CN117106872A - 一种高饲料效率奶牛分子网络标记的筛选方法 - Google Patents
一种高饲料效率奶牛分子网络标记的筛选方法 Download PDFInfo
- Publication number
- CN117106872A CN117106872A CN202310882370.1A CN202310882370A CN117106872A CN 117106872 A CN117106872 A CN 117106872A CN 202310882370 A CN202310882370 A CN 202310882370A CN 117106872 A CN117106872 A CN 117106872A
- Authority
- CN
- China
- Prior art keywords
- gene
- cows
- molecular network
- screening
- feed efficiency
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 241000283690 Bos taurus Species 0.000 title claims abstract description 80
- 235000013365 dairy product Nutrition 0.000 title claims abstract description 40
- 238000000034 method Methods 0.000 title claims abstract description 33
- 238000012216 screening Methods 0.000 title claims abstract description 28
- 239000002207 metabolite Substances 0.000 claims abstract description 46
- 108090000623 proteins and genes Proteins 0.000 claims abstract description 42
- 230000014509 gene expression Effects 0.000 claims abstract description 34
- 239000008280 blood Substances 0.000 claims abstract description 28
- 210000004369 blood Anatomy 0.000 claims abstract description 28
- 239000003550 marker Substances 0.000 claims abstract description 19
- 238000009395 breeding Methods 0.000 claims abstract description 14
- 230000001488 breeding effect Effects 0.000 claims abstract description 14
- 230000003993 interaction Effects 0.000 claims abstract description 11
- 238000012163 sequencing technique Methods 0.000 claims abstract description 10
- 238000013518 transcription Methods 0.000 claims abstract description 7
- 230000035897 transcription Effects 0.000 claims abstract description 7
- 238000010195 expression analysis Methods 0.000 claims abstract description 5
- 230000006651 lactation Effects 0.000 claims description 17
- 230000000694 effects Effects 0.000 claims description 13
- 230000037213 diet Effects 0.000 claims description 12
- 235000005911 diet Nutrition 0.000 claims description 12
- 230000037361 pathway Effects 0.000 claims description 11
- 238000004458 analytical method Methods 0.000 claims description 10
- QNAYBMKLOCPYGJ-REOHCLBHSA-N L-alanine Chemical compound C[C@H](N)C(O)=O QNAYBMKLOCPYGJ-REOHCLBHSA-N 0.000 claims description 7
- FFEARJCKVFRZRR-BYPYZUCNSA-N L-methionine Chemical compound CSCC[C@H](N)C(O)=O FFEARJCKVFRZRR-BYPYZUCNSA-N 0.000 claims description 7
- 235000004279 alanine Nutrition 0.000 claims description 7
- 229930182817 methionine Natural products 0.000 claims description 7
