OMICs approaches and technologies for understanding low-high feed efficiency traits in chicken: implication to breeding

In poultry production, there has been a trend of continuous increase in cost of feed ingredients which represents the major proportion of the production costs. Feed costs can be reduced by improving feed efficiency traits which increase the possibility of using various indigestible feed sources and decrease the environmental impact of the enhanced poultry production. Therefore, feed efficiency has been used as one of the most important economic traits of selection in the breeding program of chickens. Recently, many OMICs experimental studies have been designed to characterize biological differences between the high and low feed efficiency chicken phenotypes. Biological complexity cannot be fully captured by main individual OMICs such as genomics, transcriptomics, proteomics and metabolomics. Therefore, researchers have combined multiple assays from the same set of samples to create multi-OMICs datasets. OMICs findings are crucial in improving existing approaches to poultry breeding. The current review aimed to highlight the components of feed efficiency and general OMICs approaches and technologies. Besides, individual and multi-OMICs based understanding of chicken feed efficiency traits and the application of the acquired knowledge in the chicken breeding program were addressed.

Comparative targeted metabolomic profiles of porcine plasma and serum

Metabolomics has been used to characterise many biological matrices and obtain detailed pictures of biological systems based on many metabolites. Plasma and serum are two blood-derived biofluids commonly used to assess and monitor the organismal metabolism and obtain information on the physiological and health conditions of an animal. Plasma is the supernatant that is separated from the cellular components after centrifugation of the blood that is first added with an anticoagulant. Serum is obtained after centrifugation of the blood that has been coagulated. The choice of one or the other biofluid for metabolomic analyses is related to specific analytical needs and technical issues, to problems derived by the collection and preparation steps, in particular when specimens are sampled from animals involved in field studies. Thus far, most of the metabolomic studies that compared plasma and serum have been carried out in humans and very little is known on the pigs. In this study, we used a targeted metabolomic platform that can detect about 180 metabolites of five biochemical classes to compare plasma and serum profiles of samples collected from 24 pigs. To also obtain a cross-species comparative metabolomic analysis, information for human plasma and serum derived from the same platform was retrieved from previous studies. Statistical analyses included univariate and multivariate approaches aimed at identifying stable and/or differentially abundant metabolites between the two porcine biofluids. A total of 154 (∼83%) metabolites passed the initial quality control, indicating a good repeatability of the analytical platform in pigs. Discarded metabolites included aspartate and biogenic amines that were already reported to be unstable in human studies. More than 80% of the metabolites had similar profiles in both porcine biofluids (average correlation was 0.75). Concentrations were usually higher in serum than in plasma, in agreement with what was already reported in humans. The univariate analysis identified 44 metabolites that had statistically different concentrations between porcine plasma and serum, of which 28 metabolites were also confirmed by the multivariate analysis. The obtained picture described similarities and differences between these two biofluids in pigs and the related human-pig comparisons. The obtained information can be useful for the choice of one or the other matrix for the implementation of metabolomic studies in this livestock species. The results can also provide useful hints to valuing the pig as animal model, in particular when metabolite-derived physiological states are relevant.

Muscle growth affects the metabolome of the pectoralis major muscle in red-winged tinamou (Rhynchotus rufescens)

The aim of the present study was to identify and quantify the metabolites (metabolome analysis) of the pectoralis major muscle in male red-winged tinamou (Rhynchotus rufescens) selected for growth traits. A selection index was developed for females [body weight (BW), chest circumference (CC), and thigh circumference (TC)] and males [BW, CC, TC, semen volume, and sperm concentration] in order to divide the animals into 2 experimental groups: selection group with a higher index (TinamouS) and commercial group with a lower index (TinamouC). Twenty male offspring of the 2 groups (TinamouS, n = 10; TinamouC, n = 10) were confined for 350 d. The birds were slaughtered and pectoralis major muscle samples were collected, subjected to polar and apolar metabolites extractions and analyzed by proton nuclear magnetic resonance (1H NMR) spectroscopy. Analysis of the polar metabolomic profile identified 65 metabolites; 29 of them were differentially expressed between the experimental groups (P < 0.05). The TinamouS groups exhibited significantly higher concentrations (P < 0.05) of 25 metabolites, including anserine, aspartate, betaine, carnosine, creatine, glutamate, threonine, 3-methylhistidine, NAD+, pyruvate, and taurine. Significantly higher concentrations of cysteine, beta-alanine, lactose, and choline were observed in the TinamouC group (P < 0.05). The metabolites identified in the muscle provided information about the main metabolic pathways (higher impact value and P < 0.05), for example, phenylalanine, tyrosine and tryptophan biosynthesis; alanine, aspartate and glutamate metabolism; D-glutamine and D-glutamate metabolism; β-alanine metabolism; glycine, serine and threonine metabolism; taurine and hypotaurine metabolism; histidine metabolism; phenylalanine metabolism. The NMR spectra of apolar fraction showed 8 classes of chemical compounds. The metabolome analysis shows that the selection index resulted in the upregulation of polyunsaturated fatty acids, unsaturated fatty acids, phosphocholines, phosphoethanolamines, triacylglycerols, and glycerophospholipids. The present study suggests that, despite few generations, the selection based on muscle growth traits promoted changes in metabolite concentrations in red-winged tinamou.

Yeast culture repairs rumen epithelial injury by regulating microbial communities and metabolites in sheep

This study delves into the impact of yeast culture (YC) on rumen epithelial development, microbiota, and metabolome, with the aim of investigating YC's mechanism in regulating rumen fermentation. Thirty male lambs of Hu sheep with similar age and body weight were selected and randomly divided into three groups with 10 lambs in each group. Lambs were fed a total mixed ration [TMR; rough: concentrate (R:C) ratio ≈ 30:70] to meet their nutritional needs. The experiment adopted completely randomized design (CRD). The control group (CON) was fed the basal diet with high concentrate, to which 20 g/d of YC was added in the low dose YC group (LYC) and 40 g/d of YC in the high dose YC group (HYC). The pretrial period was 14 days, and the experimental trial period was 60 days. At the end of a 60-day trial, ruminal epithelial tissues were collected for histomorphological analysis, and rumen microorganisms were analyzed by 16S rDNA sequencing and rumen metabolites by untargeted liquid chromatography-mass spectrometry (LC-MS) metabolomics techniques. The results showed that YC improved rumen papilla development and increased rumen papilla length (p < 0.05), while decreased cuticle thickness (p < 0.05). The 16S rDNA sequencing results showed that YC reduced the relative abundance of Prevotella_1 (p < 0.05), while significantly increased the relative abundance of Ruminococcaceae_UCG-005, uncultured_bacterium_f_Lachnospiraceae, and Ruminococcus_1 genus (p < 0.05). Metabolomics analysis showed that YC changed the abundance of metabolites related to amino acid metabolism, lipid metabolism and vitamin metabolism pathways in the rumen. In summary, YC might maintain rumen health under high-concentrate diet conditions by changing rumen microbiota structure and fermentation patterns, thereby affecting rumen metabolic profiles and repairing rumen epithelial injury.

