Review: Towards the next-generation models of the rumen microbiome for enhancing predictive power and guiding sustainable production strategies

Muñoz-Tamayo, R.; Davoudkhani, M.; Fakih, I.; Robles-Rodriguez, C.E.; Rubino, F.; Creevey, C.J.; Forano, E.

doi:10.1016/j.animal.2023.100984

Cited by 6 publications

(4 citation statements)

References 72 publications

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“…The inclusion of all the IPs in the interactions impacting variation suggests that the model should be improved to better characterize the interactions. The incorporation of microbial genomic knowledge is expected to improve the representation of rumen microbial fermentation in mathematical models (Davoudkhani et al, 2024; Muñoz-Tamayo et al, 2023). The first feed distribution of the low AT treatment showed that the variation of can also be only impacted by the interactions between IPs, highlighting the importance of quantifying the interactions between IPs in SA approaches used.…”

Section: Discussionmentioning

confidence: 99%

“…The inclusion of all the IPs in the interactions impacting 𝑞 CH 4 ,g,out variation suggests that the model should be improved to better characterize the interactions. The incorporation of microbial genomic knowledge is expected to improve the representation of rumen microbial fermentation in mathematical models (Davoudkhani et al, 2024;Muñoz-Tamayo et al, 2023).…”

Section: Rate Of Methane Productionmentioning

confidence: 99%

See 1 more Smart Citation

Dynamic sensitivity analysis of a mathematical model describing the effect of the macroalgaeAsparagopsis taxiformison rumen fermentation and methane production underin vitrocontinuous conditions

Blondiaux,

Kiessé,

Eugène

et al. 2024

Preprint

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Ruminants plays an important role in global warming by emitting enteric methane (CH4) through the degradation of feeds by the rumen microbiota. To better understand the dynamics fermentation outputs, including methane and volatile fatty acids (VFA) production, mathematical models have been developed. Sensitivity analysis (SA) methods quantify the contribution of model input parameters (IP) to the variation of an output variable of interest. In animal science, SA are usually conducted in static condition. In this work, we hypothesized that including the dynamic aspect of the rumen fermentation to SA can be useful to inform on optimal experimental conditions aimed at quantifying the key mechanisms driving CH4and VFA production. Accordingly, the objective of this work was to conduct a dynamic SA of a rumen fermentation model underin vitrocontinuous conditions (close to the realin vivoconditions). Our model case study integrates the effect of the macroalgaeAsparagopsis taxiformis(AT) on the fermentation. AT has been identified as a potent CH4inhibitor via the presence of bromoform, an anti-methanogenic compound. We implemented two SA methods. We computed Shapley effects and full and independent Sobol indices over time for quantifying the contribution of 16 IPs to CH4(mol/h) and VFA (mol/l) variation. Our approach allows to discriminate the 3 contribution types of an IP to output variable variation (individual, via the interactions and via the dependence/correlation). We studied three diet scenarios accounting for several doses of AT relative to Dry Matter (DM): control (0% DM of AT), low treatment (LT: 0.25% DM of AT) and high treatment (HT: 0.50% DM of AT). Shapley effects revealed that hydrogen (H2) utilizers microbial group via its Monod H2affinity constant highly contributed (> 50%) to CH4variation with a constant dynamic over time for control and LT. A shift on the impact of microbial pathways driving CH4variation was revealed for HT. IPs associated with the kinetic of bromoform utilization and with the factor modeling the direct effect of bromoform on methanogenesis were identified as influential on CH4variation in the middle of fermentation. Whereas, VFA variation for the 3 diet scenarios was mainly explained by the kinetic of fibers degradation, showing a high constant contribution (> 30%) over time. In addition, the Sobol indices indicated that interactions between IPs played a role on CH4variation, which was not the case of VFA variation. However, these results are dependent on the way interactions are represented in the model. The simulations computed for the SA were also used to analyze prediction uncertainty. It was related to the dynamic of dry matter intake (DMI, g/h), increasing during the high intake activity periods and decreasing when the intake activity was low. Moreover, CH4(mol/h) simulations showed a larger variability than VFA simulations, suggesting that the reduction of the uncertainty of IPs describing the activity of the H2utilizers microbial group is a promising lead to reduce the overall model uncertainty. Our results highlighted the dynamic nature of the influence of metabolic pathways on CH4productions under an anti-methanogenic treatment. SA tools can be further exploited to design optimal experiments studying rumen fermentation and CH4mitigation strategies. These optimal experiments would be useful to build robust models that can guide the development of sustainable nutrition strategies.

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Section: Discussionmentioning

confidence: 99%

Section: Rate Of Methane Productionmentioning

confidence: 99%

Dynamic sensitivity analysis of a mathematical model describing the effect of the macroalgaeAsparagopsis taxiformison rumen fermentation and methane production underin vitrocontinuous conditions

Blondiaux,

Kiessé,

Eugène

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, none of these models integrate microbial genomic knowledge and thus do not capitalize on the rich information that microbial genomic sequencing provides. Integration of dynamic modelling and microbial data has the potential to improve the understanding of the rumen ecosystem, to enhance predictive power of rumen models and to help the design of microbial manipulation strategies to improve rumen function [17]. Recently, some studies have applied the genomescale metabolic approach to reconstruct metabolic networks of rumen microbes species [18][19][20] and to predict the metabolism of minimal rumen microbial consortium [21].…”

Section: Introductionmentioning

confidence: 99%

Integrating microbial abundance time series with fermentation dynamics of the rumen microbiome via mathematical modelling

Davoudkhani,

Rubino,

Creevey

et al. 2024

PLoS ONE

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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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“…However, none of these models integrate microbial genomic knowledge and thus do not capitalize on the rich information that microbial genomic sequencing provides. Integration of dynamic modelling and microbial data has the potential to improve the understanding of the rumen ecosystem, to enhance predictive power of rumen models and to help the design of microbial manipulation strategies to improve rumen function (Muñoz-Tamayo et al, 2023). Recently, some studies have applied the genome-scale metabolic approach to reconstruct metabolic networks of rumen microbes species (Fakih et al, 2023; Lee et al, 2020; Pereira et al, 2018) and to predict the metabolism of minimal rumen microbial consortium (Islam et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Integrating microbial abundance time series with fermentation dynamics of the rumen microbiomeviamathematical modelling

Davoudkhani,

Rubino,

Creevey

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

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 anin vitroRUSITEC experiment and from anin vivoexperiment with four cows. The model performance was evaluated by the coefficient of variation of the root mean square error (CRMSE). For thein vitrocase study, the mean CVRMSE were 9.8% for acetate, 14% for butyrate and 14.5% for propionate. For thein vivocase 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.

show abstract

Review: Towards the next-generation models of the rumen microbiome for enhancing predictive power and guiding sustainable production strategies

Cited by 6 publications

References 72 publications

Dynamic sensitivity analysis of a mathematical model describing the effect of the macroalgaeAsparagopsis taxiformison rumen fermentation and methane production underin vitrocontinuous conditions

Dynamic sensitivity analysis of a mathematical model describing the effect of the macroalgaeAsparagopsis taxiformison rumen fermentation and methane production underin vitrocontinuous conditions

Integrating microbial abundance time series with fermentation dynamics of the rumen microbiome via mathematical modelling

Integrating microbial abundance time series with fermentation dynamics of the rumen microbiomeviamathematical modelling

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