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Pan N, Liu Y, Zhang H, Xu Y, Bao X, Sheng S, Liang Y, Liu B, Lyu Y, Li H, Ma F, Pan H, Wang X. Oral Vaccination with Engineered Probiotic Limosilactobacillus reuteri Has Protective Effects against Localized and Systemic Staphylococcus aureus Infection. Microbiol Spectr 2023; 11:e0367322. [PMID: 36723073 PMCID: PMC10100842 DOI: 10.1128/spectrum.03673-22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 01/14/2023] [Indexed: 02/02/2023] Open
Abstract
Staphylococcus aureus is a Gram-positive bacterium responsible for most hospital-acquired (nosocomial) and community-acquired infections worldwide. The only therapeutic strategy against S. aureus-induced infections, to date, is antibiotic treatment. A protective vaccine is urgently needed in view of the emergence of antibiotic-resistant strains associated with high-mortality cases; however, no such vaccine is currently available. In our previous work, the feasibility of implementing a Lactobacillus delivery system for development of S. aureus oral vaccine was first discussed. Here, we describe systematic screening and evaluation of protective effects of engineered Lactobacillus against S. aureus infection in terms of different delivery vehicle strains and S. aureus antigens and in localized and systemic infection models. Limosilactobacillus reuteri WXD171 was selected as the delivery vehicle strain based on its tolerance of the gastrointestinal environment, adhesion ability, and antimicrobial activities in vitro and in vivo. We designed, constructed, and evaluated engineered L. reuteri strains expressing various S. aureus antigens. Among these, engineered L. reuteri WXD171-IsdB displayed effective protection against S. aureus-induced localized infection (pneumonia and skin infection) and, furthermore, a substantial survival benefit in systemic infection (sepsis). WXD171-IsdB induced mucosal responses in gut-associated lymphoid tissues, as evidenced by increased production of secretory IgA and interleukin 17A (IL-17A) and proliferation of lymphocytes derived from Peyer's patches. The probiotic L. reuteri-based oral vaccine appears to have strong potential as a prophylactic agent against S. aureus infections. Our findings regarding utilization of Lactobacillus delivery system in S. aureus vaccine development support the usefulness of this live vaccination strategy and its potential application in next-generation vaccine development. IMPORTANCE We systematically screened and evaluated protective effects of engineered Lactobacillus against S. aureus infection in terms of differing delivery vehicle strains and S. aureus antigens and in localized and systemic infection models. Engineered L. reuteri was developed and showed strong protective effects against both types of S. aureus-induced infection. Our findings regarding the utilization of a Lactobacillus delivery system in S. aureus vaccine development support the usefulness of this live vaccination strategy and its potential application in next-generation vaccine development.
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Affiliation(s)
- Na Pan
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, School of Life Sciences, Inner Mongolia University, Hohhot, China
| | - Yang Liu
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, School of Life Sciences, Inner Mongolia University, Hohhot, China
| | - Haochi Zhang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, School of Life Sciences, Inner Mongolia University, Hohhot, China
| | - Ying Xu
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, School of Life Sciences, Inner Mongolia University, Hohhot, China
| | - Xuemei Bao
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, School of Life Sciences, Inner Mongolia University, Hohhot, China
| | - Shouxin Sheng
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, School of Life Sciences, Inner Mongolia University, Hohhot, China
| | - Yanchen Liang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, School of Life Sciences, Inner Mongolia University, Hohhot, China
| | - Bohui Liu
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, School of Life Sciences, Inner Mongolia University, Hohhot, China
| | - Yueqing Lyu
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, School of Life Sciences, Inner Mongolia University, Hohhot, China
| | - Haotian Li
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, School of Life Sciences, Inner Mongolia University, Hohhot, China
| | - Fangfei Ma
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, School of Life Sciences, Inner Mongolia University, Hohhot, China
| | - Haiting Pan
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, School of Life Sciences, Inner Mongolia University, Hohhot, China
- Basic Medical College, Inner Mongolia Medical University, Hohhot, China
| | - Xiao Wang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, School of Life Sciences, Inner Mongolia University, Hohhot, China
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Lin H, Li X, Gao H, Li J, Wang Y. ISC-MTI: An IPFS and smart contract-based framework for machine learning model training and invocation. Multimed Tools Appl 2022; 81:40343-40359. [PMID: 35572386 PMCID: PMC9077977 DOI: 10.1007/s11042-022-13163-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 12/21/2021] [Accepted: 04/10/2022] [Indexed: 06/15/2023]
Abstract
Due to centralized storage, centralization problems are common in machine learning model training and invocation, which makes train data and trained models extremely vulnerable to tampering and stealing. A safe framework for training and invoking models called ISC-MTI (IPFS (InterPlanetary File System) and Smart Contract-Based Method for Storage and Invocation of Machine Learning Mobel) is proposed in this paper. The framework uses IPFS as the storage solution, EOS (Enterprise Operation System) blockchain as the smart contract platform, RSA and AES as the implementation of encrypted communication. The Action responsible for invoking the training data and trained models in the smart contract and the model training, uploading, and invoking methods are designed. The experimental results demonstrate that ISC-MTI can improve the safety of model training and invocation with losing a little efficiency. Simultaneously, ISC-MTI can provide anti-theft model capabilities, traceability, tamper resistance, reliability, and privacy for the process.
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Affiliation(s)
- Hao Lin
- School of Computer Science and Engineering, Tianjin University of Technology, Tianjin, China
- College of Data Science and Application, Inner Mongolia University of Technology, Inner Mongolia, China
- Inner Mongolia Autonomous Region Engineering and Technology Research Center of Big Data Based Software Service, Inner Mongolia, China
| | - Xiaolei Li
- College of Data Science and Application, Inner Mongolia University of Technology, Inner Mongolia, China
- Inner Mongolia Autonomous Region Engineering and Technology Research Center of Big Data Based Software Service, Inner Mongolia, China
| | - Haoyu Gao
- College of Data Science and Application, Inner Mongolia University of Technology, Inner Mongolia, China
- Inner Mongolia Autonomous Region Engineering and Technology Research Center of Big Data Based Software Service, Inner Mongolia, China
| | - Jie Li
- College of Data Science and Application, Inner Mongolia University of Technology, Inner Mongolia, China
- Inner Mongolia Autonomous Region Engineering and Technology Research Center of Big Data Based Software Service, Inner Mongolia, China
| | - Yongsheng Wang
- College of Data Science and Application, Inner Mongolia University of Technology, Inner Mongolia, China
- Inner Mongolia Autonomous Region Engineering and Technology Research Center of Big Data Based Software Service, Inner Mongolia, China
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