- 230000008569 process Effects 0.000 claims description 6
- 230000033228 biological regulation Effects 0.000 claims description 5
- 238000005259 measurement Methods 0.000 claims description 5
- 230000001105 regulatory effect Effects 0.000 claims description 5
- 101150021665 CTH gene Proteins 0.000 claims description 4
- 101150084690 LYZ gene Proteins 0.000 claims description 4
- YVPYQUNUQOZFHG-UHFFFAOYSA-N amidotrizoic acid Chemical compound CC(=O)NC1=C(I)C(NC(C)=O)=C(I)C(C(O)=O)=C1I YVPYQUNUQOZFHG-UHFFFAOYSA-N 0.000 claims description 3
- 238000005119 centrifugation Methods 0.000 claims description 3
- 238000012937 correction Methods 0.000 claims description 3
- 238000001514 detection method Methods 0.000 claims description 3
- 230000009274 differential gene expression Effects 0.000 claims description 3
- 238000010201 enrichment analysis Methods 0.000 claims description 3
- 230000035935 pregnancy Effects 0.000 claims description 3
- 238000003908 quality control method Methods 0.000 claims description 3
- 238000012360 testing method Methods 0.000 claims description 3
- 210000003462 vein Anatomy 0.000 claims description 3
- 239000003146 anticoagulant agent Substances 0.000 claims description 2
- 229940127219 anticoagulant drug Drugs 0.000 claims 1
- 230000002068 genetic effect Effects 0.000 abstract description 3
- 230000010354 integration Effects 0.000 abstract 3
- 239000000126 substance Substances 0.000 abstract 3
- 235000013336 milk Nutrition 0.000 description 10
- 239000008267 milk Substances 0.000 description 10
- 210000004080 milk Anatomy 0.000 description 10
- 241001465754 Metazoa Species 0.000 description 9
- 102000016943 Muramidase Human genes 0.000 description 7
- 108010014251 Muramidase Proteins 0.000 description 7
- 108010062010 N-Acetylmuramoyl-L-alanine Amidase Proteins 0.000 description 7
- 229960000274 lysozyme Drugs 0.000 description 7
- 235000010335 lysozyme Nutrition 0.000 description 7
- 239000004325 lysozyme Substances 0.000 description 7
- 238000004519 manufacturing process Methods 0.000 description 6
- 235000021050 feed intake Nutrition 0.000 description 5
- 235000018102 proteins Nutrition 0.000 description 4
- 102000004169 proteins and genes Human genes 0.000 description 4
- 238000012795 verification Methods 0.000 description 4
- 230000010100 anticoagulation Effects 0.000 description 3
- 230000015572 biosynthetic process Effects 0.000 description 3
- 238000002474 experimental method Methods 0.000 description 3
- 239000000203 mixture Substances 0.000 description 3
