Modeling portal drained viscera and liver fluxes of essential amino acids in dairy cattle

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Chapter 3: Modeling portal drained viscera and liver fluxes of essential amino acids in dairy cows

Abstract

The objective of this work was to predict essential amino acid (EAA) use and release by the portal drained viscera (PDV) and liver (LIV) of dairy cows. Previously derived equations were tested using data assembled from the literature, refit to the data, and modifications were undertaken to determine the best model for each EAA. The PDV clearance model predicted portal vein concentrations given inputs of absorbed and arterial fluxes of EAA with root mean squared errors (RMSE) ranging from 3.29 to 12.11 % of the observed means, and concordance correlation coefficients (CCC) ranging from 0.86 to 0.99 when using previously reported parameters. After refitting these parameters to the data, portal vein EAA concentrations were predicted with RMSE ranging from 3.19 to 8.61 % and CCC ranging from 0.93 to 1.00. Slope bias ranged from 12.42 to 55.30 % of mean squared errors, and was correlated with arterial EAA concentrations. Modifying the model to allow rate constants to vary as a function of arterial EAA concentrations reduced slope bias resulting in RMSE ranging from 1.91 to 6.48 % and CCC from 0.97 to 1.00. Alternatively, splitting the model to account for use of EAA from absorption separately from arterial use resulted in poorer predictions and un-biological parameter estimates. The liver clearance model predicted hepatic vein concentrations from arterial and portal vein input fluxes with RMSE across EAA ranging from 1.92 to 6.84 % and CCC ranging from 0.97 to 1.00 when using reported parameters. After refitting to the data, hepatic vein concentrations were predicted with RMSE ranging from 1.86 to 6.73 % and CCC ranging from 0.97 to 1.00. Significant slope bias was present for Arg, His, Lys, Phe, Thr and Val. Altering the model to represent the clearance rate constant as a function of arterial concentrations resulted in RMSE ranging from 1.76 to 6.46 % and CCC ranging from 0.97 to 1.00. The combination of PDV and liver clearance models provided similar predictions of total splanchnic use as an empirical model representing splanchnic use as a fractional proportion of absorption which had RMSE ranging from 3.01 to 8.57 % and CCC ranging from 0.95 to 0.99 with significant slope bias for majority of the EAA.

Introduction

Milk is an important food source and the primary driver of revenue for dairy farms. Ruminants convert dietary energy into products such as milk more efficiently than they convert dietary N (NRC, 2001, Bequette et al., 2003). Because of the dietary N conversion inefficiency, dairy production contributes significantly to environmental problems (Tamminga et al., 1995, Howarth et al., 2002). Much of this inefficiency is due to improperly matching individual AA to animal needs (Arriola Apelo et al., 2014b). An accurate representation of AA metabolism in dairy cows will allow construction of diets that more closely match dietary supply to animal needs, thus improving N efficiency and decreasing N excretion.
Models have been developed to evaluate N metabolism in the rumen (Dijkstra et al., 1992, NRC, 2001), liver (Hanigan et al., 2004a), and mammary glands (Hanigan et al., 2000, Hanigan et al., 2001, Hanigan et al., 2002). However, to our knowledge, there have been minimal efforts to develop a more mechanistic model of the transfer of AA from the gut lumen to milk protein. Field application models represent the transfer of AA from the gut lumen to net protein output as a set of static conversion efficiencies which lack accuracy and precision (NRC, 2001, White et al., 2017b).
Comparisons of estimated small intestinal disappearance to net portal appearance have shown that the portal drained viscera (PDV) removes approximately 33% of the net AA supply (Hanigan et al., 2004b). However, the majority of this use is from arterial supply after the AA have been delivered to general circulation (MacRae et al., 1997a), and thus the fractional use during absorption is considerably less. The liver also uses a significant proportion of the absorbed EAA on a net basis (Hanigan et al., 1998b), again with arterial supplies representing the vast majority of tissue input (Hanigan et al., 2004b). Because the absorbed supply represents a small fraction of the total flux through each tissue, fractional use during first pass is small (Estes, 2016), but overall use is significant due to constant recycling of the AA to the splanchnic tissues (Reynolds et al., 1988, Wray-Cahen et al., 1997). Net AA use by splanchnic plus mammary tissues has been shown to represent almost the entirety of net body AA use, in a non-pregnant mature cow (Larsen et al., 2015). In addition to the large proportion of AA used by the splanchnic tissues, arterial AA recycling results in variable efficiency of use, which is inconsistent with a fixed transfer efficiency used in field application models (Hanigan et al., 1998a) process-based representation of EAA flux through the post-absorptive system may yield benefits in terms of more precise descriptions of the supply of individual AA to the mammary gland, and thus more accurate and precise representations of milk protein production. From the above, such a representation should consider use during transit by, at a minimum, splanchnic and mammary tissues with the remaining body tissues considered in aggregate. We hypothesized that this process-based model of splanchnic use would provide more accurate and precise predictions of net EAA supplies available for peripheral tissue use than an empirical representation. Therefore, the objective of this work was to develop and test the PDV and liver components of such a process-based model.

  • Chapter 1: Introduction
    • Overview
    • Protein Sources
    • Importance of understanding EAA
    • Current Models
    • Model Evaluation
    • Post-Absorptive EAA
    • Absorption
    • PDV
    • Liver
    • Mammary
    • Conclusion
    Chapter 2: Predictions of rumen outflow of amino acids in dairy cattle 
    • Abstract
    • Introduction
    • Materials and Methods
    • Data Collection
    • Model Derivation
    • RUP Predictions
    • Microbial N Predictions
    • Rumen Degradable Starch
    • Rumen Degradable NDF
    • Microbial True Protein
    • Microbial EAA flow
    • Endogenous CP
    • Endogenous AA flow
    • Total EAA outflows
    • Total EAA outflows (not corrected)
    • Evaluating Prediction Errors
    • Results and Discussion
    • Conclusion
    Chapter 3: Modeling portal drained viscera and liver fluxes of essential amino acids in dairy cattle —
    • Abstract
    • Introduction
    • Materials and Methods
    • Model Description
    • PDV Model
    • Liver Model
    • Splanchnic Model
    • Data and Statistics
    • Results and Discussion
    • PDV Model
    • Hepatic Model
    • Splanchnic Model
    • Conclusion
    Chapter 4: Modeling mammary use of essential amino acids for milk production predictions in lactating dairy cattle
    • Abstract
    • Introduction
    • Material and Methods
    • Model Description
    • Mammary Model
    • Non-Splanchnic, Non-Mammary Model
    • Predictions of Arterial EAA concentrations
    • Milk Protein Model
    • Data and Statistics
    • Results and Discussion
    • Mammary Amino Acid Use
    • Non-Splanchnic, Non-Mammary Amino Acid Use
    • Predictions of Arterial Amino Acid Concentrations
    • Milk Protein Prediction
    • Conclusion

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Modeling post absorptive amino acid metabolism in dairy cattle

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