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Complete Mitochondrial Genome of the Eurasian Oystercatcher Haematopus ostralegus and Comparative Genomic Analyses in Charadriiformes

PJZ_53_6_2407-2415

Complete Mitochondrial Genome of the Eurasian Oystercatcher Haematopus ostralegus and Comparative Genomic Analyses in Charadriiformes

Chaochao Hu1,2, Xue Xu1, Wenjia Yao1, Wei Liu3, Deyun Tai1, Wan Chen4 and Qing Chang1*

1Jiangsu Key Laboratory for Biodiversity and Biotechnology, College of Life Sciences, Nanjing Normal University, Nanjing 210023, Jiangsu, China.

2Analytical and Testing Center, Nanjing Normal University, Nanjing 210023, Jiangsu, China.

3Nanjing Institute of Environmental Sciences, Ministry of Environmental Protection, Nanjing 210042, Jiangsu, China.

4College of Environment and Ecology, Jiangsu Open University (The City Vocational College of Jiangsu), Nanjing 210036, Jiangsu, China.

ABSTRACT

The Eurasian oystercatcher Haematopus ostralegus is a relatively large migratory wader with an extremely large distribution range. The objective of this study was to determine the complete mitogenome sequence of H. ostralegus, and illustrating mitogenomes structure and investigating their evolutionary relationship by comparing 31 species in Charadriiformes. The complete mitochondrial genome of H. ostralegus is a circular molecule of 16,798 bp in length and the overall nucleotide composition of H-strand was A: 31.45%, T: 23.46%, C: 31.30%, G: 13.79%. In Charadriiformes mitogenomes, AT skews values were positive, while the values of GC skew were negative. We found a significant negative correlation between CBI and ENC, and a significant positive correlation was found between CBI and G + Cc and G + C3s. The ratio of Ka/Ks of all PCGs indicated that all these genes evolved under purifying selection. Evidence of positive selection was obtained for three (ND2, ND4 and ND6) genes by at least one of the methods. This study provides a valuable resource facilitating further study of phylogenetic and evolutionary analysis for wader, and improves our understanding of the evolutionary and taxonomic research within Charadriiformes.


Article Information

Received 21 Februaary 2020

Revised 02 May 2020

Accepted 20 May 2020

Available online 14 October 2021

Authors’ Contribution

CCH and QC conceived and designed the study. XX, WJY, DYT, WL and WC collected the data. CCH, XX and WL analyzed the data and wrote the first draft of the manuscript. QC commented on the manuscript.

Key words

Mitogenome, Charadriiformes, Haematopus ostralegus

DOI: https://dx.doi.org/10.17582/journal.pjz/20200221020245

* Corresponding author: Qingchangnj@163.com

0030-9923/2021/0006-2407 $ 9.00/0

Copyright 2021 Zoological Society of Pakistan



Introduction

The resources of mitogenome have rapidly accumulated in recent years thanks to the advanced genomic sequencing, while the mitogenome has not been well studied in Charadriiformes (Baker et al., 2007; Gibson and Baker, 2012; Friesen, 2015; Hu et al., 2017). The Eurasian oystercatcher, Haematopus ostralegus (Charadriiformes, Haematopodidae), is a relatively large wader with an extremely large distribution ranging from Europe to Siberia. This species has been categorized as near threatened in red list of threatened species. Eurasian oystercatcher feeds on mussels, crabs, earth worms and all kinds of invertebrates, rarely on fish. Recent studies of H. ostralegus pay more attention on habitat ecology, foraging behavior (Schwemmer et al., 2016, 2017; Bailey et al., 2019). Based on eight polymorphic microsatellite loci, no significant genetic differentiation was observed between two groups of ‘residents’ and ‘leapfrogs’, which were divided by the observational data on the dispersal behaviour of breeding individuals in the island of Schiermonnikoog, Netherlands (Van et al., 2010). Few population genetics have been carried out, which might be the lack of basic genetics data (or molecular markers) of H. ostralegus.

