Supplementary methods, tables and figures for Journal of Dairy Science 2021 paper entitled Colostrum source and passive immunity transfer in dairy bull calves by Hue, et al.
Context Fat colour is one of the most important economic traits in the marketing of beef. There are many factors that affect fat colour, such as breed, age, diet and gender. Fat colour is observed in different ranges of colours, including white, yellow and brown. The main issue with improving fat colour is that consumer preferences of fat colour vary across the globe. Therefore, investigating the metabolic mechanisms of fat colour may provide new biomarkers for phenotyping, so as to develop effective selection strategies to achieve the locally desired fat colour. Aims This study aimed to perform a comparative metabolic analysis between white and yellow fat from crossbred cattle so as to identify potential biomarkers for the selection of fat colour and to better understand the metabolism of white and yellow fat depots. Methods Carcass samples of subcutaneous fat were collected from crossbred cattle (Simmental × Mongolian cattle) and scored for fat colour. Liquid chromatography–mass spectrophotometry analysis of extracted metabolites from the subcutaneous fat of six animals with white fat and six animals with yellow fat was performed. Key results The comparison between metabolites of white and yellow fat colour samples indicated that there were five categories of 235 significant metabolites, which included hydrocarbons, lipids and lipid-like molecules, organic acids and their derivatives, organic oxygen compounds and organoheterocyclic compounds. The principal-component analysis illustrated that yellow and white fat samples can be classified in groups; however, the metabolites of white fat samples showed greater variation than those in the yellow fat. In the white fat, there were 163 metabolites that had a higher relative abundance than in yellow fat and 72 that had a lower relative abundance than in yellow fat. 3-Hydroxyoctanoic acid, anethofuran, 9,10-DiHODE, furanoeremophilane, pregeijerene, N-glycolylneuraminic acid, and glycocholic acid were identified as the metabolites that differed the most in abundance between the white and yellow fat samples. Conclusions This study has provided insights into the metabolic differences between white and yellow fat depots and identified key metabolites associated with beef fat colour. Implications This study has provided potential biomarkers that may be useful for selection of beef fat colour in live animals.
Abstract Using 5 wild‐type strains of yeast, nonequivalence in the isolation of sterol mutants was observed. Experiments are described on the effects of sterol modifications on growth, physical and enzymic properties of Saccharomyces cerevisiae and Phytophthora cactorum . Although discontinuities in Arrhenius kinetics were observed by fluorescence anisotropy and enzymic measurements of mutants (but not wild‐types) of yeast, evidence based on membrane permeability and differential scanning calorimetry failed to support bulk lipid phase transitions as the cause for the discontinuities.
Abstract The membranes of yeast mitochondria were separated and analyzed for lipid content. The sterolto‐phospholipid molar ratio was found to be very similar between the inner and outer membranes (1∶30). These observed ratios could be substantially altered by using a crude mitochondrial pellet contaminated with a “floating lipid layer”. In this case, the sterol‐to‐phospholipid molar ratios were 1∶8 to 1∶26 for the outer and inner mitochondrial membranes, respectively.
SUMMARY Live measurements of weight, height, length, girth, fat depth, stifle- and hip-width were obtained prior to slaughter to develop prediction equations for carcass traits. The animals were boned out after slaughter and comprised 182 steers from the Southern Crossbreeding Program (progeny from Hereford cows crossed with seven sire breeds: Angus, Belgian Blue, Hereford, Jersey, Limousin, South Devon and Wagyu) and 59 steers from the Davies Gene Mapping Project (pure Limousin, pure Jersey and Limousin x Jersey). Stepwise regression was used to indicate the relative importance of variables in each model designed to estimate the percentage of meat, bone and fat from the carcass weight. The meat, bone and fat yields correspond to 70, 19 and 11% of the carcass on weight basis. The prediction equations developed accounted for 93, 87, 74 and 65% of the variation in carcass, meat, bone and fat weight respectively without breed in the model. This study has shown that some carcass traits may be determined accurately from measurements on live animal.
