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Assessing amino acid uptake and metabolism in mammary glands of lactating dairy cows intravenously infused with methionine, lysine, and histidine or with leucine and isoleucine

Open ArchivePublished:January 14, 2021DOI:https://doi.org/10.3168/jds.2020-18169

      ABSTRACT

      The objective of this study was to evaluate the effect of jugular infusions of 2 groups of AA on essential AA (EAA) transport and metabolism by mammary glands. Four Holstein cows in second lactation (66 ± 10 d in milk) were used in 4 × 4 Latin square design with a 2 × 2 factorial arrangement of treatments. Treatments were jugular infusions of saline; Met, Lys, and His (MKH); Ile and Leu (IL); or MKH plus IL (MKH+IL). Each period consisted of 8 d of no infusion followed by 8 d of jugular vein infusion of the treatment solutions. Amino acids were infused at rates of 21 g of Met, 38 g of Lys, 20 g of His, 50 g of Leu, and 22 g of Ile per day. Cows were fed a basal diet consisting of 15.2% crude protein with adequate rumen degradable protein but 15% deficient in MP based on estimates by Cornell Net Carbohydrate and Protein System (v6.5). On the last day of each period, 13C-AA derived from algae was infused into the jugular vein over 6 h, and blood and milk samples were collected before, during, and after infusion. Plasma and milk samples were analyzed for AA isotopic enrichment, and a mammary compartmental model was fitted to the data to derive bidirectional transport and metabolism rates for individual EAA. Influx of Leu increased with IL, whereas influx of other EAA was not different among treatments. Cellular efflux of Met and Lys to venous plasma represented 12 to 34% of influx, whereas cellular efflux of Phe and BCAA represented 29 to 59% of influx. Increased efflux/influx ratios of Ile and Leu with IL but not Met and Lys with MKH demonstrated that increased Ile and Leu influx was mostly returned to plasma resulting in no change in net uptake or efficiency. The isotope results showed that mammary net uptake of Lys and Ile increased during MKH infusion. Net uptake of Met increased with MKH but only in the absence of IL. Catabolism of Lys and Met only increased with MKH alone, resulting in decreased efficiency for milk protein, which demonstrated that Ile and Leu infusion can spare Lys and Met for milk protein synthesis. Total AA uptake to milk output was not different from 1, implying the catabolized Met and Lys contributed nitrogen to nonessential AA. Overall, EAA uptake and metabolism in mammary glands of dairy cows varied across individual EAA and responded differently to respective AA supplements. In addition, uptake, retention, and end use of AA by mammary tissue is variable and dependent on the mix of AA provided. This variability, depending on the mix of AA absorbed, will change the efficiency of utilization of individual AA at the mammary gland level and consequently the whole-body level. Thus, it is inaccurate to use a fixed, constant efficiency within and across AA to represent tissue activity.

      Key words

      INTRODUCTION

      Balancing the supply and profile of the EAA to match body tissue use for maintenance and production can improve the efficiency of N use for milk protein in dairy cows (
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      ). However, feeding systems currently used by the dairy industry are empirical in nature and generally focus only on Met and Lys (
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      ;
      • NRC (National Research Council)
      Nutrient Requirements of Dairy Cattle.
      ). These models inaccurately predict responses to varying AA supplies in part due to assumptions regarding efficiency of postabsorptive AA metabolism (
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      Animal grouping strategies, sources of variation, and economic factors affecting nutrient balance on dairy farms.
      ). The use of fixed conversion efficiencies for MP to milk protein (
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      ) is inaccurate, as milk protein synthesis is a variable and an integrated function of protein and energy supply (
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      demonstrated that variation in mammary net removal of individual EAA also exists, which contributes to variable efficiency of AA utilization for milk protein synthesis. In addition, mammary AA metabolism is regulated by factors such as intracellular AA concentration, the expression of the milk protein genes, or phosphorylation of translation factors (
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      ). Intracellular concentrations of several EAA can affect phosphorylation of translation factors that regulate milk protein synthesis rates (
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      Symposium review: Amino acid uptake by the mammary glands: Where does the control lie?.
      ). Thus, changes in AA uptake by mammary glands may not always translate into comparable changes in milk protein output. Failing to capture the response diversity in nutrition models likely contributes to prediction errors that leads to over-formulation of MP and reduced N efficiency (
      • NRC (National Research Council)
      Nutrient Requirements of Dairy Cattle.
      ). A better understanding of the mechanisms of AA transport and utilization in mammary glands in response to the supply and profile of AA and energy is needed to better represent the system in requirement models.
      Methionine, Lys, and possibly His are considered the limiting AA for milk protein production (
      • NRC (National Research Council)
      Nutrient Requirements of Dairy Cattle.
      ). Milk or milk protein responses to supplementation of typical North American dairy cattle diets fed at or slightly below MP requirements with Met, Lys, and His have been investigated, and the results have been inconsistent across studies (
      • NRC (National Research Council)
      Nutrient Requirements of Dairy Cattle.
      ;
      • Lee C.
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      • Parys C.
      Rumen-protected lysine, methionine, and histidine increase milk protein yield in dairy cows fed a metabolizable protein-deficient diet.
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      • Harper M.
      • Oh J.
      • Parys C.
      • Shinzato I.
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      Histidine deficiency has a negative effect on lactational performance of dairy cows.
      ). For example,
      • Sinclair K.D.
      • Garnsworthy P.C.
      • Mann G.E.
      • Sinclair L.A.
      Reducing dietary protein in dairy cow diets: implications for nitrogen utilization, milk production, welfare and fertility.
      reported no benefit in fat-corrected milk yield and only small increases in milk protein yield when Met and Lys were added in low (<15%) CP diets across 16 experiments. Other EAA, such as Ile and Leu, are not normally considered limiting, but have been linked to mTOR regulation in mammary cells (
      • Appuhamy J.A.D.R.N.
      • Knoebel N.A.
      • Nayananjalie W.A.
      • Escobar J.
      • Hanigan M.D.
      Isoleucine and leucine independently regulate mTOR signaling and protein synthesis in MAC-T cells and bovine mammary tissue slices.
      ;
      • Arriola Apelo S.I.
      • Singer L.M.
      • Lin X.Y.
      • McGilliard M.L.
      • St-Pierre N.R.
      • Hanigan M.D.
      Isoleucine, leucine, methionine, and threonine effects on mammalian target of rapamycin signaling in mammary tissue.
      ;
      • Liu G.M.
      • Hanigan M.D.
      • Lin X.Y.
      • Zhao K.
      • Jiang F.G.
      • White R.R.
      • Wang Y.
      • Hu Z.Y.
      • Wang Z.H.
      Methionine, leucine, isoleucine, or threonine effects on mammary cell signaling and pup growth in lactating mice.
      ), and Leu, in particular, plays a role in activating protein translation (
      • Saxton R.A.
      • Chantranupong L.
      • Knockenhauer K.E.
      • Schwartz T.U.
      • Sabatini D.M.
      Mechanism of arginine sensing by CASTOR1 upstream of mTORC1.
      ). However, milk protein yield response to Leu or BCAA supplementation are not well documented (
      • Arriola Apelo S.I.
      • Singer L.M.
      • Lin X.Y.
      • McGilliard M.L.
      • St-Pierre N.R.
      • Hanigan M.D.
      Isoleucine, leucine, methionine, and threonine effects on mammalian target of rapamycin signaling in mammary tissue.
      ;
      • Doelman J.
      • Kim J.J.
      • Carson M.
      • Metcalf J.A.
      • Cant J.P.
      Branched-chain amino acid and lysine deficiencies exert different effects on mammary translational regulation.
      ) and have been inconsistent (
      • Korhonen M.
      • Vanhatalo A.
      • Huhtanen P.
      Evaluation of isoleucine, leucine, and valine as a second-limiting amino acid for milk production in dairy cows fed grass silage diet.
      ;
      • Appuhamy J.A.D.R.N.
      • Knapp J.R.
      • Becvar O.
      • Escobar J.
      • Hanigan M.D.
      Effects of jugular-infused lysine, methionine, and branched-chain amino acids on milk protein synthesis in high-producing dairy cows.
      ;
      • Curtis R.V.
      • Kim J.J.M.
      • Doelman J.
      • Cant J.P.
      Maintenance of plasma branched-chain amino acid concentrations during glucose infusion directs essential amino acids to extra-mammary tissues in lactating dairy cows.
      ). Several reasons may exist for inconsistencies, including variable dietary EAA supply profiles, the supplemented BCAA were not limiting under those diet conditions, inadequate BCAA were supplemented, deficiencies of other AA limited responses, increased nonmammary EAA use, AA transport competition, or AA metabolism in the mammary glands (
      • Schwab C.G.
      • Bozak C.K.
      • Whitehouse N.L.
      • Mesbah M.M.
      Amino acid limitation and flow to duodenum at four stages of lactation. 1. Sequence of lysine and methionine limitation.
      ;
      • Varvikko T.
      • Vanhatalo A.
      • Jalava T.
      • Huhtanen P.
      Lactation and metabolic responses to graded abomasal doses of methionine and lysine in cows fed grass silage diets.
      ;
      • Socha M.T.
      • Schwab C.G.
      • Putnam D.E.
      • Whitehouse N.L.
      • Garthwaite B.D.
      • Ducharme G.A.
      Extent of methionine limitation in peak-, early-, and mid-lactation dairy cows.
      ). Better knowledge of the mechanisms mediating EAA transport, metabolism, and interactions among AA in mammary glands should help explain the diversity of responses to EAA.
      Studies have been conducted to evaluate milk protein synthesis and AA metabolism in mammary glands using the arterial-venous (A-V) difference approach, but in the absence of isotope infusions, this technique only provides estimates of net uptake and use and not bidirectional fluxes (
      • Lapierre H.
      • Lobley G.E.
      • Doepel L.
      • Raggio G.
      • Rulquin H.
      • Lemosquet S.
      Triennial Lactation Symposium: Mammary metabolism of amino acids in dairy cows.
      ;
      • Yoder P. S
      • Huang X.
      • Teixeira I.A.
      • Cant J.P.
      • Hanigan M.D.
      Effects of jugular infused methionine, lysine, and histidine as a group or leucine and isoleucine as a group on production and metabolism in lactating dairy cows.
      ).
      • Hanigan M.D.
      • Crompton L.A.
      • Metcalf J.A.
      • France J.
      Modelling mammary metabolism in the dairy cow to predict milk constituent yield, with emphasis on amino acid metabolism and milk protein production: Model construction.
      built a mammary net transport model to predict milk protein based on A-V data, and found that it only explained 53% of the observed variation in milk protein output (
      • Hanigan M.D.
      • Crompton L.A.
      • Bequette B.J.
      • Mills J.A.N.
      • France J.
      Modelling mammary metabolism in the dairy cow to predict milk constituent yield, with emphasis on amino acid metabolism and milk protein production: Model evaluation.
      ). By coupling stable isotope tracers with A-V difference techniques and compartmental modeling,
      • Hanigan M.D.
      • France J.
      • Mabjeesh S.J.
      • McNabb W.C.
      • Bequette B.J.
      High rates of mammary tissue protein turnover in lactating goats are energetically costly.
      was able to extend the information gained from measurements of mammary A-V to derivations of bidirectional AA transport and catabolism of Phe, Leu, Met, and Val.
      • Crompton L.A.
      • France J.
      • Reynolds C.K.
      • Mills J.A.N.
      • Hanigan M.D.
      • Ellis J.L.
      • Bannink A.
      • Bequette B.J.
      • Dijkstra J.
      An isotope dilution model for partitioning phenylalanine and tyrosine uptake by the mammary gland of lactating dairy cows.
      subsequently derived steady-state calculations for Phe and Tyr transport and catabolism. Use of these methods across a range of dietary conditions is lacking; thus, knowledge of in vivo transport regulation of those EAA or others has not progressed beyond those initial efforts. In the current study, a technique adapted from
      • Hanigan M.D.
      • France J.
      • Mabjeesh S.J.
      • McNabb W.C.
      • Bequette B.J.
      High rates of mammary tissue protein turnover in lactating goats are energetically costly.
      and
      • Crompton L.A.
      • France J.
      • Reynolds C.K.
      • Mills J.A.N.
      • Hanigan M.D.
      • Ellis J.L.
      • Bannink A.
      • Bequette B.J.
      • Dijkstra J.
      An isotope dilution model for partitioning phenylalanine and tyrosine uptake by the mammary gland of lactating dairy cows.
      was applied to quantify transport and metabolism of other EAA in mammary glands of high-producing cows.
      The objective of this study was to evaluate the effects of jugular infusions of Met, Lys, and His or Ile and Leu on EAA transport and metabolism in mammary glands of high-producing cows. Considering their difference in transport, metabolism, and regulating function in mammary gland, our hypothesis was that supplementation of Met, Lys, and His as a group and Ile and Leu as a group would differentially affect mammary transport and metabolism of those AA as well as others.

