Identification of natural antimicrobial peptides from bacteria through metagenomic and metatranscriptomic analysis of high-throughput transcriptome data of Taiwanese oolong teas

Discovery of antimicrobial peptides in the global microbiome with machine learning

Novel antibiotics are urgently needed to combat the antibiotic-resistance crisis. We present a machine-learning-based approach to predict antimicrobial peptides (AMPs) within the global microbiome and leverage a vast dataset of 63,410 metagenomes and 87,920 prokaryotic genomes from environmental and host-associated habitats to create the AMPSphere, a comprehensive catalog comprising 863,498 non-redundant peptides, few of which match existing databases. AMPSphere provides insights into the evolutionary origins of peptides, including by duplication or gene truncation of longer sequences, and we observed that AMP production varies by habitat. To validate our predictions, we synthesized and tested 100 AMPs against clinically relevant drug-resistant pathogens and human gut commensals both in vitro and in vivo. A total of 79 peptides were active, with 63 targeting pathogens. These active AMPs exhibited antibacterial activity by disrupting bacterial membranes. In conclusion, our approach identified nearly one million prokaryotic AMP sequences, an open-access resource for antibiotic discovery.

A novel stacking-based predictor for accurate prediction of antimicrobial peptides

Antimicrobial peptides (AMPs) are gaining acceptance and support as a chief antibiotic substitute since they boost human immunity. They retain a wide range of actions and have a low risk of developing resistance, which are critical properties to the pharmaceutical industry for drug discovery. Antibiotic sensitivity, however, is an issue that affects people all around the world and has the potential to one day lead to an epidemic. As cutting-edge therapeutic agents, AMPs are also expected to cure microbial infections. In order to produce tolerable drugs, it is crucial to understand the significance of the basic architecture of AMPs. Traditional laboratory methods are expensive and time-consuming for AMPs testing and detection. Currently, bioinformatics techniques are being successfully applied to the detection of AMPs. In this study, we have developed a novel STacking-based ensemble learning framework for AntiMicrobial Peptide (STAMP) prediction. First, we constructed 84 different baseline models by using 12 different feature encoding schemes and 7 popular machine learning algorithms. Second, these baseline models were trained and employed to create a new probabilistic feature vector. Finally, based on the feature selection strategy, we determined the optimal probabilistic feature vector, which was further utilized for the construction of our stacked model. Resultantly, the STAMP predictor achieved excellent performance during cross-validation with an accuracy and Matthew's correlation coefficient of 0.930 and 0.860, respectively. The corresponding metrics during the independent test were 0.710 and 0.464, respectively. Overall, STAMP achieved a more accurate and stable performance than the baseline models and significantly outperformed the existing predictors, demonstrating the effectiveness of our proposed hybrid framework. Furthermore, STAMP is expected to assist community-wide efforts in identifying AMPs and will contribute to the development of novel therapeutic methods and drug-design for immunity.Communicated by Ramaswamy H. Sarma.

Perspectives in Searching Antimicrobial Peptides (AMPs) Produced by the Microbiota

Changes in the structure and function of the microbiota are associated with various human diseases. These microbial changes can be mediated by antimicrobial peptides (AMPs), small peptides produced by the host and their microbiota, which play a crucial role in host-bacteria co-evolution. Thus, by studying AMPs produced by the microbiota (microbial AMPs), we can better understand the interactions between host and bacteria in microbiome homeostasis. Additionally, microbial AMPs are a new source of compounds against pathogenic and multi-resistant bacteria. Further, the growing accessibility to metagenomic and metatranscriptomic datasets presents an opportunity to discover new microbial AMPs. This review examines the structural properties of microbiota-derived AMPs, their molecular action mechanisms, genomic organization, and strategies for their identification in any microbiome data as well as experimental testing. Overall, we provided a comprehensive overview of this important topic from the microbial perspective.

