A dynamic mechanistic model of lactic acid metabolism in the rumen

Mills, J.A.N.; Crompton, L.A.; Ellis, J.L.; Dijkstra, J.; Bannink, A.; Hook, Sarah E.; Benchaar, C.

doi:10.3168/jds.2013-7582

Cited by 15 publications

(19 citation statements)

References 53 publications

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“…In vivo studies are needed to evaluate the true effect of YHP on the pH of bovine rumen. It has been previously reported that the concentration of lactic acid rapidly rises in the bovine rumen after a meal, and is positively correlated with the proportion of CF in the meal (Counotte et al, 1983;Mills et al, 2014). Lactic acid was the first individual SCFA to rise also in the present study, as demonstrated by the 6 h time points of experiments 1 and 2 in which the proportion of lactic acid was 50-70% of the total SCFAs (Tables 1 and 4, respectively).…”

Section: Resultssupporting

confidence: 56%

“…In experiment 3, lactic acid was not detected at all. The results suggested that lactic acid was a transient metabolite, as is the case in bovine rumen in vivo (Mills et al, 2014). YHP further increased the lactic acid concentration in the 6-h time point from experiment 2 (P < 0.001; Table 4).…”

Section: Resultsmentioning

confidence: 65%

“…In the bovine rumen, pH is decreased by microbial fermentation products like SCFAs (Mills et al, 2014). Since the simulation vessels of the present model system lack an absorptive surface, pH decreases through time as the fermentation products accumulate in the medium.…”

Section: Resultsmentioning

confidence: 99%

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Yeast hydrolysate product enhances ruminal fermentation in vitro

Kettunen

Vuorenmaa

Gaffney

et al. 2016

JAAN

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The present study examined the mode of action of a patented Saccharomyces cerevisiae yeast hydrolysate product (YHP) on the fermentation of bovine rumen in vitro. Three experiments were conducted. Fresh fluid from rumen-cannulated dairy cows was used as an inoculum to ferment a mixture of grass silage and concentrate feed with or without YHP. The first two experiments were batch fermentations of 12-24 h duration while the third experiment was a semi-continuous fermentation of six days. Production of gas, concentration of short chain fatty acids (SCFAs), microbial cell density and pH were measured from the fermentation medium as a function of time. In experiment 1, YHP dose-dependently stimulated the production of gas, and increased the density of microbial cells and concentration of SCFAs. Experiment 2 studied the effect of YHP on the ruminal fermentation using three ratios of concentrate feed to grass silage (25:75, 50:50, and 75:25). Both YHP and the elevated proportion of concentrate in the feed mixture significantly increased the production of gas, microbial populations and SCFAs, including propionic acid, by the ruminal microbiota. In experiment 3, YHP increased the concentration and relative proportion of propionic acid in the fermentation medium. YHP stimulated the rate of microbial fermentation of bovine ruminal microbiota, indicated by the effects on gas and SCFA production and microbial mass in these experiments. In particular, YHP increased the production of propionic acid. These results, which were likely due to modulation of microbial community by YHP, suggest that YHP enhances bovine ruminal fermentation and may thus improve the performance of these animals.

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Section: Resultssupporting

confidence: 56%

Section: Resultsmentioning

confidence: 65%

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Yeast hydrolysate product enhances ruminal fermentation in vitro

Kettunen

Vuorenmaa

Gaffney

et al. 2016

JAAN

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“…Reasons to distinguish functional classes of micro-organisms in rumen models are differences in the type of substrate fermented (type of carbohydrate in particular), the particular rumen niche they occupy with its own physical-chemical characteristics, e.g., retention time and acidity, and the specific role they exert that is related to the modeling aim; e.g., bacterial predation by protozoa and storage of polysaccharides (Dijkstra et al, 1992; Dijkstra, 1994; Figures 4A,B ) and rumen lactate metabolism (Mills et al, 2014). Mechanistic rumen models distinguish fibrolytic and amylolytic microbial activity because such a classification is well documented and in line with the distinct carbohydrate substrates for which intrinsic degradation characteristics are available.…”

Section: Introductionmentioning

confidence: 99%

“…These different states or responses might be due to a changed energy requirement for maintenance functions (Baldwin, 1995), or to regulatory mechanisms driven by changed availability of reduced co-factors (Van Lingen et al, 2016). With respect to representing the dynamics of inter-microbial relationships between various classes of micro-organisms, amongst the best documented and detailed models are the rumen protozoa metabolism model of Dijkstra (1994) and the rumen lactate metabolism model of Mills et al (2014). Some other additions to rumen models can be considered as model parameterization rather than addition of a functional class of micro-organisms if they do not include representation of the dynamics of this functional class and its inter-relationships with other classes.…”

Section: Introductionmentioning

confidence: 99%

The Contribution of Mathematical Modeling to Understanding Dynamic Aspects of Rumen Metabolism

et al. 2016

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All mechanistic rumen models cover the main drivers of variation in rumen function, which are feed intake, the differences between feedstuffs and feeds in their intrinsic rumen degradation characteristics, and fractional outflow rate of fluid and particulate matter. Dynamic modeling approaches are best suited to the prediction of more nuanced responses in rumen metabolism, and represent the dynamics of the interactions between substrates and micro-organisms and inter-microbial interactions. The concepts of dynamics are discussed for the case of rumen starch digestion as influenced by starch intake rate and frequency of feed intake, and for the case of fermentation of fiber in the large intestine. Adding representations of new functional classes of micro-organisms (i.e., with new characteristics from the perspective of whole rumen function) in rumen models only delivers new insights if complemented by the dynamics of their interactions with other functional classes. Rumen fermentation conditions have to be represented due to their profound impact on the dynamics of substrate degradation and microbial metabolism. Although the importance of rumen pH is generally acknowledged, more emphasis is needed on predicting its variation as well as variation in the processes that underlie rumen fluid dynamics. The rumen wall has an important role in adapting to rapid changes in the rumen environment, clearing of volatile fatty acids (VFA), and maintaining rumen pH within limits. Dynamics of rumen wall epithelia and their role in VFA absorption needs to be better represented in models that aim to predict rumen responses across nutritional or physiological states. For a detailed prediction of rumen N balance there is merit in a dynamic modeling approach compared to the static approaches adopted in current protein evaluation systems. Improvement is needed on previous attempts to predict rumen VFA profiles, and this should be pursued by introducing factors that relate more to microbial metabolism. For rumen model construction, data on rumen microbiomes are preferably coupled with knowledge consolidated in rumen models instead of relying on correlations with rather general aspects of treatment or animal. This helps to prevent the disregard of basic principles and underlying mechanisms of whole rumen function.

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