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Single-cell epigenome analysis reveals age-associated decay of heterochromatin domains in excitatory neurons in the mouse brain

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细胞研究（英文版） 2022年第11期32卷 1008-1021页

作者： Yanxiao Zhang Maria Luisa Amaral Chenxu Zhu Steven Francis Grieco Xiaomeng Hou Lin Lin Justin Buchanan Liqi Tong Sebastian Preissl Xiangmin Xu Bing Ren Ludwig Institute for Cancer Research La JollaCAUSA School of Life Sciences Westlake UniversityHangzhouChina Bioinformatics and Systems Biology Graduate Program University of California San DiegoLa JollaCAUSA Ludwig Institute for Cancer Research La JollaCAUSA Department of Anatomy and Neurobiology School of MedicineUniversity of CaliforniaIrvineCAUSA Center for Epigenomics University of California San DiegoLa JollaCAUSA.6Institute of Experimental and Clinical Pharmacology and ToxicologyFaculty of MedicineUniversity of FreiburgFreiburgGermany Center for Epigenomics University of California San DiegoLa JollaCAUSA.6Institute of Experimental and Clinical Pharmacology and ToxicologyFaculty of MedicineUniversity of FreiburgFreiburgGermany The Center for Neural Circuit Mapping University of CaliforniaIrvineCAUSA Department of Anatomy and Neurobiology School of MedicineUniversity of CaliforniaIrvineCAUSA Department of Cellular and Molecular Medicine University of California San Diego School of MedicineLa JollaCAUSA Ludwig Institute for Cancer Research La JollaCAUSA Center for Epigenomics University of California San DiegoLa JollaCAUSA.6Institute of Experimental and Clinical Pharmacology and ToxicologyFaculty of MedicineUniversity of FreiburgFreiburgGermany institute for Genomic Medicine University of California San DiegoLa JollaCAUSA

Loss of heterochromatin has been implicated as a cause of pre-mature aging and age-associated decline in organ functions in mammals;however,the specific cell types and gene loci affected by this type of epigenetic change have remained *** address this knowledge gap,we probed chromatin accessibility at single-cell resolution in the brains,hearts,skeletal muscles,and bone marrows from young,middle-aged,and old mice,and assessed age-associated changes at 353,126 candidate cis-regulatory elements(cCREs)across 32 major cell ***,we detected increased chromatin accessibility within specific heterochromatin domains in old mouse excitatory *** gain of chromatin accessibility at these genomic loci was accompanied by the cell-type-specific loss of heterochromatin and activation of LINE1 *** further confirmed the loss of the heterochromatin mark H3K9me3 in the excitatory neurons but not in inhibitory neurons or glial *** results reveal the cell-type-specific changes in chromatin landscapes in old mice and shed light on the scope of heterochromatin loss in mammalian aging.

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Homoharringtonine as a PHGDH inhibitor: Unraveling metabolic dependencies and developing a potent therapeutic strategy for high-risk neuroblastoma

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Biomedicine and Pharmacotherapy 2023年第1期166卷 115429-115429页

作者： Hsieh, Chiao-Hui Huang, Chen-Tsung Cheng, Yi-Sheng Hsu, Chun-Hua Hsu, Wen-Ming Chung, Yun-Hsien Liu, Yen-Lin Yang, Tsai-Shan Chien, Chia-Yu Lee, Yu-Hsuan Huang, Hsuan-Cheng Juan, Hsueh-Fen Department of Life Science National Taiwan University Taipei Taiwan Center for Computational and Systems Biology National Taiwan University Taipei Taiwan Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Institute of Plant Biology National Taiwan University Taipei Taiwan Genome and Systems Biology Degree Program National Taiwan University and Academia Sinica Taipei Taiwan Department of Agricultural Chemistry National Taiwan University Taipei Taiwan Department of Surgery National Taiwan University Hospital and National Taiwan University College of Medicine Taipei Taiwan Department of Pediatrics School of Medicine College of Medicine Taipei Medical University Taipei Taiwan Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine Taipei Taiwan

Neuroblastoma, a childhood cancer affecting the sympathetic nervous system, continues to challenge the development of potent treatments due to the limited availability of druggable targets for this aggressive illness. Recent investigations have uncovered that phosphoglycerate dehydrogenase (PHGDH), an essential enzyme for de novo serine synthesis, serves as a non-oncogene dependency in high-risk neuroblastoma. In this study, we show that homoharringtonine (HHT) acts as a PHGDH inhibitor, inducing intricate alterations in cellular metabolism, and thus providing an efficient treatment for neuroblastoma. We have experimentally verified the reliance of neuroblastoma on PHGDH and employed molecular docking, thermodynamic evaluations, and X-ray crystallography techniques to determine the bond interactions between HHT and PHGDH. Administering HHT to treat neuroblastoma resulted in effective cell elimination in vitro and tumor reduction in vivo. Metabolite and functional assessments additionally disclosed that HHT treatment suppressed de novo serine synthesis, initiating intricate metabolic reconfiguration and oxidative stress in neuroblastoma. Collectively, these discoveries highlight the potential of targeting PHGDH using HHT as a potent approach for managing high-risk neuroblastoma. © 2023 The Authors

