Variation of Detailed Protein Composition of Cow Milk Predicted from a Large Database of Mid-Infrared Spectra

Multi-tissue gene expression profiling of cows with a genetic predisposition for low and high milk urea levels

Milk urea (MU) concentration is proposed as an indicator trait for breeding toward reduced nitrogen (N) emissions and leaching in dairy. We selected 20 German Holstein cows based on MU breeding values, with 10 cows each having low (LMUg) and high (HMUg) MU genetic predisposition. Using RNA-seq, we characterized these cows to unravel molecular pathways governing post-absorptive body N pools focusing on renal filtration and reabsorption of nitrogenous compounds, hepatic urea formation and mammary gland N excretion. While we observed minor adjustments in cellular energy metabolism in different tissues associated with different MU levels, no transcriptional differences in liver ammonia detoxification were detected, despite significant differences in MU between the groups. Differential expression of AQP3 and SLC38A2 in the kidney provides evidence for higher urea concentration in the collecting duct of LMU cows than HMU cows. The mammary gland exhibited the most significant differences, particularly in tricarboxylic acid (TCA) cycle genes, amino acid transport, tRNA binding, and casein synthesis. These findings suggest that selecting for lower MU could lead to altered urinary urea (UU) handling and changes in milk protein synthesis. However, given the genetic variability in N metabolism components, the long-term effectiveness of MU-based selection in reducing N emissions remains uncertain.

Effects of A1 Milk, A2 Milk and the Opioid-like Peptide β-Casomorphin-7 on the Proliferation of Human Peripheral Blood Mononuclear Cells

Special attention is given to cow's milk and its variants, with ongoing discussions about health-related impacts primarily focusing on the A1 variant in contrast to the A2 variant. The difference between these variants lies in a single amino acid alteration at position 67 of β-casein. This alteration is presumed to make the A1 variant more susceptible to enzymatic breakdown during milk digestion, leading to an increased release of the peptide β-casomorphin-7 (BCM-7). BCM-7 is hypothesized to interact with µ-opioid receptors on immune cells in humans. Although BCM-7 has demonstrated both immunosuppressive and inflammatory effects, its direct impact on the immune system remains unclear. Thus, we examined the influence of A1 and A2 milk on Concanavalin A (ConA)-stimulated human peripheral blood mononuclear cells (PBMCs), as well as the effect of experimentally digested A1 and A2 milk, containing different amounts of free BCM-7 from β-casein cleavage. Additionally, we evaluated the effects of pure BCM-7 on the proliferation of ConA-stimulated PBMCs and purified CD4+ T cells. Milk fundamentally inhibited PBMC proliferation, independent of the β-casein variant. In contrast, experimentally digested milk of both variants and pure BCM-7 showed no influence on the proliferation of PBMCs or isolated CD4+ T cells. Our results indicate that milk exerts an anti-inflammatory effect on PBMCs, regardless of the A1 or A2 β-casein variant, which is nullified after in vitro digestion. Consequently, we deem BCM-7 unsuitable as a biomarker for food-induced inflammation.

A2 milk consumption and its health benefits: an update

Milk is a widely consumed nutrient-rich food containing protein variants such as casein A2 and A1. A1 differs from A2 in an amino acid at position 67 (Pro67 to His67). The breakdown of β-casein yields β-casomorphins (BCM), among which BCM-7 is extensively studied for its effects on the human body. Animal studies have shown that A1 β-casein milk increases digestive transit time and enhances myeloperoxidase activity. Individuals with lactose intolerance prefer A2 milk to conventional A1 milk, as BCM-7 in A1 milk can lead to inflammation and discomfort in sensitive individuals. A2 milk, which contains A2 β-casein, is believed to be more easily digestible than A1 β-casein. Its popularity has grown owing to reports linking A1 casein to diseases such as type 1 diabetes, heart disease, and autism. A2 milk has gained popularity as an alternative to A1 milk, primarily because of its potential benefits for individuals with certain diseases. This review aims to provide an updated understanding of A2 milk consumption and its health benefits. This review aims to provide an updated understanding of A2 milk consumption and its health benefits.

Effects of 2 gradual debonding strategies on machine milk yield, flow, and composition in a cow-driven cow-calf contact system

This controlled study compared the effects of 2 different gradual debonding strategies on machine milk yield, flow, and composition in a cow-driven cow-calf contact (CCC) system with automatic milking. Cows had 24 h/d access to their calves during the first weeks of lactation. In the long debonding (LDB) treatment (n = 16), a gradual reduction of cows' access to their calves was initiated 4 wk after calving over a total period of 28 d; first to 12 h/d (14 d), and then to 6 h/d (14 d). In the short debonding (SDB) treatment (n = 14), gradual reduction was initiated 6.5 wk after calving over a total period of 10 d; first to 12 h/d (5 d), and then to 6 h/d (5 d). From 6 h/d, access was finally reduced to 0 h/d for 7 d for both treatments. Machine milk yield, somatic cell count, and peak and average milk flow were automatically registered at milking. During the 9-wk study period, composite samples were analyzed for milk composition. Data were analyzed with linear mixed effect models. Results showed that machine milk yield during 24 h/d access varied between cows (range 1.2-49.9 kg/d, average ± standard deviation 13.2 ± 7.82 kg/d). The LDB cows had a higher daily machine milk yield than SDB cows at the end of and after access reduction was completed (+5.0 ± 1.63 and +5.1 ± 1.55 kg during the last 5 d of 6 h/d access, and 0 h/d access, respectively). Somatic cell count was on a healthy level, with no difference between treatments. Milk fat content increased with reduction in access, regardless of treatment. Short debonding cows tended to show higher milk protein content and lower milk lactose content than cows with a longer debonding. This study has shown that a longer debonding initiated earlier may give a higher milk yield in the short term. The variation in machine milk yield may indicate differences in milk ejection, suckling, and visiting patterns and preferences among cows.

