Cell systems最新文献_第2页

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 : 2025-09-18 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":"https://doi.org/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":"2025-09-18","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

Synthetic coevolution reveals adaptive mutational trajectories of neutralizing antibodies and SARS-CoV-2. 合成协同进化揭示了中和抗体与SARS-CoV-2的适应性突变轨迹

IF 7.7

Cell systems Pub Date : 2025-09-17 DOI: 10.1016/j.cels.2025.101391

Roy A Ehling, Mason Minot, Max D Overath, Daniel J Sheward, Jiami Han, Beichen Gao, Joseph M Taft, Margarita Pertseva, Cédric R Weber, Lester Frei, Thomas Bikias, Ben Murrell, Sai T Reddy

{"title":"Synthetic coevolution reveals adaptive mutational trajectories of neutralizing antibodies and SARS-CoV-2.","authors":"Roy A Ehling, Mason Minot, Max D Overath, Daniel J Sheward, Jiami Han, Beichen Gao, Joseph M Taft, Margarita Pertseva, Cédric R Weber, Lester Frei, Thomas Bikias, Ben Murrell, Sai T Reddy","doi":"10.1016/j.cels.2025.101391","DOIUrl":"https://doi.org/10.1016/j.cels.2025.101391","url":null,"abstract":"The COVID-19 pandemic showcased a coevolutionary race between the human immune system and SARS-CoV-2, during which the immune system generated neutralizing antibodies targeting the SARS-CoV-2 spike protein's receptor-binding domain (RBD), crucial for host cell invasion, while the virus evolved to evade antibody recognition. Here, we establish a synthetic coevolution system combining high-throughput screening of antibody and RBD variant libraries with protein mutagenesis, surface display, and deep sequencing. Additionally, to significantly extend our interrogation of sequence space, we train a protein language model that predicts antibody escape to RBD variants and demonstrate its capability to generalize to a larger mutational load and mutations at positions unseen during training. Through explainable AI techniques, we probe the model and identify biologically meaningful coevolution trends. Synthetic coevolution reveals antagonistic and compensatory mutational trajectories of neutralizing antibodies and SARS-CoV-2 variants, enhancing the understanding of this evolutionary conflict.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":"16 9","pages":"101391"},"PeriodicalIF":7.7,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145088522","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

The Adaptive Immune Receptor Repertoire Knowledge Commons: An invitation to the community. 适应性免疫受体库知识共享：对社区的邀请。

IF 7.7

Cell systems Pub Date : 2025-09-17 DOI: 10.1016/j.cels.2025.101401

Lindsay G Cowell, Scott Christley, Felix Breden, Kevin A Burns, Brian D Corrie, William D Lees, James A Overton, Bjoern Peters, Eve Richardson, Krishna M Roskin, Lonneke Scheffer, Randi Vita, Corey T Watson, Gur Yaari

引用次数: 0

Uncovering differential tolerance to deletions versus substitutions with a protein language model. 用蛋白质语言模型揭示对缺失和替代的差异耐受性。

IF 7.7

Cell systems Pub Date : 2025-09-17 Epub Date: 2025-09-05 DOI: 10.1016/j.cels.2025.101373

Grant Goldman, Prathamesh Chati, Vasilis Ntranos

引用次数: 0

Deep visual proteomics reveals an in vivo-like phenotype of orthotopically transplanted human colon organoids. 深度视觉蛋白质组学揭示了原位移植的人类结肠类器官的体内样表型。

IF 7.7

Cell systems Pub Date : 2025-09-17 Epub Date: 2025-09-10 DOI: 10.1016/j.cels.2025.101396

Frederik Post, Annika Hausmann, Sonja Kabatnik, Sophia Steigerwald, Alexandra Brand, Ditte L Clement, Jonathan Skov, Kadi Lõhmussaar, Hjalte L Larsen, Martti Maimets, Theresa L Boye, Gabriela Jez, Toshiro Sato, Casper Steenholdt, Florian Rosenberger, Andreas Mund, Ole H Nielsen, Kim B Jensen, Matthias Mann

