Kaiyuan Tang, Liqun Zhou, Xiaolong Tian, Shao-Yu Fang, Erica Vandenbulcke, Andrew Du, Johanna Shen, Hanbing Cao, Jerry Zhou, Krista Chen, Hyunu R. Kim, Zhicheng Luo, Shan Xin, Shawn H. Lin, Daniel Park, Luojia Yang, Yueqi Zhang, Kazushi Suzuki, Medha Majety, Xinyu Ling, Stanley Z. Lam, Ryan D. Chow, Ping Ren, Bo Tao, Keyi Li, Adan Codina, Xiaoyun Dai, Xingbo Shang, Suxia Bai, Timothy Nottoli, Andre Levchenko, Carmen J. Booth, Chen Liu, Rong Fan, Matthew B. Dong, Xiaoyu Zhou, Sidi Chen
{"title":"Cas12a-knock-in mice for multiplexed genome editing, disease modelling and immune-cell engineering","authors":"Kaiyuan Tang, Liqun Zhou, Xiaolong Tian, Shao-Yu Fang, Erica Vandenbulcke, Andrew Du, Johanna Shen, Hanbing Cao, Jerry Zhou, Krista Chen, Hyunu R. Kim, Zhicheng Luo, Shan Xin, Shawn H. Lin, Daniel Park, Luojia Yang, Yueqi Zhang, Kazushi Suzuki, Medha Majety, Xinyu Ling, Stanley Z. Lam, Ryan D. Chow, Ping Ren, Bo Tao, Keyi Li, Adan Codina, Xiaoyun Dai, Xingbo Shang, Suxia Bai, Timothy Nottoli, Andre Levchenko, Carmen J. Booth, Chen Liu, Rong Fan, Matthew B. Dong, Xiaoyu Zhou, Sidi Chen","doi":"10.1038/s41551-025-01371-2","DOIUrl":"https://doi.org/10.1038/s41551-025-01371-2","url":null,"abstract":"<p>The pleiotropic effects of human disease and the complex nature of gene-interaction networks require knock-in mice allowing for multiplexed gene perturbations. Here we describe a series of knock-in mice with a C57BL/6 background and with the conditional or constitutive expression of LbCas12a or of high-fidelity enhanced AsCas12a, which were inserted at the <i>Rosa26</i> locus. The constitutive expression of Cas12a in the mice did not lead to discernible pathology and enabled efficient multiplexed genome engineering. We used the mice for the retrovirus-based immune-cell engineering of CD4<sup>+</sup> and CD8<sup>+</sup> T cells, B cells and bone-marrow-derived dendritic cells, for autochthonous cancer modelling through the delivery of multiple CRISPR RNAs as a single array using adeno-associated viruses, and for the targeted genome editing of liver tissue using lipid nanoparticles. We also describe a system for simultaneous dual-gene activation and knockout (DAKO). The Cas12a-knock-in mice and the viral and non-viral delivery vehicles provide a versatile toolkit for ex vivo and in vivo applications in genome editing, disease modelling and immune-cell engineering, and for the deconvolution of complex gene interactions.</p>","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":"18 1","pages":""},"PeriodicalIF":28.1,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143660364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Theodore L. Roth, Johnathan Lu, Alison McClellan, Courtney Kernick, Oliver Takacsi-Nagy, Ansuman T. Satpathy
{"title":"Non-viral intron knock-ins for targeted gene integration into human T cells and for T-cell selection","authors":"Theodore L. Roth, Johnathan Lu, Alison McClellan, Courtney Kernick, Oliver Takacsi-Nagy, Ansuman T. Satpathy","doi":"10.1038/s41551-025-01372-1","DOIUrl":"https://doi.org/10.1038/s41551-025-01372-1","url":null,"abstract":"<p>Current methods for the precise integration of DNA sequences into the genome of human T cells predominantly target exonic regions, which limits the choice of integration site and requires complex cell-selection strategies. Here we show that non-viral intron knock-ins for incorporating synthetic exons into endogenous introns enable efficient gene targeting and selective gene knockout in successfully edited cells. In primary human T cells, the knock-in of a chimaeric antigen receptor (CAR) into the T-cell receptor alpha constant locus facilitated the purification of more than 90% CAR<sup>+</sup> T cells via the negative selection of T-cell-receptor-negative cells. The method is scalable, applicable across intronic sites, as we show for introns within four distinct endogenous surface-receptor genes, and supports the integration of large synthetic exons (longer than 5 kb), of alternative splicing architectures that preserve endogenous gene expression, and of synthetic promoters allowing for endogenous or user-defined gene regulation. Non-viral intron knock-ins expand the range of targetable genomic sites and provide a simplified and high-throughput strategy for selecting edited primary human T cells.