Beschrijving
11 ADDITIONAL FILES
Additional file 1. CSV-file with information on the Lombardy poplar trees samples used for whole genome bisulfite sequencing (WGBS) in the two methylome experiments (metadata). The raw fastq datafiles obtained by whole genome bisulfite sequencing (WGBS) are available at the Gene Expression Omnibus (GEO) database (submission GSE225596).
Additional file 2. CSV-file with mapping statistics, bisulfite conversion rates and percentages of cytosine methylation for each DNA-sample analyzed by whole genome bisulfite sequencing (WGBS). (processed data).
Additional file 3. CSV-file with the total list of GO terms that were enriched in DMRs. DMRs were identified between groups by grouping the WGBS data from 16 individual Lombardy poplar ramets by their corresponding parent-of-origin (ortet ‘HUN4’ located in Hungary, ‘ITS3’ in Italy, ‘SPC1’ in Spain and ‘UKD2’ in the UK, respectively) (processed data).
Additional file 4. POWERPOINT-file. Heatmaps with GO terms over-represented in promoters containing DMRs in CpG-context per between-group pairwise comparison. DMRs were identified between groups by grouping the WGBS data from 16 individual Lombardy poplar ramets by their corresponding parent-of-origin (ortet ‘HUN4’ located in Hungary, ‘ITS3’ in Italy, ‘SPC1’ in Spain and ‘UKD2’ in the UK, respectively). A. HUN4 versus ITS3; B. HUN4 versus UKD2, C; ITS3 versus SPC1; D. HUN4 versus SCP1, E. SPC1 versus UKD2
Additional file 5. CSV-file with the raw data of the bud set observations in the common garden experiment (raw data).
Additional file 6. HTML-file with the R source codes to reproduce the results of the bud set analysis (code, R script).
Additional file 7. A text-file representing the Snakefile (i.e. a readable Python-based workflow) including the different steps and rules of the bioinformatics of the WGBS data analyses (code, Snakefile).
Additional file 8. RMD-file with the code to reproduce the analyses to identify differential methylated predefined regions (code, R script).
Additional file 9. R-script with the code to reproduce the clustering and visualizing of the GO enrichment results (code, R script).
Supporting files 1. Zip-folder with: i) excel-files listing the genes in DMRs, and ii) PNG-files with the ‘Biological Coefficient of Variation (BCV)’-plots between any of the six pairwise comparisons of Lombardy poplars grouped per ortet and identified with Bioconductor package edgeR. DMRs were identified between groups by grouping the WGBS data from 16 individual Lombardy poplar ramets by their corresponding parent-of-origin (‘HUN4’ located in Hungary, ‘ITS3’ in Italy, ‘SPC1’ in Spain and ‘UKD2’ in the UK, respectively) (processed data).
Supporting files 2. Zip-folder with PNG-files representing heatmaps and excel-files with clustered GO terms significant over-represented in promoters and gene regions located in DMRs. DMRs were identified between groups by grouping the WGBS data from 16 individual Lombardy poplar ramets by their corresponding parent-of-origin (ortet ‘HUN4’ located in Hungary, ‘ITS3’ in Italy, ‘SPC1’ in Spain and ‘UKD2’ in the UK, respectively. The files were obtained with the Bioconductor package simplifyEnrichment (processed data).
Additional file 1. CSV-file with information on the Lombardy poplar trees samples used for whole genome bisulfite sequencing (WGBS) in the two methylome experiments (metadata). The raw fastq datafiles obtained by whole genome bisulfite sequencing (WGBS) are available at the Gene Expression Omnibus (GEO) database (submission GSE225596).
Additional file 2. CSV-file with mapping statistics, bisulfite conversion rates and percentages of cytosine methylation for each DNA-sample analyzed by whole genome bisulfite sequencing (WGBS). (processed data).
Additional file 3. CSV-file with the total list of GO terms that were enriched in DMRs. DMRs were identified between groups by grouping the WGBS data from 16 individual Lombardy poplar ramets by their corresponding parent-of-origin (ortet ‘HUN4’ located in Hungary, ‘ITS3’ in Italy, ‘SPC1’ in Spain and ‘UKD2’ in the UK, respectively) (processed data).
Additional file 4. POWERPOINT-file. Heatmaps with GO terms over-represented in promoters containing DMRs in CpG-context per between-group pairwise comparison. DMRs were identified between groups by grouping the WGBS data from 16 individual Lombardy poplar ramets by their corresponding parent-of-origin (ortet ‘HUN4’ located in Hungary, ‘ITS3’ in Italy, ‘SPC1’ in Spain and ‘UKD2’ in the UK, respectively). A. HUN4 versus ITS3; B. HUN4 versus UKD2, C; ITS3 versus SPC1; D. HUN4 versus SCP1, E. SPC1 versus UKD2
Additional file 5. CSV-file with the raw data of the bud set observations in the common garden experiment (raw data).
Additional file 6. HTML-file with the R source codes to reproduce the results of the bud set analysis (code, R script).
