Blog

Jun 29, 2023

Chromosome

Communications Biology volume 6, Article number: 797 (2023) Cite this article 2208 Accesses 2 Altmetric Metrics details Urban greening provides important ecosystem services and ideal places for urban

Communications Biology volume 6, Article number: 797 (2023) Cite this article

2208 Accesses

2 Altmetric

Metrics details

Urban greening provides important ecosystem services and ideal places for urban recreation and is a serious consideration for municipal decision-makers. Among the tree species cultivated in urban green spaces, Robinia pseudoacacia stands out due to its attractive flowers, fragrances, high trunks, wide adaptability, and essential ecosystem services. However, the genomic basis and consequences of its wide-planting in urban green spaces remains unknown. Here, we report the chromosome-level genome assembly of R. pseudoacacia, revealing a genome size of 682.4 Mb and 33,187 protein-coding genes. More than 99.3% of the assembly is anchored to 11 chromosomes with an N50 of 59.9 Mb. Comparative genomic analyses among 17 species reveal that gene families related to traits favoured by urbanites, such as wood formation, biosynthesis, and drought tolerance, are notably expanded in R. pseudoacacia. Our population genomic analyses further recover 11 genes that are under recent selection. Ultimately, these genes play important roles in the biological processes related to flower development, water retention, and immunization. Altogether, our results reveal the evolutionary forces that shape R. pseudoacacia cultivated for urban greening. These findings also present a valuable foundation for the future development of agronomic traits and molecular breeding strategies for R. pseudoacacia.

Urban greening refers to the organized or semi-organized construction of green infrastructures like urban green spaces, street trees, and hedges in cities that provide the ideal environment for urban recreation and acts as an important ecosystem service to control pollution, regulate temperature, and manage stormwater1,2,3. With the acceleration of urbanization in low-income and lower-middle-income countries, urban greening is attracting more attention from municipal decision-makers and landscape planners4,5.

In China, 2.5 × 104 km2 of built-up area (BUA) increased from 2001 to 2018 and corresponds to 47.5% of the global increase and represents the fastest speed of urbanization in the world6. The demand for green infrastructure, particularly in terms of urban planting, has become increasingly important for Chinese city designers due to rapid urbanization and the desire to meet the recreational needs and landscape perceptions of urbanites3. Their achievements marked China as the greatest contributor to urban greening for land coverage in the world from 2001–20186. Recently, other studies have shown that urban environmental change can influence four evolutionary processes: mutation, genetic drift, gene flow, and adaptation due to natural selection7,8. These urban areas represent novel ecosystems where green infrastructure construction provides precious opportunities for researchers to observe how genetic and phenotypic effects can accompany the urban greening process introduced by human activity during urbanization9.

Over the last few decades, Robinia pseudoacacia (black locust) has become one of the most widely cultivated and popular woody species in urban green areas in China10,11,12. These outcrossing, fast-growing, and nitrogen-fixing legume trees belong to the Faboideae subfamily, Fabaceae family, and originated in North America. They were then introduced to sub-Mediterranean and temperate regions, including continental Europe, Australia, and East Asia (of which China alone possesses over one million ha of plantation)13,14. In 2010, the estimated area of R. pseudoacacia plantations outside their native range was about 3 million ha, and the number keeps growing15. Currently, the species has become the second most widely planted broad-leaved tree species in the world, following Eucalyptus spp16.

R. pseudoacacia was first introduced to China and planted in Nanjing and Qingdao during the late 19th century17. Compared to other plants in urban green spaces (such as Trifolium repens, Cinnamomum camphora, and Ligustrum lucidum), R. pseudoacacia is favored by Chinese urbanites because of their rich flowers, attractive fragrances, deciduous broad leaves, and high trunks18. Additionally, its high tolerance to a wide range of soil conditions19, and high adaptability to harsh environments and low fertility enable it to thrive in urban areas across vast climatic regions while providing essential ecosystem services11,12,20,21,22. For example, in the ecologically fragile Loess Plateau, human-planted R. pseudoacacia covers >70,000 ha23,24, and has been shown to notably alter vegetation structures, soil properties, and microbial biomass and activities19,25. In Shenyang, one of the largest cities in northeastern China, R. pseudoacacia has also played an important role in improving air quality by absorbing ambient fine particulate matter with a diameter of ≤2.5 µm (PM2.5), which acts as a primary air pollutant that causes human disease12.

While the environmental effects and morphology of R. pseudoacacia in urban ecosystems have been well-researched in the past, we currently do not know much about the genomic basis and consequences of its wide distribution in urban green spaces. Notably, it is essential that we identify an accurate, complete, and contiguous genome assembly for this species, which to understand its valuable genetic variation, and apply cutting-edge molecular biology technologies. These limitations further obstruct in-depth molecular breeding of R. pseudoacacia and the proper introduction of R. pseudoacacia during green infrastructure construction in cities.

Here, we created a chromosome-level genome assembly of R. pseudoacacia that was deciphered using integrated Illumina short-read sequencing, Nanopore long-read sequencing, and chromosomal conformational capture (Hi-C) technologies. We characterized its genome in detail, including genomic structure, gene annotation, and repeat sequences. We also conducted a whole-genome re-sequencing analysis of 59 R. pseudoacacia individuals across 14 Chinese cities. Our following comprehensive population genomic survey revealed genetic relationships among all the individuals, and inferred their demographic histories. Additionally, we identified selection signatures that may be involved in urban planting. Together, our study provides a valuable resource to facilitate comparative genomics, adaptive evolution studies, and genomic-assisted breeding for R. pseudoacacia, and improves our general understanding of urban planting.

To generate a chromosome-level genome for R. pseudoacacia (Fig. 1a), the genome of a single R. pseudoacacia individual sampled from Lanzhou, China (36°2′57″N, 103°51′34″E) was sequenced using a hybrid strategy that combined Oxford Nanopore long-read sequencing, Illumina paired-end sequencing, and Hi-C technologies. Specifically, we obtained 75.6 Gb of long-read data with a read N50 of 31.7 kb, which confirms the high quality of our Oxford Nanopore sequencing libraries (Supplementary Table 1). Based on this data, we first assembled a contig-level genome assembly of 682.4 Mb, and then polished it using high-accurate Illumina reads. This assembly contained only 55 contigs with a contig N50 of 32.1 Mb, which indicates its high continuity (Table 1 and Supplementary Table 2). Our K-mer analysis based on the 111.34 Gb of Illumina paired-end data estimated that the genome was approximately 693.1 Mb with a heterozygosity rate of 1.13% (Supplementary Fig. 1 and Supplementary Table 3) that is consistent with our contig-level assembly.

a The sequenced individual of R. pseudoacacia from Lanzhou University, Gansu Province, China (36°2′57″N, 103°51′34″E). The flowers of R. pseudoacacia were demonstrated in the figure. b Genome features from the R. pseudoacacia assembly. From outer to inner: (1) genome chromosomes, (2) gene density, (3) repeat density, (4) GC (guanine-cytosine) content, and (5) synteny information.

To further improve the genome assembly, we established a Hi-C library and obtained 78.27 Gb of Hi-C reads. The assembly of R. pseudoacacia was successfully anchored onto 11 chromosomes with an improved N50 of 59.9 Mb that covered 99.3% of the raw assembly (Fig. 1b, Supplementary Fig. 2, and Supplementary Table 4), which suggests that most regions of the R. pseudoacacia genome were successfully assembled at the chromosome level. Our final assembly showed better continuity than most of the other genomes of the Fabaceae species (Supplementary Table 5).

The high quality of this genome was confirmed by multiple methods. First, we performed BUSCO analyses to determine the completeness of this genome assembly, which reported a complete score of 98.20% and indicates high completeness (Supplementary Table 6) and low redundancy of haplotype sequences (Supplementary Table 7). We also aligned Illumina paired-end whole-genome re-sequencing data obtained from other individuals and transcriptome data to this assembly. More than 97.9% of the re-sequencing data and more than 96.5% of the transcriptome data were successfully mapped to the assembly, which suggests high accuracy for this assembly.

We annotated repetitive sequences in R. pseudoacacia genome by combining both ab initio and homology-based methods. Over 405.9 Mb of sequences were identified as repetitive elements. Together, they constituted 59.47% of the genome assembly that was predominately LTRs (Supplementary Table 8). These results are consistent with previous observations in closely-related Legume species Lotus japonicus26, in which LTRs are also the most abundant type of repeat elements. Intriguingly, although the genome of the R. pseudoacacia had a larger size than the closely-related Lupinus albus and Cicer arietinum, the proportion (59.47%) of repetitive elements was still similar to what was measured in these two species. Specifically, Lupinus albus has a 451 Mb genome that contains 60% repetitive elements27, and C. arietinum has a 545 Mb genome that contains 60% repetitive elements28. The primary reason for this difference in genome size can be attributed to the influence of long terminal repeat retrotransposons (LTR-RTs) on genome size and evolution29 (Supplementary Fig. 3). LTR-RTs are responsible for over 75% of the genome in some plant species and have been identified as a major driving force behind genome expansions30. These results suggest that the large size of the R. pseudoacacia genome may be partially driven by the explosion of repetitive elements (Supplementary Fig. 3).