Animal mitogenome typically contains 13 protein-coding genes, 2 ribosomal RNAs (12S rRNA and 16S rRNA), 22 transfer RNAs (tRNAs), and a non-coding control region (Ruokonen and Kvist, 2002). During the past decade, mitogenome provides a valuable resource for further study of molecular systematics, species identification, population genetics, phylogeny, taxonomy and so on (Li et al., 2016; Du et al., 2019; Hu et al., 2020). In this study, we sequenced the complete mitogenome of H. ostralegus, which may provide a valuable resource facilitating further study of population genetics and biogeography of H. ostralegus, and provide useful information for understanding the evolutionary and taxonomic research within Charadriiformes.

Materials and methods

Ethics statement

Our experimental procedures complied with the current laws on animal welfare and research in China, and were specifically approved by Nanjing Normal University’s Animal Care and Use Committee (Permit #2011-04-008).

Sample and DNA extraction

The specimen of H. ostralegus was collected from a derelict and abandoned mist net in Dongtai, Yancheng City, Jiangsu Province, China (32°46’23.12” N, 120°57’41.68” E), in July 2017. After collection, the tissue was initially preserved in 95% ethanol in the field, and transferred to −80°C in our laboratory for long-term storage at Nanjing Normal University (specimen voucher number: NJNU-Host07). Total genomic DNA was extracted using standard phenol-chloroform methods (Sambrook and Russell, 1989). The quality of DNA was assessed through electrophoresis in a 1% agarose gel and staining with Gold View. The complete mitogenome of H. ostralegus was generated by amplification of overlapping polymerase chain reaction (PCR) fragments (Hu et al., 2017). 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. Sequences obtained were aligned and edited using the software SeqMan (DNAStar, Inc.) to generate complete mitochondrial DNA sequences.

PCR amplification and sequencing

All primers used in this study were taken from Hu et al. (2017). PCR was performed in a 30 μL system, which contained 2 × Taq PCR SuperMix (Tiangen, China) 15 μL, 10 μM of each primer (forward and reverse) 1 μL, 1 μL template DNA and 12 μL sterile double-distilled water (ddH2O). PCR profile was 5 min initial denaturation at 95 °C; 35 cycles of 30 s denaturation at 95 °C, 30 s annealing at 50–55 °C, and 1–2 min extension at 72 °C; and a final extension at 72 °C for 8 min and 4 °C hold. The PCR products were electrophoresed by 1% agarose gel and then purified and sequenced with each of the PCR primers on a DNA sequencer (ABI 3731XL).

Genome annotation and bioinformatics analysis

Sequences obtained were assembled and edited manually using the software SeqMan (DNAStar, Inc.) to generate complete mitochondrial DNA sequences. The protein-coding genes (PCGs) 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 (Lowe and Eddy, 1997), MITOS (Bernt et al., 2013), and ARWEN (Laslett and Canbäck, 2008) 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) (Perna and Kocher, 1995; Lobry, 1996). The tandem repeats were searched in the CR using the Tandem Repeats Finder program (Benson, 1999).

Codon usage and nucleotide composition statistics were estimated using DnaSP 5.1 (Librado and Rozas, 2009) and Microsoft Excel 2016. The number of variable sites, the parsimony informative sites, the singleton, and the average uncorrected pairwise distances for each PCG were calculated by MEGA 6.0 (Tamura et al., 2013). The rates of non-synonymous substitutions (Ka, π modified), synonymous substitutions (Ks, π modified), the effective number of codons (ENC) and the codon bias index (CBI) for each PCG was determined with DnaSP 5.0 (Librado and Rozas, 2009).

