Short communication: Effect of manipulating fatty acid profile on gluconeogenic gene expression in bovine primary hepatocytes.

During the peripartum period, dairy cows experience both an increase in circulating fatty acid (FA) profile and a change in circulating FA profile, which have been shown to alter regulation of gluconeogenic genes. The objective was to quantify gene expression of key enzymes involved in gluconeogenesis and FA transport into the mitochondria in primary hepatocytes in response to exposure to an FA mixture mimicking what is circulating in a transition dairy cow with or without enrichment of C16:0, C18:0, and C18:1. Primary hepatocytes were isolated from 4 Holstein bull calves 3 d of age (± standard deviation 2 d) and cultured. Twenty-four hours after plating, treatments were applied to the cells for 24-h incubation. Treatments consisted of (1) control (1% BSA), (2) 0.75 mM FA cocktail (3% C14:0, 27% C16:0, 23% C18:0, 31% C18:1, 8% C18:2, and 8% C18:3 to mimic the FA profile of dairy cattle at calving), (3) 0.90 mM FA cocktail, (4) 0.75 mM FA cocktail + 0.15 mM C16:0, (5) 0.75 mM FA cocktail + 0.15 mM C18:0, and (6) 0.75 mM FA cocktail + 0.15 mM C18:1. After harvest in Trizol (Life Technologies, Carlsbad, CA), samples were stored at -80°C until RNA extraction, purification, and reverse transcription. Abundance of mRNA was measured using quantitative real-time PCR. Expression of genes of interest [carnitine palmitoyltransferase 1A, pyruvate carboxylase, cytosolic phosphoenolpyruvate carboxykinase (PCK1), mitochondrial phosphoenolpyruvate carboxykinase, and glucose-6-phosphatase] was calculated relative to the average abundance of 2 reference genes (ribosomal protein L32 and glyceraldehyde 3-phosphate dehydrogenase), which were the most stable out of 3 tested. Data were analyzed using PROC MIXED (SAS version 9.4; SAS Institute, Cary, NC) with the fixed effect of treatment and calf in the random statement. The addition of FA compared with the 1% BSA treatment increased the expression of carnitine palmitoyltransferase 1A and cytosolic PCK1. Enrichment with individual FA did not further regulate pyruvate carboxylase or PCK1 beyond that achieved by the basal profile. These results suggest that shifts in circulating FA profile within a biological range, without a difference in the total FA concentration, have minimal effects on transcriptional regulation of hepatic gluconeogenic genes in primary bovine hepatocytes.

Feeding high oleic acid soybeans in place of conventional soybeans increases milk fat concentration.

It is well established in the literature that feeding free vegetable oils rich in oleic acid results in greater milk fat secretion than does feeding linoleic-rich oils. The objectives of these experiments were to analyze the effects of oleic and linoleic acid when fed in the form of full-fat soybeans and the interaction between soybean particle size and fatty acid (FA) profile. Soybeans were included in diets on an iso-ether extract basis and diets were balanced for crude protein using soybean meal. Experiment 1 used 63 cows (28 primiparous, PP; 35 multiparous, MP) housed in a freestall barn with Insentec roughage intake control gates (Marknesse, the Netherlands). Cows were divided into 4 mixed parity groups within the same pen. Two groups were assigned to each of the 2 diets: whole raw Plenish (WP, high oleic; Dupont-Pioneer, Johnston, IA) soybeans or whole raw conventional (WC, high linoleic) soybeans. The MP cows exhibited significantly increased milk fat yield on the WP diet compared with the WC diet. A significantly greater C18 milk FA yield by the MP cows fed WP was observed compared with those fed WC, but no difference was present in the C16 or short-chain FA yield. No effects were seen in the PP cows. Experiment 2 used 20 cows (10 PP, 10 MP) in 2 balanced 5 × 5 Latin squares within parity. Cows received 5 diets: raw WP and WC diets, raw ground Plenish and conventional soybean diets (GP and GC, respectively), and a low fat control. A significant benefit was found for the GP diet compared with the GC diet for milk fat concentration and yield. In experiment 2, no difference was observed between cows fed the WP compared with the WC diet. In experiment 2, cows consuming the Plenish diets produced less milk than when consuming the conventional soybean diets. The soybean diets resulted in significantly more C18 and less

Mining data from milk infrared spectroscopy to improve feed intake predictions in lactating dairy cows.

