Dataset Information

Recombinant Antibody-Based and Computer-Aided Comprehensive Analysis of Antibody's Equivalent Recognition Mechanism of Alternariol and Alternariol Monomethyl Ether.

ABSTRACT: Alternariol (AOH) and alternariol monomethyl ether (AME) are two main Alternaria mycotoxins that endanger human health. In this study, a single-chain antibody fragment (scFv) capable of equivalently and specifically recognizing AOH and AME was first expressed, and its equivalent recognition mechanism was discussed. According to molecular docking and dynamic simulation, the C9 site, which was always exposed outside the binding cavity, made the structural differences between AOH and AME negligible. Due to the high similarity of structures, AOH and AME interacted with almost the same amino acids on the scFv; thus, the same interaction mode and interaction force were produced. This was considered to be the most critical reason for the equivalent recognition. Thus, the exposure of common structures was considered a potential strategy to obtain the equivalent recognition antibodies, and C9 was considered the key site in the process of hapten modification. These results laid a theoretical foundation for further research on antibodies against Alternaria mycotoxins. It could promote the rapid detection of AOH and AME in food and provide a new idea for targeted preparation of antibodies that could recognize multiple hazards with similar structures.

SUBMITTER: Wang Z

PROVIDER: S-EPMC9046909 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Recombinant Antibody-Based and Computer-Aided Comprehensive Analysis of Antibody's Equivalent Recognition Mechanism of Alternariol and Alternariol Monomethyl Ether.

Wang Zile Z Chen Ling L Zheng Pimiao P Wang Jianyi J Ren Zhenhui Z Zhang Huixia H Zhang Liang L Jiang Haiyang H

Frontiers in chemistry 20220414

Alternariol (AOH) and alternariol monomethyl ether (AME) are two main <i>Alternaria</i> mycotoxins that endanger human health. In this study, a single-chain antibody fragment (scFv) capable of equivalently and specifically recognizing AOH and AME was first expressed, and its equivalent recognition mechanism was discussed. According to molecular docking and dynamic simulation, the C9 site, which was always exposed outside the binding cavity, made the structural differences between AOH and AME neg ...[more]

PMID: 35494638

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Project description:Alternaria alternata FB1 is a marine fungus identified as a candidate for plastic degradation in our previous study. This fungus has been recently shown to produce secondary metabolites with significant antimicrobial activity against various pathogens, including methicillin-resistant Staphylococcus aureus (MRSA) and the notorious aquaculture pathogen Vibrio anguillarum. The antibacterial compounds were purified and identified as alternariol (AOH) and its derivative, alternariol monomethyl ether (AME). We found that AOH and AME primarily inhibited pathogenic bacteria (MRSA or V. anguillarum) by disordering cell division and some other key physiological and biochemical processes. We further demonstrated that AOH could effectively inhibit the unwinding activity of MRSA topoisomerases, which are closely related to cell division and are the potential action target of AOH. The antibacterial activities of AOH and AME were verified by using zebrafish as the in vivo model. Notably, AOH and AME did not significantly affect the viability of normal human liver cells at concentrations that effectively inhibited MRSA or V. anguillarum. Finally, we developed the genetic operation system of A. alternata FB1 and blocked the biosynthesis of AME by knocking out omtI (encoding an O-methyl transferase), which facilitated A. alternata FB1 to only produce AOH. The development of this system in the marine fungus will accelerate the discovery of novel natural products and further bioactivity study.IMPORTANCEMore and more scientific reports indicate that alternariol (AOH) and its derivative alternariol monomethyl ether (AME) exhibit antibacterial activities. However, limited exploration of their detailed antibacterial mechanisms has been performed. In the present study, the antibacterial mechanisms of AOH and AME produced by the marine fungus Alternaria alternata FB1 were disclosed in vitro and in vivo. Given their low toxicity on the normal human liver cell line under the concentrations exhibiting significant antibacterial activity against different pathogens, AOH and AME are proposed to be good candidates for developing promising antibiotics against methicillin-resistant Staphylococcus aureus and Vibrio anguillarum. We also succeeded in blocking the biosynthesis of AME, which facilitated us to easily obtain pure AOH. Moreover, based on our previous results, A. alternata FB1 was shown to enable polyethylene degradation.

Project description:BackgroundMinimally invasive surgery is complex and associated with substantial learning curves. Computer-aided anatomy recognition, such as artificial intelligence-based algorithms, may improve anatomical orientation, prevent tissue injury, and improve learning curves. The study objective was to provide a comprehensive overview of current literature on the accuracy of anatomy recognition algorithms in intrathoracic and -abdominal surgery.MethodsThis systematic review is reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. Pubmed, Embase, and IEEE Xplore were searched for original studies up until January 2022 on computer-aided anatomy recognition, without requiring intraoperative imaging or calibration equipment. Extracted features included surgical procedure, study population and design, algorithm type, pre-training methods, pre- and post-processing methods, data augmentation, anatomy annotation, training data, testing data, model validation strategy, goal of the algorithm, target anatomical structure, accuracy, and inference time.ResultsAfter full-text screening, 23 out of 7124 articles were included. Included studies showed a wide diversity, with six possible recognition tasks in 15 different surgical procedures, and 14 different accuracy measures used. Risk of bias in the included studies was high, especially regarding patient selection and annotation of the reference standard. Dice and intersection over union (IoU) scores of the algorithms ranged from 0.50 to 0.98 and from 74 to 98%, respectively, for various anatomy recognition tasks. High-accuracy algorithms were typically trained using larger datasets annotated by expert surgeons and focused on less-complex anatomy. Some of the high-accuracy algorithms were developed using pre-training and data augmentation.ConclusionsThe accuracy of included anatomy recognition algorithms varied substantially, ranging from moderate to good. Solid comparison between algorithms was complicated by the wide variety of applied methodology, target anatomical structures, and reported accuracy measures. Computer-aided intraoperative anatomy recognition is an upcoming research discipline, but still at its infancy. Larger datasets and methodological guidelines are required to improve accuracy and clinical applicability in future research.Trial registrationPROSPERO registration number: CRD42021264226.

Dataset Information

Recombinant Antibody-Based and Computer-Aided Comprehensive Analysis of Antibody's Equivalent Recognition Mechanism of Alternariol and Alternariol Monomethyl Ether.

Publications

Recombinant Antibody-Based and Computer-Aided Comprehensive Analysis of Antibody's Equivalent Recognition Mechanism of Alternariol and Alternariol Monomethyl Ether.

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