检索结果-内蒙古大学图书馆

Isotopic Labeling-Enabled Chemical Proteomics Analysis Revealed Structural and Functional Features of Allysine Modifications in Mammalian Cells and Tissues

引用

Analytical Chemistry 2025年 2025 Apr 28页

作者： Sin, Yi-Cheng Hosny, Nora Batti Angulski, Addeli Bez Kim, Do-Hyung Metzger, Joseph M. Chen, Yue Department of Biochemistry Molecular Biology and Biophysics University of Minnesota at Twin Cities MinneapolisMN55455 United States Department of Integrative Biology and Physiology University of Minnesota at Twin Cities MinneapolisMN55455 United States Bioinformatics and Computational Biology Program University of Minnesota at Twin Cities MinneapolisMN55455 United States

Allysine is a pivotal protein post-translational modification that regulates protein interaction and activities. It is also recognized as a marker of oxidative stress under certain metabolic and physiological conditions. In this study, we developed a capture-and-release chemical proteomics workflow with heavy isotopic labeling that enables system-wide enrichment and site-specific identification of allysine as well as other carbonylated peptides, such as peptides containing glutamic semialdehyde derived from the oxidative damage of arginine and proline, with high confidence. The streamlined workflow led to the identification of 434 allysine sites on 349 proteins in human 293T and HCT116 cells and 317 allysine sites on 157 proteins in mouse muscle tissues without any treatment with an oxidative stress-inducing chemical reagent. We identified 48 histone allysine sites, including 38 sites on core histones in human 293T cells, many of which overlapped with well-characterized histone acetylation and methylation epigenetic marks. Bioinformatic analysis revealed notable characteristics of the amino acid preferences of allysine flanking sequences and the significant depletion of allysine sites in the protein secondary structure in cultured human cells. Pathway analysis showed that allysine substrates were involved in diverse cellular processes including translation, protein folding, and RNA processing in human cells and were enriched with muscle contractile fiber proteins and metabolic enzymes in mouse muscle tissue. Thus, our integrated chemical proteomics analysis revealed the structural and functional features of allysine targets under regular growth conditions in cultured human cells and mouse tissues. © 2025 American Chemical Society.

关键词： Proteolysis

来源：评论

学校读者我要写书评

暂无评论

Antibiotic resistance profiles and mutations that might affect drug susceptibility in metagenome-assembled genomes of Legionella pneumophila and Aeromonas species from municipal wastewater

引用

BMC Microbiology 2025年第1期25卷 1-9页

作者： Poopedi, Evida Pierneef, Rian Singh, Tanusha Gomba, Annancietar Department of Oral and Maxillofacial Pathology Oral Microbiology and Oral Biology Sefako Makgatho Health Sciences University Ga-Rankuwa South Africa National Institute for Occupational Health National Health Laboratory Service Johannesburg South Africa Department of Clinical Microbiology and Infectious Diseases University of the Witwatersrand Johannesburg South Africa Department of Environmental Health University of Johannesburg Doornfontein 2028 South Africa Department of Biochemistry Genetics and Microbiology University of Pretoria Pretoria South Africa Centre for Bioinformatics and Computational Biology University of Pretoria Pretoria South Africa

Antibiotic resistance (AR) has emerged as a significant global health issue. Wastewater treatment plants (WWTPs) contain diverse bacterial communities, including pathogens, and have been identified as crucial reservoirs for the emergence and dissemination of AR. The present study aimed to identify antibiotic resistance genes (ARGs) and screen for the presence of mutations associated with AR in Legionella pneumophila and Aeromonas spp. from municipal wastewater. Metagenome-assembled genomes (MAGs) of L. pneumophila and Aeromonas spp. were reconstructed to investigate the molecular mechanisms of AR in these organisms. A total of 138 nonsynonymous single nucleotide variants (SNVs) in seven genes associated with AR and one deletion mutation in the lpeB gene were identified in L. pneumophila. In Aeromonas spp., two (aph(6)-Id and aph(3’’)-Ib) and five (bla_MOX−4, bla_OXA−1143, bla_OXA−724, cepH, and imiH) ARGs conferring resistance to aminoglycosides and β-lactams were identified, respectively. Moreover, this study presents β-lactam resistance genes, bla_OXA−1143 and bla_OXA−724, for the first time in Aeromonas spp. from a municipal WWTP. In conclusion, these findings shed light on the molecular mechanisms through which clinically relevant pathogenic bacteria such as L. pneumophila and Aeromonas spp. found in natural environments like municipal wastewater acquire AR. © The Author(s) 2025.

