Cell systems最新文献_第10页

Systematic genome-wide mapping of host determinants of bacteriophage infectivity. 噬菌体感染性宿主决定因素的系统全基因组图谱。

IF 7.7

Cell systems Pub Date : 2025-11-19 Epub Date: 2025-10-15 DOI: 10.1016/j.cels.2025.101427

Chutikarn Chitboonthavisuk, Cody Martin, Phil Huss, Jason M Peters, Karthik Anantharaman, Srivatsan Raman

{"title":"Systematic genome-wide mapping of host determinants of bacteriophage infectivity.","authors":"Chutikarn Chitboonthavisuk, Cody Martin, Phil Huss, Jason M Peters, Karthik Anantharaman, Srivatsan Raman","doi":"10.1016/j.cels.2025.101427","DOIUrl":"10.1016/j.cels.2025.101427","url":null,"abstract":"Bacterial host factors regulate the infection cycle of bacteriophages. Except for some well-studied host factors (e.g., receptors or restriction-modification systems), the contribution of the rest of the host genome on phage infection remains poorly understood. We developed phage-host analysis using genome-wide CRISPR interference and phage packaging (\"PHAGEPACK\"), a pooled assay that systematically and comprehensively measures each host gene's impact on phage fitness. PHAGEPACK combines CRISPR interference with phage packaging to link host perturbation to phage fitness during active infection. Using PHAGEPACK, we constructed a genome-wide map of genes impacting T7 phage fitness in permissive E. coli, revealing pathways that affect phage packaging. When applied to the non-permissive E. coli O121, PHAGEPACK identified pathways leading to host resistance; their removal increased phage susceptibility up to a billion-fold. Bioinformatic analysis indicates that phage genomes carry homologs or truncations of key host factors, potentially for fitness advantage. In summary, PHAGEPACK offers insights into phage-host interactions, phage evolution, and bacterial resistance.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101427"},"PeriodicalIF":7.7,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13112915/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145310294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Mild HIV-specific selective forces overlaying natural CD4+ T cell dynamics explain the clonality and decay dynamics of HIV reservoir cells. 轻微的HIV特异性选择力覆盖自然CD4+ T细胞动力学解释了HIV储存库细胞的克隆和衰变动力学。

IF 7.7

Cell systems Pub Date : 2025-10-15 Epub Date: 2025-09-22 DOI: 10.1016/j.cels.2025.101402

Daniel B Reeves, Danielle N Rigau, Arianna Romero, Hao Zhang, Francesco R Simonetti, Joseph Varriale, Rebecca Hoh, Li Zhang, Kellie N Smith, Luis J Montaner, Leah H Rubin, Stephen J Gange, Nadia R Roan, Phyllis C Tien, Joseph B Margolick, Michael J Peluso, Steven G Deeks, Joshua T Schiffer, Janet D Siliciano, Robert F Siliciano, Annukka A R Antar

{"title":"Mild HIV-specific selective forces overlaying natural CD4+ T cell dynamics explain the clonality and decay dynamics of HIV reservoir cells.","authors":"Daniel B Reeves, Danielle N Rigau, Arianna Romero, Hao Zhang, Francesco R Simonetti, Joseph Varriale, Rebecca Hoh, Li Zhang, Kellie N Smith, Luis J Montaner, Leah H Rubin, Stephen J Gange, Nadia R Roan, Phyllis C Tien, Joseph B Margolick, Michael J Peluso, Steven G Deeks, Joshua T Schiffer, Janet D Siliciano, Robert F Siliciano, Annukka A R Antar","doi":"10.1016/j.cels.2025.101402","DOIUrl":"10.1016/j.cels.2025.101402","url":null,"abstract":"To determine whether HIV persistence arises from the natural dynamics of memory (m)CD4+ T cells, we compare clonal dynamics of HIV proviruses and mCD4+ T cells from the same people living with HIV (PWH) on antiretroviral therapy and from matched HIV-seronegative people (N = 51). HIV proviruses are more clonal than mCD4+ T cells but similarly clonal to antigen-specific cells. Increasing reservoir clonality over time and differential decay of intact and defective proviruses are not explained by mCD4+ T cell kinetics alone. We develop and validate a stochastic model trained on 10 quantitative data metrics, which shows that negative selection against HIV-infected cells is necessary to explain all metrics. We estimate the strength of negative selection, finding that death of cells harboring intact and defective proviruses is infrequently (∼6% and ∼2% on average) due to HIV-specific factors. Thus, our data indicate that HIV persistence is mostly, but not entirely, driven by natural mCD4+ kinetics.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101402"},"PeriodicalIF":7.7,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12694735/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145133052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Identifying an optimal perturbation to induce a desired cell state by generative deep learning. 通过生成式深度学习识别最佳扰动以诱导所需的细胞状态。