Feed intake is one of the most important components of feed efficiency in dairy systems. However, it is a difficult trait to measure in commercial operations for individual cows. Milk spectrum from mid-infrared spectroscopy has been previously used to predict milk traits, and could be an alternative to predict dry matter intake (DMI). The objectives of this study were (1) to evaluate if milk spectra can improve DMI predictions based only on cow variables; (2) to compare artificial neural network (ANN) and partial least squares (PLS) predictions; and (3) to evaluate if wavelength (WL) selection through Bayesian network (BN) improves prediction quality. Milk samples (n = 1,279) from 308 mid-lactation dairy cows [127 ± 27 d in milk (DIM)] were collected between 2014 and 2016. For each milk spectra time point, DMI (kg/d), body weight (BW, kg), milk yield (MY, kg/d), fat (%), protein (%), lactose (%), and actual DIM were recorded. The DMI was predicted with ANN and PLS using different combinations of explanatory variables. Such combinations, called covariate sets, were as follows: set 1 (MY, BW0.75, DIM, and 361 WL); set 2 [MY, BW0.75, DIM, and 33 WL (WL selected by BN)]; set 3 (MY, BW0.75, DIM, and fat, protein, and lactose concentrations); set 4 (MY, BW0.75, DIM, 33 WL, fat, protein, and lactose); set 5 (MY, BW0.75, DIM, 33 WL, and visit duration in the feed bunk); set 6 (MY, DIM, and 33 WL); set 7 (MY, BW0.75, and DIM); set-WL (included 361 WL); and set-BN (included just 33 selected WL). All models (i.e., each combination of covariate set and fitting approach, ANN or PLS) were validated with an external data set. The use of ANN improved the performance of models 2, 5, 6, and BN. The use of BN combined with ANN yielded the highest accuracy and precision. The addition of individual WL compared with milk components (set 2 vs. set 3) did not improve prediction quality when using PLS. However, when ANN was employed, the model prediction with the inclusion of 33 WL was improved over the model containing only milk components (set 2 vs. set 3; concordance correlation coefficient = 0.80 vs. 0.72; coefficient of determination = 0.67 vs. 0.53; root mean square error of prediction 2.36 vs. 2.81 kg/d). The use of ANN and the inclusion of a behavior parameter, set 5, resulted in the best predictions compared with all other models (coefficient of determination = 0.70, concordance correlation coefficient = 0.83, root mean square error of prediction = 2.15 kg/d). The addition of milk spectra information to models containing cow variables improved the accuracy and precision of DMI predictions in lactating dairy cows when ANN was used. The use of BN to select more informative WL improved the model prediction when combined with cow variables, with further improvement when combined with ANN.

The effects of adding fat to diets of lactating dairy cows on total-tract neutral detergent fiber digestibility: A meta-analysis.

The objective of this meta-analysis was to determine the effects of supplemental fat on fiber digestibility in lactating dairy cattle. Published papers that evaluated the effects of adding fat to the diets of lactating dairy cattle on total-tract neutral detergent fiber digestibility (ttNDFd) and dry matter intake (DMI) were compiled. The final data set included 108 fat-supplemented treatment means, not including low-fat controls, from 38 publications. The fat-supplemented treatment means exhibited a wide range of ttNDFd (49.4% ± 9.3, mean ± standard deviation) and DMI (21.3 kg/d ± 3.5). Observations were summarized as the difference between the treatment means for fat-supplemented diets minus their respective low-fat control means. Additionally, those differences were divided by the difference in diet fatty acid (FA) concentration between the treatment and control diets. Treatment means were categorized by the type of fat supplement. Supplementing 3% FA in the diet as medium-chain fats (containing predominately 12- and 14-carbon saturated FA) or unsaturated vegetable oil decreased ttNDFd by 8.0 and 1.2 percentage units, respectively. Adding 3% calcium salts of long-chain FA or saturated fats increased ttNDFd by 3.2 and 1.3 percentage units, respectively. No other fat supplement type affected ttNDFd. Except for saturated fats and animal-vegetable fats, supplementing dietary fat decreased DMI. When the values for changes in ttNDFd are regressed on changes in DMI there was a positive relationship, though the coefficient of determination is only 0.20. When changes in ttNDFd were regressed on changes in DMI, within individual fat supplement types, there was no relationship within calcium salt supplements. There was a positive relationship between changes in ttNDFd and changes in DMI for saturated fats. Neither relationship suggested that the increased ttNDFd with calcium salts or saturated FA was due to decreased DMI for these fat sources. A subset of the means included measured ruminal neutral detergent fiber digestion. Analysis of this smaller data set did not suggest that ruminal neutral detergent fiber digestibility is depressed by fat supplementation more than ttNDFd. Adding fats, other than those with medium-chain FA, consistently increased digestible energy density of the diet. However, due to reduced DMI, this increased energy density may not result in increased digestible nutrient intake.