Title Page
ABSTRACT
Contents
Chapter 1. Introduction 14
1.1. Seed endophytic bacteria and their plant growth promotion 15
1.2. The seed endophytic bacterial structure 16
1.3. The determinants influencing the functions and structure of endophytic commnities 17
1.4. Ecological and evolutionary aspects of endophytic bacteria on plants 18
1.5. Experimental design to assess the effects of endophytic bacteria on the plant natural selection 19
1.6. Study species; Capsella bursa-pastoris 20
1.7. Objectives of the dissertation 22
References 23
Chapter 2. Genetic Differentiation of C. bursa-pastoris in Korea and the effects of spatial and environmental variables on the genetic structure 30
2.1. Introduction 31
2.2. Materials and Methods 34
2.2.1. Seed sources collection 34
2.2.2. Climatic information collection 36
2.2.3. DNA extraction 38
2.2.4. Library preparation and RADseq 39
2.2.5. Variant calling and filtering 40
2.2.6. Genetic diversity, Pairwise differentiation, and Population structure 42
2.2.7. Statistical analysis 42
2.3. Results 47
2.3.1. Genetic diversity of natural populations and comparison among De novo assembly and reference alignments pipelines 47
2.3.2. Population differentiation among De novo assembly and reference alignment pipelines 51
2.3.3. Geographical and Environmental influences on genetic divergence of the populations 56
2.4. Discussion 59
2.4.1. Comparison among analytical pipelines 59
2.4.2. Genetic diversity and differentiation of C. bursa-pastoris populations 60
2.4.3. C. bursa-pastoris population differentiation affected by human activities 61
References 63
Chapter 3. Effects on Life History and Reproductive Traits of Seed endophytic bacteria in Capsella bursa-pastoris 72
3.1. Introduction 73
3.2. Materials and Methods 75
3.2.1. Seed collection 75
3.2.2. Bacterial isolation and identification 77
3.2.3. Bacterial phenotyping 78
3.2.4. Experimental design 80
3.2.5. Statistical analyses 81
3.3. RESULTS 83
3.3.1. Bacterial phenotype 83
3.3.2. Effects of bacterial inoculation on plant traits 85
3.3.3. Effects of germination season on post-germination traits 91
3.3.4. Natural selection for post-germination traits 93
3.4. DISCUSSION 95
References 98
Chapter 4. Seed microbiome by inoculation of seed endophytic bacteria using Nanopore sequencing 103
4.1. Introduction 104
4.2. Materials and Methods 107
4.2.1. Sampling 107
4.2.2. Microbial enrichment and DNA extraction 107
4.2.3. Bacterial DNA amplification 108
4.2.4. Nanopore sequencing library preparation 110
4.2.5. Sequence quality trimming 110
4.2.6. Sequencing information and Taxonomic assignment 110
4.2.7. Statistical analyses 111
4.3. Results 116
4.3.1. Species assignment 116
4.3.2. Influences of bacterial inoculation on next generation seed endophytic microbiome 119
4.4. Discussion 135
4.4.1. Seed endophytic microbiome by Nanopore sequencing 135
4.4.2. Effects on seed endophytic microbiome of bacterial inoculation treatments and host populations 136
References 138
Chapter 5. CONCLUSION 144
Table 2.1. Source populations of Capsella bursa-pastoris. Locations and altitudes of source populations are given. 35
Table 2.2. Nineteen environmental variables defined by WorldClim. 37
Table 2.3. Summary information on the raw sequence data before filtering 44
Table 2.4. Summary of filtering criteria and filtered-out variants. 45
Table 2.5. Nucleotide diversity (π) of Capsella bursa-pastoris populations. Average π (x 10⁻⁵) over individuals for each population is presented using different datasets. N, number of individuals. BOP,... 46
Table 2.6. Genetic differentiation between testing populations calculated using Stacks2 pipeline. Pairwise FST values are presented. BOP, Bongpyoeng; DGR, Daegwallyeong; MZS, Muju; TAB, Taebaek; JCH,...[이미지참조] 48
Table 2.7. Genetic differentiation between testing populations. The GATK pipeline with C. rubella reference genome was used. Pairwise FST values are presented. BOP, Bongpyoeng; DGR, Daegwallyeong; MZS, Muju;...[이미지참조] 49
