Genome-based source attribution using a one health Escherichia coli isolate collection from 2013-23 in Scotland

Antonia Chalka; Louise Crozier; Adriana Vallejo-Trujillo; Vesa Qarkaxhija; Alison Low; Sean McAteer; Kate E. Templeton; Sue C Tongue; Judith Evans; Geoffrey Foster; Thomas Evans; Charis A Marwick; Ahmed Raza; Benjamin J Parcell; Matthew TG Holden; Tom Mcneilly; Stephen Fitzgerald; Mairi Mitchell; Nuno Silva; Emily Robertshaw-McFarlane; Scott Hamilton; Elizabeth Wells; Clare Hamilton; Eleanor Watson; David Findlay; Julie Bolland; John Redshaw; David Walker; Jane Heywood; Charlotte King; Craig Baker-Austin; Athina Papadopoulou; Andy Powell; Gavin K Paterson; Genever Morgan; Jacqui Mcelhiney; David L. Gally

doi:10.1101/2025.11.25.25340865

Genome-based source attribution using a one health Escherichia coli isolate collection from 2013-23 in Scotland

Antonia Chalka
, Louise Crozier
, Adriana Vallejo-Trujillo
, Vesa Qarkaxhija
, Alison Low
, Sean McAteer
, Kate E. Templeton
, Sue C Tongue
, Judith Evans
, Geoffrey Foster
, Thomas Evans
, Charis A Marwick
, Ahmed Raza
, Benjamin J Parcell
, Matthew TG Holden
, Tom Mcneilly
, Stephen Fitzgerald
, Mairi Mitchell
, Nuno Silva
, Emily Robertshaw-McFarlane

Scott Hamilton, Elizabeth Wells, Clare Hamilton, Eleanor Watson, David Findlay, Julie Bolland, John Redshaw, David Walker, Jane Heywood, Charlotte King, Craig Baker-Austin, Athina Papadopoulou, Andy Powell, Gavin K Paterson, Genever Morgan, Jacqui Mcelhiney, David L. Gally

Population Health and Genomics

Research output: Working paper/Preprint › Preprint

Abstract

Random Forest based source attribution models were developed from a ‘one health’ resource comprising 4,230 high-quality whole genome assemblies from E. coli. These were isolated from a wide range of sources, predominantly originating in Scotland, including wastewater, livestock, food, and clinical infections of humans and dogs. Using these models, we derived a probabilistic assignment of E. coli isolates from food, shellfish and water samples to potential livestock and human sources of contamination. The incorporation of E. coli sequences from wastewater alongside those from human clinical infections, enabled us to capture a wide diversity of human strains in our analyses. The sequence types (STs) of isolates from human bacteraemia and urinary tract infections (UTI) were compared with livestock and food isolates. While only 2.3% of the E. coli isolated from food samples in the study were from STs primarily associated with human bacteraemia and UTI, the models found a livestock signal associated with 15% of the human clinical isolates. In the food and private water samples, livestock-human co-attribution of E. coli isolates was common and consistent with routine human exposure to specific subsets of livestock E. coli, potentially a result of selection during food and water processing. Overall, this research demonstrates the potential value of including source attribution models in national surveillance programmes to understand the transmission of E. coli through the agri-food chain and support risk management to protect public health.

Original language	English
Publisher	medRxiv
Number of pages	21
DOIs	https://doi.org/10.1101/2025.11.25.25340865
Publication status	Published - 25 Nov 2025

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Pre-printSubmitted manuscript, 1.44 MBLicence: CC BY-NC

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Chalka, A., Crozier, L., Vallejo-Trujillo, A., Qarkaxhija, V., Low, A., McAteer, S., Templeton, K. E., Tongue, S. C., Evans, J., Foster, G., Evans, T., Marwick, C. A., Raza, A., Parcell, B. J., Holden, M. TG., Mcneilly, T., Fitzgerald, S., Mitchell, M., Silva, N., ... Gally, D. L. (2025). Genome-based source attribution using a one health Escherichia coli isolate collection from 2013-23 in Scotland. medRxiv. https://doi.org/10.1101/2025.11.25.25340865

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title = "Genome-based source attribution using a one health Escherichia coli isolate collection from 2013-23 in Scotland",

abstract = "Random Forest based source attribution models were developed from a {\textquoteleft}one health{\textquoteright} resource comprising 4,230 high-quality whole genome assemblies from E. coli. These were isolated from a wide range of sources, predominantly originating in Scotland, including wastewater, livestock, food, and clinical infections of humans and dogs. Using these models, we derived a probabilistic assignment of E. coli isolates from food, shellfish and water samples to potential livestock and human sources of contamination. The incorporation of E. coli sequences from wastewater alongside those from human clinical infections, enabled us to capture a wide diversity of human strains in our analyses. The sequence types (STs) of isolates from human bacteraemia and urinary tract infections (UTI) were compared with livestock and food isolates. While only 2.3\% of the E. coli isolated from food samples in the study were from STs primarily associated with human bacteraemia and UTI, the models found a livestock signal associated with 15\% of the human clinical isolates. In the food and private water samples, livestock-human co-attribution of E. coli isolates was common and consistent with routine human exposure to specific subsets of livestock E. coli, potentially a result of selection during food and water processing. Overall, this research demonstrates the potential value of including source attribution models in national surveillance programmes to understand the transmission of E. coli through the agri-food chain and support risk management to protect public health.",