Additional file 7. A text-file representing the Snakefile (i.e. a readable Python-based workflow) including the different steps and rules of the bioinformatics of the WGBS data analyses (code, Snakefile).
Additional file 8. RMD-file with the code to reproduce the analyses to identify differential methylated predefined regions (code, R script).
Additional file 9. R-script with the code to reproduce the clustering and visualizing of the GO enrichment results (code, R script).
Supporting files 1. Zip-folder with: i) excel-files listing the genes in DMRs, and ii) PNG-files with the ‘Biological Coefficient of Variation (BCV)’-plots between any of the six pairwise comparisons of Lombardy poplars grouped per ortet and identified with Bioconductor package edgeR. DMRs were identified between groups by grouping the WGBS data from 16 individual Lombardy poplar ramets by their corresponding parent-of-origin (‘HUN4’ located in Hungary, ‘ITS3’ in Italy, ‘SPC1’ in Spain and ‘UKD2’ in the UK, respectively) (processed data).
Supporting files 2. Zip-folder with PNG-files representing heatmaps and excel-files with clustered GO terms significant over-represented in promoters and gene regions located in DMRs. DMRs were identified between groups by grouping the WGBS data from 16 individual Lombardy poplar ramets by their corresponding parent-of-origin (ortet ‘HUN4’ located in Hungary, ‘ITS3’ in Italy, ‘SPC1’ in Spain and ‘UKD2’ in the UK, respectively. The files were obtained with the Bioconductor package simplifyEnrichment (processed data).
Abstract
Background: Despite the increasing number of epigenomic studies in plants, little is known about the forces that shape the methylome in long-lived woody perennials. The Lombardy poplar (Populus nigra cv. 'Italica' Duroi) offers an ideal opportunity to investigate the impact of the individual environmental history of trees on the methylome.
Results: We present the results of three interconnected experiments on Lombardy poplar. In the first experiment, we investigated methylome variability during a growing season and across vegetatively reproduced generations. We found that ramets collected over Europe and raised in common conditions have stable methylomes in symmetrical CG-contexts. In contrast, seasonal dynamics occurred in methylation patterns in CHH-context. In the second experiment, we investigated whether methylome patterns of plants grown in a non-parental environment correlate with the parental climate. We did not observe any biological relevant pattern that significantly correlates with the parental climate. Finally, we investigated whether the parental environment has persistent carry-over effects on the vegetative offspring's' phenotype. We combined new bud set observations of three consecutive growing seasons with former published bud set data. Using a linear mixed effects analysis, we found a statistically significant but weak short-term, parental carry-over effect on the timing of bud set. However, this effect was negligible compared to the direct effects of the offspring environment.
Conclusions: Genome-wide cytosine methylation patterns in symmetrical GC-context are stable in Lombardy poplar and appear to be mainly the result of random processes. In this widespread poplar clone, methylation patterns in GC-context can be used as bio-markers to infer a common ancestor and thus to investigate the environmental history of a specific Lombardy poplar on short time-scales. The Lombardy poplar shows high phenotypic plasticity in a novel environment which enabled this clonal tree to adapt and survive all over the temperate regions of the world.
Results: We present the results of three interconnected experiments on Lombardy poplar. In the first experiment, we investigated methylome variability during a growing season and across vegetatively reproduced generations. We found that ramets collected over Europe and raised in common conditions have stable methylomes in symmetrical CG-contexts. In contrast, seasonal dynamics occurred in methylation patterns in CHH-context. In the second experiment, we investigated whether methylome patterns of plants grown in a non-parental environment correlate with the parental climate. We did not observe any biological relevant pattern that significantly correlates with the parental climate. Finally, we investigated whether the parental environment has persistent carry-over effects on the vegetative offspring's' phenotype. We combined new bud set observations of three consecutive growing seasons with former published bud set data. Using a linear mixed effects analysis, we found a statistically significant but weak short-term, parental carry-over effect on the timing of bud set. However, this effect was negligible compared to the direct effects of the offspring environment.
Conclusions: Genome-wide cytosine methylation patterns in symmetrical GC-context are stable in Lombardy poplar and appear to be mainly the result of random processes. In this widespread poplar clone, methylation patterns in GC-context can be used as bio-markers to infer a common ancestor and thus to investigate the environmental history of a specific Lombardy poplar on short time-scales. The Lombardy poplar shows high phenotypic plasticity in a novel environment which enabled this clonal tree to adapt and survive all over the temperate regions of the world.
Versie
Version 3:
Renaming of file names according to the publisher's guidelines
Additional file 2 includes also bisulfite conversion rates per sample
Renaming of file names according to the publisher's guidelines
Additional file 2 includes also bisulfite conversion rates per sample
| Datum ter beschikking | 5-dec.-2022 |
|---|---|
| Temporele dekking | 2017 - 2022 |
| Geografische dekking | Europe |
Thematische Lijst 2020
- Bos
Taxonomische lijst
- populier (Populus spp.)
Beleidsmatig
- milieubeleid
Geografische lijst
- Europa
Technologisch
- genetische technieken