Cell systems最新文献

How well do contextual protein encodings learn structure, function, and evolutionary context?

Cell systems Pub Date : 2025-02-28 DOI: 10.1016/j.cels.2025.101201

Sai Pooja Mahajan, Fátima A Dávila-Hernández, Jeffrey A Ruffolo, Jeffrey J Gray

{"title":"How well do contextual protein encodings learn structure, function, and evolutionary context?","authors":"Sai Pooja Mahajan, Fátima A Dávila-Hernández, Jeffrey A Ruffolo, Jeffrey J Gray","doi":"10.1016/j.cels.2025.101201","DOIUrl":"https://doi.org/10.1016/j.cels.2025.101201","url":null,"abstract":"In proteins, the optimal residue at any position is determined by its structural, evolutionary, and functional contexts-much like how a word may be inferred from its context in language. We trained masked label prediction models to learn representations of amino acid residues in different contexts. We focus questions on evolution and structural flexibility and whether and how contextual encodings derived through pretraining and fine-tuning may improve representations for specialized contexts. Sequences sampled from our learned representations fold into template structure and reflect sequence variations seen in related proteins. For flexible proteins, sampled sequences traverse the full conformational space of the native sequence, suggesting that plasticity is encoded in the template structure. For protein-protein interfaces, generated sequences replicate wild-type binding energies across diverse interfaces and binding strengths in silico. For the antibody-antigen interface, fine-tuning recapitulate conserved sequence patterns, while pretraining on general contexts improves sequence recovery for the hypervariable H3 loop. A record of this paper's transparent peer review process is included in the supplemental information.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101201"},"PeriodicalIF":0.0,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143569193","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

Large-scale control over collective cell migration using light-activated epidermal growth factor receptors.

Cell systems Pub Date : 2025-02-26 DOI: 10.1016/j.cels.2025.101203

Kevin Suh, Richard H Thornton, Long Nguyen, Payam E Farahani, Daniel J Cohen, Jared E Toettcher

{"title":"Large-scale control over collective cell migration using light-activated epidermal growth factor receptors.","authors":"Kevin Suh, Richard H Thornton, Long Nguyen, Payam E Farahani, Daniel J Cohen, Jared E Toettcher","doi":"10.1016/j.cels.2025.101203","DOIUrl":"https://doi.org/10.1016/j.cels.2025.101203","url":null,"abstract":"Receptor tyrosine kinases (RTKs) play key roles in coordinating cell movement at both single-cell and tissue scales. The recent development of optogenetic tools for controlling RTKs and their downstream signaling pathways suggests that these responses may be amenable to engineering-based control for sculpting tissue shape and function. Here, we report that a light-controlled epidermal growth factor (EGF) receptor (OptoEGFR) can be deployed in epithelial cells for precise, programmable control of long-range tissue movements. We show that in OptoEGFR-expressing tissues, light can drive millimeter-scale cell rearrangements to densify interior regions or produce rapid outgrowth at tissue edges. Light-controlled tissue movements are driven primarily by phosphoinositide 3-kinase (PI3K) signaling, rather than diffusible ligands, tissue contractility, or ERK kinase signaling as seen in other RTK-driven migration contexts. Our study suggests that synthetic, light-controlled RTKs could serve as a powerful platform for controlling cell positions and densities for diverse applications, including wound healing and tissue morphogenesis.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101203"},"PeriodicalIF":0.0,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143560400","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

Virus targeting as a dominant driver of interfacial evolution in the structurally resolved human-virus protein-protein interaction network.

Cell systems Pub Date : 2025-02-21 DOI: 10.1016/j.cels.2025.101202

Wan-Chun Su, Yu Xia

{"title":"Virus targeting as a dominant driver of interfacial evolution in the structurally resolved human-virus protein-protein interaction network.","authors":"Wan-Chun Su, Yu Xia","doi":"10.1016/j.cels.2025.101202","DOIUrl":"https://doi.org/10.1016/j.cels.2025.101202","url":null,"abstract":"Regions on a host protein that interact with virus proteins (exogenous interfaces) frequently overlap with those that interact with other host proteins (endogenous interfaces), resulting in competition between hosts and viruses for these shared interfaces (mimic-targeted interfaces). Yet, the evolutionary consequences of this competitive relationship on the host are not well understood. Here, we integrate experimentally determined structures and homology-based templates of protein complexes with protein-protein interaction networks to construct a high-resolution human-virus structural interaction network. We perform site-specific evolutionary rate analyses on this structural interaction network and find that exogenous-specific interfaces evolve faster than endogenous-specific interfaces. Mimic-targeted interfaces evolve as fast as exogenous-specific interfaces, despite being targeted by both human and virus proteins. Our findings suggest that virus targeting plays a dominant role in host interfacial evolution within the context of domain-domain interactions and that mimic-targeted interfaces on human proteins are the key battleground for a mammalian-specific host-virus evolutionary arms race.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101202"},"PeriodicalIF":0.0,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143538243","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

Should biology put complexity first?