Imputation of missing milk Fourier transform mid-infrared spectra using existing milk spectral databases: A strategy to improve the reliability of breeding values and predictive models

The use of milk Fourier transform mid-infrared (FT-MIR) spectrometry to develop management and breeding tools for dairy farmers and industry is growing and supported by the availability of numerous new predicted phenotypes to assess the nutritional quality of milk and its technological properties, but also the animal health and welfare status and its environmental fingerprint. For genetic evaluations, having a long-term and representative spectral dairy herd improvement (DHI) database improves the reliabilities of estimated breeding values (EBV) from these phenotypes. Unfortunately, most of the time, the raw spectral data used to generate these estimations are not stored. Moreover, many reference measurements of those phenotypes, needed during the FT-MIR calibration step, are available from past research activities but lack spectra records. So, it is impossible to use them to improve the FT-MIR models. Consequently, there is a strong interest in imputing those missing spectra. The innovative objective of this study was to use the existing large spectral DHI database to estimate missing spectra by selecting probable spectra using, as the match criteria, common dairy traits recorded for a long time by DHI organizations. We tested 4 match criteria combinations. Combination 1 required to have equal fat and protein contents between the sample for which a spectrum was to be estimated and the reference samples in the DHI database. Combination 2 also required an equal urea content. Combination 3 requested equal fat, protein, and lactose contents. Finally, combination 4 included all criteria. When more than one spectrum was found during the search, their average was the estimated spectrum for the query sample. Concretely, this study estimated missing spectra for 1,700 samples using 2,000,000 spectral DHI records. For assessing the effect of this spectral estimation on the prediction quality, FT-MIR equations were used to predict 11 phenotypes, selected as their quantification used different FT-MIR regions. They were related to the milk fat and mineral composition, lactoferrin content, quantity of eructed methane, body weight (BW), and dry matter intake. The accuracy between predictions obtained from actual and estimated spectra was evaluated by calculating the mean absolute error (MAE). The criteria in the fourth and second combinations were too strict to estimate a spectrum for most samples. Indeed, for many samples, no spectra with the same values for those matching criteria was found. The third match criteria combination had a poorer prediction performance for all studied traits and spectral absorptions than the first combination due to fewer matched samples available to compute the missing spectrum. By allowing a range for matching lactose content (±0.1 g/dL milk), we showed that this new combination increased the number of selected samples to compute missing spectra and predict better the infrared absorption at different wavenumbers, especially those related to the lactose quantification. The prediction performance was further improved by performing queries on the entire Walloon DHI spectral database (6,625,570 spectra), and it varied among the studied phenotypes. Without considering the traits used for the matching, the best predictions were obtained for the content of saturated fatty acids (MAE = 0.15 g/dL milk) and BW (MAE = 12.80 kg). Yet, the predictions for the unsaturated fatty acids were less accurate (MAE = 0.13 and 0.018 g/dL milk for monounsaturated and polyunsaturated fatty acids), likely because of the poorer predictions of spectral regions related to long-chain fatty acids. Similarly, poorer predictions were observed for the amount of methane eructed by dairy cows (MAE = 47.02 g/d), likely because it is not directly related to fat content or composition. Prediction accuracies for the remaining traits were also low. In conclusion, we observed that increasing the number of relevant matching criteria helps improve the quality of FT-MIR predicted phenotypes and the number of spectra used during the search. So, it would be of great interest to test in the future the suitability of the developed methodology with large-scale international spectral databases to improve the reliability of EBV from these FT-MIR-based phenotypes and the robustness of FT-MIR predictive models.

Comparative analysis of total protein, casein, lactose, and fat content in milk of cows suffering from subclinical and clinical mastitis caused by Streptococcus spp

Introduction

The aim of the study was to analyse the total protein (TP), casein (CAS), lactose (LAC), and fat content of milk from cows with subclinical (SCM) and clinical mastitis (CM) caused by Streptococcus spp.

Material and Methods

A total of 60 milk samples from diseased cows and 30 milk samples from healthy cows were included in the study. Milk samples were taken from Holstein-Friesian cows from four dairy farms in Lublin Province. The bacteriological examination of the milk was performed and the somatic cells count in 1 mL of milk was determined using a SomaCount FC automatic cell counter. Determination of TP, CAS, LAC, FAT and FA levels in milk was carried out using a DairySpec FT automated Fourier transform infrared spectrometer.