Screening and Functional Analyses of Novel Cecropins from Insect Transcriptome

Antibiotic resistance is a significant and growing threat to global public health. However, antimicrobial peptides (AMPs) have shown promise as they exhibit a broad spectrum of antibacterial activities with low potential for resistance development. Insects, which inhabit a wide range of environments and are incredibly diverse, remain largely unexplored as a source of novel AMPs. To address this, we conducted a screening of the representative transcriptomes from the 1000 Insect Transcriptome Evolution (1KITE) dataset, focusing on the homologous reference genes of Cecropins, the first identified AMPs in insects known for its high efficiency. Our analysis identified 108 Cecropin genes from 105 insect transcriptomes, covering all major hexapod lineages. We validated the gene sequences and synthesized mature peptides for three identified Cecropin genes. Through minimal inhibition concentration and agar diffusion assays, we confirmed that these peptides exhibited antimicrobial activities against Gram-negative bacteria. Similar to the known Cecropin, the three Cecropins adopted an alpha-helical conformation in membrane-like environments, efficiently disrupting bacterial membranes through permeabilization. Importantly, none of the three Cecropins demonstrated cytotoxicity in erythrocyte hemolysis tests, suggesting their safety in real-world applications. Overall, this newly developed methodology provides a high-throughput bioinformatic pipeline for the discovery of AMP, taking advantage of the expanding genomic resources available for diverse organisms.

Computational exploration of the global microbiome for antibiotic discovery

Novel antibiotics are urgently needed to combat the antibiotic-resistance crisis. We present a machine learning-based approach to predict prokaryotic antimicrobial peptides (AMPs) by leveraging a vast dataset of 63,410 metagenomes and 87,920 microbial genomes. This led to the creation of AMPSphere, a comprehensive catalog comprising 863,498 non-redundant peptides, the majority of which were previously unknown. We observed that AMP production varies by habitat, with animal-associated samples displaying the highest proportion of AMPs compared to other habitats. Furthermore, within different human-associated microbiota, strain-level differences were evident. To validate our predictions, we synthesized and experimentally tested 50 AMPs, demonstrating their efficacy against clinically relevant drug-resistant pathogens both in vitro and in vivo. These AMPs exhibited antibacterial activity by targeting the bacterial membrane. Additionally, AMPSphere provides valuable insights into the evolutionary origins of peptides. In conclusion, our approach identified AMP sequences within prokaryotic microbiomes, opening up new avenues for the discovery of antibiotics.

Mechanisms underlying the effects, and clinical applications, of oral microbiota in lung cancer: current challenges and prospects

The oral cavity contains a site-specific microbiota that interacts with host cells to regulate many physiological processes in the human body. Emerging evidence has suggested that changes in the oral microbiota can increase the risk of lung cancer (LC), and the oral microbiota is also altered in patients with LC. Human and animal studies have shown that oral microecological disorders and/or specific oral bacteria may play an active role in the occurrence and development of LC through direct and/or indirect mechanisms. These studies support the potential of oral microbiota in the clinical treatment of LC. Oral microbiota may therefore be used in the prevention and treatment of LC and to improve the side effects of anticancer therapy by regulating the balance of the oral microbiome. Specific oral microbiota in LC may also be used as screening or predictive biomarkers. This review summarizes the main findings in research on oral microbiome-related LC and discusses current challenges and future research directions.

Antimicrobial peptides with anticancer activity: Today status, trends and their computational design

Some antimicrobial peptides have been shown to be able to inhibit the proliferation of cancer cell lines. Various strategies for treating cancers with active peptides have been pursued. According to the reports, anticancer peptides are important therapeutic peptides, which can act through two distinct pathways: they either just create pores in the cell membrane, or they have a vital intracellular target. In this review, publications up to Sep. 2021 had extracted form Scopus and PubMed using "antimicrobial peptide" and "anticancer peptide" as keywords. In second step, "computational design" related publications extracted. Among publications, those have similar scopes were classified and selected based on mechanisms of action and application. In this review, the most recent advances in the field of antimicrobial peptides with anti-cancer activities have been summarized. Freely available webservers such as AntiCP, ACPP, iACP, iACP-GAEnsC, ACPred are discussed here. In conclusion, despite some limitations of ACPs such as production cost and challenges, short half-life and toxicity on normal cells, the beneficial properties of AMPs make some of them good therapeutic agents for cancer therapy. Towards designing novel ACPs, the computational methods have substantial position and have been used progressively, today.