Plasma metabolomic profiling in two rabbit lines divergently selected for intramuscular fat content

This study provides a thorough comparison of the plasma metabolome of two rabbit lines divergently selected for intramuscular fat content (IMF). The divergent selection led to a correlated response in the overall adiposity, turning these lines into a valuable animal material to study also the genetics of obesity. Over 900 metabolites were detected, and the adjustment of multivariate models, both discriminant and linear, allowed to identify 322 with differential abundances between lines, which also adjusted linearly to the IMF content. The most affected pathways were those of lipids and amino acids, with differences between lines ranging from 0.23 to 6.04 standard deviations, revealing a limited capacity of the low-IMF line to obtain energy from lipids, and a greater branched-chain amino acids catabolism in the high-IMF line related to its increased IMF content. Additionally, changes in metabolites derived from microbial activity supported its relevant role in the lipid deposition. Future research will focus on the analysis of the metabolomic profile of the cecum content, and on the integration of the several -omics datasets available for these lines, to help disentangle the host and microbiome biological mechanisms involved in the IMF deposition.

Effect of Rumen-Protected Methionine on Metabolic Profile of Liver, Muscle and Blood Serum Samples of Growing German Simmental Bulls Fed Protein-Reduced Diets

This study aimed to determine the metabolic response of growing German Simmental bulls fed rations low in crude protein (CP) supplemented with rumen-protected methionine (RPMET). In total, 69 bulls (on average 238 ± 11 days of age at start and 367 ± 25 kg of bodyweight) were assigned to three dietary treatments (n = 23/group): Positive control (CON; 13.7% CP; 2.11 g methionine/kg DM), negative control deficient in CP (RED; 9.04% CP; 1.56 g methionine/kg DM) and crude protein-deficient ration supplemented with RPMET (RED+RPMET; 9.04% CP; 2.54 g methionine/kg DM). At slaughter, samples of liver, muscle and blood serum were taken and underwent subsequent metabolomics profiling using a UHPLC-QTOF-MS system. A total of 6540 features could be detected. Twenty metabolites in the liver, five metabolites in muscle and thirty metabolites in blood serum were affected (p < 0.05) due to dietary treatments. In total, six metabolites could be reliably annotated and were thus subjected to subsequent univariate analysis. Reduction in dietary CP had minimal effect on metabolite abundance in target tissues of both RED and RED+RPMET bulls as compared to CON bulls. The addition of RPMET altered the hepatic anti-oxidant status in RED+RPMET bulls compared to both RED and CON bulls. Results exemplify nutrient partitioning in growing German Simmental bulls: bulls set maintenance as the prevailing metabolic priority (homeostasis) and nutrient trafficking as the second priority, which was directed toward special metabolic functions, such as anti-oxidant pathways.

Differential Responses of Physiological Parameters, Production Traits, and Blood Metabolic Profiling between First- and Second-Parity Holstein Cows in the Comparison of Spring versus Summer Seasons

Heat stress (HS) negatively influences cows' welfare and productivity. Therefore, a better understanding of the physiological and molecular mechanisms of HS responses from multiple parities is paramount for the development of effective management and breeding strategies. In comparison with first-parity cows in the spring (Spring-1), first-parity cows in the summer (Summer-1) had a significantly higher rectal temperature (RT), respiration rate (RR), drooling score (DS), and daily activity (DA), while lower (P < 0.05) daily rumination (DR), seven-day average milk yield (7AMY), milk yield on sampling day (MY_S), milk yield on test day (MY_T), and lactose percentage (LP) were observed. When comparing the spring (Spring-2) and summer (Summer-2) of the second-parity cows, significant differences were also found in RT, RR, DS, DA, and DR (P < 0.05), corresponding to similar trends with the first parity while having smaller changes. Moreover, significantly negative impacts on performance traits were only observed on fat percentage (FP) and LP. These results showed that there were different biological responses between first- and second-parity Holstein cows. Further, 18 and 17 metabolites were involved in the seasonal response of first- and second-parity cows, respectively. Nine differential metabolites were shared between the two parities, and pathway analyses suggested that cows had an inhibited tricarboxylic acid cycle, increased utilization of lipolysis, and a dysregulated gut microbiome during the summer. The metabolites identified exclusively for each parity highlighted the differences in microbial response and host amino acid metabolism between two parities in response to HS. Moreover, glucose, ethanol, and citrate were identified as potential biomarkers for distinguishing individuals between Spring-1 and Summer-1. Ethanol and acetone were better predictors for distinguishing individuals between Spring-2 and Summer-2. Taken together, the present study demonstrated the impact of naturally induced HS on physiological parameters, production traits, and the blood metabolome of Holstein cows. There are different biological responses and regulation mechanisms between first- and second-parity Holstein cows.

Plasma metabolomics reveals major changes in carbohydrate, lipid, and protein metabolism of abruptly weaned beef calves

¹H NMR-based metabolomics was used to study the effect of abrupt weaning on the blood metabolome of beef calves. Twenty Angus calves (258 ± 5 kg BW; 5 to 6 months old) were randomly assigned to a non-weaned (NW) group that remained grazing with their dam or a weaned (W) group that underwent abrupt separation from their dam to a separate paddock on d 0 of the study. Body weight, behaviour, and blood samples for cortisol and metabolomics were measured at d 0, 1, 2, 7, and 14 of the study. On d 1 and 2, W calves spent less time grazing and ruminating, and more time vocalising and walking, had a greater concentration of cortisol, NEFA, 3-hydroxybutyrate, betaine, creatine, and phenylalanine, and lesser abundance of tyrosine (P < 0.05) compared to NW calves. Compared to NW calves at d 14, W calves had greater (P < 0.01) relative abundance of acetate, glucose, allantoin, creatinine, creatine, creatine phosphate, glutamate, 3-hydroxybutyrate, 3-hydroxyisobutyrate, and seven AA (alanine, glutamate, leucine, lysine, phenylalanine, threonine and valine) but lesser (P < 0.05) relative abundance of low density and very low-density lipids, and unsaturated lipids. Both PCA and OPLS-DA showed no clustering or discrimination between groups at d 0 and increasing divergence to d 14. Blood metabolomics is a useful tool to quantify the acute effects of stress in calves during the first 2 days after abrupt weaning, and longer-term changes in carbohydrate, lipid and protein metabolism due to nutritional changes from cessation of milk intake and greater reliance on forage intake.

The time elapsed between assessments of blood metabolome and live weight affects associations between the abundance of metabolites and growth rate in beef cattle

INTRODUCTION

This study aimed to assess the associations between the relative abundance (RA) of blood metabolites and growth rate (i.e., live weight change, LWC) calculated using different intervals of time between live weight (LW) measurements from the metabolome assessment.

METHODS

Grazing beef cattle were raised for 56 days and blood samples from each animal were taken on day 57. Live weight was continuously measured using an automatic in-paddock weighing scale. The RA of plasma metabolites were determined using proton nuclear magnetic resonance (NMR). Live weight data were filtered for outliers and one LW record was selected every 1, 7, 14, 21, 28, 35, 42, 49 and 56 days before the metabolome assessment (LWC1 to LWC56, respectively). Live weight change was then re-calculated for each interval between LW data selected.

RESULTS

Associations between LWC calculations and the RA of metabolites were greatly affected by the interval of time between LW data selected. Thus, the number of significant associations decreased from 9 for LWC1 to 5 for LWC35 whereas no significant associations were found for LWC56 (P > 0.05). There were 7 metabolites negatively associated with LWC1 including leucine, 2-hydroxybutyrate, valine, creatinine, creatine, phenylalanine and methylhistidine; however, correlations were positive for 2 lipids. The strength of the correlation coefficients decreased as the length of the interval between LW measures increased although this reduction was greater for some metabolites such as leucine compared to others such as lipids. Our findings suggest that the time frame in which a particular response variable, such as LWC, is measured and metabolomic samples are taken could largely impact associations and thus conclusions drawn.