{"title":"Deep visual proteomics reveals an in vivo-like phenotype of orthotopically transplanted human colon organoids.","authors":"Frederik Post, Annika Hausmann, Sonja Kabatnik, Sophia Steigerwald, Alexandra Brand, Ditte L Clement, Jonathan Skov, Kadi Lõhmussaar, Hjalte L Larsen, Martti Maimets, Theresa L Boye, Gabriela Jez, Toshiro Sato, Casper Steenholdt, Florian Rosenberger, Andreas Mund, Ole H Nielsen, Kim B Jensen, Matthias Mann","doi":"10.1016/j.cels.2025.101396","DOIUrl":"10.1016/j.cels.2025.101396","url":null,"abstract":"Intestinal epithelial damage predisposes to disorders like inflammatory bowel disease (IBD), with organoid transplantation emerging as a potential treatment. However, it is not known how well organoids recapitulate in vivo intestinal epithelial cells (IECs). We employed deep visual proteomics (DVP), integrating AI-guided cell classification, laser microdissection, and ultra-high-sensitivity proteomics at the single-cell level to generate an in-depth proteome resource of IECs directly isolated from the human colon and organoids. While in vitro organoids display high proliferation and low functional signatures, xenotransplantation induces a remarkable shift toward an in vivo-like phenotype. We recapitulated this transition by modifying culture conditions. Our data provide a comprehensive spatial proteomics resource and validate xenotransplanted organoids as suitable models for studying human IEC behavior with unprecedented molecular detail and demonstrate their clinical potential for patients with IBD and other intestinal disorders. A record of this paper's transparent peer review process is included in the supplemental information.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101396"},"PeriodicalIF":7.7,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145042593","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

Evaluation of machine learning-assisted directed evolution across diverse combinatorial landscapes. 跨不同组合景观的机器学习辅助定向进化评价。

IF 7.7

Cell systems Pub Date : 2025-09-17 Epub Date: 2025-09-10 DOI: 10.1016/j.cels.2025.101387

Francesca-Zhoufan Li, Jason Yang, Kadina E Johnston, Emre Gürsoy, Yisong Yue, Frances H Arnold

{"title":"Evaluation of machine learning-assisted directed evolution across diverse combinatorial landscapes.","authors":"Francesca-Zhoufan Li, Jason Yang, Kadina E Johnston, Emre Gürsoy, Yisong Yue, Frances H Arnold","doi":"10.1016/j.cels.2025.101387","DOIUrl":"10.1016/j.cels.2025.101387","url":null,"abstract":"Various machine learning-assisted directed evolution (MLDE) strategies have been shown to identify high-fitness protein variants more efficiently than typical directed evolution approaches. However, limited understanding of the factors influencing MLDE performance across diverse proteins has hindered optimal strategy selection for wet-lab campaigns. To address this, we systematically analyzed multiple MLDE strategies, including active learning and focused training using six distinct zero-shot predictors, across 16 diverse protein fitness landscapes. By quantifying landscape navigability with six attributes, we found that MLDE offers a greater advantage on landscapes that are more challenging for directed evolution, especially when focused training is combined with active learning. Despite varying levels of advantage across landscapes, focused training with zero-shot predictors leveraging distinct evolutionary, structural, and stability knowledge sources consistently outperforms random sampling for both binding interactions and enzyme activities. Our findings provide practical guidelines for selecting MLDE strategies for protein engineering. A record of this paper's transparent peer review process is included in the supplemental information.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101387"},"PeriodicalIF":7.7,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145042533","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

scTrace+: Enhancing cell fate inference by integrating the lineage-tracing and multi-faceted transcriptomic similarity information. scTrace+：通过整合谱系追踪和多方面的转录组相似性信息，增强细胞命运推断。

IF 7.7

Cell systems Pub Date : 2025-09-17 Epub Date: 2025-09-10 DOI: 10.1016/j.cels.2025.101398

Wenbo Guo, Zeyu Chen, Xinqi Li, Jingmin Huang, Qifan Hu, Jin Gu

{"title":"scTrace+: Enhancing cell fate inference by integrating the lineage-tracing and multi-faceted transcriptomic similarity information.","authors":"Wenbo Guo, Zeyu Chen, Xinqi Li, Jingmin Huang, Qifan Hu, Jin Gu","doi":"10.1016/j.cels.2025.101398","DOIUrl":"10.1016/j.cels.2025.101398","url":null,"abstract":"Deciphering the cell state dynamics is crucial for understanding biological processes. Single-cell lineage-tracing technologies provide an effective way to track single-cell lineages by heritable DNA barcodes, but the high missing rates of lineage barcodes and the intra-clonal heterogeneity bring great challenges to dissecting the mechanisms of cell fate decision. Here, we systematically evaluate the features of single-cell lineage-tracing data and then develop an algorithm, scTrace+, to enhance the cell dynamic traces by incorporating multi-faceted transcriptomic similarities into lineage relationships via a kernelized probabilistic matrix factorization model. We assess its feasibility and performance by conducting ablation and benchmarking experiments on multiple real datasets and show that scTrace+ can accurately predict the fates of cells. Further, scTrace+ effectively identifies some important driver genes implicated in cellular fate decisions of diverse biological processes, such as cell differentiation or tumor drug responses. A record of this paper's transparent peer review process is included in the supplemental information.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101398"},"PeriodicalIF":7.7,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145042600","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