Table 2.8. Genetic differentiation between testing populations. The GATK pipeline with C. bursa-pastoris reference genome was used. Pairwise FST values are presented. BOP, Bongpyoeng; DGR, Daegwallyeong;...[이미지참조] 50
Table 2.9. Results of hierarchical analysis of molecular analysis (AMOVA) using SNP genotyping. The Stacks2_1105 dataset was used for the analysis. N=182. 53
Table 2.10. Summary of multiple matrix regression with randomization (MMRR) with commonality analysis (CA). The Stacks2_1105 dataset was used for the analysis. R² = 0.4764 58
Table 3.1. Geographic and climatic information of testing populations. Average values for 20 years from 1997 to 2017 are given. MT, monthly mean temperature; MT_S, mean temperature in spring season; MT_A, mean temperature in autumn season; MHT, mean of monthly highest temperature; MLT,... 76
Table 3.2. Bacterial isolates used in this study and results of plant growth-promoting (PGP) trait assays. Type strains with the highest 16S rRNA sequence similarity (GenBank accession number), the average of the PGP traits, and standard errors are presented. Abbreviations of plant populations are shown in... 84
Table 3.3. Results of generalized mixed model analyses comparing plant traits among bacterial treatments and plant populations. The block, genotype nested by plant population, and genotype by... 87
Table 3.4. Genotypic selection analysis in each treatment and analysis of covariance to examine differences in the strength of natural selection among bacterial treatments. Only bolting plants were used. 94
Table 4.1. Summary of Nanopore sequencing results. Raw reads counts are shown. Bacterial classification rates and proportions of each domain and cyanobacteria are given. 112
Table 4.2. Relative abundance of Bacillus altitudinis between treatments and in populations in each treatment 117
Table 4.3. Relative abundance of Pantoea vagans between treatments and in populations in each treatment 118
Table 4.4. The results from two-way PERMANOVA and two-way ANOVA for bacteria with high abundance in the phylum, family, and genus between endophytic treatments and populations. F values,... 121
Table 4.5. The results from one-way PERMANOVA for genus with high abundance between endophytic treatments. F values, df (degree of freedom), and R² are given. † P < 0.10, * P < 0.05, ** P < 0.01, *** P < 0.01. 124
Table 4.6. The results from one-way ANOVA for genus with high abundance between endophytic treatments in BOP population. F values, df (degree of freedom), and R² are given. † P < 0.10, * P < 0.05,... 126
Table 4.7. The results from one-way ANOVA for genus with high abundance between endophytic treatments in MZS population. F values, df (degree of freedom), and R² are given. † P < 0.10, * P <... 128
Table 4.8. The results from one-way ANOVA for genus with high abundance between endophytic treatments in DMY population. F values, df (degree of freedom), and R² are given. † P < 0.10, * P <... 130
Table 4.9. The results from one-way ANOVA for genus with high abundance between endophytic treatments in ICH population. F values, df (degree of freedom), and R² are given. † P < 0.10, * P < 0.05,... 132
Figure 1.1. Summary of plant growth promoting abilities of endophytic bacteria. 16
Figure 1.2. Abiotic and biotic factors affecting the structure of endophytic community. 17
Figure 1.3. Schematic summary of experimental design for common garden experiments to assess the effects of endophytic bacteria on life history traits and fitness of host plants. 2) to 5) steps of 1.5... 19
Figure 2.1. Seed sources of C. bursa-pastoris natural populations. Names of source populations are given in Table 1. 34
Figure 2.2. An example of gel electrophoresis after DNA extraction to confirm DNA fragmentation. DGR29, DGR31, and BOP6 (white) had a bright and thick band at the top,... 39
Figure 2.3. Results of principle component analysis (PCA) (a) and discriminant analysis of principal components (DAPC) (b). The Stacks2_1105 dataset was used. Box of each... 52