      MATERIALS AND METHODS

      Animals and Treatments

      All animal procedures were conducted at the Virginia Tech Kentland Dairy Farm (Blacksburg, VA, USA) and approved by the Virginia Tech Animal Care and Use Committee. This work was conducted with a subset of the animals within the experiment of
      • Yoder P. S
      • Huang X.
      • Teixeira I.A.
      • Cant J.P.
      • Hanigan M.D.
      Effects of jugular infused methionine, lysine, and histidine as a group or leucine and isoleucine as a group on production and metabolism in lactating dairy cows.
      . Detailed information on animal housing, experimental design, treatments, infusions, the basal diet, and feed management and sampling are described in that publication. Briefly, 8 second-lactation Holstein cows were blocked into 2 groups by DIM and randomly assigned to 1 of 4 treatment sequences according to a 2 × 2 factorial arrangement within 2 replicated 4 × 4 Latin squares. Four cows in block 1 (66 ± 10 DIM) were chosen for the isotope infusion study, and are the subjects for the results reported herein. Treatments were jugular infusions of saline (CON); methionine, lysine, and histidine (MKH); isoleucine and leucine (IL); and the combination of MKH and IL (MKH+IL). Infusion rates were 21 g of methionine, 38 g of lysine, 20 g of histidine, 50 g of leucine, and 22 g of isoleucine per day based on meta-analytical work in progress. The AA supply from diets were predicted using version 6.55 of the Cornell Net Carbohydrate and Protein System model within the NDS Professional ration formulation software (version 6.55; RUM&N, NDS Professional, Reggio Nell'Emilia, Emilia-Romagna, Italy). Each experimental period consisted of 8 d without infusions followed by 10 d of AA infusion into the jugular vein. Cows were housed in a freestall barn fitted with Calan gates between infusion periods and in metabolism stalls during infusion periods. Cows were fed a basal diet with 15.2% CP and adequate RDP, but 15% deficient in MP based on estimates by the Cornell Net Carbohydrate and Protein System (ver. 6.55). Animals were fed ad libitum once daily in the freestall barn and twice daily in the metabolism stalls from d 9 to 14 with a target minimum refusal of 5%. From 1200 h on d 15 through the isotope infusion study on d 16, cows were fed in equal portions every 2 h at 100% of the observed previous 7-d DMI average to establish stable feed intake. Milk production, feed intake, and BW were recorded daily.

      Isotope Infusion and Sampling

      On d 8 of each period, 4 cows were fitted with one jugular catheter (90 cm × 2.03-mm i.d. microrenathane, Braintree Scientific Inc., Braintree, MA) for continuous AA infusion. Catheters were placed on alternate sides of the neck in subsequent periods. On d 16, jugular infusion of 1 g of a mix of 13C labeled AA (U-13C, 97–99% enriched, Cambridge Isotope Laboratories, Andover, MA) dissolved in 100 mL saline was initiated at 1300 h and maintained for 6 h. The isotope solution was administered through a separate infusion line connected to the single jugular catheter with a y-connector to allow merged infusion of the unlabeled AA treatment solutions and the isotope solution. The infused isotope was a mix of AA derived from algae grown under an atmosphere of 13CO2.
      Blood was sampled from an indwelling abdominal vein catheter and by venipuncture from coccygeal vessels (8 mL each) at −24, −0.5, 0.5, 1, 1.5, 2, 3, 4, 5, 6, 8, 10 h relative to the start of the infusion, and stored on ice until processing at the end of the blood sampling period. Plasma was prepared from the blood samples by centrifugation at 1,600 × g for 15 min at 4°C and stored at −20°C until further analysis. Although coccygeal vessel blood may be arterial or venous blood, it has previously been observed that tail metabolism has an insignificant effect on metabolite concentrations (
      • Emery R.S.
      • Brown L.D.
      • Bell J.W.
      Correlation of milk fat with dietary and metabolic factors in cows fed restricted-roughage rations supplemented with magnesium oxide or sodium bicarbonate.
      ), thus was considered equivalent to arterial plasma regardless of which vessel was sampled.
      Cows were milked in the metabolism stalls at −1, 2, 4, 6, and 12 h relative to the start of the isotope infusion. Milk weights were recorded, and 10-mL subsamples were collected at each time point. Five milliliters of oxytocin (20 USP/mL) was administered intramuscularly at each milking to help ensure complete milk removal. Milk samples were deproteinized by centrifugation at 10,000 × g for 10 min at 4°C after acidification with sulfosalicylic acid (SSA, 10%, wt/vol). The resulting protein pellets were washed 1× with SSA (10%, wt/vol) followed by centrifugation at 10,000 × g for 10 min at 4°C, and storage at −20°C until further analysis.

      Sample Analysis

      Two subsamples (1 mL) of each plasma sample were deproteinized by centrifugation at 16,000 × g for 15 min at 4°C after acidification with SSA (8% wt/vol). One of the samples was spiked with an external tracer containing a complete mix of U-[15N, 13C]-AA for determination of AA concentrations by isotope dilution. Milk protein samples were hydrolyzed in 6 N HCl containing 0.1% phenol at 110°C for 21 h under an atmosphere of N gas to limit oxidation, and filtered to remove insoluble material (
      • Gehrke C.W.
      • Wall Sr., L.L.
      • Absheer J.S.
      • Kaiser F.E.
      • Zumwalt R.W.
      Sample preparation for chromatography of amino acids: Acid hydrolysis of proteins.
      ). Deproteinized plasma and hydrolyzed milk protein samples were desalted by ion exchange chromatography (Bio-Rad Resin AG 50W-X8*, 100–200 mesh; Bio-Rad, Hercules, CA), eluted into silanized glass vials using 2N ammonium hydroxide, and dried as described by
      • Calder A.G.
      • Garden K.E.
      • Anderson S.E.
      • Lobley G.
      Quantitation of blood and plasma amino acids using isotope dilution electron impact gas chromatography/mass spectrometry with U-13C amino acids as internal standards.
      . The unspiked, desalted plasma sample and the milk protein sample were derivatized as described by
      • Walsh R.G.
      • He S.
      • Yarnes C.T.
      Compound-specific delta13C and delta15N analysis of amino acids: A rapid, chloroformate-based method for ecological studies.
      and analyzed to determine isotopic ratios of 13C to 12C using a gas chromatograph linked to an isotope ratio mass spectrometry via a combustion oven (GC-C-IRMS, Thermo Scientific, Waltham, MA). The spiked plasma sample was treated to create N-(tert-butyldimethyl) AA derivatives by incubation with N-methyl-N-(tert-butyldimethylsilyl)-trifluoracetamide (Selectra-SIL; UCT Inc., Bristol, PA) at 70°C for 1 h. Derivatives were separated and mass ion ratios were determined using a gas chromatograph coupled to a quadrupole mass spectrometer (GC-MS; Thermo Scientific) as described by
      • Calder A.G.
      • Garden K.E.
      • Anderson S.E.
      • Lobley G.
      Quantitation of blood and plasma amino acids using isotope dilution electron impact gas chromatography/mass spectrometry with U-13C amino acids as internal standards.
      . Amino acid concentrations were derived from observed isotope dilutions using a gravimetric standard curve.

      Model Derivation

      All modeling work was completed using R Studio (version 1.2.1335; R 3.5.3; https://rstudio.com/). The 5-pool dynamic model of
      • Hanigan M.D.
      • France J.
      • Mabjeesh S.J.
      • McNabb W.C.
      • Bequette B.J.
      High rates of mammary tissue protein turnover in lactating goats are energetically costly.
      was modified and expanded for use herein. A schematic of the model is provided in Figure 1. The model represents uptake and metabolism of a single AA. Briefly, state variables were defined for total and isotopically labeled AA (denoted by an *) in: (1) arterial plasma (QaAA, Q*aAA), (2) nonmammary tissue protein (QbtAA, Q*btAA), (3) extracellular mammary fluid (QxAA, Q*xAA), (4) intracellular mammary fluid (QnAA, Q*nAA), and (5) mammary tissue protein (QmtAA, Q*mtAA). We attempted to separate nonmammary and mammary constitutive protein into slow and fast turnover pools as undertaken by
      • Hanigan M.D.
      • France J.
      • Mabjeesh S.J.
      • McNabb W.C.
      • Bequette B.J.
      High rates of mammary tissue protein turnover in lactating goats are energetically costly.
      , but this resulted in nonunique solutions for parameter estimates due to the shorter infusion time used in the current work (6 h vs. 30 h). Thus, we fixed the fraction of the total protein that was included in the fast pool to a value that yielded the greatest log-likelihood value across infusions determined by an iterative local sensitivity analysis, and derived fast turnover while assuming the slow pool did not participate.
      Figure thumbnail gr1
      Figure 1Flow diagram depicting a model of total (A) and labeled (B) AA exchange between arterial plasma and body tissue, with AA catabolism in nonmammary tissues and bidirectional AA transport and AA metabolism in mammary tissue.
      A full description of the model is provided in the Appendix and stoichiometric constants used in the model are displayed in Supplemental Table S1 (http://hdl.handle.net/10919/100999). We assumed the udder and body protein pools were fixed in size over the course of the infusion, and thus protein degradation was set equal to synthesis. All other fluxes depicted in Figure 1 were explicitly represented as mass action functions.
      The FME package of R was used to conduct parameter identifiability and model fitting (
      • Soetaert K.
      • Petzoldt T.
      Inverse modelling, sensitivity and monte carlo analysis in R using package FME.
      ). Model inputs required for the simulation are presented in Supplemental Tables S2 and S3 (http://hdl.handle.net/10919/100999). Mammary plasma flow (MPF) and average AA concentrations in arterial and venous plasma were used to calculate total AA fluxes from arterial to extracellular and from extracellular to venous pools. Mammary plasma flow was estimated using the Fick principle with Phe and Tyr as the internal markers (
      • Cant J.P.
      • DePeters E.J.
      • Baldwin R.L.
      Mammary amino acid utilization in dairy cows fed fat and its relationship to milk protein depression.
      ). We assumed there was no significant cow effect on milk AA profiles as isotope ratios of milk AA were not different among animals. Utilization of AA for milk protein was thus derived from observed milk protein production rates during the sampling window using standard AA stoichiometries for milk true protein (
      • Lapierre H.
      • Lobley G.E.
      • Doepel L.
      • Raggio G.
      • Rulquin H.
      • Lemosquet S.
      Triennial Lactation Symposium: Mammary metabolism of amino acids in dairy cows.
      ). Amino acid catabolism in mammary glands includes AA oxidation and transamination, which were calculated by difference of AA net uptake and output in milk. Amino acids used for purposes other than milk protein were aggregated into AA clearance, which was calculated as the difference of AA absorption and uptake by mammary glands. The AA absorption was derived from model fits to the data. Initial plasma, extracellular and intracellular AA pools were calculated as the mean AA concentration in the pool across time points multiplied by the estimated pool volumes. The AA concentrations in the plasma pool were determined from background plasma samples. The AA concentrations in the extracellular pool were assumed to equal AA concentrations in venous plasma (
      • Hanigan M.D.
      • France J.
      • Wray-Cahen D.
      • Beever D.E.
      • Lobley G.E.
      • Reutzel L.
      • Smith N.E.
      Alternative models for analyses of liver and mammary transorgan metabolite extraction data.
      ). Intracellular AA concentrations were taken from the literature (
      • Hanigan M.D.
      • France J.
      • Mabjeesh S.J.
      • McNabb W.C.
      • Bequette B.J.
      High rates of mammary tissue protein turnover in lactating goats are energetically costly.
      ). Extracellular volume was assumed to be 20% of mammary tissue wet weight and intracellular volume was total wet weight minus extracellular volume and tissue DM (
      • Hanigan M.D.
      • France J.
      • Mabjeesh S.J.
      • McNabb W.C.
      • Bequette B.J.
      High rates of mammary tissue protein turnover in lactating goats are energetically costly.
      ). Plasma volume was assumed to be 14.8% of BW, which includes both blood and interstitial space (
      • Shell T.M.
      • Early R.J.
      • Carpenter J.R.
      • Vincent D.L.
      • Buckley B.A.
      Prepartum nutrition and solar radiation in beef cattle: I. Relationships of body fluid compartments, packed cell volume, plasma urea nitrogen, and estrogens to prenatal development.
      ). The total protein-bound AA pool was estimated from body weight and AA concentrations in body tissue (Supplemental File, Equation S4, http://hdl.handle.net/10919/100999). The mammary protein-bound AA pool was estimated from mammary weight and the AA concentrations in mammary tissue (Supplemental File, Equation S5). The AA composition of tissue protein was as reported by
      • Williams A.
      The amino acid, collagen and mineral composition of preruminant calves.
      . The initial mass of the isotopically labeled AA pools was set equal to the unlabeled pool sizes multiplied by the observed background enrichments for plasma AA 1 h before the start of the isotope infusions.
      Model parameters were derived for each infusion by fitting the model to observed isotopic enrichment in plasma, extracellular, and intracellular pools using modCost and modFit functions within the FME package (
      • Soetaert K.
      • Petzoldt T.
      Inverse modelling, sensitivity and monte carlo analysis in R using package FME.
      ). Parameter bounds were set to minima of 0 and maxima of 1 for all parameters except for absorption and mammary influx rate constants, which were set to 5,000 µmol/min and 10 min−1 as maxima, respectively. Isotopic enrichment in the extracellular space was assumed to be equal to venous enrichment (
      • Hanigan M.D.
      • France J.
      • Wray-Cahen D.
      • Beever D.E.
      • Lobley G.E.
      • Reutzel L.
      • Smith N.E.
      Alternative models for analyses of liver and mammary transorgan metabolite extraction data.
      ). Intracellular isotopic enrichment can be obtained by tissue biopsy, but it is also represented by the isotope enrichment in milk protein with a lag time of 81 min between synthesis and secretion of casein into milk (
      • Hanigan M.D.
      • France J.
      • Mabjeesh S.J.
      • McNabb W.C.
      • Bequette B.J.
      High rates of mammary tissue protein turnover in lactating goats are energetically costly.
      ). We used the latter approach for this work.
      Initial parameter estimates by treatment and AA were derived by maximizing the log-likelihood using the Nelder-Mead algorithm. Following the initial fit, residual errors were calculated, and data points were removed where the studentized residual errors exceeded an absolute value of 2. These represented 5% of the data set. These outliers were generally also visually apparent when the observed and predicted data were plotted against sampling time. Final parameter estimates were derived after removal of these outliers.
      Root mean squared errors as a percentage of the mean (RMSE) were calculated from mean squared residual errors, and the latter was partitioned into mean bias, slope bias, and dispersion (
      • Bibby J.
      • Toutenburg H.
      Prediction and Improved Estimation in Linear Models.
      ). The concordance correlation coefficient was also calculated to provide a dimensionless evaluation of precision and accuracy.

