Effects of metabolizable protein concentration, amino acid profile, and fiber source on the messenger RNA expression of skeletal muscle in peripartum dairy cows.

After parturition, dairy cows mobilize AA from skeletal muscle to meet metabolizable protein (MP) requirements. High mobilization may compromise cow health and longer-term milk production. Postpartum diets with higher MP concentrations, improved AA profiles, or MP increased at the expense of forages rather than nonforage fiber sources may attenuate muscle catabolism; however, the molecular mechanisms responsible need investigation. We evaluated mRNA expression in the longissimus dorsi of cows fed postpartum diets differing in MP concentration, AA profile, and fiber source. From 0 to 25 d after parturition, 40 multiparous cows received the following diets: (1) 13% deficient in MP (D-MP), (2) adequate in MP using primarily soy protein (A-MP), (3) adequate in MP using blends of proteins and individual AA to improve the AA profile (Blend), or (4) similar to Blend except additional protein replaced forage (Blend-fNDF). Biopsies were taken approximately -5, 7, and 25 d relative to parturition. Greater dietary MP concentration (D-MP vs. A-MP and Blend) decreased expression of genes related to protein synthesis (MTOR, RPS6KB1) and degradation (FOXO1), inflammation (IFNG, TLR4), and endoplasmic reticulum (ER) stress (HSPA5, DDIT) and increased genes associated with lipogenesis (PPARG) and glucose oxidation (LDH, MB). In Blend versus A-MP (i.e., effect of AA profile), expression related to apoptosis (CASP8) and inflammation (TNFA) decreased and genes associated with cell cycle progression (E2F1) and fast-twitch glycolytic muscle fiber type (MYH4) increased. Less forage (Blend-fNDF vs. Blend) decreased genes associated with lipogenesis (PPARG, ACACA) and ER stress (BCL2, DDIT3, EIF2AK3, PPP1R15A) and increased genes associated with inflammation (TNF), inhibition of myogenesis (MSTN), and autophagy (PEBP1). In summary and based on mRNA expression, increasing MP supply may attenuate muscle turnover and ER stress. However, an unbalanced AA supply reduced cell cycle progression and protein synthesis. Lower energy supplies may reduce cell growth and cause autophagy.

Estimating digestible energy values of feeds and diets and integrating those values into net energy systems.

The California Net Energy System (CNES) used a combination of measured and tabular metabolizable energy (ME) values and changes in body composition gain to determine net energy requirements for maintenance and gain and their corresponding dietary concentrations. The accuracy of the CNES depends on the accuracy of the feed ME values. Feed or diet ME values can be measured directly but are expensive and require specialized facilities; therefore, most ME values are estimated from digestible energy (DE) values, which are often estimated from the concentration of total digestible nutrients (TDN). Both DE and TDN values are often from tables and not based on actual nutrient analysis. The use of tabular values eliminates important within-feed variation in composition and digestibility. Furthermore, the use of TDN to estimate DE does not account for important variation in the gross energy value of feeds. A better approach would be to estimate DE concentration directly from nutrient composition or in vitro (or in situ) digestibility measurements. This approach incorporates within-feed variation into the energy system and eliminates the issues of using TDN. A widely used summative equation based on the commonly measured feed fractions (ash, crude protein, neutral detergent fiber, and fat) has been shown to accurately estimate DE concentrations of many diets for cattle; however, deficiencies in that equation have been identified and include an overestimation of DE provided by fat and an exaggerated negative effect of intake on digestibility. Replacing the nonfiber carbohydrate term (which included everything that was not measured) in the equation with measured starch concentration and residual organic matter (i.e., nonfiber carbohydrate minus starch) should improve accuracy by accounting for more variation in starch digestibility. More accurate estimates of DE will improve the accuracy of ME values, which will ultimately lead to more accurate NE values.