检索结果-内蒙古大学图书馆

INFERRING MECHANISTIC PARAMETERS OF SOMATIC HYPERMUTATION USING NEURAL NETWORKS AND APPROXIMATE BAYESIAN COMPUTATION

引用

Annals of Applied Statistics 2025年第1期19卷 720-743页

作者： Fisher, Thayer Sung, Kevin Simon, Noah Fukuyama, Julia Matsen, Frederick A. Department of Biostatistics University of Washington United States Computational Biology Program Fred Hutchinson Cancer Center United States Department of Statistics Indiana University United States

Somatic hypermutation (SHM) is a critical enzyme-mediated process of the adaptive immune response in which antibodies acquire mutations to enhance antigen binding. Despite abundant research elucidating the biochemical basis of SHM, and substantial sequence data available for parameterization, previous computational models of SHM have not been explicitly mechanistic. In this paper we bridge this gap by developing a probabilistic latent variable model encapsulating a sequence of interacting steps, thus formulating the biochemical underpinnings of SHM in a mathematical framework. However, fitting this latent variable model is challenging. To navigate this complexity, we employ an approximate Bayesian computation strategy integrated with neural networks. We are able to estimate almost all of the parameters of the model to good accuracy but find that the parameters involving the boundaries of the nucleotide stripping process are slightly more challenging, given the type of data available. © Institute of Mathematical Statistics, 2025.

关键词： adaptive immunology Bayesian statistics computational biology likelihood-free inference

来源：评论

学校读者我要写书评

暂无评论

Engineering mtDNA deletions by reconstituting end joining in human mitochondria

引用

Cell 2025年第10期188卷 2778-2793.e21,24页

作者： Fu, Yi Land, Max Kavlashvili, Tamar Cui, Ruobing Kim, Minsoo DeBitetto, Emily Lieber, Toby Ryu, Keun Woo Choi, Elim Masilionis, Ignas Saha, Rahul Takizawa, Meril Baker, Daphne Tigano, Marco Lareau, Caleb A. Reznik, Ed Sharma, Roshan Chaligne, Ronan Thompson, Craig B. Sfeir, Agnel Molecular Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New York NY United States Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New York NY United States Cancer Biology and Genetics Program Memorial Sloan Kettering Cancer Center New York NY United States Computational Oncology Department of Epidemiology and Biostatistics Memorial Sloan Kettering Cancer Center New York NY United States Department of Pathology and Genomic Medicine Thomas Jefferson University Philadelphia PA United States Howard Hughes Medical Institute New York NY United States

Recent breakthroughs in the genetic manipulation of mitochondrial DNA (mtDNA) have enabled precise base substitutions and the efficient elimination of genomes carrying pathogenic mutations. However, reconstituting mtDNA deletions linked to mitochondrial myopathies remains challenging. Here, we engineered mtDNA deletions in human cells by co-expressing end-joining (EJ) machinery and targeted endonucleases. Using mitochondrial EJ (mito-EJ) and mito-ScaI, we generated a panel of clonal cell lines harboring a ∼3.5 kb mtDNA deletion across the full spectrum of heteroplasmy. Investigating these cells revealed a critical threshold of ∼75% deleted genomes, beyond which oxidative phosphorylation (OXPHOS) protein depletion, metabolic disruption, and impaired growth in galactose-containing media were observed. Single-cell multiomic profiling identified two distinct nuclear gene deregulation responses: one triggered at the deletion threshold and another progressively responding to heteroplasmy. Ultimately, we show that our method enables the modeling of disease-associated mtDNA deletions across cell types and could inform the development of targeted therapies. © 2025 Elsevier Inc.

关键词： DOGMA-seq end joining mitochondrial pathologies mtDNA mtDNA deletion

来源：评论

学校读者我要写书评

暂无评论

Machine learning approaches enable the discovery of therapeutics across domains

引用

Molecular Therapy 2025年第5期33卷 2269-2278页

作者： Chhibbar, Prabal Das, Jishnu Centre for Systems Immunology Department of Immunology School of Medicine University of Pittsburgh Pittsburgh 15213 PA United States Department of Computational and Systems Biology School of Medicine University of Pittsburgh Pittsburgh 15213 PA United States Integrative Systems Biology PhD Program School of Medicine University of Pittsburgh Pittsburgh 15213 PA United States