AMPpred-EL: An effective antimicrobial peptide prediction model based on ensemble learning

Antimicrobial peptides (AMPs) are important for the human immune system and are currently applied in clinical trials. AMPs have been received much attention for accurate recognition. Recently, several computational methods for identifying AMPs have been proposed. However, existing methods have difficulty in accurately predicting AMPs. In this paper, we propose a novel AMP prediction method called AMPpred-EL based on an ensemble learning strategy. AMPred-EL is constructed based on ensemble learning combined with LightGBM and logistic regression. Experimental results demonstrate that AMPpred-EL outperforms several state-of-the-art methods on the benchmark datasets and then improves the efficiency performance.

Interaction and Application of Molds and Yeasts in Chinese Fermented Foods

Fermentation is an ancient food preservation and processing technology with a long history of thousands of years, that is still practiced all over the world. Fermented foods are usually defined as foods or beverages made by controlling the growth of microorganisms and the transformation of raw and auxiliary food components, which provide the human body with many beneficial nutrients or health factors. As fungus widely used in traditional Chinese fermented foods, molds and yeasts play an irreplaceable role in the formation of flavor substances and the production of functional components in fermented foods. The research progress of molds and yeasts in traditional Chinese fermented foods from traditional to modern is reviewed, including the research on the diversity, and population structure of molds and yeasts in fermented foods. The interaction between fermenting mold and yeast and the latest research results and application development prospects of related industries were discussed.

Metabolic and molecular modelling of zebrafish gut biome to unravel antimicrobial peptides through metagenomics

Recently efforts have been taken for unravelling mysteries between host-microbe interactions in gut microbiome studies of model organisms through metagenomics. Co-existence and the co-evolution of the microorganisms is the significant cause of the growing antimicrobial menace. There needs a novel approach to develop potential antimicrobials with capabilities to act directly on the resistant microbes with reduced side effects. One such is to tap them from the natural resources, preferably the gut of the most closely related animal model. In this study, we employed metagenomics approaches to identify the large taxonomic genomes of the zebra fish gut. About 256 antimicrobial peptides were identified using gene ontology predictions from Macrel and Pubseed servers. Upon the property predictions, the top 10 antimicrobial peptides were screened based on their action against many resistant bacterial species, including Klebsiella pneumoniae, Pseudomonas aeruginosa, Staphylococcus aureus, E. coli, and Bacillus cereus. Metabolic modelling and flux balance analysis (FBA) were computed to conclude the antibiotic such as tetracycline, cephalosporins, puromycin, neomycin biosynthesis pathways were adopted by the microbiome as protection strategies. Molecular modelling strategies, including molecular docking and dynamics, were performed to estimate the antimicrobial peptides' binding against the target-putative nucleic acid binding lipoprotein and confirm stable binding. One specific antimicrobial peptide with the sequence "MPPYLHEIQPHTASNCQTELVIKL" showed promising results with 53% hydrophobic residues and a net charge +2.5, significant for the development of antimicrobial peptides. The said peptide also showed promising interactions with the target protein and expressed stable binding with docking energy of -429.34 kcal/mol and the average root mean square deviation of 1 A⁰. The study is a novel approach focusing on tapping out potential antimicrobial peptides to be developed against most resistant bacterial species.

