Predicting Foodborne Pathogens and Probiotics Taxa within poultry-related microbiomes using a machine learning approach

Authors: AYOOLA, MOSES - Mississippi State University; PILLAI, NISHA - Mississippi State University; NANDURI, BINDU - Mississippi State University; Rothrock, Michael; RAMKUMAR, MAHALINGAM - Mississippi State University

Submitted to: Animal Microbiome
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/23/2023
Publication Date: 11/15/2023
Citation: Ayoola, M., Pillai, N., Nanduri, B., Rothrock Jr, M.J., Ramkumar, M. 2023. Predicting Foodborne Pathogens and Probiotics Taxa within poultry-related microbiomes using a machine learning approach. Animal Microbiome. https://doi.org/10.1186/s42523-023-00260-w.
DOI: https://doi.org/10.1186/s42523-023-00260-w

Interpretive Summary: Background Microbiomes that can serve as an indicator of gut, intestinal, and general health of humans and animals are largely in''uenced by food consumed and contaminant bioagents. Microbiome studies usually focus on estimating the alpha (within sample) and beta (similarity/dissimilarity among samples) diversities. This study took a combinatorial approach and applied machine learning to microbiome data to predict the presence of disease-causing pathogens and their association with known/potential probiotic taxa. Here, 16S rRNA gene high-throughput Illumina sequencing of temporal pre-harvest (feces, soil) samples of 41 pastured poultry ''ocks from southeastern U.S. farms were used to generate the relative abundance of operational taxonomic units (OTUs) as machine learning input. Unique genera from the OTUs were used as predictors of the prevalence of foodborne pathogens (Salmonella, Campylobacter, and Listeria) at different stages of poultry growth (START (2–4 weeks old), MID (5–7 weeks old), END (8–11 weeks old)), association with farm management practices and physicochemical properties. Result While we did not see any signi''cant associations between known probiotics and Salmonella or Listeria, we observed signi''cant negative correlations between known probiotics (Bacillus and Clostridium) and Campylobacter at mid-timepoint of sample collection. Our data indicates a negative correlation between potential probiotics and Campylobacter at both early and end-timepoint of sample collection. Furthermore, our model prediction shows that changes in farm operations such as how often the houses are moved on pasture, age at which chickens are introduced to the pasture, diet composition, presence of other animals on the farm could favorably increase the abundance and activity of probiotics that could reduce Campylobacter prevalence. Conclusion Integration of microbiome data with farm management practices using machine learning provided insights on how to reduce Campylobacter prevalence and transmission along the farm-to-fork continuum. Altering management practices to support proliferation of bene''cial probiotics to reduce pathogen prevalence identi''ed here could constitute a complementary method to the existing but ineffective interventions such as vaccination and bacteriophage cocktails usage. Study ''ndings also corroborate the presence of bacterial genera such as Caloramator, DA101, Parabacteroides, Faecalibacterium as potential probiotics.

Technical Abstract: Background Microbiomes that can serve as an indicator of gut, intestinal, and general health of humans and animals are largely in''uenced by food consumed and contaminant bioagents. Microbiome studies usually focus on estimating the alpha (within sample) and beta (similarity/dissimilarity among samples) diversities. This study took a combinatorial approach and applied machine learning to microbiome data to predict the presence of disease-causing pathogens and their association with known/potential probiotic taxa. Here, 16S rRNA gene high-throughput Illumina sequencing of temporal pre-harvest (feces, soil) samples of 41 pastured poultry ''ocks from southeastern U.S. farms were used to generate the relative abundance of operational taxonomic units (OTUs) as machine learning input. Unique genera from the OTUs were used as predictors of the prevalence of foodborne pathogens (Salmonella, Campylobacter, and Listeria) at different stages of poultry growth (START (2–4 weeks old), MID (5–7 weeks old), END (8–11 weeks old)), association with farm management practices and physicochemical properties. Result While we did not see any signi''cant associations between known probiotics and Salmonella or Listeria, we observed signi''cant negative correlations between known probiotics (Bacillus and Clostridium) and Campylobacter at mid-timepoint of sample collection. Our data indicates a negative correlation between potential probiotics and Campylobacter at both early and end-timepoint of sample collection. Furthermore, our model prediction shows that changes in farm operations such as how often the houses are moved on pasture, age at which chickens are introduced to the pasture, diet composition, presence of other animals on the farm could favorably increase the abundance and activity of probiotics that could reduce Campylobacter prevalence. Conclusion Integration of microbiome data with farm management practices using machine learning provided insights on how to reduce Campylobacter prevalence and transmission along the farm-to-fork continuum. Altering management practices to support proliferation of bene''cial probiotics to reduce pathogen prevalence identi''ed here could constitute a complementary method to the existing but ineffective interventions such as vaccination and bacteriophage cocktails usage. Study ''ndings also corroborate the presence of bacterial genera such as Caloramator, DA101, Parabacteroides, Faecalibacterium as potential probiotics.