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Science China Life Sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

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Noise response in monomolecular closed systems

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Journal of the Chinese Chemical Society 2023年第3期70卷 747-758页

作者： Fajiculay, Erickson Hsu, Chao-Ping Institute of Chemistry Academia Sinica Taipei Taiwan Bioinformatics Program Institute of Statistical Science Taiwan International Graduate Program Academia Sinica Taipei Taiwan Institute of Bioinformatics and Structure Biology National Tsinghua University Hsinchu City Taiwan Physics Division National Center for Theoretical Sciences Taipei Taiwan Genome and Systems Biology Degree Program National Taiwan University Taipei Taiwan

One of the primary interests in understanding biological systems is the interaction between noise and cellular regulation. Noise levels can be altered by rate constant perturbations, which may result in an imbalance of important biological functions. Although the theory of noise localization can estimate noise perturbation response, it is currently limited to open systems, where the majority of biological networks belong. However, during their lifetime, some components of biological systems participate in reactions that can be categorized under a closed system. Therefore, a counterpart theory for closed systems is desirable. In this work, steady-state perturbations of monomolecular closed systems and ways to estimate the response of noise as (co)variances are explored. We extend the structural sensitivity analysis for analyzing noise response patterns by using additional clues derived from multinomial assumptions. We identified scenarios in which this combination might fail in noise prediction, which we call noise flip, arising from the nonlinear dependence of variance upon composition. Nevertheless, theoretical intuition and simulation show that noise flip diminishes with increasing system size. This work provides an efficient and scalable means to estimate noise responses with potential applications in model discrimination, network-wide response scanning, and reconstruction. © 2023 The Chemical Society Located in Taipei & Wiley-VCH GmbH.

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Lymphatic endothelial mTORC1 instructs metabolic and developmental signaling during lymphangiogenesis

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Developmental Cell 2025年

作者： Han, Fei Simeroth, Summer Zhu, Jie Gryniuk, Irma Pranay, Atul Chen, Weiqing Wang, Yuan Cai, Yuanyuan Shen, Zhiyuan Wang, Guangyu Griffin, Courtney T. Xia, Lijun Yu, Pengchun Cardiovascular Biology Research Program Oklahoma Medical Research Foundation Oklahoma City 73104 OK United States Department of Cell Biology University of Oklahoma Health Sciences Center Oklahoma City 73104 OK United States Aging and Metabolism Research Program Oklahoma Medical Research Foundation Oklahoma City 73104 OK United States Center for Bioinformatics and Computational Biology Houston Methodist Research Institute Houston TX United States Department of Physiology Biophysics & Systems Biology Weill Cornell Graduate School of Medical Science Weill Cornell Medicine Cornell University New York NY United States Department of Radiation Oncology Rutgers Cancer Institute and Robert Wood Johnson Medical School Rutgers University New Brunswick NJ United States Center for Cardiovascular Regeneration Houston Methodist Research Institute Houston TX United States Center for RNA Therapeutics Houston Methodist Research Institute Houston TX United States Department of Cardiothoracic Surgery Weill Cornell Medicine Cornell University New York NY United States Department of Biochemistry and Physiology University of Oklahoma Health Sciences Center Oklahoma City OK United States

The lymphatic vasculature comprises lymphatic capillaries and collecting vessels. To support lymphatic development, lymphatic endothelial cells (LECs) utilize nutrients to fuel lymphangiogenic processes. Meanwhile, LECs maintain constant prospero homeobox 1 (PROX1) expression critical for lymphatic specification. However, molecular mechanisms orchestrating nutrient metabolism while sustaining PROX1 levels in LECs remain unclear. Here, we show that loss of RAPTOR, an indispensable mechanistic target of rapamycin complex 1 (mTORC1) component, downregulates PROX1 and impairs lymphatic capillary growth and differentiation of collecting lymphatics in mice. Mechanistically, mTORC1 inhibition in mouse and human LECs causes Myc reduction, which decreases hexokinase 2 (HK2) and glutaminase (GLS), inhibiting glycolysis and glutaminolysis. Myc or HK2/GLS ablation impedes lymphatic capillary and collecting vessel formation. Interestingly, mTORC1 regulation of PROX1 is independent of Myc-HK2/GLS signaling. Moreover, genetic interaction analysis indicates that Myc and PROX1 play crucial roles in mTORC1-regulated lymphatic development. Collectively, our findings identify mTORC1 as a key regulator of metabolic programs and PROX1 expression during lymphangiogenesis. © 2025 Elsevier Inc.