CONCLUSIONS

Depending on the variable to be explored, rapid changes in cattle metabolome may not be reflected in correlations if they are not assessed close in time. Our findings suggest that LWC should be measured for a period shorter than 28 days before the metabolome assessment as the number of significant associations decreases when LWC is measured for longer periods.

Metabolite and Proteomic Profiling of Serum Reveals the Differences in Molecular Immunity between Min and Large White Pig Breeds

Pig diseases seriously threaten the health of pigs and the benefits of pig production. Previous research has indicated that Chinese native pigs, such as the Min (M) pig, has a better disease resistance ability than Large White (LW) pigs. However, the molecular mechanism of this resistance is still unclear. In our study, we used serum untargeted metabolomics and proteomics, interrogated to characterize differences in the molecular immunities between six resistant and six susceptible pigs raised in the same environment. A total of 62 metabolites were identified as being significantly exhibited in M and LW pigs. Ensemble feature selection (EFS) machine learning methods were used to predict biomarkers of metabolites and proteins, and the top 30 were selected and retained. Weighted gene co-expression network analysis (WGCNA) confirmed that four key metabolites, PC (18:1 (11 Z)/20:0), PC (14:0/P-18: 0), PC (18:3 (6 Z, 9 Z, 12 Z)/16:0), and PC (16:1 (9 Z)/22:2 (13 Z, 16 Z)), were significantly associated with phenotypes, such as cytokines, and different pig breeds. Correlation network analysis showed that 15 proteins were significantly correlated with the expression of both cytokines and unsaturated fatty acid metabolites. Quantitative trait locus (QTL) co-location analysis results showed that 13 of 15 proteins co-localized with immune or polyunsaturated fatty acid (PUFA)-related QTL. Moreover, seven of them co-localized with both immune and PUFA QTLs, including proteasome 20S subunit beta 8 (PSMB8), mannose binding lectin 1 (MBL1), and interleukin-1 receptor accessory protein (IL1RAP). These proteins may play important roles in regulating the production or metabolism of unsaturated fatty acids and immune factors. Most of the proteins could be validated with parallel reaction monitoring, which suggests that these proteins may play an essential role in producing or regulating unsaturated fatty acids and immune factors to cope with the adaptive immunity of different pig breeds. Our study provides a basis for further clarifying the disease resistance mechanism of pigs.

Predictive blood biomarkers of sheep pregnancy and litter size

Early detection of sheep pregnancy and the prediction of how many lambs a pregnant ewe delivers affects sheep farmers in a number of ways, most notably with regard to feed management, lambing rate, and sheep/lamb health. The standard practice for direct detection of sheep pregnancy and litter size (PLS) is ultrasonography. However, this approach has a number of limitations. Indirect measurement of PLS using blood biomarkers could offer a simpler, faster and earlier route to PLS detection. Therefore, we undertook a large-scale metabolomics study to identify and validate predictive serum biomarkers of sheep PLS. We conducted a longitudinal experiment that analyzed 131 serum samples over five timepoints (from seven days pre-conception to 70 days post-conception) from six commercial flocks in Alberta and Ontario, Canada. Using LC–MS/MS and NMR, we identified and quantified 107 metabolites in each sample. We also identified three panels of serum metabolite biomarkers that can predict ewe PLS as early as 50 days after breeding. These biomarkers were then validated in separate flocks consisting of 243 animals yielding areas-under-the-receiver-operating-characteristic-curve (AU-ROC) of 0.81–0.93. The identified biomarkers could lead to the development of a simple, low-cost blood test to measure PLS at an early stage of pregnancy, which could help optimize reproductive management on sheep farms.

Applications of Omics Technology for Livestock Selection and Improvement

Conventional animal selection and breeding methods were based on the phenotypic performance of the animals. These methods have limitations, particularly for sex-limited traits and traits expressed later in the life cycle (e.g., carcass traits). Consequently, the genetic gain has been slow with high generation intervals. With the advent of high-throughput omics techniques and the availability of multi-omics technologies and sophisticated analytic packages, several promising tools and methods have been developed to estimate the actual genetic potential of the animals. It has now become possible to collect and access large and complex datasets comprising different genomics, transcriptomics, proteomics, metabolomics, and phonemics data as well as animal-level data (such as longevity, behavior, adaptation, etc.,), which provides new opportunities to better understand the mechanisms regulating animals’ actual performance. The cost of omics technology and expertise of several fields like biology, bioinformatics, statistics, and computational biology make these technology impediments to its use in some cases. The population size and accurate phenotypic data recordings are other significant constraints for appropriate selection and breeding strategies. Nevertheless, omics technologies can estimate more accurate breeding values (BVs) and increase the genetic gain by assisting the section of genetically superior, disease-free animals at an early stage of life for enhancing animal productivity and profitability. This manuscript provides an overview of various omics technologies and their limitations for animal genetic selection and breeding decisions.

Comparisons of carcass traits, meat quality, and serum metabolome between Shaziling and Yorkshire pigs

This study aims to compare the meat quality of Shaziling and Yorkshire pigs and to find the potential indicator in serum for superior meat quality. Six Shaziling and Yorkshire pigs at 30, 60, 90, 150, 210, and 300 d of age were selected to examine carcass traits, meat quality, and serum metabolome. The results showed that the body weight, carcass length, and loin eye area of Shaziling pigs at 150, 210, and 300 d of age were significantly lower than those of Yorkshire pigs (P < 0.05). Shaziling pigs at 150 and 300 d of age had significantly lower backfat thickness than Yorkshire pigs (P < 0.05). Compared with Yorkshire pigs, Shaziling pigs at all 6 ages had a lower lean percentage and a higher fat percentage (P < 0.05). At 60, 90, and 150 d of age, the post-mortem pH-decline, b∗ value (yellowness), and drip loss of Shaziling pigs were significantly lower than those of Yorkshire pigs (P < 0.05). Moreover, at 150 d of age, Shaziling pigs had significantly higher a∗ value (redness) and intramuscular fat (IMF) content than Yorkshire pigs (P < 0.05). Correlation analysis between the top 40 metabolites and phenotypes indicated that L-carnitine had positive correlations with fat percentage, pH_24h, and IMF content, but had negative correlations with lean percentage, L∗ value (lightness), and b∗ value (P < 0.05). Serum L-carnitine content, fat percentage, pH_24h, and IMF content all decreased first and then increased as the pigs grew, which verified the positive correlations between L-carnitine and these phenotypes. In conclusion, Shaziling pigs have a slower growth rate but a better meat quality than Yorkshire pigs. The meat quality of Shaziling pigs is the best from 150 to 210 d of age. This study suggests that a higher serum L-carnitine content is a promising indicator for better meat quality.