Results

Total protein in milk from HE was significantly higher than in milk from cows with mastitis (4.04% vs 3.57% in milk from SCM cows and 3.7% in milk from CM cows, P = 0.001). The CAS level was 2.73% in milk from CM cows and 2.92% in milk from SCM cows vs 3.30% in milk from HE cows, P = 0.001. The changes in CAS and TP in milk resulted in a significant difference in the CAS/TP ratio (81.7% in milk from HE cows vs 73.8% in milk from CM cows). A decrease in levels was also recorded for LAC (4.8% in milk from HE cows vs 4.51% in milk from SCM cows and 4.01% in milk from CM cows, P = 0.001). The fat level was significantly higher in milk from healthy cows than in milk from cows with mastitis (4.0% vs 2.3% in milk from SCM cows and 1.64% in milk from CM cows, P = 0.001).

Conclusion

It should be emphasised that the decrease in the levels of TP, LAC and FAT was significant not only in milk from CM cows but also in milk from SCM cows. This is very unfavourable, because the reduction in the main milk components results in poor quality dairy products and impairs line processes.

Relationship between Milk Protein Polymorphism and Selected Cows' Reproductive Indices

This research sought to determine the effect of selected milk protein polymorphisms on the reproduction parameters of 598 black and white Polish Holstein-Friesian cattle. The analyzed genes were kappa-casein (CSN3) and beta-lactoglobulin (BLG). The following reproductive indexes were selected: the age at first calving, the interpregnancy period, the calving interval, and the insemination index. The influence of the identified genotypes on the course of parturition in cows was also analyzed. Source data were collected for each cow from the beginning of the herd life and reproduction to its culling from the herd or the end of its third lactation. Data on the age at first calving, the amount of semen portions for artificial insemination (insemination index), the interpregnancy period, and the calving interval for each cow were also collected. A contingency analysis was performed through contingency tables using a Pearson's chi-squared test for each CSN3 and BLG genotype. The results show that the BB genotype of the kappa-casein gene was associated with the most favorable values of reproductive indicators. In the case of the calving interval, the values were significantly more favorable than those of other genotypes (p ≤ 0.05). No effect of beta-lactoglobulin polymorphism on the analyzed reproductive indices was observed. On the other hand, in the case of the interpregnancy period, significant statistical differences were obtained between the AA and BB genotypes. The analyzed kappa-casein and beta-lactoglobulin genotypes did not significantly influence the course of parturition in cows. To conclude, the genotype polymorphism BB CSN3 is the most favorable for the performance of the cows in the examined herd.

Patterned Biolayers of Protein Antigens for Label-Free Biosensing in Cow Milk Allergy

This paper focuses on creating one-dimensional diffractive grooved structures of antigen proteins on glass substrates for the label-free detection of antibodies to dairy allergens. In particular, the fabrication of protein structures is carried out by combining microcontact printing with physisorption, imines coupling, and thiol-ene click chemistry. The work first sets up these patterning methods and discusses and compares the main aspects involved in them (structure, biolayer thickness, functionality, stability). Homogeneous periodic submicron structures of proteins are created and characterized by diffractive measurements, AFM, FESEM, and fluorescence scanning. Then, this patterning method is applied to proteins involved in cow milk allergy, and the resulting structures are implemented as optical transducers to sense specific immunoglobulins G. In particular, gratings of bovine serum albumin, casein, and β-lactoglobulin are created and assessed, reaching limits of detection in the range of 30-45 ng·mL^-1 of unlabeled antibodies by diffractive biosensing.

Fourier transform mid-infrared milk screening to improve milk production and processing

Milk mid-infrared spectrometry has been used for many years to quantify major milk compounds. Recently, much research has been conducted to extend the use of this technology to predict new, relevant phenotypes to assess the animals' welfare and the nutritional quality of milk, as well as its technological quality and environmental footprint. The transition from the research stage to field implementation is not easy, due to intrinsic and extrinsic constraints, but some developments can be considered to address these issues.

Demographic pattern of A1/A2 beta casein variants indicates conservation of A2 type haplotype across native cattle breeds (Bos indicus) of India

Genetic variations of the beta casein gene hold importance because of their probable association with human health. Comparative sequence analysis of β-casein gene across Indian native, crossbred and exotic breeds in India revealed 15 SNPs and 4 INDELs corresponding to 14 haplotypes. The frequency of A2 type haplotype was maximum (0.941) across all Indian native breeds. Among the 15 variants reported for taurine breeds, only three (A1, A2 and B) were observed in analysed populations. Allelic profiling of A1/A2 β-casein variants in ~ 4000 animals belonging to three cattle types and breeding bulls also revealed the predominance of A2 allele (0.95) in Indian cattle. The high proportion of A2 allele/haplotype indicates that Indian native cattle are the best suited to meet the demands for A2 milk globally. However, a higher percentage of heterozygous genotype (A1A2) in breeding bulls warrants the need to screen sire lines so as to drift the herd towards A2.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-022-03232-0.

Beclomethasone dipropionate and sodium cromoglycate protect against airway hyperresponsiveness in a human ex vivo model of cow's milk aspiration

Background

Recurrent cow's milk (CM) aspiration is often associated with gastroesophageal reflux in infants and toddlers and it seems to be implicated in the etiology of different inflammatory lung disorders. This study aimed to investigate ex vivo the impact of CM aspiration on human airways and whether treatment with beclomethasone dipropionate (BDP) or sodium cromoglycate (SCG) may prevent the potential CM-induced airway hyperresponsiveness (AHR).