- 230000008901 benefit Effects 0.000 description 2
- 235000019577 caloric intake Nutrition 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 2
- 230000035558 fertility Effects 0.000 description 2
- 239000004615 ingredient Substances 0.000 description 2
- 244000144972 livestock Species 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 239000003147 molecular marker Substances 0.000 description 2
- 230000035764 nutrition Effects 0.000 description 2
- 235000016709 nutrition Nutrition 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 230000009897 systematic effect Effects 0.000 description 2
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 1
- 102000020018 Cystathionine gamma-Lyase Human genes 0.000 description 1
- 108010045283 Cystathionine gamma-lyase Proteins 0.000 description 1
- 238000000729 Fisher's exact test Methods 0.000 description 1
- 206010018910 Haemolysis Diseases 0.000 description 1
- 208000037147 Hypercalcaemia Diseases 0.000 description 1
- 208000007976 Ketosis Diseases 0.000 description 1
- GUBGYTABKSRVRQ-QKKXKWKRSA-N Lactose Natural products OC[C@H]1O[C@@H](O[C@H]2[C@H](O)[C@@H](O)C(O)O[C@@H]2CO)[C@H](O)[C@@H](O)[C@H]1O GUBGYTABKSRVRQ-QKKXKWKRSA-N 0.000 description 1
- 238000003559 RNA-seq method Methods 0.000 description 1
- 238000012098 association analyses Methods 0.000 description 1
- 235000015278 beef Nutrition 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000031018 biological processes and functions Effects 0.000 description 1
- 230000001851 biosynthetic effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 229910052799 carbon Inorganic materials 0.000 description 1
- 238000005235 decoking Methods 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 210000003754 fetus Anatomy 0.000 description 1
- 238000009472 formulation Methods 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 229920000669 heparin Polymers 0.000 description 1
- ZFGMDIBRIDKWMY-PASTXAENSA-N heparin Chemical compound CC(O)=N[C@@H]1[C@@H](O)[C@H](O)[C@@H](COS(O)(=O)=O)O[C@@H]1O[C@@H]1[C@@H](C(O)=O)O[C@@H](O[C@H]2[C@@H]([C@@H](OS(O)(=O)=O)[C@@H](O[C@@H]3[C@@H](OC(O)[C@H](OS(O)(=O)=O)[C@H]3O)C(O)=O)O[C@@H]2O)CS(O)(=O)=O)[C@H](O)[C@H]1O ZFGMDIBRIDKWMY-PASTXAENSA-N 0.000 description 1
- 229960001008 heparin sodium Drugs 0.000 description 1
- 244000144980 herd Species 0.000 description 1
- 230000000148 hypercalcaemia Effects 0.000 description 1
- 208000030915 hypercalcemia disease Diseases 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000004140 ketosis Effects 0.000 description 1
