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NPJ Biodiversity 2024年第1期3卷 28页

作者： Mc Cartney, Ann M. Formenti, Giulio Mouton, Alice De Panis, Diego Marins, Luísa S. Leitão, Henrique G. Diedericks, Genevieve Kirangwa, Joseph Morselli, Marco Salces-Ortiz, Judit Escudero, Nuria Iannucci, Alessio Natali, Chiara Svardal, Hannes Fernández, Rosa De Pooter, Tim Joris, Geert Strazisar, Mojca Wood, Jonathan M. D. Herron, Katie E. Seehausen, Ole Watts, Phillip C. Shaw, Felix Davey, Robert P. Minotto, Alice Fernández, José M. Böhne, Astrid Alegria, Carla Alioto, Tyler Alves, Paulo C. Amorim, Isabel R. Aury, Jean-Marc Backstrom, Niclas Baldrian, Petr Baltrunaite, Laima Barta, Endre Bedhom, Bertrand Belser, Caroline Bergsten, Johannes Bertrand, Laurie Bilandija, Helena Binzer-Panchal, Mahesh Bista, Iliana Blaxter, Mark Borges, Paulo A. V. Dias, Guilherme Borges Bosse, Mirte Brown, Tom Bruggmann, Rémy Buena-Atienza, Elena Burgin, Josephine Buzan, Elena Cariani, Alessia Casadei, Nicolas Chiara, Matteo Chozas, Sergio Jr, Fedor Čiampor Crottini, Angelica Cruaud, Corinne Cruz, Fernando Dalen, Love Biase, Alessiode Delcampo, Javier Delic, Teo Dennis, Aliceb. Derks, Martijn F. L. Diroma, Maria Angela Djan, Mihajla Duprat, Simone Eleftheriadi, Klara Feulner, Philine G. D. Flot, Jean-François Forni, Giobbe Fosso, Bruno Fournier, Pascal Fournier-Chambrillon, Christine Gabaldon, Toni Garg, Shilpa Gissi, Carmela Giupponi, Luca Gomez-Garrido, Jessica González, Josefa Grilo, Miguel L. Grüning, Björn Guerin, Thomas Guiglielmoni, Nadege Gut, Marta Haesler, Marcel P. Hahn, Christoph Halpern, Balint Harrison, Peter W. Heintz, Julia Hindrikson, Maris Höglund, Jacob Howe, Kerstin Hughes, Graham M. Istace, Benjamin Cock, Mark J. Janžekovič, Franc Jonsson, Zophonias O. Joye-Dind, Sagane Koskimäki, Janne J. Krystufek, Boris Kubacka, Justyna Kuhl, Heiner Kusza, Szilvia Labadie, Karine Lähteenaro, Meri Lantz, Henrik Lavrinienko, Anton Leclère, Lucas Lopes, Ricardo Jorge Madsen, Ole Magdelenat, Ghislaine Magoga, Giulia Manousaki, Tereza Mappes, Tapio Marques, Joao Pedro Redondo, Gemma I. Martinez Maumus, Florian McCarthy, Shane A. M Genomics Institute University of California SantaCruz CA United States TheVertebrate Genome Laboratory The Rockefeller University NewYork NY United States Department of Biology University of Florence Sesto Fiorentino Italy InBios-Conservation Genetics Laboratory University of Liege Liege Belgium Leibniz Institut für Zoo und Wildtierforschung Berlin Germany Berlin Center for Genomics in Biodiversity Research Berlin Germany Department of Biology University of Antwerp Antwerp Belgium Institute of Zoology University of Cologne Cologne Germany Department of Chemistry Life Sciences and Environmental Sustainability University of Parma Parma Italy Institute of Evolutionary Biology (CSIC-Universitat Pompeu Fabra Barcelona Spain Naturalis Biodiversity Center Leiden Netherlands Neuromics Support Facility VIB Center for Molecular Neurology VIB Antwerp Belgium Neuromics Support Facility Department of Biomedical SciencesUniversity ofAntwerp Antwerp Belgium Tree of Life WellcomeSanger Institute Cambridge Hinxton United Kingdom School of Biology and Environmental Science University College Dublin Belfield Ireland Aquatic Ecology & Evolution Institute of Ecology & Evolution University of Bern Bern Switzerland Department of Fish Ecology & Evolution Eawag Kastanienbaum Switzerland Department of Biological and Environmental Science University of Jyvaskyla Jyvaskyla Finland The EarlhamInstitute NorwichResearchPark Norwich United Kingdom Digital Science London United Kingdom Barcelona Supercomputing Center Spanish National Bioinformatics Institute ELIXIR Spain Getafe Spain Leibniz Institute for the Analysis of Biodiversity Change Museum Koenig Bonn Bonn Germany CE3C—Centre for Ecology Evolution and Environmental Changes & CHANGE—Global Change and Sustainability Institute Faculdade de Ciências Universidade de Lisboa Campo Grande Lisboa Portugal Centro Nacional de Análisis Genómico (CNAG) Barcelona Spain Universitat de Barcelona (UB) Barcelona Spain CIBIO Cen