Cell systems Pub Date : 2025-02-19 Epub Date: 2025-02-10 DOI: 10.1016/j.cels.2025.101197

Philip Ball

引用次数: 0

DeST-OT: Alignment of spatiotemporal transcriptomics data.

Cell systems Pub Date : 2025-02-19 Epub Date: 2025-01-27 DOI: 10.1016/j.cels.2024.12.001

Peter Halmos, Xinhao Liu, Julian Gold, Feng Chen, Li Ding, Benjamin J Raphael

引用次数: 0

A genetic toolbox for engineering C. acnes.

Cell systems Pub Date : 2025-02-19 DOI: 10.1016/j.cels.2025.101199

Y Erin Chen

引用次数: 0

Spatial transcriptomics-aided localization for single-cell transcriptomics with STALocator.

Cell systems Pub Date : 2025-02-19 Epub Date: 2025-02-03 DOI: 10.1016/j.cels.2025.101195

Shang Li, Qunlun Shen, Shihua Zhang

{"title":"Spatial transcriptomics-aided localization for single-cell transcriptomics with STALocator.","authors":"Shang Li, Qunlun Shen, Shihua Zhang","doi":"10.1016/j.cels.2025.101195","DOIUrl":"10.1016/j.cels.2025.101195","url":null,"abstract":"Single-cell RNA-sequencing (scRNA-seq) techniques can measure gene expression at single-cell resolution but lack spatial information. Spatial transcriptomics (ST) techniques simultaneously provide gene expression data and spatial information. However, the data quality of the spatial resolution or gene coverage is still much lower than the quality of the single-cell transcriptomics data. To this end, we develop a ST-Aided Locator for single-cell transcriptomics (STALocator) to localize single cells to corresponding ST data. Applications on simulated data showed that STALocator performed better than other localization methods. When applied to the human brain and squamous cell carcinoma data, STALocator could robustly reconstruct the relative spatial organization of critical cell populations. Moreover, STALocator could enhance gene expression patterns for Slide-seqV2 data and predict genome-wide gene expression data for fluorescence in situ hybridization (FISH) and Xenium data, leading to the identification of more spatially variable genes and more biologically relevant Gene Ontology (GO) terms compared with the raw 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":"101195"},"PeriodicalIF":0.0,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143191539","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

Macrophage memory emerges from coordinated transcription factor and chromatin dynamics.

Cell systems Pub Date : 2025-02-19 Epub Date: 2025-02-11 DOI: 10.1016/j.cels.2025.101171

Andrew G Wang, Minjun Son, Aleksandr Gorin, Emma Kenna, Abinash Padhi, Bijentimala Keisham, Adam Schauer, Alexander Hoffmann, Savaş Tay

{"title":"Macrophage memory emerges from coordinated transcription factor and chromatin dynamics.","authors":"Andrew G Wang, Minjun Son, Aleksandr Gorin, Emma Kenna, Abinash Padhi, Bijentimala Keisham, Adam Schauer, Alexander Hoffmann, Savaş Tay","doi":"10.1016/j.cels.2025.101171","DOIUrl":"10.1016/j.cels.2025.101171","url":null,"abstract":"Cells of the immune system operate in dynamic microenvironments where the timing, concentration, and order of signaling molecules constantly change. Despite this complexity, immune cells manage to communicate accurately and control inflammation and infection. It is unclear how these dynamic signals are encoded and decoded and if individual cells retain the memory of past exposure to inflammatory molecules. Here, we use live-cell analysis, ATAC sequencing, and an in vivo model of sepsis to show that sequential inflammatory signals induce memory in individual macrophages through reprogramming the nuclear factor κB (NF-κB) network and the chromatin accessibility landscape. We use transcriptomic profiling and deep learning to show that transcription factor and chromatin dynamics coordinate fine-tuned macrophage responses to new inflammatory signals. This work demonstrates how macrophages retain the memory of previous signals despite single-cell variability and elucidates the mechanisms of signal-induced memory in dynamic inflammatory conditions like sepsis.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101171"},"PeriodicalIF":0.0,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143412008","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

Subspecies phylogeny in the human gut revealed by co-evolutionary constraints across the bacterial kingdom. 人类肠道的亚种系统发育揭示了跨细菌王国的共同进化约束。

