The mitochondrial genome of pin-tailed snipe Gallinago stenura, and its implications for the phylogeny of Charadriiformes

Chaochao Hu; Chenling Zhang; Lei Sun; Yi Zhang; Wenli Xie; Baowei Zhang; Qing Chang

doi:10.1371/journal.pone.0175244

Abstract

The Charadriiformes, among the most diverse orders of bird, is a good source to research on evolution. The mitochondrial genome sequencing database has rapidly increased in recent years, while Charadriiformes mitogenome has not been well studied. In this research, we determined the complete mitogenome sequence of Gallinago stenura, and comparatively analysed 20 mitogenomes of Charadriiformes. The mitogenomes display moderate size variation, and most of variation due to mutations in the control region. In 13 protein-coding genes, we found: 1. The GC skews are always negative, while the negative AT skews are found in 5 genes, 2. The average uncorrected pairwise distances reveal heterogeneity of evolutionary rate for each gene, 3. The ATG and TAA, respectively, are observed the most commonly start and stop codon. The highest dN/dS is detected for ATP8 (0.16) among Charadriiformes, while the lowest for COI (0.01), indicating that 13 protein-coding genes are evolving under the purifying selection. Predicted secondary structures of tRNAs indicate that the sequences and structures of anticodon, amino acceptor, and TψC arms are highly conserved, and most nucleotide variation is restricted to dihydrouridine arms with obvious indel polymorphisms. A total of 15 conserved sequence boxes were recognized in the control regions, and the 4 bp (5’-AAAC-3’) and 7 bp (5’- AAACAAC -3’) repeat sequences occurred frequently. Phylogenomic analysis based on the nearly complete mitochondrial genomes strongly supported the monophyly of the order, and the suborder Charadrii is at the basal of Charadriiformes. Moreover, our results well resolved the complexity family-level relationships and clearly depicted the evolutionary processes of Charadriiformes, based on 12 mitochondrial protein-coding genes from 18 families. This study improves our understanding of mitogenomic structure and evolution, which can provide further insights into our understanding of phylogeny and taxonomy in Charadriiformes.

Citation: Hu C, Zhang C, Sun L, Zhang Y, Xie W, Zhang B, et al. (2017) The mitochondrial genome of pin-tailed snipe Gallinago stenura, and its implications for the phylogeny of Charadriiformes. PLoS ONE 12(4): e0175244. https://doi.org/10.1371/journal.pone.0175244

Editor: Bi-Song Yue, Sichuan University, CHINA

Received: January 4, 2017; Accepted: March 22, 2017; Published: April 6, 2017

Copyright: © 2017 Hu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The complete mitogenome of Gallinago stenura is deposited in GenBank of NCBI under accession number KY056596.

Funding: This research was supported by grants from the Program of National Natural Science Foundation of China (#31071901 and #41271057) and Natural Science Research of Jiangsu Higher Education Institutions of China (#16KJB180012).

Competing interests: The authors have declared that no competing interests exist.

Introduction

The order Charadriiformes (Vertebrate: Aves), known as shorebirds, is a diverse order containing approximately 384 species belonging to 94 genera of 19 families, and has members distributing in all parts of the world. Because of the diversity in their ecology, morphology, behavior, distribution and other adaptive characters, Charadriiformes provide potential insight into evolutionary forces acting on life-history traits [1–4]. Because of convergent evolution, the use of morphological and biochemical methods to reconstruct Charadriiformes phylogeny is unresolved and poorly supported [5, 6]. Based on the genetic analysis (mitochondrial and/or nuclear genes), the majority of previous analyses revealed that the order can be divided taxonomically into three major clades (Charadrii, Scolopaci, and Lari), and place the Charadrii as sister to the other two groups [7–11]. However, the complexity family-level relationships of Charadriiformes have not been well resolved, especially in the deep nodes of the phylogenetic trees (Fig 1). For example, in the major clade of Lari, many studies suggest that the Sternidae and Rynchopidae are nearest phylogenetic neighbors (Fig 1B and 1C), others place the Sternidae as sister to the other two families (Fig 1A and 1D). Another potential conflict in topology is the major clade of Charadrii, the relationship of Chionidae, Pluvianellidae and Burhinidae (Fig 1).

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Fig 1. Family-level phylogenetic relationships of Charadriiformes.

(A) based on RAG-1 [12], (B) based on 14 mitochondrial genes [8], (C) based on 3 mitochondrial genes and 3 nuclear genes [11], and (D) based on 3 mitochondrial genes and 1 nuclear gene [13].

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Animal mitogenome is an approximately 16 kb circular molecules [14]. This genome typically contains 13 protein-coding genes, 2 ribosomal RNAs (12S rRNA and 16S rRNA), 22 transfer RNAs (tRNAs), and a non-coding control region (CR) [15, 16]. The genome-level characteristics of mitogenomes include the base composition, codon usage, gene order arrangement, tRNA and rRNA gene secondary structures, and the signaling genes control of replication and transcription [14, 17]. Such information has been used to show potential in resolving ancient patterns of evolutionary history, and also for developing conservation management programs for endangered species [8, 18, 19]. In the past decade, the number of sequenced mitogenomes have accumulated and these sequences have been widely used in biological research, especially when studying molecular evolution, genetic structure, and phylogeography [20–27]. Especially, a number of studies performed using mitochondrial genome sequences to elucidate avian phylogeny [21, 22, 28, 29].

Although some studies have used a few mitochondrial and/or nuclear genes in reconstructing family-level phylogeny within Charadriiformes [4, 8, 11–13, 30], mitochondrial genome sequences and structures have proven to be useful for elucidation of evolutionary relationships, and the comprehensive study of the entire mitogenomes within Charadriiformes are still not well studied. Despite recent rapid increases in the available information on bird mitogenomes, only 19 complete mitogenomes sequences have been reported in database of Charadriiformes (Table 1). Of these sequenced mitogenomes, 5 were from species in the suborder Charadrii (Haematopodidae, Recurvirostridae and Charadriidae), 5 in Scolopaci (Jacanidae and Scolopacidae), and 9 in Lari (Laridae, Stercorariidae and Alcidae).

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Table 1. List of the complete mitogenomes sequences included in this study.

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In this study, we sequenced and annotated the complete mitogenome of the pin-tailed snipe (Gallinago stenura) from the family of Scolopacidae, and this species is most common migrant snipe. We compared the newly sequenced mitogenome with previously published sequences, we tried to address: (1) features of Charadriiformes mitogenomes, (2) rates and patterns of mitochondrial gene evolution within Charadriiformes, and (3) the adaptation of the Charadriiformes mitogenome to different environment based on dN/dS analysis. And then, we have collected sequences of 12 protein-coding genes from 18 families to assess the family-level relationships of Charadriiformes. This study contributes to more molecular information on mitogenomic structure and evolution, which can provide further insights into our understanding of phylogeny and taxonomy in Charadriiformes.

Materials and methods

Ethical statement

No specific permits were required for the specimens collected for this study. The specimen was common in China and the field studies did not involve endangered or protected species. Our experimental procedures complied with the current laws on animal welfare and research in China, and were specifically approved by the Animal Research Ethics Committee of Nanjing Normal University.

Sample collection and DNA extraction

The muscle tissue sample of G. stenura was collected from a derelict and abandoned mist net in Rudong, Jiangsu Province, China (32°32'43.41" N, 121°06'09.01" E). The tissue was preserved in absolute ethanol and stored at −80°C in our laboratory at Nanjing Normal University (specimen voucher: NJNU-Gst01). Total genomic DNA was extracted using standard phenol-chloroform methods [31].