A genomic database of all Earth’s eukaryotic species could contribute to many scientific discoveries;however, only a tiny fraction of species have genomic information available. In 2018, scientists across the world united under the Earth BioGenome Project (EBP), aiming to produce a database of highquality reference genomes containing all ~1.5 million recognized eukaryotic species. As the European node of the EBP, the European Reference Genome Atlas (ERGA) sought to implement a new decentralised, equitable and inclusive model for producing reference genomes. For this, ERGA launched a Pilot Project establishing the first distributed reference genome production infrastructure and testing it on 98 eukaryotic species from 33 European countries. Here we outline the infrastructure and explore its effectiveness for scaling high-quality reference genome production, whilst considering equity and inclusion. The outcomes and lessons learned provide a solid foundation for ERGA while offering key learnings to other transnational, national genomic resource projects and the EBP. © The Author(s) 2024, corrected publication 2024.

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Ten quick tips for deep learning in biology

arXiv

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arXiv 2021年

作者： Lee, Benjamin D. Gitter, Anthony Greene, Casey S. Raschka, Sebastian Maguire, Finlay Titus, Alexander J. Kessler, Michael D. Lee, Alexandra J. Chevrette, Marc G. Stewart, Paul Allen Britto-Borges, Thiago Cofer, Evan M. Yu, Kun-Hsing Carmona, Juan Jose Fertig, Elana J. Kalinin, Alexandr A. Signal, Beth Lengerich, Benjamin J. Triche, Timothy J. Boca, Simina M. In-Q-Tel Labs School of Engineering and Applied Sciences Harvard University Department of Genetics Harvard Medical School United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison MadisonWI United States Morgridge Institute for Research MadisonWI United States Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine AuroraCO United States Center for Health AI University of Colorado School of Medicine AuroraCO United States Department of Statistics University of Wisconsin Madison United States Faculty of Computer Science Dalhousie University Canada University of New Hampshire Bioeconomy.XYZ United States Department of Oncology Johns Hopkins University United States Institute for Genome Sciences University of Maryland School of Medicine United States Genomics and Computational Biology Graduate Program University of Pennsylvania United States Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania United States Wisconsin Institute for Discovery Department of Plant Pathology University of Wisconsin-Madison United States Department of Biostatistics and Bioinformatics Moffitt Cancer Center TampaFL United States Section of Bioinformatics and Systems Cardiology Klaus Tschira Institute for Integrative Computational Cardiology University Hospital Heidelberg Germany University Hospital Heidelberg Germany Lewis-Sigler Institute for Integrative Genomics Princeton University PrincetonNJ United States Graduate Program in Quantitative and Computational Biology Princeton University PrincetonNJ United States Department of Biomedical Informatics Harvard Medical School United States Department of Pathology Brigham and Women's Hospital United States Philips Healthcare CambridgeMA United States Philips Research

Machine learning is a modern approach to problem-solving and task automation. In particular, machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling. Artificial neural networks are a particular class of machine learning algorithms and models that evolved into what is now described as deep learning. Given the computational advances made in the last decade, deep learning can now be applied to massive data sets and in innumerable contexts. Therefore, deep learning has become its own subfield of machine learning. In the context of biological research, it has been increasingly used to derive novel insights from high-dimensional biological data. To make the biological applications of deep learning more accessible to scientists who have some experience with machine learning, we solicited input from a community of researchers with varied biological and deep learning interests. These individuals collaboratively contributed to this manuscript's writing using the GitHub version control platform and the Manubot manuscript generation toolset. The goal was to articulate a practical, accessible, and concise set of guidelines and suggestions to follow when using deep learning. In the course of our discussions, several themes became clear: the importance of understanding and applying machine learning fundamentals as a baseline for utilizing deep learning, the necessity for extensive model comparisons with careful evaluation, and the need for critical thought in interpreting results generated by deep learning, among others. © 2021, CC BY.

