Type 2 Nep1-Like Proteins from the Biocontrol Oomycete Pythium oligandrum Suppress Phytophthora capsici Infection in Solanaceous Plants

As a non-pathogenic oomycete, the biocontrol agent Pythium oligandrum is able to control plant diseases through direct mycoparasite activity and boosting plant immune responses. Several P. oligandrum elicitors have been found to activate plant immunity as microbe-associated molecular patterns (MAMPs). Necrosis- and ethylene-inducing peptide 1 (Nep1)-like proteins (NLPs) are a group of MAMPs widely distributed in eukaryotic and prokaryotic plant pathogens. However, little is known about their distribution and functions in P. oligandrum and its sister species Pythium periplocum. Here, we identified a total of 25 NLPs from P. oligandrum (PyolNLPs) and P. periplocum (PypeNLPs). Meanwhile, we found that PyolNLPs/PypeNLPs genes cluster in two chromosomal segments, and our analysis suggests that they expand by duplication and share a common origin totally different from that of pathogenic oomycetes. Nine PyolNLPs/PypeNLPs induced necrosis in Nicotiana benthamiana by agroinfiltration. Eight partially purified PyolNLPs/PypeNLPs were tested for their potential biocontrol activity. PyolNLP5 and PyolNLP7 showed necrosis-inducing activity in N. benthamiana via direct protein infiltration. At sufficient concentrations, they both significantly reduced disease severity and suppressed the in planta growth of Phytophthora capsici in solanaceous plants including N. benthamiana (tobacco), Solanum lycopersicum (tomato) and Capsicum annuum (pepper). Our assays suggest that the Phytophthora suppression effect of PyolNLP5 and PyolNLP7 is irrelevant to reactive oxygen species (ROS) accumulation. Instead, they induce the expression of antimicrobial plant defensin genes, and the induction depends on their conserved nlp24-like peptide pattern. This work demonstrates the biocontrol role of two P. oligandrum NLPs for solanaceous plants, which uncovers a novel approach of utilizing NLPs to develop bioactive formulae for oomycete pathogen control with no ROS-caused injury to plants.

Comparative Genomics Reveals Prophylactic and Catabolic Capabilities of Actinobacteria within the Fungus-Farming Termite Symbiosis

ABSTRACT Actinobacteria, one of the largest bacterial phyla, are ubiquitous in many of Earth’s ecosystems and often act as defensive symbionts with animal hosts. Members of the phylum have repeatedly been isolated from basidiomycete-cultivating fungus-farming termites that maintain a monoculture fungus crop on macerated dead plant substrate. The proclivity for antimicrobial and enzyme production of Actinobacteria make them likely contributors to plant decomposition and defense in the symbiosis. To test this, we analyzed the prophylactic (biosynthetic gene cluster [BGC]) and metabolic (carbohydrate-active enzyme [CAZy]) potential in 16 (10 existing and six new genomes) termite-associated Actinobacteria and compared these to the soil-dwelling close relatives. Using antiSMASH, we identified 435 BGCs, of which 329 (65 unique) were similar to known compound gene clusters, while 106 were putatively novel, suggesting ample prospects for novel compound discovery. BGCs were identified among all major compound categories, including 26 encoding the production of known antimicrobial compounds, which ranged in activity (antibacterial being most prevalent) and modes of action that might suggest broad defensive potential. Peptide pattern recognition analysis revealed 823 (43 unique) CAZymes coding for enzymes that target key plant and fungal cell wall components (predominantly chitin, cellulose, and hemicellulose), confirming a substantial degradative potential of these bacteria. Comparison of termite-associated and soil-dwelling bacteria indicated no significant difference in either BGC or CAZy potential, suggesting that the farming termite hosts may have coopted these soil-dwelling bacteria due to their metabolic potential but that they have not been subject to genome change associated with symbiosis. IMPORTANCE Actinobacteria have repeatedly been isolated in fungus-farming termites, and our genome analyses provide insights into the potential roles they may serve in defense and for plant biomass breakdown. These insights, combined with their relatively higher abundances in fungus combs than in termite gut, suggest that they are more likely to play roles in fungus combs than in termite guts. Up to 25% of the BGCs we identify have no similarity to known clusters, indicating a large potential for novel chemistry to be discovered. Similarities in metabolic potential of soil-dwelling and termite-associated bacteria suggest that they have environmental origins, but their consistent presence with the termite system suggests their importance for the symbiosis.