</p>","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":"229 1","pages":""},"PeriodicalIF":28.1,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143569638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Guanhua Zhu, Chowdhury Rafeed Rahman, Victor Getty, Denis Odinokov, Probhonjon Baruah, Hanaé Carrié, Avril Joy Lim, Yu Amanda Guo, Zhong Wee Poh, Ngak Leng Sim, Ahmed Abdelmoneim, Yutong Cai, Lakshmi Narayanan Lakshmanan, Danliang Ho, Saranya Thangaraju, Polly Poon, Yi Ting Lau, Anna Gan, Sarah Ng, Si-Lin Koo, Dawn Q. Chong, Brenda Tay, Tira J. Tan, Yoon Sim Yap, Aik Yong Chok, Matthew Chau Hsien Ng, Patrick Tan, Daniel Tan, Limsoon Wong, Pui Mun Wong, Iain Beehuat Tan, Anders Jacobsen Skanderup
{"title":"A deep-learning model for quantifying circulating tumour DNA from the density distribution of DNA-fragment lengths","authors":"Guanhua Zhu, Chowdhury Rafeed Rahman, Victor Getty, Denis Odinokov, Probhonjon Baruah, Hanaé Carrié, Avril Joy Lim, Yu Amanda Guo, Zhong Wee Poh, Ngak Leng Sim, Ahmed Abdelmoneim, Yutong Cai, Lakshmi Narayanan Lakshmanan, Danliang Ho, Saranya Thangaraju, Polly Poon, Yi Ting Lau, Anna Gan, Sarah Ng, Si-Lin Koo, Dawn Q. Chong, Brenda Tay, Tira J. Tan, Yoon Sim Yap, Aik Yong Chok, Matthew Chau Hsien Ng, Patrick Tan, Daniel Tan, Limsoon Wong, Pui Mun Wong, Iain Beehuat Tan, Anders Jacobsen Skanderup","doi":"10.1038/s41551-025-01370-3","DOIUrl":"10.1038/s41551-025-01370-3","url":null,"abstract":"The quantification of circulating tumour DNA (ctDNA) in blood enables non-invasive surveillance of cancer progression. Here we show that a deep-learning model can accurately quantify ctDNA from the density distribution of cell-free DNA-fragment lengths. We validated the model, which we named ‘Fragle’, by using low-pass whole-genome-sequencing data from multiple cancer types and healthy control cohorts. In independent cohorts, Fragle outperformed tumour-naive methods, achieving higher accuracy and lower detection limits. We also show that Fragle is compatible with targeted sequencing data. In plasma samples from patients with colorectal cancer, longitudinal analysis with Fragle revealed strong concordance between ctDNA dynamics and treatment responses. In patients with resected lung cancer, Fragle outperformed a tumour-naive gene panel in the prediction of minimal residual disease for risk stratification. The method’s versatility, speed and accuracy for ctDNA quantification suggest that it may have broad clinical utility. A deep-learning model can accurately quantify circulating tumour DNA from the density distribution of cell-free DNA-fragment lengths in plasma from patients with cancer and from healthy individuals.","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":"9 3","pages":"307-319"},"PeriodicalIF":26.8,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143569680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yuqi Sun, Weimin Tan, Zhuoyao Gu, Ruian He, Siyuan Chen, Miao Pang, Bo Yan
{"title":"A data-efficient strategy for building high-performing medical foundation models","authors":"Yuqi Sun, Weimin Tan, Zhuoyao Gu, Ruian He, Siyuan Chen, Miao Pang, Bo Yan","doi":"10.1038/s41551-025-01365-0","DOIUrl":"https://doi.org/10.1038/s41551-025-01365-0","url":null,"abstract":"<p>Foundation models are pretrained on massive datasets. However, collecting medical datasets is expensive and time-consuming, and raises privacy concerns. Here we show that synthetic data generated via conditioning with disease labels can be leveraged for building high-performing medical foundation models. We pretrained a retinal foundation model, first with approximately one million synthetic retinal images with physiological structures and feature distribution consistent with real counterparts, and then with only 16.7% of the 904,170 real-world colour fundus photography images required in a recently reported retinal foundation model (RETFound). The data-efficient model performed as well or better than RETFound across nine public datasets and four diagnostic tasks; and for diabetic-retinopathy grading, it used only 40% of the expert-annotated training data used by RETFound. We also support the generalizability of the data-efficient strategy by building a classifier for the detection of tuberculosis on chest X-ray images. The text-conditioned generation of synthetic data may enhance the performance and generalization of medical foundation models.