黑木耳多糖-乳清蛋白复合物的制备及其抗原性的研究

蛋白质和多糖在控制条件下通过美拉德反应会发生一定程度的共价复合,能显示更优越的性能。采用黑木耳多糖作为糖基供体,用糖基化的手段与牛乳中乳清蛋白结合形成木耳多糖-乳清蛋白复合物,并在现有的条件下探索不同质量比与不同反应时间对糖基化进程的影响,采用间接竞争ELISA法测定复合物中β-乳球蛋白和α-乳白蛋白抗原性的影响。结果表明,乳清蛋白与黑木耳多糖质量比为1：1,反应时间为24h,是糖基化反应最佳条件并且能有效减低乳清蛋白抗原性,其中β-乳球蛋白抗原性降低率为75.7%,α-乳白蛋白抗原性降低率为25%。     

Short communication: Effect of kefir grains on proteolysis of major milk proteins

The effect of kefir grains on the proteolysis of major milk proteins in milk kefir and in a culture of kefir grains in pasteurized cheese whey was followed by reverse phase-HPLC analysis. The reduction of κ-, α-, and β-caseins (CN), α-lactalbumin (α-LA), and β-lactoglobulin (β-LG) contents during 48 and 90 h of incubation of pasteurized milk (100 mL) and respective cheese whey with kefir grains (6 and 12 g) at 20°C was monitored. Significant proteolysis of α-LA and κ-, α-, and β-caseins was observed. The effect of kefir amount (6 and 12 g/100 mL) was significant for α-LA and α- and β-CN. α-Lactalbumin and β-CN were more easily hydrolyzed than α-CN. No significant reduction was observed with respect to β-LG concentration for 6 and 12 g of kefir in 100 mL of milk over 48 h, indicating that no significant proteolysis was carried out. Similar results were observed when the experiment was conducted over 90 h. Regarding the cheese whey kefir samples, similar behavior was observed for the proteolysis of α-LA and β-LG: α-LA was hydrolyzed between 60 and 90% after 12 h (for 6 and 12 g of kefir) and no significant β-LG proteolysis occurred. The proteolytic activity of lactic acid bacteria and yeasts in kefir community was evaluated. Kefir milk prepared under normal conditions contained peptides from proteolysis of α-LA and κ-, α-, and β-caseins. Hydrolysis is dependent on the kefir:milk ratio and incubation time. β-Lactoglobulin is not hydrolyzed even when higher hydrolysis time is used. Kefir grains are not appropriate as adjunct cultures to increase β-LG digestibility in whey-based or whey-containing foods.     

Interactions of milk proteins during heat and high hydrostatic pressure treatments — A Review

Pressure treatment of β-lactoglobulin (β-LG), whey protein concentrate (WPC), whey protein isolate and skim milk has been explored by many groups using a wide range of techniques. In general terms, heat treatment and pressure treatment have similar effects: denaturing and aggregating the whey proteins and diminishing the number of viable microorganisms. However, there are significant differences between the effects of the two treatments on protein unfolding and the subsequent thiol-catalysed disulfide-bond interchanges that lead to different structures and product characteristics. Application of a range of techniques has given insight into the subtle differences between the pathways from native proteins to the final product mix. This review covers some of the techniques used and their strengths, and the probable pathways from native protein to the final products. β-LG is one of the most pressure-sensitive proteins and α-lactalbumin (α-LA) is one of the most pressure resistant. In a heated WPC system, bovine serum albumin is very sensitive and β-LG is more resistant. In a heated milk system, β-LG reacts with κ-casein (κ-CN) and not with α_S2-CN, but, in pressure-treated milk, β-LG forms adducts with either κ-CN or α_S2-CN. In both treatments, the role of β-LG is central to the ongoing reactions, involving α-LA and κ-CN in heated systems but involving κ-CN, α_S2-CN and α-LA in pressurized systems.Industrial relevanceHigh hydrostatic pressure (HHP) processing, as opposed to heat treatment, has received much attention recently as a means of processing milk proteins. This review examines the differences in the denaturation pathways that give rise to different final products.     

