Skip to main content
Download PDF

Plastid genome and its phylogenetic implications of Asiatic Spiraea (Rosaceae)

BMC Plant Biology volume 24, Article number: 23 (2024) Cite this article

Abstract

Background

Spiraea L. is a genus comprising approximately 90 species that are distributed throughout the northern temperate regions. China is recognized as the center of species diversity for this genus, hosting more than 70 species, including 47 endemic species. While Spiraea is well-known for its ornamental value, its taxonomic and phylogenetic studies have been insufficient.

Results

In this study, we conducted sequencing and assembly of the plastid genomes (plastomes) of 34 Asiatic Spiraea accessions (representing 27 Asiatic Spiraea species) from China and neighboring regions. The Spiraea plastid genome exhibits typical quadripartite structures and encodes 113–114 genes, including 78–79 protein-coding genes (PCGs), 30 tRNA genes, and 4 rRNA genes. Linear regression analysis revealed a significant correlation between genome size and the length of the SC region. By the sliding windows method, we identified several hypervariable hotspots within the Spiraea plastome, all of which were localized in the SC regions. Our phylogenomic analysis successfully established a robust phylogenetic framework for Spiraea, but it did not support the current defined section boundaries. Additionally, we discovered that the genus underwent diversification after the Early Oligocene (~ 30 Ma), followed by a rapid speciation process during the Pliocene and Pleistocene periods.

Conclusions

The plastomes of Spiraea provided us invaluable insights into its phylogenetic relationships and evolutionary history. In conjunction with plastome data, further investigations utilizing other genomes, such as the nuclear genome, are urgently needed to enhance our understanding of the evolutionary history of this genus.

Peer Review reports

Background

Spiraea L., a member of the tribe Spiraeeae of Amygdaloideae (Rosaceae), comprises approximately 90 species (http://www.plantsoftheworldonline.org), and is distributed across the northern temperate regions of the world. China is recognized as the center of diversity for this genus, with over 70 species, including 47 endemic species [1]. Spiraea is renowned for its ornamental value, particularly its attractive white, pink or purple flowers that bloom profusely in spring or summer, forming heads composed of numerous tiny blooms. Additionally, certain varieties of Spiraea with golden foliage, such as Spiraea × vanhouttei ‘Pink Ice’ and ‘Gold Fountain’, have been developed. To fully exploit their horticultural value, a comprehensive taxonomic and phylogenetic study of Spiraea taxa is necessary. However, the phylogeny of Spiraea has not been extensively investigated thus far.

The traditional infrageneric classification of Spiraea is primarily based on inflorescence types, including panicles, compound corymbs, corymb, umbel, and fascicle, as established by Poyarkova [2], Rehder [3], and Yü and Kuan [4]. For Chinese Spiraea taxa, four sections (i.e., sects. Spiraea, Calospira, Chamaedryon, and Glomerati) and ten series within the genus have been widely accepted [4]. Previous phylogenetic analyses have revealed issues with traditional classifications based on inflorescence morphology, as the majority of sections are considered paraphyletic or polyphyletic [5,6,7,8,9,10]. However, most nodes in these phylogenetic trees could not receive strong support values based on limited molecular fragments, highlighting the need for the development of robust molecular markers within Spiraea.

The plastid, an essential organelle in plant cells, plays a crucial role in plant growth and development [11]. Compared to nuclear and mitochondrial genomes, the plastid genome possesses unique characteristics, such as uniparental inheritance, absence of recombination, conserved genome structure and gene content, and moderate evolutionary rate. These features make the plastid genome highly valuable for resolving phylogenetic relationships at various taxonomic levels [12]. Furthermore, the assembly of plastid genomes is relatively straightforward due to their high cellular copy numbers and the development of assembly algorithms [13]. As a result, the number of complete plastid genomes has significantly increased in recent years, thanks to the advancement of high-throughput DNA sequencing technologies. Plastid genomes have proven to be invaluable in elucidating the phylogenetic backbone of Rosaceae and uncovering its evolutionary history [14]. The number of plastid genomes within the Rosaceae family has also rapidly increased in recent years (e.g., Fragaria [15, 16], Rubus [17], and Rosa [18, 19]), providing a stronger understanding of phylogenetic relationships at the species level and the phylogeographical patterns within the family. However, a limited number of plastid genomes of Spiraea species have been reported [20,21,22,23], and no solid divergence times of the genus have been inferred. Therefore, it is crucial to conduct comparative analyses with more Spiraea plastid genomes to better identify the evolutionary pattern of the plastid genome within Spiraea and assess its phylogenetic relationships.

In this study, we assembled the complete plastomes for 34 Spiraea accessions representing 27 Asiatic species using several available Spiraea plastomes from GenBank. Our objectives were to (1) examine the variation in plastome structure and features of Spiraea, (2) identify the hypervariable loci across these Spiraea plastomes, (3) infer the phylogenomic relationships of Spiraea, and (4) determine the divergence time of Spiraea. This study will contribute to a better understanding of the evolution of Spiraea based on plastome sequences.

Materials and methods

Plant materials, DNA extraction, and sequencing

In this study, we collected a total of 27 Asiatic Spiraea species and 34 accessions, representing four sections (i.e., sects. Calospira, Chamaedryon, Glomerati and Spiraea) of two subgenera (i.e., subgs. Protospiraea and Metaspiraea) in Spiraea. Among these accessions, 25 were newly collected from Hangzhou Botanical Garden. Voucher specimens were formally identified by Qiao-Ling Zhang and deposited at the Herbarium of South China Botanical Garden (IBSC), Chinese Academy of Sciences (CAS). Silica-gel dried leaves were used for DNA extraction by the cetyltrimethylammonium bromide (CTAB) method [24]. Paired-end (PE) sequencing libraries were constructed and sequenced on an MGISEQ-2000 (MGI, Shenzhen, China) at the Beijing Genomics Institution (BGI, Wuhan, China). Additionally, twelve plastid genomes of Spiraea were directly downloaded from GenBank (Accessed on February 5, 2023), which belong to nine Spiraea species and three outgroups (i.e., Malus baccata (L.) Borkh., Rosa cymose Tratt. and Sibiraea angustata (Rehd.) Hand.-Mazz.) for phylogenetic inferences. Among all accessions, four Spiraea species (Spiraea japonica L.F., S. × vanhouttei (Birot) Zabel, S. cantoniensis Lour., and S. prunifolia Siebold & Zucc.) have multiple individuals. The detailed sampling information is summarized in Table S1.

Plastid genome assembly, annotation, and comparison

Plastomes of 25 Spiraea accessions were newly assembled using GetOrangelle [25] with the reference plastome of Spiraea mongolica (NC_051992). These complete plastid sequences were annotated using the Plastid Genome Annotator (PGA) [26] and manually checked and adjusted with the aid of Geneious 2019 [27]. The circular physical maps were drawn using OrganellarGenome DRAW (ORDRAW) [28].

The total length of the plastomes, as well as the lengths of the large single copy (LSC), small single copy (SSC), and inverted repeat regions (IRA and IRB), gene content, and GC content of all Spiraea species were analyzed using Geneious. The relationships among the genome size and LSC length, IR length, intron length, and intergenic spacer length of Spiraea species were tested using least squares linear regression analysis in R 4.2.2 [29]. The boundary information between the IRs and SC regions was analyzed in Geneious.

Divergent hotspot analyses

The plastid genome sequences were aligned using MAFFT v7.308 [30]. The nucleotide polymorphism (Pi) of plastid genomes was calculated using the sliding window method with a length of 600 bp and a step size of 200 bp in DnaSP v6 [31]. The sequence heterogeneity within 34 Spiraea plastid genomes was visualized using mVISTA with the Shuffle-LAGAN model [32].