Using a customized gene prediction pipeline that incorporated ab initio, homology, and transcriptome-based approaches, we predicted 33,187 protein-coding genes in the R. pseudoacacia genome (Supplementary Table 9). The average length of the genes was 4492 bp with an average of 5 exons (Supplementary Table 9). We matched these predicted genes to functional annotations deposited in five databases, including TrEMBL, SWISS-PROT, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and InterPro. More than 99% of these genes were functionally annotated (Supplementary Table 10). Our official gene set for R. pseudoacacia genome covered more than 97% of BUSCO core genes (Supplementary Table 11), which suggests that this gene set was robust, and that most of the genes in this genome were functionally conserved. We further investigated the syntenic genes based on this gene set and found that there were 2357 syntenic blocks that ranged in size from 5 to 393 gene pairs in the R. pseudoacacia genome (Fig. 1b), which indicates the occurrence of whole-genome duplication (WGD) events.

To reveal the phylogenetics of R. pseudoacacia and its evolutionary trajectories, we compared its genome with those of 14 plant species of the family Fabaceae and used Arabidopsis thaliana as an outgroup. Based on 273 strictly single-copy orthologous genes from these 16 plant genomes, we established a genome-scale phylogenetic tree using maximum likelihood (ML) methods (Fig. 2a). Our phylogenetic tree showed that R. pseudoacacia was located close to the clade consisting of Medicago truncatula, Trifolium pratense, C. arietinum, and L. japonicus. These results are in accordance with other previous studies31,32. By estimating the divergence time for each node based on the 4DTv sites from the single-copy orthologous genes, we found that the divergence time between the R. pseudoacacia and other four species (M. truncatula, T. pratense, C. arietinum, and L. japonicus) was about 41.31 million years ago (Ma).

a Phylogenetic tree for R. pseudoacacia and the other 14 species from Fabaceae, with A. thaliana as the outgroup. Bootstrap values for the nodes were 100. The estimated divergence time for each node is indicated with bars as the 95% confidence intervals (CI). The reported WGD, WGT, and the used fossil constraint are also labeled. Genome statistics for each species are shown to the right. b Ks distributions reveal WGD events during the evolution of R. pseudoacacia, G. max, and M. truncatula, respectively (Supplementary Data 3). c Collinear relationship between R. pseudoacacia, G. max, and M. truncatula is represented by a ratio of 4:2:2 (G. max : R. pseudoacacia : M. truncatula). Interspecific syntenic blocks that span more than 14,000 genes are indicated with lines, and some of the 4:2:2 blocks are highlighted in red.

To provide insights into the evolution history of the R. pseudoacacia genome, we compared its synonymous substitution rates (Ks) with genomes of G. max and M. truncatula to identify the homologous gene pairs and to detect syntenic relationships between these species. The rate of Ks curves of collinear gene pairs suggested that the genomes of R. pseudoacacia underwent two rounds of whole-genome duplication (WGD). The older event, known as the γ event, is shared by all core eudicot lineages, while the more recent event occurred ~59 million years ago and is shared by all species in the Faboideae subfamily33 (Fig. 2b). Notably, unlike G. max34, R. pseudoacacia did not experience a species-specific WGD event (Fig. 2b). Furthermore, we detected a total of 88,836 gene pairs among these three species, and classified them into 2304 syntenic blocks, covering 662 Mb (97.73%) of the R. pseudoacacia assembly. Consistent with the Ks analysis, we also found a 2:4 syntenic relationship between R. pseudoacacia and G. max, and a 2:2 syntenic relationship between R. pseudoacacia and M. truncatula (Fig. 2c and Supplementary Fig. 4). A selected gene family was comprised of the same ratio of gene copies from different species analysed above, which also confirms these two WGD events (Supplementary Fig. 5). These results provide additional evidence that there was no R. pseudoacacia-specific WGD event.

During the long-term evolutionary history, the expansion of gene families is likely to lead to their functional diversifications (e.g., neofunctionalization and sub-functionalization), and are further expected to contribute to dynamic adaptations that increase their survival in plants35,36. To understand the main reasons for the large gene set in R. pseudoacacia, we performed gene family analyses of the 16 plant species to reveal their contribution to adaptive divergence. A total of 31,508 R. pseudoacacia genes (94.9%) were clustered into 15,789 gene families where 612 gene families were specific to R. pseudoacacia. Our results showed 2256 gene families were expanded in the R. pseudoacacia genome and ranked third among all the analysed species (Fig. 2a).

From these comparisons, duplicated genes that have experienced functional diversifications are likely to reflect novel traits in a species37. Our GO enrichment analysis found that the expanded gene families in R. pseudoacacia were significantly overrepresented in three major functional categories, including wood formation, biosynthesis, and drought tolerance (Q < 0.05, count >35; Fig. 3a and Supplementary Table 12), which likely contributed to the species-specific characteristics leading to their wide cultivation in urban greening. This result is in accordance with previous physiological observations in R. pseudoacacia and highlights the traits that rendered it an advantageous choice for urban planners. Specifically, R. pseudoacacia is visually attractive and economically competitive in urban planting because of its main mechanism of fast growth that then follows with nutrient accumulation and early death that then opens up a growth spot38,39.

a A number of 11 GO categories related to wood formation, biosynthesis, and drought tolerance are significantly enriched in the expanded gene families of R. pseudoacacia (all P < 0.05). b Gene numbers for the identified laccase (LAC) gene family between each species. The two-tailed one-sample Student’s t-test was performed to examine the statistical significances, which are denoted with asterisks (*** P < 0.001). c Maximum likelihood (ML) tree based on the protein sequences of the LAC genes in R. pseudoacacia and A. thaliana. The analysis is performed under the PROTGAMMAGTR model with 100 bootstraps.

In this study, we found that the laccase (LAC) gene family was significantly (P < 0.05) expanded in the R. pseudoacacia genome. The laccase (LAC) gene family encodes enzymes that catalyze oxidation-reduction reactions and is wildly reported to be involved in lignin biosynthesis, which is essential for wood formation and improves the water transport capacity of plants40. Here, we found that there were 31 genes that belong to the LAC gene family in the R. pseudoacacia genome from manually inspecting these genes in 15 species, verifying their conserved Cu-oxidase domains (Cu-oxidase, Cu-oxidase_2, Cu-oxidase_3) and gene structures (Supplementary Data 1). We also found that R. pseudoacacia contained a significantly larger (P = 7.11 × 10−7) number (also the largest number) of LAC genes than all other analysed species in Leguminosae, except for G. max and G. soja which experienced an additional round of WGD (Fig. 3b). To reveal the evolutionary trajectories of these LAC genes, we further constructed the phylogenetic tree of the LAC gene family between R. pseudoacacia and A. thaliana (Fig. 3c and Supplementary Table 13). The phylogenetic tree clustered 48 LACs (31 from R. pseudoacacia and 17 from A. thaliana, respectively) into seven groups. Each group comprised 8, 5, 9, 5, 8, 4, and 9 members. We also observed a noticeable expansion of LACs for R. pseudoacacia in most groups, and the expansion of LACs could highlight their probable contribution to the stronger capacity of wood formation in R. pseudoacacia41,42.

R. pseudoacacia is widely cultivated in Chinese cities, especially in central China. To explore the species-specific adaptative traits of R. pseudoacacia for urban planting, we sampled 59 R. pseudoacacia individuals from 14 counties across five provinces in China (Fig. 4a and Supplementary Table 14) with a median sampling size of five individuals for each county. The whole genomes of these 59 individuals were re-sequenced to an average depth of 14×. Approximately 98% of these re-sequencing data have been aligned to the reference genome and covers more than 90% of the genome assembly, which reflected the high quality of this re-sequencing dataset (Supplementary Table 15). We detected a total of 17,986,674 high-quality single nucleotide polymorphisms (SNPs) from these 59 individuals.

a Geographic distribution of the R. pseudoacacia samples sequenced in this study. The population IDs were labeled in the figure. b Principal component analysis (PCA). The numbers in brackets represent the fraction of variance explained by each component. The outlier individuals were labeled in black text. c Maximum likelihood (ML) tree and ADMIXTURE analysis. The phylogenetic analysis was performed based on the whole-genome SNPs of 59 R. pseudoacacia individuals, with individual IDs marked alongside the corresponding sections of the ADMIXTURE results. For each ID, the prefix before “−” corresponds to the sampling site number displayed in panel a, while the suffix after it indicates the individual number. Three L. japonicus individuals were used as the outgroup. d Demographic history inferred by the PSMC model. e Observed genome-wide sequence diversity (π) for R. pseudoacacia and other species. The species names of cultivars (including landraces) and wild lineages were labeled in blue and black colors, respectively.

