Engineering Nanoparticle-Coated Bacteria as Oral DNA Vaccines for Cancer Immunotherapy

dna疫苗 免疫疗法 细菌 癌症 癌症免疫疗法 免疫系统 微生物学 癌症研究 生物 免疫学 免疫 遗传学
作者
Qinglian Hu,Min Wu,Chun Fang,Changyong Cheng,Mengmeng Zhao,Weihuan Fang,Paul K. Chu,Ping Yuan,Guping Tang
出处
期刊:Nano Letters [American Chemical Society]
卷期号:15 (4): 2732-2739 被引量:276
标识
DOI:10.1021/acs.nanolett.5b00570
摘要

Live attenuated bacteria are of increasing importance in biotechnology and medicine in the emerging field of cancer immunotherapy. Oral DNA vaccination mediated by live attenuated bacteria often suffers from low infection efficiency due to various biological barriers during the infection process. To this end, we herein report, for the first time, a new strategy to engineer cationic nanoparticle-coated bacterial vectors that can efficiently deliver oral DNA vaccine for efficacious cancer immunotherapy. By coating live attenuated bacteria with synthetic nanoparticles self-assembled from cationic polymers and plasmid DNA, the protective nanoparticle coating layer is able to facilitate bacteria to effectively escape phagosomes, significantly enhance the acid tolerance of bacteria in stomach and intestines, and greatly promote dissemination of bacteria into blood circulation after oral administration. Most importantly, oral delivery of DNA vaccines encoding autologous vascular endothelial growth factor receptor 2 (VEGFR2) by this hybrid vector showed remarkable T cell activation and cytokine production. Successful inhibition of tumor growth was also achieved by efficient oral delivery of VEGFR2 with nanoparticle-coated bacterial vectors due to angiogenesis suppression in the tumor vasculature and tumor necrosis. This proof-of-concept work demonstrates that coating live bacterial cells with synthetic nanoparticles represents a promising strategy to engineer efficient and versatile DNA vaccines for the era of immunotherapy.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
机智的香菇完成签到,获得积分10
1秒前
2秒前
3秒前
烂漫的向日葵应助果粒程采纳,获得10
4秒前
核桃应助滕皓轩采纳,获得30
4秒前
4秒前
科研小狗完成签到 ,获得积分10
7秒前
8秒前
8秒前
杨同学发布了新的文献求助10
9秒前
9秒前
Rla发布了新的文献求助10
10秒前
10秒前
11秒前
能干的cen发布了新的文献求助10
12秒前
申誉杰发布了新的文献求助10
12秒前
南海子完成签到,获得积分10
14秒前
14秒前
Nole应助keyanqianjin采纳,获得10
15秒前
15秒前
prigogin应助张张采纳,获得10
16秒前
17秒前
千山发布了新的文献求助10
18秒前
华仔应助聪慧剑封采纳,获得10
20秒前
NexusExplorer应助于小小于采纳,获得10
21秒前
21秒前
21秒前
22秒前
xing_xing应助欢喜的皮卡丘采纳,获得20
22秒前
___赵发布了新的文献求助10
22秒前
大模型应助书尘采纳,获得10
24秒前
TOMORROW完成签到,获得积分10
24秒前
研友_VZG7GZ应助颂歌998采纳,获得30
24秒前
李子发布了新的文献求助10
25秒前
爱笑的芙发布了新的文献求助10
25秒前
千山完成签到,获得积分10
25秒前
深情安青应助清脆刺猬采纳,获得10
26秒前
欢欢姐姐发布了新的文献求助10
26秒前
28秒前
godzyy发布了新的文献求助10
28秒前
高分求助中
Markov Chain Monte Carlo 10000
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Common Foundations of American and East Asian Modernisation: From Alexander Hamilton to Junichero Koizumi 5000
How to Use Machine Learning in Chemistry: An Introduction 1000
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
Discerning Saints: Moralization of Intrinsic Motivation and Selective Prosociality at Work 500
Handbuch Trainingswissenschaft – Trainingslehre 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7583692
求助须知:如何正确求助?哪些是违规求助? 9162363
关于积分的说明 19606904
捐赠科研通 7165670
什么是DOI,文献DOI怎么找? 3266302
关于科研通互助平台的介绍 2431200
邀请新用户注册赠送积分活动 2257786