检索结果-内蒙古大学图书馆

KLF2 maintains lineage fidelity and suppresses CD8 T cell exhaustion during acute LCMV infection

Science (New York, N.Y.) 2025年第6735期387卷 eadn2337页

作者： Fagerberg, Eric Attanasio, John Dien, Christine Singh, Jaiveer Kessler, Emily A. Abdullah, Leena Shen, Jian Hunt, Brian G. Connolly, Kelli A. De Brouwer, Edward He, Jiaming Iyer, Nivedita R. Buck, Jessica Borr, Emily R. Damo, Martina Foster, Gena G. Giles, Josephine R. Huang, Yina H. Tsang, John S. Krishnaswamy, Smita Cui, Weiguo Joshi, Nikhil S. Department of Immunobiology Yale University School of Medicine New Haven CT United States Program in Computational Biology & Bioinformatics Yale University New Haven CT United States Department of Microbiology and Immunology Geisel School of Medicine at Dartmouth NH United States Department of Pathology Feinberg School of Medicine at Northwestern University Chicago IL United States Department of Genetics and Computer Science Yale University School of Medicine New Haven CT United States Department of Medicine University of Chicago Chicago IL United States Section of Hematology Department of Internal Medicine Yale University School of Medicine New Haven CT United States Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania Philadelphia PA United States Institute for Immunology Perelman School of Medicine University of Pennsylvania Philadelphia PA United States Parker Institute for Cancer Immunotherapy Perelman School of Medicine University of Pennsylvania Philadelphia PA United States Yale Center for Systems and Engineering Immunology Yale University School of Medicine New Haven CT United States New Haven CT United States Department of Biomedical Engineering Yale University New Haven CT United States Computational Biology and Bioinformatics Program Yale University CT USA New Haven United States Applied Math Program Yale University New Haven CT United States

Naïve CD8 T cells have the potential to differentiate into a spectrum of functional states during an immune response. How these developmental decisions are made and what mechanisms exist to suppress differentiation toward alternative fates remains unclear. We employed in vivo CRISPR-Cas9-based perturbation sequencing to assess the role of ~40 transcription factors (TFs) and epigenetic modulators in T cell fate decisions. Unexpectedly, we found that knockout of the TF Klf2 resulted in aberrant differentiation to exhausted-like CD8 T cells during acute infection. KLF2 was required to suppress the exhaustion-promoting TF TOX and to enable the TF TBET to drive effector differentiation. KLF2 was also necessary to maintain a polyfunctional tumor-specific progenitor state. Thus, KLF2 provides effector CD8 T cell lineage fidelity and suppresses the exhaustion program.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Transcriptional regulation by PHGDH drives amyloid pathology in Alzheimer's disease

引用

Cell 2025年

作者： Chen, Junchen Hadi, Fatemeh Wen, Xingzhao Zhao, Wenxin Xu, Ming Xue, Shuanghong Lin, Pei Calandrelli, Riccardo Richard, John Lalith Charles Song, Zhixuan Li, Jessica Amani, Alborz Liu, Yang Chen, Xu Zhong, Sheng Shu Chien-Gene Lay Department of Bioengineering University of California San Diego La Jolla San Diego CA United States Institute of Engineering in Medicine University of California San Diego La Jolla San Diego CA United States Program in Bioinformatics and Systems Biology University of California San Diego La Jolla San Diego CA United States School of Biological Sciences University of California San Diego La Jolla San Diego CA United States Department of Neurosciences University of California San Diego La Jolla San Diego CA United States Neuroscience Graduate Program University of California San Diego La Jolla San Diego CA United States

Virtually all individuals aged 65 or older develop at least early pathology of Alzheimer's disease (AD), yet most lack disease-causing mutations in APP, PSEN, or MAPT, and many do not carry the APOE4 risk allele. This raises questions about AD development in the general population. Although transcriptional dysregulation has not traditionally been a hallmark of AD, recent studies reveal significant epigenomic changes in late-onset AD (LOAD) patients. We show that altered expression of the LOAD biomarker phosphoglycerate dehydrogenase (PHGDH) modulates AD pathology in mice and human brain organoids independent of its enzymatic activity. PHGDH has an uncharacterized role in transcriptional regulation, promoting the transcription of inhibitor of nuclear factor kappa-B kinase subunit alpha (IKKa) and high-mobility group box 1 (HMGB1) in astrocytes, which suppress autophagy and accelerate amyloid pathology. A blood-brain-barrier-permeable small-molecule inhibitor targeting PHGDH's transcriptional function reduces amyloid pathology and improves AD-related behavioral deficits. These findings highlight transcriptional regulation in LOAD and suggest therapeutic strategies beyond targeting familial mutations. © 2025 The Author(s)

关键词： Alzheimer's disease amyloid pathology astrocyte biomarker LOAD PHGDH small molecule therapeutic candidate transcriptional regulation

来源：评论

学校读者我要写书评

暂无评论

Predicting dynamic cellular protein-RNA interactions by deep learning using in vivo RNA structures

