Chromosome-level genome assembly of Albizia odoratissima and effect of flavonoid metabolic pathways under drought stress

Chromosome-level genome assembly of Albizia odoratissima and effect of flavonoid metabolic pathways under drought stress | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Chromosome-level genome assembly of Albizia odoratissima and effect of flavonoid metabolic pathways under drought stress Feng Gao, Shuoxing Wei, Hanbiao Ou, Zhihui Wang, Guoping Yin, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6840333/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 31 Oct, 2025 Read the published version in BMC Plant Biology → Version 1 posted 11 You are reading this latest preprint version Abstract Albizia odoratissima is a valuable drought-tolerant native tree species in the dry and hot river valleys of China, which has important ecological and economic values. Exploring its genetic background and phylogenetic direction will be conducive to its further exploitation and use, and promote the process of vegetation restoration in the dry hot river valley region. A genome assembly of approximately 719.88 Mb was achieved at the contig level, featuring a contig N50 of 53.74 Mb. Of this, 98.58% of gene sequences were organized into 13 pseudochromosomes. The A. odoratissima genome contained 96.96% of conserved genes, including 1,538 intact single-copy genes and 42 intact duplicated genes. It had an angiosperm palaeotripling event and the last whole genome duplication event occurred approximately 62.9 million years ago. A. odoratissima shares 8,936 gene families with five other legume species, while 1,420 gene families are unique to A. odoratissima . Under drought stress, photosynthesis was significantly inhibited to reduce water consumption, osmoregulatory substances were significantly increased to alleviate osmotic stress, and flavonoids were increased to enhance antioxidant capacity through the up-regulation of AoANS gene expression, thereby improving drought tolerance. High-quality reference genomes generated through molecular studies are advancing research into the molecular mechanisms of A. odoratissima . Albizia odoratissima chromosome-level genome genome-wide replication event selenocompound metabolism drought stress Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Albizia odoratissima , a diploid (2n=26) deciduous tree species, is part of the Leguminosae family. The species is distributed mainly across Southeast Asian nations, such as China, India, Vietnam [ 1] . A. odoratissima 's rapid growth, favorable wood properties, drought resistance, and robust regenerative capacity have enabled its extensive cultivation in Guangxi, Hainan, and other regions of China. A. odoratissima has significant economic value as its wood, which is characterized as a dense hardwood with a density of approximately 0.843 g/cm 3 , is aesthetically pleasing, exhibiting a tight grain and dense texture, while also offering durability and stability. The wood of this species resists cracking, deformation after drying, and decay and insect infestation in the heartwood [ 2] . Owing to these qualities, the wood from A. odoratissima is considered excellent for crafting high-quality furniture, carving, construction, shipbuilding, and other applications [ 3] . A. odoratissima holds substantial ecological importance as a leading nitrogen-fixing tree species due to its rhizobia. A. odoratissima normally grows in a nitrogen-limited environment, thereby reducing the amounts of nitrogen fertilizer to be applied for its cultivation, ultimately reducing the production costs. A. odoratissima is also drought-tolerant and an evergreen species, with its flowering period spanning the months from April to July. The species also has a certain ornamental value and may be used as a street tree. Currently, most of the A. odoratissima resources are located in the wild, and due to serious logging damage and difficulty in achieving natural regeneration, the sustainable utilization and development of this species have been critically hampered. The rapid development of genome sequencing and assembly technologies has enabled deciphering the genomes of a large number of species. A. odoratissima breeding remains in the traditional phase, with molecular breeding efforts confined to the development of EST-SSR markers [ 4] . The existing literature on deciphering the genome of A. odoratissima is scarce, with the initial studies reporting that A. odoratissima has a simple genome of approximately 700 Mb in size [ 5] . With the availability of better and more advanced technologies at present, rapid and efficient deciphering and analysis of the entire genome of A. odoratissima appears possible. Flavonoid synthesis pathways and the enzymes involved in their synthesis are highly conserved in plants, and their contents are not only regulated by the intrinsic genetic mechanisms of the plant, but also by environmental stresses such as water, salt damage, UV-B, temperature, and heavy metal toxicity [ 6 , 7 ]. For example, drought stress led to a significant increase in the content of metabolites involved in flavonoid biosynthesis, such as cinnamic acid, p-coumaric acid, and trans-cinnamic acid, the expression of genes involved in flavonoid biosynthesis, such as flavanone 3′-hydroxylase ( F3′H ), as well as the total flavonoid content of Ginkgo biloba leaves [ 8] . Flavonoids also play an important role in plant abiotic stress resistance due to their strong antioxidant capacity to scavenge excess reactive oxygen species (ROS) induced by the above abiotic stresses in plants. For example, the stress resistance of several plants is strongly correlated with the flavonoid content in the body [ 9 , 10] . Overexpression of key genes involved in flavonoid biosynthesis, such as chalconeisomerase 2 ( CHI2 ), F3′H , and production of anthocyanin pigment 1 ( PAP1 ), enhances flavonoid biosynthesis in Arabidopsis thaliala , Oryza sativa and Pohlia nutans , among others, and increase antioxidant properties, thereby significantly enhancing the resistance of these plants to salt, drought and cold damage [ 11 , 12 , 13] . A. odoratissima is a drought-tolerant tree species with significant ecological and economic importance in China's dry-hot river valleys, however, high-quality reference genomes are lacking. This study utilized three-generation HiFi sequencing and Hi-C technology to sequence and assemble a chromosome-level reference genome of A. odoratissima . The genetic background of A. odoratissim a was elucidated through genome functional annotation, unique gene family analysis, and phylogenetic tree construction. Flavonoids, essential secondary metabolites, exhibit strong antioxidant properties, regulate leaf stomatal closure, absorb UV-B spectra, and complex heavy metal elements. These functions are crucial for plant defense against abiotic stress, as changes in flavonoid content are induced by such stress. However, information on genes encoding key enzymes for flavonoid biosynthesis in acacia under drought stress remains unavailable. 2. Materials and Methods 2.1. Materials This study utilized A. odoratissima from the dry and hot valley region near the Nanpanjiang River (106°17′1″E, 24°51′47″N) as the sample wood material. The sample site, located 7 km from the river, is representative of this area's environmental conditions.Five fresh young leaves were collected from each side of the tree crown, immediately placed in a centrifuge tube, frozen in liquid nitrogen, preserved on dry ice, and sent to Beijing Baimaike Biotechnology Co. 2.2. Genome sequencing The genome sequencing study utilized the tender leaves of A. odoratissima as plant material. Genomic DNA was extracted from A. odoratissima tender leaves using an optimized CTAB method for whole genome sequencing.Two short DNA fragment libraries were constructed and sequenced using the Illumina HiSeq X Ten platform, producing around 69 Gb of clean reads.The genome size was estimated using KmerGenie with a k-mer size of 17 [ 14] . A CCS library of SMART cells was sequenced using the PacBio Sequel II platform, employing the CCS model to produce approximately 54 Gb of HIFI reads. A library was constructed using the Hind III restriction enzyme and sequenced with Hi-C and paired-end methods on the Illumina Novaseq 6000 platform, yielding about 168 Gb (~230x) of clean reads. 2.3. Genome assembly The HiFiasm software [ 15] was employed to reassemble the PacPio CCS HiFi reads data, using default parameters, and the contig-level A. odoratissima genome was obtained.The Hi-C data was processed and assessed using the Hi-C-Pro software [ 16] . The evaluated data facilitated the assembly of A. odoratissima 's chromosome-level genome. LACHESIS [ 17] software was then employed for grouping, sorting, and orienting the genome sequences, followed by manual mapping and inspection, which finally generated the chromosome-level version of the genome of this species. 2.4. Genome annotation 2.4.1. Repeat sequence annotation Repeat sequences in A. odoratissima were annotated using RepeatModeler (v4.0.9) and RepeatMasker (v1.0.8) [ 18] through homology alignment and de novo prediction strategies.Subsequently, TEclass was utilized to classify the unknown repeat sequences.The TRF package [ 19] was used next to identify the tandem repeat sequences, and all group results were integrated.Afterward, LTR_Finde and LTRharvest were used with default parameters to identify all long terminal repeat sequences (LTRs) [ 20 , 21] in the genome. The LTR_retriever software [ 22 , 23] was then employed to identify the LTR-RTs among the determined LTRs. 2.4.2. Gene model prediction De novo prediction was conducted using Augustus v2.4 [ 24] and SNAP (2006-07-28) [ 25] . Homology-based prediction was conducted based on related species using GeMoMa [ 26] v1.7. The transcriptome prediction utilized two primary methods: reference-based and de novo assembly-based predictions. Reference-based transcripts were primarily acquired using Hisat v2.0.4 [ 27] and Stringtie v1.2.3 [ 28] , with gene prediction subsequently performed using GeneMarkS-T v5.1 [ 29] . The de novo transcripts were primarily assembled with Trinity (v2.11) [ 30] , and gene prediction was subsequently performed using PASA v2.0.2 [ 31] . EVM v1.1.1 [ 32] was used to consolidate the prediction results from the three methods, and PASA v2.0.2 was applied to refine the integrated data. 2.4.3. Gene functional annotation The predicted gene sequences were annotated using the NR (202009, ftp://ftp.ncbi.nlm.nih.gov/blast/db), EggNOG (5.0, http://eggnog5.embl.de/download/eggnog_5.0/) [ 33] GO (20200615, http://geneontology.org), KEGG (20191220, http://www.genome.jp/kegg) [ 34] , SWISS-PROT (202005, http://ftp.ebi.ac.uk/pub/databases/swissprot) [ 35] , and Pfam (v33 .1, http://pfam.xfam.org) [ 36] databases. 2.4.4. Non-coding RNA prediction The tRNAscan-SE v1.3.1 [ 37] was utilized to identify the tRNA. The prediction of rRNA primarily utilized the Rfam database (version 12.0) [ 38] . The barrnap (v 0.9) software was employed for the prediction. The miRNA was identified through the miRbase database [ 39] . The predictions for snoRNA and snRNA utilized the Rfam (v 12.0) database with Infenal 1.1 [ 40] . 2.4.5. Pseudogenome annotation GenBlastA v1.0.4 [ 41] was employed for alignment, and the homologous gene sequences (possible genes) were sought on the genome after masking the genuine gene loci. GeneWise v2.4.1 [ 42] was used to identify immature stop codons and frameshift mutations in gene sequences, resulting in the discovery of 190 pseudogenes. 2.5. Gene family cluster analysis and phylogenetic analysis 2.5.1. Gene family cluster analysis Orthofinder v2.4 [ 43] was used to classify protein sequences from 16 species into families, utilizing the diamond alignment method with an e-value of 0.001. PANTHER v 15 database [ 44] was used for the annotation of the obtained gene families. GO and KEGG enrichment analyses identified 54,643 species-specific gene families. A Venn analysis was conducted for the gene families of A. odoratissima , Cajanus cajan , G. max , Lotus japonicus , and Cicer arietinum . 2.5.2. Phylogenetic analysis Using IQ-TREE v1.6.11, a maximum likelihood phylogenetic tree was constructed from 1,174 single-copy gene sequences, representing 81.2% of the species, with 1,000 bootstraps. The result was visualized using Evolview (http://www.evolgenius.info/evolview). Oryza sativa was used as the outgroup.The species divergence times were calculated using the PAML software [ 45] . Fossil divergence times were sourced from the TimeTree website (http://www.timetree.org/): G. max and C. cajan diverged 11.7-27.5 Mya; G. max and O. sativa diverged 115-308 Mya; Eucommia ulmoides and O. europaea diverged 87-104 Mya; G. max and P. trichocarpa diverged 101-131 Mya. These fossil times were obtained using the software based on the algorithm. CAFÉ software [ 46] was utilized to analyze the gene family expansion and contraction in the species. 2.5.3. Analysis of genome-wide duplication events The command-line tool WGDi [ 47] , developed in Python, was used to identify whole-genome duplication (WGD) events in A. odoratissima . BLASTP (E < 1e-5) was used to compare protein sequences within each genome ( A. odoratissima , soybean, pigeonpea, peanut (diploid), and grape) and between genomes ( A. odoratissima and soybean; pigeonpea and peanut (diploid); pigeonpea and soybean) to identify homologous genes. Subsequently, gene position and chromosome length information for these genomes were acquired. WGDi facilitated the generation of dot plots and the collinearity analysis. Homologous gene pairs were identified, and yn00 in PAML was used to compute their non-synonymous (Ka) and synonymous (Ks) values. Subsequently, the results facilitated the creation of the Ks frequency distribution diagram. Finally, the peak value was fitted using the median Ks value from the frequency distribution of the blocks.The WGD event time was estimated using the equation Ks = t/2r, where the molecular clock rate (r) was 7 × 10–9. 