Phylogenetic analysis

For phylogenetic analysis, PCGs of mitochondrial genomes of 32 Charadriiformes species were used (Table II). Each mitochondrial gene was aligned individually using Muscle in MEGA X (Kumar et al., 2018), and subsequently edited and trimmed. Based on 15 mitochondrial genes (13 PCGs, 12S and 16S) of Charadriiformes species, phylogenetic analysis was performed using Bayesian Inference (BI) and Maximum likelihood analysis (ML), with Columba livia (KP168712) and Gallus gallusa (KM096864) as outgroups. To determine the optimal partitioning of the data, the best-fit partitioning scheme and the most appropriate nucleotide evolution model for each partition were implemented in Partition Finder 1.1.1 (Lanfear et al., 2012). BI method was performed using MrBayes 3.1.2 (Ronquist and Huelsenbeck, 2003). Four Markov Chains Monte Carlo (MCMC) chains were run for 1.0 × 106 generations. Two independent runs were performed to allow additional confirmation of the convergence of MCMC runs. ML analysis was performed with RAxML 8.0.0 (Stamatakis, 2014). The 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.

 

Table I. Characteristics of the mitochondrial genome of Haematopus ostralegus.

Gene

Strand

Position

Intergenic

nucleotides

Overlapping

nucleotides

Size (bp)

No. of codons

Anti-

Codon

Start codon

Stop codon

tRNAPhe

H

1–71

71

GAA

12S rRNA

H

73–1040

1

968

tRNAVal

H

1041–1112

72

TAC

16S rRNA

H

1113–2699

1587

tRNALeu (UUR)

H

2700–2773

74

TAA

ND1

H

2776–3753

2

978

325

ATG

AGG

tRNAIle

H

3752–3823

2

72

GAT

tRNAGln

L

3833–3903

9

71

TTG

tRNAMet

H

3903–3971

1

69

CAT

ND2

H

3972–5010

1039

345

ATG 

T–

tRNATrp

H

5011–5080

70

TCA

tRNAAla

L

5082–5150

1

69

TGC

tRNAAsn

L

5153–5225

2

73

GTT

tRNACys

L

5230–5296

4

67

GCA

tRNATyr

L

5296–5366

1

71

GTA

COI

H

5368–6918

1

1551

516

GTG

AGG

tRNASer (UCN)

L

6910–6983

9

74

TGA

tRNAAsp

H

6986–7054

2

69

GTC

COII

H

7056–7739

1

684

227

ATG

TAA

tRNALys

H

7741–7811

1

71

TTT

ATP8

H

7813–7977

1

165

54

ATG

TAA

ATP6

H

7968–8651

10

684

227

ATG

TAA

COIII

H

8651–9434

1

784

261

ATG

T–

tRNAGly

H

9435–9503

69

TCC

ND3

H

9504–9855

352

117

ATT 

TAA

tRNAArg

H

9858–9926

2

69

TCG

ND4L

H

9928–10224

1

297

98

ATG

TAA

ND4

H

10218–11595

7

1378

459

ATG

T–

tRNAHis

H

11596–11665

70

GTG

tRNASer (AGY)

H

11666–11731

66

GCT

tRNALeu (CUN)

H

11731–11801

1

71

TAG

ND5

H

11802–13616

1815

604

GTG

TAA 

Cyt b

H

13630–14772

13

1143

380

ATG

TAA

tRNAThr

H

14778–14847

5

70

TGT

tRNAPro

L

14862–14931

14

70

TGG

ND6

L

14953–15474

11

522

173

ATG

T–

tRNAGlu

L

15478–15549

3

72

TTC

CR

H

15550–16798

1249

 

Tests of selection

The effect of natural selection on the evolution of the mtDNA PCGs was assessed by comparing the number of nonsynonymous changes per nonsynonymous sites (dN) with that of synonymous changes per synonymous site (dS) (Yang et al., 2000). We further estimated the impact of selection along the mtDNA phylogeny of seabirds using codon models to assess the rates of synonymous and nonsynonymous substitutions. Four methods [Single Likelihood Ancestral Counting (SLAC), Fixed Effects Likelihood (FEL), Random Effects Likelihood (REL), and the mixed effects model of evolution (MEME)] implemented on the DATAMONKEY web server (http://www.datamonkey.org/; last accessed March 12, 2019) were used (Delport et al., 2010), by choosing the vertebrate mitochondrial DNA genetic code. The phylogeny estimated in this work, unrooted and excluding the outgroup, was used in all analyses of selection.

