Biology subjects

Sundberg, B.

Publications and source records attributed to Sundberg, B..

3 recordsLinked to original sources

bioRxiv2017

Ethylene signaling induces gelatinous layers with typical features of tension wood in hybrid aspen

Research conductedThe phytohormone ethylene impacts secondary stem growth in plants by stimulating cambial activity, xylem development and fiber over vessel formation. Here we report the effect of ethylene on secondary cell wall formation and the molecular connection between ethylene signaling and wood formation.\n\nMethodsWe applied exogenous ethylene or its precursor 1-aminocyclopropane-1-carboxylic acid (ACC) to wild type and ethylene insensitive hybrid aspen trees (Populus tremula x tremuloides) and studied secondary cell wall anatomy, chemistry and ultrastructure. We furthermore analyzed the transcriptome (RNA Seq) after ACC application to wild type and ethylene insensitive trees.\n\nKey resultsWe demonstrate that ACC and ethylene induce gelatinous-layers (G-layers) and alter the fiber cell wall cellulose microfibril angle. G-layers are tertiary wall layers rich in cellulose, typically found in tension wood of aspen trees. A vast majority of transcripts affected by ACC are downstream of ethylene perception and include a large number of transcription factors (TFs). Motif-analyses reveal potential connections between ethylene TFs (ERFs, EIN3/EIL1) and wood formation.\n\nConclusionG-layer formation upon ethylene application suggests that the increase in ethylene biosynthesis observed during tension wood formation is important for its formation. Ethylene-regulated TFs of the ERF and EIN3/EIL1 type could transmit the ethylene signal.

plant biology↗

bioRxiv2016

High-spatial-resolution transcriptome profiling reveals uncharacterized regulatory complexity underlying cambial growth and wood formation in Populus tremula

Trees represent the largest terrestrial carbon sink and a renewable source of ligno-cellulose. There is significant scope for yield and quality improvement in these largely undomesticated species, and efforts to engineer new, elite varieties will benefit from an improved understanding of the transcriptional network underlying cambial growth and wood formation. We generated high-spatial-resolution RNA Sequencing data spanning the secondary phloem, vascular cambium and wood forming tissues. The transcriptome comprised 28,294 expressed, previously annotated genes, 78 novel protein-coding genes and 567 long intergenic non-coding RNAs. Most paralogs originating from the Salicaceae whole genome duplication were found to have diverged expression, with the notable exception of those with high expression during secondary cell wall deposition. Co-expression network analysis revealed that the regulation of the transcriptome underlying cambial growth and wood formation comprises numerous modules forming a continuum of active processes across the tissues. The high spatial resolution enabled identification of novel roles for characterised genes involved in xylan and cellulose biosynthesis, regulators of xylem vessel and fiber differentiation and lignification. The associated web resource (AspWood, http://aspwood.popgenie.org) integrates the data within a set of interactive tools for exploring the expression profiles and co-expression network.

plant biology↗

bioRxiv2016

Evaluating the accuracy of genomic prediction of growth and wood traits in two Eucalyptus species and their F1 hybrids

BackgroundGenomic prediction is a genomics assisted breeding methodology that can increase genetic gains by accelerating the breeding cycle and potentially improving the accuracy of breeding values. In this study, we used 41,304 informative SNPs genotyped in a Eucalyptus breeding population involving 90 E.grandis and 78 E.urophylla parents and their 949 F1 hybrids to develop genomic prediction models for eight phenotypic traits - basic density and pulp yield, circumference at breast height and height and tree volume scored at age thee and six years. Based on different genomic prediction methods we assessed the impact of the composition and size of the training/validation sets and the number and genomic location of SNPs on the predictive ability (PA).\n\nResultsHeritabilities estimated using the realized genomic relationship matrix (GRM) were considerably higher than estimates based on the expected pedigree, mainly due to inconsistencies in the expected pedigree that were readily corrected by the GRM. Moreover, GRM more precisely capture Mendelian sampling among related individuals, such that the genetic covariance was based on the actual proportion of the genome shared between individuals. PA improved considerably when increasing the size of the training set and by enhancing relatedness to the validation set. Prediction models trained on pure species parents could not predict well in F1 hybrids, indicating that model training has to be carried out in hybrid populations if one is to predict in hybrid selection candidates. The different genomic prediction methods provided similar results for all traits, therefore GBLUP or rrBLUP represents better compromises between computational time and prediction efficiency. Only slight improvement was observed in PA when more than 5,000 SNPs were used for all traits. Using SNPs in intergenic regions provided slightly better PA than using SNPs sampled exclusively in genic regions.\n\nConclusionsEffects of training set size and composition and number of SNPs used are the most important factors for model prediction rather than prediction method and the genomic location of SNPs. Furthermore, training the prediction model on pure parental species provide limited ability to predict traits in interspecific hybrids. Our results provide additional promising perspectives for the implementation of genomic prediction in Eucalyptus breeding programs.

genetics↗