IF 7.7

Cell systems Pub Date : 2025-10-15 Epub Date: 2025-09-24 DOI: 10.1016/j.cels.2025.101405

Younghyun Han, Hyunjin Kim, Chun-Kyung Lee, Kwang-Hyun Cho

{"title":"Identifying an optimal perturbation to induce a desired cell state by generative deep learning.","authors":"Younghyun Han, Hyunjin Kim, Chun-Kyung Lee, Kwang-Hyun Cho","doi":"10.1016/j.cels.2025.101405","DOIUrl":"10.1016/j.cels.2025.101405","url":null,"abstract":"Controlling cell states is pivotal in biological research, yet understanding the specific perturbations that induce desired changes remains challenging. To address this, we present PAIRING (perturbation identifier to induce desired cell states using generative deep learning), which identifies cellular perturbations leading to the desired cell state. PAIRING embeds cell states in the latent space and decomposes them into basal states and perturbation effects. The identification of optimal perturbations is achieved by comparing the decomposed perturbation effects with the vector representing the transition toward the desired cell state in the latent space. We demonstrate that PAIRING can identify perturbations transforming given cell states into desired states across different types of transcriptome datasets. PAIRING is employed to identify perturbations that lead colorectal cancer cells to a normal-like state. Moreover, simulating gene expression changes using PAIRING provides mechanistic insights into the perturbation. We anticipate that it will have a broad impact on therapeutic development, potentially applicable across various biological domains.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101405"},"PeriodicalIF":7.7,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145152170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Molecular armor: Simple rules to keep proteins (re)soluble. 分子盔甲：保持蛋白质（再）可溶性的简单规则。

IF 7.7

Cell systems Pub Date : 2025-10-15 DOI: 10.1016/j.cels.2025.101428

Saurabh Mathur, Alexander I Alexandrov, Samhita R Radhakrishnan, Emmanuel D Levy

引用次数: 0

Weakly supervised peptide-TCR binding prediction facilitates neoantigen identification. 弱监督肽- tcr结合预测有助于新抗原鉴定。

IF 7.7

Cell systems Pub Date : 2025-10-15 Epub Date: 2025-09-22 DOI: 10.1016/j.cels.2025.101403

Yuli Gao, Yicheng Gao, Siqi Wu, Danlu Li, Chi Zhou, Fangliangzi Meng, Kejing Dong, Xueying Zhao, Ping Li, Aibin Liang, Qi Liu