Integrative analyses of genomic and metabolomic data reveal genetic mechanisms associated with carcass merit traits in beef cattle

Improvement of carcass merit traits is a priority for the beef industry. Discovering DNA variants and genes associated with variation in these traits and understanding biological functions/processes underlying their associations are of paramount importance for more effective genetic improvement of carcass merit traits in beef cattle. This study integrates 10,488,742 imputed whole genome DNA variants, 31 plasma metabolites, and animal phenotypes to identify genes and biological functions/processes that are associated with carcass merit traits including hot carcass weight (HCW), rib eye area (REA), average backfat thickness (AFAT), lean meat yield (LMY), and carcass marbling score (CMAR) in a population of 493 crossbred beef cattle. Regression analyses were performed to identify plasma metabolites associated with the carcass merit traits, and the results showed that 4 (3-hydroxybutyric acid, acetic acid, citric acid, and choline), 6 (creatinine, L-glutamine, succinic acid, pyruvic acid, L-lactic acid, and 3-hydroxybutyric acid), 4 (fumaric acid, methanol, D-glucose, and glycerol), 2 (L-lactic acid and creatinine), and 5 (succinic acid, fumaric acid, lysine, glycine, and choline) plasma metabolites were significantly associated with HCW, REA, AFAT, LMY, and CMAR (P-value < 0.1), respectively. Combining the results of metabolome-genome wide association studies using the 10,488,742 imputed SNPs, 103, 160, 83, 43, and 109 candidate genes were identified as significantly associated with HCW, REA, AFAT, LMY, and CMAR (P-value < 1 × 10^-5), respectively. By applying functional enrichment analyses for candidate genes of each trait, 26, 24, 26, 24, and 28 significant cellular and molecular functions were predicted for HCW, REA, AFAT, LMY, and CMAR, respectively. Among the five topmost significantly enriched biological functions for carcass merit traits, molecular transport and small molecule biochemistry were two top biological functions associated with all carcass merit traits. Lipid metabolism was the most significant biological function for LMY and CMAR and it was also the second and fourth highest biological function for REA and HCW, respectively. Candidate genes and enriched biological functions identified by the integrative analyses of metabolites with phenotypic traits and DNA variants could help interpret the results of previous genome-wide association studies for carcass merit traits. Our integrative study also revealed additional potential novel genes associated with these economically important traits. Therefore, our study improves understanding of the molecular and biological functions/processes that influence carcass merit traits, which could help develop strategies to enhance genomic prediction of carcass merit traits with incorporation of metabolomic data. Similarly, this information could guide management practices, such as nutritional interventions, with the purpose of boosting specific carcass merit traits.

The metabolomics profile of growth rate in grazing beef cattle

This study aimed to determine the relationship between the metabolome and changes in growth rate (i.e., liveweight change, LWC) and molasses-lick block supplement intake (MLB) of grazing cattle. Weaner beef cattle were fed for 220 days with a sequence of feed types and blood samples, growth rate, and supplement intake were taken on five points in time. The relative abundance (RA) of plasma metabolites were determined using proton nuclear magnetic resonance (NMR). Sixty-four per cent of the metabolites identified were associated with LWC but only 26% with MLB intake (P < 0.05). Periods with faster growth rate showed high availability of amino acids (i.e., valine, leucine, isoleucine, phenylalanine and tyrosine), acetate, and 3-hydroxybutyrate. Periods with lower growth rate were associated with high RA of lipids, choline and acetate. The metabolic profile of individual animals during a period of compensatory growth (after periods of poor performance) showed that high-performing animals were characterised by lower RA of amino acids (i.e., valine, leucine, isoleucine, methylhistidine), creatinine, creatine, pyruvate, 3-hydroxybutyrate, and acetyl groups. It is speculated that high-performing animals have faster uptake of these metabolites from the bloodstream. Cattle growth rate over time was associated with their metabolome which could be used to ensure that the availability of certain metabolites promoting growth is tailored in feed supplements to improve production.

Genomic Prediction Methods Accounting for Nonadditive Genetic Effects

The use of genomic information for prediction of future phenotypes or breeding values for the candidates to selection has become a standard over the last decade. However, most procedures for genomic prediction only consider the additive (or substitution) effects associated with polymorphic markers. Nevertheless, the implementation of models that consider nonadditive genetic variation may be interesting because they (1) may increase the ability of prediction, (2) can be used to define mate allocation procedures in plant and animal breeding schemes, and (3) can be used to benefit from nonadditive genetic variation in crossbreeding or purebred breeding schemes. This study reviews the available methods for incorporating nonadditive effects into genomic prediction procedures and their potential applications in predicting future phenotypic performance, mate allocation, and crossbred and purebred selection. Finally, a brief outline of some future research lines is also proposed.

Identification of candidate genes and enriched biological functions for feed efficiency traits by integrating plasma metabolites and imputed whole genome sequence variants in beef cattle

BACKGROUND

Feed efficiency is one of the key determinants of beef industry profitability and sustainability. However, the cellular and molecular background behind feed efficiency is largely unknown. This study combines imputed whole genome DNA variants and 31 plasma metabolites to dissect genes and biological functions/processes that are associated with residual feed intake (RFI) and its component traits including daily dry matter intake (DMI), average daily gain (ADG), and metabolic body weight (MWT) in beef cattle.

RESULTS

Regression analyses between feed efficiency traits and plasma metabolites in a population of 493 crossbred beef cattle identified 5 (L-valine, lysine, L-tyrosine, L-isoleucine, and L-leucine), 4 (lysine, L-lactic acid, L-tyrosine, and choline), 1 (citric acid), and 4 (L-glutamine, glycine, citric acid, and dimethyl sulfone) plasma metabolites associated with RFI, DMI, ADG, and MWT (P-value < 0.1), respectively. Combining the results of metabolome-genome wide association studies using 10,488,742 imputed SNPs, 40, 66, 15, and 40 unique candidate genes were identified as associated with RFI, DMI, ADG, and MWT (P-value < 1 × 10-5), respectively. These candidate genes were found to be involved in some key metabolic processes including metabolism of lipids, molecular transportation, cellular function and maintenance, cell morphology and biochemistry of small molecules.

CONCLUSIONS

This study identified metabolites, candidate genes and enriched biological functions/processes associated with RFI and its component traits through the integrative analyses of metabolites with phenotypic traits and DNA variants. Our findings could enhance the understanding of biochemical mechanisms of feed efficiency traits and could lead to improvement of genomic prediction accuracy via incorporating metabolite data.

Overview of Metabolomic Analysis and the Integration with Multi-Omics for Economic Traits in Cattle

Metabolomics has been applied to measure the dynamic metabolic responses, to understand the systematic biological networks, to reveal the potential genetic architecture, etc., for human diseases and livestock traits. For example, the current published results include the detected relevant candidate metabolites, identified metabolic pathways, potential systematic networks, etc., for different cattle traits that can be applied for further metabolomic and integrated omics studies. Therefore, summarizing the applications of metabolomics for economic traits is required in cattle. We here provide a comprehensive review about metabolomic analysis and its integration with other omics in five aspects: (1) characterization of the metabolomic profile of cattle; (2) metabolomic applications in cattle; (3) integrated metabolomic analysis with other omics; (4) methods and tools in metabolomic analysis; and (5) further potentialities. The review aims to investigate the existing metabolomic studies by highlighting the results in cattle, integrated with other omics studies, to understand the metabolic mechanisms underlying the economic traits and to provide useful information for further research and practical breeding programs in cattle.

Analytical Methodology for a Metabolome Atlas of Goat's Plasma, Milk and Feces Using 1H-NMR and UHPLC-HRMS

Metabolomics has been increasingly used in animal and food sciences. Animal health is one of the most important factor that can also alter animal integrity and welfare. Some studies have already investigated the link between health and metabolic profile of dairy animals. These studies in metabolomics often consider a single type of sample using a single analytical platform (nuclear magnetic resonance or mass spectrometry). Only few studies with multi-platform approaches are also used with a single or a multi type of sample, but they mainly consider dairy cows’ metabolome although dairy goats present similar diseases, that it could be interesting to detect early to preserve animal health and milk production. This study aims to create a metabolic atlas of goat plasma, milk and feces, based on healthy animals. Our study describes a standard operating procedure for three goat matrices: blood plasma, milk, and feces using multiple platforms (NMR (¹H), UHPLC (RP)-MS and UHPLC (HILIC)-MS) that follows a unique sample preparation procedure for each sample type to be analyzed on multi-platforms basis. Our method was evaluated for its robustness and allowed a better characterization of goat metabolic profile in healthy conditions.