Results and discussion

Genome organization

The circular mitogenome of H. ostralegus is 16,798 bp in length with 13 PCGs, 2 ribosomal RNAs (12S rRNA and 16S rRNA), 22 transfer RNA genes, and a non-coding region (Table I). The annotated mitogenome of H. ostralegus has been deposited in GenBank (accession number: MH727533). The graphical mitogenome map was visualized using the software OGDRAW (Lohse et al., 2013), and the map was shown in Figure 1. The overall nucleotide composition was A: 31.45%, T: 23.46%, C: 31.30%, G: 13.79%. Among the 37 genes, nine genes (tRNAGln, tRNAAla, tRNAAsn, tRNACys, tRNATyr, tRNASer, ND6, tRNAPro and tRNAGlu) were encoded on the light strand, and the remaining 28 genes were encoded on the heavy strand. Gene overlaps have been found at 7 gene junctions, spanning 1–10 nucleotides, for a total of 25 nucleotides. The longest overlap (10 bp) exists between ATP8 and ATP6. The intergenic spacer regions occurred 19 times, spanning 1–14 bp, for a total of 81 bp. The gene order of the H. ostralegus mitogenome is identical to that of Charadriiformes mitogenomes, without showing any structural rearrangement.

Nucleotide composition

The 32 mitogenomes within Charadriiformes were summarized and compared (Table II). The differences in length are almost due to the size variation of the non-coding region. The nucleotide composition showed highly similar nucleotide composition biases towards AT rich (mean= 55.74, SD= 0.86) (Table II), which is consistent with previous avian mitogenomes (Hu et al., 2017). AT and GC skews are a measure of compositional asymmetry. In Charadriiformes mitogenomes, AT skew values were positive, while the values of GC skew were negative. The AT and GC skew values observed were 0.12 ± 0.02 (mean ± SD) and −0.38 ± 0.01, respectively. 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 (Hassanin et al., 2005; Hassanin, 2006).


 

Protein-coding genes (PCGs)

The total length of 13 PCGs is 11,392bp accounting for 67.82% of the complete genome. The nucleotide composition of PCGs can be found (Table II). All PCGs began with the ATG start codon, except ND3 (started with ATT) and ND5 (started with GTG). Three types of termination codons are used in this mitogenome, two PCGs (ND1and COI) use the complete stop codon AGG, seven PCGs (COII, ATP8, ATP6, ND3, ND4L, ND5 and Cyt b) stop with TAA, but the remaining four PCGs (ND2, COIII, ND4, and ND6) terminate in the incomplete stop codons T–.

Nucleotide composition bias is also reflected in the codon usage pattern. Among 62 amino acid encoding codons, CUA-Leu (L), AUC- Ile (I), and UUC-Phe (F) were the most frequently used codons. The least frequent codons were CCG-Pro (P), ACG- Thr (T) and CGG- Arg (R). Relative synonymous codon frequencies (RSCU) values were summarized in Table III, revealed that the degenerate codon usage at the third codon positions is generally biased to use more As and Ts than Gs and Cs. The most common amino acids were Leu, Ile and Phe, which are invariably rich in mitochondrial proteins of other birds (Li et al., 2014). However, Charadriiformes species used more codon GGA for amino acid Gly than other three degenerate codons (GGG, GGC, and GGU) (Hu et al., 2017).

 

Table II. Comparative nucleotide compositions of 32 species used in Charadriiformes.

Family/Species

Accession no.