关键词： Learning algorithms

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Model of metabolism and gene expression predicts proteome allocation in Pseudomonas putida

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NPJ systems biology and applications 2025年第1期11卷 55页

作者： Juan D Tibocha-Bonilla Vishant Gandhi Chloe Lieng Oriane Moyne Rodrigo Santibáñez-Palominos Karsten Zengler Bioinformatics and Systems Biology Graduate Program University of California San Diego 9500 Gilman Drive La Jolla CA 92093-0760 USA. Department of Bioengineering University of California San Diego La Jolla CA 92093-0412 USA. Department of Pediatrics University of California San Diego 9500 Gilman Drive La Jolla CA 92093-0760 USA. Department of Bioengineering University of California San Diego La Jolla CA 92093-0412 USA. kzengler@ucsd.edu. Department of Pediatrics University of California San Diego 9500 Gilman Drive La Jolla CA 92093-0760 USA. kzengler@ucsd.edu. Center for Microbiome Innovation University of California San Diego 9500 Gilman Drive La Jolla CA 92093-0403 USA. kzengler@ucsd.edu. Program in Materials Science and Engineering University of California San Diego 9500 Gilman Drive La Jolla CA 92093-0418 USA. kzengler@ucsd.edu.

The genome-scale model of metabolism and gene expression (ME-model) for Pseudomonas putida KT2440, iPpu1676-ME, provides a comprehensive representation of biosynthetic costs and proteome allocation. Compared to a metabolic-only model, iPpu1676-ME significantly expands on gene expression, macromolecular assembly, and cofactor utilization, enabling accurate growth predictions without additional constraints. Multi-omics analysis using RNA sequencing and ribosomal profiling data revealed translational prioritization in P. putida, with core pathways, such as nicotinamide biosynthesis and queuosine metabolism, exhibiting higher translational efficiency, while secondary pathways displayed lower priority. Notably, the ME-model significantly outperformed the M-model in alignment with multi-omics data, thereby validating its predictive capacity. Thus, iPpu1676-ME offers valuable insights into P. putida's proteome allocation and presents a powerful tool for understanding resource allocation in this industrially relevant microorganism.

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Current md forcefields fail to capture key features of protein structure and fluctuations: a case study of cyclophilin a and t4 lysozyme

arXiv

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arXiv 2020年

作者： Mei, Zhe Grigas, Alex T. Treado, John D. Corres, Gabriel Melendez Vuorte, Maisa Sammalkorpi, Maria Regan, Lynne Levine, Zachary A. O’Hern, Corey S. Department of Chemistry Yale University New HavenCT06520 United States Integrated Graduate Program in Physical and Engineering Biology Yale University New HavenCT06520 United States Graduate Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States Department of Mechanical Engineering and Materials Science Yale University New HavenCT06520 United States Department of Biology UPR Humacao Puerto RicoHumacao00792 Puerto Rico Department of Chemistry and Materials Science School of Chemical Engineering Aalto University Aalto Finland Department of Bioproducts and Biosystems School of Chemical Engineering Aalto University Aalto Finland Institute of Quantitative Biology Biochemistry and Biotechnology Center for Synthetic and Systems Biology School of Biological Sciences University of Edinburgh Edinburgh United Kingdom Department of Pathology Yale School of Medicine New HavenCT06520 United States Department of Molecular Biophysics and Biochemistry Yale University New HavenCT06520 United States Department of Physics Yale University New HavenCT06520 United States Department of Applied Physics Yale University New HavenCT06520 United States

