Summary: Heterozygous alleles are widespread in outcrossing and clonally propagated woody plants. The variation in heterozygosity that underlies population adaptive evolution and phenotypic variation, however, remains largely unknown. Here, we describe a de novo chromosome‐level genome assembly of Populus tomentosa, an economic and ecologically important native tree in northern China. By resequencing 302 natural accessions, we determined that the South subpopulation (Pop_S) encompasses the ancestral strains of P. tomentosa, while the Northwest subpopulation (Pop_NW) and Northeast subpopulation (Pop_NE) experienced different selection pressures during population evolution, resulting in significant population differentiation and a decrease in the extent of heterozygosity. Analysis of heterozygous selective sweep regions (HSSR) suggested that selection for lower heterozygosity contributed to the local adaptation of P. tomentosa by dwindling gene expression and genetic load in the Pop_NW and Pop_NE subpopulations. Genome‐wide association studies (GWAS) revealed that 88 single nucleotide polymorphisms (SNPs) within 63 genes are associated with nine wood composition traits. Among them, the selection for the homozygous AA allele in PtoARF8 is associated with reductions in cellulose and hemicellulose contents by attenuating PtoARF8 expression, and the increase in lignin content is attributable to the selection for decreases in exon heterozygosity in PtoLOX3 during adaptive evolution of natural populations. This study provides novel insights into allelic variations in heterozygosity associated with adaptive evolution of P. tomentosa in response to the local environment and identifies a series of key genes for wood component traits, thereby facilitating genomic‐based breeding of important traits in perennial woody plants.

Soil nutrient restrictions are the main environmental conditions limiting plant growth, development, yield, and quality. Phosphorus (P), an essential macronutrient, is one of the most significant factors that vastly restrains the growth and development of plants. Although the total P is rich in soil, its bio-available concentration is still unable to meet the requirements of plants. To maintain P homeostasis, plants have developed lots of intricate responsive and acclimatory mechanisms at different levels, which contribute to administering the acquisition of inorganic phosphate (Pi), translocation, remobilization, and recycling of Pi. In recent years, significant advances have been made in the exploration of the utilization of P in annual plants, while the research progress in woody perennial plants is still vague. In the meanwhile, compared to annual plants, relevant reviews about P utilization in woody perennial plants are scarce. Therefore, based on the importance of P in the growth and development of plants, we briefly reviewed the latest advances on the genetic and molecular mechanisms of plants to uphold P homeostasis, P sensing, and signaling, ion transporting and metabolic regulation, and proposed the possible sustainable management strategies to fasten the P cycle in modern agriculture and new directions for future studies.

High-temperature stress is a threat to plant development and survival. Long noncoding RNAs (lncRNAs) participate in plant stress responses, but their functions in the complex stress response network remain unknown. Poplar contributes to terrestrial ecological stability. In this study, we identified 204 high-temperature-responsive lncRNAs in an abiotic stress-tolerant poplar (Populus simonii) species using strand-specific RNA sequencing (ssRNA-seq). Mimicking overexpressed and repressed candidate lncRNAs in poplar was used to illuminate their regulation pattern on targets using nano sheet mediation. These lncRNAs were predicted to target 185 genes, of which 100 were cis genes and 119 were trans genes. Gene Ontology enrichment analysis showed that anatomical structure morphogenesis and response to stress and signaling were significantly enriched. Among heat-responsive LncRNAs, TCONS_00202587 binds to upstream sequences via its secondary structure and interferes with target gene transcription. TCONS_00260893 enhances calcium influx in response to high-temperature treatment by interfering with a specific variant/isoform of the target gene. Heterogeneous expression of these two lncRNA targets promoted photosynthetic protection and recovery, inhibited membrane peroxidation, and suppressed DNA damage in Arabidopsis under heat stress. These results showed that lncRNAs can regulate their target genes by acting as potential RNA scaffolds or through the RNA interference pathway.