- 239000008101 lactose Substances 0.000 description 1
- 208000030159 metabolic disease Diseases 0.000 description 1
- 230000002503 metabolic effect Effects 0.000 description 1
- 238000002493 microarray Methods 0.000 description 1
- 238000012775 microarray technology Methods 0.000 description 1
- 238000003068 pathway analysis Methods 0.000 description 1
- 244000144977 poultry Species 0.000 description 1
- 230000008844 regulatory mechanism Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 230000014616 translation Effects 0.000 description 1
Classifications
-
- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
- C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
- C12Q1/6869—Methods for sequencing
-
- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
- C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
- C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
- C12Q1/6888—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for detection or identification of organisms
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G16B5/00—ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks
-
- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- C12Q2600/00—Oligonucleotides characterized by their use
- C12Q2600/124—Animal traits, i.e. production traits, including athletic performance or the like
-
- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- C12Q2600/00—Oligonucleotides characterized by their use
- C12Q2600/156—Polymorphic or mutational markers
Landscapes
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Health & Medical Sciences (AREA)
- Engineering & Computer Science (AREA)
- Proteomics, Peptides & Aminoacids (AREA)
- Organic Chemistry (AREA)
- Physics & Mathematics (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Analytical Chemistry (AREA)
- Molecular Biology (AREA)
- General Health & Medical Sciences (AREA)
- Biophysics (AREA)
- Biotechnology (AREA)
- Wood Science & Technology (AREA)
- Zoology (AREA)
- Genetics & Genomics (AREA)
- Biochemistry (AREA)
- Immunology (AREA)
- General Engineering & Computer Science (AREA)
- Microbiology (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Biology (AREA)
- Medical Informatics (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Theoretical Computer Science (AREA)
- Physiology (AREA)
- Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)
Abstract
本发明涉及一种高饲料效率奶牛分子网络标记的筛选方法。本发明提供了一种鉴定高饲料效率奶牛的多组学整合方法及基因‑代谢物分子网络标记。根据所述多组学整合方法和基因‑代谢物标记,可对高饲料效率奶牛进行鉴定。所述多组学整合方法为:采集待测奶牛的血液进行真核转录组和非靶向代谢组测序,利用差异表达分析方法对转录组进行差异表达基因筛选,再利用代谢物与筛选的差异表达基因构建基于STITCH的相互作用网络,即PubMed文献数据中共同使用且高度可信的交互作用,从而得到整合基因‑代谢物分子网络标记。本发明为高饲料效率奶牛的分子育种提供了理论依据和遗传基础。
