Fighting Infection and AMR in broiler farming: AI, omics and smart sensing for diagnostics, treatment selection and gut microbiome improvement
Lead Research Organisation:
University of Nottingham
Department Name: School of Veterinary Medicine and Sci
Abstract
The fight against enteric infections while containing the uprise of antimicrobial resistance, represents one of the major challenges in contemporary broiler farming, with repercussions on both bird and consumer's health. Key to future, better solutions for surveillance, diagnostics and treatment selection, is to gain an improved understanding of the bird's gut microbiome, exploring the modifications its population of commensals and opportunistic pathogens undergo as a consequence of infection, treatment and development of resistant traits.
In this project, we plan to explore the broiler gut microbiome, focusing on infection and resistance in relation to pathogens typically found in the gastrointestinal tract of the birds: Clostridium perfringens, Enterococcus cecorum, Escherichia coli and Salmonella spp. We cover also scenarios of co-infection with viruses causing dysbiosis of gut microbiome. We consider resistance/susceptibility to 8 classes of antibiotics: tetracyclines, sulphonamides, beta-lactams, fluoroquinolones, polymyxins, macrolides, diaminopyrimidines, aminoglycosides, whose use as therapeutics is diffused in the UK. We plan to collect a large amount of heterogeneous data from farms, feed and birds, covering normal production periods and infection events. Data will include results of microbiological analysis, whole-genome sequencing, shotgun metagenomics and phenotyping performed on faecal samples, on-farm management practices, as well as environmental sensor data and bird imaging. We propose to use machine learning and cloud computing to perform large-scale data mining and ultimately unravel the network of possible interactions amongst the observable variables, following broilers along their life cycle, and capturing episodes of infection, treatment and development of single or multi-drug resistance. Acquired knowledge may provide hints at the selection of observable variables acting as biomarkers, i.e, targetable by future solutions for real-time livestock monitoring, to detect/forecast infection or the presence/insurgence of resistant traits, and to support precision diagnostics and bespoke treatment selection. The results may also suggest routes to improve the birds gut microbiome, for example via feed additives, making it more robust to infection while at the same time inhibiting the development of resistance.
In this project, we plan to explore the broiler gut microbiome, focusing on infection and resistance in relation to pathogens typically found in the gastrointestinal tract of the birds: Clostridium perfringens, Enterococcus cecorum, Escherichia coli and Salmonella spp. We cover also scenarios of co-infection with viruses causing dysbiosis of gut microbiome. We consider resistance/susceptibility to 8 classes of antibiotics: tetracyclines, sulphonamides, beta-lactams, fluoroquinolones, polymyxins, macrolides, diaminopyrimidines, aminoglycosides, whose use as therapeutics is diffused in the UK. We plan to collect a large amount of heterogeneous data from farms, feed and birds, covering normal production periods and infection events. Data will include results of microbiological analysis, whole-genome sequencing, shotgun metagenomics and phenotyping performed on faecal samples, on-farm management practices, as well as environmental sensor data and bird imaging. We propose to use machine learning and cloud computing to perform large-scale data mining and ultimately unravel the network of possible interactions amongst the observable variables, following broilers along their life cycle, and capturing episodes of infection, treatment and development of single or multi-drug resistance. Acquired knowledge may provide hints at the selection of observable variables acting as biomarkers, i.e, targetable by future solutions for real-time livestock monitoring, to detect/forecast infection or the presence/insurgence of resistant traits, and to support precision diagnostics and bespoke treatment selection. The results may also suggest routes to improve the birds gut microbiome, for example via feed additives, making it more robust to infection while at the same time inhibiting the development of resistance.