关键词： Antibiotic resistance Metagenome-assembled genome Molecular mechanisms Municipal wastewater Mutations

来源：评论

学校读者我要写书评

暂无评论

Engineering Mice to Study Human Immunity

引用

Annual Review of Immunology 2025年第1期43卷 451-487页

作者： Sefik, Esen Xiao, Tianli Chiorazzi, Michael Odell, Ian Zhang, Fengrui Agrawal, Kriti Micevic, Goran Flavell, Richard A. Department of Immunobiology Yale School of Medicine New Haven CT United States Howard Hughes Medical Institute Yale School of Medicine New Haven CT United States Department of Internal Medicine Section of Medical Oncology Yale School of Medicine New Haven CT United States Department of Dermatology Yale School of Medicine New Haven CT United States Computational Biology and Bioinformatics Program Yale University New Haven CT United States Department of Pathology Yale School of Medicine New Haven CT United States

Humanized mice, which carry a human hematopoietic and immune system, have greatly advanced our understanding of human immune responses and immunological diseases. These mice are created via the transplantation of human hematopoietic stem and progenitor cells into immunocompromised murine hosts further engineered to support human hematopoiesis and immune cell growth. This article explores genetic modifications in mice that enhance xeno-tolerance, promote human hematopoiesis and immunity, and enable xenotransplantation of human tissues with resident immune cells. We also discuss genetic editing of the human immune system, provide examples of how humanized mice with humanized organs model diseases for mechanistic studies, and highlight the roles of these models in advancing knowledge of organ biology, immune responses to pathogens, and preclinical drugs tested for cancer treatment. The integration of multi-omics and state-of-the art approaches with humanized mouse models is crucial for bridging existing human data with causality and promises to significantly advance mechanistic studies. Copyright © 2025 by the author(s).

关键词： cancer human hematopoietic stem and progenitor cells human immunity human organ modeling humanized mice immunological diseases infectious diseases xenotransplantation

来源：评论

学校读者我要写书评

暂无评论

From rare to more common: The emerging role of omics in improving understanding and treatment of severe inflammatory and hyperinflammatory conditions

引用

Journal of Allergy and Clinical Immunology 2025年第5期155卷 1435-1450页

作者： Keskitalo, Salla Seppänen, Mikko R.J. del Sol, Antonio Varjosalo, Markku Institute of Biotechnology Helsinki Institute of Life Science HiLIFE University of Helsinki Helsinki Finland Pediatric Research Center New Children's Hospital University of Helsinki and HUS Helsinki University Hospital Helsinki Finland Translational Immunology Research Program University of Helsinki Helsinki Finland European Reference Network Rare Immunodeficiency Autoinflammatory and Autoimmune Diseases Network (ERN RITA) Core Center Utrecht Netherlands Computational Biology Group Luxembourg Centre for Systems Biomedicine (LCSB) University of Luxembourg Esch-sur-Alzette Luxembourg Computational Biology Group Basque Research and Technology Alliance (CIC bioGUNE-BRTA) Derio Spain Ikerbasque Basque Foundation for Science Bilbao Spain Department of Biochemistry and Developmental Biology and Translational Cancer Medicine Program Faculty of Medicine University of Helsinki Finland Helsinki Finland

Inflammation is a pathogenic driver of many diseases, including atherosclerosis and rheumatoid arthritis. Hyperinflammation can be seen as any inflammatory response that is deleterious to the host, regardless of cause. In medicine, hyperinflammation is defined as severe, deleterious, and fluctuating systemic or local inflammation with presence of a cytokine storm. It has been associated with rare autoinflammatory disorders. However, advances in omics technologies, including genomics, proteomics, and metabolomics, have revealed it to be more common, occurring in sepsis and severe coronavirus disease 2019. With a focus on proteomics, this review highlights the key role of omics in this shift. Through an exploration of research, we present how omics technologies have contributed to improved diagnostics, prognostics, and targeted therapeutics in the field of hyperinflammation. We also discuss the integration of advanced technologies, multiomics approaches, and artificial intelligence in analyzing complex datasets to develop targeted therapies, and we address their potential for revolutionizing the clinical aspects of hyperinflammation. We emphasize personalized medicine approaches for effective treatments and outline challenges, including the need for standardized methodologies, robust bioinformatics tools, and ethical considerations regarding data privacy. This review aims to provide a comprehensive overview of the molecular mechanisms underpinning hyperinflammation and underscores the potential of omics technologies in enabling successful clinical management. © 2025 The Authors

关键词： artificial intelligence biomarker discovery cytokine storm genomics Hyperinflammation inflammation metabolomics multiomics omics technologies proteomics