Primer design, PCR amplification and sequencing

The primers were designed based on sequence-conserved regions, which were identified using multiple alignments of the complete mitogenomes of 19 closely related sequences downloaded from GenBank (Table 1). Using the primer pairs newly designed for this study, PCR amplifications were carried out in a final reaction volume of 25 μL, which contained 10 × buffer 3 μL, 25 mmol/L MgCl₂ 2 μL, 2 mmol/L dNTPs 2 μL, 10 μM of each primer (forward and reverse) 1 μL, 1 μL template DNA, 1U Taq DNA polymerase (Takara, Japan), and sterile double-distilled water (ddH₂O) to make up a final volume of 25 μL. Amplifications were performed in an Applied Biosystems 2720 thermal cycler with heated lid under the following conditions: 95°C for 5 min (initial denaturation); then 35 cycles of 95°C for 30 s (denaturation), 50–55°C for 30 s (annealing), and 72°C for 1 min (extension); and a final extension at 72°C for 8 min and 4°C hold. Annealing temperature was changed from 50 to 55°C to improve the quality of PCR products as necessary. PCR products were excised by electrophoresis on a 1% agarose gel and visualized by Ethidium Bromide staining. And then the PCR products were purified using a gel extraction kit (Promega) and sequenced with each of the PCR primers on an ABI 377 sequencer.

Genome annotation and sequence analysis

Sequences obtained were assembled and edited using the software SeqMan (DNAStar, Inc.) to generate complete mitochondrial DNA sequences. The protein-coding genes were determined by open reading frame finder implemented at the NCBI website with the vertebrate mitochondrial genetic code, and then finally confirmed by sequence comparisons with the reported Charadriiformes mitogenomes. The tRNAscan-SE 1.21 [32], MITOS [33], and ARWEN [34] were used to confirm tRNA annotation results. The skewing of the nucleotide composition was calculated according to the following formulas: AT skew = (A–T) / (A + T) and GC skew = (G − C) / (G + C) [35, 36]. The tandem repeats were searched in the CR using the Tandem Repeats Finder program [37].

The number of variable sites, the parsimony informative sites, the singleton, and the average uncorrected pairwise distances for each protein-coding gene were calculated by MEGA 6.0 [38]. The rates of non-synonymous substitutions (Ka, π modified), and synonymous substitutions (Ks, π modified) for each protein-coding genes were determined with DnaSP 5.0 [39]. The ratio of nonsynonymous substitution rate to synonymous substitution rate (ω = dN/dS) was calculated with Datamonkey [40], by choosing the vertebrate mitochondrial DNA genetic code, with in the SLAC method (P < 0.1). The ratio of nonsynonymous substitution rate (dN) to synonymous substitution rate (dS) is widely used as an indicator of selective pressure at the sequence level among different species. It is commonly accepted that ω > 1, ω = 1, and ω < 1 generally indicate positive selection (replacement substitutions increase fitness), neutral mutation, and negative selection (remove such substitutions from the gene pool), respectively [41].

Phylogenetic analysis

Two datasets were assembled for phylogenetic analyses: (1) the first dataset (Dataset 1) for mitogenomic phylogeny of Charadriiformes, 13 protein-coding genes, 12S and 16S, of 20 Charadriiformes species were used (Table 1), (2) the second dataset (Dataset 2) for the family-level phylogeny of Charadriiformes, 12 protein-coding genes (with excluding ND6) of 40 species were used (S1 Table). The two aligned datasets were 13,965 and 10,773 bp in length for the Dataset 1 and Dataset 2 matrices, respectively. In all phylogenetic analyses, Columba livia, Gallus gallusa, Phodilus badius, and Zenaida auriculata were used as outgroups [10, 12, 42, 43]. Before reconstructing the phylogenetic trees, sequence alignment was carried out. Each mitochondrial gene was aligned individually using Muscle in MEGA 6.0 [38]. The 13 protein-coding genes were translated into amino acids, and then aligned using Muscle in MEGA 6.0 [38] with default parameters for each gene, and finally retranslated into nucleotide sequences after removing the stop codons.

The phylogenetic trees were inferred under the maximum likelihood (ML) and Bayesian inference (BI), which were performed with RAxML 8.2.4 [44] and MrBayes 3.2.2 [45], respectively. To determine the optimal partitioning of the data, we explored three partition schemes in phylogenetic analyses. The data set was partitioned a priori on the basis of genes and codon positions. The three partitioning schemes are: (1) single (not partitioning), (2) each gene as one partition, (3) partitioned by each of the three codon positions in each gene. The best-fit partitioning scheme and the most appropriate nucleotide evolution model for each partition were implemented in PartitionFinder 1.1.1 [46]. Unpartitioned scheme using a GTR+ I + G substitution model. For ML analysis, node support was calculated with a GTRGAMMA model via rapid bootstrapping (-f a -x option) with ten runs and 1,000 replications to estimate the best topology. For the BI analysis, two independent runs of four Markov Chains Monte Carlo (MCMC) chains (one cold chain and three hot chains) were simultaneously run for 2.0 × 10⁷ generations, with sampling conducted every 1000 generations. The first 25% of the initial trees were discarded as ‘‘burn-in” and the remaining trees were used to calculate 50% majority rule consensus tree and Bayesian posterior probabilities. The MCMC runs were repeated twice to confirm consistent approximation of the posterior parameter distributions. The convergence of MCMC runs and effective sample sizes (ESS > 200) were checked by plotting the log likelihood scores against the generation times using the program Tracer 1.6 (http://beast.bio.ed.ac.uk/Tracer).

Results and discussion

Genome organization

The mitogenome of G. stenura is a closed circular molecule of 16,899 bp in length (GenBank No. KY056596), and its map is shown in Fig 2. The completely sequenced mitogenomes from Charadriiformes encode a CR, and a typical set of 37 mitochondrial genes containing 13 protein-coding genes, 2 rRNA genes (12S rRNA and 16S rRNA), and 22 tRNA genes. Gene arrangement is highly conserved within the Charadriiformes mitogenomes, without showing any structural rearrangement [10, 14]. The gene of ND6 and 8 tRNA are encoded on the light strand, whereas the other genes are located on the heavy strand (S2 Table). The mitogenomes of the twenty completely sequenced Charadriiformes species displayed moderate size variation, the mean size was 16,807 bp (SD = 179.66, n = 20), ranging from 16,357 bp (Sternula albifrons) to 17,135 bp (Vanellus cinereus) (Table 1). Most of the size variation primarily due to length mutation in the CR. In the G. stenura mitogenome, gene overlaps occurred 10 times, spanning 1–10 nucleotides, for a total of 35 nucleotides. The longest overlap (10 bp) existed between ATP8 and ATP6, and the pairs of COI/tRNA^Ser overlap 9 nucleotides, whereas 5 pairs (16srRNA/tRNA^Leu, tRNA^Gln/tRNA^Met, tRNA^Cys/tRNA^Tyr, ATP6/COIII and tRNA^Ser/tRNA^Leu) overlap only one nucleotide (S2 Table). The intergenic spacer regions occurred 17 times, spanning 1–12 bp, for a total of 69 bp (S2 Table).

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Fig 2. Circular map of the mitochondrial genome of Gallinago stenura.

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Nucleotide composition

The overall mean base composition of Charadriiformes mitogenomes was: A, 31.20%; C, 30.60%; T, 24.44% and G, 13.76%. Twenty Charadriiformes mitogenomes are consistently biased towards AT rich, ranging from 54.39% (Chroicocephalus saundersi) to 58.35% (G. stenura) (Table 2), which is consistent with previous avian mitogenomes [22]. Interestingly, higher A + T content is observed not only in the CR but also in protein-coding genes and rRNA (Fig 3). The lowest A + T content is found in the gene of 12S rRNA (19 species) or ND1 (only in Scolopax rusticola), whereas the highest A + T content is found in ATP8 (11 species) or CR (9 species).

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Fig 3. The A + T content in Charadriiformes mitogenomes.

Each point represents a species.

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Table 2. Nucleotide composition of mitochondrial genomes in Charadriiformes.

