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Expression strategies for the efficient synthesis of antimicrobial peptides in plastids.

ABSTRACT: Antimicrobial peptides (AMPs) kill microbes or inhibit their growth and are promising next-generation antibiotics. Harnessing their full potential as antimicrobial agents will require methods for cost-effective large-scale production and purification. Here, we explore the possibility to exploit the high protein synthesis capacity of the chloroplast to produce AMPs in plants. Generating a large series of 29 sets of transplastomic tobacco plants expressing nine different AMPs as fusion proteins, we show that high-level constitutive AMP expression results in deleterious plant phenotypes. However, by utilizing inducible expression and fusions to the cleavable carrier protein SUMO, the cytotoxic effects of AMPs and fused AMPs are alleviated and plants with wild-type-like phenotypes are obtained. Importantly, purified AMP fusion proteins display antimicrobial activity independently of proteolytic removal of the carrier. Our work provides expression strategies for the synthesis of toxic polypeptides in chloroplasts, and establishes transplastomic plants as efficient production platform for antimicrobial peptides.

SUBMITTER: Hoelscher MP

PROVIDER: S-EPMC9532397 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature

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Expression strategies for the efficient synthesis of antimicrobial peptides in plastids.

Hoelscher Matthijs P MP Forner Joachim J Calderone Silvia S Krämer Carolin C Taylor Zachary Z Loiacono F Vanessa FV Agrawal Shreya S Karcher Daniel D Moratti Fabio F Kroop Xenia X Bock Ralph R

Nature communications 20221004 1

Antimicrobial peptides (AMPs) kill microbes or inhibit their growth and are promising next-generation antibiotics. Harnessing their full potential as antimicrobial agents will require methods for cost-effective large-scale production and purification. Here, we explore the possibility to exploit the high protein synthesis capacity of the chloroplast to produce AMPs in plants. Generating a large series of 29 sets of transplastomic tobacco plants expressing nine different AMPs as fusion proteins, w ...[more]

PMID: 36195597

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Project description:The increasing number of antibiotic resistant bacteria motivates prospective research toward discovery of new antimicrobial active substances. There are, however, controversies concerning the cost-effectiveness of such research with regards to the description of new substances with novel cellular interactions, or description of new uses of existing substances to overcome resistance. Although examination of bacteria isolated from remote locations with limited exposure to humans has revealed an absence of antibiotic resistance genes, it is accepted that these genes were both abundant and diverse in ancient living organisms, as detected in DNA recovered from Pleistocene deposits (30,000 years ago). Indeed, even before the first clinical use of antibiotics more than 60 years ago, resistant organisms had been isolated. Bacteria can exhibit different strategies for resistance against antibiotics. New genetic information may lead to the modification of protein structure affecting the antibiotic carriage into the cell, enzymatic inactivation of drugs, or even modification of cellular structure interfering in the drug-bacteria interaction. There are still plenty of new genes out there in the environment that can be appropriated by putative pathogenic bacteria to resist antimicrobial agents. On the other hand, there are several natural compounds with antibiotic activity that may be used to oppose them. Antimicrobial peptides (AMPs) are molecules which are wide-spread in all forms of life, from multi-cellular organisms to bacterial cells used to interfere with microbial growth. Several AMPs have been shown to be effective against multi-drug resistant bacteria and have low propensity to resistance development, probably due to their unique mode of action, different from well-known antimicrobial drugs. These substances may interact in different ways with bacterial cell membrane, protein synthesis, protein modulation, and protein folding. The analysis of bacterial transcriptome may contribute to the understanding of microbial strategies under different environmental stresses and allows the understanding of their interaction with novel AMPs.

Project description:BackgroundCurrent methods in machine learning provide approaches for solving challenging, multiple constraint design problems. While deep learning and related neural networking methods have state-of-the-art performance, their vulnerability in decision making processes leading to irrational outcomes is a major concern for their implementation. With the rising antibiotic resistance, antimicrobial peptides (AMPs) have increasingly gained attention as novel therapeutic agents. This challenging design problem requires peptides which meet the multiple constraints of limiting drug-resistance in bacteria, preventing secondary infections from imbalanced microbial flora, and avoiding immune system suppression. AMPs offer a promising, bioinspired design space to targeting antimicrobial activity, but their versatility also requires the curated selection from a combinatorial sequence space. This space is too large for brute-force methods or currently known rational design approaches outside of machine learning. While there has been progress in using the design space to more effectively target AMP activity, a widely applicable approach has been elusive. The lack of transparency in machine learning has limited the advancement of scientific knowledge of how AMPs are related among each other, and the lack of general applicability for fully rational approaches has limited a broader understanding of the design space.MethodsHere we combined an evolutionary method with rough set theory, a transparent machine learning approach, for designing antimicrobial peptides (AMPs). Our method achieves the customization of AMPs using supervised learning boundaries. Our system employs in vitro bacterial assays to measure fitness, codon-representation of peptides to gain flexibility of sequence selection in DNA-space with a genetic algorithm and machine learning to further accelerate the process.ResultsWe use supervised machine learning and a genetic algorithm to find a peptide active against S. epidermidis, a common bacterial strain for implant infections, with an improved aggregation propensity average for an improved ease of synthesis.ConclusionsOur results demonstrate that AMP design can be customized to maintain activity and simplify production. To our knowledge, this is the first time when codon-based genetic algorithms combined with rough set theory methods is used for computational search on peptide sequences.

Dataset Information

Expression strategies for the efficient synthesis of antimicrobial peptides in plastids.

Publications

Expression strategies for the efficient synthesis of antimicrobial peptides in plastids.

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