Globular proteins undergo thermal fluctuations in solution, while maintaining an overall well-defined folded structure. In particular, studies have shown that the core structure of globular proteins differs in small, but significant ways when they are solved by x-ray crystallography versus solution-based NMR spectroscopy. Given these discrepancies, it is unclear whether molecular dynamics (MD) simulations can accurately recapitulate protein conformations. We therefore perform extensive MD simulations across multiple force fields and sampling techniques to investigate the degree to which computer simulations can capture the ensemble of conformations observed in experiments. By analyzing fluctuations in the atomic coordinates and core packing, we show that conformations sampled in MD simulations both move away from and sample a larger conformational space than the ensemble of structures observed in NMR experiments. However, we find that adding inter-residue distance restraints that match those obtained via Nuclear Overhauser Effect measurements enables the MD simulations to sample more NMR-like conformations, though significant differences between the core packing features in restrained MD and the NMR ensemble remain. Given that the protein structures obtained from the MD simulations possess smaller and less dense protein cores compared to those solved by NMR, we suggest that future improvements to MD forcefields should aim to increase the packing of hydrophobic residues in protein cores. © 2020, CC BY.

关键词： Molecular dynamics

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Tumor-selective activity of RAS-GTP inhibition in pancreatic cancer

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Nature 2024年第8013期629卷 927-936页

作者： Urszula N Wasko Jingjing Jiang Tanner C Dalton Alvaro Curiel-Garcia A Cole Edwards Yingyun Wang Bianca Lee Margo Orlen Sha Tian Clint A Stalnecker Kristina Drizyte-Miller Marie Menard Julien Dilly Stephen A Sastra Carmine F Palermo Marie C Hasselluhn Amanda R Decker-Farrell Stephanie Chang Lingyan Jiang Xing Wei Yu C Yang Ciara Helland Haley Courtney Yevgeniy Gindin Karl Muonio Ruiping Zhao Samantha B Kemp Cynthia Clendenin Rina Sor William P Vostrejs Priya S Hibshman Amber M Amparo Connor Hennessey Matthew G Rees Melissa M Ronan Jennifer A Roth Jens Brodbeck Lorenzo Tomassoni Basil Bakir Nicholas D Socci Laura E Herring Natalie K Barker Junning Wang James M Cleary Brian M Wolpin John A Chabot Michael D Kluger Gulam A Manji Kenneth Y Tsai Miroslav Sekulic Stephen M Lagana Andrea Califano Elsa Quintana Zhengping Wang Jacqueline A M Smith Matthew Holderfield David Wildes Scott W Lowe Michael A Badgley Andrew J Aguirre Robert H Vonderheide Ben Z Stanger Timour Baslan Channing J Der Mallika Singh Kenneth P Olive Department of Medicine Vagelos College of Physicians and Surgeons Columbia University Irving Medical Center New York NY USA. Herbert Irving Comprehensive Cancer Center Columbia University Irving Medical Center New York NY USA. Revolution Medicines Redwood City CA USA. Department of Cell Biology and Physiology University of North Carolina at Chapel Hill Chapel Hill NC USA. University of Pennsylvania Perelman School of Medicine Department of Medicine Philadelphia PA USA. Cancer Biology and Genetics Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New York NY USA. Lineberger Comprehensive Cancer Center University of North Carolina at Chapel Hill Chapel Hill NC USA. Department of Pharmacology University of North Carolina at Chapel Hill Chapel Hill NC USA. Department of Medical Oncology Dana-Farber Cancer Institute Boston MA USA. Harvard Medical School Boston MA USA. University of Pennsylvania Perelman School of Medicine Abramson Cancer Center Philadelphia PA USA. The Broad Institute of Harvard and MIT Cambridge MA USA. Department of Systems Biology Columbia University Irving Medical Center New York NY USA. Bioinformatics Core Memorial Sloan Kettering Cancer Center New York NY USA. UNC Michael Hooker Proteomics Center University of North Carolina at Chapel Hill Chapel Hill NC USA. Department of Surgery Vagelos College of Physicians and Surgeons Columbia University Irving Medical Center New York NY USA. Department of Pathology H. Lee Moffitt Cancer Center and Research Institute Tampa FL USA. Department of Tumor Microenvironment and Metastasis H. Lee Moffitt Cancer Center and Research Institute Tampa FL USA. Department of Pathology and Cell Biology Columbia University Irving Medical Center New York NY USA. Department of Oncology The Johns Hopkins University School of Medicine Baltimore MD USA. J. P. Sulzberger Columbia Genome Center Columbia University New York NY USA. Department of Biochemistry and Molecular Biop