2.6. Drought stress experiment This study used healthy individual trees from natural forests of Albizia odoratissima in the dry-hot valley region of the Nanpan River, Leye County, Guangxi, China (106°17′1″E, 24°51′47″N, Figure 2.2) as sample sources. The sample trees were located approximately 7 km straight-line distance from the Nanpan River, within a typical dry-hot valley zone. Tender shoot segments from these trees served as explants for asexual propagation of experimental seedlings. Tissue-cultured seedlings derived from this propagation were used for the drought stress experiment. Seedlings were cultivated in plastic pots (top diameter: 20 cm; bottom diameter: 12 cm; height: 6.5 cm). The growth substrate consisted of a mixture of yellow laterite, coconut coir, and rice husk at a ratio of 5:3:2, ensuring normal seedling growth. The initial soil water content was 35.26%. After one year of growth in a greenhouse, seedlings with uniform height and ground diameter were selected for the drought stress experiment. The average seedling height was 80.3 cm, and the average ground diameter was 0.67 cm. The experiment comprised a control group (watered daily) and a drought stress treatment group (no watering), with 60 seedlings per group and three biological replicates. Three randomly selected and tagged seedlings from both the control and stress groups were used for sampling. Leaves from the same canopy layer were collected from both groups at 10 days (d) and 20 d of stress. A Li-6400 photosynthesis system was used to measure the transpiration rate (Tr), net photosynthetic rate (Pn), intercellular CO₂ concentration (Ci), and stomatal conductance (Gs) at three leaf positions (the third or fourth leaf from the apical bud) on the tagged seedlings. Average values for each parameter were calculated. Proline content, soluble protein content and soluble sugar content were determined by acid ninhydrin, Kaumas Brilliant Blue and anthrone colourimetric methods in leaves under drought stress and the control group respectively [ 48] . 2.7. Transcriptome sequencing and analysis Transcriptome samples were collected from seedlings subjected to the drought stress experiment described in Section 2.1.1. Leaves were harvested at Day 10 (Drought stress: DS10, Control: CK10) and Day 20 (Drought stress: DS20, Control: CK20) from plants exhibiting uniform size, absence of pests/diseases, and healthy growth. Immediately after collection, leaves were snap-frozen in liquid nitrogen. Three biological replicates were performed for each experimental group. Total RNA was extracted using the RNAsimple Total RNA Kit (Tiangen, Beijing, China). RNA concentration was quantified using a NanoDrop ND-1000 spectrophotometer (Wilmington, DE, USA), and RNA integrity was assessed (via RIN value) using an Agilent 2100 Bioanalyzer (Palo Alto, CA, USA). Libraries were constructed and subjected to paired-end sequencing on the Illumina HiSeq4000 platform to obtain transcriptomic data. 2.8. Sample preparation for LC-MS analysis of differentially accumulated metabolites The chromatographic column was Waters ACQUITY UPLC HSS T3 C18 1.8 µm, 2.1 mm * 100 mm. The mobile phase: Phase A was ultrapure water (0.1% formic acid), and phase B was acetonitrile (0.1% formic acid). The elution gradient: 0 min water/acetonitrile (95:5 V/V), 10.0 min 5:95 V/V, 11.0 min 5:95 V/V, 11.1 min 95:5 V/V, 15.0 min 95:5 V/V. The flow rate was 0.4 mL/min, column temperature 40 ℃, injection volume 2 μL, electrospray ionization source temperature 550 ℃, mass spectrometry voltage 5500 V, -4500 V, ion source gas I 55 psi, gas II 60 psi, curtain gas 25 psi, and the collision-induced dissociation parameters were set to high. In the triple quadrupole, each ion pair was scanned and detected according to the optimized declustering potential and collision energy. 2.9. Statistical analysis Metabolite identification and abundance calculation were performed using Analyst 1.6.3 software combined with a local metabolite database. Principal Component Analysis (PCA) results revealed the overall metabolic differences between sample groups and the variation within groups. Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA) was conducted on the metabolites to obtain the Variable Importance in Projection (VIP) scores from the multivariate OPLS-DA model. Significant differential metabolites were screened based on combined threshold criteria (Fold Change ≥ 1.5 or ≤ 0.67 and VIP ≥ 1). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed on the differential metabolites to reveal their potential biological functions. Differential genes (DEGs) and differential metabolites (DAMs) from the same comparison groups were co-mapped onto KEGG pathways to visualize their relationships. The KGML sub-database within KEGG was utilized to analyze and display the network relationships between genes and metabolites. TBtools v1.098 was employed to normalize and visualize DEGs and DAMs in the metabolic pathways of CK10 vs DS10 and CK20 vs DS20, with final figures generated using Adobe Illustrator 2022. 3. Results 3.1. Genome assembly and annotation of A. odoratissima The whole-genome assembly and annotation step was conducted using a high-quality and precious timber tree species named A. odoratissima (Fig. 1 a), which is a diploid plant with 13 pairs of homologous chromosomes (2n = 2x = 26) (Fig. S1 ). Using the frequency distribution with k-mer = 21, the genome size of A. odoratissima was predicted to be 729.45 Mb, with a heterozygosity rate of 1.86%, indicating a diploid genome (Fig. S1 ). Sequencing on the Sequel II platform [ ] yielded approximately 54.03 Gb of Pacbio CCS HIFI reads, achieving around 75x coverage (Table S1 ). The A. odoratissima genome was then assembled de novo using HiFiasm. The contig-level genome size was 783 Mb. The assembly size was 48 Mb, achieving a contig N50 of 53.74 Mb and a GC content of 33.63% (Fig. 1 d). Approximately 168.05 Gb (~ 230x) of Hi-C reads were sequenced using the Illumina platform (Table S1 ). Hi-C data underwent quality control with HiC-Pro (Table S2 ). The quality-controlled Hi-C data were used to correct the contig assembly, resulting in a final contig-level genome size of 719.88 Mb, aligning closely with the predicted size. The genome's N50 was 54.41 Mb with a GC content of 33.62%. Following the application of LACHESIS software and subsequent manual refinements, 719.88 Mb, representing 98.58% of the genome, was anchored to 13 pseudochromosomes (Fig. S2 ), with just 4 gaps and the chromosome lengths ranging from 35.96 to 76.01 Mb (Fig. 1 b, Fig. 1 c, Fig. 1 d, Fig. S2 , Table S3). The second-generation Illumina reads were aligned to the assembled genome, showing that 454,948,025 reads (98.67%) mapped to the genome, with 434,527,624 reads (94.24%) being perfect matches (Table S4). The subsequent use of the CEMGA software screened out 233 of the 248 highly conserved CEGs, achieving a recall rate of 93.95% (Table S4). Further, the BUSCO analysis could identify 1580 of the 1614 complete gene models in the genome, with a completeness rate of 97.9% (Table S4). The assembled A. odoratissima genome exhibited high accuracy and completeness. A total of 31,457 protein-coding gene models were identified in the A. odoratissima genome using a combination of de novo, homology, and RNAseq-based prediction methods (Fig. 1 d, Table S5). The average lengths for genes and CDS were 4124.27 bp and 1267.99 bp, respectively (Fig. 1 d). Comparative analysis with related species showed that A. odoratissima 's predicted gene models, as well as its average gene, CDS, exon, and intron lengths, fall within expected ranges (Table S6). The BUSCO analysis could identify 1565 of the 1614 complete gene models (96.96%) among these predicted genes (Table S7), indicating that the prediction results of the A. odoratissima gene model were of high quality. Meanwhile, 2027 ribosomal RNAs (rRNAs), 598 transfer RNAs (tRNAs), 140 microRNAs (miRNAs), 83 small nuclear RNAs (snRNAs), and 90 small nucleolar RNAs (snoRNAs) were predicted (Table S5). A total of 190 pseudogenes were predicted, spanning 697,366 bp with an average length of 3,670.35 bp (Table S5). The functional prediction of these 31,457 gene models revealed that 29,871 (94.96%), 29,703 (94.42%), 24,833 (78.95%), and 22,753 (72.33%) gene models were annotated in the NCBI NR, TrEMBL, GO, and KEGG databases (Fig.S3), respectively.In total, 29,930 (95.15%) genes from all databases were functionally annotated (Fig.S3, Table S8). The findings suggest that the gene annotations for A. odoratissima are of high quality and suitable for further research. Simultaneously, a total of 411.75 Mb (56.38% of the genome) of repetitive sequences were identified in the A. odoratissima genome. LTRs comprised the majority of repeated sequences, amounting to 301.10 Mb and representing 41.29% of the genome (Table S9). Among the LTRs, the Copia type accounted for 28.88% and the Gypsy type accounted for 40.69% in terms of length (Fig.S5). A total of 372 LTR families with over 100 copies and 57 LTR families with over 750 copies were identified (Fig.S4). LINEs and SINEs comprised 2.53% and 0.14% of the genome, respectively (Table S9). DNA transposons constituted 12.43% of the genome (Table S9). In addition, 23.57 Mb of the tandem repeat sequences were identified, accounting for 3.23% of the total genome length of A. odoratissima (Table S10). 3.2. Comparative genomes, gene rent evolution, and whole-genome duplication event analysis A.odoratissima was subjected to gene family clustering analysis with seven leguminous plant species, namely, G. max , L. japonicus , C. arietinum , Trifolium pratense , Phaseolus vulgaris , C. cajan , and Medicago sativa , and also with eight other plant species, from different families, namely Vitis vinifera , O. sativa , Olea europaea , Populus trichocarpa , E. ulmoides , Nicotiana attenuata , Juglans regia , and Arabidopsis thaliana using OrthoFinder. A total of 54,643 gene families were obtained, among which 1,930 were common among these 16 species (Table S10, Table S11, Fig.S5).The clustering analysis of A. odoratissima with the leguminous species C. cajan , L. japonicus , G. max , and C. arietinum revealed 8,936 gene families shared among the five genomes, with 1420 gene families unique to A. odoratissima (Fig. 2 a). KEGG enrichment analysis of these unique gene families revealed the top five enriched metabolic pathways: diterpenoid biosynthesis, plant-pathogen interaction, selenocompound metabolism, biosynthesis of secondary metabolites, and anthocyanin biosynthesis (Fig.S6). The GO enrichment analysis identified the top five biological processes for these unique gene families, focusing on cytokinin biosynthesis, hormone biosynthesis, hormone metabolism, cytokinin metabolism, and cellular hormone metabolism (Fig.S7). An ML phylogenetic tree was constructed using 1174 single-copy genes from 16 species (Fig. 2 e) by employing IQ-TREE and using rice as the outgroup. The tree was calibrated using the species fossil differentiation times available on ‘timetree’, and an evolutionary tree illustrating the differentiation times was obtained. A. odoratissima was clustered with the other seven species of the Leguminosae family, while it diverged from the other leguminous plants around 45.78 to 79.24 years ago (Fig. 2 e). Using CAFF software, the analysis of gene family dynamics in A. odoratissima indicated an expansion of 177 gene families and a contraction of 10 gene families (Fig. 2 e). The enrichment analysis of expanded gene families identified the top five KEGG metabolic pathways: sesquiterpenoid and triterpenoid biosynthesis, monoterpene biosynthesis, selenocompound metabolism, betalain biosynthesis, and stilbenoid, diarylheptanoid, and gingerol biosynthesis (Fig.S8). The top five biological processes enriched in the GO enrichment analysis were telomere maintenance, protein phosphorylation, DNA recombination, recognition of pollen, and DNA repair (Fig.S9). The synonymous substitution rates (Ks) for self-homologous genes in A. odoratissima , G. max , C. cajan , and A. hypogaea were calculated (Fig. 2 c). Two distinct peaks appeared in the curve of A. odoratissima , with the ancient triplication event in angiosperms (Fig. 3 c).The KS peak value for A. odoratissima was similar to that of A. hypogaea compared to other species. Using the formula t = ks/2r, the calculated r value was approximately 6.97×10–9.Accordingly, the occurrence time of the recent WGD event for A. odoratissima was approximately 62.9 Mya (Fig. 2 c). Previous studies have demonstrated that most leguminous plants share a WGD event that occurred at ~ 65 Mya. The proximity of the most recent WGD event of A. odoratissim a to this timeframe [~ 65 Mya] suggests that this species shared the WGD event with leguminous plants.According to the evolutionary tree (Fig. 2 c), this WGD event aligned closely with the estimated divergence time of A. odoratissima from other leguminous species. Accordingly, it was speculated that A. odoratissima began differentiating from the other species belonging to the Leguminosae family after the WGD event. Subsequently, a collinearity analysis was performed for A. odoratissima , V. vinifera , and G. max . The comparative collinearity analysis between A. odoratissima , V. vinifera , and G. max revealed a 1:2 relationship in the collinear gene pairs of A. odoratissima with both V. vinifera and G. max (Fig. 2 b).This finding supports the hypothesis that A. odoratissima experienced only one whole-genome duplication following the ancient angiosperm triplication event.The LTR insertion time of A. odoratissima suggests a burst around 0.2 Mya, aligning with similar insertion bursts in G. max and L. japonicus , which occurred between 0.3 and 0.7 Mya (Fig. 2 d). 3.3. Transcriptome and differentially expressed genes (DEGs) analysis under drought stress With the continuation of drought, water content, net photosynthetic rate, transpiration rate and stomatal conductance of Acacia aromatica leaves showed a highly significant decrease, while the interstitial CO 2 concentration increased by 29.87% ( p < 0.01) (Fig. 3 A-E). Leaf proline, soluble sugar, and soluble protein contents were significantly increased by 2,217.81%, 49.01%, and 20.07%, respectively, compared to each other ( p < 0.01) (Fig. 3 F-H). 3.4. Transcriptome and differentially expressed genes (DEGs) analysis under drought stress The clean data volume for each sample exceeded 6.6 Gb, with Q20 scores all above 96% (Table S12). Correlation analysis indicated that the correlation among the three biological replicates for each sample exceeded 90%, demonstrating the reliability of the data (Fig. S10 A and B). DS10 vs CK10: 4,949 DEGs were upregulated (Fig. 4 A). These were primarily enriched in processes such as "Biosynthesis and metabolism of amino acid-related substances," "Biosynthesis and metabolism of lipid-related substances," and "Flavonoid biosynthesis" (Fig. 4 C). In contrast, 3,179 DEGs were downregulated (Fig. 4 A), mainly enriched in processes including "Photosynthesis" and "Biosynthesis and metabolism of alkaloids" (Fig. S10C). DS20 vs CK20: 4,954 DEGs were upregulated (Fig. 4 B). These were primarily enriched in processes such as "Biosynthesis and metabolism of sugar-, amino acid-, and flavonoid-related substances" (Fig. 4 D). Conversely, 7,929 DEGs were downregulated (Fig. 4 B), mainly enriched in processes including "Photosynthesis", "ABC transporters", and "Biosynthesis and metabolism of alkaloids" (Fig. S10D). 