Size (bp)

Protein coding genes

A (%)

T (%)

G (%)

C (%)

AT skew

GC skew

Family: Haematopodidae

Haematopus ater

AY074886

16791

31.06

23.57

13.31

32.06

0.14

-0.41

Haematopus ostralegus

MH727533

16789

30.79

23.37

13.57

32.27

0.14

-0.41

Family: Recurvirostridae

Recurvirostra avosetta

KP757766

16897

31.24

23.40

13.27

32.09

0.14

-0.41

Himantopus himantopus

KY623656

17378

31.38

23.07

13.18

32.37

0.15

-0.42

Family: Charadriidae

Vanellus cinereus

KM404175

17074

31.06

23.69

13.15

32.09

0.13

-0.42

Vanellus vanellus

KM577158

16795

31.09

23.86

13.33

31.71

0.13

-0.41

Pluvialis fulva

KX639757

16854

30.60

23.48

13.39

32.53

0.13

-0.42

Charadrius placidus

KY419888

16895

30.70

23.79

13.56

31.94

0.13

-0.40

Charadrius alexandrinus

MF565382

16903

30.98

24.41

13.02

31.58

0.12

-0.42

Family: Jacanidae

Jacana jacana

KJ631049

16975

31.21

24.99

13.30

30.50

0.11

-0.39

Family: Scolopacidae

Scolopax rusticola

KM434134

16984

30.10

24.84

13.75

31.31

0.10

-0.39

Arenaria interpres

AY074885

16725

30.70

25.04

13.64

30.63

0.10

-0.38

Eurynorhynchus pygmeus

KP742478

16707

31.65

26.43

12.72

29.19

0.09

-0.39

Gallinago stenura

KY056596

16899

30.20

25.55

13.80

30.45

0.08

-0.38

Numenius phaeopus

KP308149

17091

29.73

25.31

14.18

30.78

0.08

-0.37

Xenus cinereus

KX644890

16817

31.13

25.14

13.02

30.71

0.11

-0.40

Tringa erythropus

KX230491

16683

30.85

25.41

13.19

30.56

0.10

-0.40

Tringa semipalmata inornata

MF036175

16603

30.08

25.83

13.77

30.32

0.08

-0.38

Limosa lapponica

KX371106

16732

30.43

24.40

13.54

31.62

0.11

-0.40

Family: Stercorariidae

Stercorarius maccormicki

KM401546

16669

30.66

24.97

13.21

31.16

0.10

-0.40

Family: Alcidae

Synthliboramphus antiquus

AP009042

16730

30.47

24.85

13.36

31.32

0.10

-0.40

Synthliboramphus wumizusume

KT592378

16714

30.61

25.12

13.44

30.84

0.10

-0.39

Family: Laridae

Chroicocephalus brunnicephalus

JX155863

16769

30.07

24.07

13.93

31.92

0.11

-0.39

Chroicocephalus ridibundus

KM577662

16807

30.04

24.10

13.96

31.90

0.11

-0.39

Chroicocephalus saundersi

JQ071443

16725

29.68

24.07

14.26

32.00

0.10

-0.38

Larus crassirostris

KM507782

16746

29.97

24.44

13.90

31.69

0.10

-0.39

Larus dominicanus

AY293619

16701

30.03

24.43

13.84

31.70

0.10

-0.39

Larus vegae

KT943749

16379

30.81

25.90

13.22

30.07

0.09

-0.39

Ichthyaetus relictus

KC760146

16586

30.19

24.31

13.70

31.81

0.11

-0.40

Gelochelidon nilotica

MF582631

16748

30.29

25.54

13.73

30.44

0.08

-0.38

Sterna hirundo

MF582632

16707

29.96

25.69

13.99

30.36

0.08

-0.37

Sternula albifrons

KT350612

16357

30.60

26.42

13.41

29.57

0.07

-0.38

 

Table III. Codon usage in charadriiformes mitochondrial protein-coding genes. A total of 3,763 codons for analyzed, excluding the start and stop codons. AA, amino acid; RSCU, relative synonymous codon usage; n= frequency of each codon; %= n/3737.