Broad-spectrum RAS inhibition has the potential to benefit roughly a quarter of human patients with cancer whose tumours are driven by RAS mutations. RMC-7977 is a highly selective inhibitor of the active GTP-bound forms of KRAS, HRAS and NRAS, with affinity for both mutant and wild-type variants. More than 90% of cases of human pancreatic ductal adenocarcinoma (PDAC) are driven by activating mutations in KRAS. Here we assessed the therapeutic potential of RMC-7977 in a comprehensive range of PDAC models. We observed broad and pronounced anti-tumour activity across models following direct RAS inhibition at exposures that were well-tolerated in vivo. Pharmacological analyses revealed divergent responses to RMC-7977 in tumour versus normal tissues. Treated tumours exhibited waves of apoptosis along with sustained proliferative arrest, whereas normal tissues underwent only transient decreases in proliferation, with no evidence of apoptosis. In the autochthonous KPC mouse model, RMC-7977 treatment resulted in a profound extension of survival followed by on-treatment relapse. Analysis of relapsed tumours identified Myc copy number gain as a prevalent candidate resistance mechanism, which could be overcome by combinatorial TEAD inhibition in vitro. Together, these data establish a strong preclinical rationale for the use of broad-spectrum RAS-GTP inhibition in the setting of PDAC and identify a promising candidate combination therapeutic regimen to overcome monotherapy resistance.

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RETRACTION: RETRACTED: Crystal structure of a Baeyer-Villiger flavin-containing monooxygenase from Staphylococcus aureus MRSA strain MU50 (Retraction of 2014)

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PROTEINS-STRUCTURE FUNCTION AND bioinformatics 2018年第2期86卷 269-269页

作者： Hwang, William C. Xu, Qingping Wu, Bainan Godzik, Adam Bioinformatics Core Joint Center for Structural Genomics (JCSG) La Jolla California 92037 Bioinformatics and Systems Biology Program Sanford-Burnham Medical Research Institute La Jolla California 92037.

The above article from Proteins: Structure, Function, and bioinformatics, published online on 5 August 2014 in Wiley Online Library (http://***/doi/10.1002/prot.24661/full), has been retracted by agreement between William C. Hwang, Qingping Xu, Bainan Wu, Adam Godzik, the Editor‐in‐Chief, Bertrand E. Garcia‐Moreno, and Wiley Periodicals, Inc. The retraction has been agreed because submission was made without agreement from co‐author Adam Godzik.

关键词： Baeyer-Villiger reaction FMO antibiotics resistance methicillin vancomycin

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Publisher Correction: Comprehensive molecular characterization of mitochondrial genomes in human cancers

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Nature genetics 2023年第5期55卷 893页

作者： Yuan Yuan Young Seok Ju Youngwook Kim Jun Li Yumeng Wang Christopher J Yoon Yang Yang Inigo Martincorena Chad J Creighton John N Weinstein Yanxun Xu Leng Han Hyung-Lae Kim Hidewaki Nakagawa Keunchil Park Peter J Campbell Han Liang Department of Bioinformatics and Computational Biology The University of Texas MD Anderson Cancer Center Houston TX USA. Cancer Genome Project Wellcome Trust Sanger Institute Hinxton UK. Graduate School of Medical Science and Engineering Korea Advanced Institute of Science and Technology Daejeon Korea. Department of Health Science and Technology Samsung Advanced Institute for Health Science and Technology Sungkyunkwan University School of Medicine Seoul Korea. Quantitative and Computational Biosciences Graduate Program Baylor College of Medicine Houston TX USA. Division of Biostatistics The University of Texas Health Science Center at Houston School of Public Health Houston TX USA. Department of Medicine and Dan L. Duncan Cancer Center Division of Biostatistics Baylor College of Medicine Houston TX USA. Department of Systems Biology The University of Texas MD Anderson Cancer Center Houston TX USA. Department of Applied Mathematics and Statistics Johns Hopkins University Baltimore MD USA. Department of Biochemistry and Molecular Biology The University of Texas Health Science Center at Houston McGovern Medical School Houston TX USA. Department of Biochemistry Ewha Womans University School of Medicine Seoul Korea. Laboratory for Cancer Genomics RIKEN Center for Integrative Medical Sciences Yokohama Japan. Division of Hematology/Oncology Samsung Medical Center Sungkyunkwan University School of Medicine Seoul Korea. kpark@skku.edu. Cancer Genome Project Wellcome Trust Sanger Institute Hinxton UK. pc8@sanger.ac.uk. Cambridge University Hospitals NHS Foundation Trust Cambridge UK. pc8@sanger.ac.uk. Department of Bioinformatics and Computational Biology The University of Texas MD Anderson Cancer Center Houston TX USA. hliang1@***. Quantitative and Computational Biosciences Graduate Program Baylor College of Medicine Houston TX USA. hliang1@***. Department of Systems Biology The University of Texas MD Anderson Cancer Center Houston TX USA. hliang1@mdanderson

