Discriminant canonical tool for inferring the effect of αS1, αS2, β, and κ casein haplotypes and haplogroups on zoometric/linear appraisal breeding values in Murciano-Granadina goats

Genomic tools have shown promising results in maximizing breeding outcomes, but their impact has not yet been explored. This study aimed to outline the effect of the individual haplotypes of each component of the casein complex (αS1, β, αS2, and κ-casein) on zoometric/linear appraisal breeding values. A discriminant canonical analysis was performed to study the relationship between the predicted breeding value for 17 zoometric/linear appraisal traits and the aforementioned casein gene haplotypic sequences. The analysis considered a total of 41,323 zoometric/linear appraisal records from 22,727 primiparous does, 17,111 multiparous does, and 1,485 bucks registered in the Murciano-Grandina goat breed herdbook. Results suggest that, although a lack of significant differences (p > 0.05) was reported across the predictive breeding values of zoometric/linear appraisal traits for αS1, αS2, and κ casein, significant differences were found for β casein (p < 0.05). The presence of β casein haplotypic sequences GAGACCCC, GGAACCCC, GGAACCTC, GGAATCTC, GGGACCCC, GGGATCTC, and GGGGCCCC, linked to differential combinations of increased quantities of higher quality milk in terms of its composition, may also be connected to increased zoometric/linear appraisal predicted breeding values. Selection must be performed carefully, given the fact that the consideration of apparently desirable animals that present the haplotypic sequence GGGATCCC in the β casein gene, due to their positive predicted breeding values for certain zoometric/linear appraisal traits such as rear insertion height, bone quality, anterior insertion, udder depth, rear legs side view, and rear legs rear view, may lead to an indirect selection against the other zoometric/linear appraisal traits and in turn lead to an inefficient selection toward an optimal dairy morphological type in Murciano-Granadina goats. Contrastingly, the consideration of animals presenting the GGAACCCC haplotypic sequence involves also considering animals that increase the genetic potential for all zoometric/linear appraisal traits, thus making them recommendable as breeding animals. The relevance of this study relies on the fact that the information derived from these analyses will enhance the selection of breeding individuals, in which a desirable dairy type is indirectly sought, through the haplotypic sequences in the β casein locus, which is not currently routinely considered in the Murciano-Granadina goat breeding program.

An approach on detection, quantification, technological properties, and trends market of A2 cow milk

The genetic variant A2 β-casein integrates the casein protein group in milk and has been often associated with positive health outcomes. Therefore, this review explores the present understanding of A2 β-casein, including detection methods and the market trends for dairy from A2 milk. Also, the interaction of A2 β-casein with αs₁-casein and κ-casein genotypes was examined in terms of technological impacts on A2 milk. A limited number of preliminary studies has aimed to investigate the sensorial and technological impacts of β-casein variants in milk matrices, for instance, in yogurt and other derivatives. Nevertheless, considering studies carried out so far, it is concluded that the manufacture of dairy products from A2 milk is perfectly feasible, as the products presented slight differences when compared to those derived from traditional milk. In one of the works, sensitive drops in rennet coagulation time and curd firmness values were observed in cheese traits. However, it is relevant to point out that variant A of κ-casein plays a negative role in the coagulation features of milk. Therefore, alterations in the pattern of cheese-making properties are not uniquely related to β-casein variants. Attempts to produce A2 β-casein in laboratory (non-natural source), through biosynthesis, for example, have not been found so far. This knowledge gap offers a promising area for future studies concerning proteins and bioactive peptide production.

Acid and Rennet Coagulation Properties of A2 Milk

This study investigated the acid and rennet milk coagulation properties of A2 milk (β-casein (CN) A2A2 genotype), in comparison to a control milk (blend of A2A1/A1A1/A2A2 genotypes). Acid and rennet coagulation were evaluated using the Optigraph^® system, measuring the coagulation time, aggregation rate, and gel density or curd firmness. The acidification kinetics were monitored using a CINAC^® system, evaluating the time to reach pH 4.6, the acidification rate, the maximum acidification rate, the time required to reach it, and the latency time. The water-holding capacity of acid milk gels and the potential yield, total solids, and syneresis of enzymatic gels were also evaluated. Some variables were highly influenced by the farm factor, showing the importance of the effect of extrinsic parameters. Acid and enzymatic coagulation times were not affected in either milk. The A2 milk presented higher acid gel density and latency time than the control milk. Although the differences in water-holding capacity were not statistically significant, the A2 milk presented lower values, related with the higher gel density. The A2 milk also showed higher rennet aggregation rate and curd firmness than the control milk. Potential yield and syneresis were higher in the A2 milk, which is in accordance with the higher firmness of curd. Coagulation results and gel and curd properties indicate that it is possible to manufacture acid and rennet coagulation dairy products from A2 milk with no major differences when compared with a control milk.