Phylogenetic analyses and molecular dating

Phylogenetic analysis was performed on all 34 Spiraea plastid genome sequences, with M. baccata (MK571561), R. cymosa (NC_051550), and Si. Angustata (NC_054238) used as outgroups. The 79 protein-coding genes (PCGs) and whole plastid genome sequences (including all coding and non-coding regions) were aligned separately using MAFFT and concatenated into a sequence matrix using AMAS [33]. Maximum likelihood (ML) and Bayesian inference (BI) trees were each inferred using RAxML v8.2.10 [34] and MrBayes v3.2.6 [35]. The ML tree was constructed using RAxML under the GTRGAMMA model with 1,000 bootstrap replicates. BI was performed with MrBayes in the GTR + G + I model on XSEDE (the CIPRES Science Gateway, [36]). The Markov chain Monte Carlo (MCMC) chains were run for 10 × 106 generations, and stationarity was assessed using TRACER v1.7.1 (effective sample size (ESS) > 200) [37]. The first 25% of runs were discarded as burn-in, and the remaining trees were used to construct the consensus tree. Bayesian posterior probabilities (BPPs) were used to estimate support for each branch. The phylogenetic tree file was visualized in FigTree v1.4.3 [38].

To estimate the divergence time of Spiraea, we retrieved the other 86 plastid genomes of Rosales taxa from GenBank (Table S1). A total of 78 PCGs extracted from 120 plastid genomes were extracted and aligned separately by MAFFT. Maximum likelihood trees were constructed for each of these 78 PCGs using RAxML, and molecular clock-like genes (clock-like genes) were screened from the 78 gene trees by using the SortaDate package [39]. The top-ten clock-like genes were selected and aligned using MAFFT, and then concatenated using AMAS for further divergence time estimation.

The concatenated matrix was used to estimate the divergence time using BEAST v2.7 [40]. The yule tree prior was used with an optimized relaxed clock (ORC) and GTR + GAMMA was selected for the molecular clock model. We ran four independent analyses, each consisting of 400 million generations and sampled every 10,000 generations. The results from these analyses were combined using the LogCombiner package. Convergence was assessed using TRACER, based on the effective sample size (ESS) of each parameter with a likelihood > 200, following the method described in [41]). After discarding the first 25% of trees as burn-in, the maximum clade credibility (MCC) tree was generated using the TreeAnnotator package. The mean nodal heights and age estimation with 95% highest posterior densities (HPDs) were visualized using FigTree.

Twelve reliable fossils from Rosaceae were used for calibration, as described by Xiang et al. [42]. The fossil information and calibration settings are shown in Table S2. Notably, Spiraea-like fossils (fruits and leaves) have been found in the Early Eocene upland floras of the Okanogan Highlands of northeastern Washington State and British Columbia, Canada [42, 43]. Therefore, the stem age of Spiraea was estimated using a standard lognormal prior distribution with a mean of 0.25, a deviation of 0.3, and an offset of 48 Ma, which roughly matched the hypothesized age (49–50 Ma).

To examine the general patterns of diversification through time of Spiraea, lineage-through-time (LTT) plots were calculated using APE v3.5 [44]. To account for uncertainty in the dating estimates, the 1,000 random trees from the 4,000 converged trees from BEAST were used to calculate the 95% confidence interval (CI), and all outgroups were removed.

Selection pressure analyses

We excluded PCGs with lengths less than 300 bp. A total of 50 PCGs were chosen and aligned using the MUSCLE algorithm [45]. Stop codons were removed from the aligned sequences using MEGA 11 [46]. The resulting aligned sequences were then converted to PML format using PhyloSuite v1.2.2 [47].

To calculate the non-synonymous substitution rate (dN), synonymous substitution rate (dS), and ratio values (ω, dN/dS) of each protein-coding gene, we employed the Site Model (M0) using EasycodeML v1.4 [48]. The PCGs were categorized into seven functional groups based on the classification by Wicke et al. [49]: ATP synthase, NADPH dehydrogenase, cytochrome b/f complex, photosystem, ribosomal proteins, RNA polymerase, and other genes.

Likelihood ratio tests (LRT) were conducted to determine the presence/absence of selection. The log-likelihood statistic was calculated as 2∆L = 2(L1- L0). This value was then compared with a χ2 distribution with degrees of freedom calculated from the difference in the number of parameters between the models. L0 represents the log-likelihood under the null model, while L1 represents the likelihood under the alternative model. Model M0 assumes a single value of ω for all sites and lineages, while model M3 includes a discrete distribution of ω values. Model M1 assumes two site classes, and was compared with M2, which allows for three site classes including positive selection. Models M7 and M8 were also compared, with M7 assuming a beta distribution for variation in ω and M8 allowing for positive selection. Positive selection can only be inferred when models M2, M3, or M8 indicate codons with a ω ratio > 1 and the likelihood ratio test for positive selection is significant at P < 0.05. Bayesian empirical Bayes (BEB) was used to identify sites potentially under positive selection.

Results

Structural features and genome composition of plastid genomes in Spiraea

In this study, we analyzed the plastid genomes of 34 individuals from the Spiraea genus, representing 27 different Asiatic species. We obtained nine Spiraea plastid genome sequences from GenBank. The complete plastid genomes exhibited the typical quadripartite circular structure, consisting of a large single-copy (LSC) region, a small single-copy (SSC) region, and two inverted repeat (IR) regions (Fig. 1). The length of the genomes ranged from 153,822 bp (S. japonica, MZ981784) to 158,637 bp (S. insularis), while the LSC region length ranged from 82,226 bp (S. japonica) to 86,997 bp (S. insularis). The shortest SSC region was 26,333 bp, found in four species: S. ovalis, S. henryi, S. nipponica, and S. trichocarpa, while the longest SSC region was 26,492 bp in S. martini. The IR region length ranged from 18,700 bp (S. laeta) to 18,957 bp (S. ovalis). The GC content of the Spiraea plastid genome ranged from 36.6% to 36.9%. This information is summarized in Table S3. Linear regression analysis showed a significant correlation between genome size and the length of the SC region (R2 = 0.99, P < 0.01) and intergenic spacer (IGS) length (R2 = 0.21, P < 0.01), but no correlation with the IR region (R2 = 0.03, P = 0.31) or intron length (R2 = 0.02, P = 0.44) (Fig. 2).

Fig. 1
figure 1

The plastid genome map of Asiatic Spiraea species. The genes belonging to different functional groups are shown in different colors. Genes inside and outside of the external circle are transcribed in clockwise and counterclockwise directions, respectively. The inner circle represents the quadripartite structure, with two copies of the inverted repeat (IRA and IRB), an LSC, and an SSC region in black with GC content in dark gray and AT content in light gray

Full size image
Fig. 2
figure 2

The relationships between the length of different parts of plastome and plastome size within Spiraea. a Correlation of the IR length and plastome size. b Correlation of the intron length and plastome size. c Correlation of the SC length and plastome size. d Correlation of the IGS length and plastome size

Full size image

The Spiraea plastid genomes encoded a total of 113–114 genes, including 78–79 protein-coding genes (PCGs), 30 tRNA genes, and 4 rRNA genes (Table S3). Seventeen genes contained one intron, namely, atpF, clpP, petB, petD, ndhA, ndhB, rpoC1, rpl2, rpl16, rps12, rps16, trnA-UGC, trnI-GAU, trnG-UCC, trnK-UUU, trnL-UAA, and trnV-UAC. One gene, ycf3, contained two introns. Additionally, seventeen genes were located in the IR region, including ndhB, rpl2, rpl23, rps7, rps12, rrn4.5, rrn5, rrn16, rrn23, trnA-UGC, trnI-CAU, trnI-GAU, trnL-CAA, trnN-GUU, trnR-ACG, trnV-GAC, and ycf2 (Table S4). The psaA gene was absent in the accession of S. japonica var. japonica (MZ981784).