3.5. Metabolome and Differential accumulation metabolites (DAMs) analysis under drought stress To analyze changes in metabolite content and metabolic pathways in leaves under drought stress, untargeted broad-spectrum metabolomics analysis was performed on leaves from drought-stressed and control groups at day 10 and day 20. Principal Component Analysis (PCA) results showed that the contribution rates of PC1 and PC2 were 32.6% and 20.6%, respectively. The Pearson correlation coefficients among the five sample groups were close and approached 1, indicating good intra-group repeatability and significant inter-group correlations. This confirms the reliability of the metabolomics data for subsequent analysis (Fig. S11 A and B). A total of 729 metabolites were identified in leaves, classified into 25 categories. Phenolic acids were the most abundant category with 103 metabolites (14.03%), followed by amino acids and derivatives with 75 metabolites (10.22%), and chalcones ranking third with 64 metabolites (8.72%). Lipids constituted the smallest category with only 1 metabolite (0.14%) (Fig. S11C). Comparative analysis of metabolites between drought-stressed and control groups at days 10 and 20 revealed: CK10 vs DS10: 224 DAMs (64 upregulated, 164 downregulated); CK20 vs DS20: 143 DAMs (56 upregulated, 87 downregulated) (Fig. 5 A and B). Metabolically enriched pathways common to both time points included: Arginine and proline metabolism, D-Amino acid metabolism, ABC transporters, Tropane, piperidine and pyridine alkaloid biosynthesis, et al. These findings demonstrate that drought stress regulates the synthesis and accumulation of metabolites in leaves. It can be inferred that A. odoratissima primarily responds to drought stress through metabolic pathways involving alkaloids and amino acids (Fig. 5 C and D). 3.6. Flavonoid metabolic responses under drought stress of A. odoratissima A differential analysis was conducted on the flavonoid metabolic pathway in A. odoratissima under drought stress, identifying 13 DEGs and 7 DAMs (Fig. 6 ). Compared to the control group, the contents of both catechin and epicatechin increased significantly after 10 days of drought stress, but decreased after 20 days of drought stress. The expression levels of key enzyme genes ( AoANS : Aod13G001010 , Aod05G020040 , Aod07G019900 ) catalyzing epicatechin formation declined in the later stages of drought stress. Notably, the increased expression of Aod07G019900 at day 10 may be one reason for the significant rise in epicatechin content at that time point. The expression of key enzyme genes ( Aod11G016970 , Aod13G004920 ) catalyzing catechin formation was significantly lower than the control group at day 20, leading to reduced catechin content. This is likely due to drought stress exceeding the plant's tolerance range, impairing its self-regulation capacity to enhance drought resistance. These results indicate that A. odoratissima promotes flavonoid biosynthesis in its leaves under drought stress by upregulating the expression of AoANS ( Aod07G019900 ). 4. Discussion A. odoratissima is a valuable tree species native. The species is recognized for its excellent adaptability to diverse environmental conditions and, therefore, warrants comprehensive genome sequencing studies. These studies would assist in comprehending the genomic structure of the species and its functions while also having significant implications in unraveling the origins and evolution of this species, revealing the essential functional genes, thereby facilitating the molecular marker-assisted selection (MAS) process in breeding programs. Therefore, the present study attempted to decipher the complete genome of A. odoratissima using third-generation sequencing methods to generate high-quality HiFi reads, coupled with Hi-C assisted assembly techniques resulting in a chromosomal-level genome assembly. This groundbreaking effort achieved a chromosome-level whole-genome assembly for the woody plant species, anchoring about 98.58% (719.88 Mb) of gene sequences onto 13 pseudochromosomes. The constructed A. odoratissima genome was determined to have a total of 1614 complete gene models. BUSCO analysis showed 97.9% completeness in gene models, reflecting the assembled genome's high integrity and sequence accuracy. This assembly would serve as a reliable reference genome database for future investigations targeting crucial gene functionalities, genetic improvements, and molecular markers relevant to this tree species. Concurrently, the present study predicted the presence of 2027 rRNAs, 598 tRNAs, 140 miRNAs, 83 snRNAs, and 90 snoRNAs in the constructed A. odoratissima genome. These RNA-level predictions and annotations would serve as substantial reference data for future post-genomic studies conducted for this species, particularly to elucidate the transcriptional regulatory mechanisms in response to environmental stimuli. Repetitive sequences constitute a substantial proportion of plant genomes. The analysis of repetitive sequences conducted for the genome of A. odoratissima constructed in the present study revealed that these sequences accounted for 56.38% of the plant’s genome, with the LTRs accounting for a 42.29% proportion. Notably, the Gypsy-type LTRs accounted for 40.69% of the repetitive sequences, with 372 of these LTR families having over 100 copies. This finding suggested the potential significance of the long-term evolutionary processes of A. odoratissima and implied that the genome expansion in A. odoratissima might progress relatively slowly compared to the other economically significant tree species, such as spruce and camellia due to the evolutionary context of the former. Comparative genomics involves comparing the known genes and genome structures with genome maps and sequencing data to understand gene functions, expressions, mechanisms, and species evolution. This study utilized the annotated high-quality A. odoratissima genome to compare 16 plant genomes, identifying 54,643 gene families. Among these, 1,930 families were common gene families shared among these 16 species. The highest number of shared gene families was 8,936, which was observed among the 5 species within the Leguminosae family. A. odoratissima , on the other hand, had just 1,420 unique gene families. Gene families tend to be relatively conserved across species. Examining the 1,420 unique gene families of A. odoratissima , potentially linked to its species specificity, was essential for understanding its evolutionary development. The gene families were then analyzed for enrichment using KEGG and GO. Accordingly, it was inferred that the rapid growth and differential environmental response of A. odoratissima was related to the enriched pathways revealed in the KEGG analysis for these specific gene families. These pathways were key to plant growth, development, and lignin synthesis [ , ] . The top five enriched biological processes revealed in the GO also suggested that A. odoratissima has distinctive hormone-related cell growth regulatory mechanisms compared to other plants. Single-copy gene families were utilized to construct a maximum likelihood phylogenetic tree. In this tree, A. odoratissima clustered with seven other species within the Leguminosae family, although its divergence from them occurred approximately 45.78 to 79.24 Mya. A. odoratissima showed notable expansions in gene families associated with the biosynthesis of sesquiterpenoids, triterpenoids, monoterpenes, seleno-compounds, betalains, and stilbenoids, diarylheptanoids, and gingerols, when compared to 16 other plant species. The functional annotations of these expanded gene families strongly suggest the presence of unique environmental responses and growth regulatory mechanisms in A. odoratissima compared to the other species. These results would be useful in identifying the genes associated with species traits, analyzing genes under positive selection during species evolution, and identifying genes related to the environmental adaptability of A. odoratissima . Water acts as a solvent to regulate physiological, biochemical and metabolic reactions within cells, which ultimately affects plant growth and development. As the duration of drought increased, the water content of the leaves of A. odoratissima gradually decreased and showed a significant difference between the 10th d and the control, indicating that drought reduced the water content of the leaves of A. odoratissima . Reduced water induces stomatal closure, which controls gas exchange and water loss, leading to a decrease in the net photosynthetic rate and transpiration rate, and at the same time promotes photorespiration as well as the accumulation of intercellular CO 2 , which ultimately inhibits growth and energy consumption and improves the tolerance of A. odoratissima . This mechanism may be a trade-off strategy of the plant under drought conditions to improve its adaptation and tolerance by moderately inhibiting growth [ , , ] . Drought stress rapidly induces the production of ROS, promotes lipid peroxidation and MDA formation, destroys the integrity of cell membranes, alters the morphology and structure of tissues and organs, and ultimately inhibits plant growth, yield and quality [ ] . It has been shown that increasing the synthesis and accumulation of intracellular osmoregulatory substances (soluble sugars, soluble proteins and proline.) and lowering the osmotic potential in order to reduce the water loss from their own bodies is an important physiological and biochemical mechanism for the plants to remove ROS and improve their drought tolerance [ , ] . In this study, with the prolongation of drought stress, A. odoratissima alleviated cellular osmotic stress by significantly up-regulating the synthesis of osmoregulatory substances, such as proline and soluble sugars (proline content in the leaves surged by 2,217.81%, p < 0.01), a mechanism that is highly similar to drought tolerance strategies in the Leguminous plants [ ] . Drought stress promoted the accumulation of free proline in A. odoratissima leaves, which could polymerise with some intracellular compounds to form a hydrophilic colloid-like substance [ ] , thus reducing the osmotic potential and water loss, and ultimately improving the drought resistance of A. odoratissima . The accumulated soluble sugars and soluble proteins in the leaves of A. odoratissima can reduce the osmotic potential of the cells, prevent the loss of intracellular water, and improve the drought resistance [ ] . In addition, the accumulated soluble sugars and soluble proteins can be rapidly degraded, providing the prerequisite substances and energy for the synthesis of membrane lipids and other substances, which has the function of stabilising the structural integrity of cell membranes and protoplasts, and thus improving the acacia's adaptability to drought stress [ ] . Under drought stress, up-regulated differentially expressed genes (DEGs) were primarily enriched in metabolic processes related to sugars and amino acids in leaves of A. odoratissima . This indicates that metabolic activities involving amino acids and sugars become more pronounced in the leaves as the plant adapts to drought stress. This response may occur because reduced internal water content and scarcity of growth resources under drought conditions drive increases in amino acid and sugar levels to sustain survival and facilitate environmental adaptation. Conversely, down-regulated DEGs were mainly enriched in processes such as photosynthesis and signal transduction. This suggests that genes regulating photosynthesis are suppressed and down-regulated following drought stress, leading to a reduced photosynthetic rate in A. odoratissima leaves. Drought stress primarily affects plant leaf photosynthesis through two mechanisms: Stomatal Limitation: Drought stress reduces stomatal conductance, impeding the entry of CO₂ (the raw material for photosynthesis) into the leaves. This directly lowers the photosynthetic rate. Consequently, the depletion of endogenous substrates within the leaves is accelerated. Pathways related to the C-cycle and amino acid metabolism persistently respond to drought stress to maintain normal leaf growth. Non-stomatal Limitation: When leaf water potential drops below a critical threshold, the structure of chloroplasts is damaged, and the activity of Photosystem II (PSII) is constrained. This subsequently inhibits electron transport and photophosphorylation, resulting in decreased photosynthesis. Therefore, photosynthesis-related pathways are impaired or suppressed, and associated genes exhibit sustained low expression. This constitutes a drought resistance mechanism by reducing leaf energy expenditure [ ] . At both day 10 and day 20 of drought stress, metabolites in A. odoratissima were enriched in amino acid metabolic pathways. Amino acid metabolism is a fundamental process for plant growth and development, providing essential proteins and supplying energy to sustain vital activities. Amino acids play multiple roles in regulating plant tolerance to abiotic stress, acting as osmotic regulators, ROS scavengers, and precursors for energy-related metabolites [ , , ]. Under drought stress, proline content in A. odoratissima accumulated dramatically. Proline can be rapidly synthesized into osmotic compounds, reflecting the plant's internal osmotic pressure. It serves as a stress-indicator amino acid and plays a crucial role in maintaining cellular osmotic potential, thereby supporting normal cell growth and development and ultimately enhancing the drought resistance of A. odoratissima [ ].These findings are largely consistent with studies on Taxus cuspidata , which also showed significant increases in sugars and amino acids to combat drought stress [ ]. Under drought conditions, A. odoratissima regulated the expression of multiple key enzyme genes involved in the synthesis of amino acids such as valine, leucine, isoleucine, lysine, and proline. The expression levels of these genes increased with prolonged drought duration, which is likely to help scavenge ROS and improve the drought tolerance of A. odoratissima . The amino acid content gradually increased over the drought period. This may be attributed to reduced water content within the plant limiting the water required for normal growth, thus prompting an increase in amino acids to sustain survival and adapt to environmental changes. Drought induces plant cells to produce excessive amounts of reactive oxygen species (ROS), which act as a stress signalling molecule as well as oxidatively damaging biomolecules, affecting the integrity of cell membranes, interfering with the normal metabolic processes of plant cells, and even causing cell death [ ] . Flavonoids have strong antioxidant capacity to resist oxidative stress damage under drought stress in plants, thus effectively responding to the adverse effects of drought [ ] . AoANS was specifically up-regulated at 10 d of drought stress, promoting epicatechin synthesis, decreasing ROS accumulation under drought stress, and increasing the antioxidant capacity and thus enhancing the drought tolerance of A. odoratissima . 