Estimation of milk production in suckling mares and factors influencing their milk yield.

Research about mare's milk is mainly focused on quality and information about quantity is incomplete partly due to the lack of a consensus on the method of measuring milk yield. The live weight, body condition at foaling and age of mares are factors influencing milk yield. The influence of mare parity, however, remains unclear. Over a period of 2 years (2018-2019), milk yield was evaluated on 65 mares (51 multiparous and 13 primiparous). Mares and foals were kept in a group at pasture. One method of milk yield measurement and one proxy method were applied; milking and weight-suckle-weight (WSW), respectively. The procedure was performed at five timepoints during the lactation period (3-30-60-90 and 180 days) without repetition. The relevance of WSW was addressed by studying the correlation between the two methods on 23 individuals. Factors influencing milk yield, through milking data, were studied on 57 individuals. Data was divided into two subsets. The first was an explanatory matrix containing the live weight of mares 24 h after parturition, parity, age, year of lactation and foal gender. The second was a response matrix containing data from milking at the five timepoints of the lactation. A correlation was found (RV = 0.41) between milking and WSW at day 3, however no correlation was found for other timepoints (RV ≤ 0.15). The live weight of the mare 24 h after foaling, age and parity appeared to have a significant impact on milk production (P < 0.05). Thus, older or multiparous mares showed a higher milk yield than younger or primiparous mares. In addition, mares with a higher live weight after foaling produced more milk than those with a lower live weight. Overall, results can lead us to two main conclusions. First, the WSW method performed at five different timepoints of the lactation, but without repeated measurements, is not an efficient way to estimate the milk yield of mares. Secondly, results concerning the live weight and age of mares were in accordance with previous studies. The influence of parity was also highlighted, confirming trends showed by other authors. Age and parity are closely related in our population, making it difficult to differentially assess their effects. Being able to identify the impact of both factors independently would benefit several sectors of the horse industry from sport to mare milk producers.

KIRREL is differentially expressed in adipose tissue from 'fertil+' and 'fertil-' cows: in vitro role in ovary?

We have previously shown that dairy cows carrying the 'fertil-' haplotype for one quantitative trait locus affecting female fertility located on the bovine chromosome three (QTL-F-Fert-BTA3) have a significantly lower conception rate and body weight after calving than cows carrying the 'fertil+' haplotype. Here, we compared by Tiling Array the expression of genes included in the QTL-F-Fert-BTA3 in 'fertil+' and 'fertil-' adipose tissue one week after calving when plasma non-esterified fatty acid concentrations were greater in 'fertil-' animals. We observed that thirty-one genes were overexpressed whereas twelve were under-expressed in 'fertil+' as compared to 'fertil-' cows (P < 0.05). By quantitative PCR and immunoblot we confirmed that adipose tissue KIRREL mRNA and protein were significantly greater expressed in 'fertil+' than in 'fertil-'. KIRREL mRNA is abundant in bovine kidney, adipose tissue, pituitary, and ovary and detectable in hypothalamus and mammary gland. Its expression (mRNA and protein) is greater in kidney of 'fertil+' than 'fertil-' cows (P < 0.05). KIRREL (mRNA and protein) is also present in the different ovarian cells with a greater expression in granulosa cells of 'fertil+' than 'fertil-' cows. In cultured granulosa cells, recombinant KIRREL halved steroid secretion in basal state (P < 0.05). It also decreased cell proliferation (P < 0.05) and in vitro oocyte maturation (P < 0.05). These results were associated to a rapid increase in MAPK1/3 and MAPK14 phosphorylation in granulosa cells and to a decrease in MAPK1/3 phosphorylation in oocyte. Thus, KIRREL could be a potential metabolic messenger linking body composition and fertility.

Diet-induced modifications to milk composition have long-term effects on offspring growth in rabbits.