      Statistical Analysis

      One cow was diagnosed with clinical mastitis during period 2, and her data from that period was discarded. Energy-corrected milk yield was calculated as described by
      • Bernard J.K.
      Milk production and composition responses to the source of protein supplements in diets containing wheat middlings.
      :
      ECM = [(12.86 × kg of fat) + (7.04 × kg of protein) + (0.3246 × kg of milk)].


      Milk production, milk composition and DMI during the infusion period (d 9 to 16), model-derived rate constants, and predicted AA flux rates were analyzed as a 2 × 2 factorial design using the lmer function of the lme4 package in R Studio (version 1.2.1335; R 3.5.3). The model was:
      Yijkl = µ + MKHi + ILj + MKH×ILij + Periodk + Cowl + eijkl,


      where Yijkl was the dependent variable, µ was the overall mean of Y, MKHi was the fixed effect of Met, Lys and His infusion (df = 1), ILj was the fixed effect of Ile and Leu infusion (df = 1), MKH×ILij was the interaction of MKH and IL (df = 1), Periodk was the random effect of period (df = 3), and Cowl was the random effect of cow (df = 3). Main effects and interactions were declared significant at P ≤ 0.05 and trends at 0.05 < P ≤ 0.10. Denominator degrees of freedom for all tests were adjusted using the Kenward-Roger option. Residual errors were evaluated for homogeneity and outliers. If studentized residuals exceeded an absolute value of 2, the sample was removed from the data for statistical analysis (<5% of the data set). When the interaction was significant, a post hoc means-separation test was conducted using the lsmeansLT package with Kenward-Roger and Tukey options. A t-test was also conducted to evaluate mammary AA efficiency for each treatment relative to the null assumption of 100%. The difference was declared significant at P ≤ 0.05 and trends at 0.05 < P ≤ 0.10.

      RESULTS AND DISCUSSION

      Dry matter and dietary CP intake were not significantly affected by treatments, but all AA infusions numerically increased total CP intake (Table 1). Milk protein yield increased with MKH (P < 0.01) but not affected with IL (P = 0.14). Infusion of IL increased milk yield by 1.45 kg/d (P = 0.04), whereas no effect was observed in response to MKH (P = 0.91). The observations herein are a subset of the results from the larger experiment by
      • Yoder P. S
      • Huang X.
      • Teixeira I.A.
      • Cant J.P.
      • Hanigan M.D.
      Effects of jugular infused methionine, lysine, and histidine as a group or leucine and isoleucine as a group on production and metabolism in lactating dairy cows.
      , and the intake and production results are similar to and consistent with the full set of animals. Numeric differences in production results were observed between the 2 studies, which was likely due to the reduced power in using fewer animals (4 vs. 8). Production responses have been thoroughly discussed in the prior publication (
      • Yoder P. S
      • Huang X.
      • Teixeira I.A.
      • Cant J.P.
      • Hanigan M.D.
      Effects of jugular infused methionine, lysine, and histidine as a group or leucine and isoleucine as a group on production and metabolism in lactating dairy cows.
      ). Production results for the 4 animals used herein are provided to support the isotope work and the model validation.
      Table 1Effect of jugular AA infusions on intake, milk production parameters, and nitrogen efficiency
      Data are presented as least-squares treatment means; n = 4 for all treatments.
      Item
      DMI = basal diet only; ECM = [(12.86 × kg of fat) + (7.04 × kg of protein) + (0.3246 × kg of milk)]; milk nitrogen efficiency = milk protein yield (kg/d)/total CP intake (kg/d).
      Treatment
      Treatments = jugular infusion; CON = 3 L of 0.9% saline; MKH = 21 g/d of Met, 20 g/d of His, and 38 g/d of Lys; IL = 22 g/d of Ile and 50 g/d of Leu; MKH+IL = 21 g/d of Met, 20 g/d of His, 38 g/d of Lys, 22 g/d of Ile, and 50 g/d of Leu; all AA treatments were administered in 3 L of 0.9% saline.
      SEMEffect (P-value)
      CONMKHILMKH + ILMKHILMKH × IL
      Intake
       DMI, kg/d24.324.824.324.01.200.810.540.54
       Dietary CP, kg/d3.693.773.683.650.1810.820.540.54
       Infused AA, g/d078.971.9150.30.10<0.01<0.010.53
       Total CP, kg/d3.693.863.763.780.1680.300.950.42
      Milk production
       Milk, kg/d50.851.052.452.32.500.910.040.80
       ECM50.652.851.751.23.800.430.800.18
       Milk protein %3.003.162.963.160.114<0.010.570.47
       Milk protein, kg/d1.521.611.541.650.083<0.010.140.64
       Milk fat %3.60
      Least squares means within a row with different superscripts are considered significantly different (P< 0.05) or tend to be different (P < 0.1).
      3.68
      Least squares means within a row with different superscripts are considered significantly different (P< 0.05) or tend to be different (P < 0.1).
      3.54
      Least squares means within a row with different superscripts are considered significantly different (P< 0.05) or tend to be different (P < 0.1).
      3.31
      Least squares means within a row with different superscripts are considered significantly different (P< 0.05) or tend to be different (P < 0.1).
      0.4080.28<0.010.02
       Milk fat, kg/d1.81
      Least squares means within a row with different superscripts are considered significantly different (P< 0.05) or tend to be different (P < 0.1).
      1.90
      Least squares means within a row with different superscripts are considered significantly different (P< 0.05) or tend to be different (P < 0.1).
      1.84
      Least squares means within a row with different superscripts are considered significantly different (P< 0.05) or tend to be different (P < 0.1).
      1.75
      Least squares means within a row with different superscripts are considered significantly different (P< 0.05) or tend to be different (P < 0.1).
      0.2430.960.220.05
       Lactose %5.055.055.064.980.0840.320.440.43
       Lactose, kg/d2.582.552.652.600.1090.350.070.75
       MUN (mg/dL)8.088.808.057.791.0020.460.090.12
       Milk nitrogen efficiency41.841.741.444.62.530.180.280.15
      a,b Least squares means within a row with different superscripts are considered significantly different (P< 0.05) or tend to be different (P < 0.1).
      1 Data are presented as least-squares treatment means; n = 4 for all treatments.
      2 DMI = basal diet only; ECM = [(12.86 × kg of fat) + (7.04 × kg of protein) + (0.3246 × kg of milk)]; milk nitrogen efficiency = milk protein yield (kg/d)/total CP intake (kg/d).
      3 Treatments = jugular infusion; CON = 3 L of 0.9% saline; MKH = 21 g/d of Met, 20 g/d of His, and 38 g/d of Lys; IL = 22 g/d of Ile and 50 g/d of Leu; MKH+IL = 21 g/d of Met, 20 g/d of His, 38 g/d of Lys, 22 g/d of Ile, and 50 g/d of Leu; all AA treatments were administered in 3 L of 0.9% saline.
      Although the infused tracer contained all AA excepting Asn, Gln, and Trp, this study focused solely on EAA transport and metabolism in mammary glands as the NEAA can be synthesized by the animal, which prevents derivation of unique transport and metabolism estimates given the techniques used. Additional limitations include the inability to assess Arg as it is destroyed during the chemical derivation process, and the poor derivatization efficiency for His resulting in values near baseline and with high variability (
      • Walsh R.G.
      • He S.
      • Yarnes C.T.
      Compound-specific delta13C and delta15N analysis of amino acids: A rapid, chloroformate-based method for ecological studies.
      ). Results for His were therefore excluded from further analysis.
      Although absorption rates were derived by model fits, they are overestimated due to not fully capturing isotope exchange with the slow turnover protein pool over the 6 h sampling period. This has been discussed previously (
      • Estes K.A.
      • White R.R.
      • Yoder P.S.
      • Pilonero T.
      • Schramm H.
      • Lapierre H.
      • Hanigan M.D.
      An in vivo stable isotope-based approach for assessment of absorbed amino acids from individual feed ingredients within complete diets.
      ), and can be addressed by difference from the control treatment. However, we did not attempt such calculations herein as the primary purpose of that portion of the model was to provide a continuous prediction of arterial plasma enrichment as an input to the mammary model, which was achieved. Therefore, the absorption and clearance rates are not presented or discussed.

