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1.
Food Microbiol ; 70: 113-119, 2018 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-29173617

RESUMO

Globally, unpasteurized milk products are vehicles for the transmission of brucellosis, a zoonosis responsible for cases of foodborne illness in the United States and elsewhere. Existing PCR assays to detect Brucella species are restricted by the resolution of band sizes on a gel or the number of fluorescent channels in a single real-time system. The Luminex bead-based suspension array is performed in a 96-well plate allowing for high throughput screening of up to 100 targets in one sample with easily discernible results. We have developed an array using the Bio-Plex 200 to differentiate the most common Brucella species: B. abortus, B. melitensis, B. suis, B. suis bv5, B. canis, B. ovis, B. pinnipedia, and B. neotomae, as well as Brucella genus. All probes showed high specificity, with no cross-reaction with non-Brucella strains. We could detect pure DNA from B. abortus, B. melitensis, and genus-level Brucella at concentrations of ≤5 fg/µL. Pure DNA from all other species tested positive at concentrations well below 500 fg/µL and we positively identified B. neotomae in six artificially contaminated cheese and milk products. An intra-laboratory verification further demonstrated the assay's accuracy and robustness in the rapid screening (3-4 h including PCR) of DNA.


Assuntos
Técnicas de Tipagem Bacteriana/métodos , Brucella/isolamento & purificação , Brucelose/microbiologia , Análise de Sequência com Séries de Oligonucleotídeos/métodos , Animais , Técnicas de Tipagem Bacteriana/instrumentação , Brucella/classificação , Brucella/genética , Brucelose/transmissão , DNA Bacteriano/genética , Humanos , Leite/microbiologia , Análise de Sequência com Séries de Oligonucleotídeos/instrumentação , Sensibilidade e Especificidade , Ovinos
2.
mSystems ; 9(6): e0141523, 2024 Jun 18.
Artigo em Inglês | MEDLINE | ID: mdl-38819130

RESUMO

Wastewater surveillance has emerged as a crucial public health tool for population-level pathogen surveillance. Supported by funding from the American Rescue Plan Act of 2021, the FDA's genomic epidemiology program, GenomeTrakr, was leveraged to sequence SARS-CoV-2 from wastewater sites across the United States. This initiative required the evaluation, optimization, development, and publication of new methods and analytical tools spanning sample collection through variant analyses. Version-controlled protocols for each step of the process were developed and published on protocols.io. A custom data analysis tool and a publicly accessible dashboard were built to facilitate real-time visualization of the collected data, focusing on the relative abundance of SARS-CoV-2 variants and sub-lineages across different samples and sites throughout the project. From September 2021 through June 2023, a total of 3,389 wastewater samples were collected, with 2,517 undergoing sequencing and submission to NCBI under the umbrella BioProject, PRJNA757291. Sequence data were released with explicit quality control (QC) tags on all sequence records, communicating our confidence in the quality of data. Variant analysis revealed wide circulation of Delta in the fall of 2021 and captured the sweep of Omicron and subsequent diversification of this lineage through the end of the sampling period. This project successfully achieved two important goals for the FDA's GenomeTrakr program: first, contributing timely genomic data for the SARS-CoV-2 pandemic response, and second, establishing both capacity and best practices for culture-independent, population-level environmental surveillance for other pathogens of interest to the FDA. IMPORTANCE: This paper serves two primary objectives. First, it summarizes the genomic and contextual data collected during a Covid-19 pandemic response project, which utilized the FDA's laboratory network, traditionally employed for sequencing foodborne pathogens, for sequencing SARS-CoV-2 from wastewater samples. Second, it outlines best practices for gathering and organizing population-level next generation sequencing (NGS) data collected for culture-free, surveillance of pathogens sourced from environmental samples.


Assuntos
COVID-19 , SARS-CoV-2 , United States Food and Drug Administration , Águas Residuárias , SARS-CoV-2/genética , Estados Unidos/epidemiologia , Águas Residuárias/virologia , COVID-19/epidemiologia , COVID-19/transmissão , COVID-19/prevenção & controle , COVID-19/virologia , Humanos , Pandemias/prevenção & controle , Genoma Viral/genética , Vigilância Epidemiológica Baseada em Águas Residuárias
3.
Genome Announc ; 6(21)2018 May 24.
Artigo em Inglês | MEDLINE | ID: mdl-29798913

RESUMO

The bacterial microbiome of flour recalled for possible Escherichia coli O121 contamination was characterized before (hour 0) and after (hour 24) enrichment using shotgun sequencing. At hour 0, Staphylococcus (46.8 to 66.5%) and Pantoea (12.6 to 21.0%) bacteria were dominant. At hour 24, Enterobacter (28.7 to 70.9%) and Klebsiella (25.6 to 68.6%) bacteria dominated, and Escherichia coli ranged from 0.3 to 17.9%.

4.
Front Microbiol ; 8: 1587, 2017.
Artigo em Inglês | MEDLINE | ID: mdl-28868052

RESUMO

16S rRNA community profiling continues to be a useful tool to study microbiome composition and dynamics, in part due to advances in next generation sequencing technology that translate into reductions in cost. Reliable taxonomic identification to the species-level, however, remains difficult, especially for short-read sequencing platforms, due to incomplete coverage of the 16S rRNA gene. This is especially true for Salmonella enterica, which is often found as a low abundant member of the microbial community, and is often found in combination with several other closely related enteric species. Here, we report on the evaluation and application of Resphera Insight, an ultra-high resolution taxonomic assignment algorithm for 16S rRNA sequences to the species level. The analytical pipeline achieved 99.7% sensitivity to correctly identify S. enterica from WGS datasets extracted from the FDA GenomeTrakr Bioproject, while demonstrating 99.9% specificity over other Enterobacteriaceae members. From low-diversity and low-complexity samples, namely ice cream, the algorithm achieved 100% specificity and sensitivity for Salmonella detection. As demonstrated using cilantro and chili powder, for highly complex and diverse samples, especially those that contain closely related species, the detection threshold will likely have to be adjusted higher to account for misidentifications. We also demonstrate the utility of this approach to detect Salmonella in the clinical setting, in this case, bloodborne infections.

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