Technical Summary
The gut microbiome is composed of harmless symbionts, commensal bacteria, and opportunistic pathogens, all of which play crucial roles in animal health and disease. In physiological conditions the gut microbiome is stable, but when perturbative events occur (e.g., dietary changes, infections, stress, antibiotic administration) the population of microbiota changes, influencing health and protection against infections and colonisation. These changes may involve new resistant bacteria becoming permanent residents, or transferring resistance to the commensals. In poultry farming, all these mechanisms are still largely unknown, but the importance of studying the gut microbiome in connection to farming productivity has been acknowledged, recognising also the existence of numerous environmental and practice-related factors influencing gut modifications. The aim of this project is to introduce novel approaches to precision farming, based on a better understanding of infection and resistance of specific pathogens (Clostridium perfringens, Enterococcus cecorum, Escherichia coli and Salmonella) and relationships with the gut microbiome. We will collect a large amount of heterogeneous data covering a broad range of targets (birds, soil, feed, water, air), involving a broad range of sources (sensing, imaging, microbiological analysis, whole-genome sequencing, shotgun metagenomics, on-farm management practices), and covering multiple time points and conditions. We will use machine learning and cloud computing to perform large-scale data mining and ultimately unravel the network of interactions amongst the observable variables, following broilers along their life cycle, and capturing episodes of infection, treatment and development of single or multi-drug resistance. The acquired knowledge will be used to select a viable set of monitorable variables to implement real-time forecasting and diagnostics of infection and AMR, and to devise decision support tools for treatment selection
Organisations
- University of Nottingham (Lead Research Organisation, Project Partner)
- Scottish Government (Co-funder)
- Department for Environment Food and Rural Affairs (Co-funder)
- Dept of Agri, Env & Rural Affairs DAERA (Co-funder)
- Cardiff University (Collaboration)
- AB Agri Ltd (Collaboration)
- AGRI-FOOD AND BIOSCIENCES INSTITUTE (Collaboration)
- Agri Food and Biosciences Institute (Project Partner)
- CARDIFF UNIVERSITY (Project Partner)
- AB Agri (Project Partner)
- Innovation for Agriculture (Project Partner)
- Slate Hall Veterinary Services Ltd (Project Partner)
| Description | Influence through membership on international organisation such as JPIAMR: The Joint Programming Initiative on Antimicrobial Resistance, JPIAMR, is a global collaborative organisation and platform, engaging 29 nations to curb antimicrobial resistance (AMR) with a One Health approach |
| Geographic Reach | Multiple continents/international |
| Policy Influence Type | Participation in a guidance/advisory committee |
| Impact | Participating in drafting roadmap of actions, guide lines, workshops |
| URL | https://www.jpiamr.eu/ |
| Description | Training and educational developments for postgraduates and research users |
| Geographic Reach | Local/Municipal/Regional |
| Policy Influence Type | Influenced training of practitioners or researchers |
| Impact | Capacity building in a LMIC, international collaborations, educational developments |
| Description | Monitoring the gut microbiome via AI and omics: a new approach to detect infection and AMR and to support novel therapeutics in broiler precision farm, BBSRC |
| Amount | £997,288 (GBP) |
| Funding ID | BB/X017370/1 |
| Organisation | University of Nottingham |
| Sector | Academic/University |
| Country | United Kingdom |
| Start | 06/2023 |
| End | 05/2026 |
| Title | Development of an innovative software powered by machine learning for better solutions for surveillance, diagnostics and treatment selection applied to livestock farming |
| Description | We are developing a real-time predictive method that is able to predict know and unknown genes and/or mutations underlying antimicrobial resistance circulating in the farm. The development of the method started earlier however it is from this award that we are developing the capability to correlate microbiome to co-infections and AMR and not just to single infections and AMR. The method relies on machine learning large-scale data mining to unravel the network of possible interactions amongst observable variables I the farm (e.g usage of antibiotics, temperature and humidity), following broilers along their life cycle, and capturing episodes of infection, treatment and development of single or multi-drug resistance. The future application of the method via a cloud system can provide hints at the selection of observable variables acting as biomarkers, i.e, targetable by future solutions for real-time livestock monitoring, to detect/forecast infection or the presence/insurgence of resistant traits, and to support precision diagnostics and bespoke treatment selection. The results may also suggest routes to improve the birds gut microbiome, for example via feed additives, making it more robust to infection while at the same time inhibiting the development of resistance. Our solution may have important implications to fight AMR. The method starts from whole genome sequence of a specific pathogen or multiple. 