Description
技术领域
本发明属于奶牛育种标志物技术领域,具体涉及一种高饲料效率奶牛分子标记的筛选方法,基于该方法筛选得到的整合基因-代谢物分子网络标记及其在奶牛育种领域的应用。
背景技术
公开该背景技术部分的信息仅仅旨在增加对本发明的总体背景的理解,而不必然被视为承认或以任何形式暗示该信息构成已经成为本领域一般技术人员所公知的现有技术。
饲料效率通常表示奶牛采食量与产出量的转化率,关系到畜牧生产的可持续性和盈利能力。一方面,乳制品的需求不断增长;同时,乳制品发展伴随着较高的碳排放,影响奶牛养殖的可持续性。因此,提高畜牧产业中的饲料效率具有重要意义。
动物多组学,特别是筛选与饲料效率提高或降低表达相关的潜在基因,可能有助于实现上述目标。采食量与饲料效率与高品质牛奶制品的生产相关(产量、脂肪含量、蛋白质、乳糖和其他牛奶成分)。高饲料效率有助于降低饲料成本,增加牛奶生产商利润。因此,测量饲料效率对改善环境和提高牛奶生产商的利润很重要。
剩余采食量(RFI)用来描述动物的饲料效率,包括肉牛和奶牛。RFI是通过实际采食量(或者实际能量摄入量)与估计采食量(或者估计能量摄入量)的差值,进而直接测定饲料的净效率或者能量的净利用率。RFI是一个估计饲料转化率和动物饲喂效率的重要指标,其直接估计了饲料使用的净效率,即低RFI被认为是吃的少产的多,而高RFI被认为是吃的多产的少。低RFI的动物相比高RFI动物具有更高的饲料效率。同时,RFI的遗传力在0.1和0.38之间,因此,在育种研究中,RFI是重要的遗传优势。
全基因组关联分析(GWAS)与饲料效率相关的基因调控机制在家禽领域相当成熟。转录组学分析可用于研究动物生产和健康中重要的系统基因组或系统生物学的组成部分方法。近10年来,奶牛的转录组学已能够通过基因表达微阵列来确定候选基因牛奶产量、蛋白质产量、生育能力和代谢疾病,例如酮症和低钙血症。转录组学分析能够给出一定组织中所有基因表达谱的快照并深入了解与特定性状相关的基因功能。微阵列技术已经成为近年来动物科学研究的主要平台;然而,这种趋势已越来越多地被RNA-Seq技术取代。代谢组学越来越多地用于测量奶牛乳制品的动态代谢反应。与其他组学研究方法相比,代谢组学可以在动物身上实现高通量、低成本地测量用于预测RFI。此外,代谢物可以涉及从DNA到RNA再到更下游蛋白质的整个生物过程,更接近于可观测的表型。
发明内容
基于上述分析,本发明认为,通过代谢物预测RFI表型可用于筛选低RFI的动物,以更好地管理畜群或用于繁殖。因此,本发明通过转录组与代谢组,利用差异表达分析方法对转录组进行差异表达基因筛选,再利用代谢物与筛选的差异表达基因构建基于STITCH的相互作用网络,提供了一种整合基因-代谢物分子网络标记,即胱硫醚γ裂合酶(CTH基因)-蛋氨酸和溶菌酶(LYZ基因)-丙氨酸分子标记,可以更快更有效的选择高饲料效率奶牛进行育种。
基于上述技术效果,本发明提供如下的技术内容:
第一方面,提供一种高饲料效率奶牛分子网络标记的筛选方法,包括如下步骤:
获取样本奶牛分别饲喂高营养饲料及低营养饲料一段时间;获取奶牛的血液样品分别进行真核转录组测序及非靶向代谢组学检测得到转录组待分析数据及代谢物,对比高营养饲料及低营养饲料组奶牛的待分析数据得到差异表达基因;
将所述差异表达基因与代谢物进行KEGG富集分析,通过相对介数的中心性计算节点度量的重要性,并进行拓扑分析,以解析基因与代谢物共同的调控通路;通过代谢物的重要性度量之和筛选通路影响值较高的调控通路;将所述调控通路中的差异表达基因和代谢物,构建基于STITCH的相互作用网络,即可得到用于筛选高饲料效率奶牛的分子网络标记。
上述筛选方法可应用于筛选可育高效产奶后代的标志物,优选应用的对象为三胎以内的奶牛个体。
所述血液样品通过抗凝管采集尾静脉全血,采用全血进行真核转录组测序,离心制备血浆进行非靶向代谢组测序。优选的方案中,上述全血进行真核转录组测序后首先得到原始数据,通过FastQC软件对该原始数据进行去接头质控,得到所述待分析数据。
所述差异表达基因的分析方法如下:通过STAR软件将待分析数据比对到牛的Bostaurus ARS-UCD1.3参考基因组上,通过HTSeq软件计算每头奶牛的基因表达水平,然后通过DESeq2软件对进行高营养饲料及低营养饲料组奶牛之间的差异基因表达分析,所用分析模型如下:
yijkl=μ+Parityi+Dietj+Lactationk+(Diet*Lactation)jk+eijkl;
其中yijkl为胎次第i个水平,饲料第j个水平,泌乳天数第k个水平的第l次重复的基因表达值;μ为总体平均值;Parityi为胎次第i个水平的效应;Dietj为饲料第j个水平的效应;Lactationk为泌乳天数第k个水平的效应;(Diet*Lactation)jk为Dietj与Lactationk的交互作用;eijkl为随机误差;最后,通过FDR(错误发现率)进行多重检验矫正,FDR<0.05的基因为差异表达基因。
所述通路影响值的计算方式如下:
PI=ΣICmatched/ΣICall
其中PI是通路影响值,ICmatched是匹配代谢物重要性测量值,ICall是所有代谢物重要性测量值。
本发明基于上述筛选方法得到的为一种整合基因-代谢物分子网络标记,即CTH基因-蛋氨酸和LYZ基因-丙氨酸分子网络标记;根据本发明的验证,LYZ高表达同时血液中蛋氨酸含量高,和/或CTH低表达同时血液中丙氨酸含量高的奶牛个体具有更高的饲料利用效率。
第三方面,提供第二方面所述分子网络标记在奶牛育种领域的应用。
第四方面,提供一种奶牛育种方法,包括如下步骤:
获取奶牛血液样本,检测LYZ基因及血液中蛋氨酸表达含量,和/或CTH基因及血液中丙氨酸的表达量,筛选其中LYZ高表达同时蛋氨酸含量高,和/或CTH低表达同时丙氨酸含量高表现的个体作为亲本进行育种。
以上一个或多个方案的有益效果在于:
现有高效率奶牛标志物通常为单一基因标记,这种标志物的选择误差较大,一个基因表达,经过翻译到蛋白表达,然后蛋白发生结构变化生成代谢物,代谢物运输作用于最终表型,这个过程的每一步都可能受到机体本身或者环境影响,从基因标记到最终表型中间过程复杂而且遥远,准确性有限。
本发明提供的这种整合基因-代谢物分子网络标记,即基因表达与特定代谢物形成的分子网络,从基因和代谢物两个维度对高饲料效率奶牛进行表征,直接锁定了基因表达-性状体现这一过程中的两个关键要素,大大减少其他因素的影响,提高标志物的准确性。
附图说明
构成本发明的一部分的说明书附图用来提供对本发明的进一步理解,本发明的示意性实施例及其说明用于解释本发明,并不构成对本发明的不当限定。
图1为实施例中所述差异表达基因和代谢组代谢物的通路分析;
图2为实施例中所述基于STITCH的相互作用网络。
具体实施方式
应该指出,以下详细说明都是例示性的,旨在对本发明提供进一步的说明。除非另有指明,本文使用的所有技术和科学术语具有与本发明所属技术领域的普通技术人员通常理解的相同含义。
需要注意的是,这里所使用的术语仅是为了描述具体实施方式,而非意图限制根据本发明的示例性实施方式。如在这里所使用的,除非上下文另外明确指出,否则单数形式也意图包括复数形式,此外,还应当理解的是,当在本说明书中使用术语“包含”和/或“包括”时,其指明存在特征、步骤、操作、器件、组件和/或它们的组合。
为了使得本领域技术人员能够更加清楚地了解本发明的技术方案,以下将结合具体的实施例详细说明本发明的技术方案。
实施例
一、整合基因-代谢物分子网络标记筛选高饲料效率奶牛
本实施例中,在山东济南周边的一个约含有500头成母牛的规模化牛场中挑选100头成母牛,挑选标准:胎次3胎以内、饲喂标准一致、饲料成分相近、泌乳天数相近;将该100头奶牛随机分为两组,分别饲喂配方1(低营养配方)及配方2(高营养培养)。基于上述100头成母牛,本实施例依据以下方式对其中高饲料效率的个体进行筛选:
1、奶牛情况统计及饲料组成:100头奶牛的胎次、泌乳天数统计量如表1,两种不同的饲料成分如表2。
表1.济南某奶牛场100头被挑选奶牛的统计表
表2.饲料成分配方
2、针对每头奶牛,用9ml的肝素钠抗凝管采集奶牛尾静脉全血,每个个体2管全血,采集完轻柔颠倒采血管4次,确保抗凝剂充分发挥抗凝作用。将其中1管全血进行真核转录组测序。将另1管全血进行实验室4℃离心,1600g离心10分钟,得到无溶血的血浆,进行非靶向代谢组学检测。
3、通过FastQC软件对100头奶牛的血液转录组的原始数据进行去接头质控,得到待分析数据。通过STAR软件将待分析数据比对到牛(Bos taurus)的ARS-UCD1.3参考基因组上。通过HTSeq软件计算每头奶牛的基因表达水平,然后通过DESeq2软件进行差异基因表达分析,所用分析模型如下:yijkl=μ+Parityi+Dietj+Lactationk+(Diet*Lactation)jk+eijkl。
其中yijkl为胎次第i个水平,饲料第j个水平,泌乳天数第k个水平的第l次重复的基因表达值;μ为总体平均值;Parityi为胎次第i个水平的效应;Dietj为饲料第j个水平的效应;Lactationk为泌乳天数第k个水平的效应;(Diet*Lactation)jk为Dietj与Lactationk的交互作用;eijkl为随机误差。
最后,通过FDR(错误发现率)进行多重检验矫正,即FDR<0.05的基因为差异表达基因。其中,显著差异表达基因如表3。
表3.显著差异表达基因
4、通过过度代表分析(ORA)并采用Fishers精确检验方法,对表3得到的差异表达基因和代谢组代谢物,进行京都基因与基因组百科全书(KEGG)富集分析。通过相对介数的中心性来计算节点度量的重要性,并进行拓扑分析,以解析基因与代谢物共同的调控通路(图2)。其中,每个通路中的通路影响值是通过匹配的代谢物的重要性度量之和除以所有代谢物的重要性度量之和来计算的。
所述通路影响值的计算方式如下:
PI=ΣICmatched/ΣICall
其中PI是通路影响值,ICmatched是匹配代谢物重要性测量值,ICall是所有代谢物重要性测量值。
根据上述通路影响值计算结果,在氨基酰基-tRNA生物合成(aminoacyl-tRNAbiosynthesis)调控通路中,具有较多相关差异表达基因,其所富集的代谢组代谢物如表4。
表4.氨基酰基-tRNA生物合成调控通路所富集的代谢物
5、确定氨基酰基-tRNA生物合成调控通路中的差异表达基因和代谢组代谢物,并构建基于STITCH的相互作用网络,即PubMed文献数据中共同使用且高度可信的交互作用,从而得到整合基因-代谢物分子网络标记。如图2所示,依据上述筛选方法,本实施例证实了,高饲料效率个体中,溶菌酶(LYZ)基因表达量显著高且胱胱硫氨酸-裂解酶(CTH)基因表达量显著低,证实了LYZ基因、CTH基因以及CTH基因-代谢物(蛋氨酸,Methionine)和LYZ基因-代谢物(丙氨酸,Alanine)分子网络标记,可用于筛选高饲料效率奶牛。
效果验证
本实施例筛选的100头奶牛中,有25头为上述步骤5中确定的高饲料效率奶牛。本实施例从剩余奶牛中随机选择25头与上述高饲料效率奶牛相同条件下进行饲喂实验验证,对比结果如表4,通过连续20天的饲喂实验验证,结果显示高饲料效率奶牛比低饲料效率奶牛总共多产奶30.12kg左右,平均每天比低饲料效率奶牛多产奶1.51kg左右。这些高饲料效率奶牛可用于高效生产,或者培育下一代,提高奶牛养殖的经济效益。
表5.高低饲料效率奶牛20天饲喂实验的产奶量对比表
以上所述仅为本发明的优选实施例而已,并不用于限制本发明,对于本领域的技术人员来说,本发明可以有各种更改和变化。凡在本发明的精神和原则之内,所作的任何修改、等同替换、改进等,均应包含在本发明的保护范围之内。