Plastid genome variations within Spiraea

Our study revealed a significant level of conservation in the genome structure, gene order, and gene content of all Spiraea plastid genomes. Figure S1 demonstrates that the inverted repeat (IR) region exhibited relatively stable characteristics compared to the large single copy (LSC) and small single copy (SSC) regions, with strikingly higher GC content and lower nucleotide and gap variations (Fig. 3a). The conservation of all rRNA genes was observed, along with certain protein-coding genes, such as psbA, atpA, rpoB, psbC, psbD, rps4, rbcL, accD, petA, psbB, ycf2, ndhD, and rps7.

Fig. 3
figure 3

Basic features of Spireae plastid genomes. a The nucleotide diversity (Pi), GC content, and gap proportion in the plastid genome sequences of Spiraea detected by sliding window analysis, using 600 bp windows and a 200 bp step size. b Nonsynonymous (dN) and synonymous (dS) substitution rates and dN/dS for each protein-coding gene within the Spiraea plastid genomes

Full size image

In the comprehensive analysis of 34 Spiraea species’ plastid genomes, we observed that the length of the rps19 gene ranged from 279 to 282 bp, all of which were located in the LSC-IRB region (Fig. S2). Among them, a segment of 108 bp to 252 bp extended into the IRB region, resulting in the duplication of the rps19 gene in the IRA region. The ycf1 gene was situated in the SSC-IRA region, with a length ranging from 1,046 bp to 1,064 bp in the IRA region. However, the total length of the ycf1 gene in S. martini was only 1,131 bp, with only 82 bp in the SSC region, while S. veitchii exhibited the longest length of 4,577 bp. Additionally, a duplication of the ycf1 pseudogene was observed in the IRB region. The position of the ndhF gene varied, with 22 Spiraea accessions harboring it in the SSC region, while it appeared in the IRB-SSC region in the remaining accessions. The conserved trnH-GUG gene, spanning 74 bp to 75 bp, was distributed in the SSC-IRA region (Fig. S2).

Sliding window analysis demonstrated that nucleotide polymorphisms in Spiraea plastid genomes were more conserved in the IR region than in the SC region, and non-coding regions exhibited more pronounced variation than coding regions (Fig. 3a). The nucleotide polymorphism (Pi) ranged from 0 to 0.0138, with an average value of 0.0036. Relatively high Pi values (> 0.011) were observed in ndhF-rpl32, trnH-GUG-psbA, trnG-UCC-atpA, petN-psbM, trnT-UGU-trnL-UAA, rpl16, rpl32-trnL-UAG, and ycf1.

Selection pressure analyses

This study investigated the adaptive evolution of Spiraea by analyzing the ratio of dN/dS. A ratio greater than one indicates positive selection, a ratio less than one indicates purifying selection, and a ratio equal to one indicates neutral evolution. Analysis of 50 PCGs revealed that the average value of dN was 0.0074, with the highest value observed for rpl22 (0.0394) and the lowest being 0. The average value of dS was 0.0449, with the highest value observed for rps14 (0.1179) and the lowest values observed for ycf2 (0.0022). The average value of dN/dS was 0.2122, with the highest value observed for ycf2 (1.7749) and the lowest being 0.0001 (Fig. 3b).

Most genes exhibited a significantly larger dS compared to dN, indicating purifying selection. However, the dN/dS ratio of the ycf2 gene (1.7749) was greater than one, suggesting that this gene has undergone significant positive selection (P < 0.05; Tables S5-6). When categorizing the protein-coding genes based on their function, it was found that the other type genes had the highest dN value (0.0127), while photosystem genes had the lowest dN value (0.0016). In terms of dS, ribosomal protein genes had the highest value (0.0599), while RNA polymerase genes had the lowest value (0.0265). Except for the ycf2 gene whose dN/dS value was greater than 1, the rest of the functional genes exhibited values less than 1. Among these groups, other type genes had the highest dN/dS value (0.4261), while photosystem genes had the lowest (0.0401) (Fig. 4).

Fig. 4
figure 4

Boxplot of the values of the nonsynonymous (dN) and synonymous (dS) substitution rates and dN/dS in each functional gene group

Full size image

Phylogenetic analyses and divergence time estimates

For this study, a total of 34 Spiraea accessions and three outgroups were selected to construct a maximum likelihood (ML) tree and Bayesian inference using the protein-coding genes (PCGs) and whole plastid genome sequences. The ML tree, Bayesian inference, and BEAST MCC tree based on PCGs showed similar phylogenetic topographies, as shown in Fig. 5. Five main clades were identified with high support in the ML tree (Fig. 5a). However, the whole plastid genome sequences only recovered four of these main clades with full support values (Fig. S3). Clade II, identified by PCGs, could not be recovered, and its three branches within the clade were not strongly supported (Fig. S3). Overall, the traditionally defined subgenera and sections of Spiraea could not be supported by any datasets or phylogenetic methods (Fig. 5a-b; Fig. S3; Table S1).

Fig. 5
figure 5

Phylogeny, divergence time estimate, and LTT plot for Spiraea. a Cladogram of the maximum likelihood (ML) phylogenetic tree based on 79 protein-coding genes (PCGs). All main clades in both trees (ML and BI) are identical. Node labels are marked with the bootstrap values and Bayesian posterior probabilities (bootstrap value/posterior probability). The upper left shows the ML tree with branch length. b Maximum clade-credibility tree (MCC tree) from BEAST. Node labels represent estimated divergence times in millions of years. The confidence intervals are provided in Fig. S4. c Lineage-through-time plot for Spiraea

Full size image

To estimate the divergence date, the top-ten clock-like genes were filtered out using Sortadate. These genes were accD, atpA, matK, ndhA, ndhD, ndhF, ccsA, rpoB, rpoC2, and ycf1. Twelve reliable fossils from Rosaceae were used. The stem age of Spiraea was estimated to be 49.24 Ma (95% HPD: 48.64–50.09 Ma), with diversification occurring since the Early Oligocene (~ 30 Ma, Fig. S4). Most extant Spiraea lineages (29 out of 34) originated from the Pliocene period, and 25 taxa diverged from their most recent common ancestor (MRCA) since the Pleistocene (Fig. 5b). The LTT plot showed a clear shift in lineages since approximately 5 Ma, followed by a rapid accumulation after approximately 2 Ma (Fig. 5c).

Discussion

Plastid genome features and molecular variation patterns of Spiraea

The plastid is a vital organelle in plant cells, playing a crucial role in plant growth and development [50]. While significant changes in plastome organization have been observed in various distantly related plant groups, such as conifers, the IR-lacking clade (IRLC) of Fabaceae, and some parasitic flowering plants [51], the plastid genome tends to remain relatively stable across major plant lineages. It typically consists of a quadripartite structure, with two copies of a large inverted repeat (IR) separated by small (SSC) and large (LSC) single-copy regions [51]. The typical plastome organization is also found in Spiraea (Fig. 1; Table S1). In our study, we examined the plastid genome of Spiraea and identified several variation patterns within these genomes.