Leaf margins are complex plant morphological features that contribute to leaf shape diversity, which affects plant structure, yield, and adaptation. Although several leaf margin regulators have been identified to date, the genetic basis of their natural variation has not been fully elucidated. In this study, we profiled two distinct leaf morphology types (serrated and smooth) using the persistent homology mathematical framework (PHMF) in two poplar species (Populus tomentosa and Populus simonii, respectively). A combined genome-wide association study (GWAS) and expression quantitative trait nucleotide (eQTN) mapping were applied to create a leaf morphology control module using data from P. tomentosa and P. simonii populations. Natural variation in leaf margins was associated with YABBY11 (YAB11) transcript abundance in poplar. In P. tomentosa, PtoYAB11 carries a premature stop codon (PtoYAB11^PSC), resulting in the loss of its positive regulation of NGATHA-LIKE1 (PtoNGAL-1) and RIBULOSE BISPHOSPHATE CARBOXYLASE LARGE SUBUNIT (PtoRBCL). Overexpression of PtoYAB11^PSC promoted serrated leaf margins, enlarged leaves, enhanced photosynthesis, and increased biomass. Overexpression of PsiYAB11 in P. tomentosa promoted smooth leaf margins, higher stomatal density, and greater light damage repair ability. In poplar, YAB11-NGAL1 is sensitive to environmental conditions, acts as a positive regulator of leaf margin serration, and may also link environmental signaling to leaf morphological plasticity.

As an essential component in reducing anthropogenic CO₂ emissions to the atmosphere, tree planting is the key to keeping carbon dioxide emissions under control. In 1992, the United Nations agreed to take action at the Earth Summit to stabilize and reduce net zero global anthropogenic CO₂ emissions. Tree planting was identified as an effective method to offset CO₂ emissions. A high net photosynthetic rate (Pn) with fast-growing trees could efficiently fulfill the goal of CO₂ emission reduction. Net photosynthetic rate model can provide refernece for plant’s stability of photosynthesis productivity. Using leaf phenotype data to predict the Pn can help effectively guide tree planting policies to offset CO₂ release into the atmosphere. Tree planting has been proposed as one climate change solution. One of the most popular trees to plant are poplars. This study used a Populus simonii (P. simonii) dataset collected from 23 artificial forests in northern China. The samples represent almost the entire geographic distribution of P. simonii. The geographic locations of these P. simonii trees cover most of the major provinces of northern China. The northwestern point reaches (36°30’N, 98°09’E). The northeastern point reaches (40°91’N, 115°83’E). The southwestern point reaches (32°31’N, 108°90’E). The southeastern point reaches (34°39’N, 113°74’E). The collected data on leaf phenotypic traits are sparse, noisy, and highly correlated. The photosynthetic rate data are nonnormal and skewed. Many machine learning algorithms can produce reasonably accurate predictions despite these data issues. Influential outliers are removed to allow an accurate and precise prediction, and cluster analysis is implemented as part of a data exploratory analysis to investigate further details in the dataset. We select four regression methods, extreme gradient boosting (XGBoost), support vector machine (SVM), random forest (RF) and generalized additive model (GAM), which are suitable to use on the dataset given in this study. Cross-validation and regularization mechanisms are implemented in the XGBoost, SVM, RF, and GAM algorithms to ensure the validity of the outputs. The best-performing approach is XGBoost, which generates a net photosynthetic rate prediction that has a 0.77 correlation with the actual rates. Moreover, the root mean square error (RMSE) is 2.57, which is approximately 35 percent smaller than the standard deviation of 3.97. The other metrics, i.e., the MAE, R², and the min-max accuracy are 1.12, 0.60, and 0.93, respectively. This study demonstrates the ability of machine learning models to use noisy leaf phenotype data to predict the net photosynthetic rate with significant accuracy. Most net photosynthetic rate prediction studies are conducted on herbaceous plants. The net photosynthetic rate prediction of P. simonii, a kind of woody plant, illustrates significant guidance for plant science or environmental science regarding the predictive relationship between leaf phenotypic characteristics and the Pn for woody plants in northern China.