Identifying the factors driving divergence is a key research topic of speciation genomics (Wolf and Ellegren, 2017). Although there has been progress in identification of individual speciation genes and genomic islands of divergence in speciation (Nosil and Feder, 2013; Renaut et al., 2013), it remains an open question as to whether there are general genomic patterns and processes of molecular evolution accompanying the divergence of species. Natural selection and drift are recognized as the major processes promoting divergence. However, the relative roles of these processes, the proportion of the genome affected by natural selection, and the strength of selection in generating genomic divergence have remained poorly understood (Kimura, 1979; Noor and Bennett, 2009; Barrett and Hoekstra, 2011; Nosil and Feder, 2013). A few recent studies have uncovered repeated patterns of genomic divergence driven by selection in species formation (Gagnaire et al., 2013; Arnegard et al., 2014; Renaut et al., 2014; Soria-Carrasco et al., 2014) and suggested that divergence may often result from changes at a relatively small subset of genes (e.g., Conte et al., 2012).

Geographic speciation is a common driving force in generating biodiversity (Coyne and Allen Orr, 1998). Given sufficient time and/or selection pressures, genomic divergence between allopatric populations is expected (Coyne and Allen Orr, 1998; Nosil, 2008). However, how genes diverge over time at a genomic scale after major geographic isolation that completely interrupts gene flow has remained an unanswered question. Meta-analyses of closely related species that span a range of time scales of geographic isolation will be, therefore, particularly beneficial for gaining insights into this question (Nosil et al., 2009). Such studies might allow us to reconstruct how genomic divergence unfolds as speciation proceeds through time.

Putatively orthologous (single-copy) genes (POGs) identified in RNA-seq data sets of closely related species offer a valuable genome-wide window on molecular divergence of a unique subset of the genome following speciation (Feder et al., 2012; De Smet et al., 2013). Investigating how these genes diverge during speciation and subsequent evolution contributes an important perspective to speciation genomics. Such studies may illuminate the functional divergence of the allopatric genomes and the underlying ecological forces that shape them. The forests of eastern Asia (EA) and eastern North America (ENA) share 65 genera of seed plants with closely related species occurring in the two areas (Li, 1952; Boufford and Spongberg, 1983; Wu, 1983; Wen, 1999; Wen et al., 2010; Wen et al., 2016). This well-known floristic disjunction is a major phytogeographic pattern of the Northern Hemisphere and has been known since the time of Linnaeus (Wen, 1999). The origins of the disjunction and impact on plant evolution have been investigated over the past 30 years (Xiang et al., 1998; Wen, 1999; Xiang et al., 2000; Wen, 2001; Xiang and Soltis, 2001; Donoghue and Smith, 2004; Harris et al., 2013; Manos and Meireles, 2015; Qian et al., 2017). Many of these genera are small with one to a few species. Previous analyses using mainly plastid and internal transcribed spacer (ITS) DNA sequence markers revealed that the divergence times of disjunct sister species in EA and ENA varied greatly from the Miocene to the Pleistocene or even more recently (15 mya to <2.0 mya), although some disjunct clades in the two areas diverged earlier (Xiang et al., 2000; Wen, 2001; Milne and Abbott, 2002; Donoghue and Smith, 2004; Harris et al., 2013). These genera therefore represent different stages of allopatric divergence and are ideal for studying genomic divergence associated with geographic speciation.

In this paper, we compare the divergence patterns of genes identified as POGs in leaf transcriptomes of 20 allopatric species/variety pairs (or taxon pairs), all but four of which represent the EA-ENA floristic disjunction (Table 1). The 16 EA-ENA disjunct species or subspecies/variety pairs represent diverse clades of flowering genera with two to several species: Campsis (two spp.) – Lamiales, Convallaria (three vars. of 1 sp.) – Asparagales, Cornus (two subclades, Cornus-1 with eight spp., Cornus-2 with two spp.) – Cornales, Cotinus (two spp.) – Sapindales, Croomia (three spp.) – Pandanales, Gelsemium (three spp.) – Gentianales, Hamamelis (six spp.) – Saxifagales, Liquidambar (4–15 spp.) – Saxifragales, Liriodendron (two spp.) – Magnoliales, Meehania (seven spp.) – Lamiales, Menispermum (two spp.) – Ranunculales, Nelumbo (two spp.) – Proteales, Penthorum (two spp.)– Saxifragales, Phryma (two spp. or two vars. of 1 sp.) – Lamiales, Sassafras (three spp.) – Magnoliales, and Saururus (two spp.) – Piperales (Appendix 1—figure 1). These EA-ENA disjunct genera are among the classic examples of the EA-ENA floristic disjunction (Li, 1952; Wu, 1983; Wen, 1999) and have one to a few species isolated in the two areas, and in a few cases, a species also occurs in western North America (e.g., Cornus-1) and/or southwestern Asia and southeastern Europe (e.g., Convallaria, Liquidambar, Nelumbo). The species pairs we compared in these genera (Table 1) span a range of divergence times (Xiang et al., 2000; Wen, 2001; Milne and Abbott, 2002; Wen et al., 2010).

As noted, four of the 20 pairs represent other allopatric pairs (i.e., Acorus, two spp. – Acorales, Calycanthus, three spp. – Laurales, Dysosma, seven spp. – Ranunculales). These additional pairs were included in the study to increase our sampling of geographically separated taxon pairs for comparison and to cover a wide window of times, from recent divergence of speciation to further divergence of the descendant species. The species pairs analyzed follow: one EA-Western North American (WNA) pair (Calycanthus chinensis – C. occidentalis), one Asia/S. Europe-ENA pair (Cotinus coggygria – C. obovatus), one EA-EA/NA pair (Acorus gramineus – A. calamus), and one Asian pair (Dysosma versipellis – D. pleiantha; Ranunculales) (Table 1). Species of Acorus and Dysosma partially overlap in their geographic ranges, but have diverged in habitat and do not occur together. Acorus calamus naturally occurs throughout China and adjacent countries, and in North America in swamps, pond sides, and standing water below 2800 m. Acorus gramineus occurs in eastern, southern, and western China, and adjacent countries of EA in dense forests, moist rocky stream banks, and meadows below 2600 m. Dysosma versipellis and D. pleiantha both occur in eastern, central, and southern China, but D. versipellis is found in deciduous forests while D. pleiantha occurs in evergreen forests.

Additional information on the 20 taxon pairs and their distributions can be obtained from the Flora of China (http://www.efloras.org) and the Flora of North America (http://floranorthamerica.org/). The 20 taxon pairs we compared span a range of divergence times (Xiang et al., 1998; Xiang et al., 2000; Milne and Abbott, 2002; Donoghue and Smith, 2004; Wen et al., 2010; Harris et al., 2013; Wen et al., 2016), mostly representing sister species or varieties; however, in a few cases, the selected species represent sister clades of one vs. two species (Calycanthus, Gelsemium, Sassafras), one vs. a few species (Hamamelis), or two or three vs. a few species (Cornus-1, Liquidambar).

We examined the pattern of variation of synonymous substitutions (Ks; the number of synonymous substitutions per synonymous site), nonsynonymous substitutions (Ka; the number of nonsynonymous substitutions per nonsynonymous site), and the ratio (Ka/Ks) for each POG within each taxon pair using a custom pipeline (Appendix 1—figure 2) and compared the patterns among taxon pairs representing these diverse angiosperm lineages. The ratio of nonsynonymous to synonymous nucleotide substitutions (Ka/Ks) is probably the most common measure of selection pressure and for identifying positive selection (Ka/Ks > 1) (Yang, 2003) in molecular evolutionary studies. The method is known to be a conservative approach to call positive selection on a gene as it uses an average Ka/Ks ratio across all nucleotide sites. We also examined the relative proportions of genes under different levels of selection forces and identify and annotate genes under strong positive selection based on the Ka/Ks values (>2). Finally, we tested if the level of molecular divergence at synonymous sites (as measured by Ks at the peak frequency) reflects the relative divergence time of the species pair. Results from such analyses will illuminate the genomic pattern of genetic divergence of these genes under geographic isolation, and may also shed light on a small window of genomic divergence that unfolds as speciation proceeds through time. Furthermore, this study will provide a ‘genome-wide’ view of evolutionary divergence of species exhibiting the EA-ENA phytogeographic pattern, in comparison to previous understanding based on a few gene regions. The POGs examined in this study represented 52–68% of the total orthologous gene families in the transcriptome data (Supplementary file 1).

We stress that the POGs in leaf transcriptomes analyzed here are putative orthologs between the two species of each biogeographic pair found in the transcriptomes by OrthoMCL followed by customized scripts (see Methods below). It is possible, therefore, that some of these genes may have more than one copy in the genome (as a small gene family), but only one of them was expressed to a detectable level in the leaf. In such cases, it is possible that the two single-copy transcripts from the taxon pair may represent paralogous copies of the low-copy gene family, making the gene comparison non-orthologous. However, we think the likelihood of non-orthology of these genes is probably very low because in each case the two taxa are either sister species or intraspecific taxa, or represent the products of recently diverged sister clades where conservation of expression of a gene copy may be expected. We therefore do not think that the low probability of non-homology will affect our conclusions derived from thousands of putatively orthologous comparisons. However, we do want to make it clear that the discussion and conclusions about ‘genome-wide’ pattern of evolutionary divergence and processes in this paper are restricted to these genes of the genome. Nonetheless these POGs represent a significant portion of the gene families expressed in the transcriptome of each species (52%–68%; Supplementary file 1).

Comparison of the rates of nonsynonymous and synonymous substitutions provides a useful tool for understanding the mechanisms of DNA sequence evolution. A ratio of nonsynonymous (Ka) to synonymous (Ks) nucleotide substitutions greater than one is a common method for identifying positive selection in molecular evolutionary studies (Yang, 2003). We are aware that uncertainty in Ka/Ks ratios is high when Ks is zero or close to zero. This uncertainty may lead to inflation of Ka/Ks ratios or false identification of genes under strong positive selection due to a high, infinite, or undefined Ka/Ks value. To minimize this problem, we removed genes with Ks = 0 and genes with Ka/Ks = 99 for all analyses and only focused on genes with Ka/Ks > 2 as candidates of strong positive selection for gene annotation analysis. Our detailed examination of the Ka and Ks data (see Discussion below) provided evidence supporting the candidate genes under positive selection detected in the study are unlikely artifacts of small Ks. Further, the Ka/Ks > 2 ratios in these genes are unlikely a result of saturation of synonymous substitutions. Saturation of synonymous substitutions is often expected at deeper phylogenetic levels. We further used alternative method (Wagner, 2007) to check for signals of positive selection in these genes with Ka/Ks > 2. This method detects variation clusters of aggregated nucleotide substitutions that are too closely spaced to be observed by chance alone (violating the prediction from neutral evolution). Both the Ka/Ks and variation cluster methods may underestimate the number of genes under positive selection because some genes with Ka/Ks < 1 may be under positive selection at only a few sites and these sites may not be necessarily clustered in the gene. However, our goal is not to determine exactly how many POGs are under positive selection, and this caveat does not affect our comparisons of average selection pressures among genes or comparisons of general patterns of gene divergence across species pairs. The genes identified as under positive selection by both methods are likely the true positive selection genes and will provide top candidates for future studies for their functions in driving allopatric divergence in these taxa.

The number of POGs in the leaf transcriptomes of the 20 biogeographic taxon pairs varies from 8596 to 15161 (Table 1). Among these POGs, 2241 are shared by over 50% of the taxa, 333 are shared by over 80% of the taxa, 79 are shared by over 90% of the taxa, and seven are shared by all of the taxa. Most of these genes exhibit low divergence, with synonymous substitution (Ks) values less than 0.1 and nonsynonymous substitution values (Ka) less than 0.04. Less than 1% of the genes exhibit greater divergence with Ks values greater than 0.15 (Figure 1) and Ka values greater than 0.05 (Figure 2). The Ks values at peak frequency when plotted in intervals of 0.01 range among the taxon pairs from <0.01 to<0.06 (Figure 1). In Convallaria, Liriodendron, Cotinus, Menispermum, and Campsis, the Ks values at peak frequency are less than 0.01, while in Cornus-2, Dysosma, Nelumbo, Penthorum, and Sassafras, they are between 0.01 and 0.02. In Calycanthus, Liquidambar, Meehania, Menispermum, and Phryma, they are between 0.02 and 0.03, and in Cornus-1, Croomia, Hamamelis, and Saururus, they are between 0.03 and 0.04. The largest values are observed in Acorus and Gelsemium, between 0.05 and 0.06 (Supplementary file 2). A portion of the POGs, 53–237 among the genera, were aligned to putative single-copy or low-copy genes shared by seed plants (Li et al., 2017), and 75–365 were mapped to strict single-copy genes shared by 20 angiosperm genomes (De Smet et al., 2013) (for method of the mapping analysis, see Methods).

Figure 1

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Figure 2

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The Ka/Ks ratios of the POGs exhibit high similarity in patterns of variation among the 20 taxon pairs. In all pairs, the ratios at peak frequencies are highly similar and more than 90% of the genes are overall under purifying selection (referred to as purifying selection in short) based on their average Ka/Ks ratios < 0.9 across all sites within the genes. The exception was Nelumbo, which has 87.6% of the genes with Ka/Ks ratios < 0.9. The greatest proportion of genes under purifying selection was observed in Gelsemium, which had 94.9% of the genes with Ka/Ks ratios < 0.9. A very small proportion, from 1.6% (in Acorus) to 3.7% (in Nelumbo), is evolving nearly neutrally with Ka/Ks ratios in the range of 0.9–1.1, and another small proportion, from 3.1% (in Gelsemium) to 8.8% (in Nelumbo), is under putative positive selection based on values of Ka/Ks ratios greater than 1.1. A very small proportion, from 0.6% (in Gelsemium) to 2.0% (in Nelumbo), is under putative strong positive selection with Ka/Ks ratios ranging from 2 to 7 and rarely to 17 (Figure 3). The proportion of genes under truly positive selection may be smaller the numbers reported here due to uncertainty in estimation of Ka/Ks ratios when Ks is small.

Figure 3

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We arbitrarily divided the genes under purifying selection into three categories, Ka/Ks < 0.1 as genes under strong purifying selection, 0.1 < Ka/Ks < 0.5 as genes under moderate purifying selection, and 0.5 < Ka/Ks < 0.9 as genes under relaxed purifying selection. Among the taxon pairs, we observed 41% (in Convallaria) to 68% (in Gelsemium) of the POGs fall into the category of moderate purifying selection, 11% (in Gelsemium) to 36% (in Convallaria) fall into the category of strong purifying selection, while 9.7% (in Acorus) to 19.5% (in Nelumbo) fall into the category of relaxed purifying selection. In all taxon pairs, the relative abundance of POGs under the different categories of selection pressures follows the same order, from high to low, as moderate purifying selection, strong purifying selection, relaxed purifying selection, weak/moderate positive selection, putatively neutral, and strong positive selection (Figure 4; Supplementary file 3).

Figure 4

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To investigate if variation in Ks values at peak frequency reflects variation in biogeographic isolation times of taxon pairs, phylogenetic analyses were conducted using the seven POGs shared by all taxa and the 79 POGs shared by >90% of the taxa, respectively. The results recovered the relationships of the 20 lineages congruent with the APG IV summary phylogeny and classification (The Angiosperm Phylogeny Group, 2016) (Figure 5; Figure 5—figure supplement 1). Divergence times of the biogeographic pairs estimated using total substitutions of these two sets of gene sequence data and BEAST (Drummond and Rambaut, 2007) were similar, with the results from 79 genes without the fossil constraint from Cornus slightly younger (Figure 5; Figure 5—figure supplement 1). The estimated divergence times suggested that isolation of the species pairs occurred in an interval between the late Miocene and Pleistocene (~10.67 –~1.69 mya; Figure 5). Species divergence time was positively correlated with the Ks value at peak frequency across taxon pairs (Figure 5; r = 0.8034; p<0.001; for values used for the correlation analysis, see Supplementary file 4). On the other hand, the Ks values at peak frequency of the taxon pairs are: (1) positively correlated with the abundance of genes under moderate purifying selection (0.1 < Ka/Ks <0.5; r = 0.9324; p<0.001), (2) negatively correlated with the abundance of genes under putative positive selection (Ka/Ks >=1.0, r = - 0.5344; p=0.015; 1.1 <= Ka/Ks <2, r = - 0.62; p=0.004), and (3) negatively correlated with the abundance of genes under putative strong purifying selection (Ka/Ks <0.1; r = - 0.7940; p<0.001) (Figure 6; Supplementary file 3, Supplementary file 10). The divergence times of taxon pairs are also positively correlated with the abundance of genes under moderate purifying selection (0.1 < Ka/Ks <0.5; r = 0.73; p<0.001) and negatively correlated with the abundance of genes under strong purifying selection (Ka/Ks <0.1; r = - 0.64; p<0.009; Figure 5; Supplementary file 4). The number of genes under strong positive selection (Ka/Ks >2) varies among the taxon pairs from 72 (in Campsis) to 184 (in Nelumbo) (Table 2). In each pair, approximately half of these genes have GO annotations (Table 2) that, in combination among all pairs, mapped to a total of 223 GO terms, varying from 21 (in Cornus-2) to 46 (in Convallaria) in each taxon pair (Supplementary file 5). Many of these GO terms are unique to a single lineage (100/223 = 44.8%).

Figure 5 with 1 supplement see all

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Figure 6

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A majority (59) of these lineage-specific GO terms belongs to the Biological Process (BP) categorization, while 23 belong to the Molecular Function (MF) ontology, and 16 belong to the Cell Components (CC) ontology (Supplementary file 5). Fourteen of the BP GO terms were annotated to responses to various stimuli (biotic or abiotic, external or internal stimuli), defense response, immune process, or signal transduction and were distributed among 11 taxon pairs, with each term was present in 1–3 pairs. In all, 124 of the 223 GO terms (55.2%) are shared by two or more of the 20 taxon pairs. Among these, 12 were shared by 10–15 taxon pairs. These 12 genes were annotated to the nucleus, protein complex, hydrolase activity, zinc ion binding, RNA binding, ATP binding, structural constituent of ribosome, oxidoreductase activity, cellular protein modification process, translation, single-organism biosynthetic process, and oxidation-reduction process (Supplementary file 5). A single GO term is shared by all 20 taxon pairs and is annotated to integral component of membrane (ICM) in the CC category (Supplementary file 5). The specific genes and the relative abundance of genes within each of the CC, BP, and MP categories vary among the taxon pairs (Figure 7; Figure 7—figure supplement 1; Figure 7—figure supplement 2). However, within the CC category, all pairs have the greatest proportion annotated to ICM (Figure 7), although they vary in the exact number (Table 3). The analysis of the Ka/Ks > 2 genes using the variation cluster method (Wagner, 2007) showed most of them (>57% in all taxon pairs, >70% in 15 pairs) were also under positive selection (indicated by significant p value for test of clustered nucleotide substitutions) (Supplementary file 6).

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Sequences of ortologous gene families and pairs of POGs sequences (prior to EST Scan) used for calculation of Ka and Ks have been submitted to Dryad (https://doi.org/10.5061/dryad.f1f0q44). Raw transcriptome data have been submitted to NCBI SRA database with BioProject number PRJNA508825 and BioSample number from SAMN10534244 to SAMN10534283 (Supplementary file 11).

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Author details

1. Yibo Dong

   1. Department of Plant and Microbial Biology, North Carolina State University, Raleigh, United States
   2. Plant Biology Division, Noble Research Institute, Ardmore, United States

   Contribution

   Software, Formal analysis, Investigation, Visualization, Methodology, Writing—original draft, Writing—review and editing

   Contributed equally with

   Shichao Chen

   Competing interests

   No competing interests declared

2. Shichao Chen

   1. Florida Museum of Natural History, University of Florida, Gainesville, United States
   2. Department of Biology, University of Florida, Gainesville, United States
   3. School of Life Sciences and Technology, Tongji University, Shanghai, China

   Contribution

   Formal analysis, Investigation, Methodology, Writing—original draft, Writing—review and editing

   Contributed equally with

   Yibo Dong

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7160-4245

3. Shifeng Cheng

   Beijing Genomics Institute, Shenzhen, China

   Contribution

   Data curation, Software, Formal analysis, Investigation, Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

4. Wenbin Zhou

   Department of Plant and Microbial Biology, North Carolina State University, Raleigh, United States

   Contribution

   Software, Formal analysis, Investigation, Visualization, Writing—review and editing

   Competing interests

   No competing interests declared

5. Qing Ma

   Department of Plant and Microbial Biology, North Carolina State University, Raleigh, United States

   Contribution

   Investigation, Writing—review and editing, Collection of experimental materials

   Competing interests

   No competing interests declared

6. Zhiduan Chen

   State Key Laboratory of Systematic and Evolutionary Botany, Institute of Botany, Chinese Academy of Sciences, Beijing, China

   Contribution

   Resources, Investigation, Writing—review and editing

   Competing interests

   No competing interests declared

7. Cheng-Xin Fu

   Laboratory of Systematic & Evolutionary Botany and Biodiversity, College of Life Sciences, Zhejiang University, Hangzhou, China

   Contribution

   Resources, Investigation, Project administration, Writing—review and editing

   Competing interests

   No competing interests declared

8. Xin Liu

   Beijing Genomics Institute, Shenzhen, China

   Contribution

   Conceptualization, Resources, Funding acquisition, Investigation, Writing—review and editing

   For correspondence

   liuxin@genomics.cn

   Competing interests

   No competing interests declared

9. Yun-peng Zhao

   Laboratory of Systematic & Evolutionary Botany and Biodiversity, College of Life Sciences, Zhejiang University, Hangzhou, China

   Contribution

   Conceptualization, Resources, Funding acquisition, Investigation, Project administration, Writing—review and editing

   For correspondence

   ypzhao913@gmail.com

   Competing interests

   No competing interests declared

10. Pamela S Soltis

    Florida Museum of Natural History, University of Florida, Gainesville, United States

    Contribution

    Conceptualization, Resources, Supervision, Funding acquisition, Investigation, Methodology, Project administration, Writing—review and editing

    For correspondence

    psoltis@flmnh.ufl.edu

    Competing interests

    No competing interests declared

11. Gane Ka-Shu Wong

    1. Beijing Genomics Institute, Shenzhen, China
    2. Department of Biological Sciences, University of Alberta, Edmonton, Canada
    3. Department of Medicine, University of Alberta, Edmonton, Canada

    Contribution

    Conceptualization, Funding acquisition, Investigation, Writing—review and editing

    For correspondence

    gane@ualberta.ca

    Competing interests

    No competing interests declared

12. Douglas E Soltis

    1. Florida Museum of Natural History, University of Florida, Gainesville, United States
    2. Department of Biology, University of Florida, Gainesville, United States

    Contribution

    Conceptualization, Resources, Data curation, Supervision, Funding acquisition, Investigation, Methodology, Project administration, Writing—review and editing

    For correspondence

    dsoltis@ufl.edu

    Competing interests

    No competing interests declared

13. Qiu-Yun(Jenny) Xiang

    Department of Plant and Microbial Biology, North Carolina State University, Raleigh, United States

    Contribution

    Conceptualization, Resources, Data curation, Supervision, Funding acquisition, Investigation, Methodology, Writing—original draft, Project administration, Writing—review and editing

    For correspondence

    jenny_xiang@ncsu.edu

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-9016-0678

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