Figure 2.4. The second-order rate of change in the log-likelihood of the data (lnP[D]) (△K) at consecutive K-values. STRUCTURE HARVERSTER was used to calculate △K... 54
Figure 2.5. Average posterior probabilities of assignment of individuals at K = 2, K = 6, and K = 10. Each vertical line represents an individual and the proportion of its genome that is assigned to distinct clusters.... 55
Figure 2.6. Isolation by distance (IBD) and isolation by environment (IBE) of C. bursa-pastoris populations. Correlations between linearized FST and geographic distance (a), latitude distance...[이미지참조] 57
Figure 2.7. Environmental gradients (BIO3) of C. bursa-pastoris populations in Korea Peninsula 58
Figure 3.1. A photograph of C. bursa-pastoris (a) and locations of seed sources and common garden experiment (b). The GPS coordinates of source populations are given. BOP, Bongpyeong, Gwanwon-... 75
Figure 3.2. Effects of endophyte treatment on germination rates and proportion of spring germinants. Letters on each bar represent significant differences by Tukey's post-hoc test at a 0.05 significance level.... 86
Figure 3.3. Effects of endophyte treatment on lifehistory and reproductive traits. Letters on each bar represent significant differences by Tukey's post-hoc test at a 0.05 significance level. C, control without... 88
Figure 3.4. Variation among testing plant populations in lifehistory and reproductive traits. Letters on each bar represent significant differences by Tukey's post-hoc test at a 0.05 significance level.... 89
Figure 3.5. Comparison of inflorescence height among endophyte treatments in each population. Letters on each bar represent significant differences by Tukey's post-hoc test at a 0.05 significance level.... 90
Figure 3.6. Path analyses models showing the effects of bacterial inoculation on plant life history and reproductive traits. Bacterial treatments are analyzed separately, and standardized path coefficients are... 92
Figure 4.1. Gel electrophoresis of PCR product with different types of plant and microbial DNA combination and 16s rRNA primer sets. CbBc2 (1:10 of plant bacterial DNA), CbBc3 (10:1 of plant... 109
Figure 4.2. Gel electrophoresis bacterial DNA extracted from seed after PCR with 27f and 1492r primer sets for gel elution. 109
Figure 4.3. Relative sequence abundance of endophytic bacteria from seeds of the five treatments; a) phylum, b) family, and c) genus. This abundance was represented by bacterial sequences with >... 122
Figure 4.4. Non-metric multidimensional scaling (nMDS) to compare the microbial communities between treatments. Each polygon is each endophytic treatment, and dots are samples in the treatment.... 123
Figure 4.5. Relative sequence abundance of endophytic bacteria from seeds of the four populations after dividing into each population at genus level; a) BOP, b) MZS, c) DMY, and d) ICH This abundance was represented by... 125
Figure 4.6. Relative sequence abundance of (top 10) genera of endophytic bacteria from seeds of the BOP population. These figures are shown in case of bacteria with significance between treatments from one-... 127
Figure 4.7. Relative sequence abundance of (top 10) genera of endophytic bacteria from seeds of the MZS population. These figures are shown in case of bacteria with significance between treatments from... 129
Figure 4.8. Relative sequence abundance of (top 10) genera of endophytic bacteria from seeds of the DMY population. These figures are shown in case of bacteria with significance between treatments from... 131
Figure 4.9. Relative sequence abundance of (top 10) genera of endophytic bacteria from seeds of the ICH population. These figures are shown in case of bacteria with significance between treatments from one-... 133
Figure 4.10. Non-metric multidimensional scaling (nMDS) to compare the microbial communities at genus level between treatments by each population. Each polygon is each endophytic treatment, and dots are samples... 134