Browsing by Subject "Non-starch polysaccharides"

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Modulation of hepatic insulin and glucagon signaling by nutritional factors in broiler chicken
(2022) Petrilla, Janka; Mátis, Gábor; Mackei, Máté; Kulcsár, Anna; Sebők, Csilla; Papp, Márton; Gálfi, Péter; Fébel, Hedvig; Huber, Korinna; Neogrády, Zsuzsanna
Influencing the endocrine metabolic regulation of chickens by nutritional factors might provide novel possibilities for improving animal health and productivity. This study was designed to evaluate the impact of dietary cereal type (wheat-based (WB) vs. maize-based (MB) diets), crude protein level (normal (NP) vs. lowered (LP)), and sodium (n-)butyrate (1.5 g/kg diet) supplementation (vs. no butyrate) on the responsiveness of hepatic glucagon receptor (GCGR), insulin receptor beta (IRβ) and mammalian target of rapamycin (mTOR) in the phase of intensive growth of chickens. Liver samples of Ross 308 broiler chickens (Gallus gallus domesticus) were collected on day 21 for quantitative real-time polymerase chain reaction and Western blot analyses. Hepatic GCGR and mTOR gene expressions were up-regulated by WB and LP diet. GCGR and IRβ protein level decreased in groups with butyrate supplementation; however, the quantity of IRβ and mTOR protein increased in WB groups. Based on these data, the applied dietary strategies may be useful tools to modulate hepatic insulin and glucagon signaling of chickens in the period of intensive growth. The obtained results might contribute to the better understanding of glycemic control of birds and increase the opportunity of ameliorating insulin sensitivity, hence, improving the production parameters and the welfare of broilers.
Variability of amino acid digestibility of cereal grains in laying hens
(2017) Zuber, Tobias; Rodehutscord, Markus
It was the objective of this doctoral thesis to generate a comprehensive data set of AA digestibility values of cereal grains in laying hens by using a strictly standardized assay procedure. Additionally, the suitability of two approaches to predict AA digestibility was examined. For this purpose, 80 genotypes of triticale, rye, corn, and wheat grains (n = 20 each) were grown as part of the “GrainUp” project. Apart from corn, the cereal species were grown under identical environmental conditions. The grain samples were comprehensively analyzed according to their physical properties, chemical composition, and gross energy concentration. The concentration of crude protein in the grain samples of triticale, rye, corn, and wheat was in the range of 113-138, 108-127, 78-112, and 125-162 g/kg dry matter, respectively. Additionally, the in vitro solubility of nitrogen (N) was determined in the grains after pretreatment with porcine pepsin and pancreatin. The animal trial comprised 16 Latin Squares (6x6), distributed among six subsequent runs. Thus, each run contained two to three Latin Squares. Cecectomized laying hens were individually housed in metabolism cages and fed either on a basal diet containing 500 g/kg cornstarch or one of the 80 cereal diets, with the cornstarch being replaced with a grain sample, for eight days. During the last four days, feed intake was recorded and excreta were collected quantitatively twice daily. After each collection period, the hens were group-housed in a floor pen for two days and offered a conventional layer diet. Amino acid digestibility of the grain samples was calculated using a linear regression approach. Relationships between AA digestibility and single analyzed fractions or the in vitro solubility of N of the cereal grains were examined by calculating Pearson product-moment correlation coefficients. Prediction equations to estimate AA digestibility were calculated by multiple regression analysis using a stepwise selection approach. Therefore, the variables were pooled according to their characteristics, and the prediction equations were calculated for the digestibility of each AA using each pool. The variables were offered in a linear or linear plus quadratic fashion and classified as significant predictors at P<0.10. The equations were assessed based on the adjusted R² and the root-mean-square error. The AA digestibility varied widely within and among the cereal species. The mean digestibility of lysine was 74% (digestibility range: 68-80%), 49% (35-59%), 79% (64-85%), and 80% (69-87%) for triticale, rye, corn, and wheat grains, respectively. A similar ranking was observed for methionine with a mean digestibility of 83% (digestibility range: 77-86%), 67% (57-75%), 91% (86-94%) and 84% (70-93%) for triticale, rye, corn, and wheat grains, respectively. Correlation analysis showed inconsistent results within and across the cereal species. Among the physical characteristics, significant correlations were detected for the thousand seed weight and the digestibility of a few AA in wheat, and for the test weight and the digestibility of a few AA in rye and corn. Significant correlations between NSP fractions and the digestibility of essential AA were detected only for rye grains. In this crop, the concentration of arabinoxylans and total NSP in the grains was negatively correlated with the digestibility of arginine, leucine, phenylalanine, and threonine. The concentration of crude protein in corn grains was positively correlated with the digestibility of essential AA, except isoleucine, tryptophan, and valine. In contrast, only a few significant positive correlations between crude protein concentration and essential AA digestibility were found for triticale and rye grains. No significant correlations were found for wheat grains in this regard. The in vitro solubility of N was negatively and positively correlated with the digestibility of a few AA in triticale and rye grains, respectively. The accuracy of the predictive equations was generally low (adjusted R² below 0.7 in most cases), and varied considerably between both pools of variables for the same AA and the same pool of variables for different AA. Thus, single or several physical or chemical characteristics could not explain the variation in AA digestibility in laying hens and the development of prediction equations sufficiently precise for the practical application was not possible.