5. Conclusions About 719.88 Mb of contig level genome was obtained from the assembly based on triple sequencing technology, contig N50 was 53.74 Mb, and 98.58% of gene sequences were assembled on 13 pseudochromosomes. Comparative analyses of A. odoratissima with 15 plant genomes revealed a total of 54,643 gene families, 1,930 shared gene families, with the latest WGD at about 62.9 Mya shared with legumes. 8,936 gene families shared by A. odoratissima with five homozygous legumes, and 1,420 gene families specific to A. odoratissima , of which 177 gene families underwent expansion, 177 gene families expanded and 10 contracted. Enhanced drought tolerance by synergistic inhibition of photosynthesis, activation of osmoregulation and elevation of antioxidant defences in A. odoratissima . This study serves as a crucial resource for advancing research and molecular breeding of A. odoratissima , aiming to develop drought-tolerant varieties and enhance its economic value. Overall, this high-quality reference genome provides insights into the genetic background and phylogeny of A. odoratissima . This research lays the groundwork for advancing genetic enhancement and breeding of A. odoratissima . Declarations Acknowledgements Not applicable. CRediT authorship contribution statement Feng Gao: Writing-original draft, Writing-review & editing, Methodology, Formal analysis, Data curation. Shuoxing Wei: Writing-review & editing, Investigation, Funding acquisition. Hanbiao Ou: Writing-review & editing, Writing-original draft, Supervision, Investigation, Conceptualization. Zhihui Wang: Writing-review & editing, Investigation. Guoping Yin: Writing-review & editing, Supervision, Funding acquisition, Conceptualization. Shizhi Wen: Writing-original draft, Methodology, Formal analysis, Data curation. Chunhe Yu: Funding acquisition. Zhifeng Lu: Funding acquisition. Jianwu Chen: Methodology. Fundings Funding for this research was provided by the Guangxi Key Research and Development Program (Projects 2025GXNSFAA069945, AB240100090 and AB21220026) and self-financed forestry science and technology projects in Guangxi (Guangxi Forestry Research [2022ZC]).105 and 2023GXZCLK 35). Data Availability Statement The corresponding author will provide the data supporting this article upon reasonable request. The raw genome and transcriptome sequencing data for A. odoratissima are available in the Genome Sequence Archive (https://www.ncbi.nlm.nih.gov/ accession no. PRJCA023416). Conflicts of Interest Ethics approval and consent to participate All samples collected fully adhere to national and local legal requirements. The plant samples used in the study were neither listed as nationally protected nor gathered from national parks or natural reserves. No specific permissions were necessary for their collection according to national and local laws.Consent for publication. 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Nakabayashi R., Yonekura‐Sakakibara K., Urano K., Suzuki M., Yamada Y., Nishizawa T., Matsuda F., Kojima M., Sakakibara H.,Shinozaki K., Michael A.J., Tohge T., Yamazaki M., and Saito K.. Enhancement of oxidative and drought tolerance inArabidopsis by overaccumulation of antioxidant flavonoids. The Plant Journal. 2014;77(3):367-379. Additional Declarations No competing interests reported. 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Institute","correspondingAuthor":false,"prefix":"","firstName":"Hanbiao","middleName":"","lastName":"Ou","suffix":""},{"id":471432992,"identity":"6ee4cb74-0155-4533-8ead-b1eac788a2d3","order_by":3,"name":"Zhihui Wang","email":"","orcid":"","institution":"Guangxi Forestry Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Zhihui","middleName":"","lastName":"Wang","suffix":""},{"id":471432993,"identity":"f1cd817d-2447-41c5-9556-e8001816afa2","order_by":4,"name":"Guoping Yin","email":"","orcid":"","institution":"Guangxi Forestry Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Guoping","middleName":"","lastName":"Yin","suffix":""},{"id":471432994,"identity":"ec85de83-7d1b-4c45-a90b-883840f5727b","order_by":5,"name":"Shizhi Wen","email":"","orcid":"","institution":"Central South University of Forestry and Technology","correspondingAuthor":false,"prefix":"","firstName":"Shizhi","middleName":"","lastName":"Wen","suffix":""},{"id":471432995,"identity":"a2660fb0-f2f3-4d71-9232-13291704cabd","order_by":6,"name":"Chunhe Yu","email":"","orcid":"","institution":"Yachang State-owned Forest Farm","correspondingAuthor":false,"prefix":"","firstName":"Chunhe","middleName":"","lastName":"Yu","suffix":""},{"id":471432996,"identity":"e1825b85-1ba8-4df9-ac9c-66b7fb241db9","order_by":7,"name":"Zhifeng Lu","email":"","orcid":"","institution":"Yachang State-owned Forest Farm","correspondingAuthor":false,"prefix":"","firstName":"Zhifeng","middleName":"","lastName":"Lu","suffix":""},{"id":471432997,"identity":"2cf9a255-e2ba-46e1-93e9-3d7767ab2154","order_by":8,"name":"Jianwu Chen","email":"","orcid":"","institution":"Guangxi Forestry Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Jianwu","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2025-06-07 03:38:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6840333/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6840333/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12870-025-07523-5","type":"published","date":"2025-10-31T15:57:46+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":84817281,"identity":"9212981c-f4ff-499a-97fd-4defae782d95","added_by":"auto","created_at":"2025-06-17 15:48:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":19856884,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eA. odoratissima\u003c/em\u003egenome assembly and characteristics of superior quality. \u003cstrong\u003eA: \u003c/strong\u003ePhotograph of \u003cem\u003eA. odoratissima\u003c/em\u003e.The five photographs at the bottom depict, from top to bottom, fruits, flowers, lateral branches, seeds, and resolved wood. \u003cstrong\u003eB: \u003c/strong\u003eThe Hi-C interaction heatmap plot of \u003cem\u003eA. odoratissima\u003c/em\u003e genome. \u003cstrong\u003eC: \u003c/strong\u003eThe genomic feature circle map of \u003cem\u003eA. odoratissima\u003c/em\u003e. \u003cstrong\u003eD: \u003c/strong\u003eContig-level genomic statistics of \u003cem\u003eA. odoratissima\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6840333/v1/f758fbbb6ec13d3b2543eba3.png"},{"id":84818965,"identity":"5fe45909-1fe9-4693-8676-8ebbc71bcc83","added_by":"auto","created_at":"2025-06-17 15:56:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2754054,"visible":true,"origin":"","legend":"\u003cp\u003ePhylogenetic analysis of the \u003cem\u003eA. odoratissima\u003c/em\u003e genome. \u003cstrong\u003eA:\u003c/strong\u003e Analysis of gene families shared by \u003cem\u003eA. odoratissima\u003c/em\u003e, \u003cem\u003eG. max, C. cajan, L. japonicus and C. arietinum\u003c/em\u003e. \u003cstrong\u003eB:\u003c/strong\u003e The genome collinearity between \u003cem\u003eA. odoratissima\u003c/em\u003e, \u003cem\u003eG. max\u003c/em\u003e and \u003cem\u003eV. vinifera\u003c/em\u003e. \u003cstrong\u003eC:\u003c/strong\u003e Rates of base synonymous substitutions (Ks) for \u003cem\u003eA.odoratissima\u003c/em\u003eand 4 other species. \u003cstrong\u003eD:\u003c/strong\u003e The insertion burst time of LTR in \u003cem\u003eA. odoratissima\u003c/em\u003e. \u003cstrong\u003eE:\u003c/strong\u003e Phylogenetic tree for \u003cem\u003eA. odoratissima\u003c/em\u003e and 15 other plants.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6840333/v1/744cc8faee1d4d088d856878.png"},{"id":84817275,"identity":"32b8e019-61e0-41bd-ad54-6ab3d3a74d10","added_by":"auto","created_at":"2025-06-17 15:48:52","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":623634,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of drought on photosynthesis and endogenous hormones in \u003cem\u003eA. odoratissima\u003c/em\u003e leaves.\u003cstrong\u003e A:\u003c/strong\u003e Net photosynthetic rate (Pn). \u003cstrong\u003eB:\u003c/strong\u003e Stomatal conductance (Gs). \u003cstrong\u003eC:\u003c/strong\u003e Intercellular carbon dioxide concentration (Ci). \u003cstrong\u003eD:\u003c/strong\u003e transpiration rate (Tr). \u003cstrong\u003eE:\u003c/strong\u003e Mositure content. \u003cstrong\u003eF: \u003c/strong\u003eProline. \u003cstrong\u003eG: \u003c/strong\u003eSoluble sugar. \u003cstrong\u003eH: \u003c/strong\u003eSoluble protein.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6840333/v1/f9fd3cf07c3ab2acd4a386a9.png"},{"id":84818966,"identity":"a7352454-0d6f-4634-a805-01f6a371e411","added_by":"auto","created_at":"2025-06-17 15:56:52","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2145672,"visible":true,"origin":"","legend":"\u003cp\u003eDEGs analysis. \u003cstrong\u003eA:\u003c/strong\u003e DEGs analysis of CK10d vs DS10d. \u003cstrong\u003eB:\u003c/strong\u003e DEGs analysis of CK20d vs DS20d. \u003cstrong\u003eC:\u003c/strong\u003e KEGG enrichment analysis of up-regulated DEGs in CK10d vs DS10d. \u003cstrong\u003eD:\u003c/strong\u003e KEGG enrichment analysis of down-regulated DEGs in CK10d vs DS10d.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6840333/v1/9f485e58c83a1ec9bed5815b.png"},{"id":84817280,"identity":"091d1aec-1275-42a8-87ef-8dcb12f4d4a3","added_by":"auto","created_at":"2025-06-17 15:48:52","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1434726,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential accumulation metabolites (DAMs) analysis. \u003cstrong\u003eA:\u003c/strong\u003e DAMs of CK10d vs DS10d.\u003cstrong\u003e B:\u003c/strong\u003e DAMs of CK20d vs DS20d. \u003cstrong\u003eC:\u003c/strong\u003e KEGG enrichment analysis of DAMs in CK10d vs DS10d. \u003cstrong\u003eD:\u003c/strong\u003e KEGG enrichment analysis of DAMs in CK20d vs DS20d.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6840333/v1/a7783c58be1287177b1bfcf0.png"},{"id":84817285,"identity":"0c4a329b-17b7-49be-a051-249be8d6fe9a","added_by":"auto","created_at":"2025-06-17 15:48:52","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":818835,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of flavanol metabolism pathways of \u003cem\u003eA. odoratissima\u003c/em\u003e under drought stress.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6840333/v1/4af04dc0bb3b1f5f627bec00.png"},{"id":95040652,"identity":"c5a974fb-8b20-42e2-a84e-06902c1acea1","added_by":"auto","created_at":"2025-11-03 16:10:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":26808583,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6840333/v1/5994ea6c-79f5-4de4-bd7b-c56339ad7467.pdf"},{"id":84817271,"identity":"7343a9a6-04a8-4a2b-9a1b-066f80193d50","added_by":"auto","created_at":"2025-06-17 15:48:51","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":47513,"visible":true,"origin":"","legend":"","description":"","filename":"schedules.docx","url":"https://assets-eu.researchsquare.com/files/rs-6840333/v1/d9ee89a62307ef7c97110e70.docx"},{"id":84817273,"identity":"6c76f0f0-d929-47fd-88a4-80cde9a44e10","added_by":"auto","created_at":"2025-06-17 15:48:52","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1797382,"visible":true,"origin":"","legend":"","description":"","filename":"diagram.docx","url":"https://assets-eu.researchsquare.com/files/rs-6840333/v1/e5e379475b36ded8482570a6.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Chromosome-level genome assembly of Albizia odoratissima and effect of flavonoid metabolic pathways under drought stress","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e\u003cem\u003eAlbizia odoratissima\u003c/em\u003e, a diploid (2n=26) deciduous tree species, is part of the \u003cem\u003eLeguminosae\u003c/em\u003e family. The species is distributed mainly across Southeast Asian nations, such as China, India, Vietnam [\u003csup\u003e1]\u003c/sup\u003e. \u003cem\u003eA. odoratissima\u003c/em\u003e's rapid growth, favorable wood properties, drought resistance, and robust regenerative capacity have enabled its extensive cultivation in Guangxi, Hainan, and other regions of China. \u003cem\u003eA. odoratissima\u003c/em\u003e has significant economic value as its wood, which is characterized as a dense hardwood with a density of approximately 0.843 g/cm\u003csup\u003e3\u003c/sup\u003e, is aesthetically pleasing, exhibiting a tight grain and dense texture, while also offering durability and stability. The wood of this species resists cracking, deformation after drying, and decay and insect infestation in the heartwood [\u003csup\u003e2]\u003c/sup\u003e. Owing to these qualities, the wood from \u003cem\u003eA. odoratissima\u003c/em\u003e is considered excellent for crafting high-quality furniture, carving, construction, shipbuilding, and other applications [\u003csup\u003e3]\u003c/sup\u003e. \u003cem\u003eA. odoratissima\u003c/em\u003e holds substantial ecological importance as a leading nitrogen-fixing tree species due to its rhizobia. \u003cem\u003eA. odoratissima\u003c/em\u003e normally grows in a nitrogen-limited environment, thereby reducing the amounts of nitrogen fertilizer to be applied for its cultivation, ultimately reducing the production costs. \u003cem\u003eA. odoratissima\u003c/em\u003e is also drought-tolerant and an evergreen species, with its flowering period spanning the months from April to July. The species also has a certain ornamental value and may be used as a street tree. Currently, most of the \u003cem\u003eA. odoratissima\u003c/em\u003e resources are located in the wild, and due to serious logging damage and difficulty in achieving natural regeneration, the sustainable utilization and development of this species have been critically hampered.\u003c/p\u003e\n\u003cp\u003eThe rapid development of genome sequencing and assembly technologies has enabled deciphering the genomes of a large number of species. \u003cem\u003eA. odoratissima\u003c/em\u003e breeding remains in the traditional phase, with molecular breeding efforts confined to the development of EST-SSR markers [\u003csup\u003e4]\u003c/sup\u003e. The existing literature on deciphering the genome of \u003cem\u003eA. odoratissima\u003c/em\u003e is scarce, with the initial studies reporting that \u003cem\u003eA. odoratissima\u003c/em\u003e has a simple genome of approximately 700 Mb in size [\u003csup\u003e5]\u003c/sup\u003e. With the availability of better and more advanced technologies at present, rapid and efficient deciphering and analysis of the entire genome of \u003cem\u003eA. odoratissima\u003c/em\u003e appears possible.