author = "Antonia Chalka and Louise Crozier and Adriana Vallejo-Trujillo and Vesa Qarkaxhija and Alison Low and Sean McAteer and Templeton, \{Kate E.\} and Tongue, \{Sue C\} and Judith Evans and Geoffrey Foster and Thomas Evans and Marwick, \{Charis A\} and Ahmed Raza and Parcell, \{Benjamin J\} and Holden, \{Matthew TG\} and Tom Mcneilly and Stephen Fitzgerald and Mairi Mitchell and Nuno Silva and Emily Robertshaw-McFarlane and Scott Hamilton and Elizabeth Wells and Clare Hamilton and Eleanor Watson and David Findlay and Julie Bolland and John Redshaw and David Walker and Jane Heywood and Charlotte King and Craig Baker-Austin and Athina Papadopoulou and Andy Powell and Paterson, \{Gavin K\} and Genever Morgan and Jacqui Mcelhiney and Gally, \{David L.\}",

note = "The copyright holder for this preprint is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.",

year = "2025",

month = nov,

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doi = "10.1101/2025.11.25.25340865",

language = "English",

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Chalka, A, Crozier, L, Vallejo-Trujillo, A, Qarkaxhija, V, Low, A, McAteer, S, Templeton, KE, Tongue, SC, Evans, J, Foster, G, Evans, T, Marwick, CA, Raza, A, Parcell, BJ, Holden, MTG, Mcneilly, T, Fitzgerald, S, Mitchell, M, Silva, N, Robertshaw-McFarlane, E, Hamilton, S, Wells, E, Hamilton, C, Watson, E, Findlay, D, Bolland, J, Redshaw, J, Walker, D, Heywood, J, King, C, Baker-Austin, C, Papadopoulou, A, Powell, A, Paterson, GK, Morgan, G, Mcelhiney, J & Gally, DL 2025 'Genome-based source attribution using a one health Escherichia coli isolate collection from 2013-23 in Scotland' medRxiv. https://doi.org/10.1101/2025.11.25.25340865

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T1 - Genome-based source attribution using a one health Escherichia coli isolate collection from 2013-23 in Scotland

AU - Chalka, Antonia

AU - Crozier, Louise

AU - Vallejo-Trujillo, Adriana

AU - Qarkaxhija, Vesa

AU - Low, Alison

AU - McAteer, Sean

AU - Templeton, Kate E.

AU - Tongue, Sue C

AU - Evans, Judith

AU - Foster, Geoffrey

AU - Evans, Thomas

AU - Marwick, Charis A

AU - Raza, Ahmed

AU - Parcell, Benjamin J

AU - Holden, Matthew TG

AU - Mcneilly, Tom

AU - Fitzgerald, Stephen

AU - Mitchell, Mairi

AU - Silva, Nuno

AU - Robertshaw-McFarlane, Emily

AU - Hamilton, Scott

AU - Wells, Elizabeth

AU - Hamilton, Clare

AU - Watson, Eleanor

AU - Findlay, David

AU - Bolland, Julie

AU - Redshaw, John

AU - Walker, David

AU - Heywood, Jane

AU - King, Charlotte

AU - Baker-Austin, Craig

AU - Papadopoulou, Athina

AU - Powell, Andy

AU - Paterson, Gavin K

AU - Morgan, Genever

AU - Mcelhiney, Jacqui

AU - Gally, David L.

N1 - The copyright holder for this preprint is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.

PY - 2025/11/25

Y1 - 2025/11/25

N2 - Random Forest based source attribution models were developed from a ‘one health’ resource comprising 4,230 high-quality whole genome assemblies from E. coli. These were isolated from a wide range of sources, predominantly originating in Scotland, including wastewater, livestock, food, and clinical infections of humans and dogs. Using these models, we derived a probabilistic assignment of E. coli isolates from food, shellfish and water samples to potential livestock and human sources of contamination. The incorporation of E. coli sequences from wastewater alongside those from human clinical infections, enabled us to capture a wide diversity of human strains in our analyses. The sequence types (STs) of isolates from human bacteraemia and urinary tract infections (UTI) were compared with livestock and food isolates. While only 2.3% of the E. coli isolated from food samples in the study were from STs primarily associated with human bacteraemia and UTI, the models found a livestock signal associated with 15% of the human clinical isolates. In the food and private water samples, livestock-human co-attribution of E. coli isolates was common and consistent with routine human exposure to specific subsets of livestock E. coli, potentially a result of selection during food and water processing. Overall, this research demonstrates the potential value of including source attribution models in national surveillance programmes to understand the transmission of E. coli through the agri-food chain and support risk management to protect public health.

AB - Random Forest based source attribution models were developed from a ‘one health’ resource comprising 4,230 high-quality whole genome assemblies from E. coli. These were isolated from a wide range of sources, predominantly originating in Scotland, including wastewater, livestock, food, and clinical infections of humans and dogs. Using these models, we derived a probabilistic assignment of E. coli isolates from food, shellfish and water samples to potential livestock and human sources of contamination. The incorporation of E. coli sequences from wastewater alongside those from human clinical infections, enabled us to capture a wide diversity of human strains in our analyses. The sequence types (STs) of isolates from human bacteraemia and urinary tract infections (UTI) were compared with livestock and food isolates. While only 2.3% of the E. coli isolated from food samples in the study were from STs primarily associated with human bacteraemia and UTI, the models found a livestock signal associated with 15% of the human clinical isolates. In the food and private water samples, livestock-human co-attribution of E. coli isolates was common and consistent with routine human exposure to specific subsets of livestock E. coli, potentially a result of selection during food and water processing. Overall, this research demonstrates the potential value of including source attribution models in national surveillance programmes to understand the transmission of E. coli through the agri-food chain and support risk management to protect public health.

U2 - 10.1101/2025.11.25.25340865

DO - 10.1101/2025.11.25.25340865

M3 - Preprint

BT - Genome-based source attribution using a one health Escherichia coli isolate collection from 2013-23 in Scotland

PB - medRxiv

ER -

Genome-based source attribution using a one health Escherichia coli isolate collection from 2013-23 in Scotland

Abstract

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