AA

Codon

Count

Percentage (%)

RSCU

Phe (F)

UUU

62.5

1.66

0.56

Phe (F)

UUC

161.1

4.28

1.44

Leu2 (L2)

UUA

93.2

2.48

0.85

Leu2 (L2)

UUG

20.8

0.55

0.19

Leu1 (L1)

CUU

69.4

1.84

0.63

Leu1 (L1)

CUC

141.5

3.76

1.29

Leu1 (L1)

CUA

301.9

8.02

2.75

Leu1 (L1)

CUG

31.1

0.83

0.28

Ile (I)

AUU

89.5

2.38

0.62

Ile (I)

AUC

200.5

5.33

1.38

Met (M)

AUA

143.8

3.82

1.75

Met (M)

AUG

21

0.56

0.25

Val (V)

GUU

34.6

0.92

0.83

Val (V)

GUC

45.5

1.21

1.09

Val (V)

GUA

69.6

1.85

1.66

Val (V)

GUG

17.7

0.47

0.42

Ser2 (S2)

UCU

39.1

1.04

0.83

Ser2 (S2)

UCC

83

2.21

1.76

Ser2 (S2)

UCA

99.2

2.64

2.10

Ser2 (S2)

UCG

7.6

0.20

0.16

Pro (P)

CCU

34.3

0.91

0.63

Pro (P)

CCC

71.7

1.91

1.31

Pro (P)

CCA

106.3

2.82

1.94

Pro (P)

CCG

6.4

0.17

0.12

Thr (T)

ACU

61.9

1.64

0.71

Thr (T)

ACC

147.7

3.93

1.69

Thr (T)

ACA

134.6

3.58

1.54

Thr (T)

ACG

6.2

0.16

0.07

Ala (A)

GCU

60.4

1.61

0.84

Ala (A)

GCC

122.6

3.26

1.70

Ala (A)

GCA

98.9

2.63

1.37

Ala (A)

GCG

7.1

0.19

0.10

Tyr (Y)

UAU

34.2

0.91

0.61

Tyr (Y)

UAC

77.5

2.06

1.39

His (H)

CAU

30.4

0.81

0.56

His (H)

CAC

77.9

2.07

1.44

Gln (Q)

CAA

87.5

2.33

1.83

Gln (Q)

CAG

8.3

0.22

0.17

Asn (N)

AAU

25.3

0.67

0.39

Asn (N)

AAC

106.1

2.82

1.61

Lys (K)

AAA

79.2

2.10

1.84

Lys (K)

AAG

7

0.19

0.16

Asp (D)

GAU

18.4

0.49

0.59

Asp (D)

GAC

44

1.17

1.41

Glu (E)

GAA

81.2

2.16

1.74

Glu (E)

GAG

12

0.32

0.26

Cys (C)

UGU

10.2

0.27

0.70

Cys (C)

UGC

18.8

0.50

1.30

Trp (W)

UGA

96.3

2.56

1.80

Trp (W)

UGG

10.8

0.29

0.20

Arg (R)

CGU

10.1

0.27

0.57

Arg (R)

CGC

18.5

0.49

1.04

Arg (R)

CGA

37.8

1.00

2.13

Arg (R)

CGG

4.6

0.12

0.26

Ser (S1)

AGU

8.6

0.23

0.18

Ser (S1)

AGC

46.1

1.23

0.98

Gly (G)

GGU

36.7

0.98

0.67

Gly (G)

GGC

64.3

1.71

1.16

Gly (G)

GGA

88.9

2.36

1.61

Gly (G)

GGG

30.9

0.82

0.56

 

In order to study the codon usage bias among Charadriiformes, we also analyzed the correlations between ENC (effective number of codons), CBI (codon bias index), the G + C content of all codons (G + Cc), and the G + C content of the third codon position (G + C3s). We found a significant negative correlation between CBI and ENC (R = 0.94, P < 0.05), and a significant positive correlation was found between CBI and G + Cc (R = 0.50, P < 0.05) and G + C3s (R = 0.58, P < 0.05) (Fig. 2). However, other pairs were not correlated with each other pairs. These results were consistent with the neutral mutational theories, in which the G + C content of mitochondrial genome was reported to be the most significant factor in determining codon bias among organisms (Plotkin and Kudla, 2011).

