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Mining and Statistical Modeling of Natural and Variant Class IIa Bacteriocins Elucidate Activity and Selectivity Profiles across Species.

ABSTRACT: Class IIa bacteriocin antimicrobial peptides (AMPs) are a compelling alternative to current antimicrobials because of potential specific activity toward antibiotic-resistant bacteria, including vancomycin-resistant enterococci. Engineering of these molecules would be enhanced by a better understanding of AMP sequence-activity relationships to improve efficacy in vivo and limit effects of off-target activity. Toward this goal, we experimentally evaluated 210 natural and variant class IIa bacteriocins for antimicrobial activity against six strains of enterococci. Inhibitory activity was ridge regressed to AMP sequence to predict performance, achieving an area under the curve of 0.70 and demonstrating the potential of statistical models for identifying and designing AMPs. Active AMPs were individually produced and evaluated against eight enterococcus strains and four Listeria strains to elucidate trends in susceptibility. It was determined that the mannose phosphotransferase system (manPTS) sequence is informative of susceptibility to class IIa bacteriocins, yet other factors, such as membrane composition, also contribute strongly to susceptibility. A broadly potent bacteriocin variant (lactocin DT1) from a Lactobacillus ruminis genome was identified as the only variant with inhibitory activity toward all tested strains, while a novel enterocin variant (DT2) from an Enterococcus faecium genome demonstrated specificity toward Listeria strains. Eight AMPs were evaluated for proteolytic stability to trypsin, chymotrypsin, and pepsin, and three C-terminal disulfide-containing variants, including divercin V41, were identified as compelling for future in vivo studies, given their high potency and proteolytic stability.IMPORTANCE Class IIa bacteriocin antimicrobial peptides (AMPs), an alternative to traditional small-molecule antibiotics, are capable of selective activity toward various Gram-positive bacteria, limiting negative side effects associated with broad-spectrum activity. This selective activity is achieved through targeting of the mannose phosphotransferase system (manPTS) of a subset of Gram-positive bacteria, although factors affecting this mechanism are not entirely understood. Peptides identified from genomic data, as well as variants of previously characterized AMPs, can offer insight into how peptide sequence affects activity and selectivity. The experimental methods presented here identify promising potent and selective bacteriocins for further evaluation, highlight the potential of simple computational modeling for prediction of AMP performance, and demonstrate that factors beyond manPTS sequence affect bacterial susceptibility to class IIa bacteriocins.

SUBMITTER: Tresnak DT

PROVIDER: S-EPMC7642072 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Many bacteria produce antimicrobial peptides, which are also referred to as peptide bacteriocins. The class IIa bacteriocins, often designated pediocin-like bacteriocins, constitute the most dominant group of antimicrobial peptides produced by lactic acid bacteria. The bacteriocins that belong to this class are structurally related and kill target cells by membrane permeabilization. Despite their structural similarity, class IIa bacteriocins display different target cell specificities. In the search for new antibiotic substances, the class IIa bacteriocins have been identified as promising new candidates and have thus received much attention. They kill some pathogenic bacteria (e.g., Listeria) with high efficiency, and they constitute a good model system for structure-function analyses of antimicrobial peptides in general. This review focuses on class IIa bacteriocins, especially on their structure, function, mode of action, biosynthesis, bacteriocin immunity, and current food applications. The genetics and biosynthesis of class IIa bacteriocins are well understood. The bacteriocins are ribosomally synthesized with an N-terminal leader sequence, which is cleaved off upon secretion. After externalization, the class IIa bacteriocins attach to potential target cells and, through electrostatic and hydrophobic interactions, subsequently permeabilize the cell membrane of sensitive cells. Recent observations suggest that a chiral interaction and possibly the presence of a mannose permease protein on the target cell surface are required for a bacteria to be sensitive to class IIa bacteriocins. There is also substantial evidence that the C-terminal half penetrates into the target cell membrane, and it plays an important role in determining the target cell specificity of these bacteriocins. Immunity proteins protect the bacteriocin producer from the bacteriocin it secretes. The three-dimensional structures of two class IIa immunity proteins have been determined, and it has been shown that the C-terminal halves of these cytosolic four-helix bundle proteins specify which class IIa bacteriocin they protect against.

Project description:Microarray and beadchip are two most efficient techniques for measuring gene expression and methylation data in bioinformatics. Biclustering deals with the simultaneous clustering of genes and samples. In this article, we propose a computational rule mining framework, StatBicRM (i.e., statistical biclustering-based rule mining) to identify special type of rules and potential biomarkers using integrated approaches of statistical and binary inclusion-maximal biclustering techniques from the biological datasets. At first, a novel statistical strategy has been utilized to eliminate the insignificant/low-significant/redundant genes in such way that significance level must satisfy the data distribution property (viz., either normal distribution or non-normal distribution). The data is then discretized and post-discretized, consecutively. Thereafter, the biclustering technique is applied to identify maximal frequent closed homogeneous itemsets. Corresponding special type of rules are then extracted from the selected itemsets. Our proposed rule mining method performs better than the other rule mining algorithms as it generates maximal frequent closed homogeneous itemsets instead of frequent itemsets. Thus, it saves elapsed time, and can work on big dataset. Pathway and Gene Ontology analyses are conducted on the genes of the evolved rules using David database. Frequency analysis of the genes appearing in the evolved rules is performed to determine potential biomarkers. Furthermore, we also classify the data to know how much the evolved rules are able to describe accurately the remaining test (unknown) data. Subsequently, we also compare the average classification accuracy, and other related factors with other rule-based classifiers. Statistical significance tests are also performed for verifying the statistical relevance of the comparative results. Here, each of the other rule mining methods or rule-based classifiers is also starting with the same post-discretized data-matrix. Finally, we have also included the integrated analysis of gene expression and methylation for determining epigenetic effect (viz., effect of methylation) on gene expression level.

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Mining and Statistical Modeling of Natural and Variant Class IIa Bacteriocins Elucidate Activity and Selectivity Profiles across Species.

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