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Comparative analysis of the mitogenomes of two
Corydoras (Siluriformes, Loricarioidei) with nine known
Corydoras, and a phylogenetic analysis of Loricarioidei
Cheng-He Sun1,2, Qi Huang1, Xiao-Shu Zeng1, Sha Li3,4, Xiao-Li Zhang1,
Ya-Nan Zhang1, Jian Liao1, Chang-Hu Lu2, Bo-Ping Han1, Qun Zhang1
1 Department of Ecology and Institute of Hydrobiology, Jinan University, Guangzhou 510632, China
2College of Biology and the Environment, Nanjing Forestry University, Nanjing 210037, China 3Chinese
Sturgeon Research Institute, China ree Gorges Corporation, Yichang 443100, Hubei, China 4Hubei Key
Laboratory of ree Gorges Project for Conservation of Fishes, Yichang 443100, Hubei, China
Corresponding author: Qun Zhang (tqzhang@jnu.edu.cn)
Academic editor: Tihomir Stefanov|Received 22 October 2021|Accepted 6 Januar y 2022|Published 24 Januar y 2022
http://zoobank.org/7B1D7ADC-5E9D-4387-9EB8-4C1A6081E6A3
Citation: Sun C-H, Huang Q, Zeng X-S, Li S, Zhang X-L, Zhang Y-N, Liao J, Lu C-H, Han B-P, Zhang Q (2022)
Comparative analysis of the mitogenomes of two Corydoras (Siluriformes, Loricarioidei) with nine known Corydoras,
and a phylogenetic analysis of Loricarioidei. ZooKeys 1083: 89–107. https://doi.org/10.3897/zookeys.1083.76887
Abstract
Corydoras is a speciose catsh genus from South America with widely investigated phylogenetic and evo-
lutionary relationships. e complete mitogenomes of C. aeneus and C. paleatus were sequenced, assem-
bled, and annotated using next-generation sequencing. e genome arrangements, gene contents, genome
structures, base compositions, evolutionary features, codon usage, and tRNA structures of the two mi-
togenomes were compared and analyzed with nine published mitogenomes of Corydoras. Phylogenetic
analysis was performed using concatenated nucleotide sequences with 13 protein-coding genes and two
rRNAs with 44 mitogenomes of Siluriformes. ese results provide information on the mitogenomes of
eleven Corydoras species and evolutionary relationships within the suborder Loricarioidei, which may be
applicable for further phylogenetic and taxonomic studies on Siluriformes and Loricarioidei.
Keywords
Corydoras aeneus, Corydoras paleatus, genome sequencing, mitochondrial DNA, Phylogenetic tree
ZooKeys 1083: 89–107 (2022)
doi: 10.3897/zookeys.1083.76887
https://zookeys.pensoft.net
Copyright Cheng-He Sun et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC
BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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Cheng-He Sun et al. / ZooKeys 1083: 89–107 (2022)
90
Introduction
Fish mitochondrial DNA shares characteristics with other vertebrate mitochondrial
DNA (Anderson et al. 1981; Manchado et al. 2007; Xu et al. 2011), e.g., small mo-
lecular weight, simple structure, and compact arrangement. It exists in the form of a
covalently closed circular supercoil structure and contains heavy and light chains. e
genetic material can be replicated, transcribed, and translated independently from the
nuclear DNA in the cell. With few exceptions, sh mitochondrial DNA comprises 13
protein-coding genes (PCGs), 22 transfer RNA genes, two ribosomal RNA genes, origi-
nal region of light-strand replication, and control region (D-loop) (Ojala et al. 1981;
Gadaleta et al. 1989; Wolstenholme 1992 Simon et al. 1994; De Rijk et al. 1995). e
mitochondrial DNA mutates rapidly, nearly 10-fold faster than the nuclear DNA, and
the fragment length and evolution rate dier for each gene, providing molecular evi-
dence for studying dierent species (Brown et al. 1979; Pesole et al. 1999). In addition,
mitochondrial DNA is highly heterogeneous and harbors the genetic characteristics as-
sociated with maternal traits (O’Brien 1971; Michot et al. 1990; Bartlett and Davidson
1991; Meyer 1993; Beheregaray and Sunnucks 2001; Liu et al. 2002; Yoshizawa and
Johnson 2003). Hence, mitochondrial DNA can be used to identify sh groups at the
molecular level and explore geographic distribution, species origin, and species dieren-
tiation (Avise et al. 1987; Kai et al. 2002; Hrbek et al. 2007). As sh are a large group
with a complex origin in the vertebrate subphylum, studies on their phylogenetic and
evolutionary relationships performed using traditional morphological methods often
provide limited information. With advances in biotechnology, complete mitochondrial
genome sequences have been determined as a useful tool to study the phylogeny and
phylogeography of sh (Bermingham and Avise 1986; Xu et al. 2020).