Efficiency of removal of whey protein from sweet whey using polymeric microfiltration membranes

Our objective was to measure whey protein removal percentage from separated sweet whey using spiral-wound (SW) polymeric microfiltration (MF) membranes using a 3-stage, 3× process at 50°C and to compare the performance of polymeric membranes with ceramic membranes. Pasteurized, separated Cheddar cheese whey (1,080 kg) was microfiltered using a polymeric 0.3-μm polyvinylidene (PVDF) fluoride SW membrane and a 3×, 3-stage MF process. Cheese making and whey processing were replicated 3 times. There was no detectable level of lactoferrin and no intact α- or β-casein detected in the MF permeate from the 0.3-μm SW PVDF membranes used in this study. We found BSA and IgG in both the retentate and permeate. The β-lactoglobulin (β-LG) and α-lactalbumin (α-LA) partitioned between retentate and permeate, but β-LG passage through the membrane was retarded more than α-LA because the ratio of β-LG to α-LA was higher in the MF retentate than either in the sweet whey feed or the MF permeate. About 69% of the crude protein present in the pasteurized separated sweet whey was removed using a 3×, 3-stage, 0.3-μm SW PVDF MF process at 50°C compared with 0.1-μm ceramic graded permeability MF that removed about 85% of crude protein from sweet whey. The polymeric SW membranes used in this study achieve approximately 20% lower yield of whey protein isolate (WPI) and a 50% higher yield of whey protein phospholipid concentrate (WPPC) under the same MF processing conditions as ceramic MF membranes used in the comparison study. Total gross revenue from the sale of WPI plus WPPC produced with polymeric versus ceramic membranes is influenced by both the absolute market price for each product and the ratio of market price of these 2 products. The combination of the market price of WPPC versus WPI and the influence of difference in yield of WPPC and WPI produced with polymeric versus ceramic membranes yielded a price ratio of WPPC versus WPI of 0.556 as the cross over point that determined which membrane type achieves higher total gross revenue return from production of these 2 products from separated sweet whey. A complete economic engineering study comparison of the WPI and WPPC manufacturing costs for polymeric versus ceramic MF membranes is needed to determine the effect of membrane material selection on long-term processing costs, which will affect net revenue and profit when the same quantity of sweet whey is processed under various market price conditions.     

Identification and characterization of yak α-lactalbumin and β-lactoglobulin

α-Lactalbumin (α-LA) and β-lactoglobulin (β-LG) were isolated from yak milk and identified by mass spectrometry. The variant of α-LA (L8IIC8) in yak milk had 123 amino acids, and the sequence differed from α-LA from bovine milk. The amino acid at site 71 was Asn (N) in domestic yak milk, but Asp (D) in bovine and wild yak milk sequences. Yak β-LG had 2 variants, β-LG A (P02754) and β-LG E (L8J1Z0). Both domestic yak and wild yak milk contained β-LG E, but it was absent in bovine milk. The amino acid at site 158 of β-Lg E was Gly (G) in yak but Glu (E) in bovine. The yak α-LA and β-LG secondary structures were slightly different from those in bovine milk. The denaturation temperatures of yak α-LA and β-LG were 52.1°C and 80.9°C, respectively. This study provides insights relevant to food functionality, food safety control, and the biological properties of yak milk products.     