To identify the genetic structure of these samples, we performed a principal component analysis (PCA) based on their pairwise genetic distances that was calculated using autosomal SNP data. Critically, the PCs from 1–10 only explained a total of 14.15% of the genetic variance and could not separate individuals collected from the different locations (Fig. 4b and Supplementary Fig. 6). The two outlier groups were collected from two distinct counties, and were comprised of three individuals from the P5 population and two individuals from the P13 population, respectively (Supplementary Table 15). Surprisingly, they were not clustered in the same group with other individuals collected from the same or adjacent location.

To further examine this counterintuitive result, we evaluated the genetic relationships between these samples by establishing an ML phylogenetic tree. No elevated relatedness was found among the individuals sampled from the same city. In contrast, individuals collected from distinct cities were clustered together (Fig. 4c). Our phylogenetic tree also suggested that the clustering from the P5 group and the P13 group may have resulted from these samples coming from the same lineage, (i.e., they are children from the same parents and came from a similar plant nursery). Further justifications were also provided from an Admixture analysis (Fig. 4c). Our results showed that K = 1 was the best modeling choice, and that the K = 14 modeling choice that divided R. pseudoacacia samples into local sub-populations based on sampling location was firmly rejected (Supplementary Figs. 7, 8). Our partial Mantel test, designed to evaluate the influence of isolation by distance (IBD) and isolation by environment (IBE) on genetic structure, revealed no significant correlation between geography or environment and genetic differentiation (Supplementary Fig. 9). These results confirmed that these R. pseudoacacia individuals were introduced through deliberate cultivation.

After these comparisons, we performed Pairwise Sequentially Markovian Coalescent (PSMC) analyses and found that the individuals collected from the different locations shared identical ancient demographic histories (Fig. 4d). Together, these results likely indicate that there has not been enough time to diverge from each other due to the fact that all R. pseudoacacia individuals in China were introduced by humans after the 1870s43. The estimated effective population size (Ne) of the R. pseudoacacia population reached its highest around 1 million years ago, followed by a sharp decline that coincides with the Mindel glacial stage.

Currently, most cultivated plants suffer from recent bottleneck effects and inbreeding introduced by human activities, such as domestication and intercontinental introductions, which might always reduce the genetic diversity44,45. Compared to populations with high genetic diversity, populations characterized by low genetic diversity are more susceptible to the detrimental effects of genetic drift and the accumulation of deleterious variations46,47. Therefore, maintaining sufficient genetic diversity is essential for species to adapt to changing environments. Here, we found that R. pseudoacacia displayed an observed diversity of π = 8.94 × 10−3, which was noticeably higher than other cultivated species and even higher than some wild plant populations (Fig. 4e and Supplementary Table 16). These highlight the abundance of R. pseudoacacia’s gene pool and their high potential to survive in changing environments.

To reveal the genetic basis of the traits favored by urbanites at the population level, we performed a comprehensive analysis of the whole genomes of 59 cultivated R. pseudoacacia trees planted in diverse environments and examined their genomic signatures for recent selective sweeps. Recent selective sweeps are usually reflected by long and unusually frequent haplotypes and high haplotype homozygosity, which can be captured by iHS and H12 statistics, respectively48. Here, we analyzed the putative selective-sweep regions by detecting genetic regions that showed both iHS and H12 significance. These putative selective-sweep regions harbored a total of 615 positively selected genes (PSGs), indicating their potential role involved in the adaptation of R. pseudoacacia to urban environments (Fig. 5a, b and Supplementary Data 2). To further understand the evolutionary forces that accompany urban planting, Gene Ontology (GO) enrichment analyses were used to summarize the biological function of the gene set located in these candidate regions under recent selection. These analyses revealed several functional categories with enriched signals for selection that included cell wall thickening (GO:0052386), sporopollenin biosynthetic processing (GO:0080110), root meristem growth regulation (GO:0010082), and bacterium detection (GO:0016045) (Supplementary Table 17). The candidate genes showed several key characteristics favored in urban planting.

a, b The candidate PSGs identified using H1 statistics and integrated haplotype scores (iHSs), respectively. Only the genes with significant values are shown. Eleven candidate PSGs are labeled with red circles and the gene name. c–e Z-transformed H1 values and absolute values of the normalized iHS for MED18 (c), WRKY4 (d), and UBP13 (e), respectively. a–e The threshold values (H1 value ≥3 and an absolute value of normalized iHS ≥2) were labeled in the figures.

In urban plant community assemblies, the characteristics of tree flowers are one of the most important traits because they improve the aesthetics of urban streets and green spaces49,50. In this study, two genes for flower development were categorized to be under selection and were consistent with the unique traits observed in R. pseudoacacia, which suggests that these genes may be involved in the breeding processes that led to their extensive urban planting in China. For example, TPS03 encodes a catalysis enzyme that plays an important role in monoterpene synthase that may contribute to the appealing fragrance of tree flowers acclaimed by urbanites. Additionally, MED18 (Fig. 5c) encodes for the subunit of the head submodule for the plant mediator complex and has been previously shown to regulate flowering time and alter floral organ number51.

Compared to non-urban areas, municipal green spaces usually have a more shallow fertile soil layer and a reduced water retention capacity52,53,54, which bring distinct challenges to trees planted in urban areas. Here, we found that the SOS4 gene that is essential for root hair development, showed notable signatures for recent selection, as well as some genes related to water utilization, such as WRKY4, SCAB1, and RD22 (Fig. 5d). Specifically, WRKY4 and MYB52 play important roles in drought stress adaptation55,56. SCAB1 is reported to encode an actin-binding protein that controls stomatal movement, which is consistent with previous observations that R. pseudoacacia can adapt to drought conditions by reducing transpiration57. Additionally, RD22 is a drought-responsive gene that have been found to be differentially expressed during water deficit58,59. The evolution of these genes at the population level may be beneficial to increase the fitness of trees planted in green spaces, especially given that R. pseudoacacia is usually shallow-rooted.

Along with these genes, three other genes related to the immune system were also found to show footprints of recent selection. For example, UBP13 (Fig. 5e) encodes a ubiquitin-specific protease that is responsible for initial pathogen perception60, and UBC36 encodes an E2 ubiquitin-conjugating enzyme involved in dampening immune signaling61. The wall-associated receptor-like kinase gene, WAK2, also plays an important role in disease resistance62. Together, these genes influence the immune system and may reflect the fact that plants in cities face increased susceptibility to disease caused by different pathogens and pollutants compared to the ones living in the wild63. Overall, our findings highlight the genetic characteristics favored in urban planting and shed light on the evolutionary forces that accompany the process of urban greening.

In this study, we took advantage of cutting-edge genomic methods to reveal the genomic basis and consequences of the wide distribution of R. pseudoacacia in urban green spaces in China, and provide novel insights into urban greening. We first generated an annotated chromosome-level genome assembly by combining Illumina short-read sequencing, nanopore long-read sequencing, and chromosomal conformational capture (Hi-C) technologies to create the first reference genome for R. pseudoacacia (Fig. 1). Our following comparative genomic analyses showed that gene families related to traits favored by urbanites were noticeably expanded, and include wood formation, biosynthesis, and drought tolerance (Figs. 2, 3). We additionally surveyed 14 cities, collected 59 R. pseudoacacia individuals, and sequenced the whole genomes of from these samples to further reveal how genetic and phenotypic effects accompany the urban planting process introduced by urbanization (Fig. 4a).

Our comprehensive genetic structure and demographic analyses strongly indicate that the presence of R. pseudoacacia in urban green spaces is a result of deliberate cultivation rather than natural dispersal (Fig. 4b, c and Supplementary Fig. 9). Additionally, R. pseudoacacia planted in cities showed a rich genetic diversity of π = 8.94 × 10−3 (Fig. 4e) that is distinct from most cultivated plants, which suffering from recent bottleneck effects and inbreeding. Together, these effects highlight their potential to withstand environmental disruptions, which is favored by city planners. In addition to stress-resistance, esthetics is another major consideration for urban residents. Such characters have also been recovered in our selective-sweep analyses. We find genes play important roles in the biological process related to flower development, water retention, and immunization that showed very recent selection in their genomic signatures, which suggests that the massive urban planting process accompanies substantial genetic effects in plant genomes (Fig. 5).