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AT and GC skews are a measure of compositional asymmetry. In Charadriiformes mitogenomes, AT skews values were always positive, while the values of GC skew were negative. The AT skew value observed is 0.12 ± 0.02 (mean ± SD), ranging from 0.09 (Sternula albifrons) to 0.15 (Recurvirostra avosetta). The GC skews value is −0.38 ± 0.01, ranging from −0.40 (Jacana spinose) to −0.36 (S. albifrons) (Table 2). The lowest value of GC skew was always found in Jacanidae (−0.40), and highest value was found in Laridae (−0.36 to −0.37). In general, AT and GC skews in Charadriiformes mitogenomes are similar to patterns typically found in most animal mitogenomes, which positive AT skew and negative GC skew are found for H-strand, implying the specific bias toward A and C in nucleotide composition [47, 48]. The GC skew in Charadriiformes mitogenomes revealed that the ATP8 containing significantly higher skews than other regions, and the positive GC skew was not found in this work. However, there was a marked negative AT skew in ND1 (11 species), COI (three species), ND3 (five species), ND4L (one species) and CR (four species) on the H-strand (Fig 4).

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Fig 4. The AT skew and GC skew in Charadriiformes mitogenomes.

Each point represents a species.

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Protein-coding genes evolutionary rate

Total length of the protein-coding genes in each species was 11,399 bp after removing termination codons and indels. Indels were only found in ATP8, COIII, ND5, and Cyt b, and ranged from two to five amino acids. Comparison of each protein-coding gene provides a better understanding of the evolutionary patterns of molecular evolution. The length of the 13 protein-coding genes of Charadriiformes genomes showed that the gene of ND5 and ATP8 being the longest and shortest, respectively. The percent of variable positions in each gene ranged from 33.91% (COI) to 53.57% (ATP8), and the parsimony informative sites ranged from 28.32% (COIII) to 51.6% (ATP8). The analysis of variable sites showed that the ATP8 contained more variable sites. The average uncorrected pairwise distances revealed the heterogeneity of evolutionary rate for each gene, the COIII (0.13), COI (0.14) and COII (0.14) had slow evolutionary rate, whereas those of ND6 (0.20), ND2 (0.20) and ATP8 (0.24) relatively faster (Table 3). So, we can infer that the COIII was the most conserved protein-coding gene, and ATP8 the least conserved. At the nucleotide levels, three genes (COI, COII and COIII) had the lowest evolutionary rates, suggesting that they are useful candidate barcoding markers. The nucleotide composition had a positive correlation with the observed variation in each gene, indicating that the larger biased in nucleotide composition might be expected evolving more quickly (Fig 4, Table 3).

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Table 3. The mutational information and average distances among Charadriiformes calculated by gene.

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To better understand the evolutionary patterns among the 13 protein-coding genes and the role of selection in Charadriiformes species, the values of Ka, Ks, and dN/dS (ω) were calculated for each protein-coding gene, respectively (Fig 5). The Ks of ND1 was the highest, while the values of Ka and ω for ATP8 were the highest. The highest dN/dS was detected for the ATP8 gene (0.16) among Charadriiformes, while the lowest for the COI gene (0.01). The dN/dS values for all protein-coding genes were far lower than one (≤ 0.16), indicating that these genes were evolving under the purifying selection.

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Fig 5. Evolutionary rates of 13 protein-coding genes in Charadriiformes mitogenomes.

The Y-axis provides the substitution rate of mitochondrial gene. Synonymous nucleotide substitutions per synonymous site (Ks) and nonsynonymous nucleotide substitutions per nonsynonymous site (Ka) are calculated using Dnasp, and dN/dS is calculated using DataMonkey.

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The usage of start and stop codon

Start and stop codons vary among the protein-coding genes in Charadriiformes, although ATG and TAA were the most commonly observed. Start and stop codon usage biases were shown in Fig 6. Five start codons (ATG, GTG, ATT, ATC, and ATA) were detected in the 13 protein-coding genes. The most common start codon was ATG, which accounts for 78.85% of the start codons, followed by GTG (11.15%). The start codon ATG appeared in 12 protein-coding genes (with the exception of ND3), and 8 genes (COII, ATP8, ATP6, COIII, ND4L, ND4, Cyt b, and ND6) only used ATG as start codon. The start codon GTG was commonly used in the COI and ND5. Then the start codons ATT and ATC were only used in the ND3. The start codon ATA were found in the ND1, ND2, and ND3 (Fig 6), which was frequently observed in other avian orders [20]. While in the suborder Lari, the start codons ATC and ATA were not common used, as they were only found in the ND3 of S. antiquus and A. interpres [10].

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Fig 6. The usage of start and stop codons in the 13 mitochondrial protein-coding genes of the order Charadriiformes.

All genes are shown in the order of occurrence in the mitochondrial genome starting from ND1.

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Four stop codons (TAA, TAG, AGG, and AGA) and an incomplete stop codon (T–) were detected. The most common stop codon TAA served as stop codon only in six genes (COII, ATP8, ATP6, ND3, ND4L, and Cyt b) (Fig 6). In the case of other bird orders, the stop codon TAA appears only in COII, ND4L, and Cyt b [20]. The stop codon TAG appeared in the ND2 and ND6. But the stop codon TAG has so far been described in the Cyt b of Aceros corrugatus (Coraciiformes; Bucerotidae) and the ND6 of a few Columbidae species [20]. The stop codon AGG was found in COI and ND1 (Fig 6). The stop codon AGA was found in ND5, and ND1. The stop codon T–only appeared in the COIII and ND4. Truncated stop codons are common and may be completed by a poly-adenylation of the 3’-end of the mRNA via post-transcriptional [49–51].

In the gene of ND3, an extra nucleotide (C: cytosine) was present at the position 174 of 16 species, which was not observed in four species (Chroicocephalus ridibundus, C. saundersi, Synthliboramphus antiquus and Scolopax rusticola). In the species of C. saundersi, Yoon et al. (2015) found the extra base in ND3, whereas Ryu and Kwang (2012) lacked it [10, 52]. The extra base insert in ND3 has been observed in some birds and turtles [53], but it appears to be randomly happened in closely related species, even within species [10, 52]. So, we can infer that there is no evolutionary significance for the extra insertion site.

Sequence features of rRNA and tRNA genes

As other avian rRNA genes, Charadriiformes mitogenomes had 2 rRNA (12S and 16S) that were identified on the heavy strand and located at a conserved position between tRNA^Phe and tRNA^Leu genes, and separated by tRNA^Val gene (Fig 2; S2 Table). The length of 12S rRNA varied from 963 bp (Haematopus ater) to 981 bp (Vanellus cinereus) (KM404175), and the smallest and largest 16S rRNA genes were 1,589 bp (Jacana spinose and J. jacana) and 1,607 bp (V. vanellus). The average size of two combined rRNA genes was 2,566 bp (±8, n = 20), and ranged from 2,553 bp (H. ater) to 2,580 bp (V. vanellus). Therefore, there was no substantial size variation between rRNAs, since their sequence size varied slightly with few base pairs difference. In G. stenura, the combined size of the two rRNA genes was 2,571 bp.

Twenty-two tRNA genes were identified in Charadriiformes in the same genomic positions as observed in Laridae [10], with an average size ranging from 65.0 ± 0.9 bp in tRNA^{Ser (AGY)} to 74.0 ± 0.2 bp in tRNA^{Leu (UUR)}. The length difference was due to the mutation of stem and loop sizes of the dihydrouridine (DHU) and TΨC arms. In G. stenura mitogenome, the length of 22 tRNA genes ranged from 66 bp [tRNA^Ser(AGY)] to 74 bp [tRNA^Ser and tRNA^Glu] (S2 Table). Putative secondary structures of tRNAs in G. stenura indicated that 21/22 [except for tRNA^{Ser (AGY)}, which appears to lack the DHU arm] possess a typical canonical cloverleaf secondary structure, which were similar to those of other Charadriiformes species, indicated their similar functions (S1 Fig). The sequences and structures of anticodon, amino acceptor, and TψC arms were highly conserved, while the nucleotide variation was mostly restricted to DHU arms with obvious indels polymorphisms (13/22 tRNA genes; S2 Fig).

Control region

The CR of Charadriiformes mitogenomes were highly divergent, such as the length ranged from 800 bp in Sternula albifrons to 1,563 bp in V. cinereus. However, the CR duplication was not found in this study. In CR of G. stenura, the nucleotide composition was 31.97% A, 28.78% T, 11.80% G, and 27.45% C, and with an A + T bias of 60.76%. The AT skew of CR ranged from −0.07 in Ichthyaetus relictus to 0.12 in V. cinereus, and there were two species (−0.07 in I. relictus and −0.02 in Larus dominicanus) with a negative value. The mean value of GC skew was −0.31, ranging from −0.41 in S. rusticola to −0.16 in V. cinereus (KM873665).

