PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 17085705

A single-nucleotide-polymorphism-based multilocus genotyping assay for subtyping lineage I isolates of Listeria monocytogenes.

Thomas F Ducey·Brent Page·Thomas Usgaard·Monica K Borucki·Kitty Pupedis·Todd J Ward
View original source ↗Export metadata ↓

Abstract

Listeria monocytogenes is a facultative intracellular pathogen responsible for food-borne disease with high mortality rates in humans and is the leading microbiological cause of food recalls. Lineage I isolates of L. monocytogenes are a particular public health concern because they are responsible for most sporadic cases of listeriosis and the vast majority of epidemic outbreaks. Rapid, reproducible, and sensitive methods for differentiating pathogens below the species level are required for effective pathogen control programs, and the CDC PulseNet Task Force has called for the development and validation of DNA sequence-based methods for subtyping food-borne pathogens. Therefore, we developed a multilocus genotyping (MLGT) assay for L. monocytogenes lineage I isolates based on nucleotide variation identified by sequencing 23,251 bp of DNA from 22 genes distributed across seven genomic regions in 65 L. monocytogenes isolates. This single-well assay of 60 allele-specific probes captured 100% of the haplotype information contained in approximately 1.5 Mb of comparative DNA sequence and was used to reproducibly type a total of 241 lineage I isolates. The MLGT assay provided high discriminatory power (Simpson's index value, 0.91), uniquely identified isolates from the eight listeriosis outbreaks examined, and differentiated serotypes 1/2b and 4b as well as epidemic clone I (ECI), ECIa, and ECII. In addition, the assay included probes for a previously characterized truncation mutation in inlA, providing for the identification of a specific virulence-attenuated subtype. These results demonstrate that MLGT represents a significant new tool for use in pathogen surveillance, outbreak detection, risk assessment, population analyses, and epidemiological investigations. DNA sequences were deposited in the GenBank database under accession numbers DQ 812146 to DQ 812517, DQ 843664 to DQ 844598, and AY 512391 to AY 512502.

Explore related subjects

Bacterial ProteinsBacterial Typing TechniquesDNA, BacterialFlow CytometryFoodborne DiseasesGenotypeHumansListeria monocytogenesListeriosisMolecular Sequence DataPolymorphism, Single NucleotideReproducibility of ResultsSequence Analysis, DNAEvaluation StudyJournal Article

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Thomas F Ducey, Brent Page, Thomas Usgaard, Monica K Borucki, Kitty Pupedis, Todd J Ward. 2006-11-03. A single-nucleotide-polymorphism-based multilocus genotyping assay for subtyping lineage I isolates of Listeria monocytogenes.. https://doi.org/10.1128/aem.01453-06

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2026

Prediction of bacterial protein-compound interactions with only positive samples.

MOTIVATION: Prediction of Compound-Protein Interactions (CPI) in bacteria is crucial to advance various pharmaceutical and chemical engineering fields, including biocatalysis, drug discovery, and industrial processing. However, current CPI models cannot be applied for bacterial CPI prediction due to the lack of curated negative interaction samples. RESULTS: We propose a novel Positive-Unlabeled (PU) learning framework, named BIN-PU, to address this limitation. BIN-PU generates pseudo positive and negative labels from known positive interaction data, enabling effective training of deep learning models for CPI prediction. We also propose a weighted positive loss function that weights to truly positive samples. We have validated BIN-PU coupled with multiple CPI backbone models, comparing the performance with the existing PU models using bacterial cytochrome P450 (CYP) data. Extensive experiments demonstrate the superiority of BIN-PU over the benchmark models in predicting CPIs with only truly positive samples. Furthermore, we have validated BIN-PU on additional bacterial proteins obtained from literature review, human CYP datasets, and uncurated data for its reproducibility. We have also validated the CPI prediction for the uncurated CYP data with biological and biophysical experiments. BIN-PU represents a significant advancement in CPI prediction for bacterial proteins, opening new possibilities for improving predictive models in related biological interaction tasks. AVAILABILITY AND IMPLEMENTATION: The source code and data are available at https://github.com/datax-lab/CYP.

Bacterial Proteins↗
PubMed2025

ComFB, a widespread family of c-di-NMP receptor proteins.

Cyclic dimeric-GMP (c-di-GMP) is a ubiquitous bacterial second messenger that regulates a variety of cellular processes, including motility, biofilm formation, secretion, cell cycle progression, and development, and also contributes to the virulence of many bacterial pathogens. While the genes encoding c-di-GMP cyclases and hydrolases are readily identifiable in microbial genomes, known c-di-GMP receptor domains are quite few, with only PilZ and MshEN broadly distributed across bacterial phyla. Recently, a new c-di-GMP receptor, named CdgR or ComFB, has been identified in cyanobacteria and shown to regulate cell size and natural competence. We demonstrated that CdgR proteins exhibit sequence and structural similarity to the Bacillus subtilis late competence development protein ComFB, a conserved protein of unknown function associated with bacterial competence. This prompted us to hypothesize that ComFB and ComFB-like proteins could also serve as c-di-GMP receptors. Here, we comprehensively investigated the ComFB protein family and demonstrated that ComFB proteins are evolutionarily widespread among bacteria and function as a novel family of c-di-GMP receptors. We showed that ComFB proteins from Gram-positive bacteria (B. subtilis, Thermoanaerobacter brockii) and Gram-negative pathogens (Vibrio cholerae, Treponema denticola) bind c-di-GMP with high affinity. Several ComFB proteins also bind cyclic di-adenosine monophosphate (c-di-AMP), suggesting that ComFB represents a widely distributed bacterial protein family with dual specificity for c-di-GMP and c-di-AMP. Our physiological studies further showed that ComFB plays vital roles in controlling motility in a c-di-GMP-dependent manner in two phylogenetically distant bacteria, B. subtilis and the gram-negative Shewanella oneidensis, attesting to the biological relevance of ComFB as a c-di-GMP binding protein.

Bacterial Proteins↗
PubMed2024

Dynamic structural determinants in bacterial microcompartment shells.

Bacterial microcompartments (BMCs) are polyhedral structures that segregate enzymatic cargo from the cytosol via encapsulation within a protein shell. Unlike other biological polyhedra, such as viral capsids and encapsulins, BMC shells can exhibit a highly advantageous structural and functional plasticity, conforming to a variety of anabolic (CO2 fixation in carboxysomes) and catabolic (nutrient assimilation in metabolosomes) roles. Consequently, understanding the subunit properties and associated protein-protein interaction processes that guide shell assembly and function is a necessary step to fully harness BMCs as modular, biotechnological nanomachines. Here, we describe the recent insights into the dynamics of structural features of the key BMC domain (Pfam00936)-containing proteins, which serve as a structural template for BMC-H and BMC-T shell building blocks.

Bacterial Proteins↗