We present the application of large-scale multivariate mixed-model equations to the joint analysis of nine gene expression experiments in beef cattle muscle and fat tissues with a total of 147 hybridizations, and we explore 47 experimental conditions or treatments. Using a correlation-based method, we constructed a gene network for 822 genes. Modules of muscle structural proteins and enzymes, extracellular matrix, fat metabolism, and protein synthesis were clearly evident. Detailed analysis of the network identified groupings of proteins on the basis of physical association. For example, expression of three components of the z-disk, MYOZ1, TCAP, and PDLIM3, was significantly correlated. In contrast, expression of these z-disk proteins was not highly correlated with the expression of a cluster of thick (myosins) and thin (actin and tropomyosins) filament proteins or of titin, the third major filament system. However, expression of titin was itself not significantly correlated with the cluster of thick and thin filament proteins and enzymes. Correlation in expression of many fast-twitch muscle structural proteins and enzymes was observed, but slow-twitch-specific proteins were not correlated with the fast-twitch proteins or with each other. In addition, a number of significant associations between genes and transcription factors were also identified. Our results not only recapitulate the known biology of muscle but have also started to reveal some of the underlying associations between and within the structural components of skeletal muscle.
'/%3e%3c/defs%3e%3cpath fill='%23012169' d='M0 0h10080v5040H0z'/%3e%3cpath stroke='%23fff' stroke-width='.6' d='m0 0 6 3m0-3L0 3' clip-path='url(%23b)' transform='scale(840)'/%3e%3cpath stroke='%23e4002b' stroke-width='.4' d='m0 0 6 3m0-3L0 3' clip-path='url(%23c)' transform='scale(840)'/%3e%3cpath stroke='%23fff' stroke-width='840' d='M2520 0v2520M0 1260h5040'/%3e%3cpath stroke='%23e4002b' stroke-width='504' d='M2520 0v2520M0 1260h5040'/%3e%3cg fill='%23fff'%3e%3cuse xlink:href='%23d' x='2520' y='3780'/%3e%3cuse xlink:href='%23a' x='7560' y='4200'/%3e%3cuse xlink:href='%23a' x='6300' y='2205'/%3e%3cuse xlink:href='%23a' x='7560' y='840'/%3e%3cuse xlink:href='%23a' x='8680' y='1869'/%3e%3cuse xlink:href='%23e' x='8064' y='2730'/%3e%3c/g%3e%3c/svg%3e)
'/%3e%3cuse xlink:href='%23a' transform='rotate(72 0 0)'/%3e%3cuse xlink:href='%23a' transform='rotate(-72 0 0)'/%3e%3cuse xlink:href='%23a' transform='scale(-1 1) rotate(72)'/%3e%3c/g%3e%3c/defs%3e%3cpath fill='%23012169' d='M0 0h1200v600H0z'/%3e%3cpath stroke='%23FFF' stroke-width='60' d='m0 0 600 300M0 300 600 0' clip-path='url(%23b)'/%3e%3cpath stroke='%23C8102E' stroke-width='40' d='m0 0 600 300M0 300 600 0' clip-path='url(%23c)'/%3e%3cpath stroke='%23FFF' stroke-width='100' d='M300 0v300M0 150h600' clip-path='url(%23b)'/%3e%3cpath stroke='%23C8102E' stroke-width='60' d='M300 0v300M0 150h600' clip-path='url(%23b)'/%3e%3cuse xlink:href='%23d' fill='%23FFF' transform='matrix(45.4 0 0 45.4 900 120)'/%3e%3cuse xlink:href='%23d' fill='%23C8102E' transform='matrix(30 0 0 30 900 120)'/%3e%3cg transform='rotate(82 900 240)'%3e%3cuse xlink:href='%23d' fill='%23FFF' transform='rotate(-82 519.022 -457.666) scale(40.4)'/%3e%3cuse xlink:href='%23d' fill='%23C8102E' transform='rotate(-82 519.022 -457.666) scale(25)'/%3e%3c/g%3e%3cg transform='rotate(82 900 240)'%3e%3cuse xlink:href='%23d' fill='%23FFF' transform='rotate(-82 668.57 -327.666) scale(45.4)'/%3e%3cuse xlink:href='%23d' fill='%23C8102E' transform='rotate(-82 668.57 -327.666) scale(30)'/%3e%3c/g%3e%3cuse xlink:href='%23d' fill='%23FFF' transform='matrix(50.4 0 0 50.4 900 480)'/%3e%3cuse xlink:href='%23d' fill='%23C8102E' transform='matrix(35 0 0 35 900 480)'/%3e%3c/svg%3e)