Corydoras Lacépède, 1803, belongs to the order Siluriformes, suborder Loricari-
oidei, family Callichthyidae. Corydoras contains 175 valid species, which makes it the
most species-rich genus of the family Callichthyidae (Lima and Britto 2020; Tencatt
et al. 2021). e body of these sh is covered with bone plates, and the pectoral and
dorsal ns have hard spines that can be used for protection. In addition, Corydoras can
use the back end of their intestines, which is rich in blood vessels, to obtain oxygen
from air taken in at the water surface, enabling survival under environmental stress,
such as drought or insucient dissolved oxygen content in water. Corydoras catsh
are benthic omnivorous sh (Moreira et al. 2016b, 2017; Liu et al. 2019b, 2019c;
Saitoh et al. 2003). Typically, Corydoras is active only during feeding, and otherwise
hide while resting. Corydoras is primarily distributed in South America. Most species
of Corydoras gather in the middle and lower reaches of the river where the current is
relatively gentle, whereas a few live in the upper reaches of the river in rapids (Saitoh et
al. 2003; Liu et al. 2019c). Corydoras is also valuable as an ornamental sh. Some phy-
logenetic relationships in Corydoras remain unclear. e number of species reported
in relevant articles is small, which is not sucient to reect the phylogenetic variety
of the genus Corydoras (Alexandrou et al. 2011; Lujan et al. 2015; Roxo et al. 2019).
erefore, a comprehensive understanding of the relationships between dierent spe-
cies of Corydoras is essential.
Comparative analysis of the mitogenomes of eleven Corydoras 91
In this study, the complete mitogenomes of two species of Corydoras (Bronze cory-
doras C. aeneus Gill, 1858 and peppered corydoras C. paleatus Jenyns, 1842) were
sequenced, assembled, and annotated. e genome organization, gene contents, re-
peat sequences, and tRNA structures of the eleven mitogenomes were compared and
analyzed in combination with nine published mitogenomes of Corydoras (Saitoh et al.
2003; Moreira et al. 2016a, 2017; Liu et al. 2019a, b, c, d; Chen et al. 2020; Lv et al.
2020). Determining the similarities and dierences in gene orders, genetic structures,
base compositions, evolutionary features, and codon usage can provide molecular in-
sights into the taxonomic and phylogenetic characteristics of the order Siluriformes.
Based on these data, and those obtained from the NCBI database, we examined the
phylogenetic relationships among species in the suborder Loricarioidei. We also evalu-
ated the mitogenomes of eleven species of Corydoras and evolutionary relationships
within the suborder Loricarioidei, thereby providing a valuable basis for further evolu-
tionary studies on Siluriformes and Loricarioidei.
Materials and methods
Sample collection and identification
Single specimens of C. aeneus and C. paleatus were collected from the temple of Confu-
cius ower and wood sh market, Nanjing city, Jiangsu province, China (32°0'27.1"N,
118°50'11.5"E) in June 2020 and identied based on their morphological character-
istics, according to the latest taxonomic classication of sh (Popazoglo and Boeger
2000; Huysentruyt and Adriaens 2005a, b). eir geographic data and specic origins
were unknown. All fresh tissues were immediately stored at -80 °C in 95% ethanol until
DNA extraction. Total DNA was extracted from the muscle tissue using a TIANamp
Marine Animals DNA Kit DP324 (Tiangen Biotech Co., Ltd., Beijing, China) accord-
ing to the manufacturer’s instructions. DNA integrity and purity were evaluated by 1%
agarose gel electrophoresis, and DNA purity was determined with a NanoDrop 2000
(NanoDrop Technologies, Wilmington, DE, USA). DNA concentrations were quanti-
ed using a QubitR 2.0 Fluorometer (Life Technologies, Carlsbad, CA, USA). To ensure
the accuracy of morphological identication, COI primers were designed based on the
latest DNA barcoding database (NCBI and FishBase) and were amplied, sequenced,
and compared. e COI sequences are provided in the Suppl. material 1. e results of
the sequence alignment verify the accuracy of the morphological identication.
