Review
A network perspective on antimicrobial peptide combination therapies: the potential of colistin, polymyxin B and nisin

https://doi.org/10.1016/j.ijantimicag.2017.02.012Get rights and content

Highlights

  • Antimicrobial combinations involving polymyxin B, colistin (polymyxin E) or nisin are described.

  • Combination reconstruction relies on literature mining and manual expert curation.

  • Network analysis enables a bird's-eye view of current research trends.

  • The networks are available at http://sing-group.org/antimicrobialCombination/.

Abstract

Antimicrobial combinations involving antimicrobial peptides (AMPs) attract considerable attention within current antimicrobial and anti-resistance research. The objective of this study was to review the available scientific literature on the effects of antimicrobial combinations involving colistin (polymyxin E), polymyxin B and nisin, which are US Food and Drug Administration (FDA)-approved AMPs broadly tested against prominent multidrug-resistant pathogens. A bioinformatics approach based on literature mining and manual expert curation supported the reconstruction of experimental evidence on the potential of these AMP combinations, as described in the literature. Network analysis enabled further characterisation of the retrieved antimicrobial agents, targets and combinatory effects. This systematic analysis was able to output valuable information on the studies conducted on colistin, polymyxin B and nisin combinations. The reconstructed networks enable the traversal and browsing of a large number of agent combinations, providing comprehensive details on the organisms, modes of growth and methodologies used in the studies. Therefore, network analysis enables a bird's-eye view of current research trends as well as in-depth analysis of specific drugs, organisms and combinatory effects, according to particular user interests. The reconstructed knowledge networks are publicly accessible at http://sing-group.org/antimicrobialCombination/. Hopefully, this resource will help researchers to look into antimicrobial combinations more easily and systematically. User-customised queries may help identify missing and less studied links and to generate new research hypotheses.

Introduction

Antimicrobial agents have significantly improved the well-being and life expectancy of humans and animals, but their overuse has accelerated the emergence of multidrug-resistant (MDR) micro-organisms and has raised an urgent need for novel antimicrobials [1]. Repurposing of natural compounds, such as antimicrobial peptides (AMPs), and the creation of synergistic antimicrobial combinations are two attractive and increasingly explored research approaches [2].

AMPs are widespread in nature as part of the immune system of plants and animals and can be also found in fungi and bacteria. In fact, AMPs played a fundamental role in the evolution of complex multicellular organisms and are currently still effective host defence agents [3]. In their majority, these peptides are short-length (between 15 and 30 amino acids), cationic, amphipathic, gene-encoded and directed to the cell membrane [4], [5]. As single agents, the multiple mechanisms of action and the low specificity in terms of molecular targets reduce the propensity of AMP therapeutics to the development of antimicrobial resistance [4]; also, AMPs aid cellular processes such as cytokine release, chemotaxis, antigen presentation, angiogenesis and wound healing [5], [6]. Synergistic combinations of AMPs with other antimicrobials often decrease individual effective concentrations and broaden the antimicrobial spectrum, whilst reducing antimicrobial resistance, toxicity and other side effects [2], [7].

Most of these research outcomes are scattered across the ever-growing scientific bibliome, which impedes their systematic comparison. However, the development of computational workflows to integrate and analyse such textual information has the potential to automate compilation and to enable comprehensive data analysis. In previous work, we implemented the reconstruction of antimicrobial-centric knowledge networks based on literature mining and manual expert curation methodologies [8], [9].

Here, our knowledge integration approach is applied to the study of polymyxins and bacteriocins, two families of AMPs widely used in healthcare and food-related studies. In particular, this paper discusses experimental findings retrieved from the scientific literature on antimicrobial combinations involving colistin (polymyxin E), polymyxin B and nisin, which are US Food and Drug Administration (FDA)-approved AMPs broadly tested against prominent MDR pathogens such as Pseudomonas aeruginosa, Staphylococcus aureus, Escherichia coli, Listeria monocytogenes and Candida albicans [10], [11], [12].

Colistin, also known as polymyxin E, and polymyxin B belong to the polymyxin group of cationic polypeptides, i.e. cyclic, positively charged decapeptides bound to a fatty acid and derived from various species of Paenibacillus (Bacillus) polymyxa [11]; they differ in structure by only one amino acid, i.e. Leu in colistin versus Phe in polymyxin B [13]. The basic mechanism of action consists of disruption of the cell membrane by binding to the anionic part of the lipopolysaccharide (LPS). This causes a detergent effect with permeability changes in the cell envelope, leakage of cell contents and cell death [13], [14]. The polymyxins are mainly active against Gram-negative pathogens, including major nosocomial pathogens such as E. coli, Klebsiella spp., Enterobacter spp., P. aeruginosa and Acinetobacter spp. [11]. Colistin and polymyxin B are used as a last-resource treatment for infections caused by MDR Gram-negative bacteria, such as P. aeruginosa infections of the respiratory tract of cystic fibrosis patients [15], [16].

Nisin is the main representative of the AMP class of lantibiotics (lanthionine-containing antibiotics) or class I bacteriocins. These small peptides (<5 kDa) are characterised by their unusual post-translationally modified residues (e.g. lanthionine or 3-methyllanthionine), which result in the formation of rings by covalent bonding with other amino acids [17]. Nisin was first isolated from Lactococcus lactis [18] and remains the only FDA-approved and commercially available bacteriocin, being normally used as a food additive [19]. In recent years, nisin has been increasingly studied in biomedical scenarios, exploring its ability to form poration complexes in cell membranes, mainly against Gram-positive bacteria [10], [20]. Nisin has reported antimicrobial activity against major Gram-positive pathogens such as L. monocytogenes and S. aureus [21].

The three AMP-centric knowledge network reconstructions describe experimental results in an intuitive and user-customised way, enabling various analysis perspectives. AMP–drug combinations are described in terms of reported effects and experimental settings, e.g. strains, mode of growth and methodologies of analysis found in the literature. By focusing on various network features, we address different questions about the role of these AMPs in antimicrobial combinational therapy.

The reconstructed knowledge networks are publicly available at http://sing-group.org/antimicrobialCombination/.

Section snippets

Information retrieval

Information extracted from the literature using text mining methods was integrated with data from curated databases to reconstruct experimental evidence on the antimicrobial combinations of colistin, polymyxin B and nisin in a comprehensive way. The curation pipeline is depicted in Supplementary Fig. S1.

Emphasis was put on experimentally validated combinations involving any of the three AMPs and drugs, other AMPs or molecules with added antimicrobial potential. To this end, the scope of the

Overview

The number of documents retrieved from the literature was highest for colistin, followed by polymyxin B and nisin, with an approximate difference of 100 documents from one another (Table 1). The number of relevant documents was also greater for colistin, followed by nisin and polymyxin B. Interestingly, the TC was much higher for colistin than for the other two AMPs. However, when analysing the CDS, colistin has the lowest figure, which indicates that studies using colistin tend to test

Conclusions

This work presented an integrative knowledge methodology for the reconstruction of relevant experimental results on antimicrobial combination tests, based on text mining and network mining methods and techniques. This methodology enabled the reconstruction of antimicrobial combinations involving colistin, polymyxin B and nisin and supports its periodical update, i.e. the curation of new publications on these topics.

This methodology holds great potential in mining combination networks for other

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