Abstract
Background
The search for alternatives to antibiotic growth promoters in poultry production has increased interest in probiotics. However, the complexity of the interactions between probiotics, gut microbiome, and the host hinders the development of effective probiotic interventions. This study explores metabolic modelling to examine the possibility of designing informed probiotic interventions within poultry production.
Results
Genomic metabolic models of fungi were generated and simulated in the context of poultry gut microbial communities. The modelling approach correlated with short-chain fatty acid production, particularly in the caecum. Introducing fungi to poultry microbiomes resulted in strain-specific and diet-dependent effects on the gut microbiome. The impact of fungal probiotics on microbiome diversity and pathogen inhibition varied depending on the specific strain, resident microbiome composition, and host diet. This context-dependency highlights the need for tailored probiotic interventions that consider the unique characteristics of each poultry production environment.
Conclusions
This study demonstrates the potential of metabolic modelling to elucidate the complex interactions between probiotics, the gut microbiome, and diet in poultry. While the effects of specific fungal strains were found to be context-dependent, the approach itself provides a valuable tool for designing targeted probiotic interventions. By considering the specific characteristics of the host microbiome and dietary factors, this methodology could guide the deployment of effective probiotics in poultry production. However, the current work relies on computational predictions, and further in vivo validation studies are needed to confirm the efficacy of the identified probiotic candidates. Nonetheless, this study represents a significant step in using metabolic models to inform probiotic interventions in the poultry industry.
The search for alternatives to antibiotic growth promoters in poultry production has increased interest in probiotics. However, the complexity of the interactions between probiotics, gut microbiome, and the host hinders the development of effective probiotic interventions. This study explores metabolic modelling to examine the possibility of designing informed probiotic interventions within poultry production.
Results
Genomic metabolic models of fungi were generated and simulated in the context of poultry gut microbial communities. The modelling approach correlated with short-chain fatty acid production, particularly in the caecum. Introducing fungi to poultry microbiomes resulted in strain-specific and diet-dependent effects on the gut microbiome. The impact of fungal probiotics on microbiome diversity and pathogen inhibition varied depending on the specific strain, resident microbiome composition, and host diet. This context-dependency highlights the need for tailored probiotic interventions that consider the unique characteristics of each poultry production environment.
Conclusions
This study demonstrates the potential of metabolic modelling to elucidate the complex interactions between probiotics, the gut microbiome, and diet in poultry. While the effects of specific fungal strains were found to be context-dependent, the approach itself provides a valuable tool for designing targeted probiotic interventions. By considering the specific characteristics of the host microbiome and dietary factors, this methodology could guide the deployment of effective probiotics in poultry production. However, the current work relies on computational predictions, and further in vivo validation studies are needed to confirm the efficacy of the identified probiotic candidates. Nonetheless, this study represents a significant step in using metabolic models to inform probiotic interventions in the poultry industry.
| Original language | English |
|---|---|
| Article number | 267 |
| Journal | Microbiome |
| Early online date | 20 Dec 2024 |
| DOIs | |
| Publication status | Published - 20 Dec 2024 |
Data Availability Statement
All Data and scripts are available in the GitHub or in the supplementary files.Acknowledgements
We would also like to thank Sandra Castillo for her help and advice while using CarveFungi and Paula Jouhten for her valuable advice and support.Funding
This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) (219012924), AB Vista and funding from the Evolution Education Trust.