We observed large tandem repeats with two or more copies in CR with the exception of I. relictus. In most species (12/20), tandem repeat sequences only had one type of repeat unit. Tandem repeat sequences contained two types of repeat units in 5 species (V. vanellus; C. brunnicephalus, C. ridibundus, C. saundersi, and L. dominicanus). The sequences contained three and four types in Jacana spinosa and J. jacana respectively (Fig 7). Total of 15 kinds of tandem repeats were found in CR. The motifs of 5’-AAACAAC-3’ (occurs in 7 species) and 5’-AAAC-3’ (in 5 species) are most common. However, 10/15 motifs were only found in one species, suggesting the fast rate evolution in the CR. Two long motifs of n (79 nucleotides with 2 tandem repeats) and o (82 nucleotides with 5 tandem repeats) only occurred in Jacava (Fig 7). Interestingly, the five motifs (a, c, f, l and m) had large copies (Fig 7), which had a strictly conserved common sequence (5’-AAAC-3’). The CR of Charadriiformes mitogenome shows distinct sequence and structural characteristics, such as varying sizes and diverse tandem repeats, which are taxon-specific and can potentially be used as genetic markers for evolution and population genetic studies.

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Fig 7. Organization of the control region in Charadriiformes mitochondrial genomes.

The location and copy number of tandem repeats are shown by oval with Arabic numerals inside. Non-repeat regions are indicated by square box. L: Length.

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Phylogenetic analysis

Phylogenetic analysis with two inference methods (ML and BI) of 13 mitochondrial protein-coding genes, 12S and 16S (Dataset 1) for 20 Charadriiformes species revealed the identical topologies, which were highly supported by bootstrap and posterior probabilities at most nodes (Fig 8). Relationships of the order Charadriiformes based on the nearly complete mtDNA strongly support monophyly of the order Charadriiformes.

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Fig 8. The phylogenetic trees of Charadriiformes based on the 13 mitochondrial protein-coding genes, 12S and 16S dataset using BI and ML methods.

Branch lengths and topologies were obtained using Bayesian inference analysis, and the numbers above each branches are the posterior probabilities and ML bootstrap support.

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The topologies recovered using ML and BI of the combined 12 protein-coding genes (Dataset 2, 10,773 bp after alignment) were consistent (Fig 9). These relationships were well supported, as the posterior probabilities in the Bayesian tree are 1 at all nodes in the tree excepting two nodes, and similarly of the maximum likelihood bootstrap values. The order was divided into three major clades including Lari (Laridae, Sternidae, Rynchopidae, Alcidae, Stercorariidae, Glareolidae, and Turnicidae) and its sister Scolopaci (Scolopacidae, Jacanidae, Rostratulidae, Pedionomidae, and Thinocoridae), which were in turn sister to the suborder Charadrii (Charadriidae, Recurvirostridae, Haematopodidae, Chionidae, Pluvianellidae, and Burhinidae) (Fig 9), as previous analysis based on both nuclear and mitochondrial gene have indicated [8, 11–13, 30]. Within Lari, our phylogenies indicated that Laridae was paraphyletic to Sternidae plus Rynchopidae. This result is in congruence with previous comprehensive phylogenetic analyses in Charadriiformes, using a combination of mitochondrial and nuclear genes [8, 11]. The Alcidae plus Stercorariidae comprise the sister group to Laridae, Sternidae and Rynchopidae. And Glareolidae and Turnicidae are successive sister groups to these clades instead as basal members of the major clade Lari. Within Scolopaci, Rostratulidae and Jacanidae as a sister group to the Pedionomidae plus Thinocoridae, and Scolopacidae is the basal to the other. This result is in congruence with more and more recent studies [11, 13, 54]. Within Charadrii, Burhinidae is sister to Chionidae plus Pluvianellidae, Charadriidae is paraphyletic to Haematopodidae plus Recurvirostridae (Fig 9). According to the phylogenetic results, the monophyly of Charadriiformes was strongly supported in ML and BI analyses.

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Fig 9. The phylogenetic trees of Charadriiformes based on the concatenated 12 mitochondrial protein-coding genes using BI and ML methods.

Branch lengths and topologies were obtained using Bayesian inference analysis. Asterisks indicate bootstrap values of 100% and posterior probability of 1.

https://doi.org/10.1371/journal.pone.0175244.g009

Conclusion

In this study, we sequenced and annotated the complete mitogenome of Gallinago stenura. We compared 20 Charadriiformes mitogenomes to illustrate mitogenomes structure and investigate their evolutionary relationship. The Charadriiformes mitogenomes display moderate size variation, the mean size was 16,807 bp (SD = 179.66, n = 20), and most of the size variation due to mutations in the CR. Nucleotide composition is consistently biased towards AT rich, and the A + T content also varies for each protein-coding genes, such as the variation in ATP8 is the highest, whereas the variation in COIII is the lowest. In the 16 gene regions, the GC skew is always negative, with the ATP8 containing significantly higher value than other regions. However, the marked negative AT skew is found in 5 gene regions (ND1, COI, ND3, ND4L and CR). The average uncorrected pairwise distances reveal heterogeneity of evolutionary rate for each gene, the COIII, COI and COII have slow evolutionary rate, while it is relative fast for the ND6, ND2 and ATP8. In 13 protein-coding genes, Charadriiformes mitogenomes contain 5 kinds of start codons (ATG, GTG, ATT, ATC, and ATA), four stop codons (TAA, TAG, AGG, and AGA), and an incomplete stop codon (T–). The codon usage analysis revealed that the ATG and TAA are the most common start and stop codon, respectively. The start codon ATG appears in 12 protein-coding genes (with the exception of ND3) and 8 genes only used ATG as start codon. The stop codon TAA appears in 8 genes, and 6 genes (COII, ATP8, ATP6, ND3, ND4L, and Cyt b) exclusively used TAA as stop codon. Putative secondary structures of tRNAs indicate that the sequences and structures of anticodon, amino acceptor, and TψC arms are highly conserved, and most of the nucleotide variation is restricted to DHU arms with obvious indel polymorphisms. In the CR, there are 15 kinds of tandem repeats, 4 bp (5’-AAAC-3’) and 7 bp (5’- AAACAAC -3’) repeat sequences occur frequently. Phylogenomic analysis based on the nearly complete mitochondrial genomes strongly supports the monophyly of the order Charadriiformes, with the suborder Lari sister to the Scolopaci, which is in turn a sister group to the suborder Charadrii. Moreover, our results well resolved the complexity family-level relationships and clearly depicted the evolutionary processes of Charadriiformes, based on 12 mitochondrial protein-coding genes from 18 families. In future, sequencing more mitogenomes from various taxonomic levels will significantly improve our understanding of mitogenomic evolution and phylogenetic relationships in Charadriiformes.

Supporting information

S1 Table. Taxon sampling and GenBank accession numbers.

https://doi.org/10.1371/journal.pone.0175244.s001

(XLSX)

S2 Table. Gene organization of Gallinago stenura mitochondrial genome.

https://doi.org/10.1371/journal.pone.0175244.s002

(DOCX)

S1 Fig. Predicted secondary structures of the 22 tRNA genes identified in the mitogenome of G. stenura.

All tRNA genes are shown in the order of occurrence in the mitochondrial genome starting from tRNA^Phe.

https://doi.org/10.1371/journal.pone.0175244.s003

(TIF)

S2 Fig. The alignment and predicted secondary structures of the 22 tRNA genes identified in the Charadriiformes mitogenomes.

https://doi.org/10.1371/journal.pone.0175244.s004

(TIF)

Acknowledgments

The authors alone are responsible for the content and writing of the article. We thank Wei Liu and Fangzheng Zhao for assistance in the data analysis, and three anonymous referees for valuable comments on the manuscript.