Due to our extensive long-read sequencing, we were able to uncover the full genomic content of R. pseudoacacia that allows for the correct identification of many unknown structural variations and repetitive elements that will be a valuable resource for future genetic diversity studies in plant taxa. Our resources and results reported provide novel insights into the construction of urban green infrastructures at comparative and population genomic levels, as well as valuable foundations for the agronomic understanding and molecular breeding of R. pseudoacacia in the future.

Still, our current study only focused on SNPs with a relatively modest sample size. Although the current sampling is enough to explore the species-specific adaptative traits of R. pseudoacacia, further studies that involve a wider range of sampling that represent different environmental stressors combined with transcriptomic and methylation data, as well as structural variation data, are needed to enhance our knowledge on urban greening that facilitate its improvement. Additionally, R. pseudoacacia could use asexual proliferation. During the clonal evolution process, somatic mutations are a major source of genetic diversification64. Somatic mutations in citrus led to a broad spectrum of phenotypes, including changes in fruit shape, color, acidity, maturation season, developmental changes related to sterility, flowering time, and tree architecture65. Therefore, the somatic mutation rate and pattern should be considered in future studies. Ultimately, the comprehensive evaluation of data obtained from genomic, physiological, and ecological studies will become increasingly important in applied contexts that target the planting of trees in urban green spaces.

We collected one R. pseudoacacia individual (Fig. 1a) at Lanzhou University, Gansu Province, China (36°2′57″N, 103°51′34″E). One Oxford Nanopore Technologies (ONT) library, one Illumina paired-end sequencing library, one Hi-C library, and three RNA-sequencing libraries were prepared based on this individual. Specifically, we extracted high molecular weight DNA from the leaves of this individual, established an Oxford Nanopore Technologies (ONT) library, and sequenced it on the ONT PromethION platform according to the manufacturer’s instructions. We then supplemented these long reads with additional high-accuracy short reads from one Illumina paired-end sequencing library. We extracted DNA from leaves of the same individual using the CTAB method66,67, after which paired-end sequencing libraries were established, PCR amplified, and sequenced on an Illumina NovaSeq 6000 platform following the workflow recommended by Illumina. To obtain chromosome contact information and achieve chromosome-scale contiguity, we extracted DNA from the same individual, and constructed Hi-C libraries following the standard protocol described previously68, in which a 4-cutter restriction enzyme, DpnII, was used for digestion. We amplified these Hi-C sequencing libraries and sequenced them on the Illumina NovaSeq 6000 platform. To aid the gene prediction and annotation, flowers, stems, and leaves from the same individual were used for RNA sequencing. In brief, we extracted total RNA from these three tissues using TRIzol and RNA purification kits, and prepared three Illumina mRNA TruSeq libraries, respectively, following the manufacturer’s guides. RNA sequencing was performed on the Illumina NovaSeq 6000 platform.

After performing base-calling and read filtering, we corrected the per-base accuracy of the PromethION long reads (~110×) and assembled them into contigs using NextDenovo v2.3 (https://github.com/Nextomics/NextDenovo) (-task all -parallel_jobs 20 -read_cutoff 1k -genome_size 680 M). To further polish this contig-level assembly, ~111 Gb (~162×) of high-accuracy Illumina paired-end reads were generated. To remove allelic haplotigs, we utilized Purge Haplotigs v1.1.169, resulting in the final contig-level assembly. Based on these high-accuracy short reads, we fixed base errors in the contig-level assembly using NextPolish v1.2.270. These data were also used to perform k-mer analyses and estimate the genome size of R. pseudoacacia71. We then evaluated, filtered, and mapped the paired-end Hi-C reads (~114×) to this contig-level assembly using HiCUP v0.8.072. The resultant BAM file was then processed with ALLHiC v0.8.1273, which built a chromosomal-scale assembly with the contig-level assembly and the Hi-C data using the innovative “prune”, “partition”, “rescue”, and “optimize” steps.

We performed repeat and gene predictions following the customized pipeline previously described in Pascoal et al. with several modifications74. Briefly, we first constructed a de novo repeat library for R. pseudoacacia using RepeatModeler v2.075 with RECON v1.0876 and RepeatScout v1.0.677. For repetitive element identification, we used BLAST+ v2.2.3178 and RepeatMasker v4.1.079 to search the R. pseudoacacia genome against our de novo repeat library and the Repbase database v2380. LTR_retriever v2.8.781, which integrates LTRharvest82, and LTR Finder v1.0783 was then implemented to predict long terminal repeat retrotransposons (LTR elements). After, the repeat identification results from the different software packages were integrated and redundancy was eliminated to produce the final repeat annotation. We calculated the genetic distance (K) between the 5′ LTR and 3′ LTR sequences using DnaDiSt, a program within PhyliP v3.696 (Felsenstein, 2004). To estimate the insert time (T) of each LTR, we used the formula T = K/2r, where r represents the nucleotide substitution rate estimated by BASEML in the PAML package v4.9j84.

After repeat-masking the R. pseudoacacia genome, transcriptome-based, homology-based and ab initio predictions were performed. For transcriptome-based prediction, we used Trinity v2.6.685 (-seqType fq -max_memory 50 -CPU 20) to obtain a de novo R. pseudoacacia transcriptome assembly based on our RNA-Seq data. We then processed this transcriptome assembly using Program to Assemble Spliced Alignments (PASA v2.3.3)86. By mapping it to the reference genome assembly, PASA predicted open reading frames (ORFs) and gene structures. We also aligned these trimmed RNA-Seq reads to the R. pseudoacacia genome assembly using HISAT2 v2.1.087. After, an intron hint file was generated using the bam2hints function in AUGUSTUS v3.3.388. We used these results to train AUGUSTUS to perform ab initio gene prediction. For homology-based prediction, we aligned protein sequences of seven plant species from Phytozome v1389 (https://phytozome-next.jgi.doe.gov) (A. thaliana, C. arietinum, Glycine soja, Lupinus albus, Populus trichocarpa, T. pratense, and Vigna unguiculata) to the repeat-mask the R. pseudoacacia genome using TBLASTN (E < 10−5). The resultant candidate protein-coding regions were then refined and further processed by GeneWise v2.4.190 to generate a homology-based gene set with accurate splice junctions. All gene sets predicted by ab initio, homology, and transcriptome-based methods were passed into EVidenceModeler v1.1.184 to produce a consensus gene set. We further upgraded this consensus gene set by predicting untranslated regions (UTRs) and alternatively spliced isoforms based on our transcriptome data, which was implemented in PASA v2.3.3. We used BUSCO v491 with embryophyta_odb10 database92 to evaluate the completeness of the genome assemblies (-l embryophyta_odb10 -m genome -c 10 -e 1e-03) and the official gene set (-l embryophyta_odb10 -m proteins -c 10 -e 1e-03), respectively. Circos v0.6993 then was used to visualize these genomic metrics in a circular layout.

Functional annotations from this consensus official gene set were obtained based on Swiss-Prot (version 2020_04)94, TrEMBL (version 2020_04)94, NCBI non-redundant protein (NR, release 20200502)95, and InterPro v84 databases96. Specifically, we ran BLAST+ with a cut-off E value of 1E-05 and a maximum target sequence number of 20 to obtain the best-hits (-evalue 1e-5 -num_threads 30 -max_target_seqs 20), and assign descriptors of these best-hits to the predicted transcripts, respectively. InterProScan v.5.2897 was used to retrieve functional domains and Gene Ontology (GO) annotations based on the InterPro database. Additionally, we used Blast2GO98 to further assign GO terms to the genes that were not annotated by InterProScan. For the Kyoto Encyclopedia of Genes and Genomes (KEGG) annotation99, we aligned the protein sequences of the R. pseudoacacia gene set against the KEGG (family_eukaryotes) database, assigned the KEGG Orthology (KO) terms, and reconstructed pathways by submitting these sequences to the KEGG Automatic Annotation Server (KAAS)100. We then implemented clusterProfiler package v4.1.4101 to perform GO enrichment tests.

We identified orthologous genes among 16 plant species by performing an ortholog clustering analysis implemented in OrthoFinder v2.3.8102 (-S blast -M msa -t 50 -T fasttree -A mafft). The general phylogeny was then resolved by performing RAxML v8.2.12103 analysis, which constructed a whole-genome maximum likelihood (ML) phylogenetic tree for these species based on their concatenated four-fold degenerate sites (4DTv) under the GTRGAMMA model. The outgroup was set to A. thaliana, and the analysis was performed with 100 bootstraps for robustness. We then estimated the divergence time of each node in this phylogenetic tree by running the MCMCtree program in the PAML package v4.9j84 with two fossil constraints (Arachis duranensis and M. truncatula [49–58 million years ago (Ma)], as well as Glycine max & A. thaliana [98–117 Ma]) acquired from TimeTree (http://www.timetree.org/). Following these general phylogeny analyses, we passed the results obtained from the OrthoFinder pipeline to CAFE v4.2104 to test the patterns in gene family evolution (expansion/contraction).