      Model Fit Quality

      Abbreviations and units for model rate constants and fluxes are displayed in Table 2. The accuracy and precision of predicted arterial, venous (represents extracellular space), and milk protein isotope ratios (represents intracellular space with a lag time) are shown in Table 3. An example of model fit is provided in Supplemental Figure S1 (http://hdl.handle.net/10919/100999). The average RMSE across treatments and AA was less than 2%, with more than 50% of MSE on average segregating into dispersion error. The averaged concordance correlation coefficient was greater than 90% (Table 3). Thus, the isotope ratios appeared to be predicted with good precision and accuracy. The mean bias of prediction for arterial isotope ratios averaged 21%. The overestimate is visually obvious in Supplemental Figure S1A. A potential reason is that a fixed fraction of the total protein was considered in the fast pool while assuming the slow pool did not participate in AA exchange. The model must capture the kinetics of protein turnover to recreate the rise toward an apparent plateau during the isotope infusion. Use of a single pool fails to fully capture the sequestration of labeled AA in slow turnover pools, which displays more linear behavior over time. This results in an overestimate of isotope enrichment in the arterial pool which leads to an overestimate of plasma AA appearance. The slope (average 16%) and mean bias (average 16%) related to predictions of milk protein isotope ratios was likely due to variation in milking effectiveness (Table 3). Although oxytocin was used to minimize this problem (
      • Bequette B.J.
      • Backwell F.R.
      • Dhanoa M.
      • Walker A.
      • Calder A.G.
      • Wray-Cahen D.
      • Metcalf J.A.
      • Sutton J.D.
      • Beever D.E.
      • Lobley G.E.
      • MacRae J.C.
      Kinetics of blood free and milk casein-amino acid labelling in the dairy goat at two stages of lactation.
      ;
      • France J.
      • Bequette B.J.
      • Lobley G.E.
      • Metcalf J.A.
      • Wray-Cahen D.
      • Dhanoa M.S.
      • Backwell F.R.C.
      • Hanigan M.D.
      • MacRae J.C.
      • Beever D.E.
      An isotope dilution model for partitioning leucine uptake by the bovine mammary gland.
      ), it cannot guarantee 100% milk removal at each milking. If more milk was left in the udder from a prior milking, the lower enrichment from the earlier milk would dilute the isotope during the next milking interval. The residual milk bias is one-sided, leading to overall greater bias. Another potential reason is that enrichment in milk protein represents that of loaded tRNA which has been found to have lower enrichment than the general intracellular pool (
      • Wilde C.J.
      • Addey C.V.
      • Knight C.H.
      Regulation of intracellular casein degradation by secreted milk proteins.
      ). The model predicted intracellular enrichment as a well-mixed pool with inputs from the extracellular and mammary tissue bound pools, which cannot represent any gradients that may exist relative to the tRNA loading site. Thus, the model could be expected to slightly overestimate milk protein AA enrichment and underestimate venous AA enrichment when minimizing overall errors. Consistent with that expectation, the venous AA enrichments were consistently underpredicted (23% mean bias), but the milk protein AA do not appear to be overpredicted. Across treatments, AA, and pools, the absolute slope and mean biases were small considering the low RMSE, and should not have contributed significantly to parameter estimate bias.
      Table 2Abbreviations and units for model rate constants and fluxes
      Variable
      Subscript i represents the isotope-labeled AA.
      DescriptionUnit
      KaAAbtAARate constant for AA incorporation into body tissue proteinmin−1
      KxAAnAARate constant for AA uptakemin−1
      KnAAxAARate constant for AA effluxmin−1
      KnAAmtAARate constant for AA incorporation into mammary tissue proteinmin−1
      Fabsorption(i)AA absorptionμmol/min
      FaAAxAA(i)AA flux from the arterial pool to the extracellular poolμmol/min
      FaAAbtAA(i)AA flux from arterial pool to body tissue protein poolμmol/min
      FbtAAaAA(i)AA flux from body tissue protein degradation to arterial poolμmol/min
      FxAAnAA(i)AA flux from extracellular pool to intracellular poolμmol/min
      FxAAvAA(i)AA flux from extracellular pool to venous poolμmol/min
      FnAAxAA(i)AA flux from intracellular pool to extracellular poolμmol/min
      FnAAcAA(i)AA flux from intracellular pool to catabolismμmol/min
      FnAAmtAA(i)AA flux from intracellular pool to mammary tissue protein poolμmol/min
      FmtAAnAA(i)AA flux from mammary tissue protein pool to intracellular poolμmol/min
      FnAAmAA(i)AA flux from intracellular pool to milkμmol/min
      1 Subscript i represents the isotope-labeled AA.
      Table 3Evaluations of predictions of essential amino acid isotope ratios after the model was fit by treatment to the observed data
      Item
      EaAAi, ExAAi and EmAAi represent isotope ratio in arterial plasma, extracellular space, and milk.
      Treatment
      Treatments = jugular infusion; CON = 3 L of 0.9% saline; MKH = 21 g/d of Met, 20 g/d of His, and 38 g/d of Lys; IL = 22 g/d of Ile and 50 g/d of Leu; MKH+IL = 21 g/d of Met, 20 g/d of His, 38 g/d of Lys, 22 g/d of Ile, and 50 g/d of Leu; all AA treatments were administered in 3 L of 0.9% saline.
      Mean observed (%)Mean predicted (%)CCC
      CCC = concordance correlation coefficient.
      RMSE
      RMSE = root mean squared errors.
      % of mean square prediction error
      % Observed meanMean biasSlope biasDispersion
      Ile
       EaAAiCON1.1351.1380.950.8320.673.5277.48
      MKH1.1301.1330.970.5634.901.4463.66
      IL1.1271.1290.970.5518.266.6875.06
      MKH+IL1.1281.1300.960.6414.0614.4271.52
       ExAAiCON1.1381.1350.950.8212.6221.6264.32
      MKH1.1341.1310.950.6814.9419.3765.59
      IL1.1281.1270.970.554.9022.0973.01
      MKH+IL1.1281.1270.960.6021.7721.9856.25
       EmAAiCON1.1091.1100.900.890.911.2694.71
      MKH1.1131.1140.930.6815.987.9776.05
      IL1.1121.1120.980.3922.5627.9749.47
      MKH+IL1.1161.1150.980.4113.4251.7934.80
      Leu
       EaAAiCON1.1221.1250.940.7520.796.8472.52
      MKH1.1201.1230.960.5427.900.9271.19
      IL1.1161.1180.960.4926.345.4468.22
      MKH+IL1.1171.1200.960.5722.749.6067.66
       ExAAiCON1.1271.1230.930.819.3522.8966.52
      MKH1.1261.1200.920.7729.2121.3449.45
      IL1.1161.1150.970.4917.5628.2654.18
      MKH+IL1.1171.1150.970.4618.9229.7151.37
       EmAAiCON1.1011.1010.920.6512.2111.9977.07
      MKH1.1031.1040.930.655.686.1088.21
      IL1.1031.1030.960.4010.4812.8676.66
      MKH+IL1.1041.1050.900.595.111.8093.09
      Lys
       EaAAiCON1.1221.1250.940.7520.796.8472.52
      MKH1.1201.1230.960.5427.900.9271.19
      IL1.1161.1180.960.4926.345.4468.22
      MKH+IL1.1171.1200.960.5722.749.6067.66
       ExAAiCON1.1271.1230.930.819.3522.8966.52
      MKH1.1261.1200.920.7729.2121.3449.45
      IL1.1171.1140.970.4917.5628.2654.18
      MKH+IL1.1171.1150.970.4618.9229.7151.37
       EmAAiCON1.1011.1030.920.6512.2111.9977.07
      MKH1.1031.1040.930.655.686.1088.21
      IL1.1031.1030.960.4010.4812.8676.66
      MKH+IL1.1041.1050.900.595.111.8093.09
      Met
       EaAAiCON1.1071.1090.880.693.437.0588.77
      MKH1.0971.0980.930.366.4816.9376.59
      IL1.1001.1020.940.4821.8216.2163.19
      MKH+IL1.0951.0970.940.3414.545.2880.18
       ExAAiCON1.1071.1060.860.8326.1024.6857.98
      MKH1.0981.0970.860.5722.0825.8452.07
      IL1.0971.0940.930.5333.8619.6641.73
      MKH+IL1.0961.0970.940.3611.0722.5866.35
       EmAAiCON1.0991.1000.840.875.663.0991.23
      MKH1.0931.0930.940.3517.0950.2632.65
      IL1.0991.0980.950.3327.5422.1750.70
      MKH+IL1.0931.0920.880.3824.9237.6037.48
      Phe
       EaAAiCON1.1551.1600.911.3617.7511.7971.63
      MKH1.1481.1530.951.0220.992.0776.94
      IL1.1461.1530.901.2532.454.2963.26
      MKH+IL1.1501.1580.921.1738.059.0652.89
       ExAAiCON1.1651.1540.861.8124.2322.0553.57
      MKH1.1641.1500.721.4722.9910.8466.17
      IL1.1541.1460.841.5127.0110.3962.60
      MKH+IL1.1571.1470.871.5526.8514.6358.53
       EmAAiCON1.1171.1180.901.1821.7713.1067.88
      MKH1.1221.1210.970.5138.9511.7249.32
      IL1.1211.1220.960.5922.4223.4154.17
      MKH+IL1.1201.1170.841.3222.849.2367.93
      Val
       EaAAiCON1.1231.1250.960.575.4711.1682.59
      MKH1.1211.1220.970.4921.802.3675.85
      IL1.1211.1230.960.5227.687.8864.43
      MKH+IL1.1211.1240.960.6727.676.0666.27
       ExAAiCON1.1251.1240.960.6411.6633.2755.07
      MKH1.1191.1170.960.6025.2029.1145.68
      IL1.1221.1210.970.517.2836.6156.11
      MKH+IL1.1221.1200.960.636.2531.4962.26
       EmAAiCON1.1031.1050.930.6814.5121.6363.86
      MKH1.1061.1020.960.5834.996.3358.68
      IL1.1051.1050.930.622.218.5389.26
      MKH+IL1.1081.1070.930.6033.6714.2452.10
      1 EaAAi, ExAAi and EmAAi represent isotope ratio in arterial plasma, extracellular space, and milk.
      2 Treatments = jugular infusion; CON = 3 L of 0.9% saline; MKH = 21 g/d of Met, 20 g/d of His, and 38 g/d of Lys; IL = 22 g/d of Ile and 50 g/d of Leu; MKH+IL = 21 g/d of Met, 20 g/d of His, 38 g/d of Lys, 22 g/d of Ile, and 50 g/d of Leu; all AA treatments were administered in 3 L of 0.9% saline.
      3 CCC = concordance correlation coefficient.
      4 RMSE = root mean squared errors.