Distinction of different heat-treated bovine milks by native-PAGE fingerprinting of their whey proteins

Native-PAGE (polyacrylamide gel electrophoresis) was used for the simultaneous qualitative and quantitative analysis of whey proteins of raw, commercial and laboratory heat-treated bovine milks. Four whey protein bands, including β-lactoglobulin variants (β-LG A and B), could be distinctively separated in the gel. The results showed that levels of the major whey proteins were reduced by less than 23% in the pasteurized milks and by more than 85% in the UHT milks as compared with raw milk. The α-lactalbumin (α-LA) exhibited the strongest heat-tolerance: about 32% of it remained in its native state after the milk was heated at 100 °C for 10 min. About 42% of β-LG A and 53% of β-LG B were lost after the milk was heated at 75 °C for 30 min. Blood serum albumin (BSA) was lost almost completely when the milk at pH 5.0 was heated at a temperature of 75 °C or higher. The β-LGA and β-LGB were much more stable at low pH than in neutral conditions.     

Stabilizing vitamin D3 using the molten globule state of α-lactalbumin

α-Lactalbumin (α-LA) is the second most abundant bovine whey protein. It has been intensively studied because of its readiness to populate the molten globular (MG) state, a partially folded state with native levels of secondary structure but loss of tertiary structure. The MG state of α-LA exposes a significant number of hydrophobic patches that could be used to bind and stabilize small hydrophobic molecules such as vitamin D₃ (vitD). Accordingly, we tested the ability of α-LA to stabilize vitD in a pH interval from 7.4 to 2; over this pH interval, α-LA transitions from the folded state to the MG state. The MG state stabilized vitD better than the folded state and was superior to the major bovine whey protein β-lactoglobulin (β-LG), which is known to stabilize vitD. At pH 7.4, β-LG and α-LA stabilized vitD to the same extent. Tryptophan fluorescence quenching measurements indicated that α-LA has one binding site at pH 7.4 but acquires an additional binding site when the pH is lowered to pH 2 to 4. Stability measurements of the vitD in the α-LA–vitD complex at different temperatures suggest that UHT processing would lead to little loss of vitD. This study demonstrates the potential of α-LA as a component in vitD fortification, particularly for low pH applications.     

Kinetics of heat-induced interactions among whey proteins and casein micelles in sheep skim milk and aggregation of the casein micelles

The interactions among the proteins in sheep skim milk (SSM) during heat treatments (67.5–90°C for 0.5–30 min) were characterized by the kinetics of the denaturation of the whey proteins and of the association of the denatured whey proteins with casein micelles, and changes in the size and structure of casein micelles. The relationship between the size of the casein micelles and the association of whey proteins with the casein micelles is discussed. The level of denaturation and association with the casein micelles for β-lactoglobulin (β-LG) and α-lactalbumin (α-LA) increased with increasing heating temperature and time; the rates of denaturation and association with the casein micelles were markedly higher for β-LG than for α-LA in the temperature range 80 to 90°C; the Arrhenius critical temperature was 80°C for the denaturation of both β-LG and α-LA. The casein micelle size increased by 7 to 120 nm, depending on the heating temperature and the holding time. For instance, the micelle size (about 293 nm) of SSM heated at 90°C for 30 min increased by about 70% compared with that (about 174.6 nm) of unheated SSM. The casein micelle size increased slowly by a maximum of about 65 nm until the level of association of the denatured whey proteins with casein micelles reached 95%, and then increased markedly by a maximum of about 120 nm when the association level was greater than about 95%. The marked increases in casein micelle size in heated SSM were due to aggregation of the casein micelles. Aggregation of the casein micelles and association of whey protein with the micelles occurred simultaneously in SSM during heating.     

Effectiveness of mid-infrared spectroscopy for the prediction of detailed protein composition and contents of protein genetic variants of individual milk of Simmental cows