2) Use bioinformatics to retrieve from the genome all the core genes, regulatory regions, mutations (single nucleotide polymorphisms - SNPs), mobile genetic elements and also accessory genes which may have been acquired from other pathogens (via horizontal gene transfer). This step results in a large number of interesting candidates (genetic elements) for further investigation. This set may or may not contain previously annotated genes. We select a specific phenotype, that is the resistance/susceptibility profiles to a specific or multiple antibiotic treatments. 4) we use machine learning (ML) to build and train a predictor of the phenotype (e.g. resistance/susceptibility), using as input all the genetic elements found at step 2. Once the trained ML predictor has learned to predict the phenotype well, then it is capable of telling us which genetic elements (regulatory regions, mutations, accessory genes, etc.), alone or in combination, had the bigger influence in determining the correct result, hence exhibiting the highest correlation to the phenotype. The final set of ranked genetic elements returned by ML is typically a reasonably-sized set, which may contain previously annotated genes, but will also contain entirely new ones, mutations, etc. which would have been ignored in more conventional methods. In addition we can: 5) we can repeat the entire procedure, starting from the same pathogen, but considering a new biological sample collected at a different time point, or collected in different conditions (e.g. healthy vs infected, treated vs untreated, etc.) and see whether the method results in different sets of identified genetic elements. This allows us to study temporal modifications as well as condition-driven modifications. 6) to further extend the investigation, we can also perform metagenomics on the same sample used to run our analysis on a given pathogen, thus retrieving types and abundances of the entire microbial population (in this case, the gut microbiome). This paves the way to other types of correlative analyses with the results of pipeline 1-5: what was the microbiome like, when resistance was found in a specific pathogen? How the types and abundances of the entire microbial population changed between time points, treatments, environments? And how these changes affected the resistance/susceptible profiles? 7) The last step is to add any other information acquired contextually to each biological sample. E.g., information from other microbiomes in the surroundings (e.g. soil, water, etc.), environmental sensor data, bird behaviour data, farm management data, etc. As the correlative infrastructure is already established, we can include all the other information incrementally, and again use ML to search for correlations which go beyond the genome-phenotype system, thus achieving an unprecedented depth and breadth of observation. |
| Type Of Material | Improvements to research infrastructure |
| Year Produced | 2023 |
| Provided To Others? | No |
| Impact | The future application of the method via a cloud system can provide hints at the selection of observable variables acting as biomarkers, i.e, targetable by future solutions for real-time livestock monitoring, to detect/forecast infection or the presence/insurgence of resistant traits, and to support precision diagnostics and bespoke treatment selection. The results may also suggest routes to improve the birds gut microbiome, for example via feed additives, making it more robust to infection while at the same time inhibiting the development of resistance |
| Title | Development of an innovative method and software powered by machine learning for better solutions for surveillance, diagnostics and treatment selection applied to livestock farming |
| Description | We are developing a real-time predictive method that is able to predict know and unknown genes and/or mutations underlying antimicrobial resistance circulating in the farm. The development of the method started earlier however it is from this award that we are developing the capability to correlate microbiome to co-infections and AMR and not just to single infections and AMR. The method relies on machine learning large-scale data mining to unravel the network of possible interactions amongst observable variables I the farm (e.g usage of antibiotics, temperature and humidity), following broilers along their life cycle, and capturing episodes of infection, treatment and development of single or multi-drug resistance. The future application of the method via a cloud system can provide hints at the selection of observable variables acting as biomarkers, i.e, targetable by future solutions for real-time livestock monitoring, to detect/forecast infection or the presence/insurgence of resistant traits, and to support precision diagnostics and bespoke treatment selection. The results may also suggest routes to improve the birds gut microbiome, for example via feed additives, making it more robust to infection while at the same time inhibiting the development of resistance. Our solution may have important implications to fight AMR. The method starts from from whole genome sequence of a specific pathogen or multiple. 2) Use bioinformatics to retrieve from the genome all the core genes, regulatory regions, mutations (single nucleotide polymorphisms - SNPs), mobile genetic elements and also accessory genes which may have been acquired from other pathogens (via horizontal gene transfer). This step results in a large number of interesting candidates (genetic elements) for further investigation. This set may or may not contain previously annotated genes. We select a specific phenotype, that is the resistance/susceptibility profiles to a specific or multiple antibiotic treatments. 