\u003c/p\u003e\n\u003cp\u003eFlavonoid synthesis pathways and the enzymes involved in their synthesis are highly conserved in plants, and their contents are not only regulated by the intrinsic genetic mechanisms of the plant, but also by environmental stresses such as water, salt damage, UV-B, temperature, and heavy metal toxicity [\u003csup\u003e6\u003c/sup\u003e,\u003csup\u003e7\u003c/sup\u003e]. For example, drought stress led to a significant increase in the content of metabolites involved in flavonoid biosynthesis, such as cinnamic acid, p-coumaric acid, and trans-cinnamic acid, the expression of genes involved in flavonoid biosynthesis, such as \u003cem\u003eflavanone 3′-hydroxylase\u003c/em\u003e (\u003cem\u003eF3′H\u003c/em\u003e), as well as the total flavonoid content of Ginkgo biloba leaves [\u003csup\u003e8]\u003c/sup\u003e. Flavonoids also play an important role in plant abiotic stress resistance due to their strong antioxidant capacity to scavenge excess reactive oxygen species (ROS) induced by the above abiotic stresses in plants. For example, the stress resistance of several plants is strongly correlated with the flavonoid content in the body [\u003csup\u003e9\u003c/sup\u003e,\u003csup\u003e10]\u003c/sup\u003e. Overexpression of key genes involved in flavonoid biosynthesis, such as \u003cem\u003echalconeisomerase 2\u003c/em\u003e (\u003cem\u003eCHI2\u003c/em\u003e), \u003cem\u003eF3′H\u003c/em\u003e, and \u003cem\u003eproduction of anthocyanin pigment 1\u003c/em\u003e (\u003cem\u003ePAP1\u003c/em\u003e), enhances flavonoid biosynthesis in \u003cem\u003eArabidopsis thaliala\u003c/em\u003e, \u003cem\u003eOryza sativa\u003c/em\u003e and \u003cem\u003ePohlia nutans\u003c/em\u003e, among others, and increase antioxidant properties, thereby significantly enhancing the resistance of these plants to salt, drought and cold damage [\u003csup\u003e11\u003c/sup\u003e, \u003csup\u003e12\u003c/sup\u003e,\u003csup\u003e13]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eA. odoratissima\u003c/em\u003e is a drought-tolerant tree species with significant ecological and economic importance in China's dry-hot river valleys, however, high-quality reference genomes are lacking. This study utilized three-generation HiFi sequencing and Hi-C technology to sequence and assemble a chromosome-level reference genome of \u003cem\u003eA. odoratissima\u003c/em\u003e. The genetic background of \u003cem\u003eA. odoratissim\u003c/em\u003ea was elucidated through genome functional annotation, unique gene family analysis, and phylogenetic tree construction. Flavonoids, essential secondary metabolites, exhibit strong antioxidant properties, regulate leaf stomatal closure, absorb UV-B spectra, and complex heavy metal elements. These functions are crucial for plant defense against abiotic stress, as changes in flavonoid content are induced by such stress. However, information on genes encoding key enzymes for flavonoid biosynthesis in acacia under drought stress remains unavailable.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003e2.1. Materials\u003c/p\u003e\n\u003cp\u003eThis study utilized \u003cem\u003eA. odoratissima\u003c/em\u003e from the dry and hot valley region near the Nanpanjiang River (106\u0026deg;17\u0026prime;1\u0026Prime;E, 24\u0026deg;51\u0026prime;47\u0026Prime;N) as the sample wood material. The sample site, located 7 km from the river, is representative of this area\u0026apos;s environmental conditions.Five fresh young leaves were collected from each side of the tree crown, immediately placed in a centrifuge tube, frozen in liquid nitrogen, preserved on dry ice, and sent to Beijing Baimaike Biotechnology Co.\u003c/p\u003e\n\u003cp\u003e2.2. Genome sequencing\u003c/p\u003e\n\u003cp\u003eThe genome sequencing study utilized the tender leaves of \u003cem\u003eA. odoratissima\u003c/em\u003e as plant material. Genomic DNA was extracted from \u003cem\u003eA. odoratissima\u003c/em\u003e tender leaves using an optimized CTAB method for whole genome sequencing.Two short DNA fragment libraries were constructed and sequenced using the Illumina HiSeq X Ten platform, producing around 69 Gb of clean reads.The genome size was estimated using KmerGenie with a k-mer size of 17 [\u003csup\u003e14]\u003c/sup\u003e. A CCS library of SMART cells was sequenced using the PacBio Sequel II platform, employing the CCS model to produce approximately 54 Gb of HIFI reads. A library was constructed using the Hind III restriction enzyme and sequenced with Hi-C and paired-end methods on the Illumina Novaseq 6000 platform, yielding about 168 Gb (~230x) of clean reads.\u003c/p\u003e\n\u003cp\u003e2.3. Genome assembly\u003c/p\u003e\n\u003cp\u003eThe HiFiasm software [\u003csup\u003e15]\u003c/sup\u003e was employed to reassemble the PacPio CCS HiFi reads data, using default parameters, and the contig-level \u003cem\u003eA. odoratissima\u003c/em\u003e genome was obtained.The Hi-C data was processed and assessed using the Hi-C-Pro software [\u003csup\u003e16]\u003c/sup\u003e. The evaluated data facilitated the assembly of \u003cem\u003eA. odoratissima\u003c/em\u003e\u0026apos;s chromosome-level genome. LACHESIS [\u003csup\u003e17]\u003c/sup\u003e software was then employed for grouping, sorting, and orienting the genome sequences, followed by manual mapping and inspection, which finally generated the chromosome-level version of the genome of this species.\u003c/p\u003e\n\u003cp\u003e2.4. Genome annotation\u003c/p\u003e\n\u003cp\u003e2.4.1. Repeat sequence annotation\u003c/p\u003e\n\u003cp\u003eRepeat sequences in \u003cem\u003eA. odoratissima\u003c/em\u003e were annotated using RepeatModeler (v4.0.9) and RepeatMasker (v1.0.8) [\u003csup\u003e18]\u003c/sup\u003e through homology alignment and de novo prediction strategies.Subsequently, TEclass was utilized to classify the unknown repeat sequences.The TRF package [\u003csup\u003e19]\u003c/sup\u003e was used next to identify the tandem repeat sequences, and all group results were integrated.Afterward, LTR_Finde and LTRharvest were used with default parameters to identify all long terminal repeat sequences (LTRs) [\u003csup\u003e20\u003c/sup\u003e,\u003csup\u003e21]\u003c/sup\u003e in the genome. The LTR_retriever software [\u003csup\u003e22\u003c/sup\u003e,\u003csup\u003e23]\u003c/sup\u003e was then employed to identify the LTR-RTs among the determined LTRs.\u003c/p\u003e\n\u003cp\u003e2.4.2. Gene model prediction\u003c/p\u003e\n\u003cp\u003eDe novo prediction was conducted using Augustus v2.4 [\u003csup\u003e24]\u003c/sup\u003e and SNAP (2006-07-28) [\u003csup\u003e25]\u003c/sup\u003e. Homology-based prediction was conducted based on related species using GeMoMa [\u003csup\u003e26]\u003c/sup\u003e v1.7. The transcriptome prediction utilized two primary methods: reference-based and de novo assembly-based predictions. Reference-based transcripts were primarily acquired using Hisat v2.0.4 [\u003csup\u003e27]\u003c/sup\u003e and Stringtie v1.2.3 [\u003csup\u003e28]\u003c/sup\u003e, with gene prediction subsequently performed using GeneMarkS-T v5.1 [\u003csup\u003e29]\u003c/sup\u003e. The de novo transcripts were primarily assembled with Trinity (v2.11) [\u003csup\u003e30]\u003c/sup\u003e, and gene prediction was subsequently performed using PASA v2.0.2 [\u003csup\u003e31]\u003c/sup\u003e. EVM v1.1.1 [\u003csup\u003e32]\u003c/sup\u003e was used to consolidate the prediction results from the three methods, and PASA v2.0.2 was applied to refine the integrated data.\u003c/p\u003e\n\u003cp\u003e2.4.3. Gene functional annotation\u003c/p\u003e\n\u003cp\u003eThe predicted gene sequences were annotated using the NR (202009, ftp://ftp.ncbi.nlm.nih.gov/blast/db), EggNOG (5.0, http://eggnog5.embl.de/download/eggnog_5.0/) [\u003csup\u003e33]\u003c/sup\u003e GO (20200615, http://geneontology.org), KEGG (20191220, http://www.genome.jp/kegg) [\u003csup\u003e34]\u003c/sup\u003e, SWISS-PROT (202005, http://ftp.ebi.ac.uk/pub/databases/swissprot) [\u003csup\u003e35]\u003c/sup\u003e, and Pfam (v33 .1, http://pfam.xfam.org) [\u003csup\u003e36]\u003c/sup\u003e databases.\u003c/p\u003e\n\u003cp\u003e2.4.4. Non-coding RNA prediction\u003c/p\u003e\n\u003cp\u003eThe tRNAscan-SE v1.3.1 [\u003csup\u003e37]\u003c/sup\u003e was utilized to identify the tRNA. The prediction of rRNA primarily utilized the Rfam database (version 12.0) [\u003csup\u003e38]\u003c/sup\u003e. The barrnap (v 0.9) software was employed for the prediction. The miRNA was identified through the miRbase database [\u003csup\u003e39]\u003c/sup\u003e. The predictions for snoRNA and snRNA utilized the Rfam (v 12.0) database with Infenal 1.1 [\u003csup\u003e40]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e2.4.5. Pseudogenome annotation\u003c/p\u003e\n\u003cp\u003eGenBlastA v1.0.4 [\u003csup\u003e41]\u003c/sup\u003e was employed for alignment, and the homologous gene sequences (possible genes) were sought on the genome after masking the genuine gene loci. GeneWise v2.4.1 [\u003csup\u003e42]\u003c/sup\u003e was used to identify immature stop codons and frameshift mutations in gene sequences, resulting in the discovery of 190 pseudogenes.\u003c/p\u003e\n\u003cp\u003e2.5. Gene family cluster analysis and phylogenetic analysis\u003c/p\u003e\n\u003cp\u003e2.5.1. Gene family cluster analysis\u003c/p\u003e\n\u003cp\u003eOrthofinder v2.4 [\u003csup\u003e43]\u003c/sup\u003e was used to classify protein sequences from 16 species into families, utilizing the diamond alignment method with an e-value of 0.001. PANTHER v 15 database [\u003csup\u003e44]\u003c/sup\u003e was used for the annotation of the obtained gene families. GO and KEGG enrichment analyses identified 54,643 species-specific gene families. A Venn analysis was conducted for the gene families of \u003cem\u003eA. odoratissima\u003c/em\u003e, \u003cem\u003eCajanus cajan\u003c/em\u003e, \u003cem\u003eG. max\u003c/em\u003e, \u003cem\u003eLotus japonicus\u003c/em\u003e, and \u003cem\u003eCicer arietinum\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e2.5.2. Phylogenetic analysis\u003c/p\u003e\n\u003cp\u003eUsing IQ-TREE v1.6.11, a maximum likelihood phylogenetic tree was constructed from 1,174 single-copy gene sequences, representing 81.2% of the species, with 1,000 bootstraps. The result was visualized using Evolview (http://www.evolgenius.info/evolview). \u003cem\u003eOryza sativa\u003c/em\u003e was used as the outgroup.The species divergence times were calculated using the PAML software [\u003csup\u003e45]\u003c/sup\u003e. Fossil divergence times were sourced from the TimeTree website (http://www.timetree.org/): \u003cem\u003eG. max\u003c/em\u003e and \u003cem\u003eC. cajan\u003c/em\u003e diverged 11.7-27.5 Mya; \u003cem\u003eG. max\u003c/em\u003e and \u003cem\u003eO. sativa\u003c/em\u003e diverged 115-308 Mya; \u003cem\u003eEucommia ulmoides\u003c/em\u003e and \u003cem\u003eO. europaea\u003c/em\u003e diverged 87-104 Mya; \u003cem\u003eG. max\u003c/em\u003e and \u003cem\u003eP. trichocarpa\u003c/em\u003e diverged 101-131 Mya. These fossil times were obtained using the software based on the algorithm. CAF\u0026Eacute; software [\u003csup\u003e46]\u003c/sup\u003e was utilized to analyze the gene family expansion and contraction in the species.\u003c/p\u003e\n\u003cp\u003e2.5.3. Analysis of genome-wide duplication events\u003c/p\u003e\n\u003cp\u003eThe command-line tool WGDi [\u003csup\u003e47]\u003c/sup\u003e, developed in Python, was used to identify whole-genome duplication (WGD) events in \u003cem\u003eA. odoratissima\u003c/em\u003e. BLASTP (E \u0026lt; 1e-5) was used to compare protein sequences within each genome (\u003cem\u003eA. odoratissima\u003c/em\u003e, soybean, pigeonpea, peanut (diploid), and grape) and between genomes (\u003cem\u003eA. odoratissima\u003c/em\u003e and soybean; pigeonpea and peanut (diploid); pigeonpea and soybean) to identify homologous genes. Subsequently, gene position and chromosome length information for these genomes were acquired. WGDi facilitated the generation of dot plots and the collinearity analysis. Homologous gene pairs were identified, and yn00 in PAML was used to compute their non-synonymous (Ka) and synonymous (Ks) values. Subsequently, the results facilitated the creation of the Ks frequency distribution diagram. Finally, the peak value was fitted using the median Ks value from the frequency distribution of the blocks.The WGD event time was estimated using the equation Ks = t/2r, where the molecular clock rate (r) was 7 \u0026times; 10\u0026ndash;9.\u003c/p\u003e\n\u003cp\u003e2.6. Drought stress experiment\u003c/p\u003e\n\u003cp\u003eThis study used healthy individual trees from natural forests of \u003cem\u003eAlbizia odoratissima\u003c/em\u003e in the dry-hot valley region of the Nanpan River, Leye County, Guangxi, China (106\u0026deg;17\u0026prime;1\u0026Prime;E, 24\u0026deg;51\u0026prime;47\u0026Prime;N, Figure 2.2) as sample sources. The sample trees were located approximately 7 km straight-line distance from the Nanpan River, within a typical dry-hot valley zone. Tender shoot segments from these trees served as explants for asexual propagation of experimental seedlings. Tissue-cultured seedlings derived from this propagation were used for the drought stress experiment. Seedlings were cultivated in plastic pots (top diameter: 20 cm; bottom diameter: 12 cm; height: 6.5 cm). The growth substrate consisted of a mixture of yellow laterite, coconut coir, and rice husk at a ratio of 5:3:2, ensuring normal seedling growth. The initial soil water content was 35.26%. After one year of growth in a greenhouse, seedlings with uniform height and ground diameter were selected for the drought stress experiment. The average seedling height was 80.3 cm, and the average ground diameter was 0.67 cm. The experiment comprised a control group (watered daily) and a drought stress treatment group (no watering), with 60 seedlings per group and three biological replicates. Three randomly selected and tagged seedlings from both the control and stress groups were used for sampling. Leaves from the same canopy layer were collected from both groups at 10 days (d) and 20 d of stress. A Li-6400 photosynthesis system was used to measure the transpiration rate (Tr), net photosynthetic rate (Pn), intercellular CO₂ concentration (Ci), and stomatal conductance (Gs) at three leaf positions (the third or fourth leaf from the apical bud) on the tagged seedlings. Average values for each parameter were calculated. Proline content, soluble protein content and soluble sugar content were determined by acid ninhydrin, Kaumas Brilliant Blue and anthrone colourimetric methods in leaves under drought stress and the control group respectively [\u003csup\u003e48]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e2.7. Transcriptome sequencing and analysis\u003c/p\u003e\n\u003cp\u003eTranscriptome samples were collected from seedlings subjected to the drought stress experiment described in Section 2.1.1. Leaves were harvested at Day 10 (Drought stress: DS10, Control: CK10) and Day 20 (Drought stress: DS20, Control: CK20) from plants exhibiting uniform size, absence of pests/diseases, and healthy growth. Immediately after collection, leaves were snap-frozen in liquid nitrogen. Three biological replicates were performed for each experimental group. Total RNA was extracted using the RNAsimple Total RNA Kit (Tiangen, Beijing, China). RNA concentration was quantified using a NanoDrop ND-1000 spectrophotometer (Wilmington, DE, USA), and RNA integrity was assessed (via RIN value) using an Agilent 2100 Bioanalyzer (Palo Alto, CA, USA). Libraries were constructed and subjected to paired-end sequencing on the Illumina HiSeq4000 platform to obtain transcriptomic data.