引用

细胞研究（英文版） 2021年第5期31卷 495-516页

作者： Lei Sun Kui Xu Wenze Huang Yucheng T.Yang Pan Li Lei Tang Tuanlin Xiong Qiangfeng Cliff Zhang MOE Key Laboratory of Bioinformatics Beijing Advanced Innovation Center for Structural Biology and Frontier Research Center for Biological StructureCenter for Synthetic and Systems BiologySchool of Life SciencesTsinghua UniversityBeijing 100084China Tsinghua-Peking Center for Life Sciences Beijing 100084China Program in Computational Biology and Bioinformatics Yale UniversityNew HavenCT 06520USA Department of Molecular Biophysics and Biochemistry Yale UniversityNew HavenCT 06520USA

Interactions with RNA-binding proteins (RBPs) are integral to RNA function and cellular regulation,and dynamically reflect specific cellular ***,presently available tools for predicting RBP-RNA interactions employ RNA sequence and/or predicted RNA structures,and therefore do not capture their condition-dependent ***,after profiling transcriptome-wide in vivo RNA secondary structures in seven cell types,we developed PrismNet,a deep learning tool that integrates experimental in vivo RNA structure data and RBP binding data for matched cells to accurately predict dynamic RBP binding in various cellular *** results for 168 RBPs support its utility for both understanding CUP-seq results and largely extending such interaction data to accurately analyze additional cell ***,PrismNet employs an 'attention' strategy to computationally identify exact RBP-binding nucleotides,and we discovered enrichment among dynamic RBP-binding sites for structure-changing variants(riboSNitches),which can link genetic diseases with dysregulated RBP *** rich profiling data and deep learning-based prediction tool provide access to a previously inaccessible layer of cell-type-specific RBP-RNA interactions,with clear utility for understanding and treating human diseases.

关键词： bioinformatics Mechanisms of disease Molecular biology

来源：评论

学校读者我要写书评

暂无评论

Noise response in monomolecular closed systems

引用

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.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Biomedical data and AI

引用

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.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Target genes discovery through copy number alteration analysis in human hepatocellular carcinoma

引用

World Journal of Gastroenterology 2013年第47期19卷 8873-8879页

作者： De-Leung Gu Yen-Hsieh Chen Jou-Ho Shih Chi-Hung Lin Yuh-Shan Jou Chian-Feng Chen Institute of Biomedical Sciences Academia Sinica Institute of Microbiology and Immunology School of Life ScienceNational Yang-Ming University Bioinformatics Program Taiwan International Graduate ProgramAcademia Sinica Genome and Systems Biology Degree Program National Taiwan University VYM Genome Research Center National Yang-Ming University

High-throughput short-read sequencing of exomes and whole cancer genomes in multiple human hepatocellular carcinoma(HCC)cohorts confirmed previously identified frequently mutated somatic genes,such as TP53,CTNNB1 and AXIN1,and identified several novel genes with moderate mutation frequencies,including ARID1A,ARID2,MLL,MLL2,MLL3,MLL4,IRF2,ATM,CDKN2A,FGF19,PIK3CA,RPS6KA3,JAK1,KEAP1,NFE2L2,C16orf62,LEPR,RAC2,and *** classification of these mutated genes suggested that alterations in pathways participating in chromatin remodeling,Wnt/β-catenin signaling,JAK/STAT signaling,and oxidative stress play critical roles in HCC ***,because there are few druggable genes used in HCC therapy,the identification of new therapeutic targets through integrated genomic approaches remains an important *** a large amount of HCC genomic data genotyped by high density single nucleotide polymorphism arrays is deposited in the public domain,copy number alteration(CNA)analyses of these arrays is a cost-effective way to reveal target genes through profiling of recurrent and overlapping amplicons,homozygous deletions and potentially unbalanced chromosomal translocations accumulated during HCC ***,integration of CNAs with other high-throughput genomic data,such as aberrantly coding transcriptomes and non-coding gene expression in human HCC tissues and rodent HCC models,provides lines of evidence that can be used to facilitate the identification of novel HCC target genes with the potential of improving the survival of HCC patients.

关键词： Copy number alteration High-density single nucleotide polymorphism arrays Driver genes Hepatocellular carcinoma

来源：评论

学校读者我要写书评

暂无评论

Convergent Usage of Amino Acids in Human Cancers as A Reversed Process of Tissue Development

引用

Genomics, Proteomics & bioinformatics 2022年第1期20卷 147-162页

作者： Yikai Luo Han Liang Graduate Program in Quantitative and Computational Biosciences Baylor College of MedicineHoustonTX 77030USA Department of Bioinformatics and Computational Biology The University of Texas MD Anderson Cancer CenterHoustonTX 77030USA Department of Systems Biology The University of Texas MD Anderson Cancer CenterHoustonTX 77030USA

Genome-and transcriptome-wide amino acid usage preference across different species is a well-studied phenomenon in molecular evolution,but its characteristics and implication in cancer evolution and therapy remain largely ***,we analyzed large-scale transcriptome/proteome profiles,such as The Cancer Genome Atlas(TCGA),the Genotype-Tissue Expression(GTEx),and the Clinical Proteomic Tumor Analysis Consortium(CPTAC),and found that compared to normal tissues,different cancer types showed a convergent pattern toward using biosynthetically low-cost amino *** a pattern can be accurately captured by a single index based on the average biosynthetic energy cost of amino acids,termed energy cost per amino acid(ECPA).With this index,we further compared the trends of amino acid usage and the contributing genes in cancer and tissue development,and revealed their reversed ***,focusing on the liver,a tissue with a dramatic increase in ECPA during development,we found that ECPA represents a powerful biomarker that could distinguish liver tumors from normal liver samples consistently across 11 independent patient cohorts and outperforms any index based on single *** study reveals an important principle underlying cancer evolution and suggests the global amino acid usage as a system-level biomarker for cancer diagnosis.