Methods

Human isolated bronchi were contracted by electrical field stimulation (EFS_10Hz) to mimic the contractile tone induced by the parasympathetic activity and challenged with CM, fat/lactose-free CM, or human breast milk (HM). The effect of pre-treatment with beclomethasone dipropionate (BDP) and sodium cromoglycate (SCG) was also investigated on the AHR induced by CM.

Results

After a 60 min-challenge with CM 1:10 v/v and fat/lactose-free CM 1:10 v/v, ASM significantly (P ​< ​0.05) increased compared to control (+67.04 ​± ​17.08% and +77.91 ​± ​1.34%, respectively), a condition that remained stable for 150 ​min post-treatment, whereas HM did not alter ASM contractility. BDP 1 ​μM and 10 ​μM significantly (P ​< ​0.05) reduced the AHR elicited by CM (−52.49 ​± ​10.97% and −66.98 ​± ​7.90%, respectively vs. control). At the same manner, SCG 1 ​μM and 10 ​μM significantly (P ​< ​0.05) inhibited the CM-induced AHR (−59.03 ​± ​9.24% and −73.52 ​± ​7.41%, respectively vs. control).

Conclusion

CM induces AHR in human ASM by eliciting an increased parasympathetic contractile response. Preventive treatment with nebulized SCG may be indicated in infants or toddlers fed with CM, rather than with BDP due to a superior safety profile.

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Cow's milk aspiration seems to be associated with some inflammatory lung diseases.

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Cow's milk aspiration induces human airway hyperresponsiveness.

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Beclomethasone and sodium cromoglycate protect against cow's milk hyperresponsiveness in vitro.

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Inhaled sodium cromoglycate might be suitable in children at risk of cow's milk aspiration.

Selecting processing robust markers using high resolution mass spectrometry for the detection of milk in food products

BACKGROUND

Cow's milk allergy is one of the most reported food allergies in Europe. To help patients suffering from food allergies it is important to be able to detect milk in different foods. An analytical method that is gaining interest in the field of allergen detection is Ultra-High Performance Liquid Chromatography-tandem Mass Spectrometry, where the analyte is a target peptide. When these peptide biomarkers are selected the effect of food processing should be taken into account to allow a robust detection method.

OBJECTIVE

This works aims at identifying such processing stable peptide markers for milk for the Ultra-High Performance Liquid Chromatography-tandem Mass Spectrometry based detection of food allergens in different food products.

METHODS

Milk-incurred food materials that underwent several processing techniques were produced. This was followed by establishing tryptic peptide profiles from each matrix using Ultra-High Performance Liquid Chromatography-High Resolution Mass Spectrometry .

RESULT

A careful comparison of peptide profiles/intensities and the use of specific exclusion criteria resulted in the selection of 8 peptide biomarkers suitable for application in Ultra-High Performance Liquid Chromatography-tandem Mass Spectrometry based milk detection methods. One of these markers is a α-lactalbumin specific peptide, which has been determined to be stable in different incurred materials for the first time.

CONCLUSION

To our knowledge, this is the first systematic and experimentally based approach for the selection of suitable milk peptide biomarkers robust towards multiple, often applied food processing techniques for milk. Ensuring the exact knowledge of the food processing circumstances by starting from well-defined raw material and using fully controlled settings to produce incurred test material allowed the construction of a peptide database with robust markers. These robust markers can be used for the development of a robust detection method for milk in different food matrices.

Identification and in silico characterization of structural and functional impacts of genetic variants in milk protein genes in the Zebu breeds Guzerat and Gyr

Whole genome sequencing of bovine breeds has allowed identification of genetic variants in milk protein genes. However, functional repercussion of such variants at a molecular level has seldom been investigated. Here, the results of a multistep Bioinformatic analysis for functional characterization of recently identified genetic variants in Brazilian Gyr and Guzerat breeds is described, including predicted effects on the following: (i) evolutionary conserved nucleotide positions/regions; (ii) protein function, stability, and interactions; (iii) splicing, branching, and miRNA binding sites; (iv) promoters and transcription factor binding sites; and (v) collocation with QTL. Seventy-one genetic variants were identified in the caseins (CSN1S1, CSN2, CSN1S2, and CSN3), LALBA, LGB, and LTF genes. Eleven potentially regulatory variants and two missense mutations were identified. LALBA Ile60Val was predicted to affect protein stability and flexibility, by reducing the number the disulfide bonds established. LTF Thr546Asn is predicted to generate steric clashes, which could mildly affect iron coordination. In addition, LALBA Ile60Val and LTF Thr546Asn affect exonic splicing enhancers and silencers. Consequently, both mutations have the potential of affecting immune response at individual level, not only in the mammary gland. Although laborious, this multistep procedure for classifying variants allowed the identification of potentially functional variants for milk protein genes.

Analysis of Bovine Kappa-Casein Glycomacropeptide by Liquid Chromatography-Tandem Mass Spectrometry

Caseinomacropeptide (CMP) is released from bovine kappa-casein after rennet treatment and is one of the major peptides in whey protein isolate. CMP has in vitro anti-inflammatory and antibacterial activities. CMP has two major amino acid sequences with different modifications, including glycosylation, phosphorylation and oxidation. However, no previous work has provided a comprehensive profile of intact CMP. Full characterization of CMP composition and structure is essential to understand the bioactivity of CMP. In this study, we developed a top-down glycopeptidomics-based analytical method to profile CMP and CMP-derived peptides using Orbitrap mass spectrometry combined with nano-liquid chromatography with electron-transfer/higher-energy collision dissociation. The liquid chromatography–tandem mass spectrometry (LC–MS/MS) spectra of CMPs were annotated to confirm peptide sequence, glycan composition and other post-translational modifications using automatic data processing. Fifty-one intact CMPs and 159 CMP-derived peptides were identified in four samples (one CMP standard, two commercial CMP products and one whey protein isolate). Overall, this novel approach provides comprehensive characterization of CMP and CMP-derived peptides and glycopeptides, and it can be applied in future studies of product quality, digestive survival and bioactivity.

