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Long non-coding RNAs (lncRNAs) have emerged as important regulators of many bio- logical processes, although their regulatory roles remain poorly characterized in woody plants, especially in gymnosperms. A major challenge of working with lncRNAs is to assign functional annotations, since they have a low coding potential and low cross-species conservation.

We utilised an existing RNA-Sequencing resource and performed short RNA sequencing of somatic embryogenesis developmental stages in Norway spruce (Picea abies L. Karst). We implemented a pipeline to identify lncRNAs located within the intergenic space (lincRNAs) and generated a co-expression network including protein coding, lincRNA and miRNA genes.

To assign putative functional annotation, we employed a guilt-by-association approach using the co-expression network and integrated these results with annota- tion assigned using semantic similarity and co-expression. Moreover, we evaluated the relationship between lincRNAs and miRNAs, and identified which lincRNAs are conserved in other species. We identified lincRNAs with clear evidence of differential expression during somatic embryogenesis and used network connectivity to identify those with the greatest regulatory potential.

This work provides the most comprehensive view of lincRNAs in Norway spruce and is the first study to perform global identification of lincRNAs during somatic embryogen- esis in conifers. The data have been integrated into the expression visualisation tools at the PlantGenIE.org web resource to enable easy access to the community. This will facilitate the use of the data to address novel questions about the role of lincRNAs in the regulation of embryogenesis and facilitate future comparative genomics studies.

Somatic embryogenesis (SE) is a powerful model system for studying embryo development and an important method for scaling up availability of elite and climate-adapted genetic material of Norway spruce (Picea abies L. Karst). However, there are several steps during the development of the somatic embryo (Sem) that are suboptimal compared to zygotic embryo (Zem) development. These differences are poorly understood and result in substantial yield losses during plant production, which limits cost-effective large-scale production of SE plants. This study presents a comprehensive data resource profiling gene expression during zygotic and somatic embryo development to support studies aiming to advance understanding of gene regulatory programmes controlling embryo development. Transcriptome expression patterns were analysed during zygotic embryogenesis (ZE) in Norway spruce, including separated samples of the female gametophytes and Zem, and at multiple stages during SE. Expression data from eight developmental stages of SE, starting with pro-embryogenic masses (PEMs) up until germination, revealed extensive modulation of the transcriptome between the early and mid-stage maturing embryos and at the transition of desiccated embryos to germination. Comparative analysis of gene expression changes during ZE and SE identified differences in the pattern of gene expression changes and functional enrichment of these provided insight into the associated biological processes. Orthologs of transcription factors known to regulate embryo development in angiosperms were differentially regulated during Zem and Sem development and in the different zygotic embryo tissues, providing clues to the differences in development observed between Zem and Sem. This resource represents the most comprehensive dataset available for exploring embryo development in conifers.

Aspen (Populus tremula L.) is a keystone species and a model system for forest tree genomics. We present an updated resource comprising a chromosome-scale assem- bly, population genetics and genomics data. Using the resource, we explore the genetic basis of natural variation in leaf size and shape, traits with complex genetic architecture.

We generated the genome assembly using long-read sequencing, optical and high-density genetic maps. We conducted whole-genome resequencing of the Umeå Aspen (UmAsp) collection. Using the assembly and re-sequencing data from the UmAsp, Swedish Aspen (SwAsp) and Scottish Aspen (ScotAsp) collections we performed genome-wide association analyses (GWAS) using Single Nucleotide Polymorphisms (SNPs) for 26 leaf physiognomy phenotypes. We conducted Assay of Transposase Accessible Chromatin sequencing (ATAC-Seq), identified genomic regions of accessible chromatin, and subset SNPs to these regions, improving the GWAS detection rate. We identified candidate long non-coding RNAs in leaf samples, quantified their expression in an updated co-expression network, and used this to explore the functions of candidate genes identified from the GWAS.

A GWAS found SNP associations for seven traits. The associated SNPs were in or near genes annotated with developmental functions, which represent candidates for further study. Of particular interest was a !177-kbp region harbouring associations with several leaf phenotypes in ScotAsp.

We have incorporated the assembly, population genetics, genomics, and GWAS data into the PlantGenIE.org web resource, including updating existing genomics data to the new genome version, to enable easy exploration and visualisation. We provide all raw and processed data to facilitate reuse in future studies.