Multi-modal datasets have grown exponentially in the last decade. This has created an enormous demand for machine learning models that can predict complex outcomes by leveraging cellular, molecular, and humoral profiles. Corresponding inference of mechanisms can help to uncover new therapeutic targets. Here, we discuss how biological principles guide the design of predictive models and how interpretable machine learning can lead to novel mechanistic insights. We provide descriptions of multiple learning techniques and how suited they are to domain adaptations. Finally, we talk about broad learning capabilities of foundation models on large datasets and whether they can be used to provide meaningful inference about biological datasets. © 2025 The Author(s)

关键词： deep learning foundation models interpretable machine learning therapeutics

来源：评论

学校读者我要写书评

暂无评论

368 Uncovering links between innate immunity, DNA repair, and cognitive health in aging populations

引用

Journal of Clinical and Translational Science 2025年第s1期9卷 113-114页

作者： Jamie Leiriao Hannah Lords Paola Sebastiani Tufts University Candidate Bioinformatics and Computational Biology Program Boston University Clinical and Translational Science Institute Tufts University

Objectives/Goals: Neurodegenerative diseases involve progressive neuronal loss or dysfunction, often due to accumulated damage and impaired repair mechanisms. Our research evaluates the role of innate immune recognition proteins to provide insights into age-related neurodegeneration and cognitive decline. Methods/Study Population: We will utilize transcriptomic data from the Long-Life Family Study (LLFS), a cohort rich in genetic and phenotypic data related to aging and longevity. Our approach includes assessing a set of innate immune recognition proteins, also known as pattern recognition receptors (PRRs) expression across various age groups, focusing on potential correlations with cognitive performance. By analyzing serum transcriptomic profiles, we aim to map changes in expression and DNA repair genes over time, evaluating their connection to cognitive health and neurodegeneration in aging populations. Results/Anticipated Results: We anticipate that the expression of some PRRs will increase with age and correlate with cognitive decline, suggesting a role in age-related neurodegeneration. We also expect a decrease in DNA repair pathway gene expression in older age groups, contrasting with an increase in genes involved in endogenous DNA detection. These results will reveal how PRRs may function as neuroprotective factors and how their expression changes may relate to the decline in DNA repair processes with age, providing a better understanding of innate immune recognition in cognitive health. Discussion/Significance of Impact: This study will reveal the role of PRRs in aging and neurodegeneration, potentially establishing them as a key player in neuronal protection. Findings may guide future research into therapeutic strategies targeting them for Alzheimer’s and other age-related neurodegenerative diseases.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Mathematical bounds on r 2 and the effect size in case-control genome-wide association studies

引用

Theoretical Population biology 2025年 164卷 1-11页

作者： Sanjana M. Paye Michael D. Edge Department of Quantitative and Computational Biology University of Southern California United States of America University of Michigan Medical Scientist Training Program United States of America

Case-control genome-wide association studies (GWAS) are often used to find associations between genetic variants and diseases. When case-control GWAS are conducted, researchers must make decisions regarding how many cases and how many controls to include in the study. Connections between variants and diseases are made using association statistics, including χ 2 . Previous work in population genetics has shown that LD statistics, including r 2 , are bounded by the allele frequencies in the population being studied. Since varying the case fraction changes sample allele frequencies, we use the known bounds on r 2 to explore how the fraction of cases included in a study can affect statistical power to detect associations. We analyze a simple mathematical model and use simulations to study a quantity proportional to the χ 2 noncentrality parameter, which is closely related to r 2 , under various conditions. Varying the case fraction changes the χ 2 noncentrality parameter, and by extension the statistical power, with effects depending on the dominance, penetrance, and frequency of the risk allele. Our framework explains previously observed results, such as asymmetries in power to detect risk vs. protective alleles, and the fact that a balanced sample of cases and controls does not always give the best power to detect associations, particularly for highly penetrant minor risk alleles that are either dominant or recessive. We show by simulation that our results can be used as a rough guide to statistical power for association tests other than χ 2 tests of independence.

