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CSN2、CSN3 和 BLG 基因与多基因背景对奶牛乳蛋白谱的作用。

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

机构信息

Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova (Padua), 35020 Legnaro (PD), Italy.

Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova (Padua), 35020 Legnaro (PD), Italy.

出版信息

J Dairy Sci. 2022 Jul;105(7):6001-6020. doi: 10.3168/jds.2021-21421. Epub 2022 May 5.

DOI:10.3168/jds.2021-21421
PMID:35525618
Abstract

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-CN, α-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.

摘要

为了在奶牛育种计划中制定更好的选择策略,需要更深入地了解编码乳蛋白组分的主要基因的作用。本研究的目的是评估 CSN2、CSN3 和 BLG 基因型对个体蛋白组分(α-CN、α-CN、β-CN、κ-CN、β-LG、α-LA)的影响,这些蛋白组分定性地表示为总氮含量的百分比(% N),定量地表示为牛奶中的含量(g/L),以及日产量(g/d)。从特伦托省(意大利东北部)85 个商业牛群中饲养的 1,264 头瑞士褐牛中收集了 1,264 份个体牛奶样本。使用 Illumina Bovine SNP50 v.2 BeadChip(Illumina Inc.)对 989 头奶牛进行了成功的基因分型,并使用获得的 37,519 个 SNP 标记构建了基因组关系矩阵。通过反相高效液相色谱法(RP-HPLC)定量了乳蛋白组分,并鉴定了 β-CN、κ-CN 和 β-LG 遗传变异体。通过贝叶斯多性状动物模型分析了所有蛋白组分,该模型通过 Gibbs 采样实现。在该模型中,将 CSN2、CSN3 和 BLG 基因型的天数、产次效应以及品种效应设定为平坦先验,而畜群和动物加性遗传效应设定为高斯先验分布,相应的协方差矩阵假设为逆 Wishart 分布。在模型中加入 3 个主要基因的效应前后,比较了感兴趣参数的边缘后验分布。结果表明,大部分遗传方差受主要基因控制。这在定性蛋白谱中尤为明显,其遗传力高于牛奶中蛋白组分的含量及其日产量。当基因单独包含在模型中时,CSN2 是控制除 κ-CN 以外的所有酪蛋白分数的主要基因,κ-CN 直接由 CSN3 基因控制。BLG 基因对 2 种乳清蛋白的影响最大。遗传相关表明,主要基因对蛋白组分之间的关系影响很小,但通过比较不同方式表达的蛋白的相关系数,可以揭示乳腺中调节和竞争合成的潜在机制。CSN2 和 CSN3 基因对蛋白谱的影响估计表明,在基因型中 B 等位基因存在时,蛋白合成过度表达。相反,与 β-LG A 变体相比,β-LG B 变体与 β-LG 浓度较低相关,无论蛋白组分如何表达,它都会下调(或在 β-LG B 的情况下上调)所有其他蛋白组分。在设计旨在提高牛奶质量以提高奶牛养殖效率和乳制品对人类健康影响的更有效的选择计划时,应考虑这些结果。

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