First, we observed a positive relationship between the size of SC region and plastid genome size. While expansion/contraction of the IR regions is one major driver of genome size variation [51], this was not evident in Spiraea (R2 = 0.03, P = 0.31; Fig. 2a). However, there was a significant correlation between the plastid genome size and the length of the SC region (R2 = 0.99, P < 0.01; Fig. 2c). The gene content across all Spiraea plastid genomes was highly conserved, except for Spiraea japonica var. japonica (accession no. MZ981784), which had the smallest plastid genome within Spiraea and lacked an important photosynthesis-related gene, psaA. Additionally, the intergenic spacer (IGS) in the SC region may contribute to the size variation of the SC region in Spiraea (Fig. 2d).

Second, we identified several hypervariable hotspots within the Spiraea plastome, all of which were localized in the SC regions (Fig. 3a). The pattern of lower IR substitution rates compared to the SC regions has been observed in Spiraea plastid genomes (Fig. 3a; Fig. S1), as well as in other plant lineages [51]. This decreased substitution rate in the IR is likely a result of a copy-dependent repair mechanism [52, 53].

Previous studies have used nrDNA ITS sequence data and two plastid DNA loci (trnL-trnF and matK) to investigate the phylogeny of Spiraea [6,7,8,9]. However, these methods have shown limited resolution in resolving the phylogeny of Spiraea [6,7,8,9], indicating low sequence divergence. Our results partially support this hypothesis, as trnL-trnF and matK exhibit relatively low sequence divergence (each with Pi values of 0.0043 and 0.0083) compared to highly variable hotspots (such as hotspots with Pi values > 0.011) (Fig. 3a). Therefore, hypervariable loci are needed to increase the phylogenetic resolution in low-level phylogenetic or phylogeographic studies in Spiraea.

We conducted an investigation into the hypervariable hotspots across the plastid genomes within the Spiraea genus. These findings provide valuable genetic resource information for future phylogenetic and phylogeographic studies. We identified eight top hypervariable loci with Pi values larger than 0.011, namely, ndhF-rpl32, trnH-GUG-psbA, trnG-UCC-atpA, petN-psbM, trnT-UGU-trnL-UAA, rpl16, rpl32-trnL-UAG, and ycf1.

Many of these loci, such as ndhF-rpl32, trnH-GUG-psbA, trnT-UGU-trnL-UAA rpl16, rpl32ni-trnL-UAG, and ycf1, have been previously identified in studies conducted by Taberlet et al. [54], Shaw et al. [55,56,57], and Dong et al. [58]. However, it is important to consider the characteristics of these variable regions themselves. Factors such as fragment size and mutation rate (substitution saturation) can have a detrimental effect on phylogenetic analysis. For example, the trnH-psbA locus is controversial due to its high mutation rate and significant length variation caused by insertions, deletions, simple sequence repeats, and localized inversions in some plant lineages [59,60,61]. These factors can impact the accuracy of sequence alignment and subsequently affect the precision of phylogenetic inference and species identification. Within Spiraea, the trnH-psbA alignment matrix has 89 indels, indicating its unstable size. Additionally, the ycf1 locus also exhibits a high degree of sequence variability and has been proposed as a candidate barcode for land plants. However, its high mutation rate and absence in certain plant lineages, such as Poaceae [58, 62], can lead to alignment ambiguities similar to those observed in trnH-psbA [58, 63].

Third, we investigated the pattern of molecular evolution among plastid genes in Spiraea. The evolution of substitution rates among plastid genes has been extensively studied with the recent release of numerous plastid genomes. Generally, non-synonymous rates of photosynthesis-related genes differ significantly from those of housekeeping genes, indicating that photosynthesis-related genes are under stronger functional constraints [64,65,66]. Our results demonstrated that the photosynthesis-related genes in terms of dN are highly conserved, providing further support for this hypothesis (Fig. 4).

It is generally assumed that most plant species have low substitution rates of most genes in organelles, and these genes evolve under strong purifying selection. However, certain genes in the plastid have undergone positive selection, particularly those involved in photosynthesis and other metabolic pathways (e.g., [67]). The relationship between the high levels of non-synonymous rate evolution and potential selective factors, such as novel ecological conditions, is still a topic of debate [68]. In our study, we observed dN/dS ratios of 1.7749 for the ycf2 gene, which is likely experiencing strong positive selection (P < 0.05; Table S6). Despite its unclear function, ycf2 is known as the largest chloroplast gene in angiosperms and encodes a motor protein that generates ATP for inner membrane translocation [69] and plant cell survival [70]. Positive selection of the ycf2 gene has been observed in numerous species (e.g., [71,72,73,74]), indicating its adaptive importance in various environments and plant lineages.

Phylogenetic and evolutionary implications of plastid genomes within Spiraea

The inheritance of plastid genomes in plants is typically uniparental, with the mother being the primary contributor. This results in a smaller effective population size compared to nuclear genomes. Consequently, the coalescent time of plastid haplotypes is shorter, making them ideal for studying genetic variations and tracing plastid evolutionary history [75]. Plastid markers, such as rbcL, matK, and trnL-trnF, have been widely used in phylogenetic studies at different taxonomic levels, including ordinal, familial, tribal, and generic levels [12, 76]. They have also been used in lower-level studies, such as phylogeographic investigations [77, 78] and species identification [79, 80].

Previous studies on the phylogeny of Spiraea have utilized nrDNA ITS sequence data and limited plastid DNA loci (trnL-trnF and matK) [6,7,8,9]. However, the resolution of the phylogenetic relationships within Spiraea based on ITS and trnL-trnF is limited, and none of the sections defined by Rehder [3] and Yü and Lu [81] based on inflorescence morphology are monophyletic [8]. Yu et al. [9] largely supported this hypothesis, except for sect. Spiraea. The inclusion of Spiraea douglasii in sect. Spiraea is questionable and represents a clear difference between the two aforementioned studies [8, 9]. The Spiraea species sampled in our study represent all four sections found in China (Table S1; [1, 81]). Our phylogenomic analysis provides the most detailed phylogenetic backbone of Spiraea to date and strongly supports the notion that the traditionally defined sections of Spiraea are not monophyletic groups [8, 9], despite only including one species (S. salicifolia) from sect. Spiraea in this study. We also found a complex evolutionary history of Spiraea, as there are many shallow relationships within Clades I and II that could not be highly supported (Fig. 5; Fig. S3). Additionally, some taxa with multiple accessions (such as S. japonica, S. cantoniensis, and S. × vanhouttei) are recovered as paraphyletic or polyphyletic groups (Fig. 5; Fig. S3).

There may be two primary reasons why the traditional taxonomic classification and some species boundaries within Spiraea cannot be supported by plastid data. First, the phylogenetic relationships inferred from plastid data could not fully capture the evolutionary relationships among taxa, potentially due to the occurrence of pervasive hybridization/introgression events in Spiraea. Recent evidence suggests that ancient hybridization/introgression events may have resulted in cytonuclear conflicts [82]. Spiraea is a genus known to frequently undergo hybridization, with numerous hybrids arising through horticultural practices [83]. Although wild hybrids are less commonly reported, there have been instances of hybrids occurring across different sections of the genus [83, 84]. These findings indicate that reproductive isolation is not complete among Spiraea species, making hybridization a potentially common phenomenon. Consequently, a substantial amount of nuclear genetic data is necessary to further investigate the traditional classification framework and hybridization/introgression events.

Second, more detailed revisions of the infrageneric classification and species boundary delimitation of Spiraea are needed. It is evident that the traditional classification system within Spiraea is inconsistent with the results obtained from plastid data ([23]; this study), nuclear ribosomal DNA internal transcribed spacer (nrDNA ITS) sequences [6, 7], and their combined analyses [8, 9]. This inconsistency strongly suggests a problem with the traditional infrageneric classification of Spiraea. Traditionally, inflorescence morphology has been the main focus in these classification schemes [3, 81], but it is likely that this trait does not accurately reflect the true evolutionary relationships within Spiraea. Therefore, an integrative framework for infrageneric classification in the genus is needed, which should incorporate additional diagnostic traits such as seed and palynology evidence [9].