It has been clearly demonstrated that the maternal nutritional status during pregnancy and lactation has long-term effects on offspring health. In mammals, milk represents the first maternal support provided to the newborns so that its composition may play a major role in long-term programming. We therefore assessed the effects of maternal high-fat/high-sugar obesogenic (OD) or control (CD) diets on offspring growth and adiposity in the rabbit. Between 7 and 20 wk of age, the BW gain of OD milk-fed rabbits was higher than that of CD milk-fed rabbits ( < 0.05). Body fat mass measurements at 21 wk of age revealed a significant increase in body adiposity as a function of milk ingested during the neonatal period, in both female and male offspring ( < 0.05). A marked weight gain difference was observed according to the milk in both female and male offspring. Moreover, we investigated the composition in major proteins and leptin levels in milk from OD or CD diet-fed dams. Liquid chromatography-mass spectrometry analysis of individual CD skimmed milk samples enabled identification and quantification of the rabbit main milk proteins and of their main phosphorylated isoforms at 2 different stages of lactation (3 and 10 d). Here we show that the OD diet induced a reduction in the whey acidic protein content concomitantly with both an increase in serum albumin and lactoferrin contents and in the phosphorylated isoforms of the main milk proteins. Furthermore, a sharp rise in leptin levels was observed in the milk of OD diet-fed dams on Day 10 of lactation when compared with CD diet animals ( < 0.05). Taken together, these findings provide evidence that lactation is a critical window of development during which exposure to a deleterious diet is highly detrimental to long-term outcomes. Moreover, these insights suggest that it may be possible to prevent at least some of the adverse effects of inadequate maternal nutrition on the long-term metabolic outcomes of the offspring through nutritional interventions applied during the lactation period.

Differences during the first lactation between cows cloned by somatic cell nuclear transfer and noncloned cows.

Lactation performance is dependent on both the genetic characteristics and the environmental conditions surrounding lactating cows. However, individual variations can still be observed within a given breed under similar environmental conditions. The role of the environment between birth and lactation could be better appreciated in cloned cows, which are presumed to be genetically identical, but differences in lactation performance between cloned and noncloned cows first need to be clearly evaluated. Conflicting results have been described in the literature, so our aim was to clarify this situation. Nine cloned Prim' Holstein cows were produced by the transfer of nuclei from a single fibroblast cell line after cell fusion with enucleated oocytes. The cloned cows and 9 noncloned counterparts were raised under similar conditions. Milk production and composition were recorded monthly from calving until 200d in milk. At 67d in milk, biopsies were sampled from the rear quarter of the udder, their mammary epithelial cell content was evaluated, and mammary cell renewal, RNA, and DNA were then analyzed in relevant samples. The results showed that milk production did not differ significantly between cloned and noncloned cows, but milk protein and fat contents were less variable in cloned cows. Furthermore, milk fat yield and contents were lower in cloned cows during early lactation. At around 67 DIM, milk fat and protein yields, as well as milk fat, protein, and lactose contents, were also lower in cloned cows. These lower yields could be linked to the higher apoptotic rate observed in cloned cows. Apoptosis is triggered by insulin-like factor growth binding protein 5 (IGFBP5) and plasminogen activator inhibitor (PAI), which both interact with CSN1S2. During our experiments, CSN1S2 transcript levels were lower in the mammary gland of cloned cows. The mammary cell apoptotic rate observed in cloned cows may have been related to the higher levels of DNA (cytosine-5-)-methyltransferase 1 (DNMT1) transcripts, coding for products that maintain the epigenetic status of cells. We conclude, therefore, that milk production in cloned cows differs slightly from that of noncloned cows. These differences may be due, in part, to a higher incidence of subclinical mastitis. They were associated with differences in cell apoptosis and linked to variations in DNMT1 mRNA. However, milk protein and fat contents were more similar among cloned cows than among noncloned cows.

Milk from dams fed an obesogenic diet combined with a high-fat/high-sugar diet induces long-term abnormal mammary gland development in the rabbit.