Influence of Estrus on the Milk Characteristics and Mid-Infrared Spectra of Dairy Cows

Simple Summary

Some studies have confirmed the variation in milk profiles when dairy cows show estrus. However, only a few milk components, such as fat, protein, and lactose, have been investigated so far, and thus any changes in the many other parts of milk’s composition due to estrus are unknown. Milk mid-infrared (MIR) spectra consist of wavenumbers, which provide insight into the chemical composition of milk. The MIR spectrum reflects the global composition of milk, but this information is currently underused. In this study, we considered MIR wavenumbers as traits, and directly studied the spectral information as a way to study the estrus of dairy cows linked to milk composition. This research provides a deeper understanding of the milk MIR spectrum and may lead to new approaches for estrus detection in dairy cows from routine milk analysis, thereby guiding an opportune insemination time.

Abstract

Milk produced by dairy cows is a complex combination of many components. However, at present, changes in only a few milk components (e.g., fat, protein, and lactose) during the estrus cycle in dairy cows have been documented. Mid-infrared (MIR) spectroscopy is a worldwide method routinely used for milk analysis, as MIR spectra reflect the global composition of milk. Therefore, this study aimed to investigate the changes in milk MIR spectra and milk production traits (fat, protein, lactose, urea, total solids (TS), and solid not fat (SnF)) due to estrus. Cows that were successfully inseminated, leading to conception, were included. Cows confirmed to be pregnant were considered to be in estrus at the day of insemination (day 0). A general linear mixed model, which included the random effect of cows, the fixed classification effects of parity number, days in relation to estrus, as well as the interaction between parity number and days in relation to estrus, was applied to investigate the changes in milk production traits and 1060 milk infrared wavenumbers, ranging from 925 to 5011 cm⁻¹, of 371 records from 162 Holstein cows on the days before (day −3, day −2, and day −1) and on the day of estrus (day 0). The days in relation to estrus had a significant effect on fat, protein, urea, TS, and SnF, whose contents increased from day −3 to day 0. Lactose did not seem to be significantly influenced by the occurrence of estrus. The days in relation to estrus had significant effects on the majority of the wavenumbers. Besides, we found that some of the wavenumbers in the water absorption regions were significantly changed on the days before and on the day of estrus. This suggests that these wavenumbers may contain useful information. In conclusion, the changes in the milk composition due to estrus can be observed through the analysis of the milk MIR spectrum. Further analyses are warranted to more deeply explore the potential use of milk MIR spectra in the detection of estrus.

Dairy Cows' Health during Alpine Summer Grazing as Assessed by Milk Traits, Including Differential Somatic Cell Count: A Case Study from Italy

Simple Summary

Dairy herds in alpine areas usually adopt summer grazing, mainly to reduce feeding costs. This practice is related to the maintenance of local traditions and to the manufacturing of niche dairy products. However, it is important to assess the impact of this practice on cattle health. This case study investigated how milk-related health traits vary across extensive grazing during the summer period, using data collected in a dairy herd whose cows were repeatedly controlled for individual milk samples. Although the transition from barn farming to pasture led to a reduction in milk production, proper grazing management can make dairy cows more resilient in terms of udder health and metabolic efficiency. Findings of the present research report suggested that pasture can be adopted to maintain dairy herd sustainability without impairing animal health.

Abstract

Extensive summer grazing is a dairy herd management practice frequently adopted in mountainous areas. Nowadays, this activity is threatened by its high labour demand, but it is fundamental for environmental, touristic and economic implications, as well as for the preservation of social and cultural traditions. Scarce information on the effects of such low-input farming systems on cattle health is available. Therefore, the present case study aimed at investigating how grazing may affect the health status of dairy cows by using milk traits routinely available from the national milk recording scheme. The research involved a dairy herd of 52 Simmental and 19 Holstein × Simmental crossbred cows. The herd had access to the pasture according to a rotational grazing scheme from late spring up to the end of summer. A total of 616 test day records collected immediately before and during the grazing season were used. Individual milk yield was registered during the milking procedure. Milk samples were analysed for composition (fat, protein, casein and lactose contents) and health-related milk indicators (electrical conductivity, urea and β-hydroxybutyrate) using mid-infrared spectroscopy. Somatic cell count (SCC) and differential SCC were also determined. Data were analysed with a linear mixed model, which included the fixed effects of the period of sampling, cow breed, stage of lactation and parity, and the random effects of cow nested within breed and the residual. The transition from barn farming to pasture had a negative effect on milk yield, together with a small deterioration of fat and protein percentages. Health-related milk indicators showed a minor deterioration of the fat to protein ratio, differential SCC and electrical conductivity, particularly towards the end of the grazing season, whereas the somatic cell score and β-hydroxybutyrate were relatively constant. Overall, the study showed that, when properly managed, pasture grazing does not have detrimental effects on dairy cows in terms of udder health and efficiency. Therefore, the proper management of cows on pasture can be a valuable solution to preserve the economic, social and environmental sustainability of small dairy farms in the alpine regions, without impairing cows’ health.