Author Contributions

Conceptualization: CH QC.
Data curation: CH YZ WX.
Formal analysis: CH CZ.
Funding acquisition: CH QC.
Investigation: CH QC.
Methodology: CH BZ.
Project administration: CH QC.
Resources: QC.
Software: CH CZ BZ.
Supervision: CH CZ QC.
Validation: CH QC.
Visualization: CH QC WX.
Writing – original draft: CH QC.
Writing – review & editing: CH LS QC.

References

1. Barbosa A, Moreno E (1999) Hindlimb morphology and locomotor performance in waders: an evolutionary approach. Biol J Linn Soc 67: 313–330.
- View Article
- Google Scholar
2. Friesen VL (2015) Speciation in seabirds: why are there so many species…and why aren’t there more? J Ornithol 156: 1–13.
- View Article
- Google Scholar
3. Reynolds JD, Székely T (1997) The evolution of parental care in shorebirds: Life histories, ecology, and sexual selection. Behav Ecol 8: 126–134.
- View Article
- Google Scholar
4. Smith NA, Clarke JA (2015) Systematics and evolution of the Pan-Alcidae (Aves, Charadriiformes). J Avian Biol 46: 125–140.
- View Article
- Google Scholar
5. Björklund M (1994) Phylogenetic relationships among Charadriiformes: reanalysis of previous Data. Auk 111: 825–832.
- View Article
- Google Scholar
6. Chu PC (1995) Phylogenetic reanalysis of strauch's osteological data set for the Charadriiformes. Condor 97: 174–196.
- View Article
- Google Scholar
7. Livezey BC (2010) Phylogenetics of modern shorebirds (Charadriiformes) based on phenotypic evidence: analysis and discussion. Zool J Linn Soc 160: 567–618.
- View Article
- Google Scholar
8. Paton TA, Baker AJ (2006) Sequences from 14 mitochondrial genes provide a well-supported phylogeny of the Charadriiform birds congruent with the nuclear RAG-1 tree. Mol Phylogenet Evol 39: 657–667. pmid:16531074
- View Article
- PubMed/NCBI
- Google Scholar
9. Tuinen MV, Waterhouse D, Dyke GJ (2004) Avian molecular systematics on the rebound: a fresh look at modern shorebird phylogenetic relationships. J Avian Biol 35: 191–194.
- View Article
- Google Scholar
10. Yoon KB, Cho CU, Park YC (2015) The mitochondrial genome of the Saunders's gull Chroicocephalus saundersi (Charadriiformes: Laridae) and a higher phylogeny of shorebirds (Charadriiformes). Gene 572: 227–236. pmid:26165451
- View Article
- PubMed/NCBI
- Google Scholar
11. Fain MG, Houde P (2007) Multilocus perspectives on the monophyly and phylogeny of the order Charadriiformes (Aves). BMC Evol Biol 7: 324.
- View Article
- Google Scholar
12. Paton TA, Baker AJ, Groth JG, Barrowclough GF (2003) RAG-1 sequences resolve phylogenetic relationships within Charadriiform birds. Mol Phylogenet Evol 29: 268–278. pmid:13678682
- View Article
- PubMed/NCBI
- Google Scholar
13. Baker AJ, Pereira SL, Paton TA (2007) Phylogenetic relationships and divergence times of Charadriiformes genera: multigene evidence for the Cretaceous origin of at least 14 clades of shorebirds. Biol Lett 3: 205–209. pmid:17284401
- View Article
- PubMed/NCBI
- Google Scholar
14. Boore JL (1999) Animal mitochondrial genomes. Nucleic Acids Res 27: 1767–1780. pmid:10101183
- View Article
- PubMed/NCBI
- Google Scholar
15. Ruokonen M, Kvist L (2002) Structure and evolution of the avian mitochondrial control region. Mol Phylogenet Evol 23: 422–432. pmid:12099796
- View Article
- PubMed/NCBI
- Google Scholar
16. Wolstenholme DR (1992) Animal mitochondrial DNA: structure and evolution. Int Rev Cytol 141: 173–216. pmid:1452431
- View Article
- PubMed/NCBI
- Google Scholar
17. San MD, Gower DJ, Zardoya R, Wilkinson M (2006) A hotspot of gene order rearrangement by tandem duplication and random loss in the vertebrate mitochondrial genome. Mol Biol Evol 23: 227–234. pmid:16177229
- View Article
- PubMed/NCBI
- Google Scholar
18. Morgan-Richards M, Trewick SA, Bartosch-Härlid A, Kardailsky O, Phillips MJ, Mclenachan PA et al. (2008) Bird evolution: testing the Metaves clade with six new mitochondrial genomes. BMC Evol Biol 8: 20. pmid:18215323
- View Article
- PubMed/NCBI
- Google Scholar
19. Slack KE, Delsuc F, Mclenachan PA, Arnason U, Penny D (2007) Resolving the root of the avian mitogenomic tree by breaking up long branches. Mol Phylogenet Evol 42: 1–13. pmid:16854605
- View Article
- PubMed/NCBI
- Google Scholar
20. Pacheco MA, Battistuzzi FU, Lentino M, Aguilar RF, Kumar S, Escalante AA(2011) Evolution of modern birds revealed by mitogenomics: timing the radiation and origin of major orders. Mol Biol Evol 28: 1927–1942. pmid:21242529
- View Article
- PubMed/NCBI
- Google Scholar
21. Jiang L, Chen J, Wang P, Ren Q, Yuan J, Qian C, et al. (2014)The Mitochondrial Genomes of Aquila fasciata and Buteo lagopus (Aves, Accipitriformes): Sequence, Structure and Phylogenetic Analyses. PLoS ONE 10.
- View Article
- Google Scholar
22. Li X, Huang Y, Lei F (2014) Comparative mitochondrial genomics and phylogenetic relationships of the Crossoptilon species (Phasianidae, Galliformes). BMC Genomics 16: 1–12.
- View Article
- Google Scholar
23. Pozzi L, Hodgson JA, Burrell AS, Sterner KN, Raaum RL, Disotell TR (2014) Primate phylogenetic relationships and divergence dates inferred from complete mitochondrial genomes. Mol Phylogenet Evol 75: 165–183. pmid:24583291
- View Article
- PubMed/NCBI
- Google Scholar
24. Timmermans MJTN, Lees DC, Simonsen TJ (2014) Towards a mitogenomic phylogeny of Lepidoptera. Mol Phylogenet Evol 79: 169–178. pmid:24910155
- View Article
- PubMed/NCBI
- Google Scholar
25. Yuan ML, Zhang QL, Guo ZL, Wang J, Shen YY (2015) Comparative mitogenomic analysis of the superfamily Pentatomoidea (Insecta: Hemiptera: Heteroptera) and phylogenetic implications. BMC Genomics 16: 1–16.
- View Article
- Google Scholar
26. Zhang L, Sechi P, Yuan M, Jiang J, Dong Y, Qiu J (2015) Fifteen new earthworm mitogenomes shed new light on phylogeny within the Pheretima complex. Sci Rep 6: 28–44.
- View Article
- Google Scholar
27. Ballard JWO, Pichaud N (2013) Mitochondrial DNA: more than an evolutionary bystander. Funct Ecol 28: 218–231.
- View Article
- Google Scholar
28. Zhou X, Lin Q, Fang W, Chen X (2014) The complete mitochondrial genomes of sixteen ardeid birds revealing the evolutionary process of the gene rearrangements. BMC Genomics 15: 1–9.
- View Article
- Google Scholar
29. Zhou X, Yao C, Lin Q, Fang W, Chen X (2016) Complete mitochondrial genomes render the Night Heron genus Gorsachius non-monophyletic. J Ornithol 157: 1–9.
- View Article
- Google Scholar
30. Ericson PG, Envall I, Irestedt M, Norman JA (2003) Inter-familial relationships of the shorebirds (Aves: Charadriiformes) based on nuclear DNA sequence data. BMC Evol Biol 3: 149–154.
- View Article
- Google Scholar
31. Sambrook J, Russell DW (1989) Molecular cloning: a laboratory manual. v.3: Cold Spring Harbor Laboratory Press.
32. Lowe TM, Eddy SR (1997) tRNAscan-SE: a program for improved detection of transfer RNA genes in genomic sequence. Nucleic Acids Res 25: 955–964. pmid:9023104
- View Article
- PubMed/NCBI
- Google Scholar
33. Bernt M, Donath A, Jühling F, Externbrink F, Florentz C, Fritzsch G, et al. (2013) MITOS: Improved de novo metazoan mitochondrial genome annotation. Mol Phylogenet Evol 69: 313–319. pmid:22982435
- View Article
- PubMed/NCBI
- Google Scholar
34. Laslett D, Canbäck B (2008) ARWEN: a program to detect tRNA genes in metazoan mitochondrial nucleotide sequences. Bioinformatics 24: 172–175. pmid:18033792
- View Article
- PubMed/NCBI
- Google Scholar
35. Lobry JR (1996) Asymmetric substitution patterns in the two DNA strands of bacteria. Mol Biol Evol 13: 660–665. pmid:8676740
- View Article
- PubMed/NCBI
- Google Scholar
36. Perna NT, Kocher TD (1995) Patterns of nucleotide composition at fourfold degenerate sites of animal mitochondrial genomes. J Mol Evol 41: 353–358. pmid:7563121
- View Article
- PubMed/NCBI
- Google Scholar
37. Benson G (1999) Tandem repeats finder: a program to analyze DNA sequences. Nucleic Acids Res 27: 573–580. pmid:9862982
- View Article
- PubMed/NCBI
- Google Scholar
38. Tamura K, Stecher G, Peterson D, Filipski A, Kumar S (2013) MEGA6: Molecular Evolutionary Genetics Analysis version 6.0. Mol Biol Evol 30: 2725–2729. pmid:24132122
- View Article
- PubMed/NCBI
- Google Scholar
39. Librado P, Rozas J (2009) DnaSP v5: a software for comprehensive analysis of DNA polymorphism data. Bioinformatics 25: 1451. pmid:19346325
- View Article
- PubMed/NCBI
- Google Scholar
40. Delport W, Poon AFY, Frost SDW, Pond SLK (2010) Datamonkey 2010: a suite of phylogenetic analysis tools for evolutionary biology. Bioinformatics 26: 2455–2457. pmid:20671151
- View Article
- PubMed/NCBI
- Google Scholar
41. Yang Z, Nielsen R, Goldman N, Pedersen AM (2000) Codon-substitution models for heterogeneous selection pressure at amino acid sites. Genetics 155: 431–449. pmid:10790415
- View Article
- PubMed/NCBI
- Google Scholar
42. Groth JG, Barrowclough GF (1999) Basal divergences in birds and the phylogenetic utility of the nuclear RAG-1 gene. Mol Phylogenet Evol 12: 115–123. pmid:10381315
- View Article
- PubMed/NCBI
- Google Scholar
43. Paton T, Haddrath O, Baker AJ (2002) Complete mitochondrial DNA genome sequences show that modern birds are not descended from transitional shorebirds. P Roy Soc B Biol Sci 269: 839–846.
- View Article
- Google Scholar
44. Stamatakis A (2014) RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30: 1312–1313. pmid:24451623
- View Article
- PubMed/NCBI
- Google Scholar
45. Ronquist F, Teslenko M, Mark PVD, Ayres DL, Darling A, Höhna S, et al. (2012) MrBayes 3.2: efficient Bayesian phylogenetic inference and model choice across a large model space. Systemat Biol 61: 539–542.
- View Article
- Google Scholar
46. Lanfear R, Calcott B, Ho SY, Guindon S (2012) Partitionfinder: combined selection of partitioning schemes and substitution models for phylogenetic analyses. Mol Biol Evol 29: 1695–1701. pmid:22319168
- View Article
- PubMed/NCBI
- Google Scholar
47. Hassanin A (2006) Phylogeny of Arthropoda inferred from mitochondrial sequences: Strategies for limiting the misleading effects of multiple changes in pattern and rates of substitution. Mol Phylogenet Evol 38: 100–116. pmid:16290034
- View Article
- PubMed/NCBI
- Google Scholar
48. Hassanin A, Léger N, Deutsch J (2005) Evidence for multiple reversals of asymmetric mutational constraints during the evolution of the mitochondrial genome of metazoa, and consequences for phylogenetic inferences. Systemat Biol 54: 277–298.
- View Article
- Google Scholar
49. Anderson S, Bankier AT, Barrell BG, De Bruijn MHL, Coulson AR, Drouin J, et al. (1981) Sequence and organization of the human mitochondrial genome. Nature 290: 457–465. pmid:7219534
- View Article
- PubMed/NCBI
- Google Scholar
50. Lavrov DV, Boore JL, Brown WM (2002) Complete mtDNA sequences of two millipedes suggest a new model for mitochondrial gene rearrangements: duplication and nonrandom loss. Mol Biol Evol 19: 163–169. pmid:11801744
- View Article
- PubMed/NCBI
- Google Scholar
51. Ojala D, Montoya J, Attardi G (1981) TRNA punctuation model of RNA processing in human mitochondrial. Nature 290: 470–474. pmid:7219536
- View Article
- PubMed/NCBI
- Google Scholar
52. Ryu SH, Hwang UW (2012) Complete mitochondrial genome of Saunders's gull Chroicocephalus saundersi (Charadriiformes, Laridae). Mitochondr DNA 23: 134–136.
- View Article
- Google Scholar
53. Mindell DP, Sorenson MD, Dimcheff DE (1998) An Extra Nucleotide is not Translated in Mitochondrial ND3 of Some Birds and Turtles. Mol Biol Evol 15: 1568–1571. pmid:12572620
- View Article
- PubMed/NCBI
- Google Scholar
54. Gibson R, Baker A (2012) Multiple gene sequences resolve phylogenetic relationships in the shorebird suborder Scolopaci (Aves: Charadriiformes). Mol Phylogenet Evol 64: 66–72. pmid:22491071
- View Article
- PubMed/NCBI
- Google Scholar