关键词： Amino acid usage Tissue development Biosynthetic energy Diagnostic biomarker

来源：评论

学校读者我要写书评

暂无评论

Genome-scale metabolic networks in time and space

引用

Current Opinion in systems biology 2018年 8卷 51-58页

作者： Øyås, Ove Stelling, Jörg Department of Biosystems Science and Engineering and SIB Swiss Institute of Bioinformatics ETH Zurich Basel Switzerland Systems Biology PhD Program Life Science Zurich Graduate School Zurich Switzerland

Constraint-based models (CBMs) are key tools for elucidating the behavior of genome-scale metabolic networks, but the assumption of steady state hinders their application to spatiotemporally varying and multicellular systems. Models that integrate CBMs with kinetics to allow dynamic simulation through dynamic flux balance analysis (DFBA) can circumvent this problem as well as the limitations of purely kinetic models. With many technical barriers for DFBA overcome in recent years, applications traditionally focused on metabolic engineering have expanded to address problems such as evolution of microbial communities, functions of biomedically relevant biofilms, and diet effects on Parkinson's disease. By addressing substantial computational challenges, we expect that such hybrid metabolic models will pave the way towards whole-cell modeling. © 2017 Elsevier Ltd

关键词： Constraint-based models Dynamic flux balance analysis Genome-scale models Metabolic networks Multi-scale models

来源：评论

学校读者我要写书评

暂无评论

Application of Random Forest in Limited Size Human Long Non-coding RNAs Identification with Secondary Structure Features 23

Application of Random Forest in Limited Size Human Long Non-...

引用

23rd International Computer Science and Engineering Conference, ICSEC 2019

作者： Anuntakarun, Songtham Wattanapornprom, Warin Lertampaiporn, Supatcha King Mongkut's University of Technology Thonburi Bioinformatics and Systems Biology Program Bangkok Thailand University of Technology Thonburi Department of Mathematics Bangkok Thailand Bangkok Thailand

ISBN: (纸本)9781728125442

In this work, preliminary experiments of using diverse machine learning algorithms and testing of multiple relevant features to discriminate between human lncRNAs and coding/partial coding sequences was performed. This research limited the size of human lncRNAs such that they are shorter than 1000 nucleotides. Various significant features in describing RNA sequence including sequenced based features, secondary structure features, base-pair features and structural robustness features were used in this study. Then, the top 20 significant features were selected using Wilcoxon rank-sum test and discovered that the secondary structure features are the unique characteristics for identifying the human lncRNAs which are quite difference with those in the groups of shorter and longer types of ncRNAs. Such features are suitable with the rule-based classifiers like Random Forest. According to 10-folding cross validation, the random forest model has shown the highest accuracy, sensitivity and specificity as well as the lowest false positive rate among all competitors. Furthermore, the model was compared with other state-of -the-art approaches such as CPC, CPAT, RNAcon and achieved the highest accuracy of 84.5% among all the participants. © 2019 IEEE.

关键词： RNA

来源：评论

学校读者我要写书评

暂无评论

Fast and accurate calculation of protein depth by Euclidean distance transform

Fast and accurate calculation of protein depth by Euclidean ...

引用

17th Annual International Conference on Research in Computational Molecular biology, RECOMB 2013

作者： Xu, Dong Li, Hua Zhang, Yang Bioinformatics and Systems Biology Program Sanford-Burnham Medical Research Institute United States Department of Computational Medicine and Bioinformatics University of Michigan United States Integration Application Center Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9783642371943

The depth of each atom/residue in a protein structure is a key attribution that has been widely used in protein structure modeling and function annotation. However, the accurate calculation of depth is time consuming. Here, we propose to use the Euclidean distance transform (EDT) to calculate the depth, which conveniently converts the protein structure to a 3D gray-scale image with each pixel labeling the minimum distance of the pixel to the surface of the molecule (i.e. the depth). We tested the proposed EDT method on a set of 261 non-redundant protein structures. The data show that the EDT method is 2.6 times faster than the widely used method by Chakravarty and Varadarajan. The depth value by EDT method is also highly accurate, which is almost identical to the depth calculated by exhaustive search (Pearson's correlation coefficient≈1). We believe the EDT-based depth calculation program can be used as an efficient tool to assist the studies of protein fold recognition and structure-based function annotation. © 2013 Springer-Verlag.

关键词： Proteins

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：