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A retinoic acid:YAP1 signaling axis controls atrial lineage commitment

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Cell Reports 2025年第5期44卷 115687页

作者： Abraham, Elizabeth Kostina, Aleksandra Volmert, Brett Roule, Thomas Huang, Ling Yu, Jingting Williams, April E. Megill, Emily Douglas, Aidan Pericak, Olivia M. Morris, Alex Stronati, Eleonora Larrinaga-Zamanillo, Arantza Fueyo, Raquel Zubillaga, Mikel Andrake, Mark D. Akizu, Naiara Aguirre, Aitor Estaras, Conchi Department of Cardiovascular Sciences Aging + Cardiovascular Discovery Center Lewis Katz School of Medicine Temple University Philadelphia 19140 PA United States Institute for Quantitative Health Science and Engineering Division of Developmental and Stem Cell Biology Michigan State University East Lansing 48824 MI United States Department of Biomedical Engineering College of Engineering Michigan State University East Lansing 48824 MI United States Raymond G. Perelman Center for Cellular and Molecular Therapeutics The Children's Hospital of Philadelphia Philadelphia 19104 PA United States Integrative Genomics and Bioinformatics Core Salk Institute for Biological Studies La Jolla 92037 CA United States Department of Child and Adolescence Psychiatry The Children's Hospital of Philadelphia Philadelphia 19104 PA United States Department of Chemical and Systems Biology Stanford University School of Medicine Stanford 94305 CA United States Cancer Epigenetics Institute Fox Chase Cancer Center Philadelphia 19111 PA United States Molecular Modeling Facility Program in Cancer Signaling and Microenvironment Fox Chase Cancer Center Philadelphia 19111 PA United States

In cardiac progenitor cells (CPCs), retinoic acid (RA) signaling induces atrial lineage gene expression and acquisition of an atrial cell fate. To achieve this, RA coordinates a complex regulatory network of downstream effectors that is not fully identified. To address this gap, we applied a functional genomics approach (i.e., scRNA-seq and snATAC-seq) to untreated and RA-treated human embryonic stem cell (hESC)-derived CPCs. Unbiased analysis revealed that the Hippo effectors YAP1 and TEAD4 are integrated with the atrial transcription factor enhancer network and that YAP1 activates RA enhancers in CPCs. Furthermore, Yap1 deletion in mouse embryos compromises the expression of RA-induced genes, such as Nr2f2, in the CPCs of the second heart field. Accordingly, in hESC-derived patterned heart organoids, YAP1 regulates the formation of an atrial chamber but is dispensable for the formation of a ventricle. Overall, our findings revealed that YAP1 cooperates with RA signaling to induce atrial lineages during cardiogenesis. © 2025 The Author(s)

关键词： atrial differentiation cardiac enhancers cardiac progenitors chamber development CP: Developmental biology heart organoids NR2F2 retinoic acid signaling SHF TEAD4 YAP1

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A flexible liposomal polymer complex as a platform of specific and regulable immune regulation for individual cancer immunotherapy

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Journal of Experimental and Clinical Cancer Research 2023年第1期42卷 29-29页