Mid-infrared (MIR) spectroscopy was used to predict the detailed protein composition of 1,517 milk samples of Simmental cows. Contents of milk protein fractions and genetic variants were quantified by reversed-phase HPLC. The most accurate predictions were those obtained for total protein, casein (CN), α_S1-CN, β-lactoglobulin (LG), glycosylated κ-CN, and whey protein content, which exhibited coefficients of determination between predicted and measured values in cross-validation (1-VR) ranging from 0.61 to 0.78. Less favorable were results for β-CN (1-VR = 0.53), α_S2-CN, and κ-CN (1-VR = 0.49). Neither the content of α-LA nor that of γ-CN was accurately predicted by MIR. Predicting the content of the most common milk protein genetic variants (κ-CN A and B; β-CN A¹, A², and B; and β-LG A and B) was unfeasible (1-VR <0.15 for the content of κ-CN genetic variants and 1-VR <0.01 for the content of β-CN variants). The best predictions were obtained for β-LG A and β-LG B contents (1-VR of 0.60 and 0.44, respectively). Results indicated that MIR is not applicable for predicting individual milk protein composition with high accuracy. However, MIR spectroscopy predictions may play a role as indicator traits in selective breeding to enhance milk protein composition. The genetic correlation between MIR spectroscopy predictions and measures of milk protein composition needs to be investigated, as it affects the suitability of MIR spectroscopy predictions as indicator traits in selective breeding.     

Effects of milk protein variants on the protein composition of bovine milk

The effects of β-lactoglobulin (β-LG), β-casein (β-CN), and κ-CN variants and β-κ-CN haplotypes on the relative concentrations of the major milk proteins α-lactalbumin (α-LA), β-LG, α_S1-CN, α_S2-CN, β-CN, and κ-CN and milk production traits were estimated in the milk of 1,912 Dutch Holstein-Friesian cows. We show that in the Dutch Holstein-Friesian population, the allele frequencies have changed in the past 16 years. In addition, genetic variants and casein haplotypes have a major impact on the protein composition of milk and explain a considerable part of the genetic variation in milk protein composition. The β-LG genotype was associated with the relative concentrations of β-LG (A » B) and of α-LA, α_S1-CN, α_S2-CN, β-CN, and κ-CN (B > A) but not with any milk production trait. The β-CN genotype was associated with the relative concentrations of β-CN and α_S2-CN (A² > A¹) and of α_S1-CN and κ-CN (A¹ > A²) and with protein yield (A² > A¹). The κ-CN genotype was associated with the relative concentrations of κ-CN (B > E > A), α_S2-CN (B > A), α-LA, and α_S1-CN (A > B) and with protein percentage (B > A). Comparing the effects of casein haplotypes with the effects of single casein variants can provide better insight into what really underlies the effect of a variant on protein composition. We conclude that selection for both the β-LG genotype B and the β-κ-CN haplotype A²B will result in cows that produce milk that is more suitable for cheese production.     

Genetic analysis of detailed milk protein composition and coagulation properties in Simmental cattle

The objective of this study was to estimate genetic parameters for milk protein fraction contents, milk protein composition, and milk coagulation properties (MCP). Contents of α_S1-, α_S2-, β-, γ-, and κ-casein (CN), β-lactoglobulin (β-LG), and α-lactalbumin (α-LA) were measured by reversed-phase HPLC in individual milk samples of 2,167 Simmental cows. Milk protein composition was measured as percentage of each CN fraction in CN (α_S1-CN%, α_S2-CN%, β-CN%, γ-CN%, and κ-CN%) and as percentage of β-LG in whey protein (β-LG%). Rennet clotting time (RCT) and curd firmness (a₃₀) were measured by a computerized renneting meter. Heritabilities for contents of milk proteins ranged from 0.11 (α-LA) to 0.52 (κ-CN). Heritabilities for α_S1-CN%, κ-CN%, and β-CN% were similar and ranged from 0.63 to 0.69, whereas heritability of α_S2-CN%, γ-CN%, and β-LG% were 0.28, 0.18, and 0.34, respectively. Effects of CSN2-CSN3 haplotype and BLG genotype accounted for more than 80% of the genetic variance of α_S1-CN%, β-CN%, and κ-CN% and 50% of the genetic variance of β-LG%. The genetic correlations among the contents of CN fractions and between CN and whey protein fractions contents were generally low. When the data were adjusted for milk protein gene effects, the magnitude of the genetic correlations among the contents of milk protein fractions markedly increased, indicating that they undergo a common regulation. The proportion of β-CN in CN correlated negatively with κ-CN% (r = −0.44). The genetic relationships between CN and whey protein composition were trivial. Low milk pH correlated with favorable MCP. Genetically, contents and proportions of α_S1- and α_S2-CN in CN were positively correlated with RCT. The relative proportion of β-CN in CN exhibited a genetic correlation with RCT of −0.26. Both the content and the relative proportion of κ-CN in CN did not correlate with RCT. Weak curds were genetically associated with increased proportions in CN of α_S1- and α_S2-CN, decreased contents of β-CN and κ-CN, and decreased proportion of κ-CN in CN. Negligible effects on the estimated correlations between a₃₀ and κ-CN contents or proportion in CN were observed when the model accounted for milk protein gene effects. Increasing β-CN and κ-CN contents and relative proportions in CN and decreasing the content and proportions of α_S1-CN and α_S2-CN and milk pH through selective breeding exert favorable effects on MCP.     