Ancient Spiraea taxa are believed to have originated in the Early Eocene based on macrofossil evidence [42, 43]. Our study is the first to identify the divergence sequence of the Asian Spiraea taxa using twelve reliable fossil calibrations of Rosaceae. We acknowledge that the crown age inferred in this study may have been underestimated due to our limited sampling. Nevertheless, our divergence time for the genus is much older than the result of Spiraea taxa in the Qinghai-Tibetan Plateau (QTP) [6], which can be attributed to the difference in molecular markers, divergence estimate methods, and the species included in the two studies ([6]; this study). We conclude that Spiraea diversified after the Early Oligocene (~ 30 Ma), followed by a rapid speciation process during the Pliocene and Pleistocene periods (Fig. 5b-c). The Pliocene–Pleistocene period played a vital role in the speciation process of Spiraea, as most extant taxa used in this study originated during this period (Fig. 5b-c), which is supported by fossil records from the Pliocene–Pleistocene period in Asia (data from PBDB: https://paleobiodb.org/#/). The pattern of rapid radiation during this period (especially the Pleistocene) has also been observed in many plant groups in East Asia [85, 86], likely due to the effects of glaciation and climatic oscillations during the Pleistocene. East Asia, being less influenced by Quaternary glaciations, had a lower extinction rate or higher speciation rate during those times, leading to a relatively high net diversification rate [87,88,89,90]. Biotic factors, such as hybridization/introgression, possibly related to climate oscillations, have also contributed to the emergence of young species during the Pleistocene period. In theory, climate oscillations during the period may have favored introgressive hybridization/introgression upon secondary contact following initial species divergence [91, 92]. Ancestral polymorphism of plastomes could have been purified through the extent of chloroplast-capture events when hybridization/introgression frequently occurred [93], obscuring species boundaries and creating the illusion of young species pairs. Further studies, including nuclear sequences and more samplings, are needed to investigate this hypothesis.

Conclusions

In this study, we sequenced, assembled, and analyzed the plastomes of 34 Asiatic Spiraea accessions. The Spiraea plastid genome exhibits typical quadripartite structures and encodes 113–114 genes, including 78–79 protein-coding genes (PCGs), 30 tRNA genes, and 4 rRNA genes. There is a significant correlation between genome size and the length of the SC region. Several hypervariable regions within the Spiraea plastome were identified, all of which were localized in the SC regions. Our phylogenomic analysis successfully provides a phylogenetic framework for Spiraea but does not support its currently defined section boundaries. Additionally, we discovered that the genus underwent diversification after the Early Oligocene (~ 30 Ma), followed by a rapid speciation process during the Pliocene and Pleistocene periods. Although not perfect, the plastomes of Spiraea provide invaluable insights into its phylogenetic relationships and evolutionary history. Further investigations utilizing other genomes, such as the nuclear genome, are urgently needed to enhance our understanding of the evolutionary history of the genus.

Availability of data and materials

All newly derived plastid sequences from this study have been deposited in the National Center for Biotechnology Information (NCBI) (see Table S1).

References

  1. Lu LT, Alexander C. Spiraea. In: Wu CY, Raven P, editors. Flora of China (vol 9). St. Louis: Missouri Botanical Garden Press; 2003. p. 47–73.

    Google Scholar 

  2. Poyarkova AI. Spiraeoideae. Moscow: Izdatel-stvo Akademii Nauk SSSR; 1939.

    Google Scholar 

  3. Rehder A. Manual of cultivated trees and shrubs. Portland: Dioscorides Press; 1940.

    Google Scholar 

  4. Yü T, Kuan K. Taxa nova Rosacearum sinicarum (1). Acta Phytotax Sin. 1963;8(3):202–34.

    Google Scholar 

  5. Huh M. Phylogenetic relationships in the genus Spiraea (Rosaceae) inferred from the chloroplast DNA region, trnL-trnF. Ame J Plant Sci. 2012;2012(3):559–66.

    Article  Google Scholar 

  6. Khan G, Zhang FQ, Gao QB, Fu PC, Xing R, Wang JL, Liu HR, Chen SL. Phylogenetic analyses of Spiraea (Rosaceae) distributed in the Qinghai-Tibetan plateau and adjacent regions: Insights from molecular data. Plant Syst Evol. 2016;302(1):11–21.

    Article  Google Scholar 

  7. Poliakova TA, Shatokhina AV, Politov DV. Molecular phylogeny of Russian species of the genus Spiraea (Rosaceae) according to the nucleotide variability of the its nuclear rDNA region. Russ J Genet. 2022;58(11):1297–305.

    Article  CAS  Google Scholar 

  8. Potter D, Eriksson T, Evans RC, Oh S, Smedmark JEE, Morgan DR, Kerr M, Robertson KR, Arsenault M, Dickinson TA, et al. Phylogeny and classification of Rosaceae. Plant Syst Evol. 2007;266(1–2):5–43.

    Article  Google Scholar 

  9. Yu SX, Gadagkar SR, Potter D, Xu DX, Zhang M, Li ZY. Phylogeny of Spiraea (Rosaceae) based on plastid and nuclear molecular data: Implications for morphological character evolution and systematics. Perspect Plant Ecol. 2018;34:109–19.

    Article  Google Scholar 

  10. Huh M. Analysis of the phylogenetic relationships in the genus Spiraea based on the nuclear ribosomal DNA its region. J Life Sci. 2012;22:285–92.

    Article  Google Scholar 

  11. Howe CJ, Barbrook AC, Koumandou VL, Nisbet RER, Symington HA, Wightman TF. Evolution of the chloroplast genome. Philos T Roy Soc B. 2003;358(1429):99–106.

    Article  CAS  Google Scholar 

  12. Gitzendanner MA, Soltis PS, Yi TS, Li DZ, Soltis DE. Plastome phylogenetics: 30 years of inferences into plant evolution. Adv Bot Res. 2018;85:293–313.

    Article  CAS  Google Scholar 

  13. Bock R. Structure, function, and inheritance of plastid genomes. In: Bock R, editor. Cell and molecular biology of plastids. Berlin, Heidelberg: Springer Berlin Heidelberg; 2007. p. 29–63.

    Chapter  Google Scholar 

  14. Zhang SD, Jin JJ, Chen SY, Chase MW, Soltis DE, Li HT, Yang JB, Li DZ, Yi TS. Diversification of Rosaceae since the Late Cretaceous based on plastid phylogenomics. New Phytol. 2017;214(3):1355–67.

    Article  PubMed  CAS  Google Scholar 

  15. Li C, Cai C, Tao Y, Sun Z, Jiang M, Chen L, Li J. Variation and evolution of the whole chloroplast genomes of Fragaria spp. (Rosaceae). Front Plant Sci. 2021;12:754209.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Sun J, Sun R, Liu HB, Chang LL, Li ST, Zhao MZ, Shennan C, Lei JJ, Dong J, Zhong CAF, et al. Complete chloroplast genome sequencing of ten wild Fragaria species in china provides evidence for phylogenetic evolution of Fragaria. Genomics. 2021;113(3):1170–9.

    Article  PubMed  CAS  Google Scholar 

  17. Yu JJ, Fu J, Fang YP, Xiang J, Dong HJ. Complete chloroplast genomes of Rubus species (Rosaceae) and comparative analysis within the genus. BMC Genomics. 2022;23:32.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. Gao C, Li T, Zhao X, Wu C, Zhang Q, Zhao X, Wu M, Lian Y, Li Z. Comparative analysis of the chloroplast genomes of Rosa species and RNA editing analysis. BMC Plant Biol. 2023;23:318.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Zhang C, Li SQ, Xie HH, Liu JQ, Gao XF. Comparative plastid genome analyses of Rosa: insights into the phylogeny and gene divergence. Tree Genet Genomes. 2022;18:20.