What is the impact of amino acid mutations in the primary structure of caseins on the composition and functionality of milk and dairy products?

The impact of amino acid mutations within the peptide structure of bovine milk protein is important to understand as it can effect processability and subsequently effect its physiological properties. Genetic polymorphisms of bovine caseins can influence the chemical, structural, and technological properties, including casein micelle morphology, calcium distribution, network creation upon gelation, and surface activity. The A1 and A2 genetic variants of β-casein have recently acquired growing attention from both academia and industry, prompting new developments in the area. The difference between these two genetic variants is the inclusion of either proline in β-casein A2 or histidine in β-casein A1 at position 67 in the peptide chain. The aim of this review was to examine the extent to which milk and ingredient functionality is influenced by β-casein phenotype. One of the main findings of this review was although β-casein A1 was found to be the dominant variant in milks with superior acid gelation and rennet coagulation properties, milks comprised of β-casein A2 possessed greater emulsion and foam formation capabilities. The difference in the casein micelle assembly, hydrophobicity, and chaperone activity of caseins may explain the contrast in the functionality of milks containing β-casein from either A1 or A2 families. This review provides new insights into the subtle variations in the physicochemical properties of bovine milks, which could potentially support dairy producers in the development of new dairy products with different functional properties.

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Impact of β- and other caseins on the casein micelle structure and functionality.

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Proline and histidine in β-caseins play a key role in casein micelle conformation.

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Chaperone activity of β-casein A2 towards heat-induced aggregation of whey protein.

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Gels prepared of milks with β-casein A1 possess a denser and firmer structure.

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Ordered structure of β-casein A2 led to improved emulsion and foam formation.

A2 Milk: New Perspectives for Food Technology and Human Health

Although milk consumption is increasing worldwide, in some geographical regions, its consumption has persistently declined in recent decades. This fact, together with the increase in milk production prices, has caused both milk producers and the dairy industry to be immersed in a major crisis. Some possible solutions to this problem are to get people who do not currently consume milk to start drinking it again, or to market milk and dairy products with a higher added value. In this context, a type of milk called A2 has recently received attention from the industry. This type of milk, characterized by a difference in an amino acid at position 67 of the β-casein polypeptide chain, releases much smaller amounts of bioactive opioid peptide β-casomorphin 7 upon digestion, which has been linked to harmful effects on human health. Additionally, A2 milk has been attributed worse technological properties in the production of some dairy products. Thus, doubts exist about the convenience for the dairy industry to bet on this product. The aim of this review is to provide an update on the effects on human health of A2 milk, as well as its different technological properties to produce dairy products.

Role of CSN2, CSN3, and BLG genes and the polygenic background in the cattle milk protein profile

To devise better selection strategies in dairy cattle breeding programs, a deeper knowledge of the role of the major genes encoding for milk protein fractions is required. The aim of the present study was to assess the effect of the CSN2, CSN3, and BLG genotypes on individual protein fractions (α_S1-CN, α_S2-CN, β-CN, κ-CN, β-LG, α-LA) expressed qualitatively as percentages of total nitrogen content (% N), quantitatively as contents in milk (g/L), and as daily production levels (g/d). Individual milk samples were collected from 1,264 Brown Swiss cows reared in 85 commercial herds in Trento Province (northeast Italy). A total of 989 cows were successfully genotyped using the Illumina Bovine SNP50 v.2 BeadChip (Illumina Inc.), and a genomic relationship matrix was constructed using the 37,519 SNP markers obtained. Milk protein fractions were quantified and the β-CN, κ-CN, and β-LG genetic variants were identified by reversed-phase HPLC (RP-HPLC). All protein fractions were analyzed through a Bayesian multitrait animal model implemented via Gibbs sampling. The effects of days in milk, parity order, and the CSN2, CSN3, and BLG genotypes were assigned flat priors in this model, whereas the effects of herd and animal additive genetic were assigned Gaussian prior distributions, and inverse Wishart distributions were assumed for the respective co-variance matrices. Marginal posterior distributions of the parameters of interest were compared before and after the inclusion of the effects of the 3 major genes in the model. The results showed that a high portion of the genetic variance was controlled by the major genes. This was particularly apparent in the qualitative protein profile, which was found to have a higher heritability than the protein fraction contents in milk and their daily yields. When the genes were included individually in the model, CSN2 was the major gene controlling all the casein fractions except for κ-CN, which was controlled directly by the CSN3 gene. The BLG gene had the most influence on the 2 whey proteins. The genetic correlations showed the major genes had only a small effect on the relationships between the protein fractions, but through comparison of the correlation coefficients of the proteins expressed in different ways they revealed potential mechanisms of regulation and competitive synthesis in the mammary gland. The estimates for the effects of the CSN2 and CSN3 genes on protein profiles showed overexpression of protein synthesis in the presence of the B allele in the genotype. Conversely, the β-LG B variant was associated with a lower concentration of β-LG compared with the β-LG A variant, independently of how the protein fractions were expressed, and it was followed by downregulation (or upregulation in the case of the β-LG B) of all other protein fractions. These results should be borne in mind when seeking to design more efficient selection programs aimed at improving milk quality for the efficiency of dairy industry and the effect of dairy products on human health.