A rapid method to quantify casein in fluid milk by front-face fluorescence spectroscopy combined with chemometrics

Casein in fluid milk determines cheese yield and affects cheese quality. Traditional methods of measuring casein in milk involve lengthy sample preparations with labor-intensive nitrogen-based protein quantifications. The objective of this study was to quantify casein in fluid milk with different casein-to-crude-protein ratios using front-face fluorescence spectroscopy (FFFS) and chemometrics. We constructed calibration samples by mixing microfiltration and ultrafiltration retentate and permeate in different ratios to obtain different casein concentrations and casein-to-crude-protein ratios. We developed partial least squares regression and elastic net regression models for casein prediction in fluid milk using FFFS tryptophan emission spectra and reference casein contents. We used a set of 20 validation samples (including raw, skim, and ultrafiltered milk) to optimize and validate model performance. We externally tested another independent set of 20 test samples (including raw, skim, and ultrafiltered milk) by root mean square error of prediction (RMSEP), residual prediction deviation (RPD), and relative prediction error (RPE). The RMSEP for casein content quantification in raw, skim, and ultrafiltered milk ranged from 0.12 to 0.13%, and the RPD ranged from 3.2 to 3.4. The externally validated error of prediction was comparable to the existing rapid method and showed practical model performance for quality-control purposes. This FFFS-based method can be implemented as a routine quality-control tool in the dairy industry, providing rapid quantification of casein content in fluid milk intended for cheese manufacturing.     

Evaluation of commercially available, wide-pore ultrafiltration membranes for production of α-lactalbumin-enriched whey protein concentrate

Commercially available, wide-pore ultrafiltration membranes were evaluated for production of α-lactalbumin (α-LA)-enriched whey protein concentrate (WPC). In this study microfiltration was used to produce a prepurified feed that was devoid of casein fines, lipid materials, and aggregated proteins. This prepurified feed was subsequently subjected to a wide-pore ultrafiltration process that produced an α-LA-enriched fraction in the permeate. We evaluated the performance of 3 membrane types and a range of transmembrane pressures. We determined that the optimal process used a polyvinylidene fluoride membrane (molecular weight cut-off of 50 kDa) operated at transmembrane pressure (TMP) of 207 kPa. This membrane type and operating pressure resulted in α-LA purity of 0.63, α-LA:β-LG ratio of 1.41, α-LA yield of 21.27%, and overall flux of 49.46 L/m²·h. The manufacturing cost of the process for a hypothetical plant indicated that α-LA-enriched WPC 80 (i.e., with 80% protein) could be produced at $17.92/kg when the price of whey was considered as an input cost. This price came down to $16.46/kg when the price of whey was not considered as an input cost. The results of this study indicate that production of a commercially viable α-LA-enriched WPC is possible and the process developed can be used to meet worldwide demand for α-LA-enriched whey protein.     