Genome sequencing and assembly
Next-generation sequencing was performed to determine the complete mitogenome
sequence of the two species of Corydoras. e DNA libraries were sequenced on an Il-
lumina sequencing platform by Novogene Co., Ltd. (Beijing, China). Briey, the total
DNA genome was quantied and fragmented into 250-base pair (bp) fragments using
a Covaris M220 ultrasonic crushing system (Woburn, MA, USA) followed by whole-
Cheng-He Sun et al. / ZooKeys 1083: 89–107 (2022)
92
genome shotgun sequencing. According to the manufacturer’s instructions, a library
was constructed based on two indices using an Illumina TruSeq DNA PCR-Free HT
kit (San Diego, CA, USA). An Illumina Novaseq 6000 platform was used for sequenc-
ing of 150 paired-end reads approximately 4 Gb in size. Clean reads were generated
as previously described, and the remaining high-quality reads were assembled using
SPADES V3.15.2 (Bankevich et al. 2012) (http://cab.spbu.ru/software/spades/) and
SOAPDENOVO2 V2.01 (Luo et al. 2012) software. e preliminary assembly results
were compared with the NT database, and looped sequences annotated as mitochon-
drial genomes were screened. CAP3 was used to merge the splicing results from the two
software programs, and the assembly results were compared with those of related species
using MUMMER v3.23 (Delcher et al. 2003). e mitogenome composition was con-
rmed, and a complete, high-quality map of the mitochondrial genome was obtained.
Genome annotation and analysis
e tRNA genes were veried using tRNASCAN-SE V1.3.1 (Lowe and Eddy 1997)
with default settings for the vertebrate mitochondrial genetic code. e software, which
integrates multiple analysis tools, can identify 99% of the tRNA genes with a very low
number of false positives and predict the secondary structure of tRNAs. Protein-coding
regions were re-identied using GLIMMER V3.0 (Ingram et al. 2009), and manual
comparisons were performed using the SEQMAN program of LASERGENE V7.1 (Bur-
land 2000) (DNAStar, Inc., Madison, WI, USA) based on the PCGs of nine species of
Corydoras and translated into putative proteins via GenBank. e non-coding RNAs
were veried using RFAM V12.0 (Griths-Jones et al. 2003) and INFERNAL V1.1
(Nawrocki and Eddy 2013). e rRNA genes were assumed to extend to the boundaries
of anking genes, similar to the homologous regions of other published mitogenomes
of Corydoras in GenBank. e MITOS WebServer (http://mitos2.bioinf.uni-leipzig.de/
index.py) and MitoFish (Iwasaki et al. 2013) (http://mitosh.aori.u-tokyo.ac.jp/) online
tools were used for the nal annotation of the entire mitogenome sequence of the two
species of Corydoras, and the annotated mitogenomes were compared with nine pub-
lished mitogenomes of Corydoras. Base compositions, genetic distances, and relative syn-
onymous codon usage values were determined using MEGA V7.0 (Kumar et al. 1994). A
graph comparing the relative synonymous codon usage was drawn using PHYLOSUITE
V1.2.2 (Zhang et al. 2020). Strand asymmetry was analyzed using the formula: AT-skew
= (A – T)/(A + T). e numbers of non-synonymous (Ka) and synonymous (Ks) substi-
tutions and the ratio of Ka/Ks and nucleotide diversity for the nine species of Corydoras
were calculated using DNASP 5.1 (Librado and Rozas 2009). e MitoFish (http://
mitosh.aori.u-tokyo.ac.jp/) online tool was used to generate circular mitogenome maps.