To further explore the molecular mechanism of wood formation in R. pseudoacacia, we downloaded the sequences of the laccase (LAC) gene family for A. thaliana (Supplementary Table 11) from the TAIR database (https://www.arabidopsis.org/) and identified the corresponding LACs for the other 15 species. We first used BLASTP v2.2.29+ to search for the candidate orthologous to LACs against the A. thaliana LACs. HMMER v3.2.1 (http://hmmer.org/)105 was then employed to scan the domain information for each candidate gene. Only the genes that have three conserved Cu-oxidase domains (Cu-oxidase, Cu-oxidase_2, and Cu-oxidase_3) were retained. We also detected and retained the candidate genes with the four copper binding regions (HxH, HxH, HxxHxH, and HCHxxxH). A phylogenetic analysis was then carried out by RAxML to examine if the LAC candidate genes were clustered together.

Inter- and intragenomic homologous genes were identified using ColinearScan106. We first used BLASTP v2.2.29+ (E value <1 × 10−5) to search for the putative paralogous and orthologous gene pairs within or between genomes with a maximum of 20 alignments for each query sequence. The maximal collinearity gap length between genes was set to 50 for the ColinearScan. The synonymous substitution (Ks) values of the identified colinear gene pairs were then calculated using the YN00 program in the PAML package using the Nei–Gojobori method107. The median Ks value for the colinear gene pairs from each colinear block was shown in the syntenic dot plots to help distinguish event-related syntenic regions. Gaussian kernel density fitting was then performed to estimate the probability density distribution of inter- and intraspecies Ks values with the bins number set to 200. The above synteny analyses were performed using the wgdi toolkit (https://github.com/SunPengChuan/wgdi)108.

To explore the species-specific evolutionary traits of R. pseudoacacia, we surveyed 14 counties across five provinces which covered arid, humid, plateau, and plain areas, to collect population genomic data. In total, 59 individuals were sampled. DNA was extracted from the leaves of these individuals using the CTAB method. We used a Nanodrop, 1% agarose gel electrophoresis, and Qubit to check the quality and purity of the extracted DNA. Illumina paired-end libraries were then prepared for each individual, following the manufacturers’ laboratory protocols, and sequenced on the Illumina NovaSeq 6000 platform. We also downloaded three L. japonicus samples as the outgroup (NCBI accession number: SRR11495342, SRR11495343, and SRR447704). For raw reads, Scythe (https://github.com/vsbuffalo/scythe) and Sickle (https://github.com/najoshi/sickle) were used to remove the adapter sequences and filter out the low-quality sequences (quality score <20), respectively. We then mapped these clean reads to the chromosome-level R. pseudoacacia genome using BWA v0.7.17109.

We genotyped the 62 individuals (59 individuals of R. pseudoacacia and three individuals of L. japonicus) following the GATK best practices110. Before the samples were genotypes, Samtools v1.10111 and Picard v2.23.6 (http://broadinstitute.github.io/picard/) were used to sort and format the resulting files from BWA, and to remove PCR duplicates. For the GATK v3.8 best practice, the HaplotypeCaller function was used to call SNPs and InDels via local re-assembly of haplotypes. The genetic variants detected from the same population were then merged by the CombineGVCFs function to expedite downstream processes. We used GenotypeGVCFs to perform the accurate re-genotyping based on these merged gVCF files. A set of hard filtering criteria was decided based on density plots of the raw SNP dataset (QD <2.0| |MQ <40.0 || ReadPosRankSum <−8.0||FS >60.0 || HaplotypeScore >13.0 || MQRankSum <−12.5||SOR >3). In order to generate a robust set of SNPs, we additionally removed low-quality SNPs with abnormally low depth (<2 × or 1/5 average depth of the corresponding sample), extremely high depth (>50× or fivefold average depth of the corresponding sample), and low-quality scores (<30). Furthermore, we discarded SNPs that were labeled as indels and located within 5 bp of an InDel. If the genotype information was missing from most of the individuals (>13 R. pseudoacacia, >1 L. japonicus), the site was treated as unknown.

The generated VCF file was then used by Vcftools v0.1.16112 to calculate genome-wide genetic diversities. An ML phylogenetic tree was established based on whole-genome SNPs by RAxML v8.2.12103, and a reduced SNP set without SNPs in high linkage disequilibrium (LD) between each sample, was generated by PLINK v1.07113, which was based on their pairwise LD information. We then assessed the population structure of the sampled individuals by passing this reduced SNP set to ADMIXTURE v1.3.0114 with plausible numbers for the ancestral populations (K) from 1 to 14. The best module was chosen based on cross-validation error rates. To further confirm the population structure, the smartpca program in EIGENSOFT v7.2.1115 and ggplot2 package in R were used to perform principal component analysis (PCA) to visualize their results, respectively. We then performed a series of Pairwise Sequentially Markovian Coalescent analyses using PSMC v0.6.5-r67116 to reconstruct the historical trajectories of changes in effective population sizes of R. pseudoacacia. To convert the scaling time and population size to actual values, we applied a generation time of 7 years and a mutation rate of 4 × 10−9 per nucleotide per year. These conversion factors allowed us to estimate the time and size in real-world terms.

We evaluated the effects of isolation by distance (IBD) and isolation by environment (IBE) on genetic variation by Mantel tests. First, we calculated pairwise geographical distances between sampling sites using the geographical coordinates with the R package Geosphere (https://github.com/rspatial/geosphere). Secondly, we calculated the environmental distance (Euclidean distance) among the 16 populations based on the environmental variables described in the "Environmental variables" section using the dist function in R software. To measure genetic distance, we utilized Arlequin v3.5117. Finally, we conducted partial Mantel tests with 9,999 permutations using the Vegan v.2.5-7 package118 in R to test the relationship between geographical/environmental distances and genetic distances.

We used a combinatorial approach to ensure robust inferences for selection. Briefly, we used Beagle v5.4119,120 to reconstruct haplotypes from unphased SNP genotype data and impute missing data. The SelectionHapStats121 program (with the parameters: -w 50 -j 20) was then used to examine frequencies from multiple haplotypes between each other and to identify both hard and soft selective sweeps and distinguish between these two types of selection sweeps based on H12 and H2/H1 statistics. Additionally, we applied Selscan v2.0.0122 to calculate the integrated haplotype score (iHS), which is designed to detect unusual haplotypes around a particular SNP compared to the whole genotype. Subsequently, the metrics detected by these methods were synthetically evaluated to reveal robust selective sweeps. The genes with a significant H1 value (Z ≥ 3) and a significant iHS value (absolute value of normalized iHS ≥2) were then identified as the positively selected genes.

Statistical analyses (the two-tailed one-sample Student’s t-test and partial Mantel test) were performed using R (v4.1). The P values associated with gene family sizes were computed by CAFE104. The Q values for functional enrichment tests were calculated using clusterProfiler101. In population genomic analyses, we sequenced three to five individuals for each population, except for two populations, each of which only had one sample in the wild.

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Numerical sources can be found in Supplementary Data 3 for Fig. 2b and Supplementary Table 16 for Fig. 4e, respectively. All sequencing reads that support the findings of this study have been deposited in the Genome Sequence Archive (https://ngdc.cncb.ac.cn/gsa/) under project number PRJCA011483. The genome assembly file, all the annotation files, and source data for phylogenetic and population analyses are available at Figshare (doi.org/10.6084/m9.figshare.23301668). All other data are available from the corresponding authors on reasonable request.

The custom scripts and analysis pipelines have been made accessible to the public via GitHub repositories (https://github.com/myBioFun/ORTHO2TREE).

Arrington, A. Urban foraging of five non-native plants in NYC: Balancing ecosystem services and invasive species management. Urban For. Urban Green. 58, 126896 (2021).

Article Google Scholar

Bretzel, F. et al. Wildflowers: from conserving biodiversity to urban greening—A review. Urban For. Urban Green. 20, 428–436 (2016).

Article Google Scholar

Zhang, H., Chen, B., Sun, Z. & Bao, Z. Landscape perception and recreation needs in urban green space in Fuyang, Hangzhou, China. Urban For. Urban Green. 12, 44–52 (2013).

Article Google Scholar

Blanusa, T., Garratt, M., Cathcart-James, M., Hunt, L. & Cameron, R. W. F. Urban hedges: a review of plant species and cultivars for ecosystem service delivery in north-west Europe. Urban For. Urban Green. 44, 126391 (2019).

Article Google Scholar

Eisenman, T. S. et al. Urban trees, air quality, and asthma: an interdisciplinary review. Landsc. Urban Plan. 187, 47–59 (2019).

Article Google Scholar

Sun, L., Chen, J., Li, Q. & Huang, D. Dramatic uneven urbanization of large cities throughout the world in recent decades. Nat. Commun. 11, 5366 (2020).

