Antiaflatoxigenic effects of selected antifungal peptides

Various Biomimetics, Including Peptides as Antifungals

Biomimetics, which are similar to natural compounds that play an important role in the metabolism, manifestation of functional activity and reproduction of various fungi, have a pronounced attraction in the current search for new effective antifungals. Actual trends in the development of this area of research indicate that unnatural amino acids can be used as such biomimetics, including those containing halogen atoms; compounds similar to nitrogenous bases embedded in the nucleic acids synthesized by fungi; peptides imitating fungal analogs; molecules similar to natural substrates of numerous fungal enzymes and quorum-sensing signaling molecules of fungi and yeast, etc. Most parts of this review are devoted to the analysis of semi-synthetic and synthetic antifungal peptides and their targets of action. This review is aimed at combining and systematizing the current scientific information accumulating in this area of research, developing various antifungals with an assessment of the effectiveness of the created biomimetics and the possibility of combining them with other antimicrobial substances to reduce cell resistance and improve antifungal effects.

Broad-Spectrum Antimicrobial Activity of Synthetic Peptides GV185 and GV187

Optimizing synthetic antimicrobial peptides for safe and enhanced activity against fungal and bacterial pathogens is useful for genetic engineering of plants for resistance to plant pathogens and their associated mycotoxins. Nine synthetic peptides modeled after lytic peptides tachyplesin 1, D4E1 from cecropin A, and protegrin 1 were added to germinated spores of fungal species Aspergillus flavus, Rhizopus stolonifer, Fusarium oxysporum f. sp. vasinfectum, F. verticillioides, F. graminearum, Claviceps purpurea, Verticillium dahliae, and Thielaviopsis basicola and bacterial cultures of Pseudomonas syringae pv. tabaci and Xanthomonas campestris pv. campestris at different doses and inhibitory dose response curves, and were modeled to assess antimicrobial activity. Peptides GV185 and GV187, modified from tachyplesin 1, had superior abilities to inhibit fungal and bacterial growth (50% inhibitory concentrations [IC50] ranging from 0.1 to 8.7 µM). R. stolonifer (IC50 = 8.1 µM), A. flavus (IC50 = 3.1 µM), and F. graminearum (IC50 = 2.2 µM) were less inhibited by GV185 and GV187 than all the remaining fungi (IC50 = 1.4 µM) and bacteria (IC50 = 0.1 µM). Of the remaining peptides, GV193, GV195, and GV196 (IC50 range of 0.9 to 6.6 µM) inhibited fungal growth of A. flavus, F. verticillioides, and F. graminearum less than GV185 and GV187 (IC50 range of 0.8 to 3.9 µM), followed by GV197 (IC50 range of 0.8 to 9.1 µM), whereas GV190 and GV192 inhibited poorly (IC50 range of 28.2 to 36.6 µM and 15.5 to 19.4 µM, respectively) and GV198 stimulated growth. GV185 and GV187 had slightly weaker hydrophobic and cationic residues than other tachyplesin 1 modified peptides but still had unexpectedly high lytic activity. Germinated fungal spores of R. stolonifer and F. graminearum exposed to these two peptides and D4E1 and AGM182 appeared wrinkled, with perforations near potential cytoplasmic leakage, which provided evidence of plasma membrane and cell wall lysis. We conclude that peptides GV185 and GV187 are promising candidates for genetic engineering of crops for resistance to plant-pathogenic bacteria and fungi, including A. flavus and aflatoxin contamination.

Handcrafted versus non-handcrafted (self-supervised) features for the classification of antimicrobial peptides: complementary or redundant?

Antimicrobial peptides (AMPs) have received a great deal of attention given their potential to become a plausible option to fight multi-drug resistant bacteria as well as other pathogens. Quantitative sequence-activity models (QSAMs) have been helpful to discover new AMPs because they allow to explore a large universe of peptide sequences and help reduce the number of wet lab experiments. A main aspect in the building of QSAMs based on shallow learning is to determine an optimal set of protein descriptors (features) required to discriminate between sequences with different antimicrobial activities. These features are generally handcrafted from peptide sequence datasets that are labeled with specific antimicrobial activities. However, recent developments have shown that unsupervised approaches can be used to determine features that outperform human-engineered (handcrafted) features. Thus, knowing which of these two approaches contribute to a better classification of AMPs, it is a fundamental question in order to design more accurate models. Here, we present a systematic and rigorous study to compare both types of features. Experimental outcomes show that non-handcrafted features lead to achieve better performances than handcrafted features. However, the experiments also prove that an improvement in performance is achieved when both types of features are merged. A relevance analysis reveals that non-handcrafted features have higher information content than handcrafted features, while an interaction-based importance analysis reveals that handcrafted features are more important. These findings suggest that there is complementarity between both types of features. Comparisons regarding state-of-the-art deep models show that shallow models yield better performances both when fed with non-handcrafted features alone and when fed with non-handcrafted and handcrafted features together.