关键词： glutaminase glutaminolysis glycolysis hexokinase 2 lymphangiogenesis lymphatic endothelial cell lymphatic vessel mTORC1 Myc PROX1

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Innovative computational advances for disease outbreaks

Innovative computational advances for disease outbreaks

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World Congress on Engineering 2016, WCE 2016

作者： Gebreyesus, Kidane Desta Chang, Chuan-Hsiung Institute of Information Science Bioinformatics Program Taiwan International Graduate Program Academia Sinica Taipei 115 Taiwan Center for Systems and Synthetic Biology Institute of Biomedical Informatics National Yang-Ming University Taipei 112 Taiwan

ISBN: (纸本)9789881925305

SIR (susceptible, infectious, removed) epidemic models are commonly used in infectious disease study. These models use systems of differential equations to estimate epidemiological parameters. However, differential equations often suffer from numerical challenges. Here, we proposed simple, effective and robust innovative methods for estimating key infectious disease parameters. Our methods are reformed from the SIR models, by adjusting components of model compartments. We illustrated the methods using the recent large disease outbreaks, Ebola virus disease and Middle East respiratory syndrome Coronavirus.

关键词： Coronavirus

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SeqCP: A sequence-based algorithm for searching circularly permuted proteins

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Computational and Structural Biotechnology Journal 2023年 21卷 185-201页

作者： Chen, Chi-Chun Huang, Yu-Wei Huang, Hsuan-Cheng Lo, Wei-Cheng Lyu, Ping-Chiang Bioinformatics Program Institute of Information Science Taiwan International Graduate Program Academia Sinica Taipei115 Taiwan Institute of Bioinformatics and Structural Biology National Tsing Hua University Hsinchu300 Taiwan Institute of Bioinformatics and Systems Biology National Yang Ming Chiao Tung University Hsinchu300 Taiwan Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei112 Taiwan Department of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu300 Taiwan The Center for Bioinformatics Research National Yang Ming Chiao Tung University Hsinchu Taiwan

Circular permutation (CP) is a protein sequence rearrangement in which the amino- and carboxyl-termini of a protein can be created in different positions along the imaginary circularized sequence. Circularly permutated proteins usually exhibit conserved three-dimensional structures and functions. By comparing the structures of circular permutants (CPMs), protein research and bioengineering applications can be approached in ways that are difficult to achieve by traditional mutagenesis. Most current CP detection algorithms depend on structural information. Because there is a vast number of proteins with unknown structures, many CP pairs may remain unidentified. An efficient sequence-based CP detector will help identify more CP pairs and advance many protein studies. For instance, some hypothetical proteins may have CPMs with known functions and structures that are informative for functional annotation, but existing structure-based CP search methods cannot be applied when those hypothetical proteins lack structural information. Despite the considerable potential for applications, sequence-based CP search methods have not been well developed. We present a sequence-based method, SeqCP, which analyzes normal and duplicated sequence alignments to identify CPMs and determine candidate CP sites for proteins. SeqCP was trained by data obtained from the Circular Permutation Database and tested with nonredundant datasets from the Protein Data Bank. It shows high reliability in CP identification and achieves an AUC of 0.9. SeqCP has been implemented into a web server available at: http://***/SeqCP/. © 2022 The Authors

关键词： Proteins

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Choroid plexus apocrine secretion shapes CSF proteome during mouse brain development

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Nature Neuroscience 2025年 1-14页

作者： Ya’el Courtney Joshua P. Head Neil Dani Frederick B. Shipley Maria K. Lehtinen Olga V. Chechneva Cameron Sadegh Yong Zhang Michael J. Holtzman Towia A. Libermann Department of Pathology Boston Children’s Hospital and Harvard Medical School Boston MA USA Graduate Program in Neuroscience Harvard Medical School Boston MA USA Department of Cell and Developmental Biology Vanderbilt University Nashville TN USA Graduate Program in Biophysics Harvard University Cambridge MA USA Department of Neurosurgery University of California Davis Sacramento CA USA Pulmonary and Critical Care Medicine Department of Medicine Washington University School of Medicine St. Louis MO USA Proteomics Bioinformatics and Systems Biology Center Beth Israel Deaconess Medical Center and Harvard Medical School Boston MA USA