Profiling the composition and metabolic activities of microbial community in fermented grain for the Chinese strong-flavor Baijiu production by using the metatranscriptome, high-throughput 16S rRNA and ITS gene sequencings

The composition and function of microbial community analyzed by sequencing 16S rRNA/ITS gene amplicons (DNA level) were compared with those derived by using metatranscriptome sequencing (RNA level) from the same fermented grain (FG) sample, which obtained from the key fermentation time point during the Chinese strong-flavor Baijiu (CSFB) production process. The results showed that the fungi with the highest relative abundance was Saccharomyces (RNA: 83.15%, DNA: 89.74%) at the two levels. The most abundant bacterium was Kroppenstedtia (37.09%) detected only at the DNA level, while it was Streptococcus (93.75%) at the RNA level, indicating that the structures of prokaryotic communities at the two levels were quite different. For the microbial functions, a large proportion of genes of FG microorganisms related to "Metabolism" function were observed both by using PICRUSt2 analysis (DNA level) and metatranscriptome analysis (RNA level), and especially enriched in "Carbohydrate metabolism". While the proportions of genes involved in some functions were different, such as "Replication and repair", "Membrane transport" and "Environmental adaptation", with high proportions of genes involved in at the DNA level when compared those at the RNA level. Furthermore, Saccharomyces cerevisiae was the most active microbe in the top15 pathways, followed by Torulaspora dellbrueckii. During the conversion of starch to ethanol, S. cerevisiae showed high metabolic capacity, and cooperated with other microorganisms to convert pyruvate to acetaldehyde directly or through acetyl-CoA and acetate, and then acetaldehyde to ethanol. As far as we know, this is a first study to profile the microbial community and metabolic features in FG of CSFB by using a combination of DNA- and RNA- based technologies. Our findings could provide useful insights for further understanding the active microbial function, metabolic pathways and fermentation mechanism in the FG ecosystem during CSFB fermentation.

Proteomic analysis of "Oriental Beauty" oolong tea leaves with different degrees of leafhopper infestation

RATIONALE

Oriental Beauty, a type of oolong tea native to Taiwan, is highly prized by connoisseurs for its unique fruity aroma and sweet taste. Leaves of Oriental Beauty vary in appearance, aroma, and taste, depending on the degree of tea green leafhopper (Jacobiasca formosana) infestation. In this study, the aim is to investigate the differential expression of proteins in leaves with low, medium, and high degrees of leafhopper infestation.

METHODS

Proteomic techniques 2DE (two-dimensional electrophoresis) and nanoscale liquid chromatography/tandem mass spectrometry (LC/MS/MS) were used to investigate the differential expression of proteins in tea leaves with different degrees of leafhopper infestation.

RESULTS

A total of 89 proteins were found to exhibit significant differences in expression. In a gene ontology analysis, most of these proteins participated in biosynthesis, carbohydrate metabolism, transport, responses to stress, and amino acid metabolism.

CONCLUSIONS

These results indicated that the unique aroma and taste of the leaves might be influenced by their protein expression profiles, as well as related factors such as defensive responses to tea green leafhopper saliva.

dbAMP: an integrated resource for exploring antimicrobial peptides with functional activities and physicochemical properties on transcriptome and proteome data

Antimicrobial peptides (AMPs), naturally encoded from genes and generally contained 10–100 amino acids, are crucial components of the innate immune system and can protect the host from various pathogenic bacteria, as well as viruses. In recent years, the widespread use of antibiotics has inspired the rapid growth of antibiotic-resistant microorganisms that usually induce critical infection and pathogenesis. An increasing interest therefore was motivated to explore natural AMPs that enable the development of new antibiotics. With the potential of AMPs being as new drugs for multidrug-resistant pathogens, we were thus motivated to develop a database (dbAMP, http://csb.cse.yzu.edu.tw/dbAMP/) by accumulating comprehensive AMPs from public domain and manually curating literature. Currently in dbAMP there are 12 389 unique entries, including 4271 experimentally verified AMPs and 8118 putative AMPs along with their functional activities, supported by 1924 research articles. The advent of high-throughput biotechnologies, such as mass spectrometry and next-generation sequencing, has led us to further expand dbAMP as a database-assisted platform for providing comprehensively functional and physicochemical analyses for AMPs based on the large-scale transcriptome and proteome data. Significant improvements available in dbAMP include the information of AMP–protein interactions, antimicrobial potency analysis for ‘cryptic’ region detection, annotations of AMP target species, as well as AMP detection on transcriptome and proteome datasets. Additionally, a Docker container has been developed as a downloadable package for discovering known and novel AMPs on high-throughput omics data. The user-friendly visualization interfaces have been created to facilitate peptide searching, browsing, and sequence alignment against dbAMP entries. All the facilities integrated into dbAMP can promote the functional analyses of AMPs and the discovery of new antimicrobial drugs.