Phylogenetic analysis
Phylogenetic trees for the eleven mitogenomes of Corydoras within the family Calli-
chthyidae and Suborder Loricarioidei were constructed by aligning 13 PCGs and two
Comparative analysis of the mitogenomes of eleven Corydoras 93
rRNA sequences with those of 42 species of Loricarioidei, 29 species from Loricariidae,
and one species from Trichomycteridae (Table 1). e mitogenomes of Pterocryptis
cochinchinensis (Resende et al. 2016) and Silurus asotus (Nakatani et al. 2011) (ac-
cession no. NC_027107.1 and NC_015806.1, respectively, suborder Siluroidei) were
included as outgroups to root the Loricarioidei tree. All operations were performed
in PHYLOSUITE V1.2.2 (Zhang et al. 2020) software package. e nucleotide se-
quences of 13 PCGs from 44 mitogenomes were aligned in batches with MAFFT
V7.313 (Katoh and Standley 2013) (https://mat.cbrc.jp/alignment/server/) using the
Table 1. Information on 44 Siluriformes species evaluated in the study.
No. Suborder Family Taxa GenBank accession no. Length (bp) Location/Reference
1 Loricarioidei Callichthyidae Corydoras aeneusMZ571336 16604 is study
2Corydoras agassizii MN641875.1 16538 Lv et al. 2020
3Corydoras arcuatus NC_049096.1 16177 Liu et al. 2019d
4Corydoras duplicareus NC_049095.1 16632 Liu et al. 2019a
5Corydoras nattereri KT239008.1 16557 Moreira et al. 2016a
6Corydoras paleatusMZ571337 16320 is study
7Corydoras panda NC_049097.1 16398 Liu et al. 2019b
8Corydoras rabauti NC_004698.1 16711 Saitoh et al. 2003
9Corydoras schwartzi KT239007.1 15671 Moreira et al. 2017
10 Corydoras sterbai NC_048967.1 16520 Liu et al. 2019c
11 Corydoras trilineatus NC_049098.1 15359 Chen et al. 2020
12 Hoplosternum littorale KX087170.1 16262 Parente et al. 2018
13 Loricariidae Ancistomus snethlageae KX087166.1 16464 Moreira et al. 2017
14 Ancistrus cryptophthalmus MF804392.1 16333 Lv et al. 2020
15 Ancistrus multispinis KT239006.1 16539 Moreira 2018
16 Ancistrus temminckii NC_051963.1 16439 Meng et al. 2021
17 Aphanotorulus emarginatus KT239019.1 16597 Moreira et al. 2017
18 Baryancistrus xanthellus KX087167.1 16167 Moreira et al. 2017
19 Dekeyseria amazonica KX087168.1 16409 Moreira 2018
20 Hemipsilichthys nimius KT239011.1 16477 Moreira et al. 2017
21 Hisonotus thayeri KX087173.1 16269 Moreira et al. 2017
22 Hypancistrus zebra KX611143.1 16202 Magalhães et al. 2017
23 Hypoptopoma incognitum NC_028072.1 16313 Moreira et al. 2016b
24 Hypostomus anis KT239013.1 16330 Moreira et al. 2017
25 Hypostomus ancistroides NC_052710.1 16422 Rocha-Reis et al. 2020
26 Hypostomus francisci NC_045188.1 16916 Pereira et al. 2019
27 Hypostomus plecostomus NC_025584.1 16562 Liu et al. 2016
28 Kronichthys heylandi KT239014.1 16632 Moreira et al. 2017
29 Loricaria cataphracta KX087174.1 16831 Moreira et al. 2017
30 Loricariichthys castaneus KT239015.1 16521 Moreira et al. 2017
31 Loricariichthys platymetopon KT239018.1 16521 Moreira et al. 2017
32 Neoplecostomus microps KX087175.1 16523 Moreira et al. 2017
33 Otocinclus anis MT323116.1 16501 Zhang et al. 2021
34 Pareiorhaphis garbei KX087178.1 16630 Moreira et al. 2017
35 Parotocinclus maculicauda KX087179.1 16541 Moreira et al. 2017
36 Peckoltia furcata KX087180.1 16497 Moreira et al. 2017
37 Pterygoplichthys anisitsi KT239003.1 16636 Parente et al. 2017
38 Pterygoplichthys disjunctivus NC_015747.1 16667 Nakatani et al. 2011
39 Pterygoplichthys pardalis KT239016.1 16822 Moreira et al. 2017
40 Schizolecis guntheri KT239017.1 16611 Moreira et al. 2017
