菌核病(SSR)是甘藍(lán)型油菜的主要病害之一,可造成10-20%的產(chǎn)量損失,在侵染嚴(yán)重的田間甚至達(dá)到80%。SSR不僅會(huì)導(dǎo)致產(chǎn)量下降,而且引起含油量降低。因此,鑒定抗菌核病的遺傳資源對(duì)培育抗性品種具有重要意義。在本研究中,我們結(jié)合三種技術(shù)對(duì)由抗病親本和感病親本構(gòu)建的雙單倍體(DH)系,進(jìn)行了抗病基因和相關(guān)調(diào)控途徑的鑒定。我們對(duì)181個(gè)DH株系進(jìn)行了數(shù)量性狀位點(diǎn)(QTL)分析,并在QTL區(qū)域內(nèi)對(duì)2個(gè)極端抗病和感病的DH系進(jìn)行了比較轉(zhuǎn)錄組和代謝組學(xué)分析。綜合分析抗病系和感病系的所有差異基因和差異代謝物的表達(dá)情況,從中鑒定到了一些參與SSR抗病的相關(guān)基因和調(diào)控途徑。利用單核苷酸多態(tài)性(SNP)標(biāo)記和接種后的表型,我們?cè)谌齻�(gè)不同年份鑒定到了17個(gè)參與SSR抗性的QTL。沒(méi)有檢測(cè)到3年共同的QTL,但發(fā)現(xiàn)3個(gè)QTL能夠在兩年里檢測(cè)到,分別為SRA9a、SRC2a和SRC3a,它們可解釋兩年內(nèi)的表型變異分別為14.75%和11.57%,7.49%和10.38%,和7.73%和6.81%。開(kāi)花時(shí)間與抗病性呈負(fù)相關(guān),即早開(kāi)花的材料更加容易感病。莖稈寬度對(duì)抗病性只有微弱的影響�？紤]到開(kāi)花期對(duì)抗病性狀的影響...

【文章頁(yè)數(shù)】：178 頁(yè)

【學(xué)位級(jí)別】：博士

【文章目錄】：
ABSTRACT
摘要
List of Abbreviations
1.REVIEW OF LITERATURE
    1.1 General Introduction
    1.2 Factors hindering the potential production of B.napus
    1.3 Diseases of B.napus
    1.4 Sclerotinia sclerotiorum impact on B.napus
    1.5 Control of S.sclerotiorum in B.napus
        1.5.1 Chemical control
        1.5.2 Biological control
        1.5.3 Cultural control
        1.5.4 Genetic control
    1.6 Impact of flowering time on SSR resistance in B.napus
    1.7 Aims and objectives
2.MATERIALS AND METHODS
    2.1 Plant material
    2.2 Phenotyping measurements and statistical analysis
    2.3 SNP array genotyping
    2.4 Linkage mapping and QTL map construction
    2.5 Sampling for RNA-seq analysis and metabolites profiling
    2.6 RNA quantification and qualification,cDNA library construction and sequencing
    2.7 Quality control and reads mapping to the reference genome
    2.8 Differential gene expression quantification
    2.9 qRT-PCR analysis for the validation of RNA-seq data
    2.10 Map Man analysis for biotic stress genes
    2.11 GO and KEGG enrichment analysis of differentially expressed genes
    2.12 Metabolites Extraction
    2.13 UHPLC-MS/MS Analysis
    2.14 Database search for metabolites
    2.15 Data Analysis of metabolites
3.RESULTS
    3.1 Phenotyping SSR resistance in the DH population
        3.1.1 Correlation among stem width and lesion length
        3.1.2 Correlation between flowering time and disease resistance
    3.2 QTL mapping
    3.3 Integration of QTLs for SSR resistance
    3.4 Phenotype of the lines used for RNA-seq and metabolomics profiling
    3.5 RNA sequencing and differential gene expression
    3.6 Identification of putative candidate genes within QTL regions
    3.7 DEGs related to biotic stress in RNA-seq analysis
    3.8 GO enrichment analysis of DEGs from RNA-seq data
    3.9 KEGG enrichment analysis of DEGs from RNA-seq data
    3.10 Metabolites profiling
    3.11 KEGG enrichment analysis of DEMs from metabolites profiling
    3.12 Overlapping Pathways from transcriptomic and metabolomics studies
4.DISCUSSION
    4.1 Avoiding the role of flowering in identification of resistant genes and pathways
    4.2 Classification and functions of identified genes within QTL regions
    4.3 Important pathways involved in resistance
        4.3.1 The role of phenylpropanid biosynthesis pathway
        4.3.2 The role of biosynthesis of amino acids
        4.3.3 The role of arginine biosynthesis
    4.4 Validation of genes and pathways involved in resistance
5.SUMMARY
    5.1 Future Directions
    5.2 Innovations
    5.3 Future Directions
REFERENCES
APPENDICES
    Appendix A.List of Primers Used in this Study
    Appendix B.GO Categories in different comparisons
        Appendix B-1.List of Significantly Enriched GO categories in R24 vs R-mock
        Appendix B-2.List of Significantly Enriched GO categories in R24 vs S24
        Appendix B-3.List of Significantly Enriched GO categories in R48 vs R-mock
        Appendix B-4.List of Significantly Enriched GO categories in R96 vs R-mock
        Appendix B-5.List of Significantly Enriched GO categories in R96 vs S96
    Appendix C.Enriched KEGG pathways in RNA-sequencing
        Appendix C-1.Enriched KEGG pathways in R24 vs R-mock
        Appendix C-2.Enriched KEGG pathways in R24 vs S24
        Appendix C-3.Enriched KEGG pathways in R48 vs R-mock
        Appendix C-4.Enriched KEGG pathways in R48 vs S48
        Appendix C-5.Enriched KEGG pathways in R96 vs R-mock
        Appendix C-6.Enriched KEGG pathways in R96 vs S96
        Appendix C-7.Enriched KEGG pathways in S24 vs S-mock
        Appendix C-8.Enriched KEGG pathways in S48 vs S-mock
        Appendix C-9.Enriched KEGG pathways in S96 vs S-mock
    Appendix D.Significantly Enriched KEGG pathways in Metabolomics profiling
    Appendix E.DEGS for the identified KEGG pathways
        Appendix E-1.DEGs in different comparisons for phenylpropanoid biosynthesis pathway
        Appendix E-2.DEGs in different comparisons for biosynthesis of amino acids pathway
        Appendix E-3.DEGs in different comparisons for arginine biosynthesis pathway
    Appendix F.List of Publications
ACKNOWLEGEMENTS



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