Effect of Protein Genotypes on Physicochemical Properties and Protein Functionality of Bovine Milk: A Review

Milk protein comprises caseins (CNs) and whey proteins, each of which has different genetic variants. Several studies have reported the frequencies of these genetic variants and the effects of variants on milk physicochemical properties and functionality. For example, the C variant and the BC haplotype of αS1-casein (αS1-CN), β-casein (β-CN) B and A1 variants, and κ-casein (κ-CN) B variant, are favourable for rennet coagulation, as well as the B variant of β-lactoglobulin (β-lg). κ-CN is reported to be the only protein influencing acid gel formation, with the AA variant contributing to a firmer acid curd. For heat stability, κ-CN B variant improves the heat resistance of milk at natural pH, and the order of heat stability between phenotypes is BB > AB > AA. The A2 variant of β-CN is more efficient in emulsion formation, but the emulsion stability is lower than the A1 and B variants. Foaming properties of milk with β-lg variant B are better than A, but the differences between β-CN A1 and A2 variants are controversial. Genetic variants of milk proteins also influence milk yield, composition, quality and processability; thus, study of such relationships offers guidance for the selection of targeted genetic variants.

Bayesian Analysis of the Association between Casein Complex Haplotype Variants and Milk Yield, Composition, and Curve Shape Parameters in Murciano-Granadina Goats

Considering casein haplotype variants rather than SNPs may maximize the understanding of heritable mechanisms and their implication on the expression of functional traits related to milk production. Effects of casein complex haplotypes on milk yield, milk composition, and curve shape parameters were used using a Bayesian inference for ANOVA. We identified 48 single nucleotide polymorphisms (SNPs) present in the casein complex of 159 unrelated individuals of diverse ancestry, which were organized into 86 haplotypes. The Ali and Schaeffer model was chosen as the best fitting model for milk yield (Kg), protein, fat, dry matter, and lactose (%), while parabolic yield-density was chosen as the best fitting model for somatic cells count (SCC × 103 sc/mL). Peak and persistence for all traits were computed respectively. Statistically significant differences (p < 0.05) were found for milk yield and components. However, no significant difference was found for any curve shape parameter except for protein percentage peak. Those haplotypes for which higher milk yields were reported were the ones that had higher percentages for protein, fat, dry matter, and lactose, while the opposite trend was described by somatic cells counts. Conclusively, casein complex haplotypes can be considered in selection strategies for economically important traits in dairy goats.

Consequences of walking or transport by truck on milk yield and quality, as well as blood metabolites, in Holstein, Montbéliarde, and Valdostana dairy cows

In the mountains, the traditional practice of transhumance is common in dairy production systems to make use of the high-altitude summer pastures. Although the effects of highland grazing have been intensively studied with respect to cow performance and milk and cheese quality, the actual moving of the animals to the highlands and the consequences of this stressor for performance and milk quality in the days immediately following transhumance has not been investigated in detail. The aim of the present study was to compare the effects of a 6-km walk (lasting 1.5 h) to those of a truck transport of 10.5 km (lasting 1 h), simulating cow movement in transhumance systems, as well as a control treatment in which cows were not moved. The experiment included 12 late-lactating Valdostana Red Pied, 12 Montbéliarde, and 12 Holstein cows (i.e., breeds contrasting in genetic merit for milk production). Each cow was subjected to each treatment in a 3-wk Latin square design. Milk yield was measured, and milk and blood samples were taken around the transhumance simulation events. Cows of the 3 breeds responded similarly to both movement treatments. Walking decreased milk yield by 1 kg/milking, but truck transport did not affect milk yield. Both treatments led to an increase in plasma nonesterified fatty acids and milk somatic cell count compared with controls, and truck transport increased milk fat content. Milk coagulation properties were better for Valdostana Red Pied and Montbéliarde cows than for Holstein cows but were not affected by walking or truck transport. Further studies aiming to compare the 3 breeds should include a wider range of response variables over a longer term, including reproduction performance and repeated transhumance.