Phylogenetic analysis

The phylogenetic analysis resolved a well-supported clade of Charadriiformes (Fig. 3), which showed great mitochondrial divergence within the Charadriiformes. Relationships of the phylogeny strongly support monophyly of the order Charadriiformes, which has a long evolutionary history dating back at least to the late Cretaceous (Baker et al., 2007). This phylogeny was in agreement with previous family hypotheses for shorebirds (Paton and Baker, 2006; Hu et al., 2017), with the exceptions of relationships of Larus vegae (GenBank No. KT943749). This sample (Larus vegae KT943749) was sequenced on Next Generation Sequencing platform of IonTorrent, and the de novo assemblies were conducted with CLC genomics workbench v. 8.5.1 with the coverage of 1070.46. This study provides a valuable resource facilitating further study of population genetics of H. ostralegus and improves our understanding of the evolutionary and taxonomic research within Charadriiformes.


 

 

Test of selection

We compared Ka/Ks ratios for all 13 PCGs in 32 Charadriiformes species. The average value of Ka and Ks ranged from 0.01 of COI to 0.09 of ATP8 and from 0.53 of ATP8 to 0.88 of ND1, respectively (Table IV). The ratio of Ka/Ks of all PCGs was far lower than one (≤ 0.17), indicating that all these genes evolved under purifying selection.

 

Table IV. The mutational information and the test of selection results among Charadriiformes were calculated by each protein coding genes.

Gene

Length (bp)

%Vs

%Pis

%S

ts/tv

Ks

Ka

Ka/Ks

SLAC

FEL

REL

MEME

ND1

969

47.57%

40.76%

6.81%

4.67

0.88

0.04

0.05

0

0

0

4 (4, 89,194,253)

ND2

1023

54.15%

43.99%

10.17%

3.63

0.78

0.07

0.09

1(277)

1(277)

2(89,276)

COI

1545

36.76%

32.88%

3.88%

4.08

0.75

0.01

0.02

0

0

0

1(407)

COII

678

40.71%

35.10%

5.60%

4.26

0.75

0.02

0.03

1(128)

0

0

0

ATP8

162

60.49%

50.62%

10.49%

3.10

0.53

0.09

0.17

0

0

1(50)

1(17)

ATP6

678

49.71%

40.86%

8.85%

3.07

0.76

0.04

0.06

0

0

1(193)

1(115)

COIII

780

39.23%

32.44%

6.79%

3.74

0.67

0.02

0.03

0

0

0

1(153)

ND3

345

47.25%

41.74%

5.51%

4.12

0.74

0.06

0.08

0

0

0

0

ND4L

291

50.17%

42.96%

7.22%

3.43

0.73

0.05

0.07

0

0

0

1(72)

ND4

1374

50.51%

41.85%

8.66%

3.09

0.69

0.06

0.08

3(13,88,96)

1(96)

0

3(96,187,448)

ND5

1800

52.22%

41.56%

10.67%

3.26

0.66

0.07

0.10

2(439,520)

0

0

8(22,276,345,351,438,475,524,600)

Cyt b

1128

45.83%

36.17%

9.66%

2.89

0.70

0.03

0.04

1(212)

0

0

1(212)

ND6

516

53.88%

42.64%

11.24%

4.41

0.73

0.07

0.09

1(115)

1(115)

0

4(64,97,115,134)

 

We then applied several methods to detect evidence of positive selection affecting mtDNA PCGs throughout the phylogenetic tree of seabird. Several positive selection sites were found based on site-specific analyses (Table IV). There are 12 different codons in eight genes were suggested to have evolved under positive selection by REL, FEL, or SLAC. Only three codons (in ND2, ND4 and ND6) were concordant in two of the methods (Table IV). MEME, which is particularly sensitive to events of episodic selection, suggested that 27 sites of 11 genes evolved under positive selection, which may have affected several lineages along the evolution of seabirds.

ACKNOWLEDGEMENTS

This research was supported by grants from the Program of Nature Science Fund of Jiangsu Province (# BK20181076) and Qing Lan Project of Jiangsu High School (# 2019SZJS-003).

Statement of conflict of interest

The authors declare no conflict of interest.

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