Alterations to the metabolic endocrine environment during early life are crucial to mammary gland development. Among these environmental parameters, the initial nutritional event after birth is the consumption of milk, which represents the first maternal support provided to mammalian newborns. Milk is a complex fluid that exerts effects far beyond its immediate nutritional value. The present study, therefore, aimed to determine the effect of the nutritional changes during the neonatal and prepubertal periods on the adult mammary phenotype. Newborn rabbits were suckled by dams fed a high-fat/high-sugar obesogenic (OD) or a control (CON) diet and then subsequently fed either the OD or CON diets from the onset of puberty and throughout early pregnancy. Mammary glands were collected during early pregnancy (Day 8 of pregnancy). Rabbits fed with OD milk and then subjected to an OD diet displayed an abnormal development of the mammary gland: the mammary ducts were markedly enlarged (P < 0.05) and filled with abundant secretory products. Moreover, the alveolar secretory structures were disorganized, with an abnormal aspect characterized by large lumina. Mammary epithelial cells contained numerous large lipid droplets and exhibited fingering of the apical membrane and abnormally enlarged intercellular spaces filled with casein micelles. Leptin has been shown to be involved in modulating several developmental processes. We therefore analyzed its expression in the mammary gland. Mammary leptin mRNA was strongly expressed in rabbits fed with OD milk and subjected to an OD diet by comparison with the CON rabbits. Leptin transcripts and protein were localized in the epithelial cells, indicating that the increase in leptin synthesis occurs in this compartment. Taken together, these findings suggest that early-life nutritional history, in particular through the milking period, can determine subsequent mammary gland development. Moreover, they highlight the potentially important regulatory role that leptin may play during critical early-life nutritional windows with respect to long-term growth and mammary function.

Genetic parameters for functional traits in dairy cattle from daily random regression models.

The objective of the research was to estimate genetic parameters, such as heritabilities and genetic correlations, using daily test day data for milk yield (MY), milking speed (MS), dry matter intake (DMI), and body weight (BW) using random regression methodology. Data were from first lactation dairy cows (n = 320) from the Chamau research farm of the Swiss Federal Institute of Technology, Switzerland over the period from April 1994 to 2004. All traits were recorded daily using automated machines. Estimated heritabilities (h(2)) varied from 0.18 to 0.30 (mean h(2) = 0.24) for MY, 0.003 to 0.098 (mean h(2) = 0.03) for MS, 0.22 to 0.53 (mean h(2) = 0.43) for BW, and 0.12 to 0.34 (mean h(2) = 0.23) for DMI. A permanent environmental effect was included in both the univariate and bivariate models, but was assumed constant in estimating some genetic correlations because of convergence problems. Estimated genetic correlations varied from 0.31 to 0.41 between MY and MS, from -0.47 to 0.29 between MY and DMI, from -0.60 to 0.54 between MY and BW, from 0.17 to 0.26 between MS and DMI, from -0.18 to 0.25 between MS and BW, and from -0.89 to 0.29 between DMI and BW. Genetic correlations for MY, MS, DMI, and BW from calving to midlactation decreased similarly to 0.40, 0.36, 0.14, and 0.36 and, at the end of the lactation, decreased to -0.06, 0.23, -0.07, and 0.09, respectively. Daily genetic variance-covariance of many functional traits are reported for the first time and will be useful when constructing selection indexes for more than one trait based on longitudinal genetic parameters.

Use of structured antedependence models for the genetic analysis of growth curves.

Growth curve analysis is an important issue for many agricultural and laboratory species, for both phenotypic and genetic studies. The aim of this paper is to present the use of a novel statistical approach, namely the structured antedependence (SAD) models, to deal with this issue. The basic idea of these models is that an observation at time t can be explained by the previous observations. These models are especially appropriate to deal with cumulative traits such as growth, as BW at age t clearly depends on BW measures at ages (t -1), (t -2), etc. These models were applied on an INRA experimental Charolais herd data set. The data comprised BW records for 560 cows born over an 11-yr period (from 1988 to 1998) from 60 sires and 369 dams. The proposed SAD models were compared with the well-known random regression (RR) models that are already widely used in various areas of longitudinal data analysis. It was found that the SAD models fit the growth process better with far fewer parameters than the RR models (9 instead of 16 covariance parameters for the phenotypic analysis, and 14 instead of 21 for the genetic analysis). Despite this smaller number of covariance parameters, the likelihood value was found to be much higher with the SAD vs. the RR models, with a difference of 262.9 for the phenotypic analysis with a quartic polynomial for the RR and 751.5 for the genetic analysis with a cubic polynomial for both the genetic and environmental parts of the RR model. The SAD models also proved to be better able to interpolate missing values. Heritability, genetic, and environmental correlation coefficients were estimated for weights from birth to adulthood. The structured antedependence models proved, in this study, to be very appropriate to model growth data in a parsimonious and flexible way.

Modeling lactation curves and estimation of genetic parameters for first lactation test-day records of French Holstein cows.