Conserved Peptides Recognition by Ensemble of Neural Networks for Mining Protein Data – LPMO Case Study

Mining protein data is a recent promising area of modern bioinformatics. In this work, we suggested a novel approach for mining protein data – conserved peptides recognition by ensemble of neural networks (CPRENN). This approach was applied for mining lytic polysaccharide monooxygenases (LPMOs) in 19 ascomycete, 18 basidiomycete, and 18 bacterial proteomes. LPMOs are recently discovered enzymes and their mining is of high relevance for biotechnology of lignocellulosic materials. CPRENN was compared with two conventional bioinformatic methods for mining protein data – profile hidden Markov models (HMMs) search (HMMER program) and peptide pattern recognition (PPR program combined with Hotpep application). The maximum number of hypothetical LPMO amino acid sequences was discovered by HMMER. Profile HMMs search proved to be more sensitive method for mining LPMOs than conserved peptides recognition. Totally, CPRENN found 76 %, 67 %, and 65 % of hypothetical ascomycete, basidiomycete, and bacterial LPMOs discovered by HMMER, respectively. For AA9, AA10, and AA11 families which contain the major part of all LPMOs in the carbohydrate-active enzymes database (CAZy), CPRENN and PPR + Hotpep found 69–98 % and 62–95 % of amino acid sequences discovered by HMMER, respectively. In contrast with PPR + Hotpep, CPRENN possessed perfect precision and provided more complete mining of basidiomycete and bacterial LPMOs.

Functional peptides from yellowfin tuna (Thunnus albacares): Characterisation and storage stability assessment

Present study attempted the effective utilisation of tuna red meat which is a major cannery waste from tuna industry, by recovery in the form of bioactive peptides. Protein hydrolysate from yellowfin tuna red meat was, characterised for functional properties. Molecular weight profile of the derived hydrolysate revealed its heterogeneity in peptide pattern with a major distribution above 10 kDa (60%). A protein recovery of 39.64% was obtained from the raw material with a protein content of 88.57±0.66% in the derived tuna protein hydrolysate (TPH). Present study revealed TPH to have rich levels of amino acids like glutamic acid, aspartic acid, lysine and leucine while phenyl alanine, tyrosine, methionine and cysteine were found in lower amounts. Variations in foaming properties at different pH levels ranging from 2-10 indicated these properties to be maximum at pH 6.0. Similarly, emulsion stability index was highest (48.09±2.69 min) at pH of 6.0. However, emulsifying activity index increased with increase in pH. The storage stability studies carried out for TPH at ambient (28oC) and chilled storage conditions (4oC) for upto six months indicated an uptake of moisture, increase in oxidative indices as well as changes in functionality which was more prominent under ambient conditions. Results suggested protein hydrolysate from tuna red meat to be a promising source of bioactive peptides, finding suitability in formulation of functional foods as well as nutraceutical products.

Database Mining for Novel Bacterial β-Etherases, Glutathione-Dependent Lignin-Degrading Enzymes

ABSTRACT Lignin is the most abundant aromatic polymer in nature and a promising renewable source for the provision of aromatic platform chemicals and biofuels. β-Etherases are enzymes with a promising potential for application in lignin depolymerization due to their selectivity in the cleavage of β-O-4 aryl ether bonds. However, only a very limited number of these enzymes have been described and characterized so far. Using peptide pattern recognition (PPR) as well as phylogenetic analyses, 96 putatively novel β-etherases have been identified, some even originating from bacteria outside the order Sphingomonadales. A set of 13 diverse enzymes was selected for biochemical characterization, and β-etherase activity was confirmed for all of them. Some enzymes displayed up to 3-fold higher activity than previously known β-etherases. Moreover, conserved sequence motifs specific for either LigE- or LigF-type enzymes were deduced from multiple-sequence alignments and the PPR-derived peptides. In combination with structural information available for the β-etherases LigE and LigF, insight into the potential structural and/or functional role of conserved residues within these sequence motifs is provided. Phylogenetic analyses further suggest the presence of additional bacterial enzymes with potential β-etherase activity outside the classical LigE- and LigF-type enzymes as well as the recently described heterodimeric β-etherases. IMPORTANCE The use of biomass as a renewable source and replacement for crude oil for the provision of chemicals and fuels is of major importance for current and future societies. Lignin, the most abundant aromatic polymer in nature, holds promise as a renewable starting material for the generation of required aromatic structures. However, a controlled and selective lignin depolymerization to yield desired aromatic structures is a very challenging task. In this regard, bacterial β-etherases are especially interesting, as they are able to cleave the most abundant bond type in lignin with high selectivity. With this study, we significantly expanded the toolbox of available β-etherases for application in lignin depolymerization and discovered more active as well as diverse enzymes than previously known. Moreover, the identification of further β-etherases by sequence database mining in the future will be facilitated considerably through our deduced etherase-specific sequence motifs.

Peptide Pattern Recognition for high-throughput protein sequence analysis and clustering

Large collections of protein sequences with divergent sequences are tedious to analyze for understanding their phylogenetic or structure-function relation. Peptide Pattern Recognition is an algorithm that was developed to facilitate this task but the previous version does only allow a limited number of sequences as input.I implemented Peptide Pattern Recognition as a multithread software designed to handle large numbers of sequences and perform analysis in a reasonable time frame. Benchmarking showed that the new implementation of Peptide Pattern Recognition is twenty times faster than the previous implementation on a small protein collection with 673 MAP kinase sequences. In addition, the new implementation could analyze a large protein collection with 48,570 Glycosyl Transferase family 20 sequences without reaching its upper limit on a desktop computer.Peptide Pattern Recognition is a useful software for providing comprehensive groups of related sequences from large protein sequence collections.