关键词： Computational molecular docking Drug discovery High-risk neuroblastoma Metabolic reprogramming Molecular target therapy Phosphoglycerate dehydrogenase

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Publisher Correction: The ENCODE Imputation Challenge: a critical assessment of methods for cross-cell type imputation of epigenomic profiles

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Genome biology 2025年第1期26卷 31页

作者： Jacob Matthew Schreiber Carles A Boix Jin Wook Lee Hongyang Li Yuanfang Guan Chun-Chieh Chang Jen-Chien Chang Alex Hawkins-Hooker Bernhard Schölkopf Gabriele Schweikert Mateo Rojas Carulla Arif Canakoglu Francesco Guzzo Luca Nanni Marco Masseroli Mark James Carman Pietro Pinoli Chenyang Hong Kevin Y Yip Jefrey P Spence Sanjit Singh Batra Yun S Song Shaun Mahony Zheng Zhang Wuwei Tan Yang Shen Yuanfei Sun Minyi Shi Jessika Adrian Richard S Sandstrom Nina P Farrell Jessica M Halow Kristen Lee Lixia Jiang Xinqiong Yang Charles B Epstein J Seth Strattan Bradley E Bernstein Michael P Snyder Manolis Kellis William S Noble Anshul Bharat Kundaje Department of Genetics Stanford University Stanford CA USA. jmschreiber91@***. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge MA USA. Department of Genetics Stanford University Stanford CA USA. Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor MI USA. Department of Research and Development DeepSeq.AI San Francisco CA USA. RIKEN Center for Integrative Medical Sciences Yokohama Japan. Department of Empirical Inference Max Planck Institute for Intelligent Systems Stuttgart Germany. School of Life Sciences University of Dundee Dundee UK. Department of Electronics Information and Bioengineering Politecnico Di Milano Milan Italy. Department of Computational Biology University of Lausanne Lausanne Switzerland. Department of Computer Science and Engineering The Chinese University of Hong Kong Sha Tin Hong Kong. Sanford Burnham Prebys Medical Discovery Institute San Diego CA USA. Department of Electrical Engineering and Computer Sciences University of California Berkeley Berkeley CA USA. Department of Statistics University of California Berkeley Berkeley CA USA. Department of Biochemistry & Molecular Biology Center for Eukaryotic Gene Regulation Pennsylvania State University University Park PA USA. Department of Statistics Pennsylvania State University University Park PA USA. Department of Electrical and Computer Engineering Texas A&M University College Station TX USA. Altius Institute Seattle WA USA. Epigenomics Program The Broad Institute of MIT and Harvard Cambridge MA USA. Department of Genome Sciences University of Washington Seattle WA USA. Department of Computer Science Stanford University Stanford CA USA.

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A Comprehensive Survey of Human Proteome by Analyzing Large Scale Tandem Mass Spectrometry and RNA-sequencing Data

A Comprehensive Survey of Human Proteome by Analyzing Large ...

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第八届中国蛋白质组学大会

作者： Peng Li Jean Thierry-Mieg Baitang Ning James C Fuscoe Qi Lv Pengzhan Hu Ruichao Xue Christopher Mason Altuna Akalin Geng Chen Chen Zhao Weida Tong Huixiao Hong Lei Guo Hong Fang Tao Qing Ying Yu Wenzhong Xiao Danielle Thierry-Mieg Leming Shi Tieliu Shi Center for Bioinformatics and Computational Biology Shanghai Key Laboratory of Regulatory Biologythe Institute of Biomedical Sciences and School of Life SciencesEast China Normal UniversityShanghai 200241China National Center for Biotechnology InformationNational Library of MedicineNIHBethesdaMD 20894USA National Center for Toxicological ResearchFood and Drug AdministrationJeffersonARUSA Department of PhysiologyBiophysicsand Systems BiologyTri-Institutional Program(CornellSloan-Ketteringand Rockefeller)on Medicine1305 York AveUSA Center for PharmacogenomicsSchool of PharmacyFudan UniversityShanghaiChina Massachusetts General HospitalHarvard Medical School51 Blossom StBostonMA 02114USA National Center for Toxicological ResearchFood and Drug AdministrationJeffersonARUSA Center for Pharmacogenomics School of PharmacyFudan UniversityShanghaiChina