来源：评论

学校读者我要写书评

暂无评论

Catalysis-Induced Highly-Stable Interface on Porous Silicon for High-Rate Lithium-Ion Batteries

引用

Nano-Micro Letters 2025年第8期17卷 548-563页

作者： Zhuobin Han Phornphimon Maitarad Nuttapon Yodsin Baogang Zhao Haoyu Ma Kexin Liu Yongfeng Hu Siriporn Jungsuttiwong Yumei Wang Li Lu Liyi Shi Shuai Yuan Yongyao Xia Yingying Lv Research Centre of Nanoscience and Nanotechnology Shanghai UniversityShanghai 200444People’s Republic of China Department of Chemistry Faculty of ScienceSilpakorn UniversityNakhon Pathom 73000Thailand Sinopec Shanghai Research Institute of Petrochemical Technology Co. Ltd.Shanghai 201208People’s Republic of China Department of Chemistry and Center of Excellence for Innovation in Chemistry Faculty of Science Ubon Ratchathani UniversityUbon Ratchathani 34190Thailand National University of Singapore(Chongqing)Research Institute Chongqing 401123People’s Republic of China Department of Chemistry Fudan UniversityShanghai 200433People’s Republic of China Emerging Industries Institute Shanghai University Jiaxing 314006ZhejiangPeople’s Republic of China Program in Bioinformatics and Computational Biology Graduate SchoolChulalongkorn UniversityBangkok 10330Thailand

Silicon stands as a key anode material in lithium-ion battery ascribing to its high energy ***,the poor rate performance and limited cycling life remain unresolved through conventional approaches that involve carbon composites or nanostructures,primarily due to the un-controllable effects arising from the substantial formation of a solid electrolyte interphase(SEI)during the ***,an ultra-thin and homogeneous Ti doping alumina oxide catalytic interface is meticulously applied on the porous Si through a synergistic etching and hydrolysis *** defect-rich oxide interface promotes a selective adsorption of fluoroethylene carbonate,leading to a catalytic reaction that can be aptly described as“molecular concentration-in situ conversion”.The resultant inorganic-rich SEI layer is electrochemical stable and favors ion-transport,particularly at high-rate cycling and high *** robustly shielded porous Si,with a large surface area,achieves a high initial Coulombic efficiency of 84.7%and delivers exceptional high-rate performance at 25 A g^(−1)(692 mAh g^(−1))and a high Coulombic efficiency of 99.7%over 1000 *** robust SEI constructed through a precious catalytic layer promises significant advantages for the fast development of silicon-based anode in fast-charging batteries.

关键词： Catalytic interface Mesoporous Inorganic-rich SEI Silicon anode Lithium-ion batteries

来源：评论

学校读者我要写书评

暂无评论

Quantum variational autoencoder utilizing regularized mixed-state latent representations

引用

Physical Review A 2025年第4期111卷 042416-042416页

作者： Gaoyuan Wang Jonathan Warrell Prashant S. Emani Mark Gerstein Program in Computational Biology and Bioinformatics Yale University New Haven Connecticut 06520 USA Department of Molecular Biophysics and Biochemistry Yale University New Haven Connecticut 06520 USA NEC Laboratories America Princeton New Jersey 08540 USA Department of Computer Science Yale University New Haven Connecticut 06520 USA Department of Statistics & Data Science Yale University New Haven Connecticut 06520 USA Department of Biomedical Informatics & Data Science Yale University New Haven Connecticut 06520 USA

A major challenge in near-term quantum computing is its application to large real-world datasets due to scarce quantum hardware resources. One approach to enabling tractable quantum models for such datasets involves finding low-dimensional representations that preserve essential information for downstream analysis. In classical machine learning, variational autoencoders (VAEs) facilitate efficient data compression, representation learning for subsequent tasks, and novel data generation. However, no quantum model has been proposed that exactly captures all of these features for direct application to quantum data on quantum computers. Some existing quantum models for data compression lack regularization of latent representations, thus preventing direct use for generation and control of generalization. Others are hybrid models with only some internal quantum components, impeding direct training on quantum data. To address this, we present a fully quantum framework, ζ-QVAE, which encompasses all the capabilities of classical VAEs and can be directly applied to map both classical and quantum data to a lower-dimensional space, while effectively reconstructing much of the original state from it. Our model utilizes regularized mixed states to attain optimal latent representations. It accommodates various divergences for reconstruction and regularization. Furthermore, by accommodating mixed states at every stage, it can utilize the full data density matrix and allow for a training objective defined on probabilistic mixtures of input data. Doing so, in turn, makes efficient optimization possible and has potential implications for private and federated learning. In addition to exploring the theoretical properties of ζ-QVAE, we demonstrate its performance on representative genomics and synthetic data. Our results indicate that ζ-QVAE consistently learns representations that better utilize the capacity of the latent space and exhibits similar or better performance compared with