4) we use machine learning (ML) to build and train a predictor of the phenotype (e.g. resistance/susceptibility), using as input all the genetic elements found at step 2. Once the trained ML predictor has learned to predict the phenotype well, then it is capable of telling us which genetic elements (regulatory regions, mutations, accessory genes, etc.), alone or in combination, had the bigger influence in determining the correct result, hence exhibiting the highest correlation to the phenotype. The final set of ranked genetic elements returned by ML is typically a reasonably-sized set, which may contain previously annotated genes, but will also contain entirely new ones, mutations, etc. which would have been ignored in more conventional methods. In addition we can: 5) we can repeat the entire procedure, starting from the same pathogen, but considering a new biological sample collected at a different time point, or collected in different conditions (e.g. healthy vs infected, treated vs untreated, etc.) and see whether the method results in different sets of identified genetic elements. This allows us to study temporal modifications as well as condition-driven modifications. 6) to further extend the investigation, we can also perform metagenomics on the same sample used to run our analysis on a given pathogen, thus retrieving types and abundances of the entire microbial population (in this case, the gut microbiome). This paves the way to other types of correlative analyses with the results of pipeline 1-5: what was the microbiome like, when resistance was found in a specific pathogen? How the types and abundances of the entire microbial population changed between time points, treatments, environments? And how these changes affected the resistance/susceptible profiles? 7) The last step is to add any other information acquired contextually to each biological sample. E.g., information from other microbiomes in the surroundings (e.g. soil, water, etc.), environmental sensor data, bird behaviour data, farm management data, etc. As the correlative infrastructure is already established, we can include all the other information incrementally, and again use ML to search for correlations which go beyond the genome-phenotype system, thus achieving an unprecedented depth and breadth of observation. |
| Type Of Material | Computer model/algorithm |
| Year Produced | 2023 |
| Provided To Others? | No |
| Impact | Reduce usage of antibiotics Perform in real-time prediction of known and novel AMR bacterial variants Detect/forecast infection or the presence/insurgence of resistant traits, and to support precision diagnostics and bespoke treatment selection. Provide simple guidelines to farmers on the most adequate antimicrobial treatment |
| Description | Multi-sector Endemic livestock disease consortium and partnership. |
| Organisation | AB Agri Ltd |
| Country | United Kingdom |
| Sector | Private |
| PI Contribution | Thanks to this award, I was able to form a partnership featuring a unique combination of skills and expertise. The multi-lateral partnership that are directly linked to the award include the one established among: Univ. of Nottingham (UoN, PI: Dottorini); Univ. of Cardiff (UoC); Innovation for Agriculture (IfA); AB Agri Ltd; Agri-food and Biosciences Institute (AFBI), veterinary practitioner (Parker), a poultry integrator and two farms selected by AB Agri. For this specific award and partnership I have set up the consortium, assembled the partners and developed the ideas behind the project design and execution and wrote the entire proposal. For the award I have brought my research experience, management and leadership. I have significant, previous research experience in precision poultry and dairy farming, having developed machine learning solutions for monitoring and diagnostics of infection and antimicrobial resistance. This project capitalises upon work conducted by me and my team in developing computational pipelines that use machine learning (ML) technology to mine correlations between next generation sequencing data, environmental sensing and microbiological analysis, to investigate infection, antimicrobial resistance and transmission between animals, food and the environment. In contexts other than farming, relevant recent results include the identification of novel SCCmec type and variants of methicillin-resistant Staphylococcus aureus (MRSA) shared between food and humans in China (Wang W et al., 2022,J. Antimicrob. Chemother), uncovering a large number of new ARGs which would not have been visible with conventional methods, and the discovery of many new shared ARGs in animal and human isolates from multiple countries, targeting resistance profiles of E. coli to a panel of 12 antibiotics (Pearcy et al., 2022, mSystems ). Of specific interest for this partnership and award are our recent results for livestock farming. In a preliminary study covering one broiler farm in China, we uncovered new ARGs and hot-spots of infections shared by birds and farm environments, using ML methods to mine WGS data from highly resistant pathogenic and non-pathogenic E. coli in broiler guts and farm sources (Peng, et al 2022, Plos Comp Biol). A further expansion of the ML-powered method to data collected from 11 farms across 3 Chinese provinces, targeting hundreds of highly-drug resistant E. coli and S. enteriditis confirmed the robustness of the methods