Article CAS PubMed PubMed Central Google Scholar

Johnson, M. T. J. & Munshi-South, J. Evolution of life in urban environments. Science 358, eaam8327 (2017).

Article PubMed Google Scholar

Szulkin, M., Munshi-South, J. & Charmantier, A. Urban Evolutionary Biology (Oxford Univ. Press, 2020).

Grimm, N. B. et al. Global change and the ecology of cities. Science 319, 756–760 (2008).

Article CAS PubMed Google Scholar

Ma, J. et al. The characteristics of urban forest structure within the Sixth Ring Road of Beijing. Chin. J. Ecol. 38, 2318–2325 (2019).

Google Scholar

Wang, K. et al. Urban street tree species composition in 35 cities of China. Bull. Bot. Res. 40, 568–574 (2020).

Google Scholar

Zhao, X. et al. Relationship between PM2.5 adsorption and leaf surface morphology in ten urban tree species in Shenyang, China. Energy Sources A: Recovery, Utili. Environ. Eff. 41, 1029–1039 (2019).

Article CAS Google Scholar

Tu, B., Gavaland, A., Du, K. & Lu, X. Black locust in China (in French). For.êt-entreprise 177, 50–53 (2007).

Google Scholar

Wang, J. X. et al. Transcriptional profiles of emasculated flowers of black locust (Robinia pseudoacacia) determined using the cDNA-AFLP technique. Genet. Mol. Res. 14, 15822–15838 (2015).

Article CAS PubMed Google Scholar

Schneck, V. Robinie–Züchtungsansätze und Begründungsverfahren. In Deutschland/Bundesministerium für Ernährung, Landwirtschaft und Verbraucherschutz Beiträge - Agrarholz 2010, Berlin (Germany) 1–8 (2010).

Nicolescu, V.-N. et al. Ecology, growth and management of black locust (Robinia pseudoacacia L.), a non-native species integrated into European forests. J. For. Res. 31, 1081–1101 (2020).

Article CAS Google Scholar

Kehrer, J. (2020) New Horizons: Eight Perspectives on Chinese Landscape Architecture Today (Birkhäuser, 2020).

Ma, X., Chen, J., Lu, X., Zhe, Y. & Jiang, Z. HPLC coupled with quadrupole time of flight tandem mass spectrometry for analysis of glycosylated components from the fresh flowers of two congeneric species: Robinia hispida L. and Robinia pseudoacacia L. J. Sep. Sci. 44, 1537–1551 (2021).

Article CAS PubMed Google Scholar

Vítková, M., Tonika, J. & Müllerová, J. Black locust—Successful invader of a wide range of soil conditions. Sci. Total Environ. 505, 315–328 (2015).

Article PubMed Google Scholar

Dini-Papanastasi, O. & Panetsos, C. P. Relation between growth and morphological traits and genetic parameters of Robinia pseudoacacia var. monophylla D.C. in northern Greece. Silvae Genet. 49, 37–44 (2000).

Google Scholar

Guo, Q. et al. Genetic diversity and population structure of Robinia pseudoacacia from six improved variety bases in China as revealed by simple sequence repeat markers. J. For. Res. 33, 611–621 (2022).

Article CAS Google Scholar

Surles, S. E., Hamrick, J. L. & Bongarten, B. C. Allozyme variation in black locust (Robinia pseudoacacia). Can. J. For. Res. 19, 471–479 (1989).

Article Google Scholar

Cao, Y. & Chen, Y. Ecosystem C: N: P stoichiometry and carbon storage in plantations and a secondary forest on the Loess Plateau, China. Ecol. Eng. 105, 125–132 (2017).

Article Google Scholar

Qiu, L., Zhang, X., Cheng, J. & Yin, X. Effects of black locust (Robinia pseudoacacia) on soil properties in the loessial gully region of the Loess Plateau, China. Plant Soil 332, 207–217 (2010).

Article CAS Google Scholar

Mao, P. et al. Dynamic characteristics of soil properties in a Robinia pseudoacacia vegetation and coastal eco-restoration. Ecol. Eng. 92, 132–137 (2016).

Article Google Scholar

Sato, S. et al. Genome structure of the legume, Lotus japonicus. DNA Res. 15, 227–239 (2008).

Article CAS PubMed PubMed Central Google Scholar

Hufnagel, B. et al. High-quality genome sequence of white lupin provides insight into soil exploration and seed quality. Nat. Commun. 11, 492 (2020).

Article CAS PubMed PubMed Central Google Scholar

Varshney, R. K. et al. Draft genome sequence of chickpea (Cicer arietinum) provides a resource for trait improvement. Nat. Biotechnol. 31, 240–246 (2013).

Article CAS PubMed Google Scholar

Bennetzen, J. L. Patterns in grass genome evolution. Curr. Opin. Plant Biol. 10, 176–181 (2007).

Article CAS PubMed Google Scholar

Kim, S. et al. Genome sequence of the hot pepper provides insights into the evolution of pungency in Capsicum species. Nat. Genet. 46, 270–278 (2014).

Article CAS PubMed Google Scholar

Zhang, R. et al. Exploration of plastid phylogenomic conflict yields new insights into the deep relationships of Leguminosae. Syst. Biol. 69, 613–622 (2020).

Article CAS PubMed PubMed Central Google Scholar

Zhao, Y. et al. Nuclear phylotranscriptomics and phylogenomics support numerous polyploidization events and hypotheses for the evolution of rhizobial nitrogen-fixing symbiosis in Fabaceae. Mol. Plant 14, 748–773 (2021).

Article CAS PubMed Google Scholar

Wang, J. et al. Hierarchically aligning 10 legume genomes establishes a family-level genomics platform. Plant Physiol. 174, 284–300 (2017).

Article CAS PubMed PubMed Central Google Scholar

Schmutz, J. et al. Genome sequence of the palaeopolyploid soybean. Nature 463, 178–183 (2010).

Article CAS PubMed Google Scholar

Leebens-Mack, J. H. et al. One thousand plant transcriptomes and the phylogenomics of green plants. Nature 574, 679–685 (2019).

Article Google Scholar

Soltis, P. S., Marchant, D. B., Van de Peer, Y. & Soltis, D. E. Polyploidy and genome evolution in plants. Curr. Opin. Genet. Dev. 35, 119–125 (2015).

Article CAS PubMed Google Scholar

Hughes, T. E., Langdale, J. A. & Kelly, S. The impact of widespread regulatory neofunctionalization on homeolog gene evolution following whole-genome duplication in maize. Genome Res. 24, 1348–1355 (2014).

Article CAS PubMed PubMed Central Google Scholar

Boring, L. R. & Swank, W. T. The role of black locust (Robinia Pseudo-Acacia) in forest succession. J. Ecol. 72, 749–766 (1984).

Article Google Scholar

Huntley, J. C. in Silvics of North America 2. Hardwoods (eds Burns, R. M. & Honkala, B. H.) (U.S. Department of Agriculture, Forest Service, 1990).

Niu, Z. et al. A gene that underwent adaptive evolution, LAC2 (LACCASE), in Populus euphratica improves drought tolerance by improving water transport capacity. Horticulture Res. 8, 88 (2021).

Article CAS Google Scholar

Yang, J. et al. Novel gene expression profiles define the metabolic and physiological processes characteristic of wood and its extractive formation in a hardwood tree species, Robinia pseudoacacia. Plant Mol. Biol. 52, 935–956 (2003).

Article CAS PubMed Google Scholar

Yang, J., Kamdem, D. P., Keathley, D. E. & Han, K. H. Seasonal changes in gene expression at the sapwood—heartwood transition zone of black locust (Robinia pseudoacacia) revealed by cDNA microarray analysis. Tree Physiol. 24, 461–474 (2004).

Article CAS PubMed Google Scholar

Kehrer, J. (ed.) New Horizons: Eight Perspectives on Chinese Landscape Architecture Today (Birkhäuser, 2020).

Rauf, S., da Silva, J. T., Khan, A. A. & Naveed, A. Consequences of plant breeding on genetic diversity. Int. J. Plant Breed. 4, 1–21 (2010).

Google Scholar

Gaut, B. S., Seymour, D. K., Liu, Q. & Zhou, Y. Demography and its effects on genomic variation in crop domestication. Nat. Plants 4, 512–520 (2018).

Article PubMed Google Scholar

Brütting, C., Hensen, I. & Wesche, K. Ex situ cultivation affects genetic structure and diversity in arable plants. Plant Biol. 15, 505–513 (2013).

Article PubMed Google Scholar

Yang, Y. et al. Genomic effects of population collapse in a critically endangered ironwood tree Ostrya rehderiana. Nat. Commun. 9, 5449 (2018).