Effects of the detailed protein composition of milk on curd yield and composition measured by model micro-cheese curd making of individual milk samples

The effect of the contents of casein (CN) and whey protein fractions on curd yield (CY) and composition was estimated using 964 individual milk samples. Contents of α_S1-CN, α_S2-CN, β-CN, γ-CN, glycosylated κ-CN (Gκ-CN), unglycosylated κ-CN, β-LG, and α-LA of individual milk samples were measured using reversed-phase HPLC. Curd yield and curd composition were measured by model micro-cheese curd making using 25 mL of milk. Dry matter CY (DMCY) was positively associated with all casein fractions but especially with α_S1-CN and β-CN. Curd moisture decreased at increasing β-CN content and increased at increasing γ-CN and Gκ-CN content. Due to their associations with moisture, Gκ-CN and β-CN were the fractions with the greatest effect on raw CY, which decreased by 0.66% per 1-standard deviation (SD) increase in the content of β-CN and increased by 0.62% per 1-SD increase in the content of Gκ-CN. The effects due to variation in percentages of the casein fractions in total casein were less marked than those exerted by contents. A 1-SD increase in β-CN percentage in casein (+3.8% in casein) exerted a slightly negative effect on DMCY (β = ?0.05%). Conversely, increasing amounts of α_S1-CN percentage were associated with a small increase in DMCY. Hence, results suggest that, at constant casein and whey protein contents in milk, the DMCY depends to a limited extent on the variation in the α_S1-CN:β-CN ratio. κ-Casein percentage did not affect DMCY, indicating that the positive relationship detected between the content of κ-CN and DMCY can be attributed to the increase in total casein resulting from the increased amount of κ-CN and not to variation in κ-CN relative content. However, milk with increased Gκ-CN percentage in κ-CN also shows increased raw CY and produces curds with increased moisture content. Curd yield increased at increasing content and relative proportion of β-LG in whey protein, but this is attributable to an improved capacity of the curd to retain water. Results obtained in this study support the hypothesis that, besides variation in total casein and whey protein contents, variation in protein composition might affect the cheese-making ability of milk, but this requires further studies.     

Structural equation modeling for unraveling the multivariate genomic architecture of milk proteins in dairy cattle

The aims of this study were to investigate potential functional relationships among milk protein fractions in dairy cattle and to carry out a structural equation model (SEM) GWAS to provide a decomposition of total SNP effects into direct effects and effects mediated by traits that are upstream in a phenotypic network. To achieve these aims, we first fitted a mixed Bayesian multitrait genomic model to infer the genomic correlations among 6 milk nitrogen fractions [4 caseins (CN), namely κ-, β-, α_S1-, and α_S2-CN, and 2 whey proteins, namely β-lactoglobulin (β-LG) and α-lactalbumin (α-LA)], in a population of 989 Italian Brown Swiss cows. Animals were genotyped with the Illumina BovineSNP50 Bead Chip v.2 (Illumina Inc.). A Bayesian network approach using the max-min hill-climbing (MMHC) algorithm was implemented to model the dependencies or independence among traits. Strong and negative genomic correlations were found between β-CN and α_S1-CN (?0.706) and between β-CN and κ-CN (?0.735). The application of the MMHC algorithm revealed that κ-CN and β-CN seemed to directly or indirectly influence all other milk protein fractions. By integrating multitrait model GWAS and SEM-GWAS, we identified a total of 127 significant SNP for κ-CN, 89 SNP for β-CN, 30 SNP for α_S1-CN, and 14 SNP for α_S2-CN (mostly shared among CN and located on Bos taurus autosome 6) and 15 SNP for β-LG (mostly located on Bos taurus autosome 11), whereas no SNP passed the significance threshold for α-LA. For the significant SNP, we assessed and quantified the contribution of direct and indirect paths to total marker effect. Pathway analyses confirmed that common regulatory mechanisms (e.g., energy metabolism and hormonal and neural signals) are involved in the control of milk protein synthesis and metabolism. The information acquired might be leveraged for setting up optimal management and selection strategies aimed at improving milk quality and technological characteristics in dairy cattle.     