Claims (10)
1.一种高饲料效率奶牛分子网络标记的筛选方法,其特征在于,包括如下步骤:
获取样本奶牛分别饲喂高营养饲料及低营养饲料一段时间;获取奶牛的血液样品分别进行真核转录组测序及非靶向代谢组学检测得到转录组待分析数据及代谢物,对比高营养饲料及低营养饲料组奶牛的待分析数据得到差异表达基因;
将所述差异表达基因与代谢物进行KEGG富集分析,通过相对介数的中心性计算节点度量的重要性,并进行拓扑分析,以解析基因与代谢物共同的调控通路;通过代谢物的重要性度量之和筛选通路影响值较高的调控通路;将所述调控通路中的差异表达基因和代谢物,构建基于STITCH的相互作用网络,即可得到用于筛选高饲料效率奶牛的分子网络标记。
2.如权利要求1所述高饲料效率奶牛分子网络标记的筛选方法,其特征在于,所述筛选方法应用的对象为三胎以内的奶牛个体。
3.如权利要求1所述高饲料效率奶牛分子网络标记的筛选方法,其特征在于,所述血液样品通过抗凝管采集尾静脉全血,采用全血进行真核转录组测序,离心制备血浆进行非靶向代谢组测序;该全血进行真核转录组测序后首先得到原始数据,通过FastQC软件对该原始数据进行去接头质控,得到所述待分析数据。
4.如权利要求1所述高饲料效率奶牛分子网络标记的筛选方法,其特征在于,所述差异表达基因的分析方法如下:通过STAR软件将待分析数据比对到牛的Bos taurusARS-UCD1.3参考基因组上,通过HTSeq软件计算每头奶牛的基因表达水平,然后通过DESeq2软件对进行高营养饲料及低营养饲料组奶牛之间的差异基因表达分析,所用分析模型如下:
yijkl=μ+Parityi+Dietj+Lactationk+(Diet*Lactation)jk+eijkl;
其中yijkl为胎次第i个水平,饲料第j个水平,泌乳天数第k个水平的第l次重复的基因表达值;μ为总体平均值;Parityi为胎次第i个水平的效应;Dietj为饲料第J个水平的效应;Lactationk为泌乳天数第k个水平的效应;(Diet*Lactation)jk为Dietj与Lactationk的交互作用;eijkl为随机误差;最后,通过FDR进行多重检验矫正,FDR<0.05的基因为差异表达基因。
5.如权利要求1所述高饲料效率奶牛分子网络标记的筛选方法,其特征在于,所述通路影响值的计算方式如下:
PI=ΣICmatched/∑ICall
其中PI是通路影响值,ICmatched是匹配代谢物重要性测量值,ICall是所有代谢物重要性测量值。
6.一种基因-代谢物分子网络标记,其特征在于,所述分子网络标记为CTH基因-蛋氨酸或LYZ基因-丙氨酸分子网络标记。
7.一种高饲料效率奶牛的筛选方法,其特征在于,对待测奶牛的血液样本进行检测,筛选LYZ高表达同时血液中蛋氨酸含量高,和/或CTH低表达同时血液中丙氨酸含量高的奶牛个体即为高饲料效率奶牛。
8.权利要求6所述基因-代谢物分子网络标记在奶牛育种领域的应用。
9.如权利要求8所述的应用,其特征在于,所述应用方式为:筛选具有权利要求6所述分子网络标的奶牛作为亲本进行繁育。
10.一种奶牛育种方法,其特征在于,包括如下步骤:
获取奶牛血液样本,检测LYZ基因及血液中蛋氨酸表达含量,和/或CTH基因及血液中丙氨酸的表达量,筛选其中LYZ高表达同时蛋氨酸含量高,和/或CTH低表达同时丙氨酸含量高表现的个体作为亲本进行育种。
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202310882370.1A CN117106872A (zh) | 2023-07-18 | 2023-07-18 | 一种高饲料效率奶牛分子网络标记的筛选方法 |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202310882370.1A CN117106872A (zh) | 2023-07-18 | 2023-07-18 | 一种高饲料效率奶牛分子网络标记的筛选方法 |
Publications (1)
Publication Number | Publication Date |
---|---|
CN117106872A true CN117106872A (zh) | 2023-11-24 |
Family
ID=88808200
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202310882370.1A Pending CN117106872A (zh) | 2023-07-18 | 2023-07-18 | 一种高饲料效率奶牛分子网络标记的筛选方法 |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN117106872A (zh) |
-
2023
- 2023-07-18 CN CN202310882370.1A patent/CN117106872A/zh active Pending
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Liu et al. | A multi-tissue atlas of regulatory variants in cattle | |
Suravajhala et al. | Multi-omic data integration and analysis using systems genomics approaches: methods and applications in animal production, health and welfare | |
Lemmon et al. | The role of cis regulatory evolution in maize domestication | |
