Rapid geographical source attribution of Salmonella enterica serovar Enteritidis genomes using hierarchical machine learning
- University of Bristol, United Kingdom
- University of Bath, United Kingdom
- UK Health Security Agency, United Kingdom
- Utrecht University, Netherlands
Abstract
Salmonella enterica serovar Enteritidis is one of the most frequent causes of Salmonellosis globally and is commonly transmitted from animals to humans by the consumption of contaminated foodstuffs. In the UK and many other countries in the Global North, a significant proportion of cases are caused by consumption of imported food products or contracted during foreign travel, therefore making the rapid identification of the geographical source of new infections a requirement for robust public health outbreak investigations. Herein, we detail the development and application of a hierarchical machine learning model to rapidly identify and trace the geographical source of S. Enteritidis infections from whole genome sequencing data. 2,313 S. Enteritidis genomes, collected by the UKHSA between 2014-2019, were used to train a 'local classifier per node' hierarchical classifier to attribute isolates to 4 continents, 11 sub-regions and 38 countries (53 classes). The highest classification accuracy was achieved at the continental level followed by the sub-regional and country levels (macro F1: 0.954, 0.718, 0.661 respectively). A number of countries commonly visited by UK travellers were predicted with high accuracy (hF1: >0.9). Longitudinal analysis and validation with publicly accessible international samples indicated that predictions were robust to prospective external datasets. The hierarchical machine learning framework provided granular geographical source prediction directly from sequencing reads in <4 minutes per sample, facilitating rapid outbreak resolution and real-time genomic epidemiology. The results suggest additional application to a broader range of pathogens and other geographically structured problems, such as antimicrobial resistance prediction, is warranted.
Data availability
The final optimised hierarchical model as well as a pipeline for pre-processing raw read data to unitigs/patterns for input and paper data is available from https://github.com/SionBayliss/HierarchicalML with a short description and tutorial for ease of use. This end-to-end process, from FASTQ to prediction, is open access and available to users under GNU GPL licence . This depository also includes the preprocessed unitig datasets and resulting predictions. Short read sequencing data is available from the Sequence Read Archive (Bioproject: PRJNA248792). Please not that the sequence data has been previously deposited/published in the Sequence Read Archive by PHE/UKHSA and was not generated for this project.
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Public Health England - SalmonellaNCBI SRA: PRJNA248792.
Article and author information
Author details
Funding
Academy of Medical Sciences (SBF005\1089)
- Lauren A Cowley
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2023, Bayliss et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Rapid geographical source attribution of Salmonella enterica serovar Enteritidis genomes using hierarchical machine learning
eLife 12:e84167.
https://doi.org/10.7554/eLife.84167
Further reading
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- Epidemiology and Global Health
Background:
The role of circulating metabolites on child development is understudied. We investigated associations between children’s serum metabolome and early childhood development (ECD).
Methods:
Untargeted metabolomics was performed on serum samples of 5004 children aged 6–59 months, a subset of participants from the Brazilian National Survey on Child Nutrition (ENANI-2019). ECD was assessed using the Survey of Well-being of Young Children’s milestones questionnaire. The graded response model was used to estimate developmental age. Developmental quotient (DQ) was calculated as the developmental age divided by chronological age. Partial least square regression selected metabolites with a variable importance projection ≥1. The interaction between significant metabolites and the child’s age was tested.
Results:
Twenty-eight top-ranked metabolites were included in linear regression models adjusted for the child’s nutritional status, diet quality, and infant age. Cresol sulfate (β=–0.07; adjusted-p <0.001), hippuric acid (β=–0.06; adjusted-p <0.001), phenylacetylglutamine (β=–0.06; adjusted-p <0.001), and trimethylamine-N-oxide (β=–0.05; adjusted-p=0.002) showed inverse associations with DQ. We observed opposite directions in the association of DQ for creatinine (for children aged –1 SD: β=–0.05; pP=0.01;+1 SD: β=0.05; p=0.02) and methylhistidine (–1 SD: β = - 0.04; p=0.04;+1 SD: β=0.04; p=0.03).
Conclusions:
Serum biomarkers, including dietary and microbial-derived metabolites involved in the gut-brain axis, may potentially be used to track children at risk for developmental delays.
Funding:
Supported by the Brazilian Ministry of Health and the Brazilian National Research Council.
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- Epidemiology and Global Health
- Microbiology and Infectious Disease
Several areas of the world suffer a notably high incidence of Shiga toxin-producing Escherichia coli. To assess the impact of persistent cross-species transmission systems on the epidemiology of E. coli O157:H7 in Alberta, Canada, we sequenced and assembled E. coli O157:H7 isolates originating from collocated cattle and human populations, 2007–2015. We constructed a timed phylogeny using BEAST2 using a structured coalescent model. We then extended the tree with human isolates through 2019 to assess the long-term disease impact of locally persistent lineages. During 2007–2015, we estimated that 88.5% of human lineages arose from cattle lineages. We identified 11 persistent lineages local to Alberta, which were associated with 38.0% (95% CI 29.3%, 47.3%) of human isolates. During the later period, six locally persistent lineages continued to be associated with human illness, including 74.7% (95% CI 68.3%, 80.3%) of reported cases in 2018 and 2019. Our study identified multiple locally evolving lineages transmitted between cattle and humans persistently associated with E. coli O157:H7 illnesses for up to 13 y. Locally persistent lineages may be a principal cause of the high incidence of E. coli O157:H7 in locations such as Alberta and provide opportunities for focused control efforts.