Bulk milk quality as affected by cattle breed composition of the herd in mountain area

The aim of this study was to investigate the variation of gross composition, somatic cell count, urea content, and fatty acids (FA) composition of bulk milk from single-breed and multi-breed farms in alpine area, keeping either Brown Swiss (BS), Holstein Friesian (HF), Simmental (SI), or their combinations. Gross milk composition, urea content, and FA composition were predicted using mid-infrared spectroscopy. Observations were grouped in 7 combinations consisting of 3 singlebreed and 4 multi-breed types of herd. A mixed linear model was used for data analysis, accounting for the fixed effects of herd composition (7 combinations), month of sampling, year of sampling, and the interactions between herd composition and month of sampling, and between herd composition and year of sampling. Farm was included as random effect. Results highlighted that about two thirds of South Tyrolean farms were single-breed and herds with more than 20 lactating cows changed herd structure over time, switching from multi- to single-breed. Single-breed BS farms produced milk with greater fat, protein, casein, lactose, and FA content than single-breed HF and SI farms. Further, multi-breed herds including BS cows produced milk with greater fat, protein, casein, and polyunsaturated FA content than multi-breed HF+SI herds. Overall, single-breed SI farms produced milk with lower somatic cell count than other herd combinations. Despite the number of BS cows in South Tyrol has decreased in favor of SI in the last years, this breed is still the most interesting for alpine dairy farming to achieve optimal milk quality in both single- and multi-breed scenarios. The tendency to move to SI is mainly related to good milk performance of SI cows coupled with their robustness, high carcass value, high market value of calves, and adaptability to mountain farming system.

Genetic and Non-Genetic Variation of Milk Total Antioxidant Activity Predicted from Mid-Infrared Spectra in Holstein Cows

Simple Summary

The total antioxidant activity (TAA) of food is important for human health and results from the contribution of different nutraceutical compounds. Direct determination of TAA in food is time-consuming and expensive. Infrared technologies allow the prediction of difficult-to-measure traits with certain accuracy in several organic matrices, including TAA of bovine milk. In order to understand the background of TAA and identify potential strategies to improve this feature in bovine milk, we explored its non-genetic sources of variation and estimated heritability and correlations with traits of economic interest in a large database of Holstein cows.

Abstract

Food antioxidants enhance products shelf life and stability during technological treatments through the maintenance of their physical and chemical properties. Moreover, they are endowed with several positive effects on human health, including cell membranes preservation, enzyme functionality, and DNA integrity. Milk has been described in relation to a wide array of fat soluble and water-soluble antioxidant compounds, in particular vitamin A, C, and E, lactoferrin and peptides derived from casein and whey proteins. The total antioxidant activity (TAA) of milk is a novel and scarcely explored trait, defined as the sum of antioxidant contributions of the aforementioned compounds. On this background, the aims of the present study were to investigate the variability of milk TAA on a large scale exploiting predictions obtained through mid-infrared (MIR) spectroscopy and to estimate genetic parameters of this trait in Holstein cows. Individual milk samples were collected between January 2011 and December 2018 during the routine milk recording procedure. Samples were analysed for gross composition through MIR spectroscopy and MIR spectra were stored. Milk TAA was then predicted (pTAA) from the stored milk MIR spectra (111,653 test-day records of 9519 cows in 344 herds) using the previously developed prediction model; considering the prediction accuracy, pTAA might be considered a proxy of the TAA determined through the reference method. Overall, pTAA averaged 7.16 mmoL/L of Trolox equivalents, showed a nadir around 40 days after calving and increased thereafter, following a linear trend up to the end of lactation. The lowest pTAA was observed in milk sampled from June to September. Milk pTAA was heritable (0.401 ± 0.015) and genetically associated to fat yield (0.366 ± 0.049), crude protein (CP) yield (0.238 ± 0.052), fat percentage (0.616 ± 0.022) and CP percentage (0.754 ± 0.015). The official selection index of Italian Holstein put the 49% of the emphasis on fat and protein yield and percentage; therefore, it derives that an indirect favourable selection for milk pTAA should be already in progress in Italian Holstein population.

Milk as a Complex Multiphase Polydisperse System: Approaches for the Quantitative and Qualitative Analysis

Milk is a product that requires quality control at all stages of production: from the dairy farm, processing at the dairy plant to finished products. Milk is a complex multiphase polydisperse system, whose components not only determine the quality and price of raw milk, but also reflect the physiological state of the herd. Today’s production volumes and rates require simple, fast, cost-effective, and accurate analytical methods, and most manufacturers want to move away from methods that use reagents that increase analysis time and move to rapid analysis methods. The review presents methods for the rapid determination of the main components of milk, examines their advantages and disadvantages. Optical spectroscopy is a fast, non-destructive, precise, and reliable tool for determination of the main constituents and common adulterants in milk. While mid-infrared spectroscopy is a well-established off-line laboratory technique for the routine quality control of milk, near-infrared technologies provide relatively low-cost and robust solutions suitable for on-site and in-line applications on milking farms and dairy production facilities. Other techniques, discussed in this review, including Raman spectroscopy, atomic spectroscopy, molecular fluorescence spectroscopy, are also used for milk analysis but much less extensively. Acoustic methods are also suitable for non-destructive on-line analysis of milk. Acoustic characterization can provide information on fat content, particle size distribution of fat and proteins, changes in the biophysical properties of milk over time, the content of specific proteins and pollutants. The basic principles of ultrasonic techniques, including transmission, pulse-echo, interferometer, and microbalance approaches, are briefly described and milk parameters measured with their help, including frequency ranges and measurement accuracy, are given.