</p>","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":"52 1","pages":""},"PeriodicalIF":28.1,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143546025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lester Frei, Beichen Gao, Jiami Han, Joseph M. Taft, Edward B. Irvine, Cédric R. Weber, Rachita K. Kumar, Benedikt N. Eisinger, Andrey Ignatov, Zhouya Yang, Sai T. Reddy
{"title":"Deep mutational learning for the selection of therapeutic antibodies resistant to the evolution of Omicron variants of SARS-CoV-2","authors":"Lester Frei, Beichen Gao, Jiami Han, Joseph M. Taft, Edward B. Irvine, Cédric R. Weber, Rachita K. Kumar, Benedikt N. Eisinger, Andrey Ignatov, Zhouya Yang, Sai T. Reddy","doi":"10.1038/s41551-025-01353-4","DOIUrl":"https://doi.org/10.1038/s41551-025-01353-4","url":null,"abstract":"<p>Most antibodies for treating COVID-19 rely on binding the receptor-binding domain (RBD) of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2). However, Omicron and its sub-lineages, as well as other heavily mutated variants, have rendered many neutralizing antibodies ineffective. Here we show that antibodies with enhanced resistance to the evolution of SARS-CoV-2 can be identified via deep mutational learning. We constructed a library of full-length RBDs of Omicron BA.1 with high mutational distance and screened it for binding to the angiotensin-converting-enzyme-2 receptor and to neutralizing antibodies. After deep-sequencing the library, we used the data to train ensemble deep-learning models for the prediction of the binding and escape of a panel of eight therapeutic antibody candidates targeting a diverse range of RBD epitopes. By using in silico evolution to assess antibody breadth via the prediction of the binding and escape of the antibodies to millions of Omicron sequences, we found combinations of two antibodies with enhanced and complementary resistance to viral evolution. Deep learning may enable the development of therapeutic antibodies that remain effective against future SARS-CoV-2 variants.</p>","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":"24 1","pages":""},"PeriodicalIF":28.1,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143545993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zitong Jerry Wang, Abdullah S. Farooq, Yu-Jen Chen, Aman Bhargava, Alexander M. Xu, Matt W. Thomson
{"title":"Identifying perturbations that boost T-cell infiltration into tumours via counterfactual learning of their spatial proteomic profiles","authors":"Zitong Jerry Wang, Abdullah S. Farooq, Yu-Jen Chen, Aman Bhargava, Alexander M. Xu, Matt W. Thomson","doi":"10.1038/s41551-025-01357-0","DOIUrl":"10.1038/s41551-025-01357-0","url":null,"abstract":"Cancer progression can be slowed down or halted via the activation of either endogenous or engineered T cells and their infiltration of the tumour microenvironment. Here we describe a deep-learning model that uses large-scale spatial proteomic profiles of tumours to generate minimal tumour perturbations that boost T-cell infiltration. The model integrates a counterfactual optimization strategy for the generation of the perturbations with the prediction of T-cell infiltration as a self-supervised machine learning problem. We applied the model to 368 samples of metastatic melanoma and colorectal cancer assayed using 40-plex imaging mass cytometry, and discovered cohort-dependent combinatorial perturbations (CXCL9, CXCL10, CCL22 and CCL18 for melanoma, and CXCR4, PD-1, PD-L1 and CYR61 for colorectal cancer) that support T-cell infiltration across patient cohorts, as confirmed via in vitro experiments. Leveraging counterfactual-based predictions of spatial omics data may aid the design of cancer therapeutics. Minimal tumour perturbations that boost T-cell