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Davoudkhani M, Rubino F, Creevey CJ, Ahvenjärvi S, Bayat AR, Tapio I, Belanche A, Muñoz-Tamayo R. Integrating microbial abundance time series with fermentation dynamics of the rumen microbiome via mathematical modelling. PLoS One 2024; 19:e0298930. [PMID: 38507436 PMCID: PMC10954177 DOI: 10.1371/journal.pone.0298930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 02/02/2024] [Indexed: 03/22/2024] Open
Abstract
The rumen represents a dynamic microbial ecosystem where fermentation metabolites and microbial concentrations change over time in response to dietary changes. The integration of microbial genomic knowledge and dynamic modelling can enhance our system-level understanding of rumen ecosystem's function. However, such an integration between dynamic models and rumen microbiota data is lacking. The objective of this work was to integrate rumen microbiota time series determined by 16S rRNA gene amplicon sequencing into a dynamic modelling framework to link microbial data to the dynamics of the volatile fatty acids (VFA) production during fermentation. For that, we used the theory of state observers to develop a model that estimates the dynamics of VFA from the data of microbial functional proxies associated with the specific production of each VFA. We determined the microbial proxies using CowPi to infer the functional potential of the rumen microbiota and extrapolate their functional modules from KEGG (Kyoto Encyclopedia of Genes and Genomes). The approach was challenged using data from an in vitro RUSITEC experiment and from an in vivo experiment with four cows. The model performance was evaluated by the coefficient of variation of the root mean square error (CRMSE). For the in vitro case study, the mean CVRMSE were 9.8% for acetate, 14% for butyrate and 14.5% for propionate. For the in vivo case study, the mean CVRMSE were 16.4% for acetate, 15.8% for butyrate and 19.8% for propionate. The mean CVRMSE for the VFA molar fractions were 3.1% for acetate, 3.8% for butyrate and 8.9% for propionate. Ours results show the promising application of state observers integrated with microbiota time series data for predicting rumen microbial metabolism.
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Affiliation(s)
- Mohsen Davoudkhani
- INRAE, AgroParisTech, UMR Modélisation Systémique Appliquée aux Ruminants, Université Paris-Saclay, Palaiseau, France
| | - Francesco Rubino
- Institute of Global Food Security, School of Biological Sciences, Queen’s University Belfast, Northern Ireland, United Kingdom
| | - Christopher J. Creevey
- Institute of Global Food Security, School of Biological Sciences, Queen’s University Belfast, Northern Ireland, United Kingdom
| | - Seppo Ahvenjärvi
- Animal Nutrition, Production Systems, Natural Resources Institute Finland (Luke), Jokioinen, Finland
| | - Ali R. Bayat
- Animal Nutrition, Production Systems, Natural Resources Institute Finland (Luke), Jokioinen, Finland
| | - Ilma Tapio
- Genomics and Breeding, Production Systems, Natural Resources Institute Finland (Luke), Jokioinen, Finland
| | - Alejandro Belanche
- Departamento de Producción Animal y Ciencia de los Alimentos, Universidad de Zaragoza, Zaragoza, Spain
| | - Rafael Muñoz-Tamayo
- INRAE, AgroParisTech, UMR Modélisation Systémique Appliquée aux Ruminants, Université Paris-Saclay, Palaiseau, France
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Cannas A, Cabrera VE, Dougherty HC, Ellis JL, Gallo A, Huhtanen P, Kyriazakis I, McPhee M, Reed KF, Sakomura NK, van Milgen J. Editorial: The 10th international Workshop on Modelling Nutrient Digestion and Utilization in Farm Animals (MODNUT). Animal 2023; 17 Suppl 5:101067. [PMID: 38286524 DOI: 10.1016/j.animal.2023.101067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 12/18/2023] [Indexed: 01/31/2024] Open
Affiliation(s)
- A Cannas
- Department of Agricultural Sciences, University of Sassari, Italy.
| | - V E Cabrera
- Department of Animal and Dairy Sciences, University of Wisconsin, Madison, Madison, WI, United States
| | - H C Dougherty
- Department of Animal Science, University of New England, Armidale, NSW, Australia
| | - J L Ellis
- Centre for Nutrition Modelling, Department of Animal Biosciences, University of Guelph, Ontario, Canada
| | - A Gallo
- Dipartimento di Scienze animali, della nutrizione e degli alimenti (DIANA), Facoltà di Scienze Agrarie, Alimentari e Ambientali, Università Cattolica del Sacro Cuore, Piacenza, Italy
| | - P Huhtanen
- Natural Resources Institute Finland (LUKE), Production Systems, Jokioinen, Finland
| | - I Kyriazakis
- Institute for Global Food Security, Queen's University, Belfast, United Kingdom
| | - M McPhee
- NSW Department of Primary Industries, Armidale Livestock Industries Centre, University of New England, Armidale, Australia
| | - K F Reed
- Department of Animal Science, Cornell University, Ithaca, NY, United States
| | - N K Sakomura
- Department of Animal Science, School of Agricultural and Veterinary Sciences, São Paulo State University (UNESP), Jaboticabal, São Paulo, Brazil
| | - J van Milgen
- Pegase, INRAE, Institut Agro, Le Clos, Saint Gilles 35590, France
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