41 Sturisomatichthys panamensis NC_045877.1 16526 Ren et al. 2019
42 Trichomycteridae Trichomycterus areolatus AP012026.1 16657 Nakatani et al. 2011
43 Siluroidei Siluridae Pterocryptis cochinchinensis NC_027107.1 16826 Resende et al. 2016
44 Silurus asotus NC_015806.1 16593 Nakatani et al. 2011
Cheng-He Sun et al. / ZooKeys 1083: 89–107 (2022)
94
codon alignment mode. e results were optimized using MACSE V2.03 (Ranwez
et al. 2018). e nucleotide sequences of two rRNAs were aligned using the online
tool MAFFT with default settings. Ambiguously aligned regions were removed via
GBLOCKS 0.91 b with default settings. e resulting alignments were concatenated
into a single dataset with PHYLOSUITE. e best partition schemes and optimal sub-
stitution models were selected by MODELFINDER (Kalyaanamoorthy et al. 2017)
with the greedy algorithm and Bayesian information criterion (Watanabe 2013). e
best substitution models applied to each partition are listed in Suppl. material 1: Table
S1. Phylogenetic trees were constructed using two inference methods: maximum likeli-
hood (ML) and Bayesian inference (BI). ML analyses were performed with IQ-TREE
V1.6.8 with the models selected for each partition, and 1,000 bootstrap replicates were
used to estimate node reliability. Bayesian analyses were performed using MRBAYES
V3.2.6 (Huelsenbeck and Ronquist 2001). One million generations of two independ-
ent runs were performed with four chains and sampling trees every 100 generations.
e initial 25% of trees generated prior to reaching stable log-likelihood values were
discarded as burn-in. e remaining trees were used to calculate the Bayesian posterior
probabilities. e resulting phylogenetic trees and gene orders were visualized and ed-
ited using iTOL (Letunic and Bork 2016).
Results and discussion
Genome structure and organization
e complete mitogenomes of C. aeneus and C. paleatus comprising 16,604 and
16,593 bp, respectively, were submitted to GenBank (accession nos. MZ571336 and
MZ571337, respectively) (Fig. 1, Table 2). e two mitogenomes were circular and
contained 37 mitochondrial genes (13 PCGs, 22 tRNA genes, and two rRNA genes)
Figure 1. Gene maps of the two newly sequenced Corydoras species.
Comparative analysis of the mitogenomes of eleven Corydoras 95
and one D-loop. e position of each gene in the mitogenome was identical to that
in other species of Corydoras (Saitoh et al. 2003; Moreira et al. 2016a, 2017; Liu et
al. 2019a, b, c, d; Chen et al. 2020; Lv et al. 2020). One of the 13 PCGs (ND6) and
eight tRNAs (tRNA-Ala, tRNA-Cys, tRNA-Glu, tRNA-Asn, tRNA-Pro, tRNA-Gln,
tRNA-Ser(TGA), and tRNA-Tyr) were encoded by the light chain (-), whereas the
other 28 genes, including 12 PCGs, 14 tRNAs, two rRNAs, and one D-loop, were
encoded by the heavy chain (+) (Fig. 1, Table 2). e 44 mitogenomes of Siluriformes
(Nakatani et al. 2011; Liu et al. 2016; Moreira et al. 2016b, 2018; Resende et al. 2016;
Table 2. Characteristic features of Corydoras aeneus and Corydoras paleatus mitogenomes (+ denotes heavy
strand; - denotes light strand).
Feature
Position Length (bp) Start codons Stop codons
Anticodon Strand
Intergenic nucleotides
C. aeneus C. paleatus C. aeneus C. paleatus C. a C. p C. a C. p
From to Fro m to C. a C. p
tRNA-Phe 168 168 68 68 GAA + 0 0
12S rRNA 69 1014 69 1013 946 945 + 0 0
tRNA-Val 1015 1086 1014 1085 72 72 TAC + 0 0
16S rRNA 1087 2757 1086 2753 1671 1668 + 0 0
tRNA-Leu 2758 2832 2754 2828 75 75 TAA + 0 0
ND1 2833 3804 2829 3800 972 972 AT G ATG TA G TAG + 8 8
tRNA-Ile 3813