Characterization and Identification of Natural Antimicrobial Peptides on Different Organisms

Because of the rapid development of multidrug resistance, conventional antibiotics cannot kill pathogenic bacteria efficiently. New antibiotic treatments such as antimicrobial peptides (AMPs) can provide a possible solution to the antibiotic-resistance crisis. However, the identification of AMPs using experimental methods is expensive and time-consuming. Meanwhile, few studies use amino acid compositions (AACs) and physicochemical properties with different sequence lengths against different organisms to predict AMPs. Therefore, the major purpose of this study is to identify AMPs on seven categories of organisms, including amphibians, humans, fish, insects, plants, bacteria, and mammals. According to the one-rule attribute evaluation, the selected features were used to construct the predictive models based on the random forest algorithm. Compared to the accuracies of iAMP-2L (a web-server for identifying AMPs and their functional types), ADAM (a database of AMP), and MLAMP (a multi-label AMP classifier), the proposed method yielded higher than 92% in predicting AMPs on each category. Additionally, the sensitivities of the proposed models in the prediction of AMPs of seven organisms were higher than that of all other tools. Furthermore, several physicochemical properties (charge, hydrophobicity, polarity, polarizability, secondary structure, normalized van der Waals volume, and solvent accessibility) of AMPs were investigated according to their sequence lengths. As a result, the proposed method is a practical means to complement the existing tools in the characterization and identification of AMPs in different organisms.

Characterization and identification of antimicrobial peptides with different functional activities

In recent years, antimicrobial peptides (AMPs) have become an emerging area of focus when developing therapeutics hot spot residues of proteins are dominant against infections. Importantly, AMPs are produced by virtually all known living organisms and are able to target a wide range of pathogenic microorganisms, including viruses, parasites, bacteria and fungi. Although several studies have proposed different machine learning methods to predict peptides as being AMPs, most do not consider the diversity of AMP activities. On this basis, we specifically investigated the sequence features of AMPs with a range of functional activities, including anti-parasitic, anti-viral, anti-cancer and anti-fungal activities and those that target mammals, Gram-positive and Gram-negative bacteria. A new scheme is proposed to systematically characterize and identify AMPs and their functional activities. The 1st stage of the proposed approach is to identify the AMPs, while the 2nd involves further characterization of their functional activities. Sequential forward selection was employed to extract potentially informative features that are possibly associated with the functional activities of the AMPs. These features include hydrophobicity, the normalized van der Waals volume, polarity, charge and solvent accessibility-all of which are essential attributes in classifying between AMPs and non-AMPs. The results revealed the 1st stage AMP classifier was able to achieve an area under the receiver operating characteristic curve (AUC) value of 0.9894. During the 2nd stage, we found pseudo amino acid composition to be an informative attribute when differentiating between AMPs in terms of their functional activities. The independent testing results demonstrated that the AUCs of the multi-class models were 0.7773, 0.9404, 0.8231, 0.8578, 0.8648, 0.8745 and 0.8672 for anti-parasitic, anti-viral, anti-cancer, anti-fungal AMPs and those that target mammals, Gram-positive and Gram-negative bacteria, respectively. The proposed scheme helps facilitate biological experiments related to the functional analysis of AMPs. Additionally, it was implemented as a user-friendly web server (AMPfun, http://fdblab.csie.ncu.edu.tw/AMPfun/index.html) that allows individuals to explore the antimicrobial functions of peptides of interest.