{"title":"Weakly supervised peptide-TCR binding prediction facilitates neoantigen identification.","authors":"Yuli Gao, Yicheng Gao, Siqi Wu, Danlu Li, Chi Zhou, Fangliangzi Meng, Kejing Dong, Xueying Zhao, Ping Li, Aibin Liang, Qi Liu","doi":"10.1016/j.cels.2025.101403","DOIUrl":"10.1016/j.cels.2025.101403","url":null,"abstract":"The identification of T cell neoantigens is fundamental and computationally challenging in tumor immunotherapy study. Current prediction methods mainly focus on peptide properties, human leukocyte antigen (HLA) binding affinity, or single peptide-major histocompatibility complex-T cell receptor (pMHC-TCR) interactions, often overlooking the patient-specific TCR profile in evaluating neoantigen immunogenicity. This limited scope has constrained the performance and application of these tools in real-world settings for neoantigen identification. To address these limitations, we developed \"TCRBagger,\" a weakly supervised learning framework that uses the bagging of sample-specific TCR profiles to enhance personalized neoantigen identification. TCRBagger integrates three learning strategies-self-supervised, denoising, and multi-instance learning (MIL)-for modeling peptide-TCR binding to identify immunogenic neoantigens. Our comprehensive tests and applications reveal that TCRBagger outperforms existing tools by modeling peptide-TCR profile interactions, accordingly enhancing the capability of immunogenic neoantigen identification. Collectively, TCRBagger provides an unprecedented perspective and methodology for modeling the interaction between a peptide and patient-specific TCR profiles, facilitating neoantigen identification for personalized tumor immunotherapy. A record of this paper's Transparent Peer Review process is included in the supplemental information.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101403"},"PeriodicalIF":7.7,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145133121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Protein surface chemistry encodes an adaptive tolerance to desiccation. 蛋白质表面化学编码对干燥的适应性耐受性。

IF 7.7

Cell systems Pub Date : 2025-10-15 Epub Date: 2025-10-02 DOI: 10.1016/j.cels.2025.101407

Paulette Sofía Romero-Pérez, Haley M Moran, David P Cordone, Azeem Horani, Alexander Truong, Edgar Manriquez-Sandoval, John F Ramirez, Alec Martinez, Edith Gollub, Kara Hunter, Kavindu C Kolamunna, Jeffrey M Lotthammer, Ryan J Emenecker, Hui Liu, Janet H Iwasa, Thomas C Boothby, Alex S Holehouse, Stephen D Fried, Shahar Sukenik

{"title":"Protein surface chemistry encodes an adaptive tolerance to desiccation.","authors":"Paulette Sofía Romero-Pérez, Haley M Moran, David P Cordone, Azeem Horani, Alexander Truong, Edgar Manriquez-Sandoval, John F Ramirez, Alec Martinez, Edith Gollub, Kara Hunter, Kavindu C Kolamunna, Jeffrey M Lotthammer, Ryan J Emenecker, Hui Liu, Janet H Iwasa, Thomas C Boothby, Alex S Holehouse, Stephen D Fried, Shahar Sukenik","doi":"10.1016/j.cels.2025.101407","DOIUrl":"10.1016/j.cels.2025.101407","url":null,"abstract":"Cellular desiccation-the loss of nearly all water from the cell-is a recurring stress that drives widespread protein dysfunction. To survive, part of the proteome must resume function upon rehydration. Which proteins tolerate desiccation, and the molecular determinants that underlie this tolerance, are largely unknown. Here, we use quantitative mass spectrometry and structural proteomics to show that certain proteins possess an innate capacity to tolerate extreme water loss. Structural analyses point to protein surface chemistry as a key determinant of desiccation tolerance, which we test by showing that rational surface mutants can convert a desiccation-sensitive protein into a tolerant one. We also find that highly tolerant proteins are responsible for the production of small-molecule building blocks, while intolerant proteins are involved in energy-consuming processes such as ribosome biogenesis. We propose that this functional bias enables cells to kickstart their metabolism and promote cell survival following desiccation and rehydration. A record of this paper's transparent peer review process is included in the supplemental information.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101407"},"PeriodicalIF":7.7,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145226498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Accelerated design of Escherichia coli reduced genomes using a whole-cell model and machine learning. 利用全细胞模型和机器学习加速设计大肠杆菌减少基因组。

IF 7.7

Cell systems Pub Date : 2025-10-15 Epub Date: 2025-09-24 DOI: 10.1016/j.cels.2025.101392

Ioana M Gherman, Kieren Sharma, Joshua Rees-Garbutt, Wei Pang, Zahraa S Abdallah, Thomas E Gorochowski, Claire S Grierson, Lucia Marucci