关键词： Quantum computers

来源：评论

学校读者我要写书评

暂无评论

Oligodendroglia vulnerability in the human dorsal striatum in Parkinson’s disease

引用

Acta Neuropathologica 2025年第1期149卷 1-24页

作者： Barba-Reyes, Juan M. Harder, Lisbeth Marco Salas, Sergio Jaisa-aad, Methasit Muñoz-Castro, Clara Garma, Leonardo D. Rafati, Nima Nilsson, Mats Hyman, Bradley T. Serrano-Pozo, Alberto Muñoz-Manchado, Ana B. Unit of Cell Biology Department of Neuroscience Institute for Biomedical Research and Innovation of Cádiz (INiBICA) University of Cádiz Cádiz Spain Laboratory of Molecular Neurobiology Department of Medical Biochemistry and Biophysics Karolinska Institutet Stockholm Sweden Science for Life Laboratory Department of Biochemistry and Biophysics Stockholm University Stockholm Sweden Institute of Computational Biology Computational Health Center Helmholtz Munich Germany Department of Neurology Massachusetts General Hospital Boston MA United States Harvard Medical School Boston MA United States National Bioinformatics Infrastructure Sweden Science for Life Laboratory Department of Medical Biochemistry and Microbiology Uppsala University Uppsala Sweden Ciber of Mental Health (CIBERSAM) ISCIII Madrid 28029 Spain

Oligodendroglia are the responsible cells for myelination in the central nervous system and their involvement in Parkinson’s disease (PD) is poorly understood. We performed sn-RNA-seq and image-based spatial transcriptomics of human caudate nucleus and putamen (dorsal striatum) from PD and control brain donors to elucidate the diversity of oligodendroglia and how they are affected by the disease. We profiled a total of ~ 200.000 oligodendroglial nuclei, defining 15 subclasses, from precursor to mature cells, 4 of which are disease-associated. These PD-specific populations are characterized by the overexpression of heat shock proteins, as well as distinct expression signatures related to immune responses, myelination alterations, and disrupted cell signaling pathways. We have also identified impairments in cell communication and oligodendrocyte development, evidenced by changes in neurotransmitter receptors expression and cell adhesion molecules. In addition, we observed significant disruptions in oligodendrocyte development, with aberrant differentiation trajectories and shifts in cell proportions, particularly in the transition from mature oligodendrocytes to disease-associated states. Quantitative immunohistochemical analysis revealed decreased myelin levels in the PD striatum, which correlated with transcriptomic alterations. Furthermore, spatial transcriptomics mapping revealed the distinct localization of disease-associated populations within the striatum, with evidence of impaired myelin integrity. Thus, we uncover oligodendroglia as a critical cell type in PD and a potential new therapeutic target for myelin-based interventions. © The Author(s) 2025.

关键词： Myelin Neurodegeneration Oligodendrocyte Sc-RNA-seq Spatial transcriptomics Striatum

来源：评论

学校读者我要写书评

暂无评论

Pre-Training Genomic Language Model with Variants for Better Modeling Functional Genomics

SSRN

引用

SSRN 2025年

作者： Liu, Tianyu Zhang, Xiangyu Ying, Rex Zhao, Hongyu Interdepartmental Program in Computational Biology & Bioinformatics Yale University New HavenCT06511 United States Department of Biostatistics Yale University New HavenCT06511 United States Department of Computer Science Yale University New HavenCT06511 United States

Sequence-to-function models can predict gene expression from sequence data and be used to link genetic information with transcriptomics data to understand regulatory processes and their effects on complex phenotypes. The genomic language models are pre-trained with large-scale DNA sequences and can generate robust representations of these sequences by learning the genomic context. However, few studies can estimate the predictability of gene expression levels and bridge these two classes of models together to explore individualized gene expression prediction. In this manuscript, we propose UKBioBERT as a DNA language model pre-trained with genetic variants from UK BioBank. We demonstrate that UKBioBERT generates informative embeddings capable of identifying gene functions, and improving gene expression prediction in cell lines, thereby enhancing our understanding of gene expression predictability. Building upon these embeddings, we combine UKBioBERT with state-of-the-art sequence-to-function architectures, Enformer and Borzoi, to create UKBioFormer and UKBioZoi. These models exhibit better performance in predicting highly predictable gene expression levels and can be generalized across different cohorts. Furthermore, UKBioFormer effectively captures the relationship between genetic variants and expression variations, enabling in-silico mutation analyses. Collectively, our findings underscore the value of integrating genomic language models and sequence-to-function approaches for advancing functional genomics research. © 2025, The Authors. All rights reserved.

