Skip to main content
Download PDF
  • Research article
  • Open access
  • Published:

Plastome phylogeny and early diversification of Brassicaceae

BMC Genomics volume 18, Article number: 176 (2017) Cite this article

Abstract

Background

The family Brassicaceae encompasses diverse species, many of which have high scientific and economic importance. Early diversifications and phylogenetic relationships between major lineages or clades remain unclear. Here we re-investigate Brassicaceae phylogeny with complete plastomes from 51 species representing all four lineages or 5 of 6 major clades (A, B, C, E and F) as identified in earlier studies.

Results

Bayesian and maximum likelihood phylogenetic analyses using a partitioned supermatrix of 77 protein coding genes resulted in nearly identical tree topologies exemplified by highly supported relationships between clades. All four lineages were well identified and interrelationships between them were resolved. The previously defined Clade C was found to be paraphyletic (the genus Megadenia formed a separate lineage), while the remaining clades were monophyletic. Clade E (lineage III) was sister to clades B + C rather than to all core Brassicaceae (clades A + B + C or lineages I + II), as suggested by a previous transcriptome study. Molecular dating based on plastome phylogeny supported the origin of major lineages or clades between late Oligocene and early Miocene, and the following radiative diversification across the family took place within a short timescale. In addition, gene losses in the plastomes occurred multiple times during the evolutionary diversification of the family.

Conclusions

Plastome phylogeny illustrates the early diversification of cruciferous species. This phylogeny will facilitate our further understanding of evolution and adaptation of numerous species in the model family Brassicaceae.

Background

The predominantly herbaceous family Brassicaceae (Cruciferae), which has some 3700 species, includes many vegetable crops in the genera Brassica and Raphanus, sources of spices (Eutrema and Armoracia) and vegetable oils (Brassica), ornamentals (Arabis, Hesperis, Lobularia, and Matthiola), and model species in experimental biology (e.g., Arabidopsis thaliana). A robust phylogeny is crucial for diverse comparative studies. However, resolving the deep phylogeny of the family has been particularly challenging because its early evolution was extremely rapid [1–5], accompanied with ancient gene flow [6], polyploidization [7–9], and origin of novel traits [10]. Prior phylogenetic studies, which involved 325 genera and 51 tribes using sequence variations of a few chloroplast DNAs or ITS, identified four major lineages, with the basal lineage (tribe Aethionemeae) sister to the remaining three lineages (I, II, and III, i.e., core Brassicaceae) [2–5, 11–16]. The relationships between lineages within core Brassicaceae remained unsolved or inconsistent in those studies. Most recently, six clades were proposed based on phylogenetic analyses of low-copy nuclear genes retrieved from transcriptomes of 55 species [17]. The study further divided lineage II into three clades (B, C, D), but the remaining three clades were similar to the previously recognized three lineages (basal lineage, and lineages I and III). The clade E (lineage III) was sister to the remaining core Brassicaceae species (clades A + B + C or lineages I + II), but the relationship within the core were unsolved in the previous study [5]. Phylogenetic conflicts between different datasets, especially between nuclear and cytoplasmic genomes in plants, were found [18, 19], possibly suggesting complex evolutionary history.

The chloroplast genomes (plastomes), with extremely more informative sites for phylogenetic analyses than only a few DNA fragments, have proven to be highly effective in resolving disputed interrelationships in numerous plant groups [20–22]. Plastomes vary in size between 75 and 250 kb, have numerous copies in a given cell, inherited maternally in most plants, and have conserved gene content and order [23, 24]. The plastome is characterized by two usually large inverted repeat regions (IRa and IRb) separated by two single-copy regions referred to as the large single-copy region (LSC) and small single-copy region (SSC) that vary in length. Occasional structural changes, such as gene or intron losses, inversions, and rearrangements, were revealed by comparative genomic studies between groups. For examples, numerous plastome genes were lost multiple times in parasitic, nonphotosynthetic plants such as species of Cuscuta [25, 26], Epifagus [27], and Rhizanthella [28]. In photosynthetic species, the loss of chloroplast genes rarely occurs and only when the nuclear and/or mitochondrial genomes contain another functional copy or acquired one from the plastome through gene transfer [29]. Such rare cases were found in the rpl22 gene in Fagaceae and Passifloraceae [30], the rpl32 gene in Populus [31], and the infA gene in Brassicaceae [32]. Therefore, the relative stability of plastomes in plants provide highly orthologous alignments of large genome data that are valuable for phylogenetic analyses and calibrated divergence estimation [33–35].

The first plastome phylogeny of Brassicaceae have recently been presented aiming to provide a reliable temporal evolutionary framework within the entire clade of Superrosidae angiosperms and using critically evaluated fossil data for calibrating divergence-time estimates [35]. This was urgently needed because of conflicting hypotheses on Brassicaceae divergence-time estimates [36]. We built on this study [35] and expanded our sampling to 51 Brassicaceae plastomes and Cleome as outgroup. These species cover all four lineages or 5 out of 6 clades identified before [5, 17]. We aimed to examine whether the plastome dataset could: (1) significantly support the previously shown deep splits; (2) resolve the disputed interrelationships between lineages or clades; and (3) reveal any previously overlooked structural evolution within Brassicaceae plastomes.

Results

Basic characteristics of Brassicaceae chloroplast genomes

The average length of the plastomes from 53 species of Brassicales (Additional file 1: Table S1) is 154 kb, ranging from 152,659 bp in Lobularia maritima to 160,100 bp in Carica papaya (Additional file 1: Table S2). The average GC content is 36.4, 34.1, 29.3 and 42.3% for complete sequences, LSC, SSC and IR regions a and b, respectively, and varies slightly between species (Additional file 1: Table S2). As in the vast majority of angiosperms, both gene content and gene order are highly conserved, where the typical quadripartite organization harbored 132 genes including 79 protein coding genes (PCGs, 8 duplicated in IR), 30 tRNA genes (7 duplicated in IR) and 4 rRNA genes (4 duplicated in IR).

Sequence alignment and evaluation of data partitions

Based on the 77 PCGs, a gap-free supermatrix containing 64,962 sites was concatenated, of which 7611 were parsimony informative (Additional file 1: Table S3). The aligned lengths of these PCGs ranged from 84 to 6645 bp (mean = 844 bp). No significant compositional heterogeneity among sequences was detected for any genes among species (Additional file 1: Table S4). The combined 77-gene data set displayed no apparent substitution saturation (Additional file 1: Table S4). Evaluation of partition strategies suggested that the automatically determined scheme is the best according to Bayesian information criterion (BIC) and the most parameter-rich gene-codon model is generally better than the less partitioned ones, while the codon-partitioned model was favored over the gene-partitioned model (Additional file 1: Table S5).

Plastome phylogeny

Both RAxML and BEAST analyses of the concatenated sequence supermatrix produced similar topologies for the 53 species. For ML analysis, the topology and support values for specific splits varied using different partition schemes or subsets. After a visual check, no well-supported conflicts (i.e., those receiving bootstrap (BS) >90%) were found between individual genes trees. Regardless of the data partition strategy in our ML analysis, the majority of relationships across the family were consistent and well supported. The BEAST topology based on the best-partition scheme defined by partition finder produced well-resolved phylogeny for all but two nodes (Fig. 1). In line with a recent conclusion [17], the three previously proposed lineages are placed into different clades. The placement of tribe Lepidieae was unstable across the analysis, and the alternative topology could not be rejected by approximately unbiased (AU) test (P = 0.297, Table 1). However, the relationship between clades determined by plastomes is discordant with nuclear gene phylogeny [17]. Of particular interest is the recently defined Clade E, a lineage containing a majority of Lineage III species, which is sister to the combined BC clade. Thus, after the split with basal Aethionemeae species, the core Brassicaceae diverged into two large clades. The first clade included species from Lineage I (or Clade A), while the second clade consisted of species from all other major lineages or clades except for the newly identified Clade D because of the limited taxon sampling. In addition, we found that Megadenia, a genus placed in the tribe Biscutelleae of Clade C, is sister to all other species of Clades B and C. Multiple tests confirmed the relationship recovered here and rejected the alternative phylogeny as previously proposed [17] (P < 0.01, Table 1).

Fig. 1
figure 1

Phylogeny of Brassicaceae. Time-calibrated phylogeny of 51 Brassicaceae species inferred from a concatenated Bayesian analysis of 77 plastome protein-coding genes with all mutations using BEAST2. Higher taxon names appear at right. All nodes are supported with posterior probability (PP) of 1.0, except for two marked with circles (Node1: PP = 0.558; Node2: PP = 0.833). Geological periods were marked with background colors. Ma million years ago, Ple Pleistocene, Pli Pliocene, Q. Quaternary

Full size image
Table 1 Comparison of tree topology hypotheses by using likelihood
Full size table

Fossil calibration and molecular dating

We included plastomes of 75 outgroups in order to allow the use of 14 non-Brassicales calibrations (Additional file 2: Figure S2; Additional file 1: Table S6). A clock rate partition test found two partitions for the whole alignment as the best fit scheme under relaxed lognormal clock model. Overall, the calculation of divergence times were barely affected by whether fossil calibrations within the Brassicales were used (Table 2; Additional file 1: Table S7). Also, there was no effect on age estimation whether we included the questionable Thlaspi primaevum fossil [37] (here used as a conservative constrain to the Brassicaceae crown node as suggested [36]) or used the newly identified Palaeocleome lakensis fossil in the analysis [33] (Additional file 1: Table S7). According to the MCMCTREE time estimates, the core Brassicaceae and Aethionemeae began to split at 35.2 (30.0–42.5) Mya during the Eocene-Oligocene boundary (Fig. 1) while the origins of the major lineages or clades occurred between the late Oligocene and early Miocene (Table 2). These time estimates are broadly consistent with recent studies using large-scale genomic data [5, 17, 34, 35]. Remarkably, all major lineages or clades radiated within a short timescales window (~3 Myr between 17 and 20 Mya in the crown age; Fig. 1 and Table 2).

Table 2 Mean and 95% HPD Age Estimates from MCMCTree Analysis
Full size table

Gene loss across Brassicaceae

As shown in Fig. 2, of the total 79 PCGs, 77 were predicted to be functional genes while rps16 and ycf15 became pseudogenes in some species (see also Additional file 2: Figures S3 and S4). Besides, the rpl22 gene was slightly truncated in Matthiola incana. The only exception was found for Solms-laubachia eurycarpa, where 10 of the 11 ndh genes were either slightly or severely truncated due to premature stop codons.

Fig. 2
figure 2

Loss of chloroplast protein-coding genes across Brassicaceae. Below are the phylogeny of Brassicaceae species based on chloroplast genomes as shown in Fig. 1. Different chloroplast regions were indicated at the left side. Grey and red boxes indicate intact and possible pseudogenized of genes, respectively. IR inverted repeat, LSC large single-copy region, SSC small single-copy region

Full size image

Discussion

In order to generate a backbone plastome phylogeny for Brassicaceae, we assembled 20 new plastomes to encompass all four lineages and 5 out of 6 major clades. All assumed lineages and major clades were generally identified, and their phylogenetic relationships were well resolved. In particular, our plastome phylogeny from 51 species provided the following new insights compared to those based on the fewer species plastome [35] or transcriptome genes [17]. First, Clade E, a group of Lineage III, is sister only to Clades B + C instead of sister to Clades B + C + A [17]. Clade A diverged from the group comprised Clades B + C + E very early. Second, the genus Megadenia of the tribe Biscutelleae in Clade C is sister to the remaining examined species of Clades B and C. This genus was shown to be closely related to Biscutella within the tribe Biscutelleae [38–41]. Thus, the phylogenetic relationship of the genus needs further examinations when more genera are sampled. Third, our comparisons based on different datasets suggested that the saturation in the third codon and phylogenetic signals from distinct plastome regions seriously affected the divergence and statistical supports in some particular nodes. For example, the tribes Lepidieae and others of Lineage I (Clade A) showed no distinct bifurcating divergence if all mutations of the total plastome dataset were used (Fig. 1). However, when excluding the third codon or using only slow-evolving IR genes, the Lepidieae diverged from Cardamineae and the others of the Lineage I (Clade A) with high statistical support (Additional file 2: Figures S5 and S6). Pachycladon was suggested to be closely related to Crucihimalaya [37, 42] as confirmed here by all plastome mutations (Fig. 1). However, this sister relationship was not supported when the IR gene dataset was used alone (Additional file 2: Figure S6).

Taxon sampling and reliable fossils used for calibrations are extremely important to estimate the divergence of targeted phylogeny [43–45]. Due to the high conservation and stable alignments, we used 14 highly reliable fossils from other eudicot orders [46] and three Brassicales. Our calibration comparisons suggested that the calculated divergences were rarely affected by Brassicales fossils, including the debated Brassicaceae fossil [37]. The estimated divergence times for major nodes were largely compatible with previous studies [5, 17, 34, 35], and it highlighted several evolutionary events. First, the stem age of Brassicaceae is around 50.5 Mya, ~6 Mya older than estimated by Magallón et al. [46] and ~5 Mya younger than estimated by Huang et al. [17], but consistent with a recent study across the Brassicales order [33]. Second, we confirmed that Brassicaceae began to diversify ~35.2 Mya during the Eocene-Oligocene boundary [35], when a warm and humid climate dominated the world [47]. Third, all major clades or lineages radiated within a short timescale between ~20 and ~17 Mya. All of these estimates are non-significantly different from those based on the plastomes with fewer Brassicaceae species [35], but younger than those based on the low-copy nuclear genes [17] with more representatives at the genus level. Therefore, both taxon sampling and evolutionary rates of different genomes might have caused differences in the estimated node times between different datasets.

A few chloroplast genes were lost in photosynthetic plants [29]. In this study, we reaffirmed the loss of the rps16 gene in the LSC region [48] and found that the ycf15 in the IR region became a pseudogene independently in different tribes of this family. Both genes were previously lost in other plants species [29]. The validity of ycf15 as a protein-coding gene remains debated [49–51], and it may have a regulatory function [52] after the full transcription of the chloroplast genome [53]. Until now, the mechanism underlying the loss of the plastome gene in Brassicaceae has been poorly understood. The dominance of self-compatibility in the family might be related with the transfer and/or loss of some organelle genes [48, 54]. However, it should be noted that Solms-laubachia eurycarpa has lost most ndh genes. To our knowledge, this is the first report of the massive loss of the ndh genes in Brasssicales. A typical plastid genome contains 11 ndh genes that are highly conserved across most autotrophic seed plants, which indicates the presence of strong selection pressure for their retention. A complete loss of the plastid ndh gene was only reported in conifers, Gnetales, and some epiphytic plants [55, 56]. Further studies are needed to examine whether specific factors were associated with the loss of the ndh genes in the genus Solms-laubachia.

Conclusions

Recent emergence of large scale phylogenomic data have undoubtedly provided a major advancement for understanding the complex systematics and taxonomy of the Brassicaceae, while phylogenetic relationships of the entire large family is far from being fully resolved. Using 51 chloroplast genomes from species of major cruciferous lineages or clades, we were able to resolve deep splits in this important plant family and found incongruence between organelle and nuclear genomes. The updated phylogenetic framework, based on plastome analysis, can be used to test many interesting evolutionary hypothesis on the origin and early diversification of Brassicaceae species. With the rapid increase in genomic data, we envision that a further in-depth understanding of the evolution of this model plant family will soon be possible.

Methods

Taxon sampling and plastome assembly

A total of 53 Brassicales species were included in this study, among which were 51 Brassicaceae species from 28 genera representing 19 out of the 51 tribes in all four major lineages or 5 out of the 6 newly identified clades. Plastome sequences were either obtained from the NCBI (last accessed, Jan 1st, 2016) or newly assembled (Additional file 1: Table S1). For the newly sequenced plastomes, we used the Illumina HiSeq X Ten sequencing pipeline to generate at least 2 Gb of 2 × 150 bp short reads data for each sample. Reads from the SRA database were extracted with fastq-dump software implemented in the SRA toolkit. We initially filtered reads following the previous approach [57]. Then, plastome contigs were assembled using Velvet [58], which were further reordered to the Arabidopsis thaliana plastome with SAMtools [59]. We finally merged all contigs into a consensus linear sequence using Geneious version 8.0.5 [60]. The annotation was performed with CpGAVAS [61] or Plann [62], aided by manually refinement in Apollo genome editor [63] and/or Sequin software [64]. Aragorn web-interface [65] was used to predict tRNAs.

Sequence alignment and partition strategy

Protein coding genes (PCGs) were extracted from the Genbank formatted file containing all plastomes using customized Perl scripts, removing start and end codons. After excluding possible pseudogenes, a total of 77 PCGs were retained for all species except for Solms-laubachia eurycarpa, where the pseudogenized ndh genes were edited and included. Each PCG was aligned using PRANK v.130410 [66] according to the translated amino acid sequences. Ambiguous alignment regions were trimmed by using Gblocks 0.91b [67] with (−t = c) option to set sequence type to codons; otherwise the default settings were assumed.

To test the phylogenetic effects of different regions of the plastid genome, we created the following datasets based on different plastome partitions. All 77 refined PCG alignments were firstly combined into a concatenated data set and four different partitioning schemes: 1 partition (unpartitioned); 3 partitions (a separate partition for all first, second, and third codon positions); 77 partitions (one partition for each gene); and 231 partitions (a separate partition for the first and second codon positions together in each gene and a partition for the third codon position in each gene). In addition, a best-fit partitioning schemes and models were selected using the greedy search mode implemented in PartitionFinder v1.1.1 [68]. Comparisons of the five partitioning strategies and selections of corresponding nucleotide substitution models were conducted under the Bayesian information criterion (BIC). The best-fitting partitioning strategy found by PartitionFinder was used in the following analysis. In addition to the main dataset, we also extract subsets from the 77-gene alignments containing either first and second codon positions or third codon positions only to explore the effect of potential sequence saturation at third codon. The data matrices and resulting trees were deposited in TreeBase (http://purl.org/phylo/treebase/phylows/study/TB2:S20512).

Phylogenetic analysis

The concatenated data set was first evaluated by BaCoCa [69], a recently developed program to perform multiple statistical analyses on multiple nucleotide and amino-acid sequence alignments, and then analyzed with Bayesian method and maximum likelihood (ML). The percentage of PI sites of each gene was estimated by PAUP [70]. The Bayesian MCMC analysis program BEAST (version 2.3.0) [71] was used to build phylogenetic trees, with parameter settings according to Hohmann et al. [35]. The GTR + G model was used for all ML analyses using RAxML version 8.0.20, as suggested in the manual [72]. Supports for nodes were assessed with 500 rapid bootstrapping replicates. Likelihood-based tests of alternative phylogenetic hypotheses were assessed based on the concatenated data set. Site-wise log-likelihoods of all alternative hypotheses (see Table 2) were first calculated with RAxML under the GTR + G model using the option (-f g). Then, the site log-likelihood file was supplied to the CONSEL v0.1j program [73] (Shimodaira and Hasegawa 2001) to estimate P-values for each alternative hypothesis using the AU test, approximate Bayesian posterior probability test, bootstrap probability test, Kishino-Hasegawa (KH) test, weighted KH test, Shimodaira-Hasegawa test (SH), and weighted SH test.

Divergence-time estimation and fossil calibration

We used the latest MCMCTREE in the PAML4.9a package to estimate divergence times with an approximate likelihood calculation [74], which allows a gamma-Dirichlet prior to describe substitution rates across multiple loci, thereby improving the accuracy of divergence-time estimation [75]. Optimal scheme for partitioning of the molecular clock(s) was tested for using ClockstaR 2.0.1 [76]. The ML phylogenetic tree topology from the 77 concatenated PCGs was used for divergence time estimation, and the ML branch lengths were estimated using the BASEML program in PAML under the GTR substitution model. For the gamma-Dirichlet prior for the overall substitution rate (rgene gamma), we used a quite diffuse (uninformative) prior =1. We used 14 highly reliable fossils from eudicot orders and three Brassicales fossils (Additional file 1: Table S6). All fossils were carefully selected according to their original descriptions and calibrations by past researches. Based on the mean estimate from three codon partitions using the strict molecular clock assuming 136 Ma constraint at the root, an average of the eudicot-monocot split [46], the gamma-Dirichlet prior for the overall substitution rate (rgene gamma) was set at G (4, 80, 1). The gamma-Dirichlet prior for the rate-drift parameter (sigma2 gamma) was set at G (1, 10, 1).

All calibration constraints were not rigorously constrained (specified with 2.5% tail probabilities above or below their limits; this is a built-in function of MCMCTREE). The independent rates model (clock = 2 in MCMCTREE) [77] was used to specify the prior of rates among the internal nodes, which followed a log-normal distribution. The three parameters (birth rate λ, death rate μ, and sampling fraction ρ) in the birth-death process with species sampling were specified as 1, 1, and 0, respectively. After a burn-in period of 1,000,000 cycles, the MCMC run was sampled every 250 cycles until a total of 20,000 samples were collected. Two separate MCMC runs were compared for convergence with two different random seeds and similar results were observed. To explore the influence of our fossil calibrations on age estimates, we conducted four separate analyses testing the inclusion of various fossil combinations (Additional file 1: Table S7).

Abbreviations

AU:

Approximately unbiased test

BIC:

Bayesian information criterion

BP:

Bootstrap

Bp:

Base pair

GC:

Guaninecytosine

IR:

Inverted repeat region

LSC:

Large single copy region

PCGs:

Protein coding genes

SSC:

Small single copy region

References

  1. Al-Shehbaz IA, Beilstein MA, Kellogg EA. Systematics and phylogeny of the Brassicaceae (Cruciferae): an overview. Plant Syst Evol. 2006;259:89–120.

    Article  Google Scholar 

  2. Bailey CD, Koch MA, Mayer M, Mummenhoff K, O’Kane SL, Warwick SI, et al. Toward a global phylogeny of the Brassicaceae. Mol Biol Evol. 2006;23:2142–60.

    Article  CAS  PubMed  Google Scholar 

  3. Koch MA, Dobeš C, Kiefer C, Schmickl R, Klimeš L, Lysak MA. Supernetwork identifies multiple events of plastid trnF (GAA) pseudogene evolution in the Brassicaceae. Mol Biol Evol. 2007;24:63–73.

    Article  CAS  PubMed  Google Scholar 

  4. Franzke A, German D, Al-Shehbaz IA, Mummenhoff K. Arabidopsis family ties: molecular phylogeny and age estimates in Brassicaceae. Taxon. 2009;58:425–37.

    Google Scholar 

  5. Couvreur TLP, Franzke A, Al-Shehbaz IA, Bakker FT, Koch MA, Mummenhoff K. Molecular phylogenetics, temporal diversification, and principles of evolution in the mustard family (Brassicaceae). Mol Biol Evol. 2010;27:55–71.

    Article  CAS  PubMed  Google Scholar 

  6. Novikova PY, Hohmann N, Nizhynska V, Tsuchimatsu T, Ali J, Muir G, et al. Sequencing of the genus Arabidopsis identifies a complex history of nonbifurcating speciation and abundant trans-specific polymorphism. Nat Genet. 2016;48:1077–82.

    Article  CAS  PubMed  Google Scholar 

  7. Lysak MA, Koch MA, Pecinka A, Schubert I. Chromosome triplication found across the tribe Brassiceae. Genome Res. 2005;15:516–25.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Mandakova T, Lysak MA. Chromosomal phylogeny and karyotype evolution in x = 7 crucifer species (Brassicaceae). Plant Cell. 2008;20:2559–70.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Mandakova T, Joly S, Krzywinski M, Mummenhoff K, Lysak MA. Fast diploidization in close mesopolyploid relatives of Arabidopsis. Plant Cell. 2010;22:2277–90.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Edger PP, Heidel-Fischer HM, Bekaert M, Rota J, Glöckner G, Platts AE, et al. The butterfly plant arms-race escalated by gene and genome duplications. Proc Natl Acad Sci U S A. 2015;112:8362–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Al-Shehbaz IA. A generic and tribal synopsis of the Brassicaceae (Cruciferae). Taxon. 2012;61:931–54.

    Google Scholar 

  12. Kiefer M, Schmickl R, German DA, Mandáková T, Lysak MA, Al-Shehbaz IA, et al. BrassiBase: introduction to a novel knowledge database on Brassicaceae evolution. Plant Cell Physiol. 2014;55:e3.

    Article  PubMed  Google Scholar 

  13. Al-Shehbaz IA, German DA, Mummenhoff K, Moazzeni H. Systematics, tribal placements, and synopses of the Malcolmia S.L. segregates (Brassicaceae). Harv Pap Bot. 2014;19:53–71.

    Article  Google Scholar 

  14. German DA, Friesen NW. Shehbazia (Shehbazieae, Cruciferae), a new monotypic genus and tribe of hybrid origin from Tibet. Turczaninowia. 2014;17:17–23.

    Article  Google Scholar 

  15. Beilstein MA, Al-Shehbaz IA, Kellogg EA. Brassicaceae phylogeny and trichome evolution. Am J Bot. 2006;93:607–19.

    Article  CAS  PubMed  Google Scholar 

  16. Lysak MA, Koch MA, Beaulieu JM, Meister A, Leitch IJ. The dynamic ups and downs of genome size evolution in Brassicaceae. Mol Biol Evol. 2009;26:85–98.

    Article  CAS  PubMed  Google Scholar 

  17. Huang CH, Sun R, Hu Y, Zeng L, Zhang N, Cai L, et al. Resolution of Brassicaceae phylogeny using nuclear genes uncovers nested radiations and supports convergent morphological evolution. Mol Biol Evol. 2016;33:394–412.

    Article  CAS  PubMed  Google Scholar 

  18. Zeng L, Zhang Q, Sun R, Kong H, Zhang N, Ma H. Resolution of deep angiosperm phylogeny using conserved nuclear genes and estimates of early divergence times. Nat Commum. 2014;5:4956.

    Article  CAS  Google Scholar 

  19. Zhang N, Zeng L, Shan H, Ma H. Highly conserved low-copy nuclear genes as effective markers for phylogenetic analyses in angiosperms. New Phytol. 2012;195:923–37.

    Article  CAS  PubMed  Google Scholar 

  20. Ruhfel BR, Gitzendanner MA, Soltis PS, Soltis DE, Burleigh JG. From algae to angiosperms–inferring the phylogeny of green plants (Viridiplantae) from 360 plastid genomes. BMC Evol Biol. 2014;14:1.

    Article  Google Scholar 

  21. Ma PF, Zhang YX, Zeng CX, Guo ZH, Li DZ. Chloroplast phylogenomic analyses resolve deep-level relationships of an intractable bamboo tribe Arundinarieae (Poaceae). Syst Biol. 2014;63:933–50.

    Article  PubMed  Google Scholar 

  22. Carbonell-Caballero J, Alonso R, Ibañez V, Terol J, Talon M, Dopazo J. A phylogenetic analysis of 34 chloroplast genomes elucidates the relationships between wild and domestic species within the genus Citrus. Mol Biol Evol. 2015;32:2015–35.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Palmer JD. Comparative organization of chloroplast genomes. Annu Rev Genet. 1985;19:325–54.

    Article  CAS  PubMed  Google Scholar 

  24. Daniell H, Lin CS, Yu M, Chang WJ. Chloroplast genomes: diversity, evolution, and applications in genetic engineering. Genome Biol. 2016;17:134.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Funk HT, Berg S, Krupinska K, Maier UG, Krause K. Complete DNA sequences of the plastid genomes of two parasitic flowering plant species, Cuscuta reflexa and Cuscuta gronovii. BMC Plant Biol. 2007;7:45.

    Article  PubMed  PubMed Central  Google Scholar 

  26. McNeal JR, Kuehl JV, Boore JL, de Pamphilis CW. Complete plastid genome sequences suggest strong selection for retention of photosynthetic genes in the parasitic plant genus Cuscuta. BMC Plant Biol. 2007;7:57.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Wolfe KH, Morden CW, Palmer JD. Function and evolution of a minimal plastid genome from a nonphotosynthetic parasitic plant. Proc Natl Acad Sci U S A. 1992;89:10648–52.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Delannoy E, Fujii S, des Francs-Small CC, Brundrett M, Small I. Rampant gene loss in the underground orchid Rhizanthella gardneri highlights evolutionary constraints on plastid genomes. Mol Biol Evol. 2011;28:2077–86.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Magee AM, Aspinall S, Rice DW, Cusack BP, Sémon M, Perry AS, et al. Localized hypermutation and associated gene losses in legume chloroplast genomes. Genome Res. 2010;20:1700–10.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Jansen RK, Saski C, Lee SB, Hansen AK, Daniell H. Complete plastid genome sequences of three Rosids (Castanea, Prunus, Theobroma): evidence for at least two independent transfers of rpl22 to the nucleus. Mol Biol Evol. 2011;28:835–47.

    Article  CAS  PubMed  Google Scholar 

  31. Ueda M, Fujimoto M, Arimura SI, Murata J, Tsutsumi N, Kadowaki KI. Loss of the rpl32 gene from the chloroplast genome and subsequent acquisition of a preexisting transit peptide within the nuclear gene in Populus. Gene. 2007;402:51–6.

    Article  CAS  PubMed  Google Scholar 

  32. Jansen RK, Cai Z, Raubeson LA, Daniell H, Leebens-Mack J, Müller KF, et al. Analysis of 81 genes from 64 plastid genomes resolves relationships in angiosperms and identifies genome-scale evolutionary patterns. Proc Natl Acad Sci U S A. 2007;104:19369–74.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Cardinal-McTeague WM, Sytsma KJ, Hall JC. Biogeography and diversification of Brassicales: A 103million year tale. Mol Phylogenet Evol. 2016;99:204–24.

    Article  PubMed  Google Scholar 

  34. Kagale S, Robinson SJ, Nixon J, Xiao R, Huebert T, Condie J, et al. Polyploid evolution of the Brassicaceae during the Cenozoic era. Plant Cell. 2014;26:2777–91.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Hohmann N, Wolf EM, Lysak MA, Koch MA. A time-calibrated road map of Brassicaceae species radiation and evolutionary history. Plant Cell. 2015;27(10):2770–84. doi:10.1105/tpc.15.00482.

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Franzke A, Koch MA, Mummenhoff K. Turnip time travels: age estimates in Brassicaceae. Trends Plant Sci. 2016. doi:10.1016/j.tplants.2016.01.024.

    PubMed  Google Scholar 

  37. Beilstein MA, Nagalingum NS, Clements MD, Manchester SR, Mathews S. Dated molecular phylogenies indicate a Miocene origin for Arabidopsis thaliana. Proc Natl Acad Sci U S A. 2010;107:18724–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. German DA, Al-Shehbaz IA. Five additional tribes (Aphragmeae, Biscutelleae, Calepineae, Conringieae, and Erysimeae) in the Brassicaceae (Cruciferae). Harvard Pap Bot. 2008;13:165–70.

    Article  Google Scholar 

  39. German DA, Friesen N, Neuffer B, Al-Shehbaz IA, Hurka H. Contribution to ITS phylogeny of the Brassicaceae, with a special reference to some Asian taxa. Plant Syst Evol. 2009;283:33–56.

    Article  Google Scholar 

  40. Warwick SI, Mummenhoff K, Sauder CA, Koch MA, Al-Shehbaz IA. Closing the gaps: phylogenetic relationships in the Brassicaceae based on DNA sequence data of nuclear ribosomal ITS region. Plant Syst Evol. 2010;285:209–32.

    Article  CAS  Google Scholar 

  41. Artyukova EV, Kozyrenko MM, Boltenkov EV, Gorovoy PG. One or three species in Megadenia (Brassicaceae): insight from molecular studies. Genetica. 2014;142:337–50.

    Article  CAS  PubMed  Google Scholar 

  42. Zhao B, Liu L, Tan D, Wang J. Analysis of phylogenetic relationships of Brassicaceae species based on Chs sequences. Biochem Syst Ecol. 2010;38:731–9.

    Article  CAS  Google Scholar 

  43. Linder HP, Hardy CR, Rutschmann F. Taxon sampling effects in molecular clock dating: an example from the African Restionaceae. Mol Phylogenet Evol. 2005;35:569–82.

    Article  CAS  PubMed  Google Scholar 

  44. Mao K, Milne RI, Zhang L, Peng Y, Liu J, Thomas P, et al. Distribution of living Cupressaceae reflects the breakup of Pangea. Proc Natl Acad Sci U S A. 2012;109:7793–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Mao KS, Liu JQ. Current ‘relicts’ more dynamic in history than previously thought. New Phytol. 2012;196:329–31.

    Article  PubMed  Google Scholar 

  46. Magallón S, Gómez‐Acevedo S, Sánchez‐Reyes LL, Hernández‐Hernández T. A metacalibrated time‐tree documents the early rise of flowering plant phylogenetic diversity. New Phytol. 2015;207:437–53.

    Article  PubMed  Google Scholar 

  47. Zachos J, Pagani M, Sloan L, Thomas E, Billups K. Trends, rhythms, and aberrations in global climate 65 Ma to present. Science. 2001;292:686–93.

    Article  CAS  PubMed  Google Scholar 

  48. Roy S, Ueda M, Kadowaki KI, Tsutsumi N. Different status of the gene for ribosomal protein S16 in the chloroplast genome during evolution of the genus Arabidopsis and closely related species. Genes Genet Syst. 2010;85:319–26.

    Article  CAS  PubMed  Google Scholar 

  49. Goremykin V, Hirsch-Ernst KI, Wölfl S, Hellwig FH. The chloroplast genome of the “basal” angiosperm Calycanthus fertilis –structural and phylogenetic analyses. Plant Syst Evol. 2003;242:119–35.

    Article  CAS  Google Scholar 

  50. Raubeson LA, Peery R, Chumley TW, Dziubek C, Fourcade HM, Boore JL, Jansen RK. Comparative chloroplast genomics: analyses including new sequences from the angiosperms Nuphar advena and Ranunculus macranthus. BMC Genomics. 2007;8:174.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Tangphatsornruang S, Uthaipaisanwong P, Sangsrakru D, Chanprasert J, Yoocha T, Jomchai N, Tragoonrung S. Characterization of the complete chloroplast genome of Hevea brasiliensis reveals genome rearrangement, RNA editing sites and phylogenetic relationships. Gene. 2011;475:104–12.

    Article  CAS  PubMed  Google Scholar 

  52. Schmitz-Linneweber C, Maier RM, Alcaraz JP, Cottet A, Herrmann RG, Mache R. The plastid chromosome of spinach (Spinacia oleracea): complete nucleotide sequence and gene organization. Plant Mol Biol. 2001;45:307–15.

    Article  CAS  PubMed  Google Scholar 

  53. Shi C, Wang S, Xia EH, Jiang JJ, Zeng FC, Gao LZ. Full transcription of the chloroplast genome in photosynthetic eukaryotes. Sci Rep. 2016;6:30135. doi:10.1038/srep30135.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Andersson SG, Kurland CG. Reductive evolution of resident genomes. Trends Microbiol. 1998;6:263–8.

    Article  CAS  PubMed  Google Scholar 

  55. Martín M, Sabater B. Plastid ndh genes in plant evolution. Plant Physiol Biochem. 2010;48:636–45.

    Article  PubMed  Google Scholar 

  56. Blazier JC, Guisinger MM, Jansen RK. Recent loss of plastid-encoded ndh genes within Erodium (Geraniaceae). Plant Mol Biol. 2011;76:263–72.

    Article  CAS  Google Scholar 

  57. Guo X, Hao G, Ma T. The complete chloroplast genome of salt cress (Eutrema salsugineum). Mitochondrial DNA A DNA Mapp Seq Anal. 2016;27(4):2862–3. doi:10.3109/19401736.2015.1053130.

    PubMed  Google Scholar 

  58. Zerbino DR, Birney E. Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Genome Res. 2008;18:821–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The sequence alignment/map format and SAMtools. Bioinformatics. 2009;25:2078–9.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Kearse M, Moir R, Wilson A, Stones-Havas S, Cheung M, Sturrock S, et al. Geneious Basic: an integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics. 2012;28:1647–9.

    Article  PubMed  PubMed Central  Google Scholar 

  61. Liu C, Shi L, Zhu Y, Chen H, Zhang J, Lin X, Guan X. CpGAVAS, an integrated web server for the annotation, visualization, analysis, and GenBank submission of completely sequenced chloroplast genome sequences. BMC Genomics. 2012;13:715.

    Article  PubMed  PubMed Central  Google Scholar 

  62. Huang DI, Cronk QC. Plann: a command-line application for annotating plastome sequences. Appl Plant Sci. 2015;3(8). doi: 10.3732/apps.1500026.

  63. Lewis SE, Searle SMJ, Harris N, Gibson M, Iyer V, Richter J, et al. Apollo: a sequence annotation editor. Genome Biol. 2002;3:1.

    Article  Google Scholar 

  64. Clark K, Karsch-Mizrachi I, Lipman DJ, Ostell J, Sayers EW. GenBank. Nucleic Acids Res. 2016;44(D1):D67–72. doi:10.1093/nar/gkv1276.

    Article  PubMed  Google Scholar 

  65. Laslett D, Canback B. ARAGORN, a program to detect tRNA genes and tmRNA genes in nucleotide sequences. Nucleic Acids Res. 2004;32:11–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Löytynoja A, Goldman N. Phylogeny-aware gap placement prevents errors in sequence alignment and evolutionary analysis. Science. 2008;320:1632–5.

    Article  PubMed  Google Scholar 

  67. Castresana J. Selection of conserved blocks from multiple alignments for their use in phylogenetic analysis. Mol Biol Evol. 2000;17:540–52.

    Article  CAS  PubMed  Google Scholar 

  68. Lanfear R, Calcott B, Ho SY, Guindon S. PartitionFinder: combined selection of partitioning schemes and substitution models for phylogenetic analyses. Mol Biol Evol. 2012;29:1695–701.

    Article  CAS  PubMed  Google Scholar 

  69. Kück P, Struck TH. BaCoCa–A heuristic software tool for the parallel assessment of sequence biases in hundreds of gene and taxon partitions. Mol Phylogenet Evol. 2014;70:94–8.

    Article  PubMed  Google Scholar 

  70. Swofford DL. PAUP*. Phylogenetic Analysis Using Parsimony (* and Other Methods). Version 4. MA: Sinauer Associates of Sunderland; 2003.

  71. Bouckaert R, Heled J, Kühnert D, Vaughan T, Wu CH, Xie D, et al. BEAST 2: a software platform for Bayesian evolutionary analysis. PLoS Comput Biol. 2014;10:e1003537.

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Shimodaira H, Hasegawa M. CONSEL: for assessing the confidence of phylogenetic tree selection. Bioinformatics. 2001;17:1246–7.

    Article  CAS  PubMed  Google Scholar 

  74. Yang Z. PAML 4: phylogenetic analysis by maximum likelihood. Mol Biol Evol. 2007;24:1586–91.

    Article  CAS  PubMed  Google Scholar 

  75. dos Reis M, Zhu T, Yang Z. The impact of the rate prior on Bayesian estimation of divergence times with multiple loci. Syst Biol. 2014;63:555–65.

    Article  PubMed  PubMed Central  Google Scholar 

  76. Duchêne S, Molak M, Ho SY. ClockstaR: choosing the number of relaxed-clock models in molecular phylogenetic analysis. Bioinformatics. 2014;30:1017–9.

    Article  PubMed  Google Scholar 

  77. Rannala B, Yang Z. Inferring speciation times under an episodic molecular clock. Syst Biol. 2007;56:453–66.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

The authors would like to thank Qi He and Hao Bi for their technical help in the lab.

Funding

This work was supported by grants from the National Natural Science Foundation of China (31590821), and National Key Project for Basic Research (2014CB954100) and International Collaboration 111 Projects of China (2010DFA34610) from Ministry of Science and Technology of the People’s Republic of China.

Availability of data and materials

Sequence data that support the findings of this study have been deposited in GenBank (accession numbers can be found in additional files). The phylogenetic matrix and trees are available in the TreeBASE repository (Study ID 20512).

Authors’ contributions

XG and JL conceived the study and drafted the manuscript. XG performed the experiment, analyzed the data and interpreted the results. GH and LZ contributed plant material. KM and XW helped to analyze the data. DZ helped to perform the experiment and contributed the sequence data. KM, TM and QH contributed to the discussion of study design. IAA and MAK helped to improve the manuscript significantly. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Ethics approval and consent to participate

Not applicable.

Author information

Authors and Affiliations

  1. MOE Key Laboratory of Bio-Resources and Eco-Environment, College of Life Sciences, Sichuan University, 610065, Chengdu, People’s Republic of China

    Xinyi Guo, Jianquan Liu, Guoqian Hao, Lei Zhang, Kangshan Mao, Xiaojuan Wang, Dan Zhang, Tao Ma & Quanjun Hu

  2. Biodiversity Institute of Mount Emei, Mount Emei Scenic Area Management Committee, 614200, Leshan, Sichuan, People’s Republic of China

    Guoqian Hao

  3. Missouri Botanical Garden, PO Box 299, St. Louis, MO, 63166-0299, USA

    Ihsan A. Al-Shehbaz

  4. Department of Biodiversity and Plant Systematics, Im Neuenheimer Feld 345, Centre for Organismal Studies (COS) Heidelberg, Heidelberg University, 69120, Heidelberg, Germany

    Marcus A. Koch

Authors
  1. Xinyi Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jianquan Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Guoqian Hao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Kangshan Mao
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xiaojuan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Dan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Tao Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Quanjun Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Ihsan A. Al-Shehbaz
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Marcus A. Koch
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jianquan Liu.

Additional files

Additional file 1:

Table S1. Species and data discription of cp genome used in this study. Table S2. Comparison of sequence length and GC content among Brassicales chloroplast genomes. Table S3. Comparison of fast-, intermediate-, and slow-evolving plastid protein-coding genes. Table S4. Heterogeneity and saturation test results from BaCoCa analysis. Table S5. Comparison of partitioning strategies. Table S6. Species and lineage specific fossil calibrations. Table S7. Comparison of mean and 95% HPD age estimates using different fossils. (XLSX 48 kb)

Additional file 2:

Figure S1. Topologies of alternative tree hypothesis used in approximately unbiased test. Figure S2. Chronogram of Brassicaceae and 75 outgroup taxa inferred using MCMCTree. Figure S3. Alignment view of Brassicales rps16 genes in MEGA6. Figure S4. Alignment view of Brassicales ycf15 genes in MEGA6. Figure S5. A phylogeny from ML analyses of 77 PCGs using 1st an 2nd codon. Figure S6. A phylogeny from ML analyses of 77 PCGs using genes from the IR region. Figure S7. A phylogeny from ML analyses of 77 PCGs using 3rd codon. Figure S8. A phylogeny from ML analyses of 77 PCGs using all three codons. (PDF 625 kb)

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The 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.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Guo, X., Liu, J., Hao, G. et al. Plastome phylogeny and early diversification of Brassicaceae. BMC Genomics 18, 176 (2017). https://doi.org/10.1186/s12864-017-3555-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12864-017-3555-3

Keywords

BMC Genomics

ISSN: 1471-2164

Contact us