[ref1] 1. Barbosa A, Moreno E (1999) Hindlimb morphology and locomotor performance in waders: an evolutionary approach. Biol J Linn Soc 67: 313–330.
View Article
Google Scholar

[2] View Article

[3] Google Scholar

[ref2] 2. Friesen VL (2015) Speciation in seabirds: why are there so many species…and why aren’t there more? J Ornithol 156: 1–13.
View Article
Google Scholar

[5] View Article

[6] Google Scholar

[ref3] 3. Reynolds JD, Székely T (1997) The evolution of parental care in shorebirds: Life histories, ecology, and sexual selection. Behav Ecol 8: 126–134.
View Article
Google Scholar

[8] View Article

[9] Google Scholar

[ref4] 4. Smith NA, Clarke JA (2015) Systematics and evolution of the Pan-Alcidae (Aves, Charadriiformes). J Avian Biol 46: 125–140.
View Article
Google Scholar

[11] View Article

[12] Google Scholar

[ref5] 5. Björklund M (1994) Phylogenetic relationships among Charadriiformes: reanalysis of previous Data. Auk 111: 825–832.
View Article
Google Scholar

[14] View Article

[15] Google Scholar

[ref6] 6. Chu PC (1995) Phylogenetic reanalysis of strauch's osteological data set for the Charadriiformes. Condor 97: 174–196.
View Article
Google Scholar

[17] View Article

[18] Google Scholar

[ref7] 7. Livezey BC (2010) Phylogenetics of modern shorebirds (Charadriiformes) based on phenotypic evidence: analysis and discussion. Zool J Linn Soc 160: 567–618.
View Article
Google Scholar

[20] View Article

[21] Google Scholar

[ref8] 8. Paton TA, Baker AJ (2006) Sequences from 14 mitochondrial genes provide a well-supported phylogeny of the Charadriiform birds congruent with the nuclear RAG-1 tree. Mol Phylogenet Evol 39: 657–667. pmid:16531074
View Article
PubMed/NCBI
Google Scholar