Several functions were used to model the fixed part of the lactation curve and genetic parameters of milk test-day records to estimate using French Holstein data. Parametric curves (Legendre polynomials, Ali-Schaeffer curve, Wilmink curve), fixed classes curves (5-d classes), and regression splines were tested. The latter were appealing because they adjusted the data well, were relatively insensitive to outliers, were flexible, and resulted in smooth curves without requiring the estimation of a large number of parameters. Genetic parameters were estimated with an Average Information REML algorithm where the average information matrix and the first derivatives of the likelihood functions were pooled over 10 samples. This approach made it possible to handle larger data sets. The residual variance was modeled as a quadratic function of days in milk. Quartic Legendre polynomials were used to estimate (co)variances of random effects. The estimates were within the range of most other studies. The greatest genetic variance was in the middle of the lactation while residual and permanent environmental variances mostly decreased during the lactation. The resulting heritability ranged from 0.15 to 0.40. The genetic correlation between the extreme parts of the lactation was 0.35 but genetic correlations were higher than 0.90 for a large part of the lactation. The use of the pooling approach resulted in smaller standard errors for the genetic parameters when compared to those obtained with a single sample.

Contrasting models for lactation curve analysis.

Several statistical models have been proposed for the genetic evaluation of production traits in dairy cattle based on test-day records. Three main approaches have been put forward in the literature: random regression, orthogonal polynomials, and, more recently, character process models. The aim of this paper is to show how these different approaches are related, to compare their performance for the genetic analysis of lactation curves, and to assess equivalence between sire and animal models for repeated measures analyses. It was found that, with an animal model, a character process model with 11 parameters performed better, regarding the likelihood criterion, than a quartic random regression model (with 31 parameters). However, although the likelihood was higher, the genetic variance was very different with the character process model from the unstructured model, which raises important issues concerning model selection criteria. There are advantages in combining methodologies. A quadratic random regression model for the environmental part, combined with a character process model for the residual, performed better than the quartic random regression model and had fewer parameters. A character process structure allowing for a correlation pattern modeled the residual better than a simple quadratic variance, and had only one extra parameter.

Statistical models for estimating the genetic basis of repeated measures and other function-valued traits.

The genetic analysis of characters that are best considered as functions of some independent and continuous variable, such as age, can be a complicated matter, and a simple and efficient procedure is desirable. Three methods are common in the literature: random regression, orthogonal polynomial approximation, and character process models. The goals of this article are (i) to clarify the relationships between these methods; (ii) to develop a general extension of the character process model that relaxes correlation stationarity, its most stringent assumption; and (iii) to compare and contrast the techniques and evaluate their performance across a range of actual and simulated data. We find that the character process model, as described in 1999 by Pletcher and Geyer, is the most successful method of analysis for the range of data examined in this study. It provides a reasonable description of a wide range of different covariance structures, and it results in the best models for actual data. Our analysis suggests genetic variance for Drosophila mortality declines with age, while genetic variance is constant at all ages for reproductive output. For growth in beef cattle, however, genetic variance increases linearly from birth, and genetic correlations are high across all observed ages.

A link function approach to model heterogeneity of residual variances over time in lactation curve analyses.

Several studies with test-day models for the lactation curve show heterogeneity of residual variance over time. The most common approach is to divide the lactation length into subclasses, assuming homogeneity within these classes and heterogeneity between them. The main drawbacks of this approach are that it can lead to many parameters being estimated and that classes have to be arbitrarily defined, whereas the residual variance changes continuously over time. A methodology that overcomes these drawbacks is proposed here. A structural model on the residual variance is assumed in which the covariates are parametric functions of time. In this model, only a few parameters need to be estimated, and the residual variance is then a continuous function of time. The analysis of a sample data set illustrates this methodology.

EM-REML estimation of covariance parameters in Gaussian mixed models for longitudinal data analysis.

This paper presents procedures for implementing the EM algorithm to compute REML estimates of variance covariance components in Gaussian mixed models for longitudinal data analysis. The class of models considered includes random coefficient factors, stationary time processes and measurement errors. The EM algorithm allows separation of the computations pertaining to parameters involved in the random coefficient factors from those pertaining to the time processes and errors. The procedures are illustrated with Pothoff and Roy's data example on growth measurements taken on 11 girls and 16 boys at four ages. Several variants and extensions are discussed.