Somatic cell count-based selection reduces susceptibility to energy shortage during early lactation in a sheep model

Responses of selected plasma metabolites to a two-day nutritional challenge of goats divergently selected for functional longevity

Understanding the extent to which genetics × environment plays a role in shaping individual strategies to environmental challenges is of considerable interest for future selection of more resilient animals. Accordingly, the objective of this study was to evaluate the metabolic responses to a nutritional challenge of goats divergently selected for functional longevity based on plasma metabolites and the repeatability of these responses across 2 experimental farms and years. We carried out 6 different experimental trials from years 2018 to 2022 (4 trials on site Bourges (2018-21) and 2 trials (2021-22) on site Grignon) in which 267 first kidding goats, daughters of Alpine bucks divergently selected for functional longevity, longevity plus (n = 137), and longevity minus (n = 130), were exposed to a 2-d nutritional challenge in early lactation. The experiments consisted of a 5 or 7-d control period (pre-challenge) on a standard lactation diet followed by a 2-d nutritional challenge with straw-only feeding and then a 7 or 10-d recovery period on a standard lactation diet, for site Bourges and Grignon, respectively. During the challenge plasma metabolite composition was recorded daily. Linear mixed-effects models were used to analyze all traits, considering the individual as a random effect and the 2x2 treatments (i.e., genetic line and year nested in site) and litter size as fixed effects. The linear mixed-effects model using a piecewise arrangement was used to analyze the response/recovery profiles to the nutritional challenge. Random parameters estimated for each individual, using the mixed-effects models without the fixed effects of genetic line, were used in a Sparse Partial Least Square Discriminant Analysis (sPLS-DA) to compare the goat metabolism response to the challenge on a multivariate scale. The plasma metabolites, glucose, β-hydroxybutyrate (BHB), and nonesterified fatty acids (NEFA), and urea concentrations responded to the 2-d nutritional challenge. Selection for functional longevity did not affect plasma glucose, NEFA, BHB, and urea response/recoveries to a 2-d nutritional challenge. However, site, trial, and litter size affected these responses. Moreover, the plasma metabolites seem not to fully recover to prechallenge levels after the recovery phase. The sPLS-DA analysis did not discriminate between the 2 longevity lines. We observed meaningful between-individuals' variability in plasma BHB, especially on the prechallenge and rate of response and rate of recovery from the 2-d nutritional challenge (CV = 26.2%, 36.1%, and 41.2%, repeatability = 0.749, 0.322, and 0.741, respectively). Plasma NEFA recovery from challenge also demonstrated high between-individuals' variability (CV = 16.4%, repeatability = 0.323). Selection for functional longevity did not affect plasma metabolites responses to a 2-d nutritional challenge in dairy goats. Plasma NEFA and BHB response/recovery presented high between-individuals' variability, indicating individual adaptative characteristics to nutritional challenges not related to the environmental conditions but to inherent individual characteristics.

Multivariate analysis of milk metabolite measures shows potential for deriving new resilience phenotypes

In a context of growing interest in breeding more resilient animals, a noninvasive indicator of resilience would be very valuable. We hypothesized that the time-course of concentrations of several milk metabolites through a short-term underfeeding challenge could reflect the variation of resilience mechanisms to such a challenge. We submitted 138 one-year-old primiparous goats, selected for extreme functional longevity (i.e., productive longevity corrected for milk yield [60 low longevity line goats and 78 high longevity line goats]), to a 2-d underfeeding challenge during early lactation. We measured the concentration of 13 milk metabolites and the activity of 1 enzyme during prechallenge, challenge, and recovery periods. Functional principal component analysis summarized the trends of milk metabolite concentration over time efficiently without preliminary assumptions concerning the shapes of the curves. We first ran a supervised prediction of the longevity line of the goats based on the milk metabolite curves. The partial least square analysis could not predict the longevity line accurately. We thus decided to explore the large overall variability of milk metabolite curves with an unsupervised clustering. The large year × facility effect on the metabolite concentrations was precorrected for. This resulted in 3 clusters of goats defined by different metabolic responses to underfeeding. The cluster that showed higher β-hydroxybutyrate, cholesterol, and triacylglycerols increase during the underfeeding challenge was associated with poorer survival compared with the other 2 clusters. These results suggest that multivariate analysis of noninvasive milk measures show potential for deriving new resilience phenotypes.