[23] View Article

[24] PubMed/NCBI

[25] Google Scholar

[ref9] 9. Tuinen MV, Waterhouse D, Dyke GJ (2004) Avian molecular systematics on the rebound: a fresh look at modern shorebird phylogenetic relationships. J Avian Biol 35: 191–194.
View Article
Google Scholar

[27] View Article

[28] Google Scholar

[ref10] 10. Yoon KB, Cho CU, Park YC (2015) The mitochondrial genome of the Saunders's gull Chroicocephalus saundersi (Charadriiformes: Laridae) and a higher phylogeny of shorebirds (Charadriiformes). Gene 572: 227–236. pmid:26165451
View Article
PubMed/NCBI
Google Scholar

[30] View Article

[31] PubMed/NCBI

[32] Google Scholar

[ref11] 11. Fain MG, Houde P (2007) Multilocus perspectives on the monophyly and phylogeny of the order Charadriiformes (Aves). BMC Evol Biol 7: 324.
View Article
Google Scholar

[34] View Article

[35] Google Scholar

[ref12] 12. Paton TA, Baker AJ, Groth JG, Barrowclough GF (2003) RAG-1 sequences resolve phylogenetic relationships within Charadriiform birds. Mol Phylogenet Evol 29: 268–278. pmid:13678682
View Article
PubMed/NCBI
Google Scholar

[37] View Article

[38] PubMed/NCBI

[39] Google Scholar

[ref13] 13. Baker AJ, Pereira SL, Paton TA (2007) Phylogenetic relationships and divergence times of Charadriiformes genera: multigene evidence for the Cretaceous origin of at least 14 clades of shorebirds. Biol Lett 3: 205–209. pmid:17284401
View Article
PubMed/NCBI
Google Scholar

[41] View Article

[42] PubMed/NCBI

[43] Google Scholar

[ref14] 14. Boore JL (1999) Animal mitochondrial genomes. Nucleic Acids Res 27: 1767–1780. pmid:10101183
View Article
PubMed/NCBI
Google Scholar

[45] View Article

[46] PubMed/NCBI

[47] Google Scholar

[ref15] 15. Ruokonen M, Kvist L (2002) Structure and evolution of the avian mitochondrial control region. Mol Phylogenet Evol 23: 422–432. pmid:12099796
View Article
PubMed/NCBI
Google Scholar

[49] View Article

[50] PubMed/NCBI

[51] Google Scholar

[ref16] 16. Wolstenholme DR (1992) Animal mitochondrial DNA: structure and evolution. Int Rev Cytol 141: 173–216. pmid:1452431
View Article
PubMed/NCBI
Google Scholar

[53] View Article

[54] PubMed/NCBI

[55] Google Scholar

[ref17] 17. San MD, Gower DJ, Zardoya R, Wilkinson M (2006) A hotspot of gene order rearrangement by tandem duplication and random loss in the vertebrate mitochondrial genome. Mol Biol Evol 23: 227–234. pmid:16177229
View Article
PubMed/NCBI
Google Scholar

[57] View Article

[58] PubMed/NCBI

[59] Google Scholar

[ref18] 18. Morgan-Richards M, Trewick SA, Bartosch-Härlid A, Kardailsky O, Phillips MJ, Mclenachan PA et al. (2008) Bird evolution: testing the Metaves clade with six new mitochondrial genomes. BMC Evol Biol 8: 20. pmid:18215323
View Article
PubMed/NCBI
Google Scholar

[61] View Article

[62] PubMed/NCBI

[63] Google Scholar

[ref19] 19. Slack KE, Delsuc F, Mclenachan PA, Arnason U, Penny D (2007) Resolving the root of the avian mitogenomic tree by breaking up long branches. Mol Phylogenet Evol 42: 1–13. pmid:16854605
View Article
PubMed/NCBI
Google Scholar

[65] View Article

[66] PubMed/NCBI

[67] Google Scholar

[ref20] 20. Pacheco MA, Battistuzzi FU, Lentino M, Aguilar RF, Kumar S, Escalante AA(2011) Evolution of modern birds revealed by mitogenomics: timing the radiation and origin of major orders. Mol Biol Evol 28: 1927–1942. pmid:21242529
View Article
PubMed/NCBI
Google Scholar

[69] View Article

[70] PubMed/NCBI

[71] Google Scholar

[ref21] 21. Jiang L, Chen J, Wang P, Ren Q, Yuan J, Qian C, et al. (2014)The Mitochondrial Genomes of Aquila fasciata and Buteo lagopus (Aves, Accipitriformes): Sequence, Structure and Phylogenetic Analyses. PLoS ONE 10.
View Article
Google Scholar

[73] View Article

[74] Google Scholar

[ref22] 22. Li X, Huang Y, Lei F (2014) Comparative mitochondrial genomics and phylogenetic relationships of the Crossoptilon species (Phasianidae, Galliformes). BMC Genomics 16: 1–12.
View Article
Google Scholar

[76] View Article

[77] Google Scholar

[ref23] 23. Pozzi L, Hodgson JA, Burrell AS, Sterner KN, Raaum RL, Disotell TR (2014) Primate phylogenetic relationships and divergence dates inferred from complete mitochondrial genomes. Mol Phylogenet Evol 75: 165–183. pmid:24583291
View Article
PubMed/NCBI
Google Scholar

[79] View Article

[80] PubMed/NCBI

[81] Google Scholar

[ref24] 24. Timmermans MJTN, Lees DC, Simonsen TJ (2014) Towards a mitogenomic phylogeny of Lepidoptera. Mol Phylogenet Evol 79: 169–178. pmid:24910155
View Article
PubMed/NCBI
Google Scholar

[83] View Article

[84] PubMed/NCBI

[85] Google Scholar

[ref25] 25. Yuan ML, Zhang QL, Guo ZL, Wang J, Shen YY (2015) Comparative mitogenomic analysis of the superfamily Pentatomoidea (Insecta: Hemiptera: Heteroptera) and phylogenetic implications. BMC Genomics 16: 1–16.
View Article
Google Scholar

[87] View Article

[88] Google Scholar

[ref26] 26. Zhang L, Sechi P, Yuan M, Jiang J, Dong Y, Qiu J (2015) Fifteen new earthworm mitogenomes shed new light on phylogeny within the Pheretima complex. Sci Rep 6: 28–44.
View Article
Google Scholar

[90] View Article

[91] Google Scholar

[ref27] 27. Ballard JWO, Pichaud N (2013) Mitochondrial DNA: more than an evolutionary bystander. Funct Ecol 28: 218–231.
View Article
Google Scholar

[93] View Article

[94] Google Scholar

[ref28] 28. Zhou X, Lin Q, Fang W, Chen X (2014) The complete mitochondrial genomes of sixteen ardeid birds revealing the evolutionary process of the gene rearrangements. BMC Genomics 15: 1–9.
View Article
Google Scholar

[96] View Article

[97] Google Scholar

[ref29] 29. Zhou X, Yao C, Lin Q, Fang W, Chen X (2016) Complete mitochondrial genomes render the Night Heron genus Gorsachius non-monophyletic. J Ornithol 157: 1–9.
View Article
Google Scholar

[99] View Article

[100] Google Scholar

[ref30] 30. Ericson PG, Envall I, Irestedt M, Norman JA (2003) Inter-familial relationships of the shorebirds (Aves: Charadriiformes) based on nuclear DNA sequence data. BMC Evol Biol 3: 149–154.
View Article
Google Scholar

[102] View Article

[103] Google Scholar

[ref31] 31. Sambrook J, Russell DW (1989) Molecular cloning: a laboratory manual. v.3: Cold Spring Harbor Laboratory Press.