and their capacity in detecting shared ARGs between bacteria co-infecting the same animals (preliminary unpublished data). In Maciel-Guerra, et al. (Maciel-Guerra et al, 2023, ISME J) we proposed an evolution of the ML method to allow the analysis of the gut microbiome, specifically the resistome. The method included also the possibility to search for correlations with environmental sensing data. By analysing one Chinese broiler farm, we identified several clinically relevant shared ARGs and associated mobile genetic elements (bird, farm soil, abattoir surfaces and meat). Importantly, we demonstrated the existence of a core chicken gut resistome that correlates with AMR profiles of E. coli isolates (an important indicator species of AMR in the farm) taken from the same gut samples. The core resistome is itself correlated with temperature and humidity opening novel pathways for surveillance. We have recently further evolved the ML method to incorporate relative abundances of the microbial population in addition to the gut resistome. Large-scale application of the new method to 11 farms in 3 Chinese provinces (460 metagenomic samples collected in 3 years) has led to results confirming previously identified correlations and to unravelling new ones (https://assets.researchsquare.com/files/rs-2458989/v1/46849cd8-3388-46ea-8978-4627d10e99fa.pdf?c=1673626633, under review). All these works have been used to build this award and partnerships. In addition thanks to this award I was bale to set up an additional multi-lateral partnership including a larger consortium. The team expands on the existing core generated by this award with the aim to tackle endemic livestock diseases at a larger scale and include: Hook2sisters (H2S), AB Agri (ABA), Slate Hall Veterinary Services (SH), Greengage (GG), Oxford Nanopore Technology (ONT). In addition the PI of this award Dottorini also included additional colleagues from the University of Nottingham: M.Loose (ML), DH.Kim (DHK)and M.Baker (MB) is a UoN research fellow with expertise in bioinformatics working on poultry infectious diseases. |
| Collaborator Contribution | Regarding the multi-lateral partnership that are directly linked to the award it includes the: Univ. of Nottingham (UoN, PI: Dottorini); Univ. of Cardiff (UoC); Innovation for Agriculture (IfA); AB Agri Ltd; Agri-food and Biosciences Institute (AFBI, Adenuga Adewale), veterinary practitioner (Daniel Parker), a poultry integrator and two farms selected by AB Agri Ltd, Sophie Prentice). AB Agri Ltd is leader in the agri food industry and provides access to the farm and to sensor data; the University of Cardiff (UoC, Nieek Buurma) is active in the Feasibility study on the future realisation of biosensors based on identified biomarkers obtained via machine learning; AFBI provides an assessment of cost-effectiveness, in particular will provide technical and economical feasibility studies to embed biomarkers in solutions for monitoring; Slate Hall Service(Daniel Parker) has expertise in veterinary medicine and co-designed the collection protocols and help in the analysis of the results; IfA: a consortium of English agricultural societies that provides experience and infrastructure for dissemination to farmers and farmers. In addition a larger multi-lateral partnership has resulted from this award. The team expands on the existing core generated by this award with the aim to tackle endemic livestock diseases at a larger scale and include: Hook2sisters (H2S), one the of the five largest poultry producers, growing 6 million chickens a week from their regional sites in Oxfordshire, Yorkshire, Lincolnshire, East Anglia, Devon, Scotland and Wales, will provide farms and oversee their operation during the data collection periods; AB Agri (ABA), the largest UK animal feed, nutrition and agri-food technology producer, will provide consultancy and additional farms if needed (risk mitigation). Slate Hall Veterinary Services (SH), specialist in health and welfare for poultry, will oversee sample collection and monitor poultry health and therapeutics. Greengage (GG), leading provider of sensors for monitoring poultry environment (temperature, humidity, light, CO2, CO, NH3, etc.) and behaviour (vocalization, clustering, mobility), will install and run all the on-farm sensors; Oxford Nanopore Technology (ONT), developer of a new generation of DNA portable sensing technology, will oversee on-field DNA sequencing. In addition to the PI of the award Dottorini also included additional colleagues from the University of Nottingham: M.Loose (ML) is the Director of DeepSeq, the next-generation sequencing facility at UoN, with DH.Kim (DHK) is the Director of the UoN Centre for Analytical Bioscience, and M.Baker (MB) is a UoN research fellow with expertise in bioinformatics working on poultry infectious diseases. |
| Impact | This is a multidisciplinary collaborations embracing bioinformatics, machine learning, veterinary medicine, biosensor technology , microbiology, farm practice and management, smart sensing Outcome: Workshops across all partners including farmers to address challenges and design the next cutting-edge research to tackle livestock endemic diseases Generation of a large multi-disciplinary consortium to address further research questions Application for further funding |
| Start Year | 2022 |
| Description | Multi-sector Endemic livestock disease consortium and partnership. |
| Organisation | Agri-Food and Biosciences Institute |
| Country | United Kingdom |
| Sector | Public |