Article CAS PubMed PubMed Central Google Scholar

Voight, B. F., Kudaravalli, S., Wen, X. & Pritchard, J. K. A map of recent positive selection in the human genome. PLoS Biol. 5, e147 (2007).

Article PubMed Central Google Scholar

Avolio, M. L., Pataki, D. E., Trammell, T. L. E. & Endter‐Wada, J. Biodiverse cities: the nursery industry, homeowners, and neighborhood differences drive urban tree composition. Ecol. Monogr. 88, 259–276 (2018).

Article Google Scholar

Lu, P., Yu, Q., Liu, J. & Lee, X. Advance of tree-flowering dates in response to urban climate change. Agric. For. Meteorol. 138, 120–131 (2006).

Article Google Scholar

Zheng, Z., Guan, H., Leal, F., Grey, P. H. & Oppenheimer, D. G. Mediator subunit18 controls flowering time and floral organ identity in Arabidopsis. PLoS ONE 8, e53924 (2013).

Article CAS PubMed PubMed Central Google Scholar

De Kimpe, C. R. & Morel, J. L. Urban soil management: a growing concern. Soil Sci. 165, 31–40 (2000).

Article Google Scholar

Halecki, W. & Stachura, T. Evaluation of soil hydrophysical parameters along a semiurban small river: Soil ecosystem services for enhancing water retention in urban and suburban green areas. Catena 196, 104910 (2021).

Article Google Scholar

Kida, K. & Kawahigashi, M. Influence of asphalt pavement construction processes on urban soil formation in Tokyo. Soil Sci. Plant Nutr. 61, 135–146 (2015).

Article Google Scholar

Li, P., Li, X. & Jiang, M. CRISPR/Cas9-mediated mutagenesis of WRKY3 and WRKY4 function decreases salt and Me-JA stress tolerance in Arabidopsis thaliana. Mol. Biol. Rep. 48, 5821–5832 (2021).

Article CAS PubMed Google Scholar

Park, M. Y., Kang, J. & Kim, S. Y. Overexpression of AtMYB52 confers ABA hypersensitivity and drought tolerance. Mol. Cells 31, 447–454 (2011).

Article CAS PubMed PubMed Central Google Scholar

Mantovani, D., Veste, M. & Freese, D. Black locust (Robinia pseudoacacia L.) ecophysiological and morphological adaptations to drought and their consequence on biomass production and water-use efficiency. N.Z. J. For. Sci. 44, 29 (2014).

Article Google Scholar

Phillips, K. & Ludidi, N. Drought and exogenous abscisic acid alter hydrogen peroxide accumulation and differentially regulate the expression of two maize RD22-like genes. Sci. Rep. 7, 8821 (2017).

Article PubMed PubMed Central Google Scholar

Yamaguchi-Shinozaki, K. & Shinozaki, K. The plant hormone abscisic acid mediates the drought-induced expression but not the seed-specific expression of rd22, a gene responsive to dehydration stress in Arabidopsis thaliana. Mol. Gen. Genet. 238, 17–25 (1993).

Article CAS PubMed Google Scholar

Ewan, R. et al. Deubiquitinating enzymes AtUBP12 and AtUBP13 and their tobacco homologue NtUBP12 are negative regulators of plant immunity. N. Phytol. 191, 92–106 (2011).

Article CAS Google Scholar

Turek, I., Tischer, N., Lassig, R. & Trujillo, M. Multi-tiered pairing selectivity between E2 ubiquitin–conjugating enzymes and E3 ligases. J. Biol. Chem. 293, 16324–16336 (2018).

Article CAS PubMed PubMed Central Google Scholar

Gadaleta, A., Colasuonno, P., Giove, S. L., Blanco, A. & Giancaspro, A. Map-based cloning of QFhb.mgb-2A identifies a WAK2 gene responsible for Fusarium Head Blight resistance in wheat. Sci. Rep. 9, 6929 (2019).

Article PubMed PubMed Central Google Scholar

Tello, M. L. et al. in Urban Forests and Trees (eds Konijnendijk, C., Nilsson, K., Randrup, T. & Schipperijn, J.) Ch.12 (Springer, 2005).

Gaut, B. S., Díez, C. M. & Morrell, P. L. Genomics and the contrasting dynamics of annual and perennial domestication. Trends Genet. 31, 709–719 (2015).

Article CAS PubMed Google Scholar

Wang, L. et al. Somatic variations led to the selection of acidic and acidless orange cultivars. Nat. Plants 7, 954–965 (2021).

Article CAS PubMed Google Scholar

Murray, M. G. & Thampson, W. F. Rapid isolation of high molecular weight plant DNA. Nucleic Acids Res. 8, 4321–4325 (1980).

Article CAS PubMed PubMed Central Google Scholar

Sahu, S. K., Thangaraj, M. & Kathiresan, K. DNA extraction protocol for plants with high levels of secondary metabolites and polysaccharides without using liquid nitrogen and phenol. ISRN Mol. Biol. 2012, 205049 (2012).

Article PubMed PubMed Central Google Scholar

Belton, J. M. et al. Hi–C: a comprehensive technique to capture the conformation of genomes. Methods 58, 268–276 (2012).

Article CAS PubMed Google Scholar

Roach, M. J., Schmidt, S. A. & Borneman, A. R. Purge haplotigs: allelic contig reassignment for third-gen diploid genome assemblies. BMC Bioinform. 19, 460 (2018).

Article CAS Google Scholar

Hu, J., Fan, J., Sun, Z. & Liu, S. NextPolish: a fast and efficient genome polishing tool for long-read assembly. Bioinformatics 36, 2253–2255 (2020).

Article CAS PubMed Google Scholar

Sun, H., Ding, J., Piednoël, M. & Schneeberger, K. findGSE: estimating genome size variation within human and Arabidopsis using k-mer frequencies. Bioinformatics 34, 550–557 (2018).

Article CAS PubMed Google Scholar

Wingett, S. et al. HiCUP: pipeline for mapping and processing Hi-C data. F1000 Res. 4, 1310 (2015).

Article Google Scholar

Zhang, X., Zhang, S., Zhao, Q., Ming, R. & Tang, H. Assembly of allele-aware, chromosomal-scale autopolyploid genomes based on Hi-C data. Nat. Plants 5, 833–845 (2019).

Article CAS PubMed Google Scholar

Pascoal, S. et al. Field cricket genome reveals the footprint of recent, abrupt adaptation in the wild. Evol. Lett. 4, 19–33 (2020).

Article PubMed Google Scholar

Flynn, J. M. et al. RepeatModeler2 for automated genomic discovery of transposable element families. Proc. Natl Acad. Sci. USA 117, 9451–9457 (2020).

Article CAS PubMed PubMed Central Google Scholar

Bao, Z. & Eddy, S. R. Automated de novo identification of repeat sequence families in sequenced genomes. Genome Res. 12, 1269–1276 (2002).

Article CAS PubMed PubMed Central Google Scholar

Price, A. L., Jones, N. C. & Pevzner, P. A. De novo identification of repeat families in large genomes. Bioinformatics 21, i351–i358 (2005).

Article CAS PubMed Google Scholar

Camacho, C. et al. BLAST+: architecture and applications. BMC Bioinform. 10, 421 (2009).

Article Google Scholar

Tarailo-Graovac, M. & Chen, N. Using repeat masker to identify repetitive elements in genomic sequences. Curr. Protoc. Bioinformatics 5, 4.10.1–4.10.14 (2009).

Google Scholar

Bao, W., Kojima, K. K. & Kohany, O. Repbase Update, a database of repetitive elements in eukaryotic genomes. Mob. DNA 6, 11 (2015).

Article PubMed PubMed Central Google Scholar

Ou, S. & Jiang, N. LTR_retriever: a highly accurate and sensitive program for identification of long terminal repeat retrotransposons. Plant Physiol. 176, 1410–1422 (2018).

Article CAS PubMed Google Scholar

Ellinghaus, D., Kurtz, S. & Willhoeft, U. LTRharvest, an efficient and flexible software for de novo detection of LTR retrotransposons. BMC Bioinform. 9, 18 (2008).

Article Google Scholar

Xu, Z. & Wang, H. LTR_FINDER: an efficient tool for the prediction of full-length LTR retrotransposons. Nucleic Acids Res. 35, W265–W268 (2007).

Article PubMed PubMed Central Google Scholar

Yang, Z. PAML 4: phylogenetic analysis by maximum likelihood. Mol. Biol. Evol. 24, 1586–1591 (2007).

Article CAS PubMed Google Scholar

Haas, B. J. et al. De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis. Nat. Protoc. 8, 1494–1512 (2013).

Article CAS PubMed Google Scholar

Haas, B. J. et al. Automated eukaryotic gene structure annotation using EVidenceModeler and the Program to Assemble Spliced Alignments. Genome Biol. 9, R7 (2008).