作者： Chen, Chia-Hung Weng, Tzu-Han Huang, Hsiao-Hsuan Huang, Ling-Ya Huang, Kai-Yao Chen, Pin-Rong Yeh, Kuang-Yu Huang, Chi-Ting Chien, Yu-Tzu Chuang, Po-Ya Lin, Yu-Ling Tsai, Nu-Man Liu, Shih-Jen Su, Yu-Cheng Weng, Shun-Long Liao, Kuang-Wen Department of Medical Research Hsinchu MacKay Memorial Hospital Hsinchu City 30071 Taiwan Department of Dermatology MacKay Memorial Hospital Taipei City 10449 Taiwan Industrial Development Graduate Program of College of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu City 30068 Taiwan Institute of Bioinformatics and Systems Biology National Yang Ming Chiao Tung University Hsinchu City 30068 Taiwan Department of Medicine MacKay Medical College New Taipei City 25245 Taiwan Institute of Molecular Medicine and Bioengineering National Yang Ming Chiao Tung University Hsinchu City 30068 Taiwan Department of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu City 30068 Taiwan Agricultural Biotechnology Research Center Academia Sinica Taipei 11529 Taiwan Department of Medical Laboratory and Biotechnology Chung Shan Medical University Taichung City 40201 Taiwan Department of Pathology and Clinical Laboratory Chung Shan Medical University Hospital Taichung City 40201 Taiwan National Institute of Infectious Diseases and Vaccinology National Health Research Institutes Miaoli 350401 Taiwan Department of Obstetrics and Gynecology Hsinchu MacKay Memorial Hospital Hsinchu City 30071 Taiwan MacKay Junior College of Medicine Nursing and Management Taipei City 11260 Taiwan Graduate Institute of Medicine College of Medicine Kaohsiung Medical University Kaohsiung City 80708 Taiwan College of Dental Medicine Kaohsiung Medical University School of Dentistry Kaohsiung City 80708 Taiwan Department of Biotechnology and Bioindustry Sciences National Cheng Kung University Tainan City 70101 Taiwan Center for Intelligent Drug Systems and Smart Bio-Devices National Yang Ming Chiao Tung University Hsinchu City 30068 Taiwan

Background: The applicability and therapeutic efficacy of specific personalized immunotherapy for cancer patients is limited by the genetic diversity of the host or the tumor. Side-effects such as immune-related adverse events (IRAEs) derived from the administration of immunotherapy have also been observed. Therefore, regulatory immunotherapy is required for cancer patients and should be developed. Methods: The cationic lipo-PEG-PEI complex (LPPC) can stably and irreplaceably adsorb various proteins on its surface without covalent linkage, and the bound proteins maintain their original functions. In this study, LPPC was developed as an immunoregulatory platform for personalized immunotherapy for tumors to address the barriers related to the heterogenetic characteristics of MHC molecules or tumor associated antigens (TAAs) in the patient population. Here, the immune-suppressive and highly metastatic melanoma, B16F10 cells were used to examine the effects of this platform. Adsorption of anti-CD3 antibodies, HLA-A2/peptide, or dendritic cells’ membrane proteins (MP) could flexibly provide pan-T-cell responses, specific Th1 responses, or specific Th1 and Th2 responses, depending on the host needs. Furthermore, with regulatory antibodies, the immuno-LPPC complex properly mediated immune responses by adsorbing positive or negative antibodies, such as anti-CD28 or anti-CTLA4 antibodies. Results: The results clearly showed that treatment with LPPC/MP/CD28 complexes activated specific Th1 and Th2 responses, including cytokine release, CTL and prevented T-cell apoptosis. Moreover, LPPC/MP/CD28 complexes could eliminate metastatic B16F10 melanoma cells in the lung more efficiently than LPPC/MP. Interestingly, the melanoma resistance of mice treated with LPPC/MP/CD28 complexes would be reversed to susceptible after administration with LPPC/MP/CTLA4 complexes. NGS data revealed that LPPC/MP/CD28 complexes could enhance the gene expression of cytokine and chemokine pathways to st

关键词： Costimulatory molecules Immunotherapy LPPC personalized cancer therapy Tumor heterogenicity