      Plasma Amino Acid Concentrations and Uptake by Mammary Glands

      Mammary plasma flow and AA uptakes were calculated from the arteriovenous difference data. Mammary plasma flow decreased by 83 L/h (11%) in response to MKH but increased by 63 L/h (9%) with IL (Table 4). Others have observed MPF changes in response to EAA supply changes (
      • Bequette B.J.
      • Hanigan M.D.
      • Calder A.G.
      • Reynolds C.K.
      • Lobley G.E.
      • MacRae J.C.
      Amino acid exchange by the mammary gland of lactating goats when histidine limits milk production.
      ,
      • Guo C.L.
      • Li Y.T.
      • Lin X.Y.
      • Hanigan M.D.
      • Yan Z.G.
      • Hu Z.Y.
      • Hou Q.L.
      • Jiang F.G.
      • Wang Z.H.
      Effects of graded removal of lysine from an intravenously infused amino acid mixture on lactation performance and mammary amino acid metabolism in lactating goats.
      ,
      • Liu W.
      • Xia F.
      • Hanigan M.D.
      • Lin X.Y.
      • Yan Z.G.
      • White R.R.
      • Hu Z.Y.
      • Hou Q.L.
      • Wang Z.H.
      Short-term lactation and mammary metabolism responses in lactating goats to graded removal of methionine from an intravenously infused complete amino acid mixture.
      ), which is likely due to local control to maintain mammary AA balance. However, it is unclear if those signals are generated within mammary cells or externally (e.g., by endothelial cells). As intra- and extracellular AA concentrations are related (
      • Clark R.M.
      • Chandler P.T.
      • Park C.S.
      • Norman A.W.
      Extracellular amino acid effects on milk and intracellular amino acid pools with bovine mammary cells in culture.
      ); it could be either or both. If the mechanism regulating MPF is sensing AA concentrations within mammary cells, then mammary transport activity will partially dictate the MPF responses, which in turn may have an effect on AA uptake and intracellular AA concentration (
      • Madsen T.G.
      • Cieslar S.R.L.
      • Trout D.R.
      • Nielsen M.O.
      • Cant J.P.
      Inhibition of local blood flow control systems in the mammary glands of lactating cows affects uptakes of energy metabolites from blood.
      ). Further characterization of this mechanism is required to fully understand the AA transport and intracellular AA concentration in mammary glands.
      Table 4Effect of jugular AA infusions on mammary plasma flow (MPF) and net mammary uptake of amino acids
      Data are presented as least-squares treatment means; n = 4 for all treatments; net mammary uptake = MPF × (Arterial AA concentration-venous AA concentration).
      Item
      TAA = total amino acids (EAA + NEAA); EAA = Arg, His, Ile, Leu, Lys, Met, Phe, Thr, Trp, and Val; BCAA = Ile, Leu, and Val; MKH = Met, Lys, and His; IL = Ile and Leu; NEAA = Ala, Asn, Asp, Gln, Glu, Gly, Pro, Ser, and Tyr.
      Treatment
      Treatments = jugular infusion; CON = 3 L of 0.9% saline; MKH = 21 g/d of Met, 20 g/d of His, and 38 g/d of Lys; IL = 22 g/d of Ile and 50 g/d of Leu; MKH+IL = 21 g/d of Met, 20 g/d of His, 38 g/d of Lys, 22 g/d of Ile, and 50 g/d of Leu; all AA treatments were administered in 3 L of 0.9% saline.
      SEMEffect (P-value)
      CONMKHILMKH+ILMKHILMKH × IL
      MPF, L/h70665479968562.00.010.040.21
      Net mammary uptake, μmol/min
       Arg47349048347859.80.860.990.75
       His125
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      173
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      142
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      148
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      12.60.020.630.04
       Ile56464559266965.20.160.610.97
       Leu9009969711,046106.30.340.490.90
       Lys62378661169734.7<0.010.120.22
       Met162
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      237
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      180
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      177
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      22.30.030.160.02
       Phe28031428928530.10.550.690.45
       Thr35839734041841.00.090.970.53
       Trp989123.80.780.610.56
       Val813774681837108.60.430.640.21
       Ala300418420609164.30.140.130.70
       Asn12013110013225.20.350.670.64
       Asp475746549.40.170.700.92
       Gln40644337040049.90.290.220.91
       Glu48946152842565.20.280.980.52
       Gly274189235293101.30.860.570.36
       Pro27621420126848.80.950.770.10
       Ser23731624235265.40.060.630.72
       Tyr27431526828927.10.200.500.66
       TAA6,6677,3306,6837,554569.20.070.730.77
       EAA4,2874,8204,2994,767371.80.110.940.91
       BCAA2,4842,4142,2442,552235.00.550.790.35
       MKH9951,1969331,02269.10.030.050.29
       IL1,6591,6411,5631,715164.60.660.950.58
       NEAA2,4012,5112,3842,787342.70.230.520.47
      a,b Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      1 Data are presented as least-squares treatment means; n = 4 for all treatments; net mammary uptake = MPF × (Arterial AA concentration-venous AA concentration).
      2 TAA = total amino acids (EAA + NEAA); EAA = Arg, His, Ile, Leu, Lys, Met, Phe, Thr, Trp, and Val; BCAA = Ile, Leu, and Val; MKH = Met, Lys, and His; IL = Ile and Leu; NEAA = Ala, Asn, Asp, Gln, Glu, Gly, Pro, Ser, and Tyr.
      3 Treatments = jugular infusion; CON = 3 L of 0.9% saline; MKH = 21 g/d of Met, 20 g/d of His, and 38 g/d of Lys; IL = 22 g/d of Ile and 50 g/d of Leu; MKH+IL = 21 g/d of Met, 20 g/d of His, 38 g/d of Lys, 22 g/d of Ile, and 50 g/d of Leu; all AA treatments were administered in 3 L of 0.9% saline.
      Concentrations of arterial EAA tended to increase with MKH but not MKH+IL (P = 0.07), whereas venous EAA concentrations increased with IL but not MKH+IL (P = 0.03). Total arterial concentrations of AA, NEAA, and BCAA were not affected significantly, but venous concentrations increased in response to IL in the absence of MKH (P = 0.03, P = 0.04 and P = 0.09), which led to numerically decreased net uptake of total AA, NEAA, and BCAA, with IL in the absence of MKH (Table 4). However, AA uptake cannot be simply explained by AA concentrations, MPF, or transport activity in isolation, and other potential regulators may also play a role. The uptake of some EAA is facilitated by AA exchange mechanisms. For example, the L system uses concentration gradients (i.e., high intracellular concentrations) of other AA to drive L system AA removal (
      • Baumrucker C.R.
      Amino acid transport systems in bovine mammary tissue.
      ). Thus, intracellular supplies of the exchange AA could also affect AA uptake.
      Arterial and venous concentrations of Met increased with MKH (P < 0.01; Table 5, Table 6). The net uptake of Met increased with MKH but only in the absence of IL (P = 0.02). Arterial concentrations of Lys (P = 0.02) and His (P = 0.07) increased more with MKH compared with MKH+IL, whereas venous His increased with MKH (P < 0.01). These interactions implied more Lys and His were extracted from arterial plasma with MKH+IL compared with MKH. However, net uptake of Lys by mammary glands increased with MKH (P = 0.02) and net uptake of His increase with MKH only in the absence of IL (P = 0.04). Inconsistent responses in mammary uptake of these AA suggests regulation other than supply at least partially dictated uptake, and some of the infused His and Lys may have been utilized by nonmammary tissues, which is supported by numerically increased Lys flux to nonmammary tissues with MKH+IL. The sum of arterial Met, Lys, and His concentrations increased more with MKH than MKH+IL (P = 0.04), and venous concentrations increased with MKH (P < 0.01). Net uptake of Met, Lys, and His increased with MKH (P = 0.03) and decreased with IL (P = 0.05). The latter suggests that mammary catabolism of those AA was reduced with the IL infusion leading to greater efficiency within the tissue. Arterial (P = 0.05) and venous Leu (P = 0.03) increased with IL but only in the absence of MKH. Venous Ile tended to increase with only IL but not MKH+IL. The net uptake of Ile, Leu, and sum of Ile and Leu thus only numerically increased with MKH+IL, which partly verified that more IL were required to save MKH for milk protein when MKH supply increased. Changes in the arterial or venous plasma concentrations of the noninfused EAA and possibly some NEAA could have mitigated a portion of the stimulatory effects of the infused EAA, which may also explain some of the variability of plasma AA concentrations in response to EAA infusion. For example, arterial and venous Phe, Thr, Val, Gly and Tyr concentrations, venous Ser concentrations, and net uptake of Thr and Ser were affected by EAA infusions. Most AA share transport systems with one or more other AA, thus transport specificity and competition within one transport system influences transport by other systems and overall AA uptake (
      • Shennan D.B.
      • Millar I.D.
      • Calvert D.T.
      Mammary-tissue amino acid transport systems.
      ).
      Table 5Effect of jugular AA infusions on arterial plasma AA concentrations (μM)
      Data are presented as least-squares treatment means; n = 4 for all treatments.
      Item
      TAA = total amino acids (EAA + NEAA); EAA = Arg, His, Ile, Leu, Lys, Met, Phe, Thr, Trp, and Val; BCAA = Ile, Leu, and Val; MKH = Met, Lys, and His; IL = Ile and Leu; NEAA = Ala, Asn, Asp, Gln, Glu, Gly, Pro, Ser, and Tyr.
      Treatment
      Treatments = jugular infusion; CON = 3 L of 0.9% saline; MKH = 21 g/d of Met, 20 g/d of His, and 38 g/d of Lys; IL = 22 g/d of Ile and 50 g/d of Leu; MKH+IL = 21 g/d of Met, 20 g/d of His, 38 g/d of Lys, 22 g/d of Ile, and 50 g/d of Leu; all AA treatments were administered in 3 L of 0.9% saline.
      SEMEffect (P-value)
      CONMKHILMKH+ILMKHILMKH × IL
      Arg808977793.90.210.160.34
      His62
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      83
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      63
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      77
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      3.8<0.010.050.07
      Ile13514515213511.20.660.700.14
      Leu200
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      209
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      264
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      225
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      15.80.19<0.010.05
      Lys82
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      124
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      79
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      102
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      7.3<0.01<0.010.02
      Met204121372.9<0.010.430.22
      Phe47
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      48
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      49
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      41
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      2.80.160.320.08
      Thr958995776.90.100.350.35
      Trp313131310.50.990.700.86
      Val28928827723119.90.180.060.19
      Ala28928827723129.80.550.750.30
      Asn212221222.30.400.680.81
      Asp111311111.50.500.170.33
      Gln1521521621528.60.460.510.48
      Glu1059792908.00.350.110.62
      Gly24220724320222.90.010.850.80
      Pro98105105999.70.900.940.27
      Ser747686767.90.300.180.18
      Tyr33
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      35
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      34
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      25
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      3.90.090.080.04
      TAA2,0042,1042,0981,94487.00.730.690.15
      EAA1,039
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      1,147
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      1,107
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      1,036
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      53.80.670.620.07
      BCAA62464269259144.60.240.790.11
      MKH164
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      248
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      164
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      217
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      10.5<0.010.040.04
      IL335
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      354
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      415
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      360
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      26.30.340.050.08
      NEAA96595799290764.30.310.790.39
      a–c Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      1 Data are presented as least-squares treatment means; n = 4 for all treatments.
      2 TAA = total amino acids (EAA + NEAA); EAA = Arg, His, Ile, Leu, Lys, Met, Phe, Thr, Trp, and Val; BCAA = Ile, Leu, and Val; MKH = Met, Lys, and His; IL = Ile and Leu; NEAA = Ala, Asn, Asp, Gln, Glu, Gly, Pro, Ser, and Tyr.
      3 Treatments = jugular infusion; CON = 3 L of 0.9% saline; MKH = 21 g/d of Met, 20 g/d of His, and 38 g/d of Lys; IL = 22 g/d of Ile and 50 g/d of Leu; MKH+IL = 21 g/d of Met, 20 g/d of His, 38 g/d of Lys, 22 g/d of Ile, and 50 g/d of Leu; all AA treatments were administered in 3 L of 0.9% saline.
      Table 6Effect of jugular AA infusions on venous plasma AA concentration (μM)
      Data are presented as least-squares treatment means; n = 4 for all treatments.
      Item
      TAA = total amino acids (EAA + NEAA); EAA = Arg, His, Ile, Leu, Lys, Met, Phe, Thr, Trp, and Val; BCAA = Ile, Leu, and Val; MKH = Met, Lys, and His; IL = Ile and Leu; NEAA = Ala, Asn, Asp, Gln, Glu, Gly, Pro, Ser, and Tyr.
      Treatment
      Treatments = jugular infusion; CON = 3 L of 0.9% saline; MKH = 21 g/d of Met, 20 g/d of His, and 38 g/d of Lys; IL = 22 g/d of Ile and 50 g/d of Leu; MKH+IL = 21 g/d of Met, 20 g/d of His, 38 g/d of Lys, 22 g/d of Ile, and 50 g/d of Leu; all AA treatments were administered in 3 L of 0.9% saline.
      SEMEffect (P-value)
      CONMKHILMKH+ILMKHILMKH × IL
      Arg384441375.60.640.380.10
      His506753643.5<0.010.900.22
      Ile86
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      85
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      107
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      76
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      10.00.060.410.07
      Leu121
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      117
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      191
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      133
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      16.10.01<0.010.03
      Lys28
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      50
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      33
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      41
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      4.7<0.010.440.04
      Met6197222.7<0.010.210.50
      Phe22
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      19
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      27
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      16
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      3.1<0.010.410.04
      Thr61
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      53
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      68
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      40
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      5.7<0.010.590.08
      Trp313131300.50.580.210.49
      Val224
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      216
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      225
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      157
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      18.00.030.080.07
      Ala194
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      217
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      210
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      183
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      31.50.850.450.07
      Asn10
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      9
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      14
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      7
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      2.20.070.460.08
      Asp67860.90.530.910.14
      Gln1171121341177.30.140.140.42
      Glu535353526.30.900.980.94
      Gly22019122517822.50.020.740.45
      Pro76
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      85
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      90
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      75
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      9.20.560.670.04
      Ser53
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      47
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      67
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      45
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      9.2<0.010.090.03
      Tyr10
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      5
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      14
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      0
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      4.8<0.010.46<0.01
      TAA
      Treatments = jugular infusion; CON = 3 L of 0.9% saline; MKH = 21 g/d of Met, 20 g/d of His, and 38 g/d of Lys; IL = 22 g/d of Ile and 50 g/d of Leu; MKH+IL = 21 g/d of Met, 20 g/d of His, 38 g/d of Lys, 22 g/d of Ile, and 50 g/d of Leu; all AA treatments were administered in 3 L of 0.9% saline.
      1,406
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      1,426
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      1,597
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      1278
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      76.80.040.730.03
      EAA
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      667
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      700
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      782
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      615
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      52.40.110.680.03
      BCAA5430
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      417
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      523
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      365
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      41.20.020.490.04
      MKH685137931278.1<0.010.930.17
      IL7206
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      201
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      298
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      208
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      25.60.030.020.04
      NEAA8739
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      726
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      814
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      663
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      56.60.050.860.09
      a–c Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      1 Data are presented as least-squares treatment means; n = 4 for all treatments.
      2 TAA = total amino acids (EAA + NEAA); EAA = Arg, His, Ile, Leu, Lys, Met, Phe, Thr, Trp, and Val; BCAA = Ile, Leu, and Val; MKH = Met, Lys, and His; IL = Ile and Leu; NEAA = Ala, Asn, Asp, Gln, Glu, Gly, Pro, Ser, and Tyr.
      3 Treatments = jugular infusion; CON = 3 L of 0.9% saline; MKH = 21 g/d of Met, 20 g/d of His, and 38 g/d of Lys; IL = 22 g/d of Ile and 50 g/d of Leu; MKH+IL = 21 g/d of Met, 20 g/d of His, 38 g/d of Lys, 22 g/d of Ile, and 50 g/d of Leu; all AA treatments were administered in 3 L of 0.9% saline.