Forestry in Sweden largely relies on planting genetically improved seedlings after clear-cutting, and high survival and early growth of planted seedlings is vital for stand establishment, economic viability, and carbon sequestration. Norway spruce (Picea abies) and Scots pine (Pinus sylvestris) are the two most important tree species in Swedish forest stands and both are associated with a variety of ectomycorrhizal fungi. While seedlings are generally not fertilized at outplanting, previous results have shown that fertilization with arginine phosphate can increase root growth and seedling survival. However, it is not clear how this affects fungal community composition on the roots of growing seedlings. In a planting experiment sampled after one and two growing seasons, we found that planting position had the largest effects both on seedling performance and on fungal community composition and provide insight into the early stages of fungal community succession on planted Norway spruce and Scots pine seedlings. Fungal taxa present on seedlings before planting persisted on seedling roots, while some degree of novel colonization by site indigenous taxa was observed. Fertilization modified the relative abundance of some fungal taxa but did not lead to significant changes in overall community composition. In terms of seedling performance, ammonium nitrate led to increased mortality while arginine phosphate improved root growth.

Quinoa is an agriculturally important crop species originally domesticated in the Andes of central South America. One of its most important phenotypic traits is seed colour. Seed colour variation is determined by contrasting abundance of betalains, a class of strong antioxidant and free radicals scavenging colour pigments only found in plants of the order Caryophyllales. However, the genetic basis for these pigments in seeds remains to be identified. Here we demonstrate the application of machine learning (extreme gradient boosting) to identify genetic variants predictive of seed colour. We show that extreme gradient boosting outperforms the classical genome-wide association approach. We provide re-sequencing and phenotypic data for 156 South American quinoa accessions and identify candidate genes potentially controlling betalain content in quinoa seeds. Genes identified include novel cytochrome P450 genes and known members of the betalain synthesis pathway, as well as genes annotated as being involved in seed development. Our work showcases the power of modern machine learning methods to extract biologically meaningful information from large sequencing data sets.

Hydrolyzable tannins (HTs), predominant polyphenols in oaks, are widely used in grape wine aging, feed additives, and human healthcare. However, the limited availability of a high-quality reference genome of oaks greatly hampered the recognition of the mechanism of HT biosynthesis. Here, high-quality reference genomes of three Asian oak species (Quercus variabilis, Quercus aliena, and Quercus dentata) that have different HT contents were generated. Multi-omics studies were carried out to identify key genes regulating HT biosynthesis. In vitro enzyme activity assay was also conducted. Dual-luciferase and yeast one-hybrid assays were used to reveal the transcriptional regulation. Our results revealed that β-glucogallin was a biochemical marker for HT production in the cupules of the three Asian oaks. UGT84A13 was confirmed as the key enzyme for β-glucogallin biosynthesis. The differential expression of UGT84A13, rather than enzyme activity, was the main reason for different β-glucogallin and HT accumulation. Notably, sequence variations in UGT84A13 promoters led to different trans-activating activities of WRKY32/59, explaining the different expression patterns of UGT84A13 among the three species. Our findings provide three high-quality new reference genomes for oak trees and give new insights into different transcriptional regulation for understanding β-glucogallin and HT biosynthesis in closely related oak species.

Poplar (Populus) is a well-established model system for tree genomics and molecular breeding, and hybrid poplar is widely used in forest plantations. However, distinguishing its diploid homologous chromosomes is difficult, complicating advanced functional studies on specific alleles. In this study, we applied a trio-binning design and PacBio high-fidelity long-read sequencing to obtain haplotype-phased telomere-to-telomere genome assemblies for the 2 parents of the well-studied F1 hybrid “84K” (Populus alba × Populus tremula var. glandulosa). Almost all chromosomes, including the telomeres and centromeres, were completely assembled for each haplotype subgenome apart from 2 small gaps on one chromosome. By incorporating information from these haplotype assemblies and extensive RNA-seq data, we analyzed gene expression patterns between the 2 subgenomes and alleles. Transcription bias at the subgenome level was not uncovered, but extensive-expression differences were detected between alleles. We developed machine-learning (ML) models to predict allele-specific expression (ASE) with high accuracy and identified underlying genome features most highly influencing ASE. One of our models with 15 predictor variables achieved 77% accuracy on the training set and 74% accuracy on the testing set. ML models identified gene body CHG methylation, sequence divergence, and transposon occupancy both upstream and downstream of alleles as important factors for ASE. Our haplotype-phased genome assemblies and ML strategy highlight an avenue for functional studies in Populus and provide additional tools for studying ASE and heterosis in hybrids.