| PI Contribution | Thanks to this award, I was able to form a partnership featuring a unique combination of skills and expertise. The multi-lateral partnership that are directly linked to the award include the one established among: Univ. of Nottingham (UoN, PI: Dottorini); Univ. of Cardiff (UoC); Innovation for Agriculture (IfA); AB Agri Ltd; Agri-food and Biosciences Institute (AFBI), veterinary practitioner (Parker), a poultry integrator and two farms selected by AB Agri. For this specific award and partnership I have set up the consortium, assembled the partners and developed the ideas behind the project design and execution and wrote the entire proposal. For the award I have brought my research experience, management and leadership. I have significant, previous research experience in precision poultry and dairy farming, having developed machine learning solutions for monitoring and diagnostics of infection and antimicrobial resistance. This project capitalises upon work conducted by me and my team in developing computational pipelines that use machine learning (ML) technology to mine correlations between next generation sequencing data, environmental sensing and microbiological analysis, to investigate infection, antimicrobial resistance and transmission between animals, food and the environment. In contexts other than farming, relevant recent results include the identification of novel SCCmec type and variants of methicillin-resistant Staphylococcus aureus (MRSA) shared between food and humans in China (Wang W et al., 2022,J. Antimicrob. Chemother), uncovering a large number of new ARGs which would not have been visible with conventional methods, and the discovery of many new shared ARGs in animal and human isolates from multiple countries, targeting resistance profiles of E. coli to a panel of 12 antibiotics (Pearcy et al., 2022, mSystems ). Of specific interest for this partnership and award are our recent results for livestock farming. In a preliminary study covering one broiler farm in China, we uncovered new ARGs and hot-spots of infections shared by birds and farm environments, using ML methods to mine WGS data from highly resistant pathogenic and non-pathogenic E. coli in broiler guts and farm sources (Peng, et al 2022, Plos Comp Biol). A further expansion of the ML-powered method to data collected from 11 farms across 3 Chinese provinces, targeting hundreds of highly-drug resistant E. coli and S. enteriditis confirmed the robustness of the methods and their capacity in detecting shared ARGs between bacteria co-infecting the same animals (preliminary unpublished data). In Maciel-Guerra, et al. (Maciel-Guerra et al, 2023, ISME J) we proposed an evolution of the ML method to allow the analysis of the gut microbiome, specifically the resistome. The method included also the possibility to search for correlations with environmental sensing data. By analysing one Chinese broiler farm, we identified several clinically relevant shared ARGs and associated mobile genetic elements (bird, farm soil, abattoir surfaces and meat). Importantly, we demonstrated the existence of a core chicken gut resistome that correlates with AMR profiles of E. coli isolates (an important indicator species of AMR in the farm) taken from the same gut samples. The core resistome is itself correlated with temperature and humidity opening novel pathways for surveillance. We have recently further evolved the ML method to incorporate relative abundances of the microbial population in addition to the gut resistome. Large-scale application of the new method to 11 farms in 3 Chinese provinces (460 metagenomic samples collected in 3 years) has led to results confirming previously identified correlations and to unravelling new ones (https://assets.researchsquare.com/files/rs-2458989/v1/46849cd8-3388-46ea-8978-4627d10e99fa.pdf?c=1673626633, under review). All these works have been used to build this award and partnerships. In addition thanks to this award I was bale to set up an additional multi-lateral partnership including a larger consortium. The team expands on the existing core generated by this award with the aim to tackle endemic livestock diseases at a larger scale and include: Hook2sisters (H2S), AB Agri (ABA), Slate Hall Veterinary Services (SH), Greengage (GG), Oxford Nanopore Technology (ONT). In addition the PI of this award Dottorini also included additional colleagues from the University of Nottingham: M.Loose (ML), DH.Kim (DHK)and M.Baker (MB) is a UoN research fellow with expertise in bioinformatics working on poultry infectious diseases. |
| Collaborator Contribution | Regarding the multi-lateral partnership that are directly linked to the award it includes the: Univ. of Nottingham (UoN, PI: Dottorini); Univ. of Cardiff (UoC); Innovation for Agriculture (IfA); AB Agri Ltd; Agri-food and Biosciences Institute (AFBI, Adenuga Adewale), veterinary practitioner (Daniel Parker), a poultry integrator and two farms selected by AB Agri Ltd, Sophie Prentice). AB Agri Ltd is leader in the agri food industry and provides access to the farm and to sensor data; the University of Cardiff (UoC, Nieek Buurma) is active in the Feasibility study on the future realisation of biosensors based on identified biomarkers obtained via machine learning; AFBI provides an assessment of cost-effectiveness, in particular will provide technical and economical feasibility studies to embed biomarkers in solutions for monitoring; Slate Hall Service(Daniel Parker) has expertise in veterinary medicine and co-designed