Invertase plays a crucial role in carbohydrate partitioning and plant development as it catalyses the irreversible hydrolysis of sucrose into glucose and fructose. The invertase family in plants is composed of two sub-families: acid invertases, which are targeted to the cell wall and vacuole; and neutral/alkaline invertases, which function in the cytosol. In this study, 5 cell wall invertase genes (PtCWINV1-5), 3 vacuolar invertase genes (PtVINV1-3) and 16 neutral/alkaline invertase genes (PtNINV1-16) were identified in the Populus genome and found to be distributed on 14 chromosomes. A comprehensive analysis of poplar invertase genes was performed, including structures, chromosome location, phylogeny, evolutionary pattern and expression profiles. Phylogenetic analysis indicated that the two sub-families were both divided into two clades. Segmental duplication is contributed to neutral/alkaline sub-family expansion. Furthermore, the Populus invertase genes displayed differential expression in roots, stems, leaves, leaf buds and in response to salt/cold stress and pathogen infection. In addition, the analysis of enzyme activity and sugar content revealed that invertase genes play key roles in the sucrose metabolism of various tissues and organs in poplar. This work lays the foundation for future functional analysis of the invertase genes in Populus and other woody perennials.

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 two parents of the well-studied F₁ hybrid “84K” (Populus alba × P. tremula var. glandulosa). Almost all chromosomes, including the telomeres and centromeres, were completely assembled for each haplotype subgenome apart from two small gaps on one chromosome. By incorporating information from these haplotype assemblies and extensive RNA-seq data, we analyzed gene expression patterns between the two 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.