关键词： Case control studies Genome-wide association studies Linkage disequilibrium Mathematical bounds Population genetics

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

Automated model-free analysis of cryo-EM volume ensembles with SIREn

引用

Structure 2025年第5期33卷 974-987.e4页

作者： Kinman, Laurel F. Carreira, Maria V. Powell, Barrett M. Davis, Joseph H. Department of Biology Massachusetts Institute of Technology Cambridge MA United States Computational and Systems Biology Graduate Program Massachusetts Institute of Technology Cambridge MA United States

Cryogenic electron microscopy (cryo-EM) has the potential to capture snapshots of proteins in motion and generate hypotheses linking conformational states to biological function. This potential has been increasingly realized by the advent of machine learning models that allow 100s-1,000s of 3D density maps to be generated from a single dataset. How to identify distinct structural states within these volume ensembles and quantify their relative occupancies remain open questions. Here, we present an approach to inferring variable regions directly from a volume ensemble based on the statistical co-occupancy of voxels, as well as a 3D convolutional neural network that predicts binarization thresholds for volumes in an unbiased and automated manner. We show that these tools recapitulate known heterogeneity in a variety of simulated and real cryo-EM datasets and highlight how integrating these tools with existing data processing pipelines enables improved particle curation. © 2025 Elsevier Inc.

关键词： cryo-EM cryo-ET cryoDRGN deep learning structural biology

来源：评论

学校读者我要写书评

暂无评论

Protein codes promote selective subcellular compartmentalization

引用

Science (New York, N.Y.) 2025年第6738期387卷 1095-1101页

作者： Kilgore, Henry R. Chinn, Itamar Mikhael, Peter G. Mitnikov, Ilan Van Dongen, Catherine Zylberberg, Guy Afeyan, Lena Banani, Salman F. Wilson-Hawken, Susana Lee, Tong Ihn Barzilay, Regina Young, Richard A. Whitehead Institute for Biomedical Research Cambridge MA United States Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology (MIT) Cambridge MA United States Abdul Latif Jameel Clinic for Machine Learning in Health MIT Cambridge MA United States Department of Biology MIT Cambridge MA United States Department of Pathology Brigham and Women's Hospital Harvard Medical School Boston MA United States Computational and Systems Biology Program MIT Cambridge MA United States

Cells have evolved mechanisms to distribute ~10 billion protein molecules to subcellular compartments where diverse proteins involved in shared functions must assemble. In this study, we demonstrate that proteins with shared functions share amino acid sequence codes that guide them to compartment destinations. We developed a protein language model, ProtGPS, that predicts with high performance the compartment localization of human proteins excluded from the training set. ProtGPS successfully guided generation of novel protein sequences that selectively assemble in the nucleolus. ProtGPS identified pathological mutations that change this code and lead to altered subcellular localization of proteins. Our results indicate that protein sequences contain not only a folding code but also a previously unrecognized code governing their distribution to diverse subcellular compartments.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Functional classification of metabolic networks

arXiv

引用

arXiv 2025年

作者： Reyes, Jorge Dunkel, Jörn Program in Computational and Systems Biology Massachusetts Institute of Technology CambridgeMA02139 United States Department of Mathematics Massachusetts Institute of Technology CambridgeMA02139 United States

Chemical reaction networks underpin biological and physical phenomena across scales, from microbial interactions to planetary atmosphere dynamics. Bacterial communities exhibit complex competitive interactions for resources, human organs and tissues demonstrate specialized biochemical functions, and planetary atmospheres are capable of displaying diverse organic and inorganic chemical processes. Despite their complexities, comparing these networks methodically remains a challenge due to the vast underlying degrees of freedom. In biological systems, comparative genomics has been pivotal in tracing evolutionary trajectories and classifying organisms via DNA sequences. However, purely genomic classifications often fail to capture functional roles within ecological systems. Metabolic changes driven by nutrient availability highlight the need for classification schemes that integrate metabolic information. Here we introduce and apply a computational framework for a classification scheme of organisms that compares matrix representations of chemical reaction networks using the Grassmann distance, corresponding to measuring distances between the fundamental subspaces of stoichiometric matrices. Applying this framework to human gut microbiome data confirms that metabolic distances are distinct from phylogenetic distances, underscoring the limitations of genetic information in metabolic classification. Importantly, our analysis of metabolic distances reveals functional groups of organisms enriched or depleted in specific metabolic processes and shows robustness to metabolically silent genetic perturbations. The generalizability of metabolic Grassmann distances is illustrated by application to chemical reaction networks in human tissue and planetary atmospheres, highlighting its potential for advancing functional comparisons across diverse chemical reaction systems. © 2025, CC BY-NC-ND.

关键词： Earth atmosphere

来源：评论

学校读者我要写书评

暂无评论

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

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：