[ref32] 32. Lowe TM, Eddy SR (1997) tRNAscan-SE: a program for improved detection of transfer RNA genes in genomic sequence. Nucleic Acids Res 25: 955–964. pmid:9023104
View Article
PubMed/NCBI
Google Scholar

[106] View Article

[107] PubMed/NCBI

[108] Google Scholar

[ref33] 33. Bernt M, Donath A, Jühling F, Externbrink F, Florentz C, Fritzsch G, et al. (2013) MITOS: Improved de novo metazoan mitochondrial genome annotation. Mol Phylogenet Evol 69: 313–319. pmid:22982435
View Article
PubMed/NCBI
Google Scholar

[110] View Article

[111] PubMed/NCBI

[112] Google Scholar

[ref34] 34. Laslett D, Canbäck B (2008) ARWEN: a program to detect tRNA genes in metazoan mitochondrial nucleotide sequences. Bioinformatics 24: 172–175. pmid:18033792
View Article
PubMed/NCBI
Google Scholar

[114] View Article

[115] PubMed/NCBI

[116] Google Scholar

[ref35] 35. Lobry JR (1996) Asymmetric substitution patterns in the two DNA strands of bacteria. Mol Biol Evol 13: 660–665. pmid:8676740
View Article
PubMed/NCBI
Google Scholar

[118] View Article

[119] PubMed/NCBI

[120] Google Scholar

[ref36] 36. Perna NT, Kocher TD (1995) Patterns of nucleotide composition at fourfold degenerate sites of animal mitochondrial genomes. J Mol Evol 41: 353–358. pmid:7563121
View Article
PubMed/NCBI
Google Scholar

[122] View Article

[123] PubMed/NCBI

[124] Google Scholar

[ref37] 37. Benson G (1999) Tandem repeats finder: a program to analyze DNA sequences. Nucleic Acids Res 27: 573–580. pmid:9862982
View Article
PubMed/NCBI
Google Scholar

[126] View Article

[127] PubMed/NCBI

[128] Google Scholar

[ref38] 38. Tamura K, Stecher G, Peterson D, Filipski A, Kumar S (2013) MEGA6: Molecular Evolutionary Genetics Analysis version 6.0. Mol Biol Evol 30: 2725–2729. pmid:24132122
View Article
PubMed/NCBI
Google Scholar

[130] View Article

[131] PubMed/NCBI

[132] Google Scholar

[ref39] 39. Librado P, Rozas J (2009) DnaSP v5: a software for comprehensive analysis of DNA polymorphism data. Bioinformatics 25: 1451. pmid:19346325
View Article
PubMed/NCBI
Google Scholar

[134] View Article

[135] PubMed/NCBI

[136] Google Scholar

[ref40] 40. Delport W, Poon AFY, Frost SDW, Pond SLK (2010) Datamonkey 2010: a suite of phylogenetic analysis tools for evolutionary biology. Bioinformatics 26: 2455–2457. pmid:20671151
View Article
PubMed/NCBI
Google Scholar

[138] View Article

[139] PubMed/NCBI

[140] Google Scholar

[ref41] 41. Yang Z, Nielsen R, Goldman N, Pedersen AM (2000) Codon-substitution models for heterogeneous selection pressure at amino acid sites. Genetics 155: 431–449. pmid:10790415
View Article
PubMed/NCBI
Google Scholar

[142] View Article

[143] PubMed/NCBI

[144] Google Scholar

[ref42] 42. Groth JG, Barrowclough GF (1999) Basal divergences in birds and the phylogenetic utility of the nuclear RAG-1 gene. Mol Phylogenet Evol 12: 115–123. pmid:10381315
View Article
PubMed/NCBI
Google Scholar

[146] View Article

[147] PubMed/NCBI

[148] Google Scholar

[ref43] 43. Paton T, Haddrath O, Baker AJ (2002) Complete mitochondrial DNA genome sequences show that modern birds are not descended from transitional shorebirds. P Roy Soc B Biol Sci 269: 839–846.
View Article
Google Scholar

[150] View Article

[151] Google Scholar

[ref44] 44. Stamatakis A (2014) RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30: 1312–1313. pmid:24451623
View Article
PubMed/NCBI
Google Scholar

[153] View Article

[154] PubMed/NCBI

[155] Google Scholar

[ref45] 45. Ronquist F, Teslenko M, Mark PVD, Ayres DL, Darling A, Höhna S, et al. (2012) MrBayes 3.2: efficient Bayesian phylogenetic inference and model choice across a large model space. Systemat Biol 61: 539–542.
View Article
Google Scholar

[157] View Article

[158] Google Scholar

[ref46] 46. Lanfear R, Calcott B, Ho SY, Guindon S (2012) Partitionfinder: combined selection of partitioning schemes and substitution models for phylogenetic analyses. Mol Biol Evol 29: 1695–1701. pmid:22319168
View Article
PubMed/NCBI
Google Scholar

[160] View Article

[161] PubMed/NCBI

[162] Google Scholar

[ref47] 47. Hassanin A (2006) Phylogeny of Arthropoda inferred from mitochondrial sequences: Strategies for limiting the misleading effects of multiple changes in pattern and rates of substitution. Mol Phylogenet Evol 38: 100–116. pmid:16290034
View Article
PubMed/NCBI
Google Scholar

[164] View Article

[165] PubMed/NCBI

[166] Google Scholar

[ref48] 48. Hassanin A, Léger N, Deutsch J (2005) Evidence for multiple reversals of asymmetric mutational constraints during the evolution of the mitochondrial genome of metazoa, and consequences for phylogenetic inferences. Systemat Biol 54: 277–298.
View Article
Google Scholar

[168] View Article

[169] Google Scholar

[ref49] 49. Anderson S, Bankier AT, Barrell BG, De Bruijn MHL, Coulson AR, Drouin J, et al. (1981) Sequence and organization of the human mitochondrial genome. Nature 290: 457–465. pmid:7219534
View Article
PubMed/NCBI
Google Scholar

[171] View Article

[172] PubMed/NCBI

[173] Google Scholar

[ref50] 50. Lavrov DV, Boore JL, Brown WM (2002) Complete mtDNA sequences of two millipedes suggest a new model for mitochondrial gene rearrangements: duplication and nonrandom loss. Mol Biol Evol 19: 163–169. pmid:11801744
View Article
PubMed/NCBI
Google Scholar

[175] View Article

[176] PubMed/NCBI

[177] Google Scholar

[ref51] 51. Ojala D, Montoya J, Attardi G (1981) TRNA punctuation model of RNA processing in human mitochondrial. Nature 290: 470–474. pmid:7219536
View Article
PubMed/NCBI
Google Scholar

[179] View Article

[180] PubMed/NCBI

[181] Google Scholar

[ref52] 52. Ryu SH, Hwang UW (2012) Complete mitochondrial genome of Saunders's gull Chroicocephalus saundersi (Charadriiformes, Laridae). Mitochondr DNA 23: 134–136.
View Article
Google Scholar

[183] View Article

[184] Google Scholar

[ref53] 53. Mindell DP, Sorenson MD, Dimcheff DE (1998) An Extra Nucleotide is not Translated in Mitochondrial ND3 of Some Birds and Turtles. Mol Biol Evol 15: 1568–1571. pmid:12572620
View Article
PubMed/NCBI
Google Scholar

[186] View Article

[187] PubMed/NCBI

[188] Google Scholar

[ref54] 54. Gibson R, Baker A (2012) Multiple gene sequences resolve phylogenetic relationships in the shorebird suborder Scolopaci (Aves: Charadriiformes). Mol Phylogenet Evol 64: 66–72. pmid:22491071
View Article
PubMed/NCBI
Google Scholar

[190] View Article

[191] PubMed/NCBI

[192] Google Scholar

Figures

Abstract

Introduction

Materials and methods

Ethical statement

Sample collection and DNA extraction

Primer design, PCR amplification and sequencing

Genome annotation and sequence analysis

Phylogenetic analysis

Results and discussion

Genome organization

Nucleotide composition

Protein-coding genes evolutionary rate

The usage of start and stop codon

Sequence features of rRNA and tRNA genes

Control region

Phylogenetic analysis

Conclusion

Supporting information

S1 Table. Taxon sampling and GenBank accession numbers.

S2 Table. Gene organization of Gallinago stenura mitochondrial genome.

S1 Fig. Predicted secondary structures of the 22 tRNA genes identified in the mitogenome of G. stenura.

S2 Fig. The alignment and predicted secondary structures of the 22 tRNA genes identified in the Charadriiformes mitogenomes.

Acknowledgments

Author Contributions

References