Infrared Spectrometry as a High-Throughput Phenotyping Technology to Predict Complex Traits in Livestock Systems

High-throughput phenotyping technologies are growing in importance in livestock systems due to their ability to generate real-time, non-invasive, and accurate animal-level information. Collecting such individual-level information can generate novel traits and potentially improve animal selection and management decisions in livestock operations. One of the most relevant tools used in the dairy and beef industry to predict complex traits is infrared spectrometry, which is based on the analysis of the interaction between electromagnetic radiation and matter. The infrared electromagnetic radiation spans an enormous range of wavelengths and frequencies known as the electromagnetic spectrum. The spectrum is divided into different regions, with near- and mid-infrared regions being the main spectral regions used in livestock applications. The advantage of using infrared spectrometry includes speed, non-destructive measurement, and great potential for on-line analysis. This paper aims to review the use of mid- and near-infrared spectrometry techniques as tools to predict complex dairy and beef phenotypes, such as milk composition, feed efficiency, methane emission, fertility, energy balance, health status, and meat quality traits. Although several research studies have used these technologies to predict a wide range of phenotypes, most of them are based on Partial Least Squares (PLS) and did not considered other machine learning (ML) techniques to improve prediction quality. Therefore, we will discuss the role of analytical methods employed on spectral data to improve the predictive ability for complex traits in livestock operations. Furthermore, we will discuss different approaches to reduce data dimensionality and the impact of validation strategies on predictive quality.

Comparison of Fatty Acid Proportions Determined by Mid-Infrared Spectroscopy and Gas Chromatography in Bulk and Individual Milk Samples

Simple Summary

Information about fatty acid proportions in milk fat is important for many purposes, such as animal breeding, animal health control, as well as human nutrition. The routine methods for determining fatty acid proportions (e.g., mid-infrared spectroscopy) are rapid and relatively cheap, but there is a need to compare them with the reference analytical method (gas chromatography) to ensure their validity and suitability for various milk samples. The aim of this study is to compare the proportions of single fatty acids and their sums determined by utilizing both of these analytical methods and the resulting correlation coefficients. Our results show that the mid-infrared spectroscopy method is more appropriate (both for bulk and individual milk samples) for fatty acids present in high proportions of the total fat and for the sum of fatty acids (such as saturated and unsaturated) than for fatty acids with low proportions.

Abstract

Rapid analytical methods can contribute to the expansion of milk fatty acid determination for various important practical purposes. The reliability of data resulting from these routine methods plays a crucial role. Bulk and individual milk samples (60 and 345, respectively) were obtained from Czech Fleckvieh and Holstein dairy cows in the Czech Republic. The correlation between milk fatty acid (FA) proportions determined by the routine method (infrared spectroscopy in the mid-region in connection with Fourier transformation; FT-MIR) and the reference method (gas chromatography; GC) was evaluated. To validate the calibration of the FT-MIR method, a linear regression model was used. For bulk milk samples, the correlation coefficients between these methods were higher for the saturated (SFAs) and unsaturated FAs (UFAs) (r = 0.7169 and 0.9232; p < 0.001) than for the trans isomers of UFAs (TFAs) and polyunsaturated FAs (PUFAs) (r = 0.5706 and 0.6278; p < 0.001). Similar results were found for individual milk samples: r = 0.8592 and 0.8666 (p < 0.001) for SFAs and UFAs, 0.1690 (p < 0.01) for TFAs, and 0.3314 (p < 0.001) for PUFAs. The correlation coefficients for TFAs and PUFAs were statistically significant but too low for practical analytical application. The results indicate that the FT-MIR method can be used for routine determination mainly for SFAs and UFAs.

Mid-infrared spectroscopic analysis of raw milk to predict the blood nonesterified fatty acid concentrations in dairy cows

