The effects of lactoferrin (LF) on the immune system have already been shown by many studies. Unfortunately, the current methods used to measure LF levels in milk do not permit the study of the genetic variability of lactoferrin or the performance of routine genetic evaluations. The first aim of this research was to derive a calibration equation permitting the prediction of LF in milk by mid-infrared spectrometry (MIR). The calibration with partial least squares on 69 samples showed a ratio of standard error of cross-validation to standard deviation equal to 1.98. Based on this value, the calibration equation was used to establish an LF indicator trait (predicted LF; pLF) on a large number of milk samples (n = 7,690). A subsequent study of its variability was conducted, which confirmed that stage of lactation and lactation number influence the overall pLF level. Small differences in mean pLF among 7 dairy breeds were also observed. The pLF content of Jersey milk was significantly higher than that in Holstein milk. Therefore, the choice of breed could change the expected LF level. Heritability estimated for pLF was 19.7%. The genetic and phenotypic correlations between somatic cell score and pLF were 0.04 and 0.26, respectively. As somatic cell score increases in presence of mastitis, this observation seems to indicate that pLF, or a function of observed pLF, compared with expected LF might have potential as an indicator of mastitis. The negative genetic correlation (−0.36) between milk yield and pLF could indicate an undesirable effect of selection for high milk production on the overall LF level.
Lactoferrin (LTF) is a milk glycoprotein favorably associated with the immune system of dairy cows. Somatic cell count is often used as an indicator of mastitis in dairy cows, but knowledge on the milk LTF content could aid in mastitis detection. An inexpensive, rapid and robust method to predict milk LTF is required. The aim of this study was to develop an equation to quantify the LTF content in bovine milk using mid-infrared (MIR) spectrometry. LTF was quantified by enzyme-linked immunosorbent assay (ELISA), and all milk samples were analyzed by MIR. After discarding samples with a coefficient of variation between 2 ELISA measurements of more than 5% and the spectral outliers, the calibration set consisted of 2499 samples from Belgium (n 5 110), Ireland (n 5 1658) and Scotland (n 5 731). Six statistical methods were evaluated to develop the LTF equation. The best method yielded a cross-validation coefficient of determination for LTF of 0.71 and a cross-validation standard error of 50.55 mg/l of milk. An external validation was undertaken using an additional dataset containing 274 Walloon samples. The validation coefficient of determination was 0.60. To assess the usefulness of the MIR predicted LTF, four logistic regressions using somatic cell score (SCS) and MIR LTF were developed to predict the presence of mastitis. The dataset used to build the logistic regressions consisted of 275 mastitis records and 13 507 MIR data collected in 18 Walloon herds. The LTF and the interaction SCS 3 LTF effects were significant (P , 0.001 and P 5 0.02, respectively). When only the predicted LTF was included in the model, the prediction of the presence of mastitis was not accurate despite a moderate correlation between SCS and LTF (r 5 0.54). The specificity and the sensitivity of models were assessed using Walloon data (i.e. internal validation) and data collected from a research herd at the University of Wisconsin -Madison (i.e. 5886 Wisconsin MIR records related to 93 mastistis events -external validation). Model specificity was better when LTF was included in the regression along with SCS when compared with SCS alone. Correct classification of non-mastitis records was 95.44% and 92.05% from Wisconsin and Walloon data, respectively. The same conclusion was formulated from the Hosmer and Lemeshow test. In conclusion, this study confirms the possibility to quantify an LTF indicator from milk MIR spectra. It suggests the usefulness of this indicator associated to SCS to detect the presence of mastitis. Moreover, the knowledge of milk LTF could also improve the milk nutritional quality.
Bovine lactoferrin (LF) is mainly present in milk and shows important physiological and biological functions. The aim of this study was to estimate the heritability and correlation values of LF content in bovine milk with different economic traits as milk yield (MY), fat and protein percentages, and somatic cell score (SCS). Variance components of the studied traits were estimated by REML using a multiple-trait mixed model. The obtained heritability (0.22) for LF content predicted using mid-infrared spectrometry (pLF) suggested the possibility of animal selection based on the increase of LF content in milk. The phenotypic and genetic correlation values calculated between pLF and SCS were moderate (0.31 and 0.24, respectively). Furthermore, a preliminary study of bovine LF gene polymorphism effects was performed on the same production traits. By PCR, all exons of the LF gene were amplified and then sequenced. Three new polymorphisms were detected in exon 2, exon 11, and intron 8. We examined the effects of LF gene polymorphisms of exons 2, 4, 9, 11, and 15, and intron 8 on pLF, MY, fat and protein percentages, and SCS. The different observed effects did not reach a significant level probably because of the characteristics of the studied population. However, the results were promising, and LF may be a potential indicator of mastitis. Further studies are necessary to evaluate the effect of genetic selection based on LF content on the improvement of mastitis resistance.
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