cells can generate amino acids through macropinocytosis and lysosomal breakdown of extracellular proteins, which is exploited by cancer cells to grow in nutrient-poor tumors. Through genetic screens in defined nutrient conditions, we characterized LYSET, a transmembrane protein (TMEM251) selectively required when cells consume extracellular proteins. LYSET was found to associate in the Golgi with GlcNAc-1-phosphotransferase, which targets catabolic enzymes to lysosomes through mannose-6-phosphate modification. Without LYSET, GlcNAc-1-phosphotransferase was unstable because of a hydrophilic transmembrane domain. Consequently, LYSET-deficient cells were depleted of lysosomal enzymes and impaired in turnover of macropinocytic and autophagic cargoes. Thus, LYSET represents a core component of the lysosomal enzyme trafficking pathway, underlies the pathomechanism for hereditary lysosomal storage disorders, and may represent a target to suppress metabolic adaptations in cancer.
2.The human disease gene LYSET is essential for lysosomal enzyme transport and viral infection下载原文
First Author:
Christopher M. Richards①
Corresponding Author:
Thomas Braulke②, Jan E.Carette③
Affiliations:
Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, USA.①③
Department of Osteology and Biomechanics, Cell Biology of Rare Diseases, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.②
Abstract:
Lysosomes are key degradative compartments of the cell. Transport to lysosomes relies on GlcNAc-1-phosphotransferase–mediated tagging of soluble enzymes with mannose 6-phosphate (M6P). GlcNAc-1-phosphotransferase deficiency leads to the severe lysosomal storage disorder mucolipidosis II (MLII). Several viruses require lysosomal cathepsins to cleave structural proteins and thus depend on functional GlcNAc-1-phosphotransferase. We used genome-scale CRISPR screens to identify lysosomal enzyme trafficking factor (LYSET, also named TMEM251) as essential for infection by cathepsin-dependent viruses including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). LYSET deficiency resulted in global loss of M6P tagging and mislocalization of GlcNAc-1-phosphotransferase from the Golgi complex to lysosomes. Lyset knockout mice exhibited MLII-like phenotypes, and human pathogenic LYSET alleles failed to restore lysosomal sorting defects. Thus, LYSET is required for correct functioning of the M6P trafficking machinery and mutations in LYSET can explain the phenotype of the associated disorder.
3. Electrochemical potential enables dormant spores to integrate environmental signal下载原文
First Author:
Kaito Kikuchi①
Corresponding Author:
Gurol M.Suel②
Affiliations:
Molecular Biology Section, Division of Biological Sciences, Universityof California San Diego, La Jolla, CA 92093, USA.①②
UCSD Synthetic Biology Institute, University of California San Diego, La Jolla, CA 92093, USA.②
Center for Microbiome Innovation, University of California San Diego, La Jolla, CA 92093, USA.②
Abstract:
The dormant state of bacterial spores is generally thought to be devoid of biological activity. We show that despite continued dormancy, spores can integrate environmental signals over time through a preexisting electrochemical potential. Specifically, we studied thousands of individual Bacillus subtilis spores that remain dormant when exposed to transient nutrient pulses. Guided by a mathematical model of bacterial electrophysiology, we modulated the decision to exit dormancy by genetically and chemically targeting potassium ion flux. We confirmed that short nutrient pulses result in step-like changes in the electrochemical potential of persistent spores. During dormancy, spores thus gradually release their stored electrochemical potential to integrate extracellular information over time. These findings reveal a decision-making mechanism that operates in physiologically inactive cells.
4. Robust deep learning–based protein sequence design using ProteinMPNN下载原文
First Author:
J. Dauparas①
Corresponding Author: D. Baker ②
Affiliations:
Department of Biochemistry, University of Washington, Seattle, WA, USA.①②
Institute for Protein Design, University of Washington, Seattle, WA, USA.①②
Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA.②
Abstract:
Although deep learning has revolutionized protein structure prediction, almost all experimentally characterized de novo protein designs have been generated using physically based approaches such as Rosetta. Here, we describe a deep learning–based protein sequence design method, ProteinMPNN, that has outstanding performance in both in silico and experimental tests. On native protein backbones, ProteinMPNN has a sequence recovery of 52.4% compared with 32.9% for Rosetta. The amino acid sequence at different positions can be coupled between single or multiple chains, enabling application to a wide range of current protein design challenges. We demonstrate the broad utility and high accuracy of ProteinMPNN using x-ray crystallography, cryo–electron microscopy, and functional studies by rescuing previously failed designs, which were made using Rosetta or AlphaFold, of protein monomers, cyclic homo-oligomers, tetrahedral nanoparticles, and target-binding proteins.
5. Machine learning–enabled high-entropy alloy discovery下载原文
First Author:
Ziyuan Rao①
Corresponding Author: Ye Wei②,Dierk Raabe ③
Affiliations:
Max-Planck-Institut für Eisenforschung GmbH, Düsseldorf, Germany.①②③
Abstract:
High-entropy alloys are solid solutions of multiple principal elements that are capable of reaching composition and property regimes inaccessible for dilute materials. Discovering those with valuable properties, however, too often relies on serendipity, because thermodynamic alloy design rules alone often fail in high-dimensional composition spaces. We propose an active learning strategy to accelerate the design of high-entropy Invar alloys in a practically infinite compositional space based on very sparse data. Our approach works as a closed-loop, integrating machine learning with density-functional theory, thermodynamic calculations, and experiments. After processing and characterizing 17 new alloys out of millions of possible compositions, we identified two high-entropy Invar alloys with extremely low thermal expansion coefficients around 2 × 10−6per degree kelvin at 300 kelvin. We believe this to be a suitable pathway for the fast and automated discovery of high-entropy alloys with optimal thermal, magnetic, and electrical properties.