In high-yielding dairy cattle, severe postpartum negative energy balance is often associated with metabolic and infectious disorders that negatively affect production, fertility, and welfare. Mobilization of adipose tissue associated with negative energy balance is reflected through an increased level of nonesterified fatty acids (NEFA) in the blood plasma. Earlier, identification of negative energy balance through detection of increased blood plasma NEFA concentration required laborious and stressful blood sampling. More recently, attempts have been made to predict blood NEFA concentration from milk samples. In this study, we aimed to develop and validate a model to predict blood plasma NEFA concentration using the milk mid-infrared (MIR) spectra that are routinely measured in the context of milk recording. To this end, blood plasma and milk samples were collected in wk 2, 3, and 20 postpartum for 192 lactations in 3 herds. The blood plasma samples were taken in the morning, and representative milk samples were collected during the morning and evening milk sessions on the same day. To predict plasma NEFA concentration from the milk MIR spectra, partial least squares regression models were trained on part of the observations from the first herd. The models were then thoroughly validated on all other observations of the first herd and on the observations of the 2 independent herds to explore their robustness and wide applicability. The final model could accurately predict blood plasma NEFA concentrations <0.6 mmol/L with a root mean square error of prediction of <0.143 mmol/L. However, for blood plasma with >1.2 mmol/L NEFA, the model clearly underestimated the true level. Additionally, we found that morning blood plasma NEFA levels were predicted with significantly higher accuracy using MIR spectra of evening milk samples compared with MIR spectra of morning samples, with root mean square error of prediction values of, respectively, 0.182 and 0.197 mmol/L, and R² values of 0.613 and 0.502. These results suggest a time delay between variations in blood plasma NEFA and related milk biomarkers. Based on the MIR spectra of evening milk samples, cows at risk for negative energy status, indicated by detrimental morning blood plasma NEFA levels (>0.6 mmol/L), could be identified with a sensitivity and specificity of, respectively, 0.831 and 0.800. As this model can be applied to millions of historical and future milk MIR spectra, it opens an opportunity for regular metabolic screening and improved resilience phenotyping.

Variation of Blood Metabolites of Brown Swiss, Holstein-Friesian, and Simmental Cows

Simple Summary

Population-level phenotyping of blood metabolites is hardly achievable due to the limitation of reference analyses. Mid-infrared spectroscopy has recently been used to develop prediction models for major blood metabolites, allowing their determination on a large scale. The current study investigated the variation of blood β-hydroxybutyrate, non-esterified fatty acids, and urea nitrogen predicted from a large milk mid-infrared spectra database of Brown Swiss, Holstein-Friesian, and Simmental cows. Holstein-Friesian cows had the greatest concentrations of β-hydroxybutyrate and non-esterified fatty acids, and the lowest urea nitrogen in blood, which may underline an altered energy and nutritional status.

Abstract

Serum metabolic profile is a common method to monitor health and nutritional status of dairy cows, but blood sampling and analysis are invasive, time-consuming, and expensive. Milk mid-infrared spectra have recently been used to develop prediction models for blood metabolites. The current study aimed to investigate factors affecting blood β-hydroxybutyrate (BHB), non-esterified fatty acids (NEFA), and urea nitrogen (BUN) predicted from a large milk mid-infrared spectra database. Data consisted of the first test-day record of early-lactation cows in multi-breed herds. Holstein-Friesian cows had the greatest concentration of blood BHB and NEFA, followed by Simmental and Brown Swiss. The greatest and the lowest concentrations of BUN were detected for Brown Swiss and Holstein-Friesian, respectively. The greatest BHB concentration was observed in the first two weeks of lactation for Brown Swiss and Holstein-Friesian. Across the first month of lactation, NEFA decreased and BUN increased for all considered breeds. The greatest concentrations of blood BHB and NEFA were recorded in spring and early summer, whereas BUN peaked in December. Environmental effects identified in the present study can be included as adjusting factors in within-breed estimation of genetic parameters for major blood metabolites.

Effects of somatic cell score on milk yield and mid-infrared predicted composition and technological traits of Brown Swiss, Holstein Friesian, and Simmental cattle breeds

High milk somatic cell count (SCC) influences milk production and quality; however, very little is known about the effect of low SCC on milk quality, especially in terms of mineral content and coagulation properties. Thus, the present study aimed to investigate the effects of somatic cell score (SCS), calculated as log₂(SCC/100) + 3, on milk yield, composition (fat, crude protein, casein, lactose, milk urea nitrogen, protein fractions, and mineral contents), and coagulation properties of Brown Swiss, Holstein Friesian, and Simmental cows from multibreed herds. Milk composition and coagulation traits were predicted using mid-infrared spectroscopy. The data set comprised 95,591 observations of 6,940 cows in 313 multibreed herds, collected from January 2011 to December 2017. Observations were divided into 8 classes based on SCS. Statistical analysis was performed using a linear mixed model, which included breed, parity, stage of lactation, SCS class, and their interactions as fixed effects, and herd test day, cow, and residual as random effects. The probability that cows experienced SCS > 4.00 at 30 ± 5, 60 ± 5, or 90 ± 5 d after the observation test day was calculated for each SCS class, and odds ratios to the reference class (-1.00 < SCS ≤ 0.00) were reported. Results showed that the relationship between SCS and milk traits followed a third-order polynomial regression. The average loss of milk, fat, and crude protein yields were 0.43, 0.01, and 0.01 kg/d, respectively, for each SCS unit higher than 1.00. Very low SCS (<-1.00) had detrimental effects on milk yield and quality traits similar to or even stronger than high SCS (>4.00). Moreover, cows with SCS lower than -1.00 on a test day were about 7 times more likely to present high SCS within the following 90 ± 5 d than cows with SCS between -1.00 and 0.00. Breeds responded similarly to the increase of SCS, but the overall loss of fat and crude protein yields, and several minerals among Holstein Friesian were lower with increasing SCS. The best milk yield and quality were observed between SCS 0.00 and 1.00, but milk quality of Holstein Friesians started to decrease at lower SCS compared with milk quality of Brown Swiss and Simmental cows. Results suggest a breed-dependent optimum of SCS, and highlighted that very low SCS can be an indicator of udder health problems and, thus, may be used for early detection of mastitis.