The choroid plexus (ChP) regulates cerebrospinal fluid (CSF) composition, providing essential molecular cues for brain development; yet, embryonic ChP secretory mechanisms remain poorly defined. Here we identify apocrine secretion by embryonic ChP epithelial cells as a key regulator of the CSF proteome and neurodevelopment in male and female mice. We demonstrate that the activation of serotonergic 5-HT2Creceptors (by WAY-161503) triggers sustained Ca2+signaling, driving high-volume apocrine secretion in mouse and human ChP. This secretion alters the CSF proteome, stimulating neural progenitors lining the brain’s ventricles and shifting their developmental trajectory. Inducing ChP secretion in utero in mice disrupts neural progenitor dynamics, cerebral cortical architecture and offspring behavior. Additionally, illness or lysergic acid diethylamide exposure during pregnancy provokes coordinated ChP secretion in the mouse embryo. Our findings reveal a fundamental secretory pathway in the ChP that shapes brain development, highlighting how its disruption can have lasting consequences for brain health.

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Non-Coding RNA Analysis Using the Rfam Database

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Current Protocols in bioinformatics 2018年第1期62卷 e51-e51页

作者： Kalvari, Ioanna Nawrocki, Eric P. Argasinska, Joanna Quinones-Olvera, Natalia Finn, Robert D. Bateman, Alex Petrov, Anton I. European Molecular Biology Laboratory European Bioinformatics Institute Wellcome Genome Campus Hinxton Cambridge United Kingdom National Center for Biotechnology Information National Institutes of Health Department of Health and Human Services Bethesda MD United States Systems Biology Graduate Program Harvard University Cambridge MA United States

Rfam is a database of non-coding RNA families in which each family is represented by a multiple sequence alignment, a consensus secondary structure, and a covariance model. Using a combination of manual and literature-based curation and a custom software pipeline, Rfam converts descriptions of RNA families found in the scientific literature into computational models that can be used to annotate RNAs belonging to those families in any DNA or RNA sequence. Valuable research outputs that are often locked up in figures and supplementary information files are encapsulated in Rfam entries and made accessible through the Rfam Web site. The data produced by Rfam have a broad application, from genome annotation to providing training sets for algorithm development. This article gives an overview of how to search and navigate the Rfam Web site, and how to annotate sequences with RNA families. The Rfam database is freely available at http://***. © 2018 by John Wiley & Sons, Inc. Copyright © 2018 John Wiley & Sons, Inc.

关键词： genome annotation Infernal non-coding RNA Rfam RNA family

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IDH1 fine-tunes cap-dependent translation initiation

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Journal of Molecular Cell biology 2019年第10期11卷 816-828页

作者： Lichao Liu JYuyang Lu Fajin Li Xudong Xing Tong Li Xuerui Yang Xiaohua Shen Tsinghua-Peking Center for Life Sciences School of Medicine and School of Life SciencesTsinghua UniversityBeijing 100084China MOE Key Laboratory of Bioinformatics Center for Synthetic Systems BiologySchool of Life SciencesTsinghua UniversityBeijing 100084China Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program School of Life SciencesTsinghua UniversityBeijing 100084China

The metabolic enzyme isocitrate dehydrogenase 1(IDH1)catalyzes the oxidative decarboxylation of isocitrate to a-ketoglutarate(a-KG).Its mutation often leads to aberrant gene expression in ***1 was reported to bind thousands of RNA transcripts in a sequence-dependent manner;yet,the functional significance of this RNA-binding activity remains ***,we report that IDH1 promotes mRNA translation via direct associations with polysome mRNA and translation *** proteomic analysis in embryonic stem cells(ESCs)revealed strikingenrichmentof ribosomal proteins and translation regulators in IDH1-bound protein *** performed ribosomal profiling and analyzed mRNA transcripts that are associated with actively translating ***,knockout of IDH1 in ESCs led to significant downregulation of polysome-bound mRNA in IDH1 targets and subtle upregulation of ribosome densities at the start codon,indicating inefficient translation initiation upon loss of *** IDH1 to a luciferase mRNA via the MS2-MBP system promotes luciferase translation,independently of the catalytic activity of ***,IDH1 fails to enhance luciferase translation driven by an internal ribosome entry ***,these results reveal an unforeseen role of IDH1 in fine-tuning cap-dependent translation via the initiation step.