The potential for mitigation of methane emissions in ruminants through the application of metagenomics, metabolomics, and other -OMICS technologies

Ruminant supply chains contribute 5.7 gigatons of CO_2-eq per annum, which represents approximately 80% of the livestock sector emissions. One of the largest sources of emission in the ruminant sector is methane (CH₄), accounting for approximately 40% of the sectors total emissions. With climate change being a growing concern, emphasis is being put on reducing greenhouse gas emissions, including those from ruminant production. Various genetic and environmental factors influence cattle CH₄ production, such as breed, genetic makeup, diet, management practices, and physiological status of the host. The influence of genetic variability on CH₄ yield in ruminants indicates that genomic selection for reduced CH₄ emissions is possible. Although the microbiology of CH₄ production has been studied, further research is needed to identify key differences in the host and microbiome genomes and how they interact with one another. The advancement of “-omics” technologies, such as metabolomics and metagenomics, may provide valuable information in this regard. Improved understanding of genetic mechanisms associated with CH₄ production and the interaction between the microbiome profile and host genetics will increase the rate of genetic progress for reduced CH₄ emissions. Through a systems biology approach, various “-omics” technologies can be combined to unravel genomic regions and genetic markers associated with CH₄ production, which can then be used in selective breeding programs. This comprehensive review discusses current challenges in applying genomic selection for reduced CH₄ emissions, and the potential for “-omics” technologies, especially metabolomics and metagenomics, to minimize such challenges. The integration and evaluation of different levels of biological information using a systems biology approach is also discussed, which can assist in understanding the underlying genetic mechanisms and biology of CH₄ production traits in ruminants and aid in reducing agriculture’s overall environmental footprint.

Comparative untargeted metabolome analysis of ruminal fluid and feces of Nelore steers (Bos indicus)

We conducted a study to identify the fecal metabolite profile and its proximity to the ruminal metabolism of Nelore steers based on an untargeted metabolomic approach. Twenty-six Nelore were feedlot with same diet during 105 d. Feces and rumen fluid were collected before and at slaughter, respectively. The metabolomics analysis indicated 49 common polar metabolites in the rumen and feces. Acetate, propionate, and butyrate were the most abundant polar metabolites in both bio-samples. The rumen presented significantly higher concentrations of the polar compounds when compared to feces (P < 0.05); even though, fecal metabolites presented an accentuated representability of the ruminal fluid metabolites. All fatty acids present in the ruminal fluid were also observed in the feces, except for C20:2n6 and C20:4n6. The identified metabolites offer information on the main metabolic pathways (higher impact factor and P < 0.05), as synthesis and degradation of ketone bodies; the alanine, aspartate and glutamate metabolisms, the glycine, serine; and threonine metabolism and the pyruvate metabolism. The findings reported herein on the close relationship between the ruminal fluid and feces metabolic profiles may offer new metabolic information, in addition to facilitating the sampling for metabolism investigation in animal production and health routines.

Prediction of complex phenotypes using the Drosophila melanogaster metabolome

Understanding the genotype-phenotype map and how variation at different levels of biological organization is associated are central topics in modern biology. Fast developments in sequencing technologies and other molecular omic tools enable researchers to obtain detailed information on variation at DNA level and on intermediate endophenotypes, such as RNA, proteins and metabolites. This can facilitate our understanding of the link between genotypes and molecular and functional organismal phenotypes. Here, we use the Drosophila melanogaster Genetic Reference Panel and nuclear magnetic resonance (NMR) metabolomics to investigate the ability of the metabolome to predict organismal phenotypes. We performed NMR metabolomics on four replicate pools of male flies from each of 170 different isogenic lines. Our results show that metabolite profiles are variable among the investigated lines and that this variation is highly heritable. Second, we identify genes associated with metabolome variation. Third, using the metabolome gave better prediction accuracies than genomic information for four of five quantitative traits analyzed. Our comprehensive characterization of population-scale diversity of metabolomes and its genetic basis illustrates that metabolites have large potential as predictors of organismal phenotypes. This finding is of great importance, e.g., in human medicine, evolutionary biology and animal and plant breeding.

LC-MS/MS Based Metabolomics Reveal Candidate Biomarkers and Metabolic Changes in Different Buffalo Species

Consumers have shown more and more interest in high-quality and healthy dairy products and buffalo milk is commercially more viable than other milks in producing superior dairy products due to its higher contents of fat, crude protein, and total solids. Metabolomics is one of the most powerful strategies in molecular mechanism research however, little study has been focused on the milk metabolites in different buffalo species. Therefore, the aim of this study was to explore the underlying molecular mechanism of the fatty synthesis and candidate biomarkers by analyzing the metabolomic profiles. Milk of three groups of buffaloes, including 10 Mediterranean, 12 Murrah, and 10 crossbred buffaloes (Murrah × local swamp buffalo), were collected and UPLC-Q-Orbitrap HRMS was used to obtain the metabolomic profiles. Results showed that milk fatty acid in Mediterranean buffalo was significantly higher than Murrah buffalo and crossbred buffalo. A total of 1837/726 metabolites was identified in both positive and negative electrospray ionization (ESI±) mode, including 19 significantly different metabolites between Mediterranean and Murrah buffalo, and 18 different metabolites between Mediterranean and crossbred buffalo. We found 11 of the different metabolites were both significantly different between Mediterranean vs. Murrah group and Mediterranean vs crossbred group, indicating that they can be used as candidate biomarkers of Mediterranean buffalo milk. Further analysis found that the different metabolites were mainly enriched in fat synthesis related pathways such as fatty acid biosynthesis, unsaturated fatty acid biosynthesis, and linoleic acid metabolism, indicating that the priority of different pathways affected the milk fat content in different buffalo species. These specific metabolites may be used as biomarkers in the identification of milk quality and molecular breeding of high milk fat buffalo.

Rumen and Serum Metabolomes in Response to Endophyte-Infected Tall Fescue Seed and Isoflavone Supplementation in Beef Steers

Fescue toxicosis impacts beef cattle production via reductions in weight gain and muscle development. Isoflavone supplementation has displayed potential for mitigating these effects. The objective of the current study was to evaluate isoflavone supplementation with fescue seed consumption on rumen and serum metabolomes. Angus steers (n = 36) were allocated randomly in a 2 × 2 factorial arrangement of treatments including endophyte-infected (E+) or endophyte-free (E−) tall fescue seed, with (P+) or without (P−) isoflavones. Steers were provided a basal diet with fescue seed for 21 days, while isoflavones were orally administered daily. Following the trial, blood and rumen fluid were collected for metabolite analysis. Metabolites were extracted and then analyzed by UPLC-MS. The MAVEN program was implemented to identify metabolites for MetaboAnalyst 4.0 and SAS 9.4 statistical analysis. Seven differentially abundant metabolites were identified in serum by isoflavone treatment, and eleven metabolites in the rumen due to seed type (p < 0.05). Pathways affected by treatments were related to amino acid and nucleic acid metabolism in both rumen fluid and serum (p < 0.05). Therefore, metabolism was altered by fescue seed in the rumen; however, isoflavones altered metabolism systemically to potentially mitigate detrimental effects of seed and improve animal performance.