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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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Addendum to “Hypothesis: a glycoprotein-degradation complex formed by protein–protein interaction involves cytoplasmic peptide: N -glycanase” [Biochem. Biophys. Res. Commun. 302 (2003) 1–5]

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Biochemical and Biophysical Research Communications 2003年第2期303卷 732-732页

作者： Tadashi Suzuki William J Lennarz Undergraduate Program for Bioinformatics and Systems Biology and Department of Biophysics and Biochemistry Graduate School of Science University of Tokyo and PRESTO Japan Science and Technology Corporation (JST) Hongo 7-3-1 Bunkyo-ku Tokyo 113-0033 Japan Department of Biochemistry and Cell Biology and the Institute for Cell and Developmental Biology State University of New York at Stony Brook Stony Brook NY 11794-5215 USA

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Shotgun Sequencing of Southern Thailand Traditional Sweet Pickled Mango During the Production Process

SSRN

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SSRN 2023年

作者： Indrati, Niken Sirisarn, Wanchat Nuanpirom, Jiratchaya Yodsawat, Prasert Wanna, Warapond Prombutara, Pinidphon Phonsatta, Natthaporn Panya, Atikorn Maneerat, Suppasil Poungsombat, Patcha Khoomrung, Sakda Sumpavapol, Punnanee Food Microbiology and Safety Laboratory Food Science and Technology Program Faculty of Agro-Industry Prince of Songkla University Songkhla90110 Thailand Center of Genomics and Bioinformatics Research Faculty of Science Prince of Songkla University Songkhla90110 Thailand Omics Sciences and Bioinformatics Center Faculty of Science Chulalongkorn University Bangkok10330 Thailand Thailand Science Park Pathumthani12120 Thailand Center of Excellence in Innovative Biotechnology for Sustainable Utilization of Bioresources Faculty of Agro-Industry Prince of Songkla University Songkhla90110 Thailand Metabolomics and Systems Biology Department of Biochemistry Faculty of Medicine Siriraj Hospital Mahidol University Bangkok10700 Thailand Siriraj Metabolomics and Phenomics Center Faculty of Medicine Siriraj Hospital Mahidol University Bangkok10700 Thailand Faculty of Science Mahidol University Bangkok10400 Thailand Faculty of Science and Technology Songkhla Rajabhat University Songkhla90000 Thailand

Southern Thailand's traditional sweet pickled mango, known as Ma-Muang Bao Chae-Im (MBC), holds an important place in the local community. Several immersion processes, including brining and submersion in a hypertonic sugar solution, have been employed in the MBC production process. The occurrence of microbial succession during the production process affects its flavor due to the produced metabolites. However, microbial succession during the production process has received inadequate research. Therefore, this study aimed to (i) understand the microbiome alteration and (ii) investigate the functional profiling (metabolic pathways) during MBC production process. Through shotgun metagenomics, the microbial succession was shifting from bacteria in raw mango to fungi in MBC, with Ascomycota as the dominant phylum and Hanseniaspora as the dominant genus in MBC. The abundance of biosynthesis pathways was in line with the occurrence of microbial growth throughout the production process and prolonged storage. Polymyxin resistance was found as a significant pathway during brining 1 (B1-RM) and brining 2 (B2-B1). Mixed acid fermentation pathway and glyoxylate cycle were suggested to have a role in the aroma development of MBC by synthesizing metabolites related to the acidic and alcoholic aroma. © 2023, The Authors. All rights reserved.

关键词： Brining

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Quantum processor-inspired machine learning in the biomedical sciences

arXiv

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arXiv 2019年

作者： Li, Richard Y. Gujja, Sharvari Bajaj, Sweta R. Gamel, Omar E. Cilfone, Nicholas Gulcher, Jeffrey R. Lidar, Daniel A. Chittenden, Thomas W. Department of Chemistry University of Southern California Los AngelesCA United States Computational Biology and Bioinformatics Program Department of Biological Sciences University of Southern California Los AngelesCA United States Center for Quantum Information Science & Technology University of Southern California Los AngelesCA United States Computational Statistics and Bioinformatics Group Advanced Artificial Intelligence Research Laboratory Genuity Science BostonMA United States Complex Biological Systems Alliance MedfordMA United States Cancer Genetics Group Genuity Science BostonMA United States Department of Electrical Engineering University of Southern California Los AngelesCA United States Department of Physics and Astronomy University of Southern California Los AngelesCA United States Division of Genetics and Genomics Boston Children's Hospital Harvard Medical School BostonMA United States