infiltration can be discovered by using deep learning to analyse large-scale spatial omics profiles of tumours.","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":"9 3","pages":"390-404"},"PeriodicalIF":26.8,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41551-025-01357-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143546075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Justin H. Wilde, Yu-Yo Sun, Spenser R. Simpson, Ethan R. Hill, Zhongxiao Fu, Emily J. Bian, Melissa M. Kinkaid, Paulina Villanueva, Aden F. Weybright, William R. Terrell, Zoraiz Qureshi, Shashika S. Perera, Heather S. Sheppard, James R. Stone, Bijoy K. Kundu, Chia-Yi Kuan, Kiel D. Neumann
{"title":"A positron emission tomography tracer for the imaging of oxidative stress in the central nervous system","authors":"Justin H. Wilde, Yu-Yo Sun, Spenser R. Simpson, Ethan R. Hill, Zhongxiao Fu, Emily J. Bian, Melissa M. Kinkaid, Paulina Villanueva, Aden F. Weybright, William R. Terrell, Zoraiz Qureshi, Shashika S. Perera, Heather S. Sheppard, James R. Stone, Bijoy K. Kundu, Chia-Yi Kuan, Kiel D. Neumann","doi":"10.1038/s41551-025-01362-3","DOIUrl":"https://doi.org/10.1038/s41551-025-01362-3","url":null,"abstract":"<p>Reactive oxygen and nitrogen species (RONS) contribute to the pathogenesis of neurodegeneration, but the inability to detect RONS in vivo in the central nervous system has confounded the interpretation of results of clinical trials of antioxidants. Here we report the synthesis and characterization of a positron emission tomography (PET) probe, [<sup>18</sup>F]fluoroedaravone ([<sup>18</sup>F]FEDV), for the in vivo quantification of oxidative stress. Derived from the antioxidant edaravone, the probe can diffuse through the blood–brain barrier and is stable in human plasma. In mice, PET imaging with [<sup>18</sup>F]FEDV allowed for the detection of RONS after intrastriatal injection of sodium nitroprusside, in the middle cerebral artery after stroke by photothrombosis, and in brains with tauopathy. When using dynamic PET imaging coupled with parametric mapping, the sensitivity of [<sup>18</sup>F]FEDV-PET to RONS allowed for the detection of increased oxidative stress. [<sup>18</sup>F]FEDV-PET could be used to quantify RONS longitudinally in vivo and to assess the results of clinical studies of antioxidants.</p>","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":"22 1","pages":""},"PeriodicalIF":28.1,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143545992","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zhenxing Wang, Xuemin Liu, Tan Ye, Zhichen Zhai, Kai Wu, Yudi Kuang, Serge Ostrovidov, Dan Shao, Yingjun Wang, Kam W. Leong, Xuetao Shi
{"title":"3D-printed perfused models of the penis for the study of penile physiology and for restoring erectile function in rabbits and pigs","authors":"Zhenxing Wang, Xuemin Liu, Tan Ye, Zhichen Zhai, Kai Wu, Yudi Kuang, Serge Ostrovidov, Dan Shao, Yingjun Wang, Kam W. Leong, Xuetao Shi","doi":"10.1038/s41551-025-01367-y","DOIUrl":"https://doi.org/10.1038/s41551-025-01367-y","url":null,"abstract":"<p>The intricate topology of vascular networks and the complex functions of vessel-rich tissues are challenging to reconstruct in vitro. Here we report the development of: in vitro pathological models of erectile dysfunction and Peyronie’s disease; a model of the penis that includes the glans and the corpus spongiosum with urethral structures; and an implantable model of the corpus cavernosum, whose complex vascular network is critical for erectile function, via the vein-occlusion effect. Specifically, we 3D printed a hydrogel-based corpus cavernosum incorporating a strain-limiting tunica albuginea that can be engorged with blood through vein occlusion. In corpus cavernosum defects in rabbits and pigs, implantation of the 3D-printed tissue seeded with endothelial cells restored normal erectile function on electrical stimulation of the cavernous nerves as well as spontaneous erectile function within a few weeks of implantation, which allowed the animals to mate and reproduce. Our findings support the further development of 3D-printed blood-vessel-rich functional organs for transplantation.