Cell systems Pub Date : 2025-02-19 Epub Date: 2025-01-17 DOI: 10.1016/j.cels.2024.12.008

Benjamin A Doran, Robert Y Chen, Hannah Giba, Vivek Behera, Bidisha Barat, Anitha Sundararajan, Huaiying Lin, Ashley Sidebottom, Eric G Pamer, Arjun S Raman

{"title":"Subspecies phylogeny in the human gut revealed by co-evolutionary constraints across the bacterial kingdom.","authors":"Benjamin A Doran, Robert Y Chen, Hannah Giba, Vivek Behera, Bidisha Barat, Anitha Sundararajan, Huaiying Lin, Ashley Sidebottom, Eric G Pamer, Arjun S Raman","doi":"10.1016/j.cels.2024.12.008","DOIUrl":"10.1016/j.cels.2024.12.008","url":null,"abstract":"The human gut microbiome contains many bacterial strains of the same species (\"strain-level variants\") that shape microbiome function. The tremendous scale and molecular resolution at which microbial communities are being interrogated motivates addressing how to describe strain-level variants. We introduce the \"Spectral Tree\"-an inferred tree of relatedness built from patterns of co-evolutionary constraint between greater than 7,000 diverse bacteria. Using the Spectral Tree to describe over 600 diverse gut commensal strains that we isolated, whole-genome sequenced, and metabolically profiled revealed (1) widespread phylogenetic structure among strain-level variants, (2) the origins of subspecies phylogeny as a shared history of phage infections across humans, and (3) the key role of inter-human strain variation in predicting strain-level metabolic qualities. Overall, our work demonstrates the existence and metabolic importance of structured phylogeny below the level of species for commensal gut bacteria, motivating a redefinition of individual strains according to their evolutionary context. A record of this paper's transparent peer review process is included in the supplemental information.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101167"},"PeriodicalIF":0.0,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143018343","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

A genome-scale metabolic reconstruction resource of 247,092 diverse human microbes spanning multiple continents, age groups, and body sites.

Cell systems Pub Date : 2025-02-19 Epub Date: 2025-02-12 DOI: 10.1016/j.cels.2025.101196

Almut Heinken, Timothy Otto Hulshof, Bram Nap, Filippo Martinelli, Arianna Basile, Amy O'Brolchain, Neil Francis O'Sullivan, Celine Gallagher, Eimer Magee, Francesca McDonagh, Ian Lalor, Maeve Bergin, Phoebe Evans, Rachel Daly, Ronan Farrell, Rose Mary Delaney, Saoirse Hill, Saoirse Roisin McAuliffe, Trevor Kilgannon, Ronan M T Fleming, Cyrille C Thinnes, Ines Thiele

{"title":"A genome-scale metabolic reconstruction resource of 247,092 diverse human microbes spanning multiple continents, age groups, and body sites.","authors":"Almut Heinken, Timothy Otto Hulshof, Bram Nap, Filippo Martinelli, Arianna Basile, Amy O'Brolchain, Neil Francis O'Sullivan, Celine Gallagher, Eimer Magee, Francesca McDonagh, Ian Lalor, Maeve Bergin, Phoebe Evans, Rachel Daly, Ronan Farrell, Rose Mary Delaney, Saoirse Hill, Saoirse Roisin McAuliffe, Trevor Kilgannon, Ronan M T Fleming, Cyrille C Thinnes, Ines Thiele","doi":"10.1016/j.cels.2025.101196","DOIUrl":"10.1016/j.cels.2025.101196","url":null,"abstract":"Genome-scale modeling of microbiome metabolism enables the simulation of diet-host-microbiome-disease interactions. However, current genome-scale reconstruction resources are limited in scope by computational challenges. We developed an optimized and highly parallelized reconstruction and analysis pipeline to build a resource of 247,092 microbial genome-scale metabolic reconstructions, deemed APOLLO. APOLLO spans 19 phyla, contains >60% of uncharacterized strains, and accounts for strains from 34 countries, all age groups, and multiple body sites. Using machine learning, we predicted with high accuracy the taxonomic assignment of strains based on the computed metabolic features. We then built 14,451 metagenomic sample-specific microbiome community models to systematically interrogate their community-level metabolic capabilities. We show that sample-specific metabolic pathways accurately stratify microbiomes by body site, age, and disease state. APOLLO is freely available, enables the systematic interrogation of the metabolic capabilities of largely still uncultured and unclassified species, and provides unprecedented opportunities for systems-level modeling of personalized host-microbiome co-metabolism.","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101196"},"PeriodicalIF":0.0,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143416386","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