Recent advances in high-throughput genomic technologies coupled with exponential increases in computer processing and memory have allowed us to interrogate the complex aberrant molecular underpinnings of human disease from a genome-wide perspective. While the deluge of genomic information is expected to increase, a bottleneck in conventional high-performance computing is rapidly approaching. Inspired in part by recent advances in physical quantum processors, we evaluated several unconventional machine learning (ML) strategies on actual human tumor data;namely "Ising-type" methods whose objective function is formulated identically to simulated annealing and quantum annealing, as well as a restricted Boltzmann machine. Here we show for the first time the efficacy of multiple Ising-type ML algorithms for classification of high-dimensional, multi-omics human cancer data from the Cancer Genome Atlas. To assess algorithm performance, we compared these classifiers to a variety of standard ML methods. Our results indicate the feasibility of using Ising-type ML to provide competitive classification of human cancer types and associated molecular subtypes and superior performance with smaller training datasets, thus providing compelling empirical evidence for the potential future application of unconventional computing approaches in the biomedical sciences. © 2019, CC BY.

关键词： Machine learning

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Computation of Microbial Ecosystems in Time and Space (COMETS): An open source collaborative platform for modeling ecosystems metabolism

arXiv

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arXiv 2020年

作者： Dukovski, Ilija Bajić, Djordje Chacón, Jeremy M. Quintin, Michael Vila, Jean C.C. Sulheim, Snorre Pacheco, Alan R. Bernstein, David B. Riehl, William J. Korolev, Kirill S. Sanchez, Alvaro Harcombe, William R. Segrè, Daniel Bioinformatics Program Boston University BostonMA United States Biological Design Center Boston University BostonMA United States Department of Ecology and Evolutionary Biology Yale University New HavenCT United States Microbial Sciences Institute Yale University West HavenCT United States Department of Ecology Evolution and Behavior University of Minnesota St. PaulMN United States BioTechnology Institute University of Minnesota St. PaulMN United States Department of Biotechnology and Food Science Norwegian University of Science and Technology Trondheim Norway Department of Biotechnology and Nanomedicine SINTEF Industry Trondheim Norway Environmental Genomics and Systems Biology Division Lawrence Berkeley National Laboratory BerkeleyCA United States Department of Biomedical Engineering Boston University BostonMA United States Department of Physics Boston University BostonMA United States Department of Biology Boston University BostonMA United States

Genome-scale stoichiometric modeling of metabolism has become a standard systems biology tool for modeling cellular physiology and growth. Extensions of this approach are also emerging as a valuable avenue for predicting, understanding and designing microbial communities. COMETS (Computation Of Microbial Ecosystems in Time and Space) was initially developed as an extension of dynamic flux balance analysis, which incorporates cellular and molecular diffusion, enabling simulations of multiple microbial species in spatially structured environments. Here we describe how to best use and apply the most recent version of this platform, COMETS 2, which incorporates a more accurate biophysical model of microbial biomass expansion upon growth, as well as several new biological simulation modules, including evolutionary dynamics and extracellular enzyme activity. COMETS 2 provides user-friendly Python and MATLAB interfaces compatible with the well-established COBRA models and methods, and comprehensive documentation and tutorials, facilitating the use of COMETS for researchers at all levels of expertise with metabolic simulations. This protocol provides a detailed guideline for installing, testing and applying COMETS 2 to different scenarios, with broad applicability to microbial communities across biomes and scales. Copyright © 2020, The Authors. All rights reserved.

关键词： Microorganisms

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Prognostic Impacts and Dynamic Changes of Cohesin Complex Gene Mutations in De Novo Acute Myeloid Leukemia

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BLOOD 2017年 130卷 2582-2582页

作者： Tsai, Cheng-Hong Hou, Hsin-An Tang, Jih-Luh Kuo, Yuan-Yeh Chiu, Yu-Chiao Lin, Chien-Chin Lin, Liang-In Yao, Ming Li, Chi-Cheng Huang, Shang-Yi Ko, Bor-Sheng Hsu, Szu Chun Lin, Chien-Ting Wu, Shang-Ju Chen, Chien-Yuan Tsai, Woei Chou, Wen-Chien Tien, Hwei-Fang Tai-Cheng Stem Cell Therapy Center National Taiwan University Taipei Taiwan Division of Hematology Department of Internal Medicine National Taiwan University Hospital Taipei Taiwan Genome and Systems Biology Degree Program National Taiwan University Taipei Taiwan Graduate Institute of Oncology National Taiwan University Taipei Taiwan Graduate institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Department of Clinical Laboratory Sciences and Medical Biotechnology National Taiwan University Taipei Taiwan Department of Laboratory Medicine National Taiwan University Hospital Taipei Taiwan Division of Hematology Department of Laboratory Medicine National Taiwan University Hospital Taipei Taiwan

2582

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