Physiochemical characteristics and fermentation ability of milk from Czech Fleckvieh cows are related to genetic polymorphisms of β-casein, κ-casein, and β-lactoglobulin

Objective

The aim of the study was to find a possible association between the β- and κ-casein and β-lactoglobulin genotypes and important milk physiochemical and technological characteristics such as acidity, alcohol stability, the contents of some minerals and the parameters of acid fermentation ability (FEA) in Czech Fleckvieh Cattle.

Methods

Milk and blood samples were collected from 338 primiparous Czech Fleckvieh cows at the same stage of lactation. The genotypes of individual cows for κ-casein (alleles A, B, and E) and β-lactoglobulin (alleles A and B) were ascertained by polymerase chain reaction-restriction fragment length polymorphism, while their β-casein (alleles A¹, A², A³, and B) genotype was determined using melting curve genotyping analysis. The data collected were i) milk traits including active acidity (pH), titratable acidity (TA), alcohol stability (AS); calcium (Ca), phosphorus (P), sodium (Na), magnesium (Mg), and potassium (K) contents; and ii) yoghurt traits including active acidity (Y-pH), titratable acidity (Y-TA), and the counts of both Lactobacilli and Streptococci in 1 mL of yoghurt. A linear model was assumed with fixed effects of herd, year, and season of calving, an effect of the age of the cow at first calving and effects of the casein and lactoglobulin genotypes of β-CN (β-casein, CSN2), κ-CN (κ-casein, CSN3), and β-LG (β-lactoglobulin, LGB), or the three-way interaction between those genes.

Results

The genetic polymorphisms were related to the milk TA, AS, content of P and Ca, Y-pH and Lactobacilli number in the fresh yoghurt. The CSN3 genotype was significantly associated with milk AS (p<0.05). The effect of the composite CSN2-CSN3-LGB genotype on the investigated traits mostly reflected the effects of the individual genes. It significantly influenced TA (p<0.01), Y-pH (p<0.05) and the log of the Lactobacilli count (p<0.05).

Conclusion

Our findings indicate that the yoghurt fermentation test together with milk proteins genotyping could contribute to milk quality control and highlight new perspectives in dairy cattle selection.

Association of candidate gene polymorphisms with milk technological traits, yield, composition, and somatic cell score in Italian Holstein-Friesian sires

Advances in DNA-based marker technology have enabled the identification of genomic regions underlying complex phenotypic traits in livestock species. The incorporation of detected quantitative trait loci into genetic evaluation provides great potential to enhance selection accuracies, hence expediting the genetic improvement of economically important traits. The objective of the present study was to investigate 96 single nucleotide polymorphisms (SNP) located in 53 candidate genes previously reported to have effects on milk production and quality traits in a population of highly selected Holstein-Friesian bulls. A total of 423 semen samples were used to genotype the bulls through a custom oligo pool assay. Forty-five SNP in 32 genes were found to be associated with at least 1 of the tested traits. Most significant and favorable SNP trait associations were observed for polymorphisms located in CCL3 and AGPAT6 genes for fat yield (0.037 and 0.033 kg/d, respectively), DGKG gene for milk yield (0.698 kg/d), PPARGC1A, CSN1S1, and AGPAT6 genes for fat percentage (0.127, 0.113, and 0.093%, respectively), GHR gene for protein (0.064%) and casein percentage (0.053%), and TLR4 gene for fat (0.090%), protein (0.066%), and casein percentage (0.050%). Somatic cell score was favorably affected by GHR (-0.095) and POU1F1 (-0.137), and interesting SNP-trait associations were observed for polymorphisms located in CSN2, POU1F1, and AGPAT6 genes for rennet coagulation time (-0.592, -0.558, and -0.462 min, respectively), and GHR and CSN2 genes for curd firmness 30 min after rennet addition (1.264 and 1.183 mm, respectively). In addition to the influence of individual SNP, the effects of composite genotypes constructed by grouping SNP according to their individual effects on traits considered in the analysis were also examined. Favorable and significant effects on milk traits were observed for 2 composite genotypes, one including 10 SNP and the other 4 SNP. The former was associated with an increase of milk (0.075 kg/d), fat (0.097 kg/d), protein (0.083 kg/d), and casein yields (0.065 kg/d), and the latter was associated with an increase of fat (0.244%), protein (0.071%), and casein percentage (0.047%). Although further research is required to validate the identified SNP loci in other populations and breeds, our results can be considered as a preliminary foundation for further replication studies on gene-assisted selection programs.

