Dataset Information

Naive Bayes Classifiers and accompanying dataset for Pseudomonas syringae isolate characterization.

ABSTRACT: The Pseudomonas syringae species complex (PSSC) is a diverse group of plant pathogens with a collective host range encompassing almost every food crop grown today. As a threat to global food security, rapid detection and characterization of epidemic and emerging pathogenic lineages is essential. However, phylogenetic identification is often complicated by an unclarified and ever-changing taxonomy, making practical use of available databases and the proper training of classifiers difficult. As such, while amplicon sequencing is a common method for routine identification of PSSC isolates, there is no efficient method for accurate classification based on this data. Here we present a suite of five Naïve bayes classifiers for PCR primer sets widely used for PSSC identification, trained on in-silico amplicon data from 2,161 published PSSC genomes using the life identification number (LIN) hierarchical clustering algorithm in place of traditional Linnaean taxonomy. Additionally, we include a dataset for translating classification results back into traditional taxonomic nomenclature (i.e. species, phylogroup, pathovar), and for predicting virulence factor repertoires.

SUBMITTER: Fautt C

PROVIDER: S-EPMC10850129 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature

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Naïve Bayes Classifiers and accompanying dataset for Pseudomonas syringae isolate characterization.

Fautt Chad C Couradeau Estelle E Hockett Kevin L KL

Scientific data 20240207 1

The Pseudomonas syringae species complex (PSSC) is a diverse group of plant pathogens with a collective host range encompassing almost every food crop grown today. As a threat to global food security, rapid detection and characterization of epidemic and emerging pathogenic lineages is essential. However, phylogenetic identification is often complicated by an unclarified and ever-changing taxonomy, making practical use of available databases and the proper training of classifiers difficult. As su ...[more]

PMID: 38326362

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Project description:MotivationRecent CASP experiments have witnessed exciting progress on folding large-size non-humongous proteins with the assistance of co-evolution based contact predictions. The success is however anecdotal due to the requirement of the contact prediction methods for the high volume of sequence homologs that are not available to most of the non-humongous protein targets. Development of efficient methods that can generate balanced and reliable contact maps for different type of protein targets is essential to enhance the success rate of the ab initio protein structure prediction.ResultsWe developed a new pipeline, NeBcon, which uses the naïve Bayes classifier (NBC) theorem to combine eight state of the art contact methods that are built from co-evolution and machine learning approaches. The posterior probabilities of the NBC model are then trained with intrinsic structural features through neural network learning for the final contact map prediction. NeBcon was tested on 98 non-redundant proteins, which improves the accuracy of the best co-evolution based meta-server predictor by 22%; the magnitude of the improvement increases to 45% for the hard targets that lack sequence and structural homologs in the databases. Detailed data analysis showed that the major contribution to the improvement is due to the optimized NBC combination of the complementary information from both co-evolution and machine learning predictions. The neural network training also helps to improve the coupling of the NBC posterior probability and the intrinsic structural features, which were found particularly important for the proteins that do not have sufficient number of homologous sequences to derive reliable co-evolution profiles.Availiablity and implementationOn-line server and standalone package of the program are available at http://zhanglab.ccmb.med.umich.edu/NeBcon/ .Contactzhng@umich.edu.Supplementary informationSupplementary data are available at Bioinformatics online.

Project description:BackgroundPseudomonas syringae pv. phaseolicola 1448a (P. syringae 1448a), the causative agent of bean halo blight, is a bacterium capable of occupying diverse biological niches. Under conditions of iron starvation P. syringae 1448a secretes siderophores for active uptake of iron. The primary siderophore of P. syringae 1448a is pyoverdine, a fluorescent molecule that is assembled from amino acid precursors by non-ribosomal peptide synthetase (NRPS) enzymes. Whereas other species of Pseudomonas often exhibit structural variations in the pyoverdine produced by different strains, all P. syringae pathovars previously tested have been found to make an identical pyoverdine molecule. P. syringae 1448a also appears to have the genetic potential to make two secondary siderophores, achromobactin and yersiniabactin, each of which has previously been detected in different P. syringae pathovars.ResultsFive putative pyoverdine NRPS genes in P. syringae 1448a were characterized in-silico and their role in pyoverdine biosynthesis was confirmed by gene knockout. Pyoverdine was purified from P. syringae 1448a and analyzed by MALDI-TOF and MS/MS spectroscopy. Peaks were detected corresponding to the expected sizes for the pyoverdine structure previously found in other P. syringae pathovars, but surprisingly P. syringae 1448a appears to also produce a variant pyoverdine species that has an additional 71 Da monomer incorporated into the peptide side chain. Creation of pyoverdine null mutants of P. syringae 1448a revealed that this strain also produces achromobactin as a temperature-regulated secondary siderophore, but does not appear to make yersiniabactin. Pyoverdine and achromobactin null mutants were characterized in regard to siderophore production, iron uptake, virulence and growth in iron limited conditions.ConclusionsThis study provides the first evidence of a P. syringae pathovar producing a side chain variant form of pyoverdine. We also describe novel IC₅₀ and liquid CAS assays to quantify the contribution of different siderophores across a range of iron starvation conditions, and show that although achromobactin has potential to contribute to fitness its contribution is masked by the presence of pyoverdine, which is a significantly more effective siderophore. Neither pyoverdine nor achromobactin appear to be required for P. syringae 1448a to cause bean halo blight, indicating that these siderophores are not promising targets for crop protection strategies.

Dataset Information

Naive Bayes Classifiers and accompanying dataset for Pseudomonas syringae isolate characterization.

Publications

Naïve Bayes Classifiers and accompanying dataset for Pseudomonas syringae isolate characterization.

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