Brevibacillus laterosporus strains BGSP7, BGSP9 and BGSP11 isolated from silage produce broad spectrum multi-antimicrobials

Bacteria active against multi-drug resistant pathogens, isolated by direct selection of colonies from clover silage samples, produce zones of inhibition against two Gram-negative (Klebsiella pneumoniae Ni9 and Pseudomonas aeruginosa MMA83) and two Gram-positive (Staphylococcus aureus ATCC25923 and Listeria monocytogenes ATCC19111) pathogens. Isolates BGSP7, BGSP9, BGSP11 and BGSP12 produced the largest zones of inhibition against all four pathogens when grown in LB broth with aeration at 37°C. Isolates BGSP7, BGSP9, BGSP11 and BGSP12 were identified as Brevibacillus laterosporus and pulsed field gel electrophoresis and extracellular protein profiles showed that three different strains (BGSP7, BGSP9 and BGSP11) were isolated. A semi-native SDS-PAGE (sodium dodecyl sulphate-polyacrylamide gel electrophoresis) gel overlay assay showed that BGSP7 and BGSP9 produce small antimicrobial molecules of about 1.5 kDa, while BGSP11 produces antimicrobial molecules of 1.5 and 6 kDa active against S. aureus ATCC25923. Amino acid analysis of two antimicrobial molecules (1583.73 Da; from BGSP7 and 1556.31 Da; from BGSP11) revealed that they have a similar composition and differ only by virtue of the presence of a methionine which is present only in BGSP11 molecule. Genome sequencing of the three isolates revealed the presence of gene clusters associated with the production of non-ribosomally synthesized peptides (brevibacillin, bogorol, gramicidin S, plipastatin and tyrocin) and bacteriocins (laterosporulin, a lactococcin 972-like bacteriocin, as well as putative linocin M18, sactipeptide, UviB and lantipeptide-like molecules). Ultimately, the purification of a number of antimicrobial molecules from each isolate suggests that they can be considered as potent biocontrol strains that produce an arsenal of antimicrobial molecules active against Gram-positive and Gram-negative multi-resistant pathogens, fungi and insects.

Understanding antimicrobial discovery and resistance from a metagenomic and metatranscriptomic perspective: advances and applications

Our inability to cultivate most microorganisms, specifically bacteria, in the laboratory has for many years restricted our view and understanding of the bacterial meta-resistome in all living and nonliving environments. As a result, reservoirs, sources and distribution of antibiotic resistance genes (ARGS) and antibiotic-producers, as well as the effects of human activity and antibiotics on the selection and dissemination of ARGs were not well comprehended. With the advances made in the fields of metagenomics and metatranscriptomics, many of the hitherto little-understood concepts are becoming clearer. Further, the discovery of antibiotics such as lugdinin and lactocillin from the human microbiota, buttressed the importance of these new fields. Metagenomics and metatranscriptomics are becoming important clinical diagnostic tools for screening and detecting pathogens and ARGs, assessing the effects of antibiotics, other xenobiotics and human activity on the environment, characterizing the microbiome and the environmental resistome with lesser turnaround time and decreasing cost, as well as discovering antibiotic-producers. However, challenges with accurate binning, skewed ARGs databases, detection of less abundant and allelic variants of ARGs and efficient mobilome characterization remain. Ongoing efforts in long-read, phased- and single-cell sequencing, strain-resolved binning, chromosomal-conformation capture, DNA-methylation binning and deep-learning bioinformatic approaches offer promising prospects in reconstructing complete strain-level genomes and mobilomes from metagenomes.

A bioinformatics potpourri

The 16th International Conference on Bioinformatics (InCoB) was held at Tsinghua University, Shenzhen from September 20 to 22, 2017. The annual conference of the Asia-Pacific Bioinformatics Network featured six keynotes, two invited talks, a panel discussion on big data driven bioinformatics and precision medicine, and 66 oral presentations of accepted research articles or posters. Fifty-seven articles comprising a topic assortment of algorithms, biomolecular networks, cancer and disease informatics, drug-target interactions and drug efficacy, gene regulation and expression, imaging, immunoinformatics, metagenomics, next generation sequencing for genomics and transcriptomics, ontologies, post-translational modification, and structural bioinformatics are the subject of this editorial for the InCoB2017 supplement issues in BMC Genomics, BMC Bioinformatics, BMC Systems Biology and BMC Medical Genomics. New Delhi will be the location of InCoB2018, scheduled for September 26-28, 2018.