the collection protocols and help in the analysis of the results; IfA: a consortium of English agricultural societies that provides experience and infrastructure for dissemination to farmers and farmers. In addition a larger multi-lateral partnership has resulted from this award. The team expands on the existing core generated by this award with the aim to tackle endemic livestock diseases at a larger scale and include: Hook2sisters (H2S), one the of the five largest poultry producers, growing 6 million chickens a week from their regional sites in Oxfordshire, Yorkshire, Lincolnshire, East Anglia, Devon, Scotland and Wales, will provide farms and oversee their operation during the data collection periods; AB Agri (ABA), the largest UK animal feed, nutrition and agri-food technology producer, will provide consultancy and additional farms if needed (risk mitigation). Slate Hall Veterinary Services (SH), specialist in health and welfare for poultry, will oversee sample collection and monitor poultry health and therapeutics. Greengage (GG), leading provider of sensors for monitoring poultry environment (temperature, humidity, light, CO2, CO, NH3, etc.) and behaviour (vocalization, clustering, mobility), will install and run all the on-farm sensors; Oxford Nanopore Technology (ONT), developer of a new generation of DNA portable sensing technology, will oversee on-field DNA sequencing. In addition to the PI of the award Dottorini also included additional colleagues from the University of Nottingham: M.Loose (ML) is the Director of DeepSeq, the next-generation sequencing facility at UoN, with DH.Kim (DHK) is the Director of the UoN Centre for Analytical Bioscience, and M.Baker (MB) is a UoN research fellow with expertise in bioinformatics working on poultry infectious diseases. |
| Impact | This is a multidisciplinary collaborations embracing bioinformatics, machine learning, veterinary medicine, biosensor technology , microbiology, farm practice and management, smart sensing Outcome: Workshops across all partners including farmers to address challenges and design the next cutting-edge research to tackle livestock endemic diseases Generation of a large multi-disciplinary consortium to address further research questions Application for further funding |
| Start Year | 2022 |
| Description | Multi-sector Endemic livestock disease consortium and partnership. |
| Organisation | Cardiff University |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Thanks to this award, I was able to form a partnership featuring a unique combination of skills and expertise. The multi-lateral partnership that are directly linked to the award include the one established among: Univ. of Nottingham (UoN, PI: Dottorini); Univ. of Cardiff (UoC); Innovation for Agriculture (IfA); AB Agri Ltd; Agri-food and Biosciences Institute (AFBI), veterinary practitioner (Parker), a poultry integrator and two farms selected by AB Agri. For this specific award and partnership I have set up the consortium, assembled the partners and developed the ideas behind the project design and execution and wrote the entire proposal. For the award I have brought my research experience, management and leadership. I have significant, previous research experience in precision poultry and dairy farming, having developed machine learning solutions for monitoring and diagnostics of infection and antimicrobial resistance. This project capitalises upon work conducted by me and my team in developing computational pipelines that use machine learning (ML) technology to mine correlations between next generation sequencing data, environmental sensing and microbiological analysis, to investigate infection, antimicrobial resistance and transmission between animals, food and the environment. In contexts other than farming, relevant recent results include the identification of novel SCCmec type and variants of methicillin-resistant Staphylococcus aureus (MRSA) shared between food and humans in China (Wang W et al., 2022,J. Antimicrob. Chemother), uncovering a large number of new ARGs which would not have been visible with conventional methods, and the discovery of many new shared ARGs in animal and human isolates from multiple countries, targeting resistance profiles of E. coli to a panel of 12 antibiotics (Pearcy et al., 2022, mSystems ). Of specific interest for this partnership and award are our recent results for livestock farming. In a preliminary study covering one broiler farm in China, we uncovered new ARGs and hot-spots of infections shared by birds and farm environments, using ML methods to mine WGS data from highly resistant pathogenic and non-pathogenic E. coli in broiler guts and farm sources (Peng, et al 2022, Plos Comp Biol). A further expansion of the ML-powered method to data collected from 11 farms across 3 Chinese provinces, targeting hundreds of highly-drug resistant E. coli and S. enteriditis confirmed the robustness of the methods and their capacity in detecting shared ARGs between bacteria co-infecting the same animals (preliminary unpublished data). In Maciel-Guerra, et al. (Maciel-Guerra et al, 2023, ISME J) we proposed an evolution of the ML method to allow the analysis of the gut microbiome, specifically the resistome. The method included also the possibility to search for correlations with environmental sensing data. By analysing one Chinese broiler farm, we identified several clinically relevant shared ARGs and associated mobile genetic elements (bird, farm soil, abattoir surfaces and meat). Importantly, we