Identification of alleles and genotypes of beta-casein with DNA sequencing analysis in Chinese Holstein cow

The study reported in this Regional Research Communication aimed to analyse the genetic polymorphisms of β-casein in Chinese Holstein cows. β-casein has received considerable research interest in the dairy industry and animal breeding in recent years as a source not only of high quality protein, but also of bioactive peptides that may be linked to health effects. Morever, the polymorphic nature of β-casein and its association with milk production traits, composition, and quality also attracted several efforts in evaluating the allelic distribution of β-casein locus as a potential dairy trait marker. However, few data on beta-casein variants are available for the Chinese Holstein cow. In the present paper, one hundred and thirty three Holstein cows were included in the analysis. Results revealed the presence of 5 variants (A1, A2, A3, B and I), preponderance of the genotype A1A2 (0·353) and superiorities of A1/A2 alleles (0·432 and 0·459, respectively) in the population. Sequence analysis of β-casein gene in the cows showed four nucleotide changes in exon 7. Our study can provide reference and guidance for selection for superior milk for industrial applications and crossbreeding and genetic improvement programmes.

Casein polymorphism heterogeneity influences casein micelle size in milk of individual cows

Milk samples from individual cows producing small (148-155 nm) or large (177-222 nm) casein micelles were selected to investigate the relationship between the individual casein proteins, specifically κ- and β-casein phenotypes, and casein micelle size. Only κ-casein AA and β-casein A1A1, A1A2 and A2A2 phenotypes were found in the large casein micelle group. Among the small micelle group, both κ-casein and β-casein phenotypes were more diverse. κ-Casein AB was the dominant phenotype, and 3 combinations (AA, AB, and BB) were present in the small casein micelle group. A considerable mix of β-casein phenotypes was found, including B and I variants, which were only found in the small casein micelle group. The relative amount of κ-casein to total casein was significantly higher in the small micelle group, and the nonglycosylated and glycosylated κ-casein contents were higher in the milks with small casein micelles (primarily with κ-casein AB and BB variants) compared with the large micelle group. The ratio of glycosylated to nonglycosylated κ-casein was higher in the milks with small casein micelles compared with the milks with large casein micelles. This suggests that although the amount of κ-casein (both glycosylated and nonglycosylated) is associated with micelle size, an increased proportion of glycosylated κ-casein could be a more important and favorable factor for small micelle size. This suggests that the increased spatial requirement due to addition of the glycosyl group with increasing extent of glycosylation of κ-casein is one mechanism that controls casein micelle assembly and growth. In addition, increased electrostatic repulsion due to the sialyl residues on the glycosyl group could be a contributory factor.

Structural differences between bovine A(1) and A(2) β-casein alter micelle self-assembly and influence molecular chaperone activity

Within each milk protein there are many individual protein variants and marked alterations to milk functionality can occur depending on the genetic variants of each protein present. Bovine A(1) and A(2) β-casein (β-CN) are 2 variants that contribute to differences in the gelation performance of milk. The A(1) and A(2) β-CN variants differ by a single AA, the substitution of histidine for proline at position 67. β-Casein not only participates in formation of the casein micelle but also forms an oligomeric micelle itself and functions as a molecular chaperone to prevent the aggregation of a wide range of proteins, including the other caseins. Micelle assembly of A(1) and A(2) β-CN was investigated using dynamic light scattering and small-angle X-ray scattering, whereas protein functionality was assessed using fluorescence techniques and molecular chaperone assays. The A(2) β-CN variant formed smaller micelles than A(1) β-CN, with the monomer-micelle equilibrium of A(2) β-CN being shifted toward the monomer. This shift most likely arose from structural differences between the 2 β-CN variants associated with the adoption of greater polyproline-II helix in A(2) β-CN and most likely led to enhanced chaperone activity of A(2) β-CN compared with A(1) β-CN. The difference in micelle assembly, and hence chaperone activity, may provide explain differences in the functionality of homozygous A(1) and A(2) milk. The results of this study highlight that substitution of even a single AA can significantly alter the properties of an intrinsically unstructured protein such as β-CN and, in this case, may have an effect on the functionality of milk.

