Genetic Variability of Lactoferrin Content Estimated by Mid-Infrared Spectrometry in Bovine Milk

Soyeurt, Hélène; Colinet, Frédéric; Arnould, Valérie; Dardenne, Pierre; Bertozzi, Carlo; Renaville, Robert; Portetelle, Daniel; Gengler, Nicolas

doi:10.3168/jds.2006-827

Cited by 49 publications

(55 citation statements)

References 23 publications

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“…As it is possible to determine the total content of protein and some specific milk proteins, it could be possible to predict the LTF content of milk using MIR as LTF is a natural milk glycoprotein. This hypothesis was tested by Soyeurt et al (2007) who suggested the possibility of quantification of an indicator of LTF in bovine milk using MIR spectrometry with a cross-validation coefficient of determination (R 2 CV ) of 0.75. However, the study of Soyeurt et al (2007) had several limitations; only 69 samples collected in the Walloon region of Belgium were used to develop the equation through a partial least squares regression (PLS).…”

Section: Introductionmentioning

confidence: 99%

“…This hypothesis was tested by Soyeurt et al (2007) who suggested the possibility of quantification of an indicator of LTF in bovine milk using MIR spectrometry with a cross-validation coefficient of determination (R 2 CV ) of 0.75. However, the study of Soyeurt et al (2007) had several limitations; only 69 samples collected in the Walloon region of Belgium were used to develop the equation through a partial least squares regression (PLS). Recent studies to predict other components in milk have shown a benefit in the accuracy of calibration equations with (1) more complex calibration procedures, (2) a larger number of samples from a wider range of production systems and (3) greater animal diversity (Dal Zotto et al, 2008;Soyeurt et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

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Mid-infrared prediction of lactoferrin content in bovine milk: potential indicator of mastitis

Soyeurt

Bastin

Colinet

et al. 2012

Animal

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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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mid-infrared prediction of lactoferrin content in bovine milk: potential indicator of mastitis

Soyeurt

Bastin

Colinet

et al. 2012

Animal

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“…The low LTF concentration during the first 2 months of lactation and the highest during the last 3 months could be explained by the dilution effect observed by Arnould et al (2009) and Soyeurt et al (2007) who obtained a negative genetic correlation between daily milk production and LTF content of -0.25 and -0.36, respectively. A significant increase in LTF concentration in the following stages of lactation was in agreement with the results obtained by other authors (Hagiwara et al 2003, Cheng et al 2008a.…”

Section: Discussionmentioning

confidence: 99%

“…This observation confirms the previous conclusions of many reports showing the increase in lactoferrin content in milk from infected udders compared with healthy cows (Kawai et al 1999, Hagiwara et al 2003, Cheng et al 2008b, Arnould et al 2009). LTF level in milk is believed to be a good indicator of mastitis because of a high positive correlation between LTF and SCC (Lindmark-Mansson 2006, Soyeurt et al 2007, Arnould et al 2009) and is also useful in detecting the false-positive results of screening tests for antibiotic residues in milk (Malinowski et al 2008). Cheng et al (2008b) observed a significant positive correlation of lactoferrin concentrations between serum and milk in early phase of the immune response.…”

Section: Discussionmentioning

confidence: 99%

Association of polymorphism within LTF gene promoter with lactoferrin concentration in milk of Holstein cows

Zabolewicz¹,

Barcewicz²,

Brym³

et al. 2014

Polish Journal of Veterinary Sciences

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This study analyzed the association between single nucleotide polymorphism (A/C) in position -28 located in the TATA box of LTF gene and the lactoferrin concentration in bovine milk secreted by healthy and infected udders. Out of 241, 69 cows were selected into the experimental group and were divided into 3 groups according to mean value of somatic cell count (SCC): I <180 000 cells/mL, II: 180 000 -350 000 cells/mL and III > 350 000 cells/mL. In each SCC group, three LTF genotypes: AA, AC and CC were identified by PCR-SSCP method. A total of 604 milk samples were collected monthly and lactoferrin concentration was measured by ELISA. The 1-way ANOVA within SCC groups was performed to estimate association of -28 A/C genotypes with mean lactoferrin concentration per lactation. In the group of healthy cows (<180 000 cells/mL) LTF concentration in milk cows with the AA genotype (107.58 ± 17.92 μg/mL) was significantly higher than in homozygotes CC (52.09 ± 19.01 μg/mL). Unexpectedly, in cows with elevated SCC (>350 000 cells/mL) we observed a significant opposite relationship (207.21 ± 28.50 in CC vs 115.0 ± 28.6 μg/mL in AA). We hypothesized that a promoter with allele C, which cannot be recognized as a TATA sequence is becoming more accessible for other transcription factors, which may induce alternative LTF gene expression. We assume that our results demonstrate a very interesting effect of differential gene expression depending on polymorphism in a key regulatory motif (TATA box) and also on the health status of mammary tissues.

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“…Encouraging results, although less accurate, were also obtained in the prediction of individual proteins (e.g. lactoferrin, Soyeurt et al, 2007b) or minerals (Soyeurt et al, 2009b).…”

Section: Introductionmentioning

confidence: 99%

New phenotypes for new breeding goals in dairy cattle

Boichard

Brochard

2012

Animal

108

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Cattle production faces new challenges regarding sustainability with its three pillars -economic, societal and environmental. The following three main factors will drive dairy cattle selection in the future: (1) During a long period, intensive selection for enhanced productivity has deteriorated most functional traits, some reaching a critical point and needing to be restored. This is especially the case for the Holstein breed and for female fertility, mastitis resistance, longevity and metabolic diseases.(2) Genomic selection offers two new opportunities: as the potential genetic gain can be almost doubled, more traits can be efficiently selected; phenotype recording can be decoupled from selection and limited to several thousand animals. (3) Additional information from other traits can be used, either from existing traditional recording systems at the farm level or from the recent and rapid development of new technologies and precision farming. Milk composition (i.e. mainly fatty acids) should be adapted to better meet human nutritional requirements. Fatty acids can be measured through a new interpretation of the usual medium infrared spectra. Milk composition can also provide additional information about reproduction and health. Modern milk recorders also provide new information, that is, on milking speed or on the shape of milking curves. Electronic devices measuring physiological or activity parameters can predict physiological status like estrus or diseases, and can record behavioral traits. Slaughterhouse data may permit effective selection on carcass traits. Efficient observatories should be set up for early detection of new emerging genetic defects. In the near future, social acceptance of cattle production could depend on its capacity to decrease its ecological footprint. The first solution consists in increasing survival and longevity to reduce replacement needs and the number of nonproductive animals. At the individual level, selection on rumen activity may lead to decreased methane production and concomitantly to improved feed efficiency. A major effort should be dedicated to this new field of research and particularly to rumen flora metagenomics. Low input in cattle production is very important and tomorrow's cow will need to adapt to a less intensive production environment, particularly lower feed quality and limited care. Finally, global climate change will increase pathogen pressure, thus more accurate predictors for disease resistance will be required.

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Genetic Variability of Lactoferrin Content Estimated by Mid-Infrared Spectrometry in Bovine Milk

Cited by 49 publications

References 23 publications

Mid-infrared prediction of lactoferrin content in bovine milk: potential indicator of mastitis

Mid-infrared prediction of lactoferrin content in bovine milk: potential indicator of mastitis

Association of polymorphism within LTF gene promoter with lactoferrin concentration in milk of Holstein cows

New phenotypes for new breeding goals in dairy cattle

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