demonstrated the existence of a core chicken gut resistome that correlates with AMR profiles of E. coli isolates (an important indicator species of AMR in the farm) taken from the same gut samples. The core resistome is itself correlated with temperature and humidity opening novel pathways for surveillance. We have recently further evolved the ML method to incorporate relative abundances of the microbial population in addition to the gut resistome. Large-scale application of the new method to 11 farms in 3 Chinese provinces (460 metagenomic samples collected in 3 years) has led to results confirming previously identified correlations and to unravelling new ones (https://assets.researchsquare.com/files/rs-2458989/v1/46849cd8-3388-46ea-8978-4627d10e99fa.pdf?c=1673626633, under review). All these works have been used to build this award and partnerships. In addition thanks to this award I was bale to set up an additional multi-lateral partnership including a larger consortium. The team expands on the existing core generated by this award with the aim to tackle endemic livestock diseases at a larger scale and include: Hook2sisters (H2S), AB Agri (ABA), Slate Hall Veterinary Services (SH), Greengage (GG), Oxford Nanopore Technology (ONT). In addition the PI of this award Dottorini also included additional colleagues from the University of Nottingham: M.Loose (ML), DH.Kim (DHK)and M.Baker (MB) is a UoN research fellow with expertise in bioinformatics working on poultry infectious diseases. |
| Collaborator Contribution | Regarding the multi-lateral partnership that are directly linked to the award it includes the: Univ. of Nottingham (UoN, PI: Dottorini); Univ. of Cardiff (UoC); Innovation for Agriculture (IfA); AB Agri Ltd; Agri-food and Biosciences Institute (AFBI, Adenuga Adewale), veterinary practitioner (Daniel Parker), a poultry integrator and two farms selected by AB Agri Ltd, Sophie Prentice). AB Agri Ltd is leader in the agri food industry and provides access to the farm and to sensor data; the University of Cardiff (UoC, Nieek Buurma) is active in the Feasibility study on the future realisation of biosensors based on identified biomarkers obtained via machine learning; AFBI provides an assessment of cost-effectiveness, in particular will provide technical and economical feasibility studies to embed biomarkers in solutions for monitoring; Slate Hall Service(Daniel Parker) has expertise in veterinary medicine and co-designed the collection protocols and help in the analysis of the results; IfA: a consortium of English agricultural societies that provides experience and infrastructure for dissemination to farmers and farmers. In addition a larger multi-lateral partnership has resulted from this award. The team expands on the existing core generated by this award with the aim to tackle endemic livestock diseases at a larger scale and include: Hook2sisters (H2S), one the of the five largest poultry producers, growing 6 million chickens a week from their regional sites in Oxfordshire, Yorkshire, Lincolnshire, East Anglia, Devon, Scotland and Wales, will provide farms and oversee their operation during the data collection periods; AB Agri (ABA), the largest UK animal feed, nutrition and agri-food technology producer, will provide consultancy and additional farms if needed (risk mitigation). Slate Hall Veterinary Services (SH), specialist in health and welfare for poultry, will oversee sample collection and monitor poultry health and therapeutics. Greengage (GG), leading provider of sensors for monitoring poultry environment (temperature, humidity, light, CO2, CO, NH3, etc.) and behaviour (vocalization, clustering, mobility), will install and run all the on-farm sensors; Oxford Nanopore Technology (ONT), developer of a new generation of DNA portable sensing technology, will oversee on-field DNA sequencing. In addition to the PI of the award Dottorini also included additional colleagues from the University of Nottingham: M.Loose (ML) is the Director of DeepSeq, the next-generation sequencing facility at UoN, with DH.Kim (DHK) is the Director of the UoN Centre for Analytical Bioscience, and M.Baker (MB) is a UoN research fellow with expertise in bioinformatics working on poultry infectious diseases. |
| Impact | This is a multidisciplinary collaborations embracing bioinformatics, machine learning, veterinary medicine, biosensor technology , microbiology, farm practice and management, smart sensing Outcome: Workshops across all partners including farmers to address challenges and design the next cutting-edge research to tackle livestock endemic diseases Generation of a large multi-disciplinary consortium to address further research questions Application for further funding |
| Start Year | 2022 |
| Description | Elizabeth Doughman, WATT Global Media, Poultry Future Interview |
| Form Of Engagement Activity | A magazine, newsletter or online publication |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Industry/Business |
| Results and Impact | An Interview about the innovative research done to fight AMR |
| Year(s) Of Engagement Activity | 2023 |
| URL | https://www.wattpoultryusa-digital.com/wattpoultryusa/february_2023/MobilePagedReplica.action?pm=2&f... |
| Description | Workshop with farmers |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Study participants or study members |
| Results and Impact | Disseminate knowledge, expertise and technology across the different partners targeting industry, farms and academic dialogue |
| Year(s) Of Engagement Activity | 2023 |