Whole-genome association study for milk protein composition in dairy cattle

Our objective was to perform a genome-wide association study for content in bovine milk of α_S1-casein (α_S1-CN), α_S2-casein (α_S2-CN), β-casein (β-CN), κ-casein (κ-CN), α-lactalbumin (α-LA), β-lactoglobulin (β-LG), casein index, protein percentage, and protein yield using a 50K single nucleotide polymorphism (SNP) chip. In total, 1,713 Dutch Holstein-Friesian cows were genotyped for 50,228 SNP and a 2-step association study was performed. The first step involved a general linear model and the second step used a mixed model accounting for all family relationships. Associations with milk protein content and composition were detected on 20 bovine autosomes. The main genomic regions associated with milk protein composition or protein percentage were found on chromosomes 5, 6, 11, and 14. The number of chromosomal regions showing significant (false discovery rate <0.01) effects ranged from 3 for β-CN and 3 for β-LG to 12 for α_S2-CN. A genomic region on Bos taurus autosome (BTA) 6 was significantly associated with all 6 major milk proteins, and a genomic region on BTA 11 was significantly associated with the 4 caseins and β-LG. In addition, regions were detected that only showed a significant effect on one of the milk protein fractions: regions on BTA 13 and 22 with effects on α_S1-CN; regions on BTA 1, 9, 10, 17, 19, and 28 with effects on α_S2-CN; a region on BTA 6 with an effect on β-CN; regions on BTA 13 and 21 with effects on κ-CN; regions on BTA 1, 5, 9, 16, 17, and 26 with effects on α-LA; and a region on BTA 24 with an effect on β-LG. The proportion of genetic variance explained by the SNP showing the strongest association in each of these genomic regions ranged from <1% for α_S1-CN on BTA 22 to almost 100% for casein index on BTA 11. Variation associated with regions on BTA 6, 11, and 14 could in large part but not completely be explained by known protein variants of β-CN (BTA 6), κ-CN (BTA 6), and β-LG (BTA 11) or DGAT1 variants (BTA 14). Our results indicate 3 regions with major effects on milk protein composition, in addition to several regions with smaller effects involved in the regulation of milk protein composition.     

乳蛋白基因多态性对加工与营养的影响

综述了乳蛋白基因多态性和检测方法以及其对乳品加工及人类营养影响的研究进展。酪蛋白的基因型较多,除了基因差异外,还有磷酸化水平与糖基化程度等其他影响因素。乳清蛋白部分,β-乳球蛋白(-βLG)的基因型较多,而α-乳白蛋白(-αLA)的基因型较少。乳蛋白基因多态性可从蛋白水平和基因水平两方面进行检测。乳蛋白基因型会显著影响乳的加工特性,包括热稳定性、凝乳性能及干酪的产率和品质。乳蛋白基因与人类营养息息相关,随着分子技术的发展,基因多态性的应用会更广,需进一步的研究来更好地描述多态性与乳品加工及营养之间的关系。     

Evaluation of colostrum bioactive protein transfer and blood metabolic traits in neonatal lambs in the first 24 hours of life