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Causes of evolutionary divergence in prostate cancer

arXiv

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arXiv 2025年

作者： Esentürk, Emre Sahli, Atef Haberland, Valeriia Ziuboniewicz, Aleksandra Wirth, Christopher Bova, G. Steven Bristow, Robert G. Brook, Mark N. Brors, Benedikt Butler, Adam Cancel-Tassin, Géraldine Cheng, Kevin C.L. Cooper, Colin S. Corcoran, Niall M. Cussenot, Olivier Eeles, Ros A. Favero, Francesco Gerhauser, Clarissa Gihawi, Abraham Girma, Etsehiwot G. Gnanapragasam, Vincent J. Gruber, Andreas J. Hamid, Anis Hayes, Vanessa M. He, Housheng Hansen Hovens, Christopher M. Imada, Eddie Luidy Jakobsdottir, G. Maria Jung, Chol-Hee Khani, Francesca Kote-Jarai, Zsofia Lamy, Philippe Leeman, Gregory Loda, Massimo Lutsik, Pavlo Marchionni, Luigi Molania, Ramyar Papenfuss, Anthony T. Pellegrina, Diogo Pope, Bernard Queiroz, Lucio R. Rausch, Tobias Reimand, Jüri Robinson, Brian Schlomm, Thorsten Sørensen, Karina D. Uhrig, Sebastian Weischenfeldt, Joachim Xu, Yaobo Yamaguchi, Takafumi N. Zanettini, Claudio Lynch, Andy G. Wedge, David C. Brewer, Daniel S. Woodcock, Dan J. Nuffield Department of Medicine University of Oxford United Kingdom Manchester Cancer Research Centre The University of Manchester United Kingdom Norwich Medical School University of East Anglia United Kingdom Nuffield Department of Surgical Sciences University of Oxford United Kingdom Prostate Cancer Research Center Faculty of Medicine and Health Technology Tampere University Finland Manchester Cancer Research Centre Cancer Research UK Manchester Institute The University of Manchester United Kingdom Christie NHS Foundation Trust United Kingdom Div Cancer Sciences Faculty of Biology Medicine & Health University of Manchester United Kingdom The Institute of Cancer Research United Kingdom Germany Germany Medical Faculty Heidelberg Faculty of Biosciences Heidelberg University Germany Wellcome Sanger Institute United Kingdom France Sorbonne Université GRC n°5 Predictive Onco-Urology APHP Tenon Hospital France Computational Biology Program Ontario Institute for Cancer Research Canada Department of Medical Biophysics University of Toronto Canada Department of Urology Royal Melbourne Hospital Australia Department of Surgery The University of Melbourne Australia Department of Urology Western Health Australia The Royal Marsden NHS Foundation Trust London United Kingdom University of Copenhagen Denmark Finsen Laboratory Copenhagen University Hospital - Rigshospitalet Denmark Germany Cambridge Biomedical Campus Addenbrooke's Hospital Site S Wards Building United Kingdom University of Konstanz Germany School of Medical Sciences University of Sydney Faculty of Medicine & Health Australia School of Health Systems & Public Health University of Pretoria South Africa Manchester Cancer Research Centre University of Manchester United Kingdom Princess Margaret Cancer Centre University Health Network Department of Medical Biophysics University of Toronto Canada Department of Surgery The Collaborative Centre for Genomic Cancer Medicine The University of Melbourne

Cancer progression involves the sequential accumulation of genetic alterations that cumulatively shape the tumour phenotype. In prostate cancer, tumours can follow divergent evolutionary trajectories that lead to distinct subtypes, but the causes of this divergence remain unclear. While causal inference could elucidate the factors involved, conventional methods are unsuitable due to the possibility of unobserved confounders and ambiguity in the direction of causality. Here, we propose a method that circumvents these issues and apply it to genomic data from 829 prostate cancer patients. We identify several genetic alterations that drive divergence as well as others that prevent this transition, locking tumours into one trajectory. Further analysis reveals that these genetic alterations may cause each other, implying a positive-feedback loop that accelerates divergence. Our findings provide insights into how cancer subtypes emerge and offer a foundation for genomic surveillance strategies aimed at monitoring the progression of prostate cancer. © 2025, CC BY-NC-SA.

关键词： Lung cancer

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