Do deep learning models make a difference in the identification of antimicrobial peptides?

In the last few decades, antimicrobial peptides (AMPs) have been explored as an alternative to classical antibiotics, which in turn motivated the development of machine learning models to predict antimicrobial activities in peptides. The first generation of these predictors was filled with what is now known as shallow learning-based models. These models require the computation and selection of molecular descriptors to characterize each peptide sequence and train the models. The second generation, known as deep learning-based models, which no longer requires the explicit computation and selection of those descriptors, started to be used in the prediction task of AMPs just four years ago. The superior performance claimed by deep models regarding shallow models has created a prevalent inertia to using deep learning to identify AMPs. However, methodological flaws and/or modeling biases in the building of deep models do not support such superiority. Here, we analyze the main pitfalls that led to establish biased conclusions on the leading performance of deep models. Also, we analyze whether deep models truly contribute to achieve better predictions than shallow models by performing fair studies on different state-of-the-art benchmarking datasets. The experiments reveal that deep models do not outperform shallow models in the classification of AMPs, and that both types of models codify similar chemical information since their predictions are highly similar. Thus, according to the currently available datasets, we conclude that the use of deep learning could not be the most suitable approach to develop models to identify AMPs, mainly because shallow models achieve comparable-to-superior performances and are simpler (Ockham's razor principle). Even so, we suggest the use of deep learning only when its capabilities lead to obtaining significantly better performance gains worth the additional computational cost.

Biocontrol Methods in Avoidance and Downsizing of Mycotoxin Contamination of Food Crops

By increasing the resistance of seeds against abiotic and biotic stress, the possibility of cereal mold contamination and hence the occurrence of secondary mold metabolites mycotoxins decreases. The use of biological methods of seed treatment represents a complementary strategy, which can be implemented as an environmental-friendlier approach to increase the agricultural sustainability. Whereas the use of resistant cultivars helps to reduce mold growth and mycotoxin contamination at the very beginning of the production chain, biological detoxification of cereals provides additional weapons against fungal pathogens in the later stage. Most efficient techniques can be selected and combined on an industrial scale to reduce losses and boost crop yields and agriculture sustainability, increasing at the same time food and feed safety. This paper strives to emphasize the possibility of implementation of biocontrol methods in the production of resistant seeds and the prevention and reduction in cereal mycotoxin contamination.

Superoxide Dismutases in Eukaryotic Microorganisms: Four Case Studies

Various components in the cell are responsible for maintaining physiological levels of reactive oxygen species (ROS). Several different enzymes exist that can convert or degrade ROS; among them are the superoxide dismutases (SODs). If left unchecked, ROS can cause damage that leads to pathology, can contribute to aging, and may, ultimately, cause death. SODs are responsible for converting superoxide anions to hydrogen peroxide by dismutation. Here we review the role of different SODs on the development and pathogenicity of various eukaryotic microorganisms relevant to human health. These include the fungal aging model, Podospora anserina; various members of the genus Aspergillus that can potentially cause aspergillosis; the agents of diseases such as Chagas and sleeping disease, Trypanosoma cruzi and Trypanosoma brucei, respectively; and, finally, pathogenic amoebae, such as Acanthamoeba spp. In these organisms, SODs fulfill essential and often regulatory functions that come into play during processes such as the development, host infection, propagation, and control of gene expression. We explore the contribution of SODs and their related factors in these microorganisms, which have an established role in health and disease.