Selection for milk coagulation properties predicted by Fourier transform infrared spectroscopy in the Italian Holstein-Friesian breed

Milk coagulation is based on a series of physicochemical changes at the casein micelle level, resulting in formation of a gel. Milk coagulation properties (MCP) are relevant for cheese quality and yield, important factors for the dairy industry. They are also evaluated in herd bulk milk to reward or penalize producers of Protected Designation of Origin cheeses. The economic importance of improving MCP justifies the need to account for this trait in the selection process. A pilot study was carried out to determine the feasibility of including MCP in the selection schemes of the Italian Holstein. The MCP were predicted in 1,055 individual milk samples collected in 16 herds (66 ± 24 cows per herd) located in Brescia province (northeastern Italy) by means of Fourier transform infrared (FTIR) spectroscopy. The coefficient of determination of prediction models indicated moderate predictions for milk rennet coagulation time (RCT=0.65) and curd firmness (a₃₀=0.68), and poor predictions for curd-firming time (k₂₀=0.49), whereas the range error ratio (8.9, 6.9, and 9.5 for RCT, k₂₀, and a₃₀, respectively) indicated good practical utility of the predictive models for all parameters. Milk proteins were genotyped and casein haplotypes (αS₁-, β-, αS₂-, and κ-casein) were reconstructed. Data from 51 half-sib families (19.9 ± 16.4 daughters per sire) were analyzed by an animal model to estimate (1) the genetic parameters of predicted RCT, k₂₀, and a₃₀; (2) the breeding values for these predicted clotting variables; and (3) the effect of milk protein genotypes and casein haplotypes on predicted MCP (pMCP). This is the first study to estimate both genetic parameters and breeding values of pMCP, together with the effects of milk protein genotypes and casein haplotypes, that also considered k₂₀, probably the most important parameter for the dairy industry (because it indicates the time for the beginning of curd-cutting). Heritability of predicted RCT (0.26) and k₂₀ (0.31) were close to the average heritability described in literature, whereas the heritability of a₃₀ was higher (0.52 vs. 0.27). The effects of milk proteins were statistically significant and similar to those obtained on measured MCP. In particular, haplotypes including uncommon variants showed positive (B-I-A-B) or negative (B-A(1)-A-E) effects. Based on these findings, FTIR spectroscopy-pMCP is proposed as a potential selection criterion for the Italian Holstein.

Influence of micellar calcium and phosphorus on rennet coagulation properties of cows milk

The main requirement for milk processed in most cheese typologies is its rennet coagulation ability. Despite the increasing number of studies, the causes for abnormal coagulation of milk are not fully understood. The aim of this study was to ascertain relationships between milk characteristics and its rennet coagulation ability, focusing on the influence of calcium (Ca) and phosphorus (P). Ca and P are essential constituents of the micelles. Micellar P can be present as part of colloidal calcium phosphate (inorganic-P) or covalently bound to caseins as phosphate groups (casein-P). Eighty one herd milk samples (SCC<400 000 cell/ml) were classified as Optimal (8), Suboptimal (39) Poor (29) and Non-coagulating milk (5), according to their rennet coagulation parameters as assessed by lactodynamographic test. Samples were analysed for their chemical composition (basic composition, protein fractions, minerals and salt equilibria), physicochemical parameters (pH and titratable acidity) and rheological properties. Optimal milk was characterised by the highest contents of major constituents, protein fractions and minerals, lowest content of chloride and highest values of titratable acidity. Non-coagulating milk was characterised by the highest values of pH and the lowest of titratable acidity. At micellar level, Optimal milk showed the highest values of colloidal Ca, casein-P and colloidal Mg (g/100 g casein), while Non-coagulating milk showed the lowest values. Interestingly, there was no statistical difference regarding the content of colloidal inorganic-P (g/100 g casein) between Optimal and Non-coagulating milks. Overall, high mineralisation of the micelle (expressed as g inorganic-P/100 g casein) positively affect its rennetability. However, excessive mineralisation could lead to a reduction of the phosphate groups (g casein-P/100 g casein) available for curd formation.