Discriminating Dietary Responses by Combining Transcriptomics and Metabolomics Data in Nutrition Intervention Studies

Scope

Combining different “omics” data types in a single, integrated analysis may better characterize the effects of diet on human health.

Methods and results

The performance of two data integration tools, similarity network fusion tool (SNFtool) and Data Integration Analysis for Biomarker discovery using Latent variable approaches for “Omics” (DIABLO; MixOmics), in discriminating responses to diet and metabolic phenotypes is investigated by combining transcriptomics and metabolomics datasets from three human intervention studies: a postprandial crossover study testing dairy foods (n = 7; study 1), a postprandial challenge study comparing obese and non‐obese subjects (n = 13; study 2); and an 8‐week parallel intervention study that assessed three diets with variable lipid content on fasting parameters (n = 39; study 3). In study 1, combining datasets using SNF or DIABLO significantly improve sample classification. For studies 2 and 3, the value of SNF integration depends on the dietary groups being compared, while DIABLO discriminates samples well but does not perform better than transcriptomic data alone.

Conclusion

The integration of associated “omics” datasets can help clarify the subtle signals observed in nutritional interventions. The performance of each integration tool is differently influenced by study design, size of the datasets, and sample size.

Microbiome and metabolic changes in milk in response to artemisinin supplementation in dairy cows

This study aimed to explore the effects of artemisinin (ART) on the milk microbiome and metabolites of dairy cow. A total of 12 mid-lactation Holstein dairy cows with similar parity, days in milk were randomly divided into 2 groups receiving either a total mixed ration (TMR) as the control group or this TMR and 120 g/d/head ART as the ART group. The milk samples were collected weekly to determine the contents, and end-of-trial (week 8) milk samples were used to identify microbial species and metabolite profiles by 16S rRNA sequencing and LC–MS analyses, respectively. We observed that the milk fat content significantly increased by ART treatment (P < 0.05). The bacterial community richness was significantly lower in the ART group (P < 0.05), while the diversity showed no difference (P > 0.05). Compared with its abundance in the control (CON) group, Firmicutes was significantly decreased, whereas Proteobacteria was significantly increased. Furthermore, in the ART group, the relative abundances of the genera Aerococcus, Staphylococcus, Corynebacterium_1 and Facklamia were significantly lower (P < 0.01). Metabolomics analysis revealed that ART significantly increasing the concentrations of glycerophospholipids, glycerolipids and flavonoids compared with those in the CON group. An enrichment analysis of the different metabolites showed that ART mainly affected glycerophospholipid metabolism and the pantothenate and CoA biosynthesis pathways. These findings revealed that ART supplementation could affect the milk microbiota and metabolites, that glycerophospholipids and glycerolipids could be potential biomarkers in the milk response to ART feed in dairy cows, and that ART changes substances in milk by maintaining lipid metabolism in the mammary gland.

LEP and SCD polymorphisms are associated with milk somatic cell count, electrical conductivity and pH values in Holstein cows

This study was conducted to determine LEP-Sau3AI, SCD-Fnu4HI, NR1H3-HpyCH4IV and FABP4-HinII gene polymorphisms and to investigate the association between these SNPs and somatic cell count (SCC), electrical conductivity (EC) and pH in Holstein cow milk. LEP-Sau3AI polymorphism found associated with SCC (p < 0.01), EC (p < 0.01) and pH (p < 0.05). LEP-Sau3AI-BB genotype resulted with higher SCC, EC and pH compared to other genotypes. SCD-Fnu4HI polymorphism showed differences in genotypes for EC (p < 0.05) and pH (p ≤ 0.05) traits. While the highest EC value was found in SCD-Fnu4HI-CT genotype, the highest milk pH was found in genotype TT. In addition, NR1H3-HpyCH4IV genotypes was found the only associated with pH (p < 0.05) among all studied phenotypes. Based on the present findings, it was concluded that LEP and SCD genes could be used in breeding programs for improved SCC, EC and pH values in Holstein dairy cows.