Dataset Information

Identification of host-microbe interaction factors in the genomes of soft rot-associated pathogens Dickeya dadantii 3937 and Pectobacterium carotovorum WPP14 with supervised machine learning.

ABSTRACT: BACKGROUND: A wealth of genome sequences has provided thousands of genes of unknown function, but identification of functions for the large numbers of hypothetical genes in phytopathogens remains a challenge that impacts all research on plant-microbe interactions. Decades of research on the molecular basis of pathogenesis focused on a limited number of factors associated with long-known host-microbe interaction systems, providing limited direction into this challenge. Computational approaches to identify virulence genes often rely on two strategies: searching for sequence similarity to known host-microbe interaction factors from other organisms, and identifying islands of genes that discriminate between pathogens of one type and closely related non-pathogens or pathogens of a different type. The former is limited to known genes, excluding vast collections of genes of unknown function found in every genome. The latter lacks specificity, since many genes in genomic islands have little to do with host-interaction. RESULT: In this study, we developed a supervised machine learning approach that was designed to recognize patterns from large and disparate data types, in order to identify candidate host-microbe interaction factors. The soft rot Enterobacteriaceae strains Dickeya dadantii 3937 and Pectobacterium carotovorum WPP14 were used for development of this tool, because these pathogens are important on multiple high value crops in agriculture worldwide and more genomic and functional data is available for the Enterobacteriaceae than any other microbial family. Our approach achieved greater than 90% precision and a recall rate over 80% in 10-fold cross validation tests. CONCLUSION: Application of the learning scheme to the complete genome of these two organisms generated a list of roughly 200 candidates, many of which were previously not implicated in plant-microbe interaction and many of which are of completely unknown function. These lists provide new targets for experimental validation and further characterization, and our approach presents a promising pattern-learning scheme that can be generalized to create a resource to study host-microbe interactions in other bacterial phytopathogens.

SUBMITTER: Ma B

PROVIDER: S-EPMC4079955 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Identification of host-microbe interaction factors in the genomes of soft rot-associated pathogens Dickeya dadantii 3937 and Pectobacterium carotovorum WPP14 with supervised machine learning.

Ma Bing B Charkowski Amy O AO Glasner Jeremy D JD Perna Nicole T NT

BMC genomics 20140621

<h4>Background</h4>A wealth of genome sequences has provided thousands of genes of unknown function, but identification of functions for the large numbers of hypothetical genes in phytopathogens remains a challenge that impacts all research on plant-microbe interactions. Decades of research on the molecular basis of pathogenesis focused on a limited number of factors associated with long-known host-microbe interaction systems, providing limited direction into this challenge. Computational approa ...[more]

PMID: 24952641

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Project description:UnlabelledAlthough enteric human pathogens are usually studied in the context of their animal hosts, a significant portion of their life cycle occurs on plants. Plant disease alters the phyllosphere, leading to enhanced growth of human pathogens; however, the impact of human pathogens on phytopathogen biology and plant health is largely unknown. To characterize the interaction between human pathogens and phytobacterial pathogens in the phyllosphere, we examined the interactions between Pectobacterium carotovorum subsp. carotovorum and Salmonella enterica or Escherichia coli O157:H7 with regard to bacterial populations, soft rot progression, and changes in local pH. The presence of P. carotovorum subsp. carotovorum enhanced the growth of both S. enterica and E. coli O157:H7 on leaves. However, in a microaerophilic environment, S. enterica reduced P. carotovorum subsp. carotovorum populations and soft rot progression by moderating local environmental pH. Reduced soft rot was not due to S. enterica proteolytic activity. Limitations on P. carotovorum subsp. carotovorum growth, disease progression, and pH elevation were not observed on leaves coinoculated with E. coli O157:H7 or when leaves were coinoculated with S. enterica in an aerobic environment. S. enterica also severely undermined the relationship between the phytobacterial population and disease progression of a P. carotovorum subsp. carotovorum budB mutant defective in the 2,3-butanediol pathway for acid neutralization. Our results show that S. enterica and E. coli O157:H7 interact differently with the enteric phytobacterial pathogen P. carotovorum subsp. carotovorum. S. enterica inhibition of soft rot progression may conceal a rapidly growing human pathogen population. Whereas soft rotted produce can alert consumers to the possibility of food-borne pathogens, healthy-looking produce may entice consumption of contaminated vegetables.ImportanceSalmonella enterica and Escherichia coli O157:H7 may use plants to move between animal and human hosts. Their populations are higher on plants cocolonized with the common bacterial soft rot pathogen Pectobacterium carotovorum subsp. carotovorum, turning edible plants into a risk factor for human disease. We inoculated leaves with P. carotovorum subsp. carotovorum and S. enterica or E. coli O157:H7 to study the interactions between these bacteria. While P. carotovorum subsp. carotovorum enhanced the growth of both S. enterica and E. coli O157:H7, these human pathogens affected P. carotovorum subsp. carotovorum fundamentally differently. S. enterica reduced P. carotovorum subsp. carotovorum growth and acidified the environment, leading to less soft rot on leaves; E. coli O157:H7 had no such effects. As soft rot signals a food safety risk, the reduction of soft rot symptoms in the presence of S. enterica may lead consumers to eat healthy-looking but S. enterica-contaminated produce.

Dataset Information

Identification of host-microbe interaction factors in the genomes of soft rot-associated pathogens Dickeya dadantii 3937 and Pectobacterium carotovorum WPP14 with supervised machine learning.

Publications

Identification of host-microbe interaction factors in the genomes of soft rot-associated pathogens Dickeya dadantii 3937 and Pectobacterium carotovorum WPP14 with supervised machine learning.

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