引用次数: 0

Unraveling principles of thermodynamics for genome-scale metabolic networks using graph neural networks. 利用图神经网络揭示基因组尺度代谢网络的热力学原理。

IF 7.7

Cell systems Pub Date : 2025-10-15 Epub Date: 2025-09-18 DOI: 10.1016/j.cels.2025.101393

Wenchao Fan, Yonghong Hao, Xiangyu Hou, Chuyun Ding, Dan Huang, Weiyan Zheng, Ziwei Dai

{"title":"Unraveling principles of thermodynamics for genome-scale metabolic networks using graph neural networks.","authors":"Wenchao Fan, Yonghong Hao, Xiangyu Hou, Chuyun Ding, Dan Huang, Weiyan Zheng, Ziwei Dai","doi":"10.1016/j.cels.2025.101393","DOIUrl":"10.1016/j.cels.2025.101393","url":null,"abstract":"Our understanding of metabolic thermodynamics is limited by the lack of genome-scale data on the standard Gibbs free energy change (ΔrG°) of metabolic reactions. Here, we present dGbyG, a graph neural network (GNN)-based model for predicting ΔrG° with superior accuracy, versatility, robustness, and generalization ability. Integration of dGbyG predictions into metabolic networks facilitated model curation, improved flux prediction accuracy, and identified thermodynamic driver reactions (TDRs) with substantial negative values of the reaction Gibbs free energy change (ΔrG). TDRs showed distinctive network topological features and heterogeneous enzyme expression, implying coupling between reaction thermodynamics and network topology for efficient metabolic regulation. We also discovered a universal pattern of thermodynamics in linear metabolic pathways, explained by a multi-objective optimization model balancing the needs to maximize pathway flux and minimize enzyme and metabolite loads. Our work expands accessible thermodynamic data and elucidates optimality principles in metabolism at the genome scale. A record of this paper's transparent peer review process is included in the supplemental information.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101393"},"PeriodicalIF":7.7,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145093024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Learning cell-specific networks from dynamics and geometry of single cells. 从单个细胞的动力学和几何结构中学习细胞特异性网络。

IF 7.7

Cell systems Pub Date : 2025-10-15 Epub Date: 2025-10-01 DOI: 10.1016/j.cels.2025.101399

Stephen Y Zhang, Michael P H Stumpf

{"title":"Learning cell-specific networks from dynamics and geometry of single cells.","authors":"Stephen Y Zhang, Michael P H Stumpf","doi":"10.1016/j.cels.2025.101399","DOIUrl":"10.1016/j.cels.2025.101399","url":null,"abstract":"Cell dynamics and biological function are governed by intricate networks of molecular interactions. Inferring these interactions from data is a notoriously difficult inverse problem. Most existing network inference methods construct population-averaged representations of gene interaction networks, and they do not naturally allow us to infer differences in interaction activity across heterogeneous cell populations. We introduce locaTE, an information theoretic approach that leverages single-cell, dynamical information, together with geometry of the cell-state manifold, to infer cell-specific, causal gene interaction networks in a manner that is agnostic to the topology of the underlying biological trajectory. Through extensive simulation studies and applications to experimental datasets spanning mouse primitive endoderm formation, pancreatic development, and hematopoiesis, we demonstrate superior performance and the generation of additional insights, compared with standard population-averaged inference methods. We find that locaTE provides a powerful network inference method that allows us to distil cell-specific networks from single-cell data. A record of this paper's transparent peer review process is included in the supplemental information.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101399"},"PeriodicalIF":7.7,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145214592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Leveraging attention-based deep multiple instance and multiple task learning for improved neoepitope identification. 利用基于注意力的深度多实例和多任务学习改进新表位识别。

IF 7.7

Cell systems Pub Date : 2025-10-15 Epub Date: 2025-10-06 DOI: 10.1016/j.cels.2025.101404

Wei Qu, Shanfeng Zhu

引用次数: 0