    Article  CAS  Google Scholar 

  20. Choi J, Kim W, Park JY, Kang JS, Yang TJ. The complete plastid genome and nuclear ribosomal transcription unit sequences of Spiraea prunifolia f. simpliciflora (Rosaceae). Korean J Plant Taxon. 2023;53(1):32–7.

    Article  Google Scholar 

  21. Shen W, Lin J, Lin H. The chloroplast genome of Spiraea thunbergii (Rosaceae). Mitochondrial DNA Part B. 2022;7(10):1879–81.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Yang J, Kang GH, Pak JH, Kim SC. Characterization and comparison of two complete plastomes of Rosaceae species (Potentilla dickinsii var. glabrata and Spiraea insularis) endemic to Ulleung island, Korea. Int J Mol Sci. 2020;21(14):4933.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  23. Zhang SD, Yan K, Ling LZ. Characterization and phylogenetic analyses of ten complete plastomes of Spiraea species. BMC Genomics. 2023;24(1):137.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. Doyle JJ, Doyle JL. A rapid DNA isolation procedure for small quantities of fresh leaf tissue. Phytochem Bull. 1987;19:11–5.

    Google Scholar 

  25. Jin JJ, Yu WB, Yang JB, Song Y, dePamphilis CW, Yi TS, Li DZ. Getorganelle: a fast and versatile toolkit for accurate de novo assembly of organelle genomes. Genome Biol. 2020;21:241.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Qu XJ, Moore MJ, Li DZ, Yi TS. PGA: a software package for rapid, accurate, and flexible batch annotation of plastomes. Plant Methods. 2019;15:50.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Kearse M, Moir R, Wilson A, Stones-Havas S, Cheung M, Sturrock S, Buxton S, Cooper A, Markowitz S, Duran C, et al. Geneious basic: an integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics. 2012;28(12):1647–9.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Lohse M, Drechsel O, Bock R. Organellargenomedraw (OGDRAW): a tool for the easy generation of high-quality custom graphical maps of plastid and mitochondrial genomes. Curr Genet. 2007;52(5–6):267–74.

    Article  PubMed  CAS  Google Scholar 

  29. R Core Team. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2022.

    Google Scholar 

  30. Katoh K, Standley DM. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol. 2013;30(4):772–80.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  31. Rozas J, Ferrer-Mata A, Sanchez-DelBarrio JC, Guirao-Rico S, Librado P, Ramos-Onsins SE, Sanchez-Gracia A. DnaSP 6: DNA sequence polymorphism analysis of large data sets. Mol Biol Evol. 2017;34(12):3299–302.

    Article  PubMed  CAS  Google Scholar 

  32. Frazer KA, Pachter L, Poliakov A, Rubin EM, Dubchak I. VISTA: computational tools for comparative genomics. Nucleic Acids Res. 2004;32:W273–9.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  33. Borowiec ML. AMAS: A fast tool for alignment manipulation and computing of summary statistics. PeerJ. 2016;4:e1660.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Stamatakis A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics. 2014;30(9):1312–3.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. Ronquist F, Teslenko M, van der Mark P, Ayres DL, Darling A, Höhna S, Larget B, Liu L, Suchard MA, Huelsenbeck JP. Mrbayes 3.2: Efficient bayesian phylogenetic inference and model choice across a large model space. Syst Biol. 2012;61(3):539–42.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Miller MA, Pfeiffer W, Schwartz T. Creating the cipres science gateway for inference of large phylogenetic trees. In: 2010 gateway computing environments workshop (GCE). New Orleans: IEEE; 2010. p. 1–8.

  37. Rambaut A, Drummond AJ, Xie D, Baele G, Suchard MA. Posterior summarization in bayesian phylogenetics using TRACER 1.7. Syst Biol. 2018;67(5):901–4.

    Article  PubMed  CAS  Google Scholar 

  38. Rambaut A. Figtree v1.4.3. 2009. [[WWW document] URL http://tree.bio.ed.ac.uk/software/figtree]. Accessed 5 Feb 2023.

  39. Smith SA, Brown JW, Walker JF. So many genes, so little time: a practical approach to divergence-time estimation in the genomic era. PLoS ONE. 2018;13(5):e0197433.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Bouckaert R, Vaughan TG, Barido-Sottani J, Duchêne S, Fourment M, Gavryushkina A, Heled J, Jones G, Kühnert D, De Maio N, et al. BEAST 2.5: an advanced software platform for bayesian evolutionary analysis. PLOS Comput Biol. 2019;15:e1006650.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. Drummond AJ, Ho SYW, Phillips MJ, Rambaut A. Relaxed phylogenetics and dating with confidence. PLOS Biol. 2006;4:e88.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Xiang Y, Huang CH, Hu Y, Wen J, Li S, Yi T, Chen H, Xiang J, Ma H. Evolution of rosaceae fruit types based on nuclear phylogeny in the context of geological times and genome duplication. Mol Biol Evol. 2017;34(2):262–81.

    PubMed  CAS  Google Scholar 

  43. DeVore ML, Pigg KB. A brief review of the fossil history of the family Rosaceae with a focus on the Eocene Okanogan highlands of eastern Washington state, USA, and British Columbia. Canada Plant Syst Evol. 2007;266(1–2):45–57.

    Article  Google Scholar 

  44. Paradis E, Claude J, Strimmer K. APE: analyses of phylogenetics and evolution in R language. Bioinformatics. 2004;20(2):289–90.

    Article  PubMed  CAS  Google Scholar 

  45. Edgar RC. MUSCLE: a multiple sequence alignment method with reduced time and space complexity. BMC Bioinformatics. 2004;5:113.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Tamura K, Stecher G, Kumar S. MEGA11 molecular evolutionary genetics analysis version 11. Mol Biol Evol. 2021;38(7):3022–7.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  47. Zhang D, Gao FL, Jakovlic I, Zou H, Zhang J, Li WX, Wang GT. Phylosuite: an integrated and scalable desktop platform for streamlined molecular sequence data management and evolutionary phylogenetics studies. Mol Ecol Resour. 2020;20(1):348–55.

    Article  PubMed  Google Scholar 

  48. Gao FL, Chen CJ, Arab DA, Du ZG, He YH, Ho SYW. EasycodeML: a visual tool for analysis of selection using codeml. Ecol Evol. 2019;9(7):3891–8.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Wicke S, Schneeweiss GM, dePamphilis CW, Muller KF, Quandt D. The evolution of the plastid chromosome in land plants: gene content, gene order, gene function. Plant Mol Biol. 2011;76(3–5):273–97.

    Article  PubMed  CAS  Google Scholar 

  50. Gould SB, Waller RF, McFadden GI. Plastid evolution. Annu Rev Plant Biol. 2008;59(1):491–517.

    Article  PubMed  CAS  Google Scholar 

  51. Jansen RK, Ruhlman TA. Plastid genomes of seed plants. In: Bock R, Knoop V, editors. Genomics of chloroplasts and mitochondria. Dordrecht: Springer Netherlands; 2012. p. 103–26.

    Chapter  Google Scholar 

  52. Perry AS, Wolfe KH. Nucleotide substitution rates in legume chloroplast DNA depend on the presence of the inverted repeat. J Mol Evol. 2002;55(5):501–8.

    Article  PubMed  CAS  Google Scholar 

  53. Zhu AD, Guo WH, Gupta S, Fan WS, Mower JP. Evolutionary dynamics of the plastid inverted repeat: the effects of expansion, contraction, and loss on substitution rates. New Phytol. 2016;209(4):1747–56.

    Article  PubMed  CAS  Google Scholar 

  54. Taberlet P, Gielly L, Pautou G, Bouvet J. Universal primers for amplification of three non-coding regions of chloroplast DNA. Plant Mol Biol. 1991;17(5):1105–9.

    Article  PubMed  CAS  Google Scholar 

  55. Shaw J, Lickey EB, Beck JT, Farmer SB, Liu WS, Miller J, Siripun KC, Winder CT, Schilling EE, Small RL. The tortoise and the hare II: relative utility of 21 noncoding chloroplast DNA sequences for phylogenetic analysis. Am J Bot. 2005;92(1):142–66.

    Article  CAS  Google Scholar 

  56. Shaw J, Lickey EB, Schilling EE, Small RL. Comparison of whole chloroplast genome sequences to choose noncoding regions for phylogenetic studies in angiosperms: the tortoise and the hare III. Am J Bot. 2007;94(3):275–88.

    Article  PubMed  CAS  Google Scholar 

  57. Shaw J, Shafer HL, Leonard OR, Kovach MJ, Schorr M, Morris AB. Chloroplast DNA sequence utility for the lowest phylogenetic and phylogeographic inferences in angiosperms: the tortoise and the hare IV. Am J Bot. 2014;101(11):1987–2004.

    Article  PubMed  Google Scholar 

  58. Dong WP, Xu C, Li CH, Sun JH, Zuo YJ, Shi S, Cheng T, Guo JJ, Zhou SL. Ycf1, the most promising plastid DNA barcode of land plants. Sci Rep. 2015;5:8348.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  59. Kress WJ, Erickson DL. A two-locus global DNA barcode for land plants: the coding rbcL gene complements the non-coding trnH-psbA spacer region. PLoS ONE. 2007;2(6):e508.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Pang XH, Liu C, Shi LC, Liu R, Liang D, Li H, Cherny SS, Chen SL. Utility of the trnH-psbA intergenic spacer region and its combinations as plant DNA barcodes: a meta-analysis. PLoS ONE. 2012;7(11):e48833.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  61. Whitlock BA, Hale AM, Groff PA. Intraspecific inversions pose a challenge for the trnh-psba plant DNA barcode. PLoS ONE. 2010;5(7):e11533.

    Article  PubMed  PubMed Central  Google Scholar 

  62. Goremykin VV, Holland B, Hirsch-Ernst KI, Hellwig FH. Analysis of acorus calamus chloroplast genome and its phylogenetic implications. Mol Biol Evol. 2005;22(9):1813–22.

    Article  PubMed  CAS  Google Scholar 

  63. Jansen RK, Cai Z, Raubeson LA, Daniell H, Depamphilis CW, Leebens-Mack J, Muller KF, Guisinger-Bellian M, Haberle RC, Hansen AK, et al. Analysis of 81 genes from 64 plastid genomes resolves relationships in angiosperms and identifies genome-scale evolutionary patterns. P Natl Acad Sci USA. 2007;104(49):19369–74.

    Article  CAS  Google Scholar 

  64. Wicke S, Schneeweiss GM. Next-generation organellar genomics: potentials and pitfalls of high-throughput technologies for molecular evolutionary studies and plant systematics. In: Hörandl E, Appelhans M, editors. Next-generation sequencing in plant systematics. 676. Oberreifenberg: Koeltz Botanical Books; 2015.

    Google Scholar 

  65. Wu CS, Wang TJ, Wu CW, Wang YN, Chaw SM. Plastome evolution in the sole hemiparasitic genus Laurel dodder (Cassytha) and insights into the plastid phylogenomics of Lauraceae. Genome Biol Evol. 2017;9(10):2604–14.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  66. Yan XK, Liu TJ, Yuan X, Xu Y, Yan HF, Hao G. Chloroplast genomes and comparative analyses among thirteen taxa within Myrsinaceae s.str. clade (Myrsinoideae, Primulaceae). Int J Mol Sci. 2019;20:4534.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  67. Silva SR, Miranda VFO, Michael TP, PÅ‚achno BJ, Matos RG, Adamec L, Pond SLK, Lucaci AG, Pinheiro DG, Varani AM. The phylogenomics and evolutionary dynamics of the organellar genomes in Carnivorous utricularia and Genlisea species (Lentibulariaceae). Mol Phylogenet Evol. 2023;181:107711.

    Article  PubMed  CAS  Google Scholar 

  68. Ye WQ, Yap ZY, Li P, Comes HP, Qiu YX. Plastome organization, genome-based phylogeny and evolution of plastid genes in podophylloideae (berberidaceae). Mol Phylogenet Evol. 2018;127:978–87.

    Article  Google Scholar 

  69. Kikuchi S, Asakura Y, Imai M, Nakahira Y, Kotani Y, Hashiguchi Y, Nakai Y, Takafuji K, Bedard J, Hirabayashi-Ishioka Y, et al. A ycf2-Ftshi heteromeric AAA-Atpase complex is required for chloroplast protein import. Plant Cell. 2018;30(11):2677–703.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  70. Drescher A, Ruf S, Calsa T, Carrer H, Bock R. The two largest chloroplast genome-encoded open reading frames of higher plants are essential genes. Plant J. 2000;22(2):97–104.

    Article  PubMed  CAS  Google Scholar 

  71. Chen J, Zang Y, Shang S, Liang S, Zhu ML, Wang Y, Tang XX. Comparative chloroplast genomes of zosteraceae species provide adaptive evolution insights into seagrass. Front Plant Sci. 2021;12:741152.

    Article  PubMed Central  Google Scholar 

  72. Chen YQ, Zhong H, Zhu YT, Huang YZ, Wu SS, Liu ZJ, Lan SR, Zhai JW. Plastome structure and adaptive evolution of Calanthe s.l. species. PeerJ. 2020;8:e10051.

    Article  PubMed  PubMed Central  Google Scholar 

  73. Fan WB, Wu Y, Yang J, Shahzad K, Li ZH. Comparative chloroplast genomics of Dipsacales species: Insights into sequence variation, adaptive evolution, and phylogenetic relationships. Front Plant Sci. 2018;9:689.

    Article  PubMed  PubMed Central  Google Scholar 

  74. Yang Q, Fu GF, Wu ZQ, Li L, Zhao JL, Li QJ. Chloroplast genome evolution in four montane Zingiberaceae taxa in China. Front Plant Sci. 2022;12:774482.

    Article  PubMed  Google Scholar 

  75. Petit RJ, Vendramin GG. Plant phylogeography based on organelle genes: an introduction. In: Weiss S, Ferrand N, editors. Phylogeography of southern European refugia: evolutionary perspectives on the origins and conservation of European biodiversity. Dordrecht: Springer; 2007.

    Google Scholar 

  76. Soltis D, Soltis P, Endress P, Chase MW, Manchester S, Judd W, Majure L, Mavrodiev EJC. Phylogeny and evolution of the angiosperms: revised and. updated. Chicago: The University of Chicago Press; 2018.

    Book  Google Scholar 

  77. Schaal BA, Hayworth DA, Olsen KM, Rauscher JT, Smith WA. Phylogeographic studies in plants: problems and prospects. Mol Ecol. 1998;7(4):465–74.

    Article  Google Scholar 

  78. Soltis DE, Morris AB, McLachlan JS, Manos PS, Soltis PS. Comparative phylogeography of unglaciated eastern North America. Mol Ecol. 2006;15(14):4261–93.

    Article  PubMed  Google Scholar 

  79. Hollingsworth PM, Forrest LL, Spouge JL, Hajibabaei M, Ratnasingham S, van der Bank M, Chase MW, Cowan RS, Erickson DL, Fazekas AJ, et al. A DNA barcode for land plants. P Natl Acad Sci USA. 2009;106(31):12794–7.

    Article  CAS  Google Scholar 

  80. Hollingsworth PM, Li DZ, van der Bank M, Twyford AD. Telling plant species apart with DNA: from barcodes to genomes. Philos T R Soc B. 2016;371:20150338.

    Article  Google Scholar 

  81. Yü TT, Lu LD. Spiraea. In: Yü TT, editor. Flora reipublicae Popularis Sinicae, vol. 36. Beijing: Science Press; 1974. p. 1–66.

    Google Scholar 

  82. Rose JP, Toledo CAP, Lemmon EM, Lemmon AR, Sytsma KJ. Out of sight, out of mind: Widespread nuclear and plastid-nuclear discordance in the flowering plant genus Polemonium (Polemoniaceae) suggests widespread historical gene flow despite limited nuclear signal. Syst Biol. 2021;70(1):162–80.

    Article  PubMed  CAS  Google Scholar 

  83. Zaveska Drabkova L, Pospiskova M, Businsky R. Phylogeny and infrageneric delimitation in Spiraea (Rosaceae) inferred from AFLP markers and a comparison with morphology. Bot J Linn Soc. 2017;185(4):525–41.

    Article  Google Scholar 

  84. Businsky R. Transitive inflorescence types in Spiraea (Rosaceae-Spiraeoideae) undermine the fundamental classification concept of the genus. Phyton. 2015;55(1):69–120.

    Google Scholar 

  85. Xing YW, Ree RH. Uplift-driven diversification in the Hengduan Mountains, a temperate biodiversity hotspot. P Natl Acad Sci USA. 2017;114(17):E3444–51.

    Article  CAS  Google Scholar 

  86. Zhao N, Park S, Zhang YQ, Nie ZL, Ge XJ, Kim S, Yan HF. Fingerprints of climatic changes through the late Cenozoic in southern Asian flora: Magnolia section Michelia (Magnoliaceae). Ann Bot. 2022;130(1):41–52.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  87. Adams J. Species richness: patterns in the diversity of life. Heidelberg: Springer Berlin; 2009.

  88. Axelrod D, Al-Shehbaz I, Raven P. History of the modern flora of China. In: Zhang AL, Wu SG, editors. Floristic characteristics and diversity of East Asian plants. Beijing: China: Higher Education Press; 1998. p. 43–55.

    Google Scholar 

  89. Qian H, Ricklefs RE. Large-scale processes and the Asian bias in species diversity of temperate plants. Nature. 2000;407(6801):180–2.

    Article  PubMed  CAS  Google Scholar 

  90. Yan HF, Zhang CY, Anderberg AA, Hao G, Ge XJ, Wiens JJ. What explains high plant richness in East Asia? Time and diversification in the tribe Lysimachieae (primulaceae). New Phytol. 2018;219(1):436–48.

    Article  PubMed  Google Scholar 

  91. Taylor SA, Larson EL, Harrison RG. Hybrid zones: Windows on climate change. Trends Ecol Evol. 2015;30(7):398–406.

    Article  PubMed  Google Scholar 

  92. Walter RP, Roy D, Hussey NE, Stelbrink B, Kovacs KM, Lydersen C, McMeans BC, Svavarsson J, Kessel ST, Porsmoguer SB, et al. Origins of the greenland shark (Somniosus microcephalus): Impacts of ice-olation and introgression. Ecol Evol. 2017;7(19):8113–25.

    Article  PubMed  PubMed Central  Google Scholar 

  93. Rieseberg LH, Soltis DE. Phylogenetic consequences of cytoplasmic gene flow in plants. Evol Trend Plant. 1991;5(1):65–84.

    Google Scholar 

Download references

Acknowledgements

We are grateful to Hangzhou Botanical Garden for providing plant materials. We also thank Dong-Xian Xu, Ting Wang, Tian-Wen Xiao, Lu Jin, Ning Fu and Yu-Yin Zhou for helping with sampling, specimen identification, DNA extraction and data analyses.

Funding

This work was financially supported by the Guangdong Flagship Project of Basic and Applied Basic Research (grant no. 2023B0303050001) and the National Survey of Traditional Chinese Medicine Resources from the National Administration of Traditional Chinese Medicine (grant no. GZY-KJS-2018–004).

Author information

Author notes

  1. Shu-Yan Zhang and Hai-Fei Yan contributed equally to this work.

Authors and Affiliations

  1. College of Life Sciences, South China Agricultural University, Guangzhou, 510642, China

    Shu-Yan Zhang & Gang Hao

  2. Key Laboratory of Plant Resources Conservation and Sustainable Utilization, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou, 510650, China

    Shu-Yan Zhang, Hai-Fei Yan, Lei Wei, Tong-Jian Liu & Xing Wu

  3. South China National Botanical Garden, Guangzhou, 510650, China

    Shu-Yan Zhang, Hai-Fei Yan, Lei Wei, Tong-Jian Liu & Xing Wu

  4. Guangdong Provincial Key Laboratory of Applied Botany, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou, 510650, China

    Shu-Yan Zhang, Hai-Fei Yan, Lei Wei, Tong-Jian Liu & Xing Wu

  5. Hangzhou Xixi National Wetland Park Service Center (Hangzhou Xixi National Wetland Park Ecology & Culture Research Center), Hangzhou, 310013, China

    Lin Chen & Qiao-Ling Zhang

Authors
  1. Shu-Yan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hai-Fei Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lei Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tong-Jian Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lin Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Gang Hao
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Xing Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Qiao-Ling Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

QLZ, XW, LC and GH conceived this study. SYZ and TJL conducted the experimental study. SYZ, TJL and LW performed the analyses. HFY and SYZ wrote the manuscript. QLZ and GH contributed feedback and editing of the draft manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Xing Wu or Qiao-Ling Zhang.

Ethics declarations

Ethics approval and consent to participate

This study’s material collections and experimental research followed the relevant institutional, national, and international guidelines and legislation. No specific permissions or licenses were needed.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

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

Supplementary Information

Additional file 1: Fig. S1.

Sequence identity plots of plastomes of Spiraea by mVISTA. The top line shows the orientation of genes. A cutoff of 70% identity was used for the plots, and the Y-scale represents the percentage identity ranging from 50 to 100%. Fig. S2. Comparison of LSC, IRs, and SSC junction positions among Spiraea plastomes. Fig. S3. Cladogram of the maximum likelihood (ML) phylogenetic tree constructed based on the whole plastid sequences. Node labels represent the bootstrap values (left) and Bayesian posterior probabilities (right), respectively. Fig. S4. Maximum clade-credibility tree of Rosaceae from BEAST with 12 fossil calibration points. The bar at each node indicates age with a 95% height posterior distribution. Red stars represent fossil calibration points (see Table S2). Table S1. The sampling information in this study. Table S2. Fossil calibration points used in this study. Table S3. Plastid genome information of Spiraea. Table S4. List of annotated genes in the plastid genome of Spiraea. Table S5. The value of nonsynonymous (dN), synonymous (dS) substitution rate and dN/dS in each functional gene. Table S6. The likelihood ratio and positively selected codon site tests in this study.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, SY., Yan, HF., Wei, L. et al. Plastid genome and its phylogenetic implications of Asiatic Spiraea (Rosaceae). BMC Plant Biol 24, 23 (2024). https://doi.org/10.1186/s12870-023-04697-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12870-023-04697-8

Keywords

BMC Plant Biology

ISSN: 1471-2229

Contact us