Metabolomic Profiling of Bos taurus Beef, Dairy, and Crossbred Cattle: A Between-Breeds Meta-Analysis

Cattle breeds may differ substantially in their metabolism. However, the metabolomes of dairy and beef cattle are not well-known. Knowledge of breed-specific metabolic features is essential for biomarker identification and to adopt specific nutritional strategies. The muscle hypertrophy (mh), a beef cattle phenotype present in Asturiana de los Valles (AV) but absent in Asturiana de la Montaña (AM) and Holsteins, may underlie such differences. We compared the plasma metabolomes of Holstein, AV, AM, and crossbred cattle recipients selected for meta-analysis within an embryo transfer (ET) program. Blood samples were collected on day 0 (oestrus) and day 7 (prior to ET) (N = 234 samples × 2 days). Nuclear magnetic resonance quantified N = 36 metabolites in plasma, and more metabolic differences between breeds were found on day 0 (N = 19 regulated metabolites) than on day 7 (N = 5). AV and AM largely differed from Holstein cattle (N = 55 and 35 enriched metabolic pathways, respectively); however, AV and AM differed in N = 6 enriched pathways. Metabolic activity was higher in AV than in Holstein cattle, as explained in part by the mh phenotype. The metabolomic characterization of breeds facilitates biomarker research and helps to define the healthy ranges of metabolite concentrations.

Differential Metabolomics Profiles Identified by CE-TOFMS between High and Low Intramuscular Fat Amount in Fattening Pigs

The amount of intramuscular fat (IMF) present in the loin eye area is one of the most important characteristics of high-quality pork. IMF measurements are currently impractical without a labor-intensive process. Metabolomic profiling could be used as an IMF indicator to avoid this process; however, no studies have investigated their use during the fattening period of pigs. This study examined the metabolite profiles in the plasma of two groups of pigs derived from the same Duroc genetic line and fed the same diet. Five plasma samples were collected from each individual the day before slaughter. Capillary electrophoresis-time of flight mass spectrometry (CE-TOFMS) was used to analyze the purified plasma from each sample. Principle component analysis (PCA) and partial least squares (PLS) were used to find the semi-quantitative values of the compounds. The results indicate that branched-chain amino acids are significantly associated with high IMF content, while amino acids are associated with low IMF content. These differences were validated using the quantification analyses by high-performance liquid chromatograph, which supported our results. These results suggest that the concentration of branched-chain amino acids in plasma could be an indicative biomarker for the IMF content in the loin eye area.

MEATabolomics: Muscle and Meat Metabolomics in Domestic Animals

In the past decades, metabolomics has been used to comprehensively understand a variety of food materials for improvement and assessment of food quality. Farm animal skeletal muscles and meat are one of the major targets of metabolomics for the characterization of meat and the exploration of biomarkers in the production system. For identification of potential biomarkers to control meat quality, studies of animal muscles and meat with metabolomics (MEATabolomics) has been conducted in combination with analyses of meat quality traits, focusing on specific factors associated with animal genetic background and sensory scores, or conditions in feeding system and treatments of meat in the processes such as postmortem storage, processing, and hygiene control. Currently, most of MEATabolomics approaches combine separation techniques (gas or liquid chromatography, and capillary electrophoresis)-mass spectrometry (MS) or nuclear magnetic resonance (NMR) approaches with the downstream multivariate analyses, depending on the polarity and/or hydrophobicity of the targeted metabolites. Studies employing these approaches provide useful information to monitor meat quality traits efficiently and to understand the genetic background and production system of animals behind the meat quality. MEATabolomics is expected to improve the knowledge and methodologies in animal breeding and feeding, meat storage and processing, and prediction of meat quality.

Use of Large and Diverse Datasets for 1H NMR Serum Metabolic Profiling of Early Lactation Dairy Cows

Most livestock metabolomic studies involve relatively small, homogenous populations of animals. However, livestock farming systems are non-homogenous, and large and more diverse datasets are required to ensure that biomarkers are robust. The aims of this study were therefore to (1) investigate the feasibility of using a large and diverse dataset for untargeted proton nuclear magnetic resonance (¹H NMR) serum metabolomic profiling, and (2) investigate the impact of fixed effects (farm of origin, parity and stage of lactation) on the serum metabolome of early-lactation dairy cows. First, we used multiple linear regression to correct a large spectral dataset (707 cows from 13 farms) for fixed effects prior to multivariate statistical analysis with principal component analysis (PCA). Results showed that farm of origin accounted for up to 57% of overall spectral variation, and nearly 80% of variation for some individual metabolite concentrations. Parity and week of lactation had much smaller effects on both the spectra as a whole and individual metabolites (< 3% and < 20%, respectively). In order to assess the effect of fixed effects on prediction accuracy and biomarker discovery, we used orthogonal partial least squares (OPLS) regression to quantify the relationship between NMR spectra and concentrations of the current gold standard serum biomarker of energy balance, β-hydroxybutyrate (BHBA). Models constructed using data from multiple farms provided reasonably robust predictions of serum BHBA concentration (0.05 ≤ RMSE ≤ 0.18). Fixed effects influenced the results biomarker discovery; however, these impacts could be controlled using the proposed method of linear regression spectral correction.

Blood Plasma Metabolomics Predicts Pregnancy in Holstein Cattle Transferred with Fresh and Vitrified/Warmed Embryos Produced in Vitro

Metabolomics may identify biomarkers in blood that differentiate pregnant from open embryo recipients. Fresh and vitrified/warmed, in vitro-produced embryos were transferred to Holstein recipients (discovery group). Recipient blood plasma collected on Day-0 (estrus) and Day-7 (before embryo transfer) were analyzed by nuclear magnetic resonance (N = 36 metabolites quantified). Metabolites whose concentrations differed between open and pregnant recipients were analyzed [(P < 0.05); false discovery rate (FDR) (P < 0.05)]. Biomarkers were identified in Day-7 plasma (receiver operator characteristic-area under curve (ROC-AUC) > 0.650; t-test P < 0.05; random forests, mean decrease accuracy) and cross-validated in independent Holstein, beef, and crossbred recipients (overall classification accuracy -OCA-; P < 0.05). Recipients with fresh embryos showed N = 6 biomarkers consistently on Day-40, Day-62, and at birth. Recipients with vitrified embryos showed N = 5 biomarkers on Day-40 and Day-62 but only one biomarker at birth. The most predictive biomarkers identified at birth within fresh embryos were oxoglutaric acid (ROC-AUC = 0.709; OCA = 0.812) and ornithine (ROC-AUC = 0.731; OCA = 0.727), while l-glycine was identified in vitrified embryos (ROC-AUC = 0.796; OCA = 0.667) together with other predictive biomarkers not identified at birth (Day-62: l-glutamine ROC-AUC = 0.757; OCA = 0.767) and l-lysine (Day-62: ROC-AUC = 0.680; OCA = 0.767). Pathway enrichment analysis distinguished between pregnant recipients for fresh (enriched energy oxidative metabolism from fat) and vitrified (lower lipid metabolism) embryos. Metabolomics can select individuals that will become pregnant in a defined cycle.

Rumen fluid metabolomics of beef steers differing in feed efficiency

INTRODUCTION

Beef is the most consumed red meat in the United States, and the US is the largest producer and consumer of beef cattle globally. Feed is one of the largest input costs for the beef cattle industry, accounting for 40-60% of the total input costs. Identifying methods for improving feed efficiency in beef cattle herds could result in decreased cost to both producers and consumers, as well as increased animal protein available for global consumption.