Screening of Bacillus velezensis E2 and the Inhibitory Effect of Its Antifungal Substances on Aspergillus flavus

Aspergilus flavus is the main pathogenic fungus that causes food mold. Effective control of A. flavus contamination is essential to ensure food safety. The lipopeptides (LPs) produced by Bacillus strains have been shown to have an obvious antifungal effect on molds. In this study, an antagonist strain of Bacillus velezensis with obvious antifungal activity against A. flavus was isolated from the surface of healthy rice. Using HPLC-MS analysis, the main components of LPs produced by strain E2 were identified as fengycin and iturins. Further investigations showed that LPs could inhibit the spore germination, and even cause abnormal expansion of hyphae and cell rupture. Transcriptomic analyses showed that some genes, involved in ribosome biogenesis in eukaryotes (NOG1, KRE33) and aflatoxin biosynthesis (aflK, aflR, veA, omtA) pathways in A. flavus were significantly down-regulated by LPs. In conclusion, this study provides novel insights into the cellular and molecular antifungal mechanisms of LPs against grain A. flavus contamination.

Antifungal Peptides and Proteins to Control Toxigenic Fungi and Mycotoxin Biosynthesis

The global challenge to prevent fungal spoilage and mycotoxin contamination on food and feed requires the development of new antifungal strategies. Antimicrobial peptides and proteins (AMPs) with antifungal activity are gaining much interest as natural antifungal compounds due to their properties such as structure diversity and function, antifungal spectrum, mechanism of action, high stability and the availability of biotechnological production methods. Given their multistep mode of action, the development of fungal resistance to AMPs is presumed to be slow or delayed compared to conventional fungicides. Interestingly, AMPs also accomplish important biological functions other than antifungal activity, including anti-mycotoxin biosynthesis activity, which opens novel aspects for their future use in agriculture and food industry to fight mycotoxin contamination. AMPs can reach intracellular targets and exert their activity by mechanisms other than membrane permeabilization. The mechanisms through which AMPs affect mycotoxin production are varied and complex, ranging from oxidative stress to specific inhibition of enzymatic components of mycotoxin biosynthetic pathways. This review presents natural and synthetic antifungal AMPs from different origins which are effective against mycotoxin-producing fungi, and aims at summarizing current knowledge concerning their additional effects on mycotoxin biosynthesis. Antifungal AMPs properties and mechanisms of action are also discussed.

Current Practices for Reference Gene Selection in RT-qPCR of Aspergillus: Outlook and Recommendations for the Future

Aspergillus is a genus of filamentous fungi with vast geographic and ecological distributions. Species within this genus are clinically, agriculturally and biotechnologically relevant, leading to increasing interest in elucidating gene expression dynamics of key metabolic and physiological processes. Reverse-transcription quantitative Polymerase Chain Reaction (RT-qPCR) is a sensitive and specific method of quantifying gene expression. A crucial step for comparing RT-qPCR results between strains and experimental conditions is normalisation to experimentally validated reference gene(s). In this review, we provide a critical analysis of current reference gene selection and validation practices for RT-qPCR gene expression analyses of Aspergillus. Of 90 primary research articles obtained through our PubMed query, 17 experimentally validated the reference gene(s) used. Twenty reference genes were used across the 90 studies, with beta-tubulin being the most used reference gene, followed by actin, 18S rRNA and glyceraldehyde 3-phosphate dehydrogenase. Sixteen of the 90 studies used multiple reference genes for normalisation. Failing to experimentally validate the stability of reference genes can lead to conflicting results, as was the case for four studies. Overall, our review highlights the need to experimentally validate reference genes in RT-qPCR studies of Aspergillus.

Alignment-Free Antimicrobial Peptide Predictors: Improving Performance by a Thorough Analysis of the Largest Available Data Set

In the last two decades, a large number of machine-learning-based predictors for the activities of antimicrobial peptides (AMPs) have been proposed. These predictors differ from one another in the learning method and in the training and testing data sets used. Unfortunately, the training data sets present several drawbacks, such as a low representativeness regarding the experimentally validated AMP space, and duplicated peptide sequences between negative and positive data sets. These limitations give a low confidence to most of the approaches to be used in prospective studies. To address these weaknesses, we propose novel modeling and assessing data sets from the largest experimentally validated nonredundant peptide data set reported to date. From these novel data sets, alignment-free quantitative sequence-activity models (AF-QSAMs) based on Random Forest are created to identify general AMPs and their antibacterial, antifungal, antiparasitic, and antiviral functional types. An applicability domain analysis is carried out to determine the reliability of the predictions obtained, which, to the best of our knowledge, is performed for the first time for AMP recognition. A benchmarking is undertaken between the models proposed and several models from the literature that are freely available in 13 programs (ClassAMP, iAMP-2L, ADAM, MLAMP, AMPScanner v2.0, AntiFP, AMPfun, PEPred-suite, AxPEP, CAMPR3, iAMPpred, APIN, and Meta-iAVP). The models proposed are those with the best performance in all of the endpoints modeled, while most of the methods from the literature have weak-to-random predictive agreements. The models proposed are also assessed through Y-scrambling and repeated k-fold cross-validation tests, demonstrating that the outcomes obtained by them are not given by chance. Three chemometric analyses also confirmed the relevance of the peptides descriptors used in the modeling. Therefore, it can be concluded that the models built by fixing the drawbacks existing in the literature contribute to identifying antibacterial, antifungal, antiparasitic, and antiviral peptides with high effectivity and reliability. Models are freely available via the AMPDiscover tool at https://biocom-ampdiscover.cicese.mx/.