      Mammary Transport and Uptake

      Calculation from A-V concentration differences only reflects net AA uptake, which gives inconclusive information regarding bidirectional transport activity, whereas isotope dilution data provides more information about AA transport and metabolism in mammary glands (Table 7, Table 8).
      Table 7Rate constants (min−1) and flux (μmol/min) associated with Met, Lys, and Phe exchange with protein-bound AA in nonmammary tissue and transport constants and fluxes in mammary tissue
      Data are presented as least-squares treatment means; n = 4 for all treatments.
      VariableTreatment
      Treatments = jugular infusion; CON = 3 L of 0.9% saline; MKH = 21 g/d of Met, 20 g/d of His, and 38 g/d of Lys; IL = 22 g/d of Ile and 50 g/d of Leu; MKH+IL = 21 g/d of Met, 20 g/d of His, 38 g/d of Lys, 22 g/d of Ile, and 50 g/d of Leu; all AA treatments were administered in 3 L of 0.9% saline.
      SEMEffect (P-value)
      CONMKHILMKH+ILMKHILMKH × IL
      Met
       KaAAbtAA, min−10.730.500.410.330.100.060.010.27
       Flux to nonmammary tissue, μmol/min1,4871,7798021,2682580.070.030.63
       KxAAnAA, min−10.450.520.720.470.170.440.370.22
       KnAAxAA, min−10.470.560.440.640.290.590.930.85
       Influx, μmol/min225320238290530.110.840.60
       Efflux, μmol/min688965112510.380.750.69
       Efflux/influx, %2325283412.80.720.500.88
       Net uptake,
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      μmol/min
      162
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      232
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      186
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      172
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      210.090.260.03
       KnAAmtAA, min−10.410.410.310.790.260.210.450.22
       AA to mammary tissue, μmol/min45593573200.110.900.43
       Catabolism, μmol/min−9
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      55
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      10
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      −13
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      150.050.03<0.01
       AA to milk protein, μmol/min172176177185140.450.420.83
       Milk protein, % of net uptake106
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      79
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      99
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      108
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      7.90.030.01<0.01
      Lys
       KaAAbtAA, min−10.270.260.410.330.080.580.220.64
       Flux to nonmammary tissue, μmol/min2,3932,8123,3623,4207580.720.270.79
       KxAAnAA, min−11.480.841.170.950.290.150.720.44
       KnAAxAA, min−10.210.230.280.220.090.730.710.55
       Influx, μmol/min801932780793640.160.130.24
       Efflux, μmol/min166183169101660.430.240.22
       Efflux/influx, %181719126.90.130.410.15
       Net uptake,
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      μmol/min
      62575661169230<0.010.160.30
       KnAAmtAA, min−10.310.250.330.360.130.860.400.52
       AA to mammary tissue, μmol/min2162802582161290.900.910.57
       Catabolism, μmol/min124
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      251
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      90
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      147
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      32<0.010.010.10
       AA to milk protein, μmol/min507505522545380.630.250.56
       Milk protein, % of net uptake806886795.10.010.010.29
      Phe
       KaAAbtAA, min−10.220.170.190.180.030.270.620.60
       Flux to nonmammary tissue, μmol/min1,1148609687311350.040.200.93
       KxAAnAA, min−10.430.800.460.530.210.240.510.39
       KnAAxAA, min−10.980.801.010.860.240.450.850.96
       Influx, μmol/min5956086186401060.850.760.96
       Efflux, μmol/min3102963303541040.960.680.84
       Efflux/influx, %4948534912.90.760.760.89
       Net uptake,
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      μmol/min
      283312288287290.570.680.54
       KnAAmtAA, min−10.640.440.630.820.150.970.120.11
       AA to mammary tissue, μmol/min183
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      176
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      205
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      277
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      340.140.020.09
       Catabolism, μmol/min2439141200.950.130.33
       AA to milk protein, μmol/min265272274286210.400.340.81
       Milk protein, % of net uptake9388961007.20.900.150.35
      a–c Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      1 Data are presented as least-squares treatment means; n = 4 for all treatments.
      2 Treatments = jugular infusion; CON = 3 L of 0.9% saline; MKH = 21 g/d of Met, 20 g/d of His, and 38 g/d of Lys; IL = 22 g/d of Ile and 50 g/d of Leu; MKH+IL = 21 g/d of Met, 20 g/d of His, 38 g/d of Lys, 22 g/d of Ile, and 50 g/d of Leu; all AA treatments were administered in 3 L of 0.9% saline.
      Table 8Rate constants (min−1) and flux (μmol/min) associated with BCAA exchange with protein-bound AA in nonmammary tissue and transport constants and fluxes in mammary tissue
      Data are presented as least-squares treatment means. n = 4 for all treatments.
      VariableTreatment
      Treatments = jugular infusion; CON = 3 L of 0.9% saline; MKH = 21 g/d of Met, 20 g/d of His, and 38 g/d of Lys; IL = 22 g/d of Ile and 50 g/d of Leu; MKH+IL = 21 g/d of Met, 20 g/d of His, 38 g/d of Lys, 22 g/d of Ile, and 50 g/d of Leu; all AA treatments were administered in 3 L of 0.9% saline.
      SEMEffect (P-value)
      CONMKHILMKH+ILMKHILMKH × IL
      Ile
       KaAAbtAA, min−10.120.110.120.100.030.650.870.99
       Flux to nonmammary tissue, μmol/min1,8181,6512,2941,4635740.350.780.52
       KxAAnAA, min−10.430.450.690.750.230.830.170.90
       KnAAxAA, min−10.580.590.550.620.170.820.960.81
       Influx, μmol/min8889271,0141,1041540.640.290.85
       Efflux, μmol/min3072914354381170.950.220.93
       Efflux/influx, %342949405.50.190.030.58
       Net uptake,
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      μmol/min
      576636542666540.030.940.36
       KnAAmtAA, min−10.960.690.530.590.180.470.090.27
       AA to mammary tissue, μmol/min541
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      331
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      344
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      390
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      810.160.230.05
       Catabolism, μmol/min172226157257560.200.810.99
       AA to milk protein, μmol/min399410412431320.400.340.81
       Milk protein, % of net uptake67
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      64
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      77
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      66
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      5.30.020.030.09
      Leu
       KaAAbtAA, min−10.160.140.110.100.020.420.090.90
       Flux to nonmammary tissue, μmol/min3,4852,9433,0892,4154970.190.300.88
       KxAAnAA, min−10.420.560.590.640.090.150.070.50
       KnAAxAA, min−10.53
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      0.73
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      0.86
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      0.63
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      0.150.970.250.09
       Influx, μmol/min1,3391,6071,8531,7201540.580.040.13
       Efflux, μmol/min420
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      616
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      879
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      679
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      1480.980.030.07
       Efflux/influx, %29
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      38
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      47
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      39
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      6.60.830.050.07
       Net uptake,
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      μmol/min
      9089911,0051,0411000.480.390.77
       KnAAmtAA, min−10.92
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      0.67
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      0.57
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      0.83
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      0.160.990.350.04
       AA to mammary tissue, μmol/min6945825808651620.440.450.11
       Catabolism, μmol/min2393102893251060.530.700.83
       AA to milk protein, μmol/min663681684715530.400.340.81
       Milk protein, % of net uptake736971707.10.590.910.73
      Val
       KaAAbtAA, min−10.100.130.080.100.020.180.210.88
       Flux to nonmammary tissue, μmol/min3,1873,9452,2322,5567770.430.120.75
       KxAAnAA, min−10.460.550.450.560.130.370.990.96
       KnAAxAA, min−11.051.070.930.790.220.670.170.58
       Influx, μmol/min1,79919401,6371,7863610.610.580.99
       Efflux, μmol/min1,0221,1749529643610.740.580.78
       Efflux/influx, %5459534712.90.930.470.53
       Net uptake,
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      μmol/min
      8287676858221050.600.540.20
       KnAAmtAA, min−10.680.510.670.750.180.750.400.37
       AA to mammary tissue, μmol/min6795495587421840.860.810.32
       Catabolism, μmol/min329254170283880.710.250.12
       AA to milk protein, μmol/min499513515539400.400.340.81
       Milk protein, % of net uptake65
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      67
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      76
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      66
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      6.50.070.030.02
      a–c Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      1 Data are presented as least-squares treatment means. n = 4 for all treatments.
      2 Treatments = jugular infusion; CON = 3 L of 0.9% saline; MKH = 21 g/d of Met, 20 g/d of His, and 38 g/d of Lys; IL = 22 g/d of Ile and 50 g/d of Leu; MKH+IL = 21 g/d of Met, 20 g/d of His, 38 g/d of Lys, 22 g/d of Ile, and 50 g/d of Leu; all AA treatments were administered in 3 L of 0.9% saline.

      Transport Affinity (Rate Constants)

      • Bequette B.J.
      • Hanigan M.D.
      • Calder A.G.
      • Reynolds C.K.
      • Lobley G.E.
      • MacRae J.C.
      Amino acid exchange by the mammary gland of lactating goats when histidine limits milk production.
      demonstrated the udder can respond to AA limitations by altering transport activity to maintain AA uptake. Such changes in activity could be driven by a change in the number of transporters or their AA affinity, or a change in the perfused capillary surface area. Modeling work has demonstrated that transport flexibility is required to support metabolic flexibility (
      • Hanigan M.D.
      • France J.
      • Crompton L.A.
      • Bequette J.B.
      Evaluation of a representation of the limiting amino acid theory for milk protein synthesis.
      ). In current study, infusion of MKH and IL had no effect on the Met and Lys influx rate constants (Table 7). Mammary affinity for Lys was previously observed to be unaffected by Lys supply (
      • Guo C.L.
      • Li Y.T.
      • Lin X.Y.
      • Hanigan M.D.
      • Yan Z.G.
      • Hu Z.Y.
      • Hou Q.L.
      • Jiang F.G.
      • Wang Z.H.
      Effects of graded removal of lysine from an intravenously infused amino acid mixture on lactation performance and mammary amino acid metabolism in lactating goats.
      ), but
      • Liu W.
      • Xia F.
      • Hanigan M.D.
      • Lin X.Y.
      • Yan Z.G.
      • White R.R.
      • Hu Z.Y.
      • Hou Q.L.
      • Wang Z.H.
      Short-term lactation and mammary metabolism responses in lactating goats to graded removal of methionine from an intravenously infused complete amino acid mixture.
      reported mammary affinity for Met increased when Met deficiency varied from 40 to 100%. The lack of a change in affinity (rate constants) in the current study may be due to the smaller Met supply differences among treatments (15%) or a less deficient state for the control treatment than that imposed by the single-limiting AA infusion models used by
      • Bequette B.J.
      • Hanigan M.D.
      • Calder A.G.
      • Reynolds C.K.
      • Lobley G.E.
      • MacRae J.C.
      Amino acid exchange by the mammary gland of lactating goats when histidine limits milk production.
      and
      • Liu W.
      • Xia F.
      • Hanigan M.D.
      • Lin X.Y.
      • Yan Z.G.
      • White R.R.
      • Hu Z.Y.
      • Hou Q.L.
      • Wang Z.H.
      Short-term lactation and mammary metabolism responses in lactating goats to graded removal of methionine from an intravenously infused complete amino acid mixture.
      . Changes in efflux rate constants across treatments were not evident for Met, Lys and Phe, which was consistent with influx rate constants.
      • Yoder P.S.
      Evaluation of amino acid transport and protein metabolism in the mammary gland of dairy cattle. PhD thesis.
      also reported efflux rate constants were related to influx transport regulation.
      The mammary influx (extracellular to intracellular) rate constants for Leu, Ile and Val were similar to one another (Table 8) as might be expected given their common transport system (
      • Baumrucker C.R.
      Amino acid transport systems in bovine mammary tissue.
      ). However, they were inconsistent with previous findings in mammary cells (
      • Yoder P.S.
      Evaluation of amino acid transport and protein metabolism in the mammary gland of dairy cattle. PhD thesis.
      ), which reported much lower transport affinity for Val than Ile and Leu. The rate constant for Ile and Val influx was not affected by IL, whereas the rate constant for Leu influx tended to increase with IL (P = 0.07). These seemingly divergent responses suggest some diversity among the BCAA transporters.
      • Yoder P.S.
      Evaluation of amino acid transport and protein metabolism in the mammary gland of dairy cattle. PhD thesis.
      reported that Ile transport was saturated in mammary cells at the upper end of the in vivo Ile plasma concentration range (125 µmol/L). Plasma Ile concentrations ranged up to 152 µmol/L with IL herein (Table 5); however, the numerical increases in the Ile rate constant with IL treatment suggests the transporter was not saturated. The tendency for an increase in the rate constant for Leu influx with IL along with the numerical increases for Ile suggests that other drivers of transport were complicit. In vitro work showed that AA exchange by the L system can be regulated by sodium-driven transport of Ala, Gln, or Gly [i.e., the removal of sodium or Gln and excess Ala and Met in the media inhibited uptake of System L AA substrates (
      • Jackson S.C.
      • Bryson J.M.
      • Wang H.
      • Hurley W.L.
      Cellular uptake of valine by lactating porcine mammary tissue.
      )]. However, in the current study, venous concentrations of Met (representative of that at the cell surface) and the sum of Ala, Gln, and Gly were essentially identical for the treatments with and without infused IL, and thus do not explain the apparent changes in transport activity. The observed increase in MPF is perhaps more informative, indicating a potential deficiency in supply relative to need. This is perhaps the same mechanism that drove the trend for increased transport activity. However, the deficiency driving MPF was clearly not arising from Ile or Leu given the observed increases in supply.
      The efflux rate constants were generally consistent with influx rate constants. The Leu efflux rate constant tended to increase with IL but only in absence of MKH (P = 0.09), implying that more Leu was captured and metabolized with MKH+IL (Table 8). The potential explanation for the increased Leu efflux with IL alone was that the increase in use for protein synthesis plus catabolism was less than the increase in supply elicited by mass action thus eliciting a signal that resulted in increased efflux transport activity. When MKH was supplied with IL (MKH+IL), if Leu in the cell is depleted due to influx being less than protein biosynthesis and metabolism, Leu transport out of the cell would fall due to mass action.
      • Yoder P.S.
      Evaluation of amino acid transport and protein metabolism in the mammary gland of dairy cattle. PhD thesis.
      reported that increases or decreases of AA efflux in mammary glands due to a change in extracellular supply were proportional to the increases or decreases of AA influx considering common transporters were used. However, intramammary AA exchange is also regulated by other factors including cell water movement across the cell membrane (
      • Christensen H.N.
      Role of amino acid transport and countertransport in nutrition and metabolism.
      ), and concentration gradients of other AA due to AA exchange mechanisms. The combination of diffusion and exchange effects on influx and efflux movement could result in divergent or mixed activity.