关键词： IDH1 proteomic interactome translation regulation RNA-binding protein

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A spatial genome aligner for resolving chromatin architectures from multiplexed DNA FISH (Jan, 10.1038/s41587-022-01568-9, 2023)

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NATURE BIOTECHNOLOGY 2023年第7期41卷 1027-1027页

作者： Jia, Bojing Blair Jussila, Adam Kern, Colin Zhu, Quan Ren, Bing Bioinformatics and Systems Biology Graduate Program University of California San Diego La Jolla CA USA Medical Scientist Training Program University of California San Diego La Jolla CA USA Department of Cellular and Molecular Medicine Center for Epigenomics University of California San Diego La Jolla CA USA Ludwig Institute for Cancer Research La Jolla CA USA Institute of Genomic Medicine Moores Cancer Center School of Medicine University of California San Diego La Jolla CA USA

Multiplexed fluorescence in situ hybridization (FISH) is a widely used approach for analyzing three-dimensional genome organization, but it is challenging to derive chromosomal conformations from noisy fluorescence signals, and tracing chromatin is not straightforward. Here we report a spatial genome aligner that parses true chromatin signal from noise by aligning signals to a DNA polymer model. Using genomic distances separating imaged loci, our aligner estimates spatial distances expected to separate loci on a polymer in three-dimensional space. Our aligner then evaluates the physical probability observed signals belonging to these loci are connected, thereby tracing chromatin structures. We demonstrate that this spatial genome aligner can efficiently model chromosome architectures from DNA FISH data across multiple scales and be used to predict chromosome ploidies de novo in interphase cells. Reprocessing of previous whole-genome chromosome tracing data with this method indicates the spatial aggregation of sister chromatids in S/G2 phase cells in asynchronous mouse embryonic stem cells and provides evidence for extranumerary chromosomes that remain tightly paired in postmitotic neurons of the adult mouse cortex.

关键词： Gene regulation Image processing

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Modularity detection in protein-protein interaction networks

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BMC Research Notes 2011年第1期4卷 1-6页

作者： Narayanan, Tejaswini Gersten, Merril Subramaniam, Shankar Grama, Ananth Department of Electrical and Computer Engineering University of California San Diego United States Graduate Program in Bioinformatics and Systems Biology University of California San Diego United States Department of Bioengineering University of California San Diego United States Department of Computer Science Purdue University West Lafayette IN United States

Background: Many recent studies have investigated modularity in biological networks, and its role in functional and structural characterization of constituent biomolecules. A technique that has shown considerable promise in the domain of modularity detection is the Newman and Girvan (NG) algorithm, which relies on the number of shortest-paths across pairs of vertices in the network traversing a given edge, referred to as the betweenness of that edge. The edge with the highest betweenness is iteratively eliminated from the network, with the betweenness of the remaining edges recalculated in every iteration. This generates a complete dendrogram, from which modules are extracted by applying a quality metric called modularity denoted by Q. This exhaustive computation can be prohibitively expensive for large networks such as Protein-Protein Interaction Networks. In this paper, we present a novel optimization to the modularity detection algorithm, in terms of an efficient termination criterion based on a target edge betweenness value, using which the process of iterative edge removal may be terminated. Results: We validate the robustness of our approach by applying our algorithm on real-world protein-protein interaction networks of Yeast, *** and Drosophila, and demonstrate that our algorithm consistently has significant computational gains in terms of reduced runtime, when compared to the NG algorithm. Furthermore, our algorithm produces modules comparable to those from the NG algorithm, qualitatively and quantitatively. We illustrate this using comparison metrics such as module distribution, module membership cardinality, modularity Q, and Jaccard Similarity Coefficient. Conclusions: We have presented an optimized approach for efficient modularity detection in networks. The intuition driving our approach is the extraction of holistic measures of centrality from graphs, which are representative of inherent modular structure of the underlying network, and the applic

关键词： Biological Network Jaccard Index Input Network High Betweenness Jaccard Similarity Coefficient

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