Inhibited Methanogenesis in the Rumen of Cattle: Microbial Metabolism in Response to Supplemental 3-Nitrooxypropanol and Nitrate

Abstract

3-Nitrooxypropanol (3-NOP) supplementation to cattle diets mitigates enteric CH4 emissions and may also be economically beneficial at farm level. However, the wider rumen metabolic response to methanogenic inhibition by 3-NOP and the NO2- intermediary metabolite requires further exploration. Furthermore, NO3- supplementation potently decreases CH4 emissions from cattle. The reduction of NO3- utilizes H2 and yields NO2-, the latter of which may also inhibit rumen methanogens, although a different mode of action than for 3-NOP and its NO2- derivative was hypothesized. Our objective was to explore potential responses of the fermentative and methanogenic metabolism in the rumen to 3-NOP, NO3- and their metabolic derivatives using a dynamic mechanistic modeling approach. An extant mechanistic rumen fermentation model with state variables for carbohydrate substrates, bacteria and protozoa, gaseous and dissolved fermentation end products and methanogens was extended with a state variable of either 3-NOP or NO3-. Both new models were further extended with a NO2- state variable, with NO2- exerting methanogenic inhibition, although the modes of action of 3-NOP-derived and NO3--derived NO2- are different. Feed composition and intake rate (twice daily feeding regime), and supplement inclusion were used as model inputs. Model parameters were estimated to experimental data collected from the literature. The extended 3-NOP and NO3- models both predicted a marked peak in H2 emission shortly after feeding, the magnitude of which increased with higher doses of supplement inclusion. The H2 emission rate appeared positively related to decreased acetate proportions and increased propionate and butyrate proportions. A decreased CH4 emission rate was associated with 3-NOP and NO3- supplementation. Omission of the NO2- state variable from the 3-NOP model did not change the overall dynamics of H2 and CH4 emission and other metabolites. However, omitting the NO2- state variable from the NO3- model did substantially change the dynamics of H2 and CH4 emissions indicated by a decrease in both H2 and CH4 emission after feeding. Simulations do not point to a strong relationship between methanogenic inhibition and the rate of NO3- and NO2- formation upon 3-NOP supplementation, whereas the metabolic response to NO3- supplementation may largely depend on methanogenic inhibition by NO2-.

Sensitivity analysis of a mechanistic model of rumen fermentation and methane production by rumen microbiota in the presence ofAsparagopsis taxiformis

“…For a current review on how microbial genome data can be incorporated in these mathematical models, see Munoz-Tamayo et al (17). Models developed by Muñoz-Tamayo and van Lingen include the dynamics of methanogens (10)(11)(12)(13), but they do not incorporate microbial genomic data in their analysis and thus could be further improved with the addition of this data type. Studying the effects of methane inhibitors in silico can be helpful to improve understanding of their effects on rumen fermentation, and in particular the rumen microbiome.…”

“…While these are all valid approaches to understanding the effect of A. taxiformis on methane production, a complete picture of rumen fermentation and methane output necessitates the incorporation of microbial data. Nevertheless, the Muñoz-Tamayo et al (12) and van Lingen et al (11) models serve as excellent starting points for the development of improved quantitative methods in the rumen fermentation field. As was done with the Muñoz-Tamayo et al (12) and van Lingen et al (11) models, the values of rate constants utilized in these mathematical models are often measured in isolation, i.e., considering only one reaction and the involved reactants, and these experiments are conducted under varying conditions e.g., temperature.…”

“…Nevertheless, the Muñoz-Tamayo et al (12) and van Lingen et al (11) models serve as excellent starting points for the development of improved quantitative methods in the rumen fermentation field. As was done with the Muñoz-Tamayo et al (12) and van Lingen et al (11) models, the values of rate constants utilized in these mathematical models are often measured in isolation, i.e., considering only one reaction and the involved reactants, and these experiments are conducted under varying conditions e.g., temperature. Thus, there is uncertainty in parameter values and how changes in parameter values affect the system.…”

“…LSA is straightforward in its application and interpretation, while GSA can be more involved with multiple approaches. SA has been conducted on various models of rumen fermentation (8)(9)(10)(11)(20)(21)(22), but has not been conducted in a model that incorporates the effects of A. taxiformis or that incorporates microbial data. Here we describe our adaption and further calibration of the Muñoz-Tamayo et al (12) model and the ensuing comparison to Roque et al (3) continuous in vitro RUSITEC experimental data.…”

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

“…In the category of dynamic models, three modelling structures namely Molly, Dijkstra models and Karoline have been incrementally improved over the years (Gregorini et al, 2015;Huhtanen et al, 2015;van Lingen et al, 2019). Recent modelling efforts have been done to include the dynamics of methanogens (Muñoz-Tamayo et al, 2016;van Lingen et al, 2019), thermodynamic control and the impact of methane inhibitors on the rumen fermentation pattern and methane production (Muñoz-Tamayo et al, 2021;van Lingen et al, 2021). Modelling works have also been developed to study ecological interactions within the methanogen rumen community (Lynch et al, 2019;Muñoz-Tamayo et al, 2019).…”