      Bidirectional AA Transport (Flux Rate)

      Flux is a function of the transport rate constant and precursor AA pool size, thus changes in flux were not always consistent with changes in flux rate constants. Influx and efflux of Lys were not affected by EAA infusion, although the numerical changes in influx were similar and consistent with the significant effect of MKH on net uptake measured as the difference between influx and efflux. The bidirectional fluxes of Met were not affected by treatments. Net uptake of Met increased with MKH but not MKH+IL (P = 0.03), which was likely due to numerically greater Met efflux with MKH+IL. One possible explanation was that more Met was used for catabolism instead of returning to plasma due to Ile and Leu deficiency when only MKH was infused, which was verified by increased Met catabolism with only MKH infusion (P < 0.01). On average, 27% of Met and 17% of Lys extracted by mammary glands was returned to plasma, which was consistent with previous findings (
      • Hanigan M.D.
      • France J.
      • Mabjeesh S.J.
      • McNabb W.C.
      • Bequette B.J.
      High rates of mammary tissue protein turnover in lactating goats are energetically costly.
      ). Phenylalanine efflux from the tissue to plasma ranged from 49 to 55% of influx, which was higher than for Met and Lys. The supply of MKH and IL had no effect on Phe flux or net uptake independently or additively, in contrast with
      • Berthiaume R.
      • Thivierge M.C.
      • Patton R.A.
      • Dubreuil P.
      • Stevenson M.
      • McBride B.W.
      • Lapierre H.
      Effect of ruminally protected methionine on splanchnic metabolism of amino acids in lactating dairy cows.
      who reported a linear increase in mammary extraction of Phe with rumen-protected Met supplementation. One possible reason for this inconsistency was the decreased MPF with MKH in the current study, which was not observed in Berthiaume study. The difference could also reflect potential differences in Phe and Tyr supply within the mammary glands. If Tyr were more limiting in the prior work than herein, increased use for milk protein would necessitate additional production of Tyr from Phe perhaps explaining the linear relationship in the prior work, and the lack of relationship in the current work.
      Influx of Leu increased with IL (P = 0.04), and efflux tended to increase more in response to IL alone than to MKH+IL (P = 0.07), which was consistent with Leu influx and efflux rate constants (Table 8). In agreement, numerically more Leu was used for milk protein, incorporated into mammary tissue, and catabolized with MKH+IL. Therefore, less Leu was available in the intracellular pool for transport back to the plasma. Infusion of MKH and IL had no effect on the influx and efflux of Ile, whereas the net uptake of Ile increased with MKH but not with IL (P = 0.03). The lack of change in milk protein production with IL and increased milk protein production with MKH were consistent with this response. The MKH and IL infusions had no effect on Val uptake independently or additively.
      The proportion of extracted Ile and Leu returned to the extracellular space (efflux/influx) ranged from 31 to 49%. The ratio of Ile efflux increased with IL infusions (P = 0.03) independent of MKH, whereas efflux/influx of Leu increased with IL only in the absence of MKH (P = 0.07). Efflux relative to influx was greater for Val, ranging from 47 to 59% with the lowest observed return occurring with MKH+IL. The BCAA return ratios were consistent with previous findings (
      • Hanigan M.D.
      • France J.
      • Mabjeesh S.J.
      • McNabb W.C.
      • Bequette B.J.
      High rates of mammary tissue protein turnover in lactating goats are energetically costly.
      ,
      • Yoder P.S.
      Evaluation of amino acid transport and protein metabolism in the mammary gland of dairy cattle. PhD thesis.
      ), which ranged from 17 to 80% of BCAA influx.
      • Yoder P.S.
      Evaluation of amino acid transport and protein metabolism in the mammary gland of dairy cattle. PhD thesis.
      observed that the ratio of efflux to influx increased as intracellular AA concentrations increased. Enhanced efflux/influx of Ile and Leu was apparently in response to an oversupply in the mammary cells relative to milk protein needs and no increased stimulation of catabolism.

      Mammary AA Metabolism

      Amino Acid Exchange with Mammary Tissue

      The rate constants of Met, Lys, and Phe exchange with mammary tissue protein were not affected by treatments (Table 7). The flux rate of Phe to mammary tissue tended to increase with MKH+IL but not IL or MKH alone (P = 0.09), indicating higher protein turnover with MKH+IL.
      The rate constants of Ile and Val exchange with mammary tissue protein were not affected by treatments (Table 8). The rate constant for Leu decreased with IL alone but not with MKH+IL (P = 0.04). Increased intracellular concentrations of Leu with IL would have to be offset by a reduction in the synthesis rate constant to maintain the same body protein and set protein/DNA ratio (
      • Oltjen J.W.
      • Bywater A.C.
      • Baldwin R.L.
      • Garrett W.N.
      Development of a dynamic model of beef cattle growth and composition.
      ,
      • Di Marco O.N.
      • Baldwin R.L.
      • Calvert C.C.
      Simulation of DNA, protein and fat accretion in growing steers.
      ), whereas MKH+IL infusion may not have increased intracellular Leu as much, due to numerically increased capture in milk protein.

      Amino Acids for Milk Protein Synthesis and Catabolism

      Methionine and Lys incorporated into milk protein was not affected by treatments. The catabolism of Met and Lys increased or tended to increase with MKH but only in the absence of IL (P < 0.01 for Met, P = 0.10 for Lys), implying Met and Lys catabolism increases when Ile and Leu are deficient. Lysine may be oxidized in a regulated or passive manner to provide ketogenic intermediates. For example, during 5-d infusions of Lys (9 g/d) in late lactation goats, Lys oxidation increased from 16 to 30%, representing an almost 2-fold increase in the absolute rate of oxidation (
      • Bequette B.J.
      • Backwell F.R.
      • Crompton L.A.
      Current concepts of amino acid and protein metabolism in the mammary gland of the lactating ruminant.
      ).
      Catabolism of Leu, Ile, and Val were not affected by treatment in accordance with the absence of any significant change in mammary used for milk protein. Branched-chain AA catabolism depends upon phosphorylation status of keto acid dehydrogenase, which is regulated by insulin and BCAA concentrations. The enzyme has reduced activity (phosphorylated) when insulin is high and BCAA concentrations are low, resulting in reduced catabolism (
      • Razooki Hasan H.
      • White D.A.
      • Mayer R.J.
      Extensive destruction of newly synthesized casein in mammary explants in organ culture.
      ). One possible explanation for the lack of change in Ile and Leu catabolism in the current study was that the IL infusion was not enough to elicit a change in insulin (
      • Kuhara T.
      • Ikeda S.
      • Ohneda A.
      • Sasaki Y.
      Effects of intravenous infusion of 17 amino acids on the secretion of GH, glucagon, and insulin in sheep.
      ) and intracellular Ile and Leu concentrations (
      • Clark R.M.
      • Chandler P.T.
      • Park C.S.
      • Norman A.W.
      Extracellular amino acid effects on milk and intracellular amino acid pools with bovine mammary cells in culture.
      ).