\u003c/p\u003e\n\u003cp\u003e2.8. Sample preparation for LC-MS analysis of differentially accumulated metabolites\u003c/p\u003e\n\u003cp\u003eThe chromatographic column was Waters ACQUITY UPLC HSS T3 C18 1.8 \u0026micro;m, 2.1 mm * 100 mm. The mobile phase: Phase A was ultrapure water (0.1% formic acid), and phase B was acetonitrile (0.1% formic acid). The elution gradient: 0 min water/acetonitrile (95:5 V/V), 10.0 min 5:95 V/V, 11.0 min 5:95 V/V, 11.1 min 95:5 V/V, 15.0 min 95:5 V/V. The flow rate was 0.4 mL/min, column temperature 40 ℃, injection volume 2 \u0026mu;L, electrospray ionization source temperature 550 ℃, mass spectrometry voltage 5500 V, -4500 V, ion source gas I 55 psi, gas II 60 psi, curtain gas 25 psi, and the collision-induced dissociation parameters were set to high. In the triple quadrupole, each ion pair was scanned and detected according to the optimized declustering potential and collision energy.\u003c/p\u003e\n\u003cp\u003e2.9. Statistical analysis\u003c/p\u003e\n\u003cp\u003eMetabolite identification and abundance calculation were performed using Analyst 1.6.3 software combined with a local metabolite database. Principal Component Analysis (PCA) results revealed the overall metabolic differences between sample groups and the variation within groups. Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA) was conducted on the metabolites to obtain the Variable Importance in Projection (VIP) scores from the multivariate OPLS-DA model. Significant differential metabolites were screened based on combined threshold criteria (Fold Change \u0026ge; 1.5 or \u0026le; 0.67 and VIP \u0026ge; 1). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed on the differential metabolites to reveal their potential biological functions.\u003c/p\u003e\n\u003cp\u003eDifferential genes (DEGs) and differential metabolites (DAMs) from the same comparison groups were co-mapped onto KEGG pathways to visualize their relationships. The KGML sub-database within KEGG was utilized to analyze and display the network relationships between genes and metabolites. TBtools v1.098 was employed to normalize and visualize DEGs and DAMs in the metabolic pathways of CK10 vs DS10 and CK20 vs DS20, with final figures generated using Adobe Illustrator 2022.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Genome assembly and annotation of \u003cem\u003eA. odoratissima\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eThe whole-genome assembly and annotation step was conducted using a high-quality and precious timber tree species named \u003cem\u003eA. odoratissima\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea), which is a diploid plant with 13 pairs of homologous chromosomes (2n\u0026thinsp;=\u0026thinsp;2x\u0026thinsp;=\u0026thinsp;26) (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Using the frequency distribution with k-mer\u0026thinsp;=\u0026thinsp;21, the genome size of \u003cem\u003eA. odoratissima\u003c/em\u003e was predicted to be 729.45 Mb, with a heterozygosity rate of 1.86%, indicating a diploid genome (Fig.\u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Sequencing on the Sequel II platform \u003csup\u003e[\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn49\" id=\"#FNLinkFn49\"\u003e\u003c/a\u003e\u003csup\u003e]\u003c/sup\u003e yielded approximately 54.03 Gb of Pacbio CCS HIFI reads, achieving around 75x coverage (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The \u003cem\u003eA. odoratissima\u003c/em\u003e genome was then assembled de novo using HiFiasm. The contig-level genome size was 783 Mb. The assembly size was 48 Mb, achieving a contig N50 of 53.74 Mb and a GC content of 33.63% (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). Approximately 168.05 Gb (~\u0026thinsp;230x) of Hi-C reads were sequenced using the Illumina platform (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Hi-C data underwent quality control with HiC-Pro (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). The quality-controlled Hi-C data were used to correct the contig assembly, resulting in a final contig-level genome size of 719.88 Mb, aligning closely with the predicted size. The genome's N50 was 54.41 Mb with a GC content of 33.62%. Following the application of LACHESIS software and subsequent manual refinements, 719.88 Mb, representing 98.58% of the genome, was anchored to 13 pseudochromosomes (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e), with just 4 gaps and the chromosome lengths ranging from 35.96 to 76.01 Mb (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed, Fig.\u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e, Table S3). The second-generation Illumina reads were aligned to the assembled genome, showing that 454,948,025 reads (98.67%) mapped to the genome, with 434,527,624 reads (94.24%) being perfect matches (Table S4). The subsequent use of the CEMGA software screened out 233 of the 248 highly conserved CEGs, achieving a recall rate of 93.95% (Table S4). Further, the BUSCO analysis could identify 1580 of the 1614 complete gene models in the genome, with a completeness rate of 97.9% (Table S4). The assembled \u003cem\u003eA. odoratissima\u003c/em\u003e genome exhibited high accuracy and completeness.\u003c/p\u003e \u003cp\u003eA total of 31,457 protein-coding gene models were identified in the \u003cem\u003eA. odoratissima\u003c/em\u003e genome using a combination of de novo, homology, and RNAseq-based prediction methods (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed, Table S5). The average lengths for genes and CDS were 4124.27 bp and 1267.99 bp, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). Comparative analysis with related species showed that \u003cem\u003eA. odoratissima\u003c/em\u003e's predicted gene models, as well as its average gene, CDS, exon, and intron lengths, fall within expected ranges (Table S6). The BUSCO analysis could identify 1565 of the 1614 complete gene models (96.96%) among these predicted genes (Table S7), indicating that the prediction results of the \u003cem\u003eA. odoratissima\u003c/em\u003e gene model were of high quality. Meanwhile, 2027 ribosomal RNAs (rRNAs), 598 transfer RNAs (tRNAs), 140 microRNAs (miRNAs), 83 small nuclear RNAs (snRNAs), and 90 small nucleolar RNAs (snoRNAs) were predicted (Table S5). A total of 190 pseudogenes were predicted, spanning 697,366 bp with an average length of 3,670.35 bp (Table S5). The functional prediction of these 31,457 gene models revealed that 29,871 (94.96%), 29,703 (94.42%), 24,833 (78.95%), and 22,753 (72.33%) gene models were annotated in the NCBI NR, TrEMBL, GO, and KEGG databases (Fig.S3), respectively.In total, 29,930 (95.15%) genes from all databases were functionally annotated (Fig.S3, Table S8). The findings suggest that the gene annotations for \u003cem\u003eA. odoratissima\u003c/em\u003e are of high quality and suitable for further research.\u003c/p\u003e \u003cp\u003eSimultaneously, a total of 411.75 Mb (56.38% of the genome) of repetitive sequences were identified in the A. odoratissima genome. LTRs comprised the majority of repeated sequences, amounting to 301.10 Mb and representing 41.29% of the genome (Table S9). Among the LTRs, the Copia type accounted for 28.88% and the Gypsy type accounted for 40.69% in terms of length (Fig.S5). A total of 372 LTR families with over 100 copies and 57 LTR families with over 750 copies were identified (Fig.S4). LINEs and SINEs comprised 2.53% and 0.14% of the genome, respectively (Table S9). DNA transposons constituted 12.43% of the genome (Table S9). In addition, 23.57 Mb of the tandem repeat sequences were identified, accounting for 3.23% of the total genome length of \u003cem\u003eA. odoratissima\u003c/em\u003e (Table S10).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Comparative genomes, gene rent evolution, and whole-genome duplication event analysis\u003c/h2\u003e \u003cp\u003e \u003cem\u003eA.odoratissima\u003c/em\u003e was subjected to gene family clustering analysis with seven leguminous plant species, namely, \u003cem\u003eG. max\u003c/em\u003e, \u003cem\u003eL. japonicus\u003c/em\u003e, \u003cem\u003eC. arietinum\u003c/em\u003e, \u003cem\u003eTrifolium pratense\u003c/em\u003e, \u003cem\u003ePhaseolus vulgaris\u003c/em\u003e, \u003cem\u003eC. cajan\u003c/em\u003e, and \u003cem\u003eMedicago sativa\u003c/em\u003e, and also with eight other plant species, from different families, namely \u003cem\u003eVitis vinifera\u003c/em\u003e, \u003cem\u003eO. sativa\u003c/em\u003e, \u003cem\u003eOlea europaea\u003c/em\u003e, \u003cem\u003ePopulus trichocarpa\u003c/em\u003e, \u003cem\u003eE. ulmoides\u003c/em\u003e, \u003cem\u003eNicotiana attenuata\u003c/em\u003e, \u003cem\u003eJuglans regia\u003c/em\u003e, and \u003cem\u003eArabidopsis thaliana\u003c/em\u003e using OrthoFinder. A total of 54,643 gene families were obtained, among which 1,930 were common among these 16 species (Table S10, Table S11, Fig.S5).The clustering analysis of \u003cem\u003eA. odoratissima\u003c/em\u003e with the leguminous species \u003cem\u003eC. cajan\u003c/em\u003e, \u003cem\u003eL. japonicus\u003c/em\u003e, \u003cem\u003eG. max\u003c/em\u003e, and \u003cem\u003eC. arietinum\u003c/em\u003e revealed 8,936 gene families shared among the five genomes, with 1420 gene families unique to \u003cem\u003eA. odoratissima\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). KEGG enrichment analysis of these unique gene families revealed the top five enriched metabolic pathways: diterpenoid biosynthesis, plant-pathogen interaction, selenocompound metabolism, biosynthesis of secondary metabolites, and anthocyanin biosynthesis (Fig.S6). The GO enrichment analysis identified the top five biological processes for these unique gene families, focusing on cytokinin biosynthesis, hormone biosynthesis, hormone metabolism, cytokinin metabolism, and cellular hormone metabolism (Fig.S7).\u003c/p\u003e \u003cp\u003eAn ML phylogenetic tree was constructed using 1174 single-copy genes from 16 species (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee) by employing IQ-TREE and using rice as the outgroup. The tree was calibrated using the species fossil differentiation times available on \u0026lsquo;timetree\u0026rsquo;, and an evolutionary tree illustrating the differentiation times was obtained. \u003cem\u003eA. odoratissima\u003c/em\u003e was clustered with the other seven species of the \u003cem\u003eLeguminosae\u003c/em\u003e family, while it diverged from the other leguminous plants around 45.78 to 79.24 years ago (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee). Using CAFF software, the analysis of gene family dynamics in \u003cem\u003eA. odoratissima\u003c/em\u003e indicated an expansion of 177 gene families and a contraction of 10 gene families (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee). The enrichment analysis of expanded gene families identified the top five KEGG metabolic pathways: sesquiterpenoid and triterpenoid biosynthesis, monoterpene biosynthesis, selenocompound metabolism, betalain biosynthesis, and stilbenoid, diarylheptanoid, and gingerol biosynthesis (Fig.S8). The top five biological processes enriched in the GO enrichment analysis were telomere maintenance, protein phosphorylation, DNA recombination, recognition of pollen, and DNA repair (Fig.S9).\u003c/p\u003e \u003cp\u003eThe synonymous substitution rates (Ks) for self-homologous genes in \u003cem\u003eA. odoratissima\u003c/em\u003e, \u003cem\u003eG. max\u003c/em\u003e, \u003cem\u003eC. cajan\u003c/em\u003e, and \u003cem\u003eA. hypogaea\u003c/em\u003e were calculated (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). Two distinct peaks appeared in the curve of \u003cem\u003eA. odoratissima\u003c/em\u003e, with the ancient triplication event in angiosperms (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec).The KS peak value for \u003cem\u003eA. odoratissima\u003c/em\u003e was similar to that of \u003cem\u003eA. hypogaea\u003c/em\u003e compared to other species. Using the formula t\u0026thinsp;=\u0026thinsp;ks/2r, the calculated r value was approximately 6.97\u0026times;10\u0026ndash;9.Accordingly, the occurrence time of the recent WGD event for \u003cem\u003eA. odoratissima\u003c/em\u003e was approximately 62.9 Mya (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). Previous studies have demonstrated that most leguminous plants share a WGD event that occurred at ~\u0026thinsp;65 Mya. The proximity of the most recent WGD event of \u003cem\u003eA. odoratissim\u003c/em\u003ea to this timeframe [~\u0026thinsp;65 Mya] suggests that this species shared the WGD event with leguminous plants.According to the evolutionary tree (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec), this WGD event aligned closely with the estimated divergence time of \u003cem\u003eA. odoratissima\u003c/em\u003e from other leguminous species. Accordingly, it was speculated that \u003cem\u003eA. odoratissima\u003c/em\u003e began differentiating from the other species belonging to the \u003cem\u003eLeguminosae\u003c/em\u003e family after the WGD event. Subsequently, a collinearity analysis was performed for \u003cem\u003eA. odoratissima\u003c/em\u003e, \u003cem\u003eV. vinifera\u003c/em\u003e, and \u003cem\u003eG. max\u003c/em\u003e. The comparative collinearity analysis between \u003cem\u003eA. odoratissima\u003c/em\u003e, \u003cem\u003eV. vinifera\u003c/em\u003e, and \u003cem\u003eG. max\u003c/em\u003e revealed a 1:2 relationship in the collinear gene pairs of \u003cem\u003eA. odoratissima\u003c/em\u003e with both \u003cem\u003eV. vinifera\u003c/em\u003e and \u003cem\u003eG. max\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb).This finding supports the hypothesis that \u003cem\u003eA. odoratissima\u003c/em\u003e experienced only one whole-genome duplication following the ancient angiosperm triplication event.The LTR insertion time of \u003cem\u003eA. odoratissima\u003c/em\u003e suggests a burst around 0.2 Mya, aligning with similar insertion bursts in \u003cem\u003eG. max\u003c/em\u003e and \u003cem\u003eL. japonicus\u003c/em\u003e, which occurred between 0.3 and 0.7 Mya (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Transcriptome and differentially expressed genes (DEGs) analysis under drought stress\u003c/h2\u003e \u003cp\u003eWith the continuation of drought, water content, net photosynthetic rate, transpiration rate and stomatal conductance of Acacia aromatica leaves showed a highly significant decrease, while the interstitial CO\u003csub\u003e2\u003c/sub\u003e concentration increased by 29.87% (\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.01) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-E). Leaf proline, soluble sugar, and soluble protein contents were significantly increased by 2,217.81%, 49.01%, and 20.07%, respectively, compared to each other (\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.01) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF-H).