METHODS

In this study, rumen fluid was collected from low- (n = 14) and high-RFI (n = 15) steers. Rumen fluid was filtered through a 0.22 µM syringe filter, extracted using 0.1% formic acid in acetonitrile:water:methanol (2:2:1) and injected into the Dionex UltiMate 3000 UHPLC system with an Exactive Plus Orbitrap MS. Peaks were identified using MAVEN and analyzed using MetaboAnalyst 4.0 and SAS. Significance was determined using an α ≤ 0.05.

RESULTS

Eight metabolites were greater in low-RFI steers compared to high-RFI steers, including 3,4-dihydroxyphenylacetate, 4-pyridoxate, citraconate, hypoxanthine, succinate/methylmalonate, thymine, uracil, and xylose (P ≤ 0.05). These metabolites were predominantly involved in amino acid and lipid metabolism.

CONCLUSIONS

Rumen fluid metabolomes differ in steers of varying feed efficiencies. These metabolites may be used as biomarkers of feed efficiency, and may provide insight as to factors contributing to differences in feed efficiency that may be exploited to improve feed efficiency in beef cattle herds.

Metabolomic networks and pathways associated with feed efficiency and related-traits in Duroc and Landrace pigs

Improving feed efficiency (FE) is a major goal of pig breeding, reducing production costs and providing sustainability to the pig industry. Reliable predictors for FE could assist pig producers. We carried out untargeted blood metabolite profiling in uncastrated males from Danbred Duroc (n = 59) and Danbred Landrace (n = 50) pigs at the beginning and end of a FE testing phase to identify biomarkers and biological processes underlying FE and related traits. By applying linear modeling and clustering analyses coupled with WGCNA framework, we identified 102 and 73 relevant metabolites in Duroc and Landrace based on two sampling time points. Among them, choline and pyridoxamine were hub metabolites in Duroc in early testing phase, while, acetoacetate, cholesterol sulfate, xanthine, and deoxyuridine were identified in the end of testing. In Landrace, cholesterol sulfate, thiamine, L-methionine, chenodeoxycholate were identified at early testing phase, while, D-glutamate, pyridoxamine, deoxycytidine, and L-2-aminoadipate were found at the end of testing. Validation of these results in larger populations could establish FE prediction using metabolomics biomarkers. We conclude that it is possible to identify a link between blood metabolite profiles and FE. These results could lead to improved nutrient utilization, reduced production costs, and increased FE.

Review: Recent advances in bovine in vitro embryo production: reproductive biotechnology history and methods

In vitro production (IVP) of embryos and associated technologies in cattle have shown significant progress in recent years, in part driven by a better understanding of the full potential of these tools by end users. The combination of IVP with sexed semen (SS) and genomic selection (GS) is being successfully and widely used in North America, South America and Europe. The main advantages offered by these technologies include a higher number of embryos and pregnancies per unit of time, and a wider range of potential female donors from which to retrieve oocytes (including open cyclic females and ones up to 3 months pregnant), including high index genomic calves, a reduced number of sperm required to produce embryos and increased chances of obtaining the desired sex of offspring. However, there are still unresolved aspects of IVP of embryos that limit a wider implementation of the technology, including potentially reduced fertility from the use of SS, reduced oocyte quality after in vitro oocyte maturation and lower embryo cryotolerance, resulting in reduced pregnancy rates compared to in vivo-produced embryos. Nevertheless, promising research results have been reported, and work is in progress to address current deficiencies. The combination of GS, IVP and SS has proven successful in the commercial field in several countries assisting practitioners and cattle producers to improve reproductive performance, efficiency and genetic gain.

Relationship of the blood metabolome to subsequent carcass traits at slaughter in feedlot Wagyu crossbred steers

The aim of the present study was to determine the relationships between the blood metabolome and (1) carcass traits with a focus on intramuscular fat (marbling), and (2) the length of time cattle consumed a high-starch diet in feedlot cattle. Blood samples were obtained from 181 Wagyu-crossbred steers between 300-400 days before slaughter when carcass data was collected. ¹H NMR spectroscopy identified 35 metabolites with 7 positively associated with marbling (3-hydroxybutyrate, propionate, acetate, creatine, histidine, valine, and isoleucine; P ≤ 0.05). Subcutaneous rump fat thickness was positively associated with glucose, leucine and lipids (P ≤ 0.05) and negatively associated with anserine and arabinose (P ≤ 0.05). Carcass weight and growth rate were negatively associated with 3-hydroxybutyrate (P < 0.05), and growth rate was negatively associated with creatine (P < 0.05) and positively associated with aspartate (P < 0.05). Glucose and arginine showed a significant interaction between marbling and number of days animals consumed a high-starch diet (P < 0.05). Sire was the single variable with the largest effect on the relative concentration of metabolites and carcass and production traits. Blood metabolomics helps understand fat and muscle metabolism, and is associated with genotype, and carcass and production traits in cattle offering potential biomarkers suitable to select animals for management and genetic improvement.

Very Low Crude Protein and Varying Glycine Concentrations in the Diet Affect Growth Performance, Characteristics of Nitrogen Excretion, and the Blood Metabolome of Broiler Chickens

BACKGROUND

The minimum to which dietary crude protein (CP) level for broiler chickens can be reduced without decreasing growth and the glycine equivalent (Glyequi) concentration required are not known. The plasma metabolome might reflect dietary influences on physiological processes.

OBJECTIVE

The aim of this study was to investigate the effect of 3 low CP levels with 4 Glyequi concentrations on growth and characteristics of nitrogen excretion, and to identify plasma metabolome variations.

METHODS

Male Ross308 broiler chickens were provided 1 of 12 dietary treatments in 84 metabolism cages (10/cage) from days 7 to 21. Three diets with 163 (CP163), 147 (CP147), and 132 (CP132) g CP/kg were formulated, each containing 12, 15, 18, and 21 g Glyequi/kg. Essential amino acid concentrations were the same in all diets. Animals and feed were weighed on days 7 and 21 to determine average daily gain (ADG) and gain:feed ratio (G:F). Excreta were collected from days 18 to 21 to analyze nitrogenous components, and blood was obtained on day 21 to conduct a metabolome analysis.

RESULTS

Two-factor ANOVA showed significant interaction effects for ADG, G:F, and nitrogen efficiency (P < 0.001). Reduction of CP decreased ADG and G:F, and increased nitrogen efficiency. Glyequi supplementation increased ADG (by 7.9 g/d) and G:F (by 0.07 g/g) at CP132. The ADG (by 2.4 g/d) at CP147 and G:F (by 0.02 g/g) at CP147 and CP163 increased up to 15 g Glyequi/kg. Multivariate statistical analysis showed an influence of Glyequi on plasma acylcarnitine and lysophosphatidylcholine concentrations, and a decrease of plasma phosphatidylcholine and sphingomyelin concentrations with reduced CP.

CONCLUSIONS

These results suggest that a nutrient other than Glyequi limited growth when CP was reduced from CP163 to CP147, and that the response of broiler chickens to Glyequi is dependent on the dietary CP level. Plasma metabolites indicate dietary influences on the physiological state of the animals.