Zhang et al. | Bayesian modeling reveals host genetics associated with rumen microbiota jointly influence methane emission in dairy cows | |
Hocquette | Where are we in genomics? | |
Zhang et al. | Characterization and comparative analyses of muscle transcriptomes in Dorper and small-tailed Han sheep using RNA-Seq technique | |
WO2022028624A1 (zh) | 通过测序获取微生物物种及相关信息的方法、装置、计算机可读存储介质和电子设备 | |
Ouwens et al. | A characterization of cis-and trans-heritability of RNA-Seq-based gene expression | |
Liu et al. | A comprehensive catalogue of regulatory variants in the cattle transcriptome | |
Michailidou et al. | Analysis of genome-wide DNA arrays reveals the genomic population structure and diversity in autochthonous Greek goat breeds | |
IL258999A (en) | Methods for detecting copy-number variations in next-generation sequencing | |
Chen et al. | Sequencing and characterization of divergent marbling levels in the beef cattle (longissimus dorsi muscle) transcriptome | |
Keele et al. | Genomewide association study of lung lesions in cattle using sample pooling | |
Ariyarathne et al. | Identification of genomic regions associated with concentrations of milk fat, protein, urea and efficiency of crude protein utilization in grazing dairy cows | |
CN115083521A (zh) | 一种单细胞转录组测序数据中肿瘤细胞类群的鉴定方法及系统 | |
Abdalla et al. | Genome-wide association study identifies candidate genes associated with feet and leg conformation traits in Chinese Holstein cattle | |
Xing et al. | The first high-quality reference genome of sika deer provides insights into high-tannin adaptation | |
CN117106872A (zh) | 一种高饲料效率奶牛分子网络标记的筛选方法 | |
Smaragdov et al. | Genome-wide analysis of across herd F st heterogeneity in holsteinized cattle | |
Zhong et al. | A genome-wide perspective on the diversity and selection signatures in indigenous goats using 53 K single nucleotide polymorphism array | |
Ross et al. | The genome of tropically adapted Brahman cattle (Bos taurus indicus) reveals novel genome variation in production animals | |
Zeng et al. | Genome-wide association study identifies 12 new genetic loci associated with growth traits in pigs | |
Wang et al. | PHARP: A pig haplotype reference panel for genotype imputation | |
Zhao et al. | Young SINEs in pig genomes impact gene regulation, genetic diversity, and complex traits | |
Dong et al. | A new alignment-free whole metagenome comparison tool and its application on gut microbiomes of wild giant pandas |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination |