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  • Research article
  • Open Access

Differential genetic responses to the stress revealed the mutation-order adaptive divergence between two sympatric ginger species

BMC Genomics201819:692

https://doi.org/10.1186/s12864-018-5081-3

  • Received: 14 June 2018
  • Accepted: 14 September 2018
  • Published:

Abstract

Background

Divergent genetic responses to the same environmental pressures may lead sympatric ecological speciation possible. Such speciation process possibly explains rapid sympatric speciation of island species. Two island endemic ginger species Zingiber kawagoii and Z. shuanglongensis was suggested to be independently originated from inland ancestors, but their island endemism and similar morphologies and habitats lead another hypothesis of in situ ecological speciation. For understanding when and how these two species diverged, intraspecific variation was estimated from three chloroplast DNA fragments (cpDNA) and interspecific genome-wide SNPs and expression differences after saline treatment were examined by transcriptomic analyses.

Results

Extremely low intraspecific genetic variation was estimated by cpDNA sequences in both species: nucleotide diversity π = 0.00002 in Z. kawagoii and no nucleotide substitution but only indels found in Z. shuanglongensis. Nonsignificant inter-population genetic differentiation suggests homogenized genetic variation within species. Based on 53,683 SNPs from 13,842 polymorphic transcripts, in which 10,693 SNPs are fixed between species, Z. kawagoii and Z. shuanglongensis were estimated to be diverged since 218~ 238 thousand generations ago (complete divergence since 41.5~ 43.5 thousand generations ago). This time is more recent than the time of Taiwan Island formation. In addition, high proportion of differential expression genes (DEGs) is non-polymorphic or non-positively selected, suggesting key roles of plastic genetic divergence in broaden the selectability in incipient speciation. While some positive selected DEGs were mainly the biotic and abiotic stress-resistance genes, emphasizing the importance of adaptive divergence of stress-related genes in sympatric ecological speciation. Furthermore, the higher proportional expression of functional classes in Z. kawagoii than in Z. shuanglongensis explains the more widespread distribution of Z. kawagoii in Taiwan.

Conclusions

Our results contradict the previous hypothesis of independent origination of these two island endemic ginger species from SE China and SW China. Adaptive divergent responses to the stress explain how these gingers maintain genetic differentiation in sympatry. However, the recent speciation and rapid expansion make extremely low intraspecific genetic variation in these two species. This study arise a more probable speciation hypothesis of sympatric speciation within an island via the mutation-order mechanism underlying the same environmental pressure.

Keywords

  • Adaptive divergence
  • Divergent time
  • Ecological speciation
  • Mutation-order mechanism
  • Plastic divergence
  • Positively selected DEGs

Background

Positive divergent selection is a key for driving or maintaining the species divergence in sympatry [1]. Particularly for the island species which usually physical contact, endogenous genetic barrier is important to the reproductive isolation between species of incipient speciation [2, 3]. In fact, ecological speciation could be much faster if selective pressures act on stress-related genes (e.g. cold resistance and starvation tolerance in Drosophila melanogaster [4]). Differential responses to environmental pressures reinforce and fasten the reproductive isolation [57]. If these differential responses have similar fitness advantages, selection may promote and maintain the incompatibility of genotypic combinations and is against gene flow via the Bateson-Dobzhansky–Muller incompatibility mechanism [8, 9]. This process particularly commonly used to explain the parapatric speciation of continental-island species with a continental relative [9]. In addition, ragged topography also creates ecological and altitudinal separation between populations/species (e.g. genera Howea, Metrosideros and Coprosma in Lord Howe Island [1012]) that facilitates the speciation and radiation of island species. No matter parapatic or sympatric speciation of continental-island species, the natural selection usually involves in during speciation [1316], despite a certain cases focused on the geographic isolation (allopatric speciation) [1720].

Several novel species of genus Zingiber Mill. (Zingiberaceae Martinov) in Southeast Asia have been published due to distinguishable floral variation [2124] while some ambiguous species were revised and combined [25] in recent years, implying some outstanding taxonomic problems in this genus in SE Asia. There are four native Zingiber species in Taiwan, a continental island off the continental Asia with a central mountain range up to near 4000-m height [26]. Among these four island natives, Z. kawagoii Hayata and Z. shuanglongensis C. L. Yeh & S. W. Chung are endemic to Taiwan and similar in morphology except slight differences of the rhizome color, bract size and color, and labellum color [26]. However, these two island endemic gingers were suggested to be phylogenetically separated by two inland allies Z. smilesianum Craib and Z. striolatum Diels that distributed in southwestern China (Fig. 1) [23].
Fig. 1
Fig. 1

Phylogenetic hypotheses of Z. kawagoii and Z. shuanglongensis and allies and the distribution pattern of Z. kawagoii, Z. shuanglongensis, and Z. striolatum. The left plot shows the phylogenetic inference reconstructed by Yeh et al. [23]. The right panel is an alternative phylogenetic hypothesis inferred according to the distribution. Panels in the middle of figure reveal the current distribution, potential spatial distribution inferred by Ecological Niche Modeling (ENM) in the southeastern Asian and Taiwan. The current distribution is collected from GBIF database. It is worth to notify that Z. striolatum is not distributed in Taiwan, but the ENM suggests that the Taiwan Island is proper for its distribution. The current distribution and ENM of Z. smilesianum are not shown because of only one record in GBIF

Zingiber oligophyllum, previously reported as endemic to Taiwan, is now recorded for southeastern mainland China and Hong Kong [27]. Therefore, according to Yeh et al.’s [23] phylogenetic inference, Z. kawagoii and Z. shuanglongensis could be independently derived from widespread ancestors (Fig. 1). However, phylogenetic inference based on few genetic markers is easily affected by incomplete lineage sorting, particularly in species of frequent hybridization such as species of Zingiberaceae [28, 29]. In addition, Z. smilesianum and Z. striolatum, the sisters of Z. kawagoii, are widespread in SE Asia but not distributed in Taiwan. Therefore, we propose another hypothesis of recent and sympatric speciation for Z. kawagoii and Z. shuanglongensis according to their morphological similarity, island endemism and sympatric distribution (Fig. 1).

However, despite sympatric in Taiwan, some altitudinal differences between Z. kawagoii and Z. shuanglongensis are predicted by ecological niche modeling (ENM) based on 19 bio-climatic data from WorldClim database (Fig. 1). This suggests Grinnellian niche differentiation between these two species. According to different hypotheses of species origination, such niche differentiation could be a cause (e.g. sympatric ecological speciation) or a consequence (e.g. different originations or allopatric speciation) of speciation: if the former, we expected to detect genomic signatures of positive selection (i.e. adaptive divergence); if the latter, no or few response difference to the environmental pressures is expected.

In fact, the real distributional elevations of these two species are overlap (Z. kawagoii: 300–1500 m; Z. shuanglongensis: 1000–1400 m) with similar microhabitats (under forest, forest edge, slopes, and valley) [26]. This implies these two ginger species suffer similar environmental pressures. Therefore, if there is an adaptive divergence between Z. kawagoii and Z. shuanglongensis, it should be the mutation-order speciation [5, 30], says different mutations fixed in separate populations adapting to similar selective pressure leading divergence, rather than the divergent selection between different environments [5]. Exogenous stress is a major driver leading ecological divergence via selection [2]. Hence the genomic signature of adaptive divergence to the same stress was used for testing the mutation-order mechanism in this study.

Except genetic change, expressional plasticity plays another adaptive component to environmental change [31], by which different species with the same genotype may have differential expressions to response the same stress, i.e. reaction norms [32]. Many researches have indicated the key roles of plastic divergence for the incipient ecological speciation [31, 33, 34]. We tested two contrast hypotheses of adaptive vs. plasticity divergence between two ginger congeners. The genetic response to the stress only different in expression with no signals of positive selection (i.e. no nucleotide difference, selective constraints, and neutrality) is defined as plastic divergence. In contrast, the genes that are differentially expressed and positively selected are defined as adaptive divergence.

In this study, the genome-wide analysis is used to answer question about when and how Z. kawagoii and Z. shuanglongensis divergence. The objectives of this study were twofold: the first, to clarify whether these two island species are recently divergent in Taiwan or independently derived from inland ancestors; second, to test whether and how selective pressure drives or maintains the divergence in incipient speciation. To solve these questions, the chloroplast DNA (cpDNA) sequences were used to evaluate the intraspecific polymorphisms and interspecific divergence, and the genome-wide single nucleotide polymorphisms (SNPs) and RNA expression between these two species were collected via transcriptomic (RNA-seq) technology. Both sampled species were treated with saline before the experiment of RNA-seq in order to understand the genetic response under the stress of osmotic pressure change. Besides, such mild stress helps to increase overall transcription level and is conducive to obtain more loci for inferring the divergence of these two ginger congeners [35, 36]. The obtained SNPs were collected for divergent time estimation, and missense and silent SNPs were identified to detect signals of positive selection. Cross-reference of differential expression genes (DEGs) with positively selected genes (PSGs) was further used to determine the adaptive and/or plastic divergence between Z. kawagoii and Z. shuanglongensis. This study indicates a key role of genome-wide plastic divergence with adaptive divergence in stress-resistance genes between incipient species in sympatry.

Results

Low intraspecific genetic variation estimated by cpDNA in both species

We adopted three cpDNA region to evaluate the intraspecific divergence before inferring the genome-wide divergence between these two ginger congeners, including trnH-psbA intergenic spacer, rpl16 intron, and trnL-trnF intergenic spacer. Total nine Z. kawagoii populations and three Z. shuanglongensis populations were sampled (Table 1), which cover known distribution of these two species. The concatenated cpDNA sequences revealed very low intraspecific variation in these two species (nucleotide diversity π = 0.00002 in Z. kawagoii, while only indels but no SNP were found in Z. shuanglongensis). Furthermore, very low interpopulation divergences were observed in these two ginger congeners (Table 2, all corrected intraspecific πXY < 0.001, all intraspecific FST, the index of population differentiation, were nonsignificant). These results implied very little intraspecific variation, and the low intraspecific variation might be critical during inferring the divergence between species [37].
Table 1

Sampled population in chloroplast sequence amplification

population code

location

sample size

latitude

longitude

Z. kawagoii

 ZK_DSS

Dahsushan

8

N24.233

E120.900

 ZK_WLS

Weiliaoshan

7

N22.879

E120.646

 ZK_WLFS

Wulai, Fushan

9

N24.777

E121.504

 ZK_RS

Rueishuei

9

N23.511

E121.330

 ZK_NZ

Nanzhuang

10

N24.574

E121.044

 ZK_KTS

Kantoushan

6

N23.267

E120.501

 ZK_JIL

Jiajinlin

8

N22.402

E120.840

 ZK_GK

Gukeng

10

N23.631

E120.617

 ZK_DTDL

Datong Dali Trail

12

N24.192

E121.637

Z. shuanglongensis

 ZS_DLS

Dulanshan

12

N22.894

E121.186

 ZS_EJT

Erjituan

10

N23.064

E120.717

 ZS_LJ

Lijia Forest Trail

10

N22.805

E121.032

Table 2

Pairwise FST for population for population divergence in Z. kawagoii (ZK) and Z. shuanglongensis. The corrected average interpopulation difference (πXY) were shown above the diagonal, while the pairwise FST were shown below the diagonal

 

ZK_DDS

ZK_WLS

ZK_WLFS

ZK_RS

ZK_NZ

ZK_KTS

ZK_JIL

ZK_GK

ZK_DTDL

ZS_DLS

ZS_EJT

ZS_LJ

ZK_DDS

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

22.171*

22.171*

22.171*

ZK_WLS

0.037

0.000

0.000

0.000

0.000

0.000

0.000

0.000

22.163*

22.163*

22.163*

ZK_WLFS

0.024

0.002

0.000

0.000

0.000

0.000

0.000

0.000

22.163*

22.163*

22.163*

ZK_RS

0.000

0.038

0.025

0.000

0.000

0.000

0.000

0.000

22.152*

22.152*

22.152*

ZK_NZ

0.000

0.069

0.059

0.000

0.000

0.000

0.000

0.000

22.161*

22.161*

22.161*

ZK_KTS

0.000

0.017

0.000

0.000

0.000

0.000

0.000

0.000

22.161*

22.161*

22.161*

ZK_JIL

0.000

0.026

0.011

0.000

0.000

0.000

0.000

0.000

22.171*

22.171*

22.171*

ZK_GK

0.000

0.026

0.011

0.000

0.000

0.000

0.000

0.000

22.16*

22.16*

22.16*

ZK_DTDL

0.000

0.051

0.040

0.000

0.000

0.000

0.000

0.000

22.161*

22.161*

22.161*

ZS_DLS

1.000*

0.996*

0.996*

1.000*

1.000*

1.000*

1.000*

1.000*

1.000*

0.000

0.000

ZS_EJT

1.000*

0.996*

0.996*

1.000*

1.000*

1.000*

1.000*

1.000*

1.000*

0.000

0.000

ZS_LJ

1.000*

0.996*

0.996*

1.000*

1.000*

1.000*

1.000*

1.000*

1.000*

0.000

0.000

*Significance of random allelic permutation (P < 0.05)

Species divergence between two ginger congeners estimated from transcriptomes

A total of 14.59 and 14.70 million paired-end high-quality reads, in which 2.189 and 2.206 billion bases, with sequence error rate < 1% (Q20 percentages) of 96.36% and 96.68% were generated from transcriptomes of Z. kawagoii and Z. shuanglongensis, respectively. Totally 255,711 non-redundant transcripts with N50 value of 1378 bp were generated by Trinity de novo assembly. For pseudo-reference-based assembly, contigs of both Z. kawagoii and Z. shuanglongensis were assembled together by bowtie2 [38], which results in 205,168 reference sequences (129,806 and 138,497 transcripts of Z. kawagoii and Z. shuanglongensis aligned to pseudo-reference, respectively). The mean depth of coverage is 36.426 (±147.733, Additional file 1: Figure S1), and the mean mapping quality is 23.98.

To estimate species divergent time between Z. kawagoii and Z. shuanglongensis, 53,686 SNPs were identified in 13,842 (17,302,281 nucleotides) out of 205,168 transcripts (total length 137,601,681 nucleotides, average length 670.68 nucleotides per transcript), in which 91 transcripts are multi-allelic loci. The variant rate is 3.10 × 10− 3 per site. Among these SNPs, 10,693 SNPs are fixed differences (fixation rate is 6.18 × 10− 4 per site). If assuming a constant mutation rate, the divergent time T can be estimated by the formula μ = K/2T, where μ is the mutation rate and the K is the average variants per site or the average fixed differences per site. If applying the mutation rate 6.5 × 10− 9 or 7.1 × 10− 9 substitutions/site/generation estimated from Arabidopsis thaliana [39], the time to the most recent common ancestor (TMRCA) of both Z. kawagoii and Z. shuanglongensis could be coalesced back to roughly 2.38 × 105~ 2.18 × 105 generations ago when using variant rate 3.10 × 10− 3 per site as the K. If using the fixation rate (6.18 × 10− 4 per site) as K to estimate the divergent time, the species divergence was estimated to be 4.75 × 104~ 4.35 × 104 generations ago, which was suggested as the time of completion of speciation.

The plastic species divergence inferred from high proportion of non-positively selected differential expression transcripts

Among these SNPs, 33,571 SNPs locate in exons (56.404%), 16,170 SNPs in intergenic regions (27.168%), 4 SNPs in the splice site region (0.007%), and 5466 (9.184%) and 4308 SNPs (7.238%) are found in the 5′ and 3′ untranslated region (UTR), respectively. Among 33,571 exonic SNPs, there are 14,967 missense variants (44.58%) and 18,393 synonymous variants (54.79%), and the other 213 SNPs are initiator codon variants, start lost, stop gained/lost, and stop retained variants (0.63%). Slightly smaller proportion of missense variants than the silent variants (44.58% vs. 54.79%) suggests that the overall genomic divergence of these two species is not far from neutral or nearly neutral evolutionary model [40]. The overall distributions of Ka and Ks values of orthologs are shown in Fig. 2.
Fig. 2
Fig. 2

Distribution of the Ka, Ks, and Ka/Ks ratio of ortholog pairs under 60-min saline treatment. (a) Distribution of Ka and Ks values of all orthologous unigenes reveals a general patten of higher Ks than Ka; (b) L-shape Ka/Ks distribution in all transcripts revealed that most homologous genes (96.9% transcripts) of two ginger species are conserved (Ka/Ks < 1); (c) L-shape distribution of genes with Ka/Ks > 1 indicate that most of the positively selective pressures for the species divergence are slight (1 < Ka/Ks < 2)

We checked the differentially expressed genes using a criterion log2 fold-change (log2FC) > 1 and P < 0.05, resulting in 14,402 of 205,168 transcripts (7.02%) are differentially expressed. In these 14,402 DEGs, 3505 (24.34%) and 4396 transcripts (35.52%) are unique to Z. shuanglongensis and Z. kawagoii, respectively; while 6501 DEGs (45.14%) are commonly expressed in both species, in which 3176 (22.05%) and 3325 DEGs (23.09%) are higher expressed in Z. shuanglongensis and Z. kawagoii, respectively (Fig. 3). These differential expression patterns not only reflect the congenital differences in gene expression between species but also indicate divergent physiological responses to the saline stress.
Fig. 3
Fig. 3

Comparison of relative expression (FPKM) of differentially expressed genes (DEGs) in heatmap. Only the DEGs fit criteria of log2FC > 1 and significant differences in FPKM (P < 0.05) are shown. Three major clusters of transcripts are the genes expressed in both species but differentially expressed and the genes expressed in single species only

Based on the Gene Ontology (GO) annotation, 25,710 (50.00%), 14,296 (27.80%), and 11,184 (21.75%) unigenes of Z. shuanglongensis are identified as categories Biological Process (BP), Cell Component (CC), and Molecular Function (MF), respectively; 33,689 (50.00%), 15,295 (22.70%), and 28,473 (25.66%) unigenes are assigned as BP, CC, and MF, respectively, in Z. kawagoii. Comparisons of each functional class revealed higher proportion of unique expression of functional classes in Z. kawagoii than in Z. shuanglongensis, while the commonly expressed functional classes revealed higher expression level in Z. shuanglongensis than in Z. kawagoii (Fig. 4, the GO terms with differences > 0.5% are shown only). Such differences of expression patterns suggest that, in response to the stress, Z. kawagoii may quickly and widely trigger the expression of multiple functional genes (particularly the functional classes in BP and MF) in contrast to the concentrative expression of part of main functional categories in Z. shuanglongensis. The quick response and expression of diversified genes in the face of stresses may be advantageous to adapt changeable environment (i.e. adaptability) and increase the success of colonization (i.e. dispersability) [41, 42], which explains the broader habitat ranges of Z. kawagoii than Z. shuanglongensis in Taiwan (see the current distribution in Fig. 1).
Fig. 4
Fig. 4

Differences of the gene ontology (GO) enrichments of three main categories between Z. shuanglongensis and Z. kawagoii. Only the GO terms with differences > 0.5% are shown. Apparently, more genes of certain GO terms are uniquely expressed in Z. kawagoii than in Z. shuanglongensis, particularly in categories Biological Process and Molecular Function

For testing whether such divergence is adaptive or just a phenomenon of plasticity, we further check the Ka/Ks ratio, a genetic signature of selection [43], of these DEGs. Among 5413 transcripts with exonic SNPs, small proportion of DEGs was found in transcripts with Ka/Ks > 1 (429/5413 = 7.93%, Fig. 5). In contrast, relatively high proportion of DEGs was negatively selected or under selective constraint (i.e. Ka/Ks < 1, 4684/5413 = 86.53%, Fig. 5). The result of high proportion of DEGs without under positive selection supports the plasticity hypothesis rather the adaptive hypothesis of genomic divergence between two ginger congeners. Within these positively selected DEGs inferred by log2FC > 1, only 56 DEGs were also significant differences in Fragment Per Kilobase of transcript per Million mapped reads (FPKM) (P < 0.05, Fig. 6), in which 15 DEGs were highly adaptive (Ka/Ks > 2, Table 3). These highly adaptive DEGs can be considered to be the key genetic agents that cause or maintain the differentiation of the two ginger congeners.
Fig. 5
Fig. 5

Venn diagram shows proportions of differentially expressed genes (DEGs) and polymorphic genes and the DEGs under positive selection (i.e. Ka/Ks > 1)

Fig. 6
Fig. 6

Comparison of FPKM of positively selected DEGs in heatmap. Only the DEGs fit criteria of Ka/Ks > 1, log2FC > 1 and significant differences in FPKM (P < 0.05) are shown

Table 3

List of SNPs and relative expressions of transcripts with Ka/Ks > 2 and significantly differential expression

  

SNPs

FPKM

Transcript Id

Length

Missense

Synonymous

Ka/Ks

Z. kawagoii (zk)

Z. shuanglongensis (zs)

log2FC (zs-zk)

p (zs-zk)

DN30045_c2_g6_i1

386

6

1

3

5.12

26.67

2.29

0.038

DN32090_c10_g8_i1

634

5

1

2.5

0

17.47

7.97

1.80E-05

DN32090_c10_g5_i9

722

5

1

2.5

0.44

27.29

5.67

1.09E-06

DN31496_c1_g1_i13

839

16

2

4

5.62

31.54

2.36

0.002

DN32036_c1_g2_i5

1148

13

2

3.3

9.84

1.85

−2.61

0.002

DN31537_c1_g4_i6

1778

12

2

3

14.86

4.92

−1.77

0.016

DN31659_c3_g1_i5

2061

6

1

3

20.03

4.67

−2.27

0.002

DN28640_c0_g2_i1

2844

6

1

3

5.84

1.72

−1.91

0.013

DN30152_c4_g8_i1

403

5

1

2.5

55.55

2.79

−4.29

1.13E-04

DN26431_c0_g1_i1

1066

6

1

3

22.23

3.39

−2.85

3.77E-04

DN30898_c1_g1_i2

1872

5

1

2.5

11.4

3.66

−1.8

0.015

DN32090_c10_g5_i10

1862

46

7

3.3

7.98

24.08

1.44

0.039

DN31607_c0_g4_i2

2162

11

2

2.8

27.88

5.09

−2.6

3.37E-04

DN31888_c2_g1_i14

465

7

1

3.5

2.15

83.59

5.16

5.25E-07

DN26862_c0_g2_i1

1481

7

1

3.5

1.62

6.52

1.85

0.032

The adaptive divergence between species inferred from positive selection on stress-resistance DEGs

We further checked the functions of these highly adaptive-divergent (i.e. Ka/Ks > 2) DEGs. Ten of 15 transcripts were annotated (Table 4). Five of ten annotated transcripts are disease resistance genes analogous to RGA1 (DN30898_c1_g1_i2), RGA2 (DN32090_c10_g5_i9, DN31496_c1_g1_i13), RPM1 (DN32036_c1_g2_i5), and RPP13-like protein 1 (DN32090_c10_g5_i10); one is analogous to Mitogen-activated protein kinase kinase kinase 1 (MEKK1) (DN26862_c0_g2_i1), with a function of signaling pathway to modulate gene expressions responding to biotic (pathogen defense by negatively regulating innate immunity) and abiotic stresses (cold and salinity stress-mediated MAP kinase signaling cascade); one is analogous to Peroxiredoxin-2C (DN26431_c0_g1_i1), playing a role in cell protection against oxidative stress; and one is an analog of Retrovirus-related Pol polyprotein from transposon TNT 1–94 which might transcribe in the face of stresses (DN28640_c0_g2_i1). These eight transcripts are related to the resistance of biotic and abiotic stresses. Positive selection on these stress-resistance DEGs indicated differential genetic responses to the stress may play important roles in driving or maintaining species divergence.
Table 4

Functional annotation of 15 genes listed in Table 1

   

BLASTP

BLASTX

     

Transcript Id

UniprotKB gene

Protein

e value

bit score

e value

bit score

Pfam

COGs (EggNog)

Pathways

EC number

KEGGs

DN30045_c2_g6_i1

 

.

.

.

DN32090_c10_g8_i1

 

.

.

.

DN32090_c10_g5_i9

RGA2_SOLBU

Disease resistance protein RGA2

1.80E-23

101

3.16E-24

104

PF00931.21: NB-ARC domain

.

.

DN31496_c1_g1_i13

RGA2_SOLBU

Disease resistance protein RGA2

4.85E-13

71.6

2.33E-09

61.6

PF00931.21: NB-ARC domain

.

.

DN32036_c1_g2_i5

RPM1_ARATH

Disease resistance protein RPM1

3.33E-48

176

1.61E-52

191

.

COG4886: leucine Rich Repeat

osa04626 (Plant-Pathogen Interaction)

KO:K13457

DN31537_c1_g4_i6

WAK2_ARATH

Wall-associated receptor kinase 2

3.10E-133

408

2.03E-132

407

PF13947.5: Wall-associated receptor kinase galacturonan-binding

COG0515: Serine Threonine protein kinase

2.7.11

.

PF08488.10: Wall-associated kinase

PF07645.14: Calcium-binding EGF domain

PF00069.24: Protein kinase domain

PF07714.16: Protein tyrosine kinase

DN31659_c3_g1_i5

PF03732.16: Retrotransposon gag protein

.

.

DN28640_c0_g2_i1

POLX_TOBAC

Retrovirus-related Pol polyprotein from transposon TNT 1–94

1.52E-76

277

5.95E-62

233

PF14223.5: gag-polypeptide of LTR copia-type

.

2.7.7.49

.

PF13917.5: Zinc knuckle

3.4.23

PF13976.5: GAG-pre-integrase domain

PF00665.25: Integrase core domain

DN30152_c4_g8_i1

 

.

.

.

DN26431_c0_g1_i1

PRX2C_ARATH

Peroxiredoxin-2C

1.81E-88

264

4.42E-85

257

PF02966.15: Mitosis protein DIM1

COG0678: peroxiredoxin

1.11.1.15

.

PF08534.9: Redoxin

1.11.1.7

PF00578.20: AhpC/TSA family

DN30898_c1_g1_i2

RGA1_SOLBU

Putative disease resistance protein RGA1

3.29E-34

141

1.14E-33

141

.

.

.

DN32090_c10_g5_i10

R13L1_ARATH

Putative disease resistance RPP13-like protein 1

2.70E-41

164

2.45E-31

134

PF13855.5: Leucine rich repeat

COG4886: leucine Rich Repeat

.

PF13516.5: Leucine Rich repeat

DN31607_c0_g4_i2

 

.

 

.

DN31888_c2_g1_i14

C71A1_ZINZE

Alpha-humulene 10-hydroxylase

3.00E-90

274

3.34E-90

274

PF00067.21: Cytochrome P450

 

1.14.13.150

.

DN26862_c0_g2_i1

M3K1_ARATH

Mitogen-activated protein kinase kinase kinase 1

9.38E-72

242

3.62E-60

211

PF00069.24: Protein kinase domain

ENOG410XQGS: mitogen-activated

osa04016 (Plant-Pathogen Interaction)

2.7.1

KO:K13414

PF07714.16: Protein tyrosine kinase

protein kinase kinase kinase

osa04626 (MEPK Signaling Pathway)

2.7.11.25

In addition to the transcript DN28640_c0_g2_i1 analogous to Retrovirus-related Pol polyprotein from transposon TNT 1–94, DN31659_c3_g1_i5 was also annotated as encoding a Retrotransposon gag protein (Table 4). Transcription of TE is known to increase under various kinds of stresses in several systems [4446]. For example, in Arabidopsis [47] and coral Acropora hyacinthus [48], transposons and retrotransposons are activated during heat stress and are regulated by small interfering RNAs (siRNAs); Copia retrotransposons of the Cucumis melo genome are transcriptionally induced when melon leaves are exposed to UV light [49]; differentially expression of multiple retrovirus-related Pol polyproteins between two bryophytes with divergent desiccation tolerance during cycles of rapid desiccation and rehydration in the intertidal zone differentiates their ecological niches [50]. These two transposons and retrotransposons expressed higher in Z. kawagoii than in Z. shuanglongensis (Table 4). Another abiotic stress resistance transcript DN26431_c0_g1_i1 (analog of Peroxiredoxin-2C) is also higher expressed in Z. kawagoii (Table 4). The high expression of abiotic resistance genes in Z. kawagoii may be beneficial to the adaptation to a variety of environments, which explains the wider distribution of Z. kawagoii than Z. shuanglongensis in Taiwan.

The other two transcripts are analogous to the Alpha-humulene 10-hydroxylase (DN31888_c2_g1_i14) which is a catalytic enzyme of zerumbone biosynthesis with an anticancer function, and the Wall-associated receptor kinase 2 (DN31537_c1_g4_i6) functioning in the control of cell expansion, morphogenesis and development (Table 4). The positive selection and differential expression on these genes suggest differential morphogenesis and physiological functions between Z. kawagoii and Z. shuanglongensis. Ginger (Z. officinalis Rosc.) and other zingiberaceous plants have potent antioxidant and anti-inflammatory activities and are therefore suggested to exhibit cancer preventive activity [5153]. Differential expression and positive selection on Alpha-humulene 10-hydroxylase analog (DN31888_c2_g1_i14) suggest that Z. shuanglongensis, which has higher expression level, has a higher anti-inflammatory or anticancer effect than Z. kawagoii, although more researches are necessary.

Discussion

The species divergence event was later than the island formation time

The divergence of these two ginger congeners was since 2.38 × 105~ 2.18 × 105 generations ago and completed at 4.75 × 104~ 4.35 × 104 generations ago. Since the variant rate could be overestimated due to post-speciation polymorphism, the real TMRCA may be even smaller than the estimation. Therefore, the divergence of these two island-endemic species is more recent than the time of island formation (< 5 million years ago (Mya) [5456]). If Yeh et al.’s [23] hypothesis is true (Fig. 1), Z. shuanglongensis, Z. striolatum, Z. smilesianum and Z. kawagoii must be rapidly speciated and dispersed to Taiwan and SW China several times within hundreds of thousands of years (Fig. 1). Although this hypothesis cannot be completely exclusive due to lack of comparative genetic data of Z. striolatum and Z. smilesianum, multiple long-distance dispersal with speciation events (e.g. founder speciation) in these low-migratory plants is not easy in a short time. Alternatively, the hypothesis of in situ speciation within Taiwan Island (Fig. 1, the alternative hypothesis) is more probable to explain the sympatric and recent divergence between Z. kawagoii and Z. shuanglongensis. Anyhow, Z. kawagoii and Z. shuanglongensis are undisputedly incipient species.

Adaptive plasticity broadens selectabilities in the incipient speciation

Differentiation of physiological responses to the stress can arise through phenotypic plasticity, divergent selection, or a combination thereof, leading to speciation [33, 57]. High proportion of plastic divergence (i.e. DEGs without signatures of positive selection) suggests rapid and pluralistic responses to environmental stresses (e.g. osmotic pressure change due to saline or drought) [58], which increases the variability and selectivity at the beginning of speciation [31]. Adaptive plasticity also facilitates the success of organism to colonize to new habitats, that potentially contributes to genetic differentiation and speciation [59]. The recent divergence between Z. shuanglongensis and Z. kawagoii since Middle-to-Late Pleistocene suggests that the speciation initiates in an era of rapid change of climate and environment. High proportion of plastic divergent DEGs facilitates the survival of the common ancestors of Z. shuanglongensis and Z. kawagoii. If the divergent genetic responses are equally advantageous to the environmental pressure, these two incipient species can persist [8]. Such plasticity extends the persistence of the tolerance of environmental change of populations in low genetic variability, which is common in island endemic species, and the following adaptive evolution can facilitate the persistent responses to environmental change [58, 60].

Certain nonsynonymous polymorphisms of DEGs are found to be fixed between Z. shuanglongensis and Z. kawagoii. Such divergent process is particularly revealed in pathogenic and stress-resistance genes (Table 4). In the study of Brassica rapa, pathogenic sustainability changes accompanying with the evolutionary shift of the drought response and even results in early flowering following the drought stress [61]. Although no obvious difference in flowering time between Z. shuanglongensis and Z. kawagoii, Z. kawagoii have unique expression of genes regarding the regulation of red or far-red light signaling pathway (GO:0090227). Within this GO term, the transcript DN28195_c2_g11_i1 (analog to the REPRESSOR OF UV-B PHOTOMORPHOGENESIS 2 of Arabidopsis thaliana) that involves in the regulation of photoperiodic flowering and vegetative development have significant higher expression in Z. kawagoii than in Z. shuanglongensis (log2FC = 1.866, P = 0.009), and the transcript DN29957_c0_g6_i13 (analog to the Myb transcription factor EARLY FLOWERING MYB PROTEIN, of A. thaliana) that acts as a flowering repressor is positively divergent between Z. shuanglongensis and Z. kawagoii (Ka/Ks = 1.5). Although lack direct evidences to link the expressional differences regarding the pathogenic resistance genes with the flowering regulation under the drought stress, this transcriptomic data revealed clues that the expressional plasticity of stress- and pathogen-resistance genes might be associated with the evolution of a reproductive barrier toward adaptive divergence and ecological speciation [33].

Positive selection on stress-resistance genes as an important rule underlying the maintenance of species divergence

Higher expression of genes related to responses to stress and pathogens reveals higher adaptability in Z. kawagoii than in Z. shuanglongensis. Studies on wild sunflower have shown that the positive selection for salt tolerance quantitative trait loci (QTLs) is strong enough to counteract the homogenizing effect of gene flow and facilitate the sympatric speciation [62, 63]. Divergent responses to the stresses (e.g. saline, pathogens, etc.) accompanying with the gamete incompatibility that results in reproductive barrier during incipient speciation is probably a pleiotropic effect of the evolution of stress- or pathogen-resistance genes, and the pleiotropic effect may lead to gamete defect and further support the scenario of Bateson-Dobzhansky-Muller incompatibility [3, 8, 9]. The island endemic with overlapping distributions of recently divergent Z. kawagoii and Z. shuanglongensis implies the sympatric speciation. Although the speciation drivers and speciation genes are unknown yet, the transcriptomic data revealed that positively divergent selection on stress- and pathogen-resistance DEGs is involved in blocking the genetic homogenization of gene flow between species. These stress- and pathogen-resistance DEGs are undoubtedly one of the important factors to maintain reproductive isolation in the incipient speciation of Z. kawagoii and Z. shuanglongensis in Taiwan. This is congruent with Bomblies and Weigel’s [3] autoimmunity hypothesis for the between-species incompatibility, and the adaptive divergent responses to the same stress support the mutation-order speciation sympatrically within an island.

Conclusions

In conclusion, Z. kawagoii and Z. shuanglongensis is recently diverged and is most probably sympatrically speciated underlying the adaptive divergence of stress-resistance DEGs. Since lack of obvious habitat heterogeneity between species, we suggested mutation-order speciation, a mechanism of fixation of equally advantageous mutations in different populations/incipient species in the face of the same environmental pressures, might explain the adaptive divergence of these two sympatric island species. These adaptive-divergence mechanisms are based on the expressional plasticity of incipient speciation to expand adaptabilities of incipient species. Although the speciation mechanism and origination hypothesis need more studies to validate, differential responses to the same stress in genetic variation and expression have explained how sympatric incipient species diverged when suffering identical environmental pressures.

Methods

Plant material preparation, RNA extraction, and transcriptome sequencing

Mature plants of Z. kawagoii and Z. shuanglongensis are domesticated for more than a month in the greenhouse at the National Taiwan Normal University after collection from Miaoli Nanzhuang (N24°34′24.1″, E121°02′31.7″) and Jenlun Forest Road (N25°02′0.6″, E121°33′50.8″). Before RNA extraction, the roots of samples were irrigated with 1 M NaCl for saline stress for 1 h, and then the fresh leaves were collected for RNA extraction immediately using an improved cetyltrimethylammonium bromide (CTAB) method. RNA integrity was checked through 1% agarose gel electrophoresis, and then the mRNA was purified using poly-T oligo-attached beads. The cDNA libraries of both species were constructed from qualified RNA following manufacture’s protocol (Illumina Inc. San Diego, CA, USA). Library quality was validated on Agilent 2100 Bio-analyzer (Agilent Technology, CA, USA) and real-time PCR system. The paired-end sequencing was then performed by Illumina HiSeq platform. To elucidate the within-species variation of both Z. kawagoii and Z. shuanglongensis, we adopted three chloroplast regions for studying the level of intra-specific variation, including trnH-psbA intergenic spacer (forward primer: 5′-GTT ATG CAT GAA CGT AAT GCT C-3′, reverse primer: 5’-CGC GCA TGG TGG ATT CAC AAT C-3′), rpl16 intron (forward primer: 5’-CGA AAT CGG TAG ACG CTA CG-3′, reverse primer: 5′- ATT TGA ACT GGT GAC ACG AG-3′), and trnL-trnF intergenic spacer (forward primer: 5′- GCT ATG CTT AGT GTG TGA CTC GTT A-3′,reverse primer: 5’-CTT CCT CTA TGT TGT TTA CG-3′). We sampled nine Z. kawagoii populations and three Z. shuanglongensis populations (Table 1) for amplification and sequencing using BigDye terminator kit (Applied Biosystems). The divergence index including FST and average pairwise πXY were adopted to elucidate the intraspecific diversity. The concatenated sequences were deposited on GenBank (accession number: MH758531-MH758541), and the aligned file was also deposited in Mendeley (https://data.mendeley.com/datasets/fky8hszf8n/1).

Bioinformatics analysis

The modular tool MultiQC [64] was used for evaluating read quality. With the FastQC for quality control, the reads contaminated with adapter sequences, containing N bases accounting for > 5% of the total read length, or low-quality sequences with Phred score < 28 were removed using Trimmomatic [65]. Trinity ver. 2.3.2 [66] was used to de novo assembly with a setting of minimum contig length 150 bps and the CD-HIT-EST [67] was used for removing redundant transcripts to obtain more specific unigenes with a 95% sequence identity threshold. The filtered reads data were deposited in the NCBI Sequence Read Archive (SRA) with the Bioproject accession number PRJNA437070. The backbone reference was de-novo assembled using reads from both species by bowtie2 ver. 2.3.1 [38] and the read count quantification was performed by RSEM ver. 1.2.31 [68]. Relative expression level was calculated by the FPKM using RSEM ver. 1.2.31 [68]. Differential gene expression was then estimated by edgeR ver. 3.5 [69] with a criterion of log2FC > 1 with P < 0.05. We cross-compared the significant differences of FPKM (P < 0.05) and log2FC > 1 to confirm the differential expression.

We used the Transdecoder ver. 3.0.1 [https://transdecoder.github.io/] to parse coding region of all transcripts and retrieve protein sequences. Trinotate ver. 3.0.2 [https://trinotate.github.io/] was further used for functional annotation to processing the blastx and blastp homology search in UniProtKB/Swiss-Prot database. We also used the protein families (Pfam) domain database to identity protein domain in predicted and annotated proteins. Clusters of Orthologous Groups (COGs), Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) functional and pathway annotation was also conducted. The GO enrichment analysis for gene set was further performed to compare the differences of functional expression groups between two species.

SNPs of homologous transcripts in both species were identified by samtools [70] and bcftools [71]. The SNPs data were saved as the Variant Call Format (VCF) file in which only the SNPs with quality > 30 were retained. The snpEff [72] and SnpSift [73] were used for SNP annotation and retrieve useful annotated variants, respectively. In the snpEff analysis, functional classes (missense, nonsense, and synonymous variants) and variant regions (3′- and 5’-UTR, intergenic, exonic, and splice-site regions) were classified.

Testing the adaptive-divergence and plastic-divergence hypotheses

After the SNP calling, the ratio of missense mutation rate to silent mutation rate of each gene (hereafter named “Ka/Ks”) was estimated by assuming two fold nonsynonymous sites more than synonymous sites of coding sequences. Although this is a rough estimation, the estimated Ka/Ks ratio is better than direct estimating the proportion of nonsynonymous and synonymous variants (i.e. pa/ps) for the inference of positive selection (Ka/Ks > 1). Among all transcripts with exonic SNPs, we compared the proportion of DEGs to the number of no-expressional-difference genes between those of Ka/Ks > 1 and Ka/Ks ≤ 1. Since the differential gene expression between species indicates divergent physiological responses to the environment, the positive selection on DEGs indicates the adaptive divergence. In contrast, the DEGs with Ka/Ks ≤ 1 indicate the expressional divergence is not genetically based but an acclimated response, i.e. plastic divergence.

Abbreviations

BP: 

Biological Process of the GO term

CC: 

Cell Component of the GO term

COG: 

Clusters of Orthologous Groups

cpDNA: 

Chloroplast DNA

CTAB: 

Cetyltrimethylammonium bromide

DEG: 

Differential expression genes

ENM: 

Ecological niche modeling

FPKM: 

The Fragment Per Kilobase of transcript per Million mapped reads

F ST

The fixation index, a measurement of population differentiation

GO: 

Gene Ontology

K

The average variants (or fixed differences) per site

Ka

Nonsynonymous mutation rate

KEGG: 

Kyoto encyclopedia of genes and genomes

Ks

Synonymous mutation rate

log2FC: 

log2 fold-change

MF: 

Molecular Function of the GO term

Mya: 

Million years ago

Pfam: 

The protein families

PSGs: 

Positively selected genes

Q20: 

The sequencing quality score of 20 (an error rate of 1 in 100, with a corresponding call accuracy of 99%)

QTLs: 

Quantitative trait loci

RNA-seq: 

RNA sequencing or the transcriptome sequencing

siRNA: 

small interfering RNAs

SNPs: 

single nucleotide polymorphisms

SRA: 

Sequence Read Archive

T

The divergent time

TMRCA

The time to the most recent common ancestor

UTR: 

Untranslated region

VCF: 

The Variant Call Format

μ

Mutation rate

π: 

Nucleotide diversity estimated by pairwise differences

πXY

The average nucleotide diversity estimated by π

Declarations

Acknowledgements

The authors thank Dawn Schmidt for English editing.

Funding

This research was financially supported by the Ministry of Science and Technology in Taiwan (MOST 105–2628-B-003 -002 -MY3) and was also subsidized by the National Taiwan Normal University (NTNU), Taiwan.

Availability of data and materials

The chloroplast DNA sequences were deposited in the NCBI GenBank (accession number: MH758531-MH758541). The filtered reads data were deposited in the NCBI Sequence Read Archive (SRA) with the Bioproject accession number PRJNA437070. The assembly and expression profiles in this study were deposited in the Mendeley Data (https://data.mendeley.com; https://doi.org/10.17632/mxfgjzyxm9.1).

Authors’ contributions

PL and YL conceived this study. BH, HL and ML collected materials. BH, CH, HL and ML conducted experiments. BH and PL analysed the data. PL wrote the paper. YL, CH and BH critically reviewed the manuscript. All authors participated in the discussion and read and approved the final manuscript.

Competing interest

The authors declare that they have no competing interest.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Publisher’s Note

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Open AccessThis 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.

Authors’ Affiliations

(1)
School of Life Science, National Taiwan Normal University, No. 88, Sec. 4, Ting-Chow Rd., Wenshan Dist, Taipei, 11677, Taiwan
(2)
Department of Forestry, National Chung-Hsing University, No. 250, Kuo Kuang Rd, Taichung, 402, Taiwan

References

  1. Michel AP, Sim S, Powell THQ, Taylor MS, Nosil P, Feder JL. Widespread genomic divergence during sympatric speciation. Proc Natl Acad Sci. 2010;107(21):9724–9.View ArticleGoogle Scholar
  2. Lexer C, Fay MF. Adaptation to environmental stress: a rare or frequent driver of speciation? J Evolution Biol. 2005;18(4):893–900.View ArticleGoogle Scholar
  3. Bomblies K, Weigel D. Hybrid necrosis: autoimmunity as a potential gene-flow barrier in plant species. Nat Rev Genet. 2007;8:382–93.View ArticleGoogle Scholar
  4. Greenberg AJ, Moran JR, Coyne JA, Wu C-I. Ecological adaptation during incipient speciation revealed by precise gene replacement. Science. 2003;302(5651):1754–7.View ArticleGoogle Scholar
  5. Schluter D. Evidence for ecological speciation and its alternative. Science. 2009;323(5915):737–41.View ArticleGoogle Scholar
  6. Flatscher R, Frajman B, Schönswetter P, Paun O. Environmental heterogeneity and phenotypic divergence: can heritable epigenetic variation aid speciation? Genetics Research International. 2012;2012:698421.View ArticleGoogle Scholar
  7. Stanton ML, Roy BA, Thiede DA. Evolution in stressful environments. I. Phenotypic variability, phenotypic selection, and response to selection in five distinct environmental stresses. Evolution. 2000;54(1):93–111.View ArticleGoogle Scholar
  8. Nosil P, Flaxman SM. Conditions for mutation-order speciation. Proc R Soc B Biol Sci. 2011;278(1704):399–407.View ArticleGoogle Scholar
  9. Bank C, Bürger R, Hermisson J. The limits to parapatric speciation: Dobzhansky–Muller incompatibilities in a continent–island model. Genetics. 2012;191(3):845–63.View ArticleGoogle Scholar
  10. Papadopulos AST, Baker WJ, Crayn D, Butlin RK, Kynast RG, Hutton I, Savolainen V. Speciation with gene flow on Lord Howe Island. Proc Natl Acad Sci. 2011;108(32):13188–93.View ArticleGoogle Scholar
  11. Savolainen V, Anstett M-C, Lexer C, Hutton I, Clarkson JJ, Norup MV, Powell MP, Springate D, Salamin N, Baker WJ. Sympatric speciation in palms on an oceanic island. Nature. 2006;441:210–3.View ArticleGoogle Scholar
  12. Babik W, Butlin RK, Baker WJ, Papadopulos AST, Boulesteix M, Anstett M-C, Lexer C, Hutton IAN, Savolainen V. How sympatric is speciation in the Howea palms of Lord Howe Island? Mol Ecol. 2009;18(17):3629–38.View ArticleGoogle Scholar
  13. Li J-W, Yeung CKL, Tsai P-W, Lin R-C, Yeh C-F, Yao C-T, Han L, Hung LM, Ding P, Wang Q, et al. Rejecting strictly allopatric speciation on a continental island: prolonged postdivergence gene flow between Taiwan (Leucodioptron taewanus, Passeriformes Timaliidae) and Chinese (L. canorum canorum) hwameis. Mol Ecol. 2010;19(3):494–507.View ArticleGoogle Scholar
  14. Huang B-H, Huang C-W, Huang C-L, Liao P-C. Continuation of the genetic divergence of ecological speciation by spatial environmental heterogeneity in island endemic plants. Sci Rep. 2017;7(1):5465.View ArticleGoogle Scholar
  15. Thorpe RS, Surget-Groba Y, Johansson H. Genetic tests for ecological and allopatric speciation in anoles on an island archipelago. PLoS Genet. 2010;6(4):e1000929.View ArticleGoogle Scholar
  16. Lee Y-H, Lin C-P. Pleistocene speciation with and without gene flow in Euphaea damselflies of subtropical and tropical east Asian islands. Mol Ecol. 2012;21(15):3739–56.View ArticleGoogle Scholar
  17. Tseng S-P, Wang C-J, Li S-H, Lin S-M. Within-island speciation with an exceptional case of distinct separation between two sibling lizard species divided by a narrow stream. Mol Phylogenet Evol. 2015;90:164–75.View ArticleGoogle Scholar
  18. Tseng S-P, Li S-H, Hsieh C-H, Wang H-Y, Lin S-M. Influence of gene flow on divergence dating – implications for the speciation history of Takydromus grass lizards. Mol Ecol. 2014;23(19):4770–84.View ArticleGoogle Scholar
  19. Lin S-M, Chen CA, Lue K-Y. Molecular phylogeny and biogeography of the grass lizards genus Takydromus (Reptilia: Lacertidae) of East Asia. Mol Phylogenet Evol. 2002;22(2):276–88.View ArticleGoogle Scholar
  20. Tseng H-Y, Huang W-S, Jeng M-L, Villanueva RJT, Nuñeza OM, Lin C-P. Complex inter-island colonization and peripatric founder speciation promote diversification of flightless Pachyrhynchus weevils in the Taiwan–Luzon volcanic belt. J Biogeogr. 2018;45(1):89–100.View ArticleGoogle Scholar
  21. Nurainas N, Arbain D. A new species and a new record of Zingiberaceae from Sumatra Indonesia. Taiwania. 2017;62(3):294–8.Google Scholar
  22. Chaveerach A, Mokkamul P, Sudmoon R, Tanee T. A new species of Zingiber (Zingiberaceae) from northern Thailand. Taiwania. 2007;52(2):159–63.Google Scholar
  23. Yeh C-L, Chung S-W, Kuo Y-W, Hsu T-C, Leou C-S, Hong S-J, Yeh C-R. A new species of Zingiber (Zingiberaceae) from Taiwan, China, based on morphological and molecular data. J Syst Evol. 2012;50(2):163–9.View ArticleGoogle Scholar
  24. Bai L, Leong-Škorničková J, Li D, Xia N: Taxonomic studies on Zingiber (Zingiberaceae) in China IV: Z. pauciflorum, a new species from Yunnan. Nordic Journal of Botany 2017:(in press).Google Scholar
  25. Govaerts R. Nomenclatural notes in Asian Zingiberaceae. Taiwania. 2016;61(3):269–70.Google Scholar
  26. Lin Y-C. Taxonomic study of Zingiber mill. (Zingiberaceae) in Taiwan. M.S. Thesis. Taichung, Taiwan: National Chung-Hsing University; 2017.Google Scholar
  27. Bai L, Leong-ŠKorniČKovÁ J, Xia N-H, Ye Y-S. Taxonomic studies on Zingiber (Zingiberaceae) in China III: Z. ventricosum, a new species from Yunnan, and notes on three closely related species. Phytotaxa. 2016;261(2):18.Google Scholar
  28. Liu S-C, Lu C-T, Wang J-C. Reticulate hybridization of Alpinia (Zingiberaceae) in Taiwan. J Plant Res. 2009;122(3):305–16.View ArticleGoogle Scholar
  29. Valderrama E, Richardson JE, Kidner CA, Madriñán S, Stone GN. Transcriptome mining for phylogenetic markers in a recently radiated genus of tropical plants (Renealmia L.f., Zingiberaceae). Mol Phylogenet Evol. 2018;119:13–24.View ArticleGoogle Scholar
  30. Mani GS, Clarke BC. Mutational order: a major stochastic process in evolution. Proceedings of the Royal Society of London B Biological Sciences. 1990;240(1297):29.View ArticleGoogle Scholar
  31. Pfennig DW, Wund MA, Snell-Rood EC, Cruickshank T, Schlichting CD, Moczek AP. Phenotypic plasticity's impacts on diversification and speciation. Trends Ecol Evol. 2010;25(8):459–67.View ArticleGoogle Scholar
  32. Grenier S, Barre P, Litrico I. Phenotypic plasticity and selection: nonexclusive mechanisms of adaptation. Scientifica. 2016;2016:7021701.View ArticleGoogle Scholar
  33. Nonaka E, Svanbäck R, Thibert-Plante X, Englund G, Brännström Å. Mechanisms by which phenotypic plasticity affects adaptive divergence and ecological speciation. Am Nat. 2015;186(5):E126–43.View ArticleGoogle Scholar
  34. Thibert-Plante X, Hendry AP. The consequences of phenotypic plasticity for ecological speciation. J Evolution Biol. 2011;24(2):326–42.View ArticleGoogle Scholar
  35. Asensi-Fabado M-A, Amtmann A, Perrella G. Plant responses to abiotic stress: the chromatin context of transcriptional regulation. Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms. 2017;1860(1):106–22.View ArticleGoogle Scholar
  36. Ohama N, Sato H, Shinozaki K, Yamaguchi-Shinozaki K. Transcriptional regulatory network of plant heat stress response. Trends Plant Sci. 2017;22(1):53–65.View ArticleGoogle Scholar
  37. Edwards S, Beerli P. Perspective: gene divergence, population divergence, and the variation in coalescence time in phylogeographic studies. Evolution. 2000;54(6):1839–54.PubMedGoogle Scholar
  38. Langdon WB. Performance of genetic programming optimised Bowtie2 on genome comparison and analytic testing (GCAT) benchmarks. BioData Mining. 2015;8(1):1.View ArticleGoogle Scholar
  39. Ossowski S, Schneeberger K, Lucas-Lledó JI, Warthmann N, Clark RM, Shaw RG, Weigel D, Lynch M. The rate and molecular spectrum of spontaneous mutations in Arabidopsis thaliana. Science. 2010;327(5961):92–4.View ArticleGoogle Scholar
  40. Nei M, Gojobori T. Simple methods for estimating the numbers of synonymous and nonsynonymous nucleotide substitutions. Mol Biol Evol. 1986;3(5):418–26.PubMedGoogle Scholar
  41. Lockwood BL, Sanders JG, Somero GN. Transcriptomic responses to heat stress in invasive and native blue mussels (genus <em>Mytilus</em>): molecular correlates of invasive success. J Exp Biol. 2010;213(20):3548–58.View ArticleGoogle Scholar
  42. Voelckel C, Gruenheit N, Lockhart P. Evolutionary transcriptomics and proteomics: insight into plant adaptation. Trends Plant Sci. 2017;22(6):462–71.View ArticleGoogle Scholar
  43. Nekrutenko A, Makova KD, Li W-H. The K A/K S ratio test for assessing the protein-coding potential of genomic regions: an empirical and simulation study. Genome Res. 2002;12(1):198–202.View ArticleGoogle Scholar
  44. Capy P, Gasperi G, Biémont C, Bazin C. Stress and transposable elements: co-evolution or useful parasites? Heredity. 2000;85:101–6.View ArticleGoogle Scholar
  45. Grandbastien M-A. Activation of plant retrotransposons under stress conditions. Trends Plant Sci. 1998;3(5):181–7.View ArticleGoogle Scholar
  46. Wessler SR. Plant retrotransposons: turned on by stress. Curr Biol. 1996;6(8):959–61.View ArticleGoogle Scholar
  47. Matsunaga W, Ohama N, Tanabe N, Masuta Y, Masuda S, Mitani N, Yamaguchi-Shinozaki K, Ma JF, Kato A, Ito H. A small RNA mediated regulation of a stress-activated retrotransposon and the tissue specific transposition during the reproductive period in Arabidopsis. Front Plant Sci. 2015;6:48.View ArticleGoogle Scholar
  48. Traylor-Knowles N, Rose NH, Sheets EA, Palumbi SR. Early transcriptional responses during heat stress in the coral Acropora hyacinthus. Biol Bull. 2017;232(2):91–100.View ArticleGoogle Scholar
  49. Ramallo E, Kalendar R, Schulman AH, Martínez-Izquierdo JA. Reme1, a Copia retrotransposon in melon, is transcriptionally induced by UV light. Plant Mol Biol. 2007;66(1):137.PubMedGoogle Scholar
  50. Pearson GA, Hoarau G, Lago-Leston A, Coyer JA, Kube M, Reinhardt R, Henckel K, Serrão ETA, Corre E, Olsen JL. An expressed sequence tag analysis of the intertidal brown seaweeds Fucus serratus (L.) and F. vesiculosus (L.) (Heterokontophyta, Phaeophyceae) in response to abiotic stressors. Mar Biotechnol. 2010;12(2):195–213.View ArticleGoogle Scholar
  51. Shukla Y, Singh M. Cancer preventive properties of ginger: a brief review. Food Chem Toxicol. 2007;45(5):683–90.View ArticleGoogle Scholar
  52. Haque MA, Jantan I, Arshad L, Bukhari SNA. Exploring the immunomodulatory and anticancer properties of zerumbone. Food Funct. 2017;8(10):3410–31.View ArticleGoogle Scholar
  53. Kalantari K, Moniri M, Boroumand Moghaddam A, Abdul Rahim R, Bin Ariff A, Izadiyan Z, Mohamad R. A review of the biomedical applications of zerumbone and the techniques for its extraction from ginger rhizomes. Molecules. 2017;22(10):1645.View ArticleGoogle Scholar
  54. Teng LS. Extensional collapse of the northern Taiwan mountain belt. Geology. 1996;24(10):949–52.View ArticleGoogle Scholar
  55. Chi W-R, Namson J, Suppe J. Stratigraphic record of plate interactions in the coastal range of eastern Taiwan. Mem Geol Soc China. 1981;4:155–94.Google Scholar
  56. Wang CS, Huang CP, Ke LY, Chien WJ, Hsu SK, Shyu CT, Cheng WB, Lee CS, Teng LS. Formation of the Taiwan island as a solitary wave along the Eurasian continental plate margin: magnetic and seismological evidence. Terr Atmos Ocean Sci. 2002;13(3):339–54.View ArticleGoogle Scholar
  57. Crispo E. Modifying effects of phenotypic plasticity on interactions among natural selection, adaptation and gene flow. J Evol Biol. 2008;21(6):1460–9.View ArticleGoogle Scholar
  58. Pavey SA, Collin H, Nosil P, Rogers SM. The role of gene expression in ecological speciation. Ann N Y Acad Sci. 2010;1206(1):110–29.View ArticleGoogle Scholar
  59. Agrawal AA. Phenotypic plasticity in the interactions and evolution of species. Science. 2001;294(5541):321–6.View ArticleGoogle Scholar
  60. Becklin KM, Anderson JT, Gerhart LM, Wadgymar SM, Wessinger CA, Ward JK. Examining plant physiological responses to climate change through an evolutionary lens. Plant Physiol. 2016;172(2):635–49.PubMedPubMed CentralGoogle Scholar
  61. O'Hara NB, Rest JS, Franks SJ. Increased susceptibility to fungal disease accompanies adaptation to drought in Brassica rapa. Evolution. 2016;70(1):241–8.View ArticleGoogle Scholar
  62. Lexer C, Welch ME, Durphy JL, Rieseberg LH. Natural selection for salt tolerance quantitative trait loci (QTLs) in wild sunflower hybrids: implications for the origin of Helianthus paradoxus, a diploid hybrid species. Mol Ecol. 2003;12(5):1225–35.View ArticleGoogle Scholar
  63. Lexer C, Lai Z, Rieseberg LH. Candidate gene polymorphisms associated with salt tolerance in wild sunflower hybrids: implications for the origin of Helianthus paradoxus, a diploid hybrid species. New Phytol. 2004;161(1):225–33.View ArticleGoogle Scholar
  64. Ewels P, Magnusson M, Lundin S, Käller M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics. 2016;32(19):3047–8.View ArticleGoogle Scholar
  65. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30(15):2114–20.View ArticleGoogle Scholar
  66. Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I, Adiconis X, Fan L, Raychowdhury R, Zeng Q, et al. Trinity: reconstructing a full-length transcriptome without a genome from RNA-Seq data. Nat Biotechnol. 2011;29(7):644–52.View ArticleGoogle Scholar
  67. Li W, Godzik A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics. 2006;22(13):1658–9.View ArticleGoogle Scholar
  68. Li B, Dewey CN. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics. 2011;12(1):323.View ArticleGoogle Scholar
  69. Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26(1):139–40.View ArticleGoogle Scholar
  70. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R. The sequence alignment/map format and SAMtools. Bioinformatics. 2009;25(16):2078–9.View ArticleGoogle Scholar
  71. Narasimhan V, Danecek P, Scally A, Xue Y, Tyler-Smith C, Durbin R. BCFtools/RoH: a hidden Markov model approach for detecting autozygosity from next-generation sequencing data. Bioinformatics. 2016;32(11):1749–51.View ArticleGoogle Scholar
  72. Cingolani P, Platts A, Wang LL, Coon M, Nguyen T, Wang L, Land SJ, Lu X, Ruden DM. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff. Fly. 2012;6(2):80–92.View ArticleGoogle Scholar
  73. Ruden D, Cingolani P, Patel V, Coon M, Nguyen T, Land S, Lu X. Using Drosophila melanogaster as a Model for Genotoxic Chemical Mutational Studies with a New Program, SnpSift. Front Genet. 2012;3:35.Google Scholar

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