RESUMO
The aim of this study was to describe metabolism of early-lactation dairy cows by clustering cows based on glucose, insulin-like growth factor I (IGF-I), free fatty acid, and ß-hydroxybutyrate (BHB) using the k-means method. Predictive models for metabolic clusters were created and validated using 3 sets of milk biomarkers (milk metabolites and enzymes, glycans on the immunogamma globulin fraction of milk, and Fourier-transform mid-infrared spectra of milk). Metabolic clusters are used to identify dairy cows with a balanced or imbalanced metabolic profile. Around 14 and 35 d in milk, serum or plasma concentrations of BHB, free fatty acids, glucose, and IGF-I were determined. Cows with a favorable metabolic profile were grouped together in what was referred to as the "balanced" group (n = 43) and were compared with cows in what was referred to as the "other balanced" group (n = 64). Cows with an unfavorable metabolic profile were grouped in what was referred to as the "imbalanced" group (n = 19) and compared with cows in what was referred to as the "other imbalanced" group (n = 88). Glucose and IGF-I were higher in balanced compared with other balanced cows. Free fatty acids and BHB were lower in balanced compared with other balanced cows. Glucose and IGF-I were lower in imbalanced compared with other imbalanced cows. Free fatty acids and BHB were higher in imbalanced cows. Metabolic clusters were related to production parameters. There was a trend for a higher daily increase in fat- and protein-corrected milk yield in balanced cows, whereas that of imbalanced cows was higher. Dry matter intake and the daily increase in dry matter intake were higher in balanced cows and lower in imbalanced cows. Energy balance was continuously higher in balanced cows and lower in imbalanced cows. Weekly or twice-weekly milk samples were taken and milk metabolites and enzymes (milk glucose, glucose-6-phosphate, BHB, lactate dehydrogenase, N-acetyl-ß-d-glucosaminidase, isocitrate), immunogamma globulin glycans (19 peaks), and Fourier-transform mid-infrared spectra (1,060 wavelengths reduced to 15 principal components) were determined. Milk biomarkers with or without additional cow information (days in milk, parity, milk yield features) were used to create predictive models for the metabolic clusters. Accuracy for prediction of balanced (80%) and imbalanced (88%) cows was highest using milk metabolites and enzymes combined with days in milk and parity. The results and models of the present study are part of the GplusE project and identify novel milk-based phenotypes that may be used as predictors for metabolic and performance traits in early-lactation dairy cows.
Assuntos
Biomarcadores/análise , Bovinos/metabolismo , Lactação/fisiologia , Leite/química , Ácido 3-Hidroxibutírico/análise , Ácido 3-Hidroxibutírico/sangue , Animais , Biomarcadores/sangue , Glicemia/análise , Metabolismo Energético , Ácidos Graxos não Esterificados/sangue , Feminino , Glucose/análise , Fator de Crescimento Insulin-Like I/análise , Gravidez , Espectroscopia de Infravermelho com Transformada de FourierRESUMO
Gut bacterial communities have a profound influence on the health of humans and animals. Early-life gut microbial community structure influences the development of immunological competence and susceptibility to disease. For the Thoroughbred racehorse, the significance of early-life microbial colonisation events on subsequent health and athletic performance is unknown. Here we present data from a three-year cohort study of horses bred for racing designed to explore interactions between early-life gut bacterial community structure, health events in later life and athletic performance on the racetrack. Our data show that gut bacterial community structure in the first months of life predicts the risk of specific diseases and athletic performance up to three years old. Foals with lower faecal bacterial diversity at one month old had a significantly increased risk of respiratory disease in later life which was also associated with higher relative abundance of faecal Pseudomonadaceae. Surprisingly, athletic performance up to three years old, measured by three different metrics, was positively associated with higher faecal bacterial diversity at one month old and with the relative abundance of specific bacterial families. We also present data on the impact of antibiotic exposure of foals during the first month of life. This resulted in significantly lower faecal bacterial diversity at 28 days old, a significantly increased risk of respiratory disease in later life and a significant reduction in average prize money earnings, a proxy for athletic performance. Our study reveals associations between early-life bacterial community profiles and health events in later life and it provides evidence of the detrimental impact of antimicrobial treatment in the first month of life on health and performance outcomes in later life. For the first time, this study demonstrates a relationship between early-life gut bacterial communities and subsequent athletic performance that has implications for athletes of all species including humans.
Assuntos
Fezes , Microbioma Gastrointestinal , Cavalos , Animais , Fezes/microbiologia , Doenças dos Cavalos/microbiologia , Desempenho Atlético , Bactérias/classificação , Bactérias/genética , Masculino , FemininoRESUMO
Complex carbohydrates are rapidly becoming excellent biomarker candidates because of their high sensitivity to pathological changes. However, the discovery of clinical glycobiomarkers has been slow, due to the scarcity of high-throughput glycoanalytical workflows that allow rapid glycoprofiling of large clinical sample sets. To generate high-quality quantitative glycomics data in a high-throughput fashion, we have developed a robotized platform for rapid serum-based N-glycan sample preparation. The sample preparation workflow features a fully automated, rapid glycoprotein denaturation followed by sequential enzymatic glycan release, glycan purification on solid-supported hydrazide and fluorescent labelling. This allows accurate glycan quantitation by ultra-high performance liquid chromatography (UPLC). The sample preparation workflow was automated using an eight-channel Hamilton Robotics liquid handling workstation, allowing the preparation of almost 100 samples in 14 hours with excellent reproducibility and thus should greatly facilitate serum-based glyco-biomarker discovery.