Colostrum is a unique resource that contributes to the passive transfer of immunity and plays a central role in the health status of neonatal ruminants. However, digestion and absorption of colostral proteins in the gut remain incompletely understood. Therefore, this study aimed to investigate the effect of bovine colostrum feeding on blood metabolic traits and to quantify colostral bioactive proteins in the gastrointestinal digesta and blood to evaluate intestinal transfer in neonatal lambs in the first 24 h of life. Fifty-four newborn lambs were used in this study, including 27 lambs fed pooled bovine colostrum and slaughtered at 6 (C6h), 12 (C12h), or 24 h (C24h) after birth; 18 lambs not fed any colostrum or milk and slaughtered at birth (N0h) or 24 h (N24h) after birth; and 9 milk-fed lambs slaughtered at 24 h (M24h) after birth. Lambs receiving colostrum or milk were bottle-fed within the first 2 h to obtain intakes of 8% of body weight at birth. Samples of blood and digesta from the abomasum, jejunum, and ileum were collected after slaughter. Serum concentrations of glucose, insulin, total protein, and aspartate aminotransferase were higher in colostrum-fed lambs than in N0h lambs. Serum concentrations of insulin, total protein, insulin-like growth factor 1, and γ-glutamyl transpeptidase were higher in C24h lambs than in N24h or M24h lambs. Apparent efficiencies of IgG absorption in C6h, C12h, and C24h lambs were 14.4, 26.8, and 17.2%, respectively, whereas apparent efficiencies of lactoferrin (LF), α-lactalbumin (α-LA), and β-lactoglobulin (β-LG) absorption were very low in colostrum-fed lambs, with mean values of 0.06, 0.002, and 0.003%, respectively. Concentrations of IgG, LF, α-LA, and β-LG in the digesta of the abomasum, jejunum, and ileum rapidly decreased from C6h to C24h lambs, and the disappearance rates of IgG, LF, α-LA, and β-LG were higher in lambs from C6h to C12h (62.1, 75.7, 91.3, and 95.0% for IgG, LF, α-LA, and β-LG, respectively) than from C12h to C24h (34.6, 22.5, 7.5, and 2.2% for IgG, LF, α-LA, and β-LG, respectively). These results indicated that bovine colostrum feeding improved the metabolic and immunological status of lambs, and that ingested colostral IgG was prone to intact uptake into the blood, whereas almost all ingested LF, α-LA, and β-LG disappeared in the lumen of the gastrointestinal tract in a time-dependent manner. The findings provide novel information for exploring selective absorption of colostral compounds in the small intestine of lambs.     

Effect of milk protein genetic polymorphisms on rennet and acid coagulation properties after standardisation of protein content

The effects of milk protein genetic polymorphisms on the rennet and acid coagulation properties of milk after protein standardisation were investigated. Skim milk samples were adjusted to a protein concentration of 6.07 ± 0.06% by ultrafiltration (UF) before evaluating rennet coagulation and acid coagulation properties. Only the β-lactoglobulin (β-LG) genotypes influenced the rennet-clotting time before standardisation for the total protein concentration by UF; however, this effect was confounded with the β-LG concentration. After UF-concentration, a similar protein concentration between the samples was achieved in the retentate, then the rennet clotting time and rennet curd firmness at 30 min were significantly influenced by both the κ-casein (κ-CN) and β-LG genotypes. κ-CN genotypes significantly influenced the acid coagulation properties of both skim milk and retentate. Variations in the concentration of milk proteins (mostly α_S2-CN-12P) explained most of the differences in the rennet and acid coagulation properties of milk after protein standardisation by UF.     

Characterisation of the β-lactoglobulin/α-tocopherol complex and its impact on α-tocopherol stability

β-Lactoglobulin (β-LG), the major whey protein in the milk of ruminants, has a high affinity for small hydrophobic molecules. In the present study, its interaction with lipophilic α-tocopherol, the most abundant and biologically active form of vitamin E, was investigated using circular dichroism, fluorescence, high performance liquid chromatography and turbidity analysis. The interaction did not disrupt the secondary or tertiary structures of β-LG. It was dependent on the aggregation of α-tocopherol and on β-LG/α-tocopherol ratios, with one binding site appearing to be the protein internal cavity and the other in the hydrophobic surface pocket at protein concentrations 1/10 that of the vitamin. Formation of complexes with β-LG increased the solubility of α-tocopherol. The protein also protected α-tocopherol somewhat against decomposition, which depended on the protein concentration and was most effective at a β-LG concentration of one-tenth the α-tocopherol concentration. Protein–nutrient complexes might therefore be useful in the development of functional foods.