Efficacy of short-synthetic antifungal peptides on pathogenic Aspergillus flavus

The efficacies of three short synthetic antifungal peptides were tested for their inhibitory action on pathogenic fungi, Aspergillus flavus. The sequences of the short synthetic peptides are PPD1- FRLHF, 66-10-FRLKFH, 77-3- FRLKFHF, respectively. These test peptides inhibited fungal growth and showed a membranolytic activity. The fungal biomass and ergosterol levels were significantly low in peptides treated samples. Further, the fungal cell wall component chitin was also found to be lower in peptides treated samples. Scanning electron microscopic images also showed highly wrinkled fungal mycelia. Significant membrane permeabilisation as well as potassium ion leakage was also observed in fungal samples treated with peptides. To assess the membrane damage, the uptake of Sytox green dye was employed. At tested concentration, peptides induced fungal membrane damage as evidenced by the green fluorescence. Further, at tested concentration, these peptides induced an oxidative stress in A.flavus as evidenced by an increase in the ROS production, malondialdehyde levels, increase in the antioxidant enzymes - superoxide dismutase, catalase with concomitant decrease in the reduced glutathione content. Additionally, a growth dependent reduction in aflatoxin levels were also observed in peptides treated samples. Docking studies on the interaction of the peptides with a trans-membrane protein calcium ATPase of A. flavus showed that all the peptides were able to bind to the protein with high z rank score. The activity of the calcium ATPase was significantly decreased in peptides treated fungal samples, thereby validating the docking results. Among all the tested peptides, 77-3 peptide exhibited the maximal membrane damage property.

LC-MS/MS assay coupled with carboxylic acid magnetic bead affinity capture to quantitatively measure cationic host defense peptides (HDPs) in complex matrices with application to preclinical pharmacokinetic studies

Synthetic host defense peptides (HDP) are a new class of promising therapeutic agents with potential application in a variety of diseases. RP-182 is a 10mer synthetic HDP design, which selectively reduces M2-like tumor associated macrophages via engagement with the cell surface lectin receptor MRC1/CD206 and is currently being developed as an innate immune defense regulator to improve anti-tumor immunity in immunologically cold tumors. Herein, we describe a sensitive and specific liquid chromatography (LC) coupled to quadrupole electron spray tandem mass spectrometry method to measure positively charged HDPs and HDP peptide fragments in complex biological matrices. Carboxylic acid magnetic beads were used as an affinity-capturing agent to extract the positively charged RP-182 from both mouse plasma and tissue homogenates. Beads were eluted with 0.1% (v/v) formic acid and chromatographic separation was achieved on a Waters 2.1 × 100 mm, 3.5 μm XSelect Peptide CSH C18 column with a Vanguard pre-column of the same phase. MS/MS was performed on a Thermo TSQ Quantiva triple quadrupole mass spectrometer operating in Selected Reaction Monitoring (SRM) mode fragmenting the plus three parent ion 458.9⁺³ and monitoring ions 624.0⁺², 550.5⁺², and 597.3⁺¹ for RP-182 and 462.4⁺³ > 629.1⁺², 555.5⁺², and 607.3⁺¹ for isotopic RP-182 standard. The assay had good linearity ranging from 1 ng to 1000 ng in mouse plasma with the lower limit of detection for RP-182 at 1 ng in mouse plasma with good intra- and inter-sample precision and accuracy. Recovery ranged from 66% to 77% with minimum matrix effects. The method was successfully applied to an abbreviated pharmacokinetic study in mice after single IP injection of RP-182. The method was successfully tested on a second HDP, the 17mer D4E1, and the cationic human peptide hormone ghrelin suggesting that it might be a general sensitive method applicable to quantifying HDP peptides that are difficult to extract.