Article PubMed PubMed Central Google Scholar

Kim, D., Langmead, B. & Salzberg, S. L. HISAT: a fast spliced aligner with low memory requirements. Nat. Methods 12, 357–360 (2015).

Article CAS PubMed PubMed Central Google Scholar

Stanke, M. & Morgenstern, B. AUGUSTUS: a web server for gene prediction in eukaryotes that allows user-defined constraints. Nucleic Acids Res. 33, W465–W467 (2005).

Article CAS PubMed PubMed Central Google Scholar

Goodstein, D. M. et al. Phytozome: a comparative platform for green plant genomics. Nucleic Acids Res. 40, D1178–Dd1186 (2012).

Article CAS PubMed Google Scholar

Birney, E., Clamp, M. & Durbin, R. GeneWise and genomewise. Genome Res. 14, 988–995 (2004).

Article CAS PubMed PubMed Central Google Scholar

Manni, M., Berkeley, M. R., Seppey, M., Simão, F. A. & Zdobnov, E. M. BUSCO update: novel and streamlined workflows along with broader and deeper phylogenetic coverage for scoring of eukaryotic, prokaryotic, and viral genomes. Mol. Biol. Evol. 38, 4647–4654 (2021).

Article CAS PubMed PubMed Central Google Scholar

Kriventseva, E. V. et al. OrthoDB v10: sampling the diversity of animal, plant, fungal, protist, bacterial and viral genomes for evolutionary and functional annotations of orthologs. Nucleic Acids Res. 47, D807–D811 (2019).

Article CAS PubMed Google Scholar

Krzywinski, M. & Schein, J. I. Circos: an information aesthetic for comparative genomics. Genome Res. 19, 1639–1645 (2009).

Article CAS PubMed PubMed Central Google Scholar

Bairoch, A. & Apweiler, R. The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000. Nucleic Acids Res. 28, 45–48 (2000).

Article CAS PubMed PubMed Central Google Scholar

Marchler-Bauer, A. et al. CDD: a Conserved Domain Database for the functional annotation of proteins. Nucleic Acids Res. 39, D225–D229 (2011).

Article CAS PubMed Google Scholar

Hunter, S. et al. InterPro: the integrative protein signature database. Nucleic Acids Res. 37, D211–D215 (2009).

Article CAS PubMed Google Scholar

Zdobnov, E. M. & Apweiler, R. InterProScan—an integration platform for the signature-recognition methods in InterPro. Bioinformatics 17, 847–848 (2001).

Article CAS PubMed Google Scholar

Conesa, A. & Götz, S. Blast2GO: a comprehensive suite for functional analysis in plant genomics. Int. J. Plant Genom. 2008, 619832 (2008).

Google Scholar

Kanehisa, M. & Goto, S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27–30 (2000).

Article CAS PubMed PubMed Central Google Scholar

Moriya, Y., Itoh, M., Okuda, S., Yoshizawa, A. C. & Kanehisa, M. KAAS: an automatic genome annotation and pathway reconstruction server. Nucleic Acids Res. 35, W182–W185 (2007).

Article PubMed PubMed Central Google Scholar

Wu, T. et al. clusterProfiler 4.0: a universal enrichment tool for interpreting omics data. Innovation 2, 100141 (2021).

CAS PubMed PubMed Central Google Scholar

Emms, D. M. & Kelly, S. OrthoFinder: phylogenetic orthology inference for comparative genomics. Genome Biol. 20, 238 (2019).

Article PubMed PubMed Central Google Scholar

Stamatakis, A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313 (2014).

Article CAS PubMed PubMed Central Google Scholar

De Bie, T., Cristianini, N., Demuth, J. P. & Hahn, M. W. CAFE: a computational tool for the study of gene family evolution. Bioinformatics 22, 1269–1271 (2006).

Article PubMed Google Scholar

Eddy, S. R. Accelerated profile HMM searches. PLoS Comput. Biol. 7, e1002195 (2011).

Article CAS PubMed PubMed Central Google Scholar

Wang, X. et al. Statistical inference of chromosomal homology based on gene colinearity and applications to Arabidopsis and rice. BMC Bioinform. 7, 447 (2006).

Article Google Scholar

Nei, M. & Gojobori, T. Simple methods for estimating the numbers of synonymous and nonsynonymous nucleotide substitutions. Mol. Biol. Evol. 3, 418–426 (1986).

CAS PubMed Google Scholar

Sun, P. et al. WGDI: A user-friendly toolkit for evolutionary analyses of whole-genome duplications and ancestral karyotypes. Mol. Plant 15, 1841–1851 (2022).

Article CAS PubMed Google Scholar

Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

Article CAS PubMed PubMed Central Google Scholar

DePristo, M. A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491–498 (2011).

Article CAS PubMed PubMed Central Google Scholar

Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

Article PubMed PubMed Central Google Scholar

Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011).

Article CAS PubMed PubMed Central Google Scholar

Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).

Article CAS PubMed PubMed Central Google Scholar

Alexander, D. H. & Lange, K. Enhancements to the ADMIXTURE algorithm for individual ancestry estimation. BMC Bioinform. 12, 246 (2011).

Article Google Scholar

Price, A. L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904–909 (2006).

Article CAS PubMed Google Scholar

Li, H. & Durbin, R. Inference of human population history from individual whole-genome sequences. Nature 475, 493–496 (2011).

Article CAS PubMed PubMed Central Google Scholar

Excoffier, L. & Lischer, H. E. Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows. Mol. Ecol. Resour. 10, 564–567 (2010).

Article PubMed Google Scholar

Oksanen, J. et al. vegan community ecology package version 2.5-7 (2020).

Browning, B. L., Zhou, Y. & Browning, S. R. A one-penny imputed genome from next generation reference panels. Am. J. Hum. Genet. 103, 338–348 (2018).

Article CAS PubMed PubMed Central Google Scholar

Browning, B. L., Tian, X., Zhou, Y. & Browning, S. R. Fast two-stage phasing of large-scale sequence data. Am. J. Hum. Genet. 108, 1880–1890 (2021).

Article CAS PubMed PubMed Central Google Scholar

Garud, N. R., Messer, P. W., Buzbas, E. O. & Petrov, D. A. Recent selective sweeps in North American Drosophila melanogaster show signatures of soft sweeps. PLoS Genet. 11, e1005004 (2015).

Article PubMed PubMed Central Google Scholar

Szpiech, Z. A. selscan 2.0: scanning for sweeps in unphased data. Preprint at bioRxiv 2021.10.22.465497. (2021).

Download references

We are grateful for the editors’ and three reviewers’ excellent suggestions to improve the manuscript. This study was supported by the National Natural Science Foundation of China (grant no. 32001085) and Fundamental Research Funds for Central Universities (grant no. lzujbky-2020-34).

These authors contributed equally: Zefu Wang, Xiao Zhang.

State Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystem, College of Ecology, Lanzhou University, Lanzhou, 730000, China

Zefu Wang, Weixiao Lei, Hui Zhu, Shengdan Wu & Dafu Ru

Tianjin Key Laboratory of Conservation and Utilization of Animal Diversity, College of Life Sciences, Tianjin Normal University, Tianjin, 300387, China

Zefu Wang & Xiao Zhang

Co-Innovation Center for Sustainable Forestry in Southern China, College of Biology and the Environment, Nanjing Forestry University, Nanjing, 210037, China

Zefu Wang

Institute of Loess Plateau, Shanxi University, Taiyuan, 030006, China

Bingbing Liu

You can also search for this author in PubMed Google Scholar

You can also search for this author in PubMed Google Scholar

You can also search for this author in PubMed Google Scholar

You can also search for this author in PubMed Google Scholar

You can also search for this author in PubMed Google Scholar

You can also search for this author in PubMed Google Scholar

You can also search for this author in PubMed Google Scholar

D.R., X.Z., Z.W., S.W., and B.L. conceived the project and designed the analyses; Z.W. conducted the research; Z.W., X.Z., H.Z., W.L., S.W., B.L., and D.R. analyzed the data; X.Z., Z.W., and D.R. wrote the paper; and all authors revised and approved the final manuscript.

Correspondence to Xiao Zhang, Shengdan Wu, Bingbing Liu or Dafu Ru.

The authors declare no competing interests.

Communications Biology thanks Fang Du, Jianchao Ma and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editors: Matteo Dell’Acqua and David Favero. A peer review file is available.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and Permissions

Wang, Z., Zhang, X., Lei, W. et al. Chromosome-level genome assembly and population genomics of Robinia pseudoacacia reveal the genetic basis for its wide cultivation. Commun Biol 6, 797 (2023). https://doi.org/10.1038/s42003-023-05158-6

Download citation

Received: 14 February 2023

Accepted: 19 July 2023

Published: 31 July 2023

DOI: https://doi.org/10.1038/s42003-023-05158-6

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.