关键词： Genome

来源：评论

学校读者我要写书评

暂无评论

Rationally designed antimicrobial peptides with high selectivity and efficiency against phytopathogenic Ralstonia solanecearum

引用

Science of the Total Environment 2025年 976卷 179354页

作者： Manik, Melvalia Cristin Darai, Nitchakan Rungrotmongkol, Thanyada Duan, Lian Harada, Ryuhei Shigeta, Yasuteru Hengphasatporn, Kowit Vangnai, Alisa S. Biotechnology Program Faculty of Science Chulalongkorn University Bangkok10330 Thailand Futuristic Science Research Center School of Science Walailak University Nakhon Si Thammarat80160 Thailand Drug and Cosmetics Excellence Center Walailak University Thasala Nakhon Si Thammarat80160 Thailand Program in Bioinformatics and Computational Biology Chulalongkorn University Bangkok10330 Thailand Center for Computational Sciences University of Tsukuba 1-1-1 Tennodai Ibaraki Tsukuba305-8577 Japan Center of Excellence in Biocatalyst and Sustainable Biotechnology Faculty of Science Chulalongkorn University Bangkok10330 Thailand Department of Biochemistry Faculty of Science Chulalongkorn University Bangkok10330 Thailand

Ralstonia solanacearum, the causative agent of bacterial wilt, poses a global threat to agriculture, necessitating urgent and sustainable solutions as traditional methods lose efficacy. This study developed WRF-13, a synthetic antimicrobial peptide (AMP) designed to mimic natural AMPs, exhibiting potent antibacterial and anti-biofilm activity with high specificity against R. solanacearum. Mechanistic studies, including microscopy and computational analyses, demonstrated that WRF-13 disrupts the bacterial membrane. WRF-13 remained stable across a wide pH (5–8) and temperature (25–50 °C) range, essential for field applications, and showed no detectable toxicity to mammalian or plant cells at elevated concentrations. Greenhouse trials confirmed its efficacy in reducing bacterial wilt severity up to 65 %, highlighting its potential to protect crops from R. solanacearum infection. Overall, this study highlights WRF-13 as a targeted solution for managing bacterial wilt and advancing sustainable agriculture. © 2025 Elsevier B.V.

关键词： Antimicrobial peptides Bacterial wilt Crop protection In silico-peptide design Ralstonia solanacearum

来源：评论

学校读者我要写书评

暂无评论

Gumbel-Softmax Flow Matching with Straight-Through Guidance for Controllable Biological Sequence Generation

arXiv

引用

arXiv 2025年

作者： Tang, Sophia Zhang, Yinuo Tong, Alexander Chatterjee, Pranam Department of Biomedical Engineering Duke University United States Management and Technology Program University of Pennsylvania United States Center of Computational Biology Duke-NUS Medical School Singapore Mila Quebec AI Institute Canada Université de Montréal Canada Department of Computer Science Duke University United States Department of Biostatistics and Bioinformatics Duke University United States

Flow matching in the continuous simplex has emerged as a promising strategy for DNA sequence design, but struggles to scale to higher simplex dimensions required for peptide and protein generation. We introduce Gumbel-Softmax Flow and Score Matching, a generative framework on the simplex based on a novel Gumbel-Softmax interpolant with a time-dependent temperature. Using this interpolant, we introduce Gumbel-Softmax Flow Matching by deriving a parameterized velocity field that transports from smooth categorical distributions to distributions concentrated at a single vertex of the simplex. We alternatively present Gumbel-Softmax Score Matching which learns to regress the gradient of the probability density. Our framework enables high-quality, diverse generation and scales efficiently to higher-dimensional simplices. To enable training-free guidance, we propose Straight-Through Guided Flows (STGFlow), a classifier-based guidance method that leverages straight-through estimators to steer the unconditional velocity field toward optimal vertices of the simplex. STGFlow enables efficient inference-time guidance using classifiers pre-trained on clean sequences, and can be used with any discrete flow method. Together, these components form a robust framework for controllable de novo sequence generation. We demonstrate state-of-the-art performance in conditional DNA promoter design, sequence-only protein generation, and target-binding peptide design for rare disease treatment. © 2025, CC BY-NC-ND.

关键词： Velocity distribution

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：