</p>","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":"211 1","pages":""},"PeriodicalIF":28.1,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143538300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ameya R. Kirtane, Jianling Bi, Netra U. Rajesh, Chaoyang Tang, Miguel Jimenez, Emily Witt, Megan K. McGovern, Arielle B. Cafi, Samual J. Hatfield, Lauren Rosenstock, Sarah L. Becker, Nicole Machado, Veena Venkatachalam, Dylan Freitas, Xisha Huang, Alvin Chan, Aaron Lopes, Hyunjoon Kim, Nayoon Kim, Joy E. Collins, Michelle E. Howard, Srija Manchkanti, Theodore S. Hong, James D. Byrne, Giovanni Traverso
{"title":"Radioprotection of healthy tissue via nanoparticle-delivered mRNA encoding for a damage-suppressor protein found in tardigrades","authors":"Ameya R. Kirtane, Jianling Bi, Netra U. Rajesh, Chaoyang Tang, Miguel Jimenez, Emily Witt, Megan K. McGovern, Arielle B. Cafi, Samual J. Hatfield, Lauren Rosenstock, Sarah L. Becker, Nicole Machado, Veena Venkatachalam, Dylan Freitas, Xisha Huang, Alvin Chan, Aaron Lopes, Hyunjoon Kim, Nayoon Kim, Joy E. Collins, Michelle E. Howard, Srija Manchkanti, Theodore S. Hong, James D. Byrne, Giovanni Traverso","doi":"10.1038/s41551-025-01360-5","DOIUrl":"https://doi.org/10.1038/s41551-025-01360-5","url":null,"abstract":"<p>Patients undergoing radiation therapy experience debilitating side effects because of toxicity arising from radiation-induced DNA strand breaks in normal peritumoural cells. Here, inspired by the ability of tardigrades to resist extreme radiation through the expression of a damage-suppressor protein that binds to DNA and reduces strand breaks, we show that the local and transient expression of the protein can reduce radiation-induced DNA damage in oral and rectal epithelial tissues (which are commonly affected during radiotherapy for head-and-neck and prostate cancers, respectively). We used ionizable lipid nanoparticles supplemented with biodegradable cationic polymers to enhance the transfection efficiency and delivery of messenger RNA encoding the damage-suppressor protein into buccal and rectal tissues. In mice with orthotopic oral cancer, messenger RNA-based radioprotection of normal tissue preserved the efficacy of radiation therapy. The strategy may be broadly applicable to the protection of healthy tissue from DNA-damaging agents.</p>","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":"16 1","pages":""},"PeriodicalIF":28.1,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143495271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sisi Lin, Guocheng Xie, Jun He, Lu Meng, Yan Pang, Jinyao Liu
{"title":"Enhancing phage therapy by coating single bacteriophage-infected bacteria with polymer to preserve phage vitality","authors":"Sisi Lin, Guocheng Xie, Jun He, Lu Meng, Yan Pang, Jinyao Liu","doi":"10.1038/s41551-025-01354-3","DOIUrl":"https://doi.org/10.1038/s41551-025-01354-3","url":null,"abstract":"<p>The efficacy of bacteriophages in treating bacterial infections largely depends on the phages’ vitality, which is impaired when they are naturally released from their hosts, as well as by culture media, manufacturing processes and other insults. Here, by wrapping phage-invaded bacteria individually with a polymeric nanoscale coating to preserve the microenvironment on phage-induced bacterial lysis, we show that, compared with naturally released phages, which have severely degraded proteins in their tail, the vitality of phages isolated from polymer-coated bacteria is maintained. Such latent phages could also be better amplified, and they more efficiently bound and lysed bacteria when clearing bacterial biofilms. In mice with bacterially induced enteritis and associated arthritis, latent phages released from orally administered bacteria coated with a polymer that dissolves at neutral pH had higher bioavailability and led to substantially better therapeutic outcomes than the administration of uncoated phages.</p>","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":"65 1","pages":""},"PeriodicalIF":28.1,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143485679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