      Efficiency of Mammary Uptake of AA Used for Milk Protein

      Model-derived AA efficiencies (AA to milk protein, % of net uptake; Table 7, Table 8) were very similar to calculated AA efficiency by AV difference (Table 9). Thus, they are discussed together here.
      Table 9Effect of jugular AA infusions on efficiency of AA uptake converted into milk protein (%)
      Data are presented as least-squares treatment means. n = 4 for all treatments.
      AA efficiency (%), AA to milk protein, % of net uptake
      TAA = total amino acids (EAA + NEAA); EAA = Arg, His, Ile, Leu, Lys, Met, Phe, Thr, Trp and Val; NEAA = Ala, Asn, Asp, Gln, Glu, Gly, Pro, Ser, and Tyr.
      Treatment
      Treatments = jugular infusion; CON = 3 L of 0.9% saline; MKH = 21 g/d of Met, 20 g/d of His, and 38 g/d of Lys; IL = 22 g/d of Ile and 50 g/d of Leu; MKH+IL = 21 g/d of Met, 20 g/d of His, 38 g/d of Lys, 22 g/d of Ile, and 50 g/d of Leu; all AA treatments were administered in 3 L of 0.9% saline.
      SEMEffect (P-value)
      CONMKHILMKH+ILMKHILMKH × IL
      Arg39
      Indicates the value is significantly different from 100.
      40
      Indicates the value is significantly different from 100.
      38
      Indicates the value is significantly different from 100.
      40
      Indicates the value is significantly different from 100.
      4.10.620.940.82
      His131
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      94
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      115
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      115
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      11.20.060.730.05
      Ile71
      Indicates the value is significantly different from 100.
      63
      Indicates the value is significantly different from 100.
      72
      Indicates the value is significantly different from 100.
      65
      Indicates the value is significantly different from 100.
      7.10.140.750.86
      Leu75
      Indicates the value is significantly different from 100.
      68
      Indicates the value is significantly different from 100.
      74
      Indicates the value is significantly different from 100.
      69
      Indicates the value is significantly different from 100.
      6.80.310.970.83
      Lys81
      Indicates the value is significantly different from 100.
      66
      Indicates the value is significantly different from 100.
      8678
      Indicates the value is significantly different from 100.
      5.1<0.010.010.18
      Met107
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      75
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      Indicates the value is significantly different from 100.
      101
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      106
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      8.2<0.01<0.01<0.01
      Phe9487961007.20.830.180.26
      Thr9386
      Indicates the value is significantly different from 100.
      10285
      Indicates the value is significantly different from 100.
      9.00.030.500.36
      Val67
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      Indicates the value is significantly different from 100.
      66
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      Indicates the value is significantly different from 100.
      77
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      Indicates the value is significantly different from 100.
      65
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      Indicates the value is significantly different from 100.
      7.30.020.060.04
      Ala1011059059
      Indicates the value is significantly different from 100.
      36.10.620.250.47
      Asn261
      Indicates the value is significantly different from 100.
      213
      Indicates the value is significantly different from 100.
      288
      Indicates the value is significantly different from 100.
      232
      Indicates the value is significantly different from 100.
      43.30.210.540.92
      Asp517
      Indicates the value is significantly different from 100.
      490
      Indicates the value is significantly different from 100.
      584
      Indicates the value is significantly different from 100.
      498
      Indicates the value is significantly different from 100.
      114.70.470.640.71
      Gln138
      Indicates the value is significantly different from 100.
      133
      Indicates the value is significantly different from 100.
      159
      Indicates the value is significantly different from 100.
      156
      Indicates the value is significantly different from 100.
      14.20.560.020.99
      Glu151
      Indicates the value is significantly different from 100.
      188152
      Indicates the value is significantly different from 100.
      195
      Indicates the value is significantly different from 100.
      38.00.230.890.92
      Gly9116314811251.10.660.940.20
      Pro306
      Indicates the value is significantly different from 100.
      466
      Indicates the value is significantly different from 100.
      437
      Indicates the value is significantly different from 100.
      323
      Indicates the value is significantly different from 100.
      117.20.760.940.12
      Ser294182
      Indicates the value is significantly different from 100.
      416166
      Indicates the value is significantly different from 100.
      145.90.160.650.56
      Tyr100891071026.10.180.100.54
      TAA
      Treatments = jugular infusion; CON = 3 L of 0.9% saline; MKH = 21 g/d of Met, 20 g/d of His, and 38 g/d of Lys; IL = 22 g/d of Ile and 50 g/d of Leu; MKH+IL = 21 g/d of Met, 20 g/d of His, 38 g/d of Lys, 22 g/d of Ile, and 50 g/d of Leu; all AA treatments were administered in 3 L of 0.9% saline.
      1071011081027.30.140.780.96
      EAA
      Indicates the value is significantly different from 100.
      74
      Indicates the value is significantly different from 100.
      69
      Indicates the value is significantly different from 100.
      65
      Indicates the value is significantly different from 100.
      73
      Indicates the value is significantly different from 100.
      11.20.850.810.49
      NEAA
      Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      174
      Indicates the value is significantly different from 100.
      167
      Indicates the value is significantly different from 100.
      179
      Indicates the value is significantly different from 100.
      153
      Indicates the value is significantly different from 100.
      20.90.320.750.53
      a,b Least squares means within a row with different superscripts are considered significantly different (P < 0.05) or tend to be different (P < 0.1).
      1 Data are presented as least-squares treatment means. n = 4 for all treatments.
      2 TAA = total amino acids (EAA + NEAA); EAA = Arg, His, Ile, Leu, Lys, Met, Phe, Thr, Trp and Val; NEAA = Ala, Asn, Asp, Gln, Glu, Gly, Pro, Ser, and Tyr.
      3 Treatments = jugular infusion; CON = 3 L of 0.9% saline; MKH = 21 g/d of Met, 20 g/d of His, and 38 g/d of Lys; IL = 22 g/d of Ile and 50 g/d of Leu; MKH+IL = 21 g/d of Met, 20 g/d of His, 38 g/d of Lys, 22 g/d of Ile, and 50 g/d of Leu; all AA treatments were administered in 3 L of 0.9% saline.
      * Indicates the value is significantly different from 100.
      Previous studies indicated that Met and Phe extracted from plasma were quantitatively transferred to milk protein whereas Lys was extracted in excess of milk protein output with the excess catabolized within the udder (
      • Mepham T.B.
      • Linzell J.L.
      A quantitative assessment of the contribution of individual plasma amino acids to the synthesis of milk proteins by the goaat mammary gland.
      ;
      • Bickerstaffe R.
      • Annison E.F.
      • Linzell J.L.
      The metabolism of glucose, acetate, lipids and amino acids in lactating dairy cows.
      ;
      • Guinard J.
      • Rulquin H.
      Effect of graded levels of duodenal infusions of casein on mammary uptake in lactating cows. 2. Individual amino acids.
      ).
      • Lapierre H.
      • Doepel L.
      • Milne E.
      • Lobley G.E.
      Responses in mammary and splanchnic metabolism to altered lysine supply in dairy cows.
      also reported decreased efficiency of use of extracted Lys for milk protein and increased Lys usage for NEAA synthesis when Lys was infused into cows fed a Lys-deficient diet. The efficiency of extracted Lys used for milk protein increased with IL (P = 0.01), indicating less Lys was catabolized when Ile and Leu were infused.
      • Lapierre H.
      • Lobley G.E.
      • Doepel L.
      • Raggio G.
      • Rulquin H.
      • Lemosquet S.
      Triennial Lactation Symposium: Mammary metabolism of amino acids in dairy cows.
      indicated that Met was not taken up in excess when supply increased above mammary demand for milk protein, but the current data do not support that concept as the Met output in milk was 25% lower than Met uptake with MKH alone whereas the Met efficiency (g of Met in milk/g of Met uptake) of the other 3 treatments approximated unity from both AV calculation (Table 9) and isotope dilution results (Table 7). The numerically lower Met efflux and significantly higher Met catabolism with MKH alone contributed to the lower efficiency of extracted Met used for milk protein. However, the decreased Met efficiency with MKH infusion was not observed in the full study (
      • Yoder P. S
      • Huang X.
      • Teixeira I.A.
      • Cant J.P.
      • Hanigan M.D.
      Effects of jugular infused methionine, lysine, and histidine as a group or leucine and isoleucine as a group on production and metabolism in lactating dairy cows.
      ). The discrepancy between studies cannot be explained.
      Model-derived efficiencies of Ile, Leu, and Val used for milk protein (Table 8, Table 9) are consistent with previous work (
      • Clark J.H.
      Lactational responses to postruminal administration of proteins and amino acids.
      ;
      • Mepham T.B.
      Amino acid utilization by lactating mammary gland.
      ;
      • Hanigan M.D.
      • Crompton L.A.
      • Metcalf J.A.
      • France J.
      Modelling mammary metabolism in the dairy cow to predict milk constituent yield, with emphasis on amino acid metabolism and milk protein production: Model construction.
      ). Model-derived efficiency estimates for Ile (P = 0.09) and Val (P = 0.02) use for milk protein tended to increase or significantly increased with IL alone but not MKH+IL. The AV-based calculation had the same change for Val efficiency and a numerically similar trend for Ile. The increased efficiency was likely due to decreased oxidation with IL, which was consistent with numerically decreased Ile and Val catabolism and decreased MUN with IL. However, the interaction cannot be explained here. One potential reason is that more Ile and Val may have been trans-aminated to NEAA with MKH+IL to support increased milk protein synthesis. Although previous studies have shown that the proportion of Leu that was oxidized by the mammary gland was substantially reduced (0.19 vs. 0.07) by the infusion of AA other than Leu (
      • Bequette B.J.
      • Backwell F.R.
      • Crompton L.A.
      Current concepts of amino acid and protein metabolism in the mammary gland of the lactating ruminant.
      ), the efficiency of Leu use for milk protein was not affected by MKH in the current study.
      The efficiency of extracted total AA (TAA) and other AA used for milk protein was also calculated from AV differences (Table 9). The efficiency of use of extracted TAA for milk protein averaged 100% and was not affected by treatments.
      • Hanigan M.D.
      • Crompton L.A.
      • Metcalf J.A.
      • France J.
      Modelling mammary metabolism in the dairy cow to predict milk constituent yield, with emphasis on amino acid metabolism and milk protein production: Model construction.
      ,
      • Hanigan M.D.
      • Crompton L.A.
      • Bequette B.J.
      • Mills J.A.N.
      • France J.
      Modelling mammary metabolism in the dairy cow to predict milk constituent yield, with emphasis on amino acid metabolism and milk protein production: Model evaluation.
      ) observed that the TAA were in balance across several studies and diets when conducting meta analyses, and others, including
      • Omphalius C.
      • Lapierre H.
      • Guinard-Flament J.
      • Lamberton P.
      • Bahloul L.
      • Lemosquet S.
      Amino acid efficiencies of utilization vary by different mechanisms in response to energy and protein supplies in dairy cows: Study at mammary-gland and whole-body levels.
      , have subsequently reported the same. These results demonstrate the flexibility of mammary glands in maintaining N balance. The average efficiency of EAA was lower than 100%, whereas the efficiency of NEAA was higher than 100%. As efficiency of TAA was not different from 100%, the EAA must donate their N via transamination to NEAA which were subsequently captured in milk protein. The lack of change in TAA efficiency with the EAA infusions when the animals were fed AA-deficient diets (15% MP deficiency) is inconsistent with the findings by
      • Doepel L.
      • Lapierre H.
      Changes in production and mammary metabolism of dairy cows in response to essential and nonessential amino acid infusions.
      .
      The observation that TAA are generally always in balance across the mammary glands offers an alternative reference point for deriving blood flow estimates. Calculating from only 1 or 2 EAA is subject to greater variation than utilizing 18 or 20 AA. This is particularly true for Met which generally has greater measurement error than other AA. Thus, when using the Fick principle to estimate blood flow, it seems advisable to use TAA as a basis for that calculation, rather than Met or Phe + Tyr as is commonly undertaken including herein.

      Amino Acid Exchange with Nonmammary Tissue

      The rate constant for Met incorporation into nonmammary tissue protein decreased significantly with IL and tended to decrease with MKH. The rate constant for Leu incorporated into nonmammary tissue protein tended to decrease with IL. At maturity, body tissues are programmed to maintain a constant protein to DNA ratio (
      • Oltjen J.W.
      • Bywater A.C.
      • Baldwin R.L.
      • Garrett W.N.
      Development of a dynamic model of beef cattle growth and composition.
      ;
      • Di Marco O.N.
      • Baldwin R.L.
      • Calvert C.C.
      Simulation of DNA, protein and fat accretion in growing steers.
      ). Because AA transport is tied to tissue use, any reductions in tissue use should be reflected in transport activity. Thus, an increase in arterial Leu and Met concentrations would initially act to drive body tissue protein synthesis higher, but this would be resisted either by a reduction in synthesis rates or an increase in protein degradation rates to maintain the desired protein to DNA ratio. The failure to use the extra AA present in those cells would cause a reduction in AA transport rates, which would manifest as a reduction in the tissue transport rate constant as observed for Met and the trend for Leu. However, it is difficult to mentally consider all the likely interactions among AA, energy, and hormonal signals to predict how nonmammary tissue will respond to each AA, and it seems from the current data that flux into these tissues was affected by the other factors in addition to altered plasma concentrations. The Met flux to nonmammary tissue decreased with IL but increased with MKH, and Phe flux to nonmammary tissue was decreased with MKH. Fluxes of Lys, Ile, Leu, and Val to nonmammary tissue were not affected by treatments. A response to IL was anticipated given the strong effect of at least Leu on the regulation of muscle protein synthesis (
      • Escobar J.
      • Frank J.W.
      • Suryawan A.
      • Nguyen H.V.
      • Kimball S.R.
      • Jefferson L.S.
      • Davis T.A.
      Regulation of cardiac and skeletal muscle protein synthesis by individual branched-chain amino acids in neonatal pigs.
      ). Although
      • Curtis R.V.
      • Kim J.J.M.
      • Doelman J.
      • Cant J.P.
      Maintenance of plasma branched-chain amino acid concentrations during glucose infusion directs essential amino acids to extra-mammary tissues in lactating dairy cows.
      found that infusing BCAA plus glucose directed more BCAA to skeletal muscle, that effect was likely driven more by the insulin response to the glucose infusion than to the BCAA. Overall, the current results demonstrated that EAA anabolic use by nonmammary tissues remained relatively constant across treatments as supply increased. This suggests that nonmammary body tissue net deposition is maximized at absorbed AA supply and plasma AA concentrations below those generated with the CON treatment in the current work.

      CONCLUSIONS

      Jugular infusions of MKH resulted in increased milk protein yields in high-producing dairy cows, and infusions of IL resulted in numerical increases in milk protein yields. Milk production and efficiency of EAA used for milk protein were maximized when all EAA met animal requirement estimates (MKH+IL). There was no apparent single mechanism across AA that explained the production responses, which supports our assumption that EAA transport and metabolism in mammary glands differ in response to infusion of different EAA profiles. Generally, findings in the current study demonstrated that across EAA, uptake transport activity was not significantly affected by EAA supply, and if the excess EAA were not able to stimulate milk protein synthesis, they were catabolized (Lys and Met), returned to circulation (Ile, Leu, and Val), or deposited in other mammary sinks (Phe). In addition, the fact that several parameters related to EAA transport and metabolism were affected differently by MKH in the presence or absence of IL (or vice versa) demonstrated that the deficiency or supplement of an EAA can affect the use of other EAA. Thus, it is inaccurate to use a fixed, constant efficiency within and across AA in postabsorption models. Although the general change in AA uptake and metabolism confirmed previous findings in bovine mammary cell experiments and goat mammary net balance experiments, quantitative aspects of AA transport and metabolism in high-producing cows were demonstrated here. Finally, there is not a single mechanism used across EAA to deal with altered supply of each EAA to the animal. Thus, the production response surface for each AA can be expected to be divergent and complicated when combined to predict protein synthesis rates.

      ACKNOWLEDGMENTS

      Financial support for this work was provided by Perdue Agricultural Company (Salisbury, MD); the Virginia Agricultural Experiment Station (Regional Research Project NC-2040; Blacksburg, VA) and the Hatch Program of the National Institute of Food and Agriculture, U.S. Department of Agriculture (Washington, DC); the College of Agriculture and the Life Sciences Pratt Endowment at Virginia Tech (Blacksburg, VA); and Balchem Corp. (New Hampton, NY). The AA used in the trial were donated by Evonik Nutrition & Care GmbH (Hanau, Germany). Huang was partially supported by the China Scholarship Council (Beijing, China). The authors also appreciate the support of the Hanigan laboratory students and Kentland farm staff (Virginia Tech, Blacksburg, VA). The other authors have not stated any conflicts of interest.

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