Genome-wide association studies for 30 haematological and blood clinical-biochemical traits in Large White pigs reveal genomic regions affecting intermediate phenotypes

Haematological and clinical-biochemical parameters are considered indicators of the physiological/health status of animals and might serve as intermediate phenotypes to link physiological aspects to production and disease resistance traits. The dissection of the genetic variability affecting these phenotypes might be useful to describe the resilience of the animals and to support the usefulness of the pig as animal model. Here, we analysed 15 haematological and 15 clinical-biochemical traits in 843 Italian Large White pigs, via three genome-wide association scan approaches (single-trait, multi-trait and Bayesian). We identified 52 quantitative trait loci (QTLs) associated with 29 out of 30 analysed blood parameters, with the most significant QTL identified on porcine chromosome 14 for basophil count. Some QTL regions harbour genes that may be the obvious candidates: QTLs for cholesterol parameters identified genes (ADCY8, APOB, ATG5, CDKAL1, PCSK5, PRL and SOX6) that are directly involved in cholesterol metabolism; other QTLs highlighted genes encoding the enzymes being measured [ALT (known also as GPT) and AST (known also as GOT)]. Moreover, the multivariate approach strengthened the association results for several candidate genes. The obtained results can contribute to define new measurable phenotypes that could be applied in breeding programs as proxies for more complex traits.

Application of omics technologies for a deeper insight into quali-quantitative production traits in broiler chickens: A review

The poultry industry is continuously facing substantial and different challenges such as the increasing cost of feed ingredients, the European Union's ban of antibiotic as growth promoters, the antimicrobial resistance and the high incidence of muscle myopathies and breast meat abnormalities. In the last decade, there has been an extraordinary development of many genomic techniques able to describe global variation of genes, proteins and metabolites expression level. Proper application of these cutting-edge omics technologies (mainly transcriptomics, proteomics and metabolomics) paves the possibility to understand much useful information about the biological processes and pathways behind different complex traits of chickens. The current review aimed to highlight some important knowledge achieved through the application of omics technologies and proteo-genomics data in the field of feed efficiency, nutrition, meat quality and disease resistance in broiler chickens.

Synergetic action between the rumen microbiota and bovine health

Host-rumen-microbe interactions are essential components of many physiological processes, and therefore can affect ruminant health. Classical knowledge of rumen microbiology is based on culture-dependent methodologies, which only account for 10-20% of the rumen bacterial communities. While, the advancement in DNA sequencing and bioinformatics platforms provide novel approaches to investigate the composition and dynamics of the rumen microbiota. Recent studies demonstrated that the ruminal ecosystem is highly diverse and harbors numerous microbial communities. The composition of these microbial communities are affected by various environmental factors such as nutrition and different management strategies. Disturbance in the microbial ecology of the rumen is associated with the development of various diseases. Despite the flow of recent rumen-based studies, rumen microbiota is still not fully characterized. This review provides an overview of recent efforts to characterize rumen microbiota and its potential role in rumen health and disease. Moreover, the recent effects of dietary interventions and probiotics on rumen microbiota are discussed.

Non-additive Effects in Genomic Selection

In the last decade, genomic selection has become a standard in the genetic evaluation of livestock populations. However, most procedures for the implementation of genomic selection only consider the additive effects associated with SNP (Single Nucleotide Polymorphism) markers used to calculate the prediction of the breeding values of candidates for selection. Nevertheless, the availability of estimates of non-additive effects is of interest because: (i) they contribute to an increase in the accuracy of the prediction of breeding values and the genetic response; (ii) they allow the definition of mate allocation procedures between candidates for selection; and (iii) they can be used to enhance non-additive genetic variation through the definition of appropriate crossbreeding or purebred breeding schemes. This study presents a review of methods for the incorporation of non-additive genetic effects into genomic selection procedures and their potential applications in the prediction of future performance, mate allocation, crossbreeding, and purebred selection. The work concludes with a brief outline of some ideas for future lines of that may help the standard inclusion of non-additive effects in genomic selection.

Multi-Level Integration of Environmentally Perturbed Internal Phenotypes Reveals Key Points of Connectivity between Them

The genotype and external phenotype of organisms are linked by so-called internal phenotypes which are influenced by environmental conditions. In this study, we used five existing -omics datasets representing five different layers of internal phenotypes, which were simultaneously measured in dietarily perturbed mice. We performed 10 pair-wise correlation analyses verified with a null model built from randomized data. Subsequently, the inferred networks were merged and literature mined for co-occurrences of identified linked nodes. Densely connected internal phenotypes emerged. Forty-five nodes have links with all other data-types and we denote them “connectivity hubs.” In literature, we found proof of 6% of the 577 connections, suggesting a biological meaning for the observed correlations. The observed connectivities between metabolite and cytokines hubs showed higher numbers of literature hits as compared to the number of literature hits on the connectivities between the microbiota and gene expression internal phenotypes. We conclude that multi-level integrated networks may help to generate hypotheses and to design experiments aiming to further close the gap between genotype and phenotype. We describe and/or hypothesize on the biological relevance of four identified multi-level connectivity hubs.

Livestock metabolomics and the livestock metabolome: A systematic review

Metabolomics uses advanced analytical chemistry techniques to comprehensively measure large numbers of small molecule metabolites in cells, tissues and biofluids. The ability to rapidly detect and quantify hundreds or even thousands of metabolites within a single sample is helping scientists paint a far more complete picture of system-wide metabolism and biology. Metabolomics is also allowing researchers to focus on measuring the end-products of complex, hard-to-decipher genetic, epigenetic and environmental interactions. As a result, metabolomics has become an increasingly popular “omics” approach to assist with the robust phenotypic characterization of humans, crop plants and model organisms. Indeed, metabolomics is now routinely used in biomedical, nutritional and crop research. It is also being increasingly used in livestock research and livestock monitoring. The purpose of this systematic review is to quantitatively and objectively summarize the current status of livestock metabolomics and to identify emerging trends, preferred technologies and important gaps in the field. In conducting this review we also critically assessed the applications of livestock metabolomics in key areas such as animal health assessment, disease diagnosis, bioproduct characterization and biomarker discovery for highly desirable economic traits (i.e., feed efficiency, growth potential and milk production). A secondary goal of this critical review was to compile data on the known composition of the livestock metabolome (for 5 of the most common livestock species namely cattle, sheep, goats, horses and pigs). These data have been made available through an open access, comprehensive livestock metabolome database (LMDB, available at http://www.lmdb.ca). The LMDB should enable livestock researchers and producers to conduct more targeted metabolomic studies and to identify where further metabolome coverage is needed.

The Importance of Endophenotypes to Evaluate the Relationship between Genotype and External Phenotype

With the exception of a few Mendelian traits, almost all phenotypes (traits) in livestock science are quantitative or complex traits regulated by the expression of many genes. For most of the complex traits, differential expression of genes, rather than genomic variation in the gene coding sequences, is associated with the genotype of a trait. The expression profiles of the animal's transcriptome, proteome and metabolome represent endophenotypes that influence/regulate the externally-observed phenotype. These expression profiles are generated by interactions between the animal's genome and its environment that range from the cellular, up to the husbandry environment. Thus, understanding complex traits requires knowledge about not only genomic variation, but also environmental effects that affect genome expression. Gene products act together in physiological pathways and interaction networks (of pathways). Due to the lack of annotation of the functional genome and ontologies of genes, our knowledge about the various biological systems that contribute to the development of external phenotypes is sparse. Furthermore, interaction with the animals' microbiome, especially in the gut, greatly influences the external phenotype. We conclude that a detailed understanding of complex traits requires not only understanding of variation in the genome, but also its expression at all functional levels.