ProtRNA: A protein-derived RNA language model by cross-modality transfer learning. 跨模态迁移学习的蛋白质衍生RNA语言模型。

IF 7.7

Cell systems Pub Date : 2025-09-17 Epub Date: 2025-08-22 DOI: 10.1016/j.cels.2025.101371

Ruoxi Zhang, Ben Ma, Gang Xu, Jianpeng Ma

引用次数: 0

Chemotherapy modulation by a cancer-associated microbiota metabolite. 癌症相关微生物代谢物对化疗的调节作用。

IF 7.7

Cell systems Pub Date : 2025-09-17 Epub Date: 2025-09-10 DOI: 10.1016/j.cels.2025.101397

Daniel Martinez-Martinez, Tanara V Peres, Kristin Gehling, Leonor Quintaneiro, Cecilia Cabrera, Maksym Cherevatenko, Stephen J Cutty, Lena Best, Georgios Marinos, Johannes Zimmerman, Ayesha Safoor, Despoina Chrysostomou, Joao B Mokochinski, Alex Montoya, Susanne Brodesser, Michalina Zatorska, Timothy Scott, Ivan Andrew, Holger Kramer, Masuma Begum, Bian Zhang, Bernard T Golding, Julian R Marchesi, Susumu Hirabayashi, Christoph Kaleta, Alexis R Barr, Christian Frezza, Helena M Cochemé, Filipe Cabreiro

{"title":"Chemotherapy modulation by a cancer-associated microbiota metabolite.","authors":"Daniel Martinez-Martinez, Tanara V Peres, Kristin Gehling, Leonor Quintaneiro, Cecilia Cabrera, Maksym Cherevatenko, Stephen J Cutty, Lena Best, Georgios Marinos, Johannes Zimmerman, Ayesha Safoor, Despoina Chrysostomou, Joao B Mokochinski, Alex Montoya, Susanne Brodesser, Michalina Zatorska, Timothy Scott, Ivan Andrew, Holger Kramer, Masuma Begum, Bian Zhang, Bernard T Golding, Julian R Marchesi, Susumu Hirabayashi, Christoph Kaleta, Alexis R Barr, Christian Frezza, Helena M Cochemé, Filipe Cabreiro","doi":"10.1016/j.cels.2025.101397","DOIUrl":"10.1016/j.cels.2025.101397","url":null,"abstract":"Understanding how the microbiota produces regulatory metabolites is of significance for cancer and cancer therapy. Using a host-microbe-drug-nutrient 4-way screening approach, we evaluated the role of nutrition at the molecular level in the context of 5-fluorouracil toxicity. Notably, our screens identified the metabolite 2-methylisocitrate, which was found to be produced and enriched in human tumor-associated microbiota. 2-methylisocitrate exhibits anti-proliferative properties across genetically and tissue-diverse cancer cell lines, three-dimensional (3D) spheroids, and an in vivo Drosophila gut tumor model, where it reduced tumor dissemination and increased survival. Chemical landscape interaction screens identified drug-metabolite signatures and highlighted the synergy between 5-fluorouracil and 2-methylisocitrate. Multi-omic analyses revealed that 2-methylisocitrate acts via multiple cellular pathways linking metabolism and DNA damage to regulate chemotherapy. Finally, we converted 2-methylisocitrate into its trimethyl ester, thereby enhancing its potency. This work highlights the great impact of microbiome-derived metabolites on tumor proliferation and their potential as promising co-adjuvants for cancer treatment.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101397"},"PeriodicalIF":7.7,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145042552","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 antibody sequence constraints from allelic inclusion. 从等位基因包涵体中学习抗体序列约束。

IF 7.7

Cell systems Pub Date : 2025-09-17 Epub Date: 2025-08-13 DOI: 10.1016/j.cels.2025.101368

Milind Jagota, Chloe Hsu, Thomas Mazumder, Kevin Sung, William S DeWitt, Jennifer Listgarten, Frederick A Matsen Iv, Chun Jimmie Ye, Yun S Song

引用次数: 0