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Transcriptome and differentially expressed genes (DEGs) analysis under drought stress\u003c/h2\u003e \u003cp\u003eThe clean data volume for each sample exceeded 6.6 Gb, with Q20 scores all above 96% (Table S12). Correlation analysis indicated that the correlation among the three biological replicates for each sample exceeded 90%, demonstrating the reliability of the data (Fig. S10 A and B). DS10 vs CK10: 4,949 DEGs were upregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). These were primarily enriched in processes such as \"Biosynthesis and metabolism of amino acid-related substances,\" \"Biosynthesis and metabolism of lipid-related substances,\" and \"Flavonoid biosynthesis\" (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). In contrast, 3,179 DEGs were downregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), mainly enriched in processes including \"Photosynthesis\" and \"Biosynthesis and metabolism of alkaloids\" (Fig. S10C). DS20 vs CK20: 4,954 DEGs were upregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). These were primarily enriched in processes such as \"Biosynthesis and metabolism of sugar-, amino acid-, and flavonoid-related substances\" (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). Conversely, 7,929 DEGs were downregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB), mainly enriched in processes including \"Photosynthesis\", \"ABC transporters\", and \"Biosynthesis and metabolism of alkaloids\" (Fig. S10D).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Metabolome and Differential accumulation metabolites (DAMs) analysis under drought stress\u003c/h2\u003e \u003cp\u003eTo analyze changes in metabolite content and metabolic pathways in leaves under drought stress, untargeted broad-spectrum metabolomics analysis was performed on leaves from drought-stressed and control groups at day 10 and day 20. Principal Component Analysis (PCA) results showed that the contribution rates of PC1 and PC2 were 32.6% and 20.6%, respectively. The Pearson correlation coefficients among the five sample groups were close and approached 1, indicating good intra-group repeatability and significant inter-group correlations. This confirms the reliability of the metabolomics data for subsequent analysis (Fig. S11 A and B). A total of 729 metabolites were identified in leaves, classified into 25 categories. Phenolic acids were the most abundant category with 103 metabolites (14.03%), followed by amino acids and derivatives with 75 metabolites (10.22%), and chalcones ranking third with 64 metabolites (8.72%). Lipids constituted the smallest category with only 1 metabolite (0.14%) (Fig. S11C). Comparative analysis of metabolites between drought-stressed and control groups at days 10 and 20 revealed: CK10 vs DS10: 224 DAMs (64 upregulated, 164 downregulated); CK20 vs DS20: 143 DAMs (56 upregulated, 87 downregulated) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA and B). Metabolically enriched pathways common to both time points included: Arginine and proline metabolism, D-Amino acid metabolism, ABC transporters, Tropane, piperidine and pyridine alkaloid biosynthesis, et al. These findings demonstrate that drought stress regulates the synthesis and accumulation of metabolites in leaves. It can be inferred that \u003cem\u003eA. odoratissima\u003c/em\u003e primarily responds to drought stress through metabolic pathways involving alkaloids and amino acids (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC and D).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Flavonoid metabolic responses under drought stress of \u003cem\u003eA. odoratissima\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eA differential analysis was conducted on the flavonoid metabolic pathway in \u003cem\u003eA. odoratissima\u003c/em\u003e under drought stress, identifying 13 DEGs and 7 DAMs (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Compared to the control group, the contents of both catechin and epicatechin increased significantly after 10 days of drought stress, but decreased after 20 days of drought stress. The expression levels of key enzyme genes (\u003cem\u003eAoANS\u003c/em\u003e: \u003cem\u003eAod13G001010\u003c/em\u003e, \u003cem\u003eAod05G020040\u003c/em\u003e, \u003cem\u003eAod07G019900\u003c/em\u003e) catalyzing epicatechin formation declined in the later stages of drought stress. Notably, the increased expression of \u003cem\u003eAod07G019900\u003c/em\u003e at day 10 may be one reason for the significant rise in epicatechin content at that time point. The expression of key enzyme genes (\u003cem\u003eAod11G016970\u003c/em\u003e, \u003cem\u003eAod13G004920\u003c/em\u003e) catalyzing catechin formation was significantly lower than the control group at day 20, leading to reduced catechin content. This is likely due to drought stress exceeding the plant's tolerance range, impairing its self-regulation capacity to enhance drought resistance. These results indicate that \u003cem\u003eA. odoratissima\u003c/em\u003e promotes flavonoid biosynthesis in its leaves under drought stress by upregulating the expression of \u003cem\u003eAoANS\u003c/em\u003e (\u003cem\u003eAod07G019900\u003c/em\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e \u003cem\u003eA. odoratissima\u003c/em\u003e is a valuable tree species native. The species is recognized for its excellent adaptability to diverse environmental conditions and, therefore, warrants comprehensive genome sequencing studies. These studies would assist in comprehending the genomic structure of the species and its functions while also having significant implications in unraveling the origins and evolution of this species, revealing the essential functional genes, thereby facilitating the molecular marker-assisted selection (MAS) process in breeding programs. Therefore, the present study attempted to decipher the complete genome of \u003cem\u003eA. odoratissima\u003c/em\u003e using third-generation sequencing methods to generate high-quality HiFi reads, coupled with Hi-C assisted assembly techniques resulting in a chromosomal-level genome assembly. This groundbreaking effort achieved a chromosome-level whole-genome assembly for the woody plant species, anchoring about 98.58% (719.88 Mb) of gene sequences onto 13 pseudochromosomes. The constructed \u003cem\u003eA. odoratissima\u003c/em\u003e genome was determined to have a total of 1614 complete gene models. BUSCO analysis showed 97.9% completeness in gene models, reflecting the assembled genome's high integrity and sequence accuracy. This assembly would serve as a reliable reference genome database for future investigations targeting crucial gene functionalities, genetic improvements, and molecular markers relevant to this tree species. Concurrently, the present study predicted the presence of 2027 rRNAs, 598 tRNAs, 140 miRNAs, 83 snRNAs, and 90 snoRNAs in the constructed \u003cem\u003eA. odoratissima\u003c/em\u003e genome. These RNA-level predictions and annotations would serve as substantial reference data for future post-genomic studies conducted for this species, particularly to elucidate the transcriptional regulatory mechanisms in response to environmental stimuli. Repetitive sequences constitute a substantial proportion of plant genomes. The analysis of repetitive sequences conducted for the genome of \u003cem\u003eA. odoratissima\u003c/em\u003e constructed in the present study revealed that these sequences accounted for 56.38% of the plant\u0026rsquo;s genome, with the LTRs accounting for a 42.29% proportion. Notably, the Gypsy-type LTRs accounted for 40.69% of the repetitive sequences, with 372 of these LTR families having over 100 copies. This finding suggested the potential significance of the long-term evolutionary processes of \u003cem\u003eA. odoratissima\u003c/em\u003e and implied that the genome expansion in \u003cem\u003eA. odoratissima\u003c/em\u003e might progress relatively slowly compared to the other economically significant tree species, such as spruce and camellia due to the evolutionary context of the former.\u003c/p\u003e \u003cp\u003eComparative genomics involves comparing the known genes and genome structures with genome maps and sequencing data to understand gene functions, expressions, mechanisms, and species evolution. This study utilized the annotated high-quality \u003cem\u003eA. odoratissima\u003c/em\u003e genome to compare 16 plant genomes, identifying 54,643 gene families. Among these, 1,930 families were common gene families shared among these 16 species. The highest number of shared gene families was 8,936, which was observed among the 5 species within the \u003cem\u003eLeguminosae\u003c/em\u003e family. \u003cem\u003eA. odoratissima\u003c/em\u003e, on the other hand, had just 1,420 unique gene families. Gene families tend to be relatively conserved across species. Examining the 1,420 unique gene families of \u003cem\u003eA. odoratissima\u003c/em\u003e, potentially linked to its species specificity, was essential for understanding its evolutionary development. The gene families were then analyzed for enrichment using KEGG and GO. Accordingly, it was inferred that the rapid growth and differential environmental response of \u003cem\u003eA. odoratissima\u003c/em\u003e was related to the enriched pathways revealed in the KEGG analysis for these specific gene families. These pathways were key to plant growth, development, and lignin synthesis \u003csup\u003e[\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn50\" id=\"#FNLinkFn50\"\u003e\u003c/a\u003e,\u003ca class=\"FNLink\" href=\"#Fn51\" id=\"#FNLinkFn51\"\u003e\u003c/a\u003e\u003csup\u003e]\u003c/sup\u003e. The top five enriched biological processes revealed in the GO also suggested that \u003cem\u003eA. odoratissima\u003c/em\u003e has distinctive hormone-related cell growth regulatory mechanisms compared to other plants. Single-copy gene families were utilized to construct a maximum likelihood phylogenetic tree. In this tree, \u003cem\u003eA. odoratissima\u003c/em\u003e clustered with seven other species within the \u003cem\u003eLeguminosae\u003c/em\u003e family, although its divergence from them occurred approximately 45.78 to 79.24 Mya. \u003cem\u003eA. odoratissima\u003c/em\u003e showed notable expansions in gene families associated with the biosynthesis of sesquiterpenoids, triterpenoids, monoterpenes, seleno-compounds, betalains, and stilbenoids, diarylheptanoids, and gingerols, when compared to 16 other plant species. The functional annotations of these expanded gene families strongly suggest the presence of unique environmental responses and growth regulatory mechanisms in \u003cem\u003eA. odoratissima\u003c/em\u003e compared to the other species. These results would be useful in identifying the genes associated with species traits, analyzing genes under positive selection during species evolution, and identifying genes related to the environmental adaptability of \u003cem\u003eA. odoratissima\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eWater acts as a solvent to regulate physiological, biochemical and metabolic reactions within cells, which ultimately affects plant growth and development. As the duration of drought increased, the water content of the leaves of \u003cem\u003eA. odoratissima\u003c/em\u003e gradually decreased and showed a significant difference between the 10th d and the control, indicating that drought reduced the water content of the leaves of \u003cem\u003eA. odoratissima\u003c/em\u003e. Reduced water induces stomatal closure, which controls gas exchange and water loss, leading to a decrease in the net photosynthetic rate and transpiration rate, and at the same time promotes photorespiration as well as the accumulation of intercellular CO\u003csub\u003e2\u003c/sub\u003e, which ultimately inhibits growth and energy consumption and improves the tolerance of \u003cem\u003eA. odoratissima\u003c/em\u003e. This mechanism may be a trade-off strategy of the plant under drought conditions to improve its adaptation and tolerance by moderately inhibiting growth \u003csup\u003e[\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn52\" id=\"#FNLinkFn52\"\u003e\u003c/a\u003e,\u003ca class=\"FNLink\" href=\"#Fn53\" id=\"#FNLinkFn53\"\u003e\u003c/a\u003e,\u003ca class=\"FNLink\" href=\"#Fn54\" id=\"#FNLinkFn54\"\u003e\u003c/a\u003e\u003csup\u003e]\u003c/sup\u003e. Drought stress rapidly induces the production of ROS, promotes lipid peroxidation and MDA formation, destroys the integrity of cell membranes, alters the morphology and structure of tissues and organs, and ultimately inhibits plant growth, yield and quality \u003csup\u003e[\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn55\" id=\"#FNLinkFn55\"\u003e\u003c/a\u003e\u003csup\u003e]\u003c/sup\u003e. It has been shown that increasing the synthesis and accumulation of intracellular osmoregulatory substances (soluble sugars, soluble proteins and proline.) and lowering the osmotic potential in order to reduce the water loss from their own bodies is an important physiological and biochemical mechanism for the plants to remove ROS and improve their drought tolerance \u003csup\u003e[\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn56\" id=\"#FNLinkFn56\"\u003e\u003c/a\u003e,\u003ca class=\"FNLink\" href=\"#Fn57\" id=\"#FNLinkFn57\"\u003e\u003c/a\u003e\u003csup\u003e]\u003c/sup\u003e. In this study, with the prolongation of drought stress, \u003cem\u003eA. odoratissima\u003c/em\u003e alleviated cellular osmotic stress by significantly up-regulating the synthesis of osmoregulatory substances, such as proline and soluble sugars (proline content in the leaves surged by 2,217.81%, \u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.01), a mechanism that is highly similar to drought tolerance strategies in the Leguminous plants \u003csup\u003e[\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn58\" id=\"#FNLinkFn58\"\u003e\u003c/a\u003e\u003csup\u003e]\u003c/sup\u003e. Drought stress promoted the accumulation of free proline in \u003cem\u003eA. odoratissima\u003c/em\u003e leaves, which could polymerise with some intracellular compounds to form a hydrophilic colloid-like substance \u003csup\u003e[\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn59\" id=\"#FNLinkFn59\"\u003e\u003c/a\u003e\u003csup\u003e]\u003c/sup\u003e, thus reducing the osmotic potential and water loss, and ultimately improving the drought resistance of \u003cem\u003eA. odoratissima\u003c/em\u003e. The accumulated soluble sugars and soluble proteins in the leaves of \u003cem\u003eA. odoratissima\u003c/em\u003e can reduce the osmotic potential of the cells, prevent the loss of intracellular water, and improve the drought resistance \u003csup\u003e[\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn60\" id=\"#FNLinkFn60\"\u003e\u003c/a\u003e\u003csup\u003e]\u003c/sup\u003e. In addition, the accumulated soluble sugars and soluble proteins can be rapidly degraded, providing the prerequisite substances and energy for the synthesis of membrane lipids and other substances, which has the function of stabilising the structural integrity of cell membranes and protoplasts, and thus improving the acacia's adaptability to drought stress \u003csup\u003e[\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn61\" id=\"#FNLinkFn61\"\u003e\u003c/a\u003e\u003csup\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eUnder drought stress, up-regulated differentially expressed genes (DEGs) were primarily enriched in metabolic processes related to sugars and amino acids in leaves of \u003cem\u003eA. odoratissima\u003c/em\u003e. This indicates that metabolic activities involving amino acids and sugars become more pronounced in the leaves as the plant adapts to drought stress. This response may occur because reduced internal water content and scarcity of growth resources under drought conditions drive increases in amino acid and sugar levels to sustain survival and facilitate environmental adaptation. Conversely, down-regulated DEGs were mainly enriched in processes such as photosynthesis and signal transduction. This suggests that genes regulating photosynthesis are suppressed and down-regulated following drought stress, leading to a reduced photosynthetic rate in \u003cem\u003eA. odoratissima\u003c/em\u003e leaves. Drought stress primarily affects plant leaf photosynthesis through two mechanisms: Stomatal Limitation: Drought stress reduces stomatal conductance, impeding the entry of CO₂ (the raw material for photosynthesis) into the leaves. This directly lowers the photosynthetic rate. Consequently, the depletion of endogenous substrates within the leaves is accelerated. Pathways related to the C-cycle and amino acid metabolism persistently respond to drought stress to maintain normal leaf growth. Non-stomatal Limitation: When leaf water potential drops below a critical threshold, the structure of chloroplasts is damaged, and the activity of Photosystem II (PSII) is constrained. This subsequently inhibits electron transport and photophosphorylation, resulting in decreased photosynthesis. Therefore, photosynthesis-related pathways are impaired or suppressed, and associated genes exhibit sustained low expression. This constitutes a drought resistance mechanism by reducing leaf energy expenditure \u003csup\u003e[\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn62\" id=\"#FNLinkFn62\"\u003e\u003c/a\u003e\u003csup\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAt both day 10 and day 20 of drought stress, metabolites in \u003cem\u003eA. odoratissima\u003c/em\u003e were enriched in amino acid metabolic pathways. Amino acid metabolism is a fundamental process for plant growth and development, providing essential proteins and supplying energy to sustain vital activities. Amino acids play multiple roles in regulating plant tolerance to abiotic stress, acting as osmotic regulators, ROS scavengers, and precursors for energy-related metabolites [\u003ca class=\"FNLink\" href=\"#Fn63\" id=\"#FNLinkFn63\"\u003e\u003c/a\u003e,\u003ca class=\"FNLink\" href=\"#Fn64\" id=\"#FNLinkFn64\"\u003e\u003c/a\u003e,\u003ca class=\"FNLink\" href=\"#Fn65\" id=\"#FNLinkFn65\"\u003e\u003c/a\u003e]. Under drought stress, proline content in \u003cem\u003eA. odoratissima\u003c/em\u003e accumulated dramatically. Proline can be rapidly synthesized into osmotic compounds, reflecting the plant's internal osmotic pressure. It serves as a stress-indicator amino acid and plays a crucial role in maintaining cellular osmotic potential, thereby supporting normal cell growth and development and ultimately enhancing the drought resistance of \u003cem\u003eA. odoratissima\u003c/em\u003e [\u003ca class=\"FNLink\" href=\"#Fn66\" id=\"#FNLinkFn66\"\u003e\u003c/a\u003e].These findings are largely consistent with studies on \u003cem\u003eTaxus cuspidata\u003c/em\u003e, which also showed significant increases in sugars and amino acids to combat drought stress [\u003ca class=\"FNLink\" href=\"#Fn67\" id=\"#FNLinkFn67\"\u003e\u003c/a\u003e]. Under drought conditions, \u003cem\u003eA. odoratissima\u003c/em\u003e regulated the expression of multiple key enzyme genes involved in the synthesis of amino acids such as valine, leucine, isoleucine, lysine, and proline. The expression levels of these genes increased with prolonged drought duration, which is likely to help scavenge ROS and improve the drought tolerance of \u003cem\u003eA. odoratissima\u003c/em\u003e. The amino acid content gradually increased over the drought period. This may be attributed to reduced water content within the plant limiting the water required for normal growth, thus prompting an increase in amino acids to sustain survival and adapt to environmental changes.\u003c/p\u003e \u003cp\u003eDrought induces plant cells to produce excessive amounts of reactive oxygen species (ROS), which act as a stress signalling molecule as well as oxidatively damaging biomolecules, affecting the integrity of cell membranes, interfering with the normal metabolic processes of plant cells, and even causing cell death \u003csup\u003e[\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn68\" id=\"#FNLinkFn68\"\u003e\u003c/a\u003e\u003csup\u003e]\u003c/sup\u003e. Flavonoids have strong antioxidant capacity to resist oxidative stress damage under drought stress in plants, thus effectively responding to the adverse effects of drought \u003csup\u003e[\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn69\" id=\"#FNLinkFn69\"\u003e\u003c/a\u003e\u003csup\u003e]\u003c/sup\u003e. \u003cem\u003eAoANS\u003c/em\u003e was specifically up-regulated at 10 d of drought stress, promoting epicatechin synthesis, decreasing ROS accumulation under drought stress, and increasing the antioxidant capacity and thus enhancing the drought tolerance of \u003cem\u003eA. odoratissima\u003c/em\u003e.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eAbout 719.88 Mb of contig level genome was obtained from the assembly based on triple sequencing technology, contig N50 was 53.74 Mb, and 98.58% of gene sequences were assembled on 13 pseudochromosomes. Comparative analyses of \u003cem\u003eA. odoratissima\u003c/em\u003e with 15 plant genomes revealed a total of 54,643 gene families, 1,930 shared gene families, with the latest WGD at about 62.9 Mya shared with legumes. 8,936 gene families shared by \u003cem\u003eA. odoratissima\u003c/em\u003e with five homozygous legumes, and 1,420 gene families specific to \u003cem\u003eA. odoratissima\u003c/em\u003e, of which 177 gene families underwent expansion, 177 gene families expanded and 10 contracted. Enhanced drought tolerance by synergistic inhibition of photosynthesis, activation of osmoregulation and elevation of antioxidant defences in \u003cem\u003eA. odoratissima\u003c/em\u003e. This study serves as a crucial resource for advancing research and molecular breeding of \u003cem\u003eA. odoratissima\u003c/em\u003e, aiming to develop drought-tolerant varieties and enhance its economic value. Overall, this high-quality reference genome provides insights into the genetic background and phylogeny of \u003cem\u003eA. odoratissima\u003c/em\u003e. This research lays the groundwork for advancing genetic enhancement and breeding of \u003cem\u003eA. odoratissima\u003c/em\u003e.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eCRediT authorship contribution statement\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFeng Gao:\u0026nbsp;\u003c/strong\u003eWriting-original draft, Writing-review \u0026amp; editing, Methodology, Formal analysis, Data curation. \u003cstrong\u003eShuoxing Wei:\u0026nbsp;\u003c/strong\u003eWriting-review \u0026amp; editing, Investigation, Funding acquisition. \u003cstrong\u003eHanbiao Ou:\u0026nbsp;\u003c/strong\u003eWriting-review \u0026amp; editing, Writing-original draft, Supervision, Investigation, Conceptualization. \u003cstrong\u003eZhihui Wang:\u0026nbsp;\u003c/strong\u003eWriting-review \u0026amp; editing, Investigation. \u003cstrong\u003eGuoping Yin:\u0026nbsp;\u003c/strong\u003eWriting-review \u0026amp; editing, Supervision, Funding acquisition, Conceptualization. \u003cstrong\u003eShizhi Wen:\u0026nbsp;\u003c/strong\u003eWriting-original draft, Methodology, Formal analysis, Data curation.\u0026nbsp;\u003cstrong\u003eChunhe Yu:\u0026nbsp;\u003c/strong\u003eFunding acquisition.\u003cstrong\u003e\u0026nbsp;Zhifeng Lu:\u0026nbsp;\u003c/strong\u003eFunding acquisition.\u003cstrong\u003e\u0026nbsp;Jianwu Chen:\u0026nbsp;\u003c/strong\u003eMethodology.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFundings\u003c/p\u003e\n\u003cp\u003eFunding for this research was provided by the Guangxi Key Research and Development Program (Projects 2025GXNSFAA069945, AB240100090 and AB21220026) and self-financed forestry science and technology projects in Guangxi (Guangxi Forestry Research [2022ZC]).105 and 2023GXZCLK 35).\u003c/p\u003e\n\u003cp\u003eData Availability Statement\u003c/p\u003e\n\u003cp\u003eThe corresponding author will provide the data supporting this article upon reasonable request. The raw genome and transcriptome sequencing data for \u003cem\u003eA. odoratissima\u003c/em\u003e are available in the Genome Sequence Archive (https://www.ncbi.nlm.nih.gov/ accession no. PRJCA023416).\u003c/p\u003e\n\u003cp\u003eConflicts of Interest\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eAll samples collected fully adhere to national and local legal requirements. The plant samples used in the study were neither listed as nationally protected nor gathered from national parks or natural reserves. No specific permissions were necessary for their collection according to national and local laws.Consent for publication.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":" References","content":"\u003col\u003e\n \u003cli\u003eWei SX, Liang RL, Lin J, He YH, Jiang Y, Ou HB, et al. 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The Plant Journal. 2014;77(3):367-379.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Albizia odoratissima, chromosome-level genome, genome-wide replication event, selenocompound metabolism, drought stress","lastPublishedDoi":"10.21203/rs.3.rs-6840333/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6840333/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003eAlbizia odoratissima\u003c/em\u003e is a valuable drought-tolerant native tree species in the dry and hot river valleys of China, which has important ecological and economic values. Exploring its genetic background and phylogenetic direction will be conducive to its further exploitation and use, and promote the process of vegetation restoration in the dry hot river valley region. A genome assembly of approximately 719.88 Mb was achieved at the contig level, featuring a contig N50 of 53.74 Mb. Of this, 98.58% of gene sequences were organized into 13 pseudochromosomes. The \u003cem\u003eA. odoratissima\u003c/em\u003e genome contained 96.96% of conserved genes, including 1,538 intact single-copy genes and 42 intact duplicated genes. It had an angiosperm palaeotripling event and the last whole genome duplication event occurred approximately 62.9 million years ago. \u003cem\u003eA. odoratissima\u003c/em\u003e shares 8,936 gene families with five other legume species, while 1,420 gene families are unique to \u003cem\u003eA. odoratissima\u003c/em\u003e. Under drought stress, photosynthesis was significantly inhibited to reduce water consumption, osmoregulatory substances were significantly increased to alleviate osmotic stress, and flavonoids were increased to enhance antioxidant capacity through the up-regulation of \u003cem\u003eAoANS\u003c/em\u003egene expression, thereby improving drought tolerance. High-quality reference genomes generated through molecular studies are advancing research into the molecular mechanisms of \u003cem\u003eA. odoratissima\u003c/em\u003e.\u003c/p\u003e","manuscriptTitle":"Chromosome-level genome assembly of Albizia odoratissima and effect of flavonoid metabolic pathways under drought stress","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-17 15:48:47","doi":"10.21203/rs.3.rs-6840333/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-06T11:52:52+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-29T03:25:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"59503512234753122077822068104561733711","date":"2025-07-23T23:32:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"52216569204859509614763859369826445403","date":"2025-06-26T15:20:21+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-23T10:08:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"108349293756836997863725038977546784513","date":"2025-06-20T00:54:05+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-13T07:54:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-13T07:52:36+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-12T04:32:19+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-12T00:18:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Plant Biology","date":"2025-06-12T00:15:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e9c76db1-2ac9-4bb6-b18b-ae197977ee12","owner":[],"postedDate":"June 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-03T16:06:44+00:00","versionOfRecord":{"articleIdentity":"rs-6840333","link":"https://doi.org/10.1186/s12870-025-07523-5","journal":{"identity":"bmc-plant-biology","isVorOnly":false,"title":"BMC Plant Biology"},"publishedOn":"2025-10-31 15:57:46","publishedOnDateReadable":"October 31st, 2025"},"versionCreatedAt":"2025-06-17 15:48:47","video":"","vorDoi":"10.1186/s12870-025-07523-5","vorDoiUrl":"https://doi.org/10.1186/s12870-025-07523-5","workflowStages":[]},"version":"v1","identity":"rs-6840333","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6840333","identity":"rs-6840333","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}