Dataset Information

Agritourism and Kidding Season: A Large Outbreak of Human Shiga Toxin-Producing Escherichia coli O157 (STEC O157) Infections Linked to a Goat Dairy Farm-Connecticut, 2016.

ABSTRACT: The objective of this study was to determine sources of Shiga toxin-producing Escherichia coli O157 (STEC O157) infection among visitors to Farm X and develop public health recommendations. A case-control study was conducted. Case-patients were defined as the first ill child (aged <18 years) in the household with laboratory-confirmed STEC O157, or physician-diagnosed hemolytic uremic syndrome with laboratory confirmation by serology, who visited Farm X in the 10 days prior to illness. Controls were selected from Farm X visitors aged <18 years, without symptoms during the same time period as case-patients. Environment and animal fecal samples collected from Farm X were cultured; isolates from Farm X were compared with patient isolates using whole genome sequencing (WGS). Case-patients were more likely than controls to have sat on hay bales at the doe barn (adjusted odds ratio: 4.55; 95% confidence interval: 1.41-16.13). No handwashing stations were available; limited hand sanitizer was provided. Overall, 37% (29 of 78) of animal and environmental samples collected were positive for STEC; of these, 62% (18 of 29) yielded STEC O157 highly related by WGS to patient isolates. STEC O157 environmental contamination and fecal shedding by goats at Farm X was extensive. Farms should provide handwashing stations with soap, running water, and disposable towels. Access to animal areas, including animal pens and enclosures, should be limited for young children who are at risk for severe outcomes from STEC O157 infection. National recommendations should be adopted to reduce disease transmission.

SUBMITTER: Nichols MC

PROVIDER: S-EPMC8635155 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Agritourism and Kidding Season: A Large Outbreak of Human Shiga Toxin-Producing <i>Escherichia coli</i> O157 (STEC O157) Infections Linked to a Goat Dairy Farm-Connecticut, 2016.

Nichols Megin C MC Gacek Paul P Phan Quyen Q Gambino-Shirley Kelly J KJ Gollarza Lauren M LM Schroeder Morgan N MN Mercante Alexandra A Mullins Jocelyn J Blackstock Anna A Laughlin Mark E ME Olson Samantha M SM Pizzo Eugene E Nguyen Tu Ngoc TN Mank Laurn L Holmes-Talbot Kimberly K McNutt Alycia A Noel Diane D Muyombwe Anthony A Razeq Jafar H JH Lis Mary Jane MJ Sherman Bruce B Kasacek Wayne W Whitlock Laura L Strockbine Nancy N Martin Haley H Vidyaprakash Eshaw E McCormack Patrick P Cartter Matthew M

Frontiers in veterinary science 20211116

The objective of this study was to determine sources of Shiga toxin-producing <i>Escherichia coli</i> O157 (STEC O157) infection among visitors to Farm X and develop public health recommendations. A case-control study was conducted. Case-patients were defined as the first ill child (aged <18 years) in the household with laboratory-confirmed STEC O157, or physician-diagnosed hemolytic uremic syndrome with laboratory confirmation by serology, who visited Farm X in the 10 days prior to illness. Con ...[more]

PMID: 34869720

Similar Datasets

Project description:The objective of this study was to develop a canonical, parsimoniously-informative SNP panel for subtyping Shiga-toxin producing Escherichia coli (STEC) O157:H7 that would be consistent with epidemiological, PFGE, and MLVA clustering of human specimens. Our group had previously identified 906 putative discriminatory SNPs, which were pared down to 391 SNPs based on their prevalence in a test set. The 391 SNPs were screened using a high-throughput form of TaqMan PCR against a set of clinical isolates that represent the most diverse collection of O157:H7 isolates from outbreaks and sporadic cases examined to date. Another 30 SNPs identified by others were also screened using the same method. Two additional targets were tested using standard TaqMan PCR endpoint analysis. These 423 SNPs were reduced to a 32 SNP panel with the almost the same discriminatory value. While the panel partitioned our diverse set of isolates in a manner that was consistent with epidemiological data and PFGE and MLVA phylogenies, it resulted in fewer subtypes than either existing method and insufficient epidemiological resolution in 10 of 47 clusters. Therefore, another round of SNP discovery was undertaken using comparative genomic resequencing of pooled DNA from the 10 clusters with insufficient resolution. This process identified 4,040 potential SNPs and suggested one of the ten clusters was incorrectly grouped. After its removal, there were 2,878 SNPs, of which only 63 were previously identified and 438 occurred across multiple clusters. Among highly clonal bacteria like STEC O157:H7, linkage disequilibrium greatly limits the number of parsimoniously informative SNPs. Therefore, it is perhaps unsurprising that our panel accounted for the potential discriminatory value of numerous other SNPs reported in the literature. We concluded published O157:H7 SNPs are insufficient for effective epidemiological subtyping. However, the 438 multi-cluster SNPs we identified may provide the additional information required.

Project description:Shiga toxin-producing Escherichia coli (STEC) are important foodborne pathogens and non-O157 serotypes have been gradually increasing in frequency. The non-O157 STEC population is diverse and is often characterized using serotyping and/or multilocus sequence typing (MLST). Although spacers within clustered regularly interspaced repeat (CRISPR) regions were shown to comprise horizontally acquired DNA elements, this region does not actively acquire spacers in STEC. Hence, it is useful for further characterizing non-O157 STEC and examining relationships between strains. Our study goal was to evaluate the genetic relatedness of 41 clinical non-O157 isolates identified in Michigan between 2001 and 2005 while comparing to 114 isolates from Connecticut during an overlapping time period. Whole genome sequencing (WGS) was performed, and sequences were extracted for serotyping, MLST and CRISPR analysis. Phylogenetic analysis of MLST and CRISPR data was performed using the Neighbor joining and unweighted pair group method with arithmetic mean (UPGMA) algorithms, respectively. In all, 29 serogroups were identified; eight were unique to Michigan and 13 to Connecticut. "Big-six" serogroup frequencies were similar by state (Michigan: 73.2%, Connecticut: 81.6%), though STEC O121 was not found in Michigan. The distribution of sequence types (STs) and CRISPR profiles was also similar across states. Interestingly, big-six serogroups such as O103 and O26, grouped into different STs located on distinct branches of the phylogeny, further confirming that serotyping alone is not adequate for evaluating strain relatedness. Comparatively, the CRISPR analysis identified 361 unique spacers that grouped into 80 different CRISPR profiles. CRISPR spacers 231 and 317 were isolated from 79.2% (n = 118) and 59.1% (n = 88) of strains, respectively, regardless of serogroup and ST. Spacer profiles clustered according to the MLST analysis, though some discrepancies were noted. Indeed, use of both MLST and CRISPR typing enhanced the discriminatory power when compared to the use of each tool separately. These data highlight the genetic diversity of clinical STEC from different locations and show that CRISPR profiling can be used alongside MLST to discriminate related strains. Use of targeted sequencing approaches are particularly helpful for sites without WGS capabilities and can help define which strains require additional characterization using more discriminatory methods.

Dataset Information

Agritourism and Kidding Season: A Large Outbreak of Human Shiga Toxin-Producing Escherichia coli O157 (STEC O157) Infections Linked to a Goat Dairy Farm-Connecticut, 2016.

Publications

Agritourism and Kidding Season: A Large Outbreak of Human Shiga Toxin-Producing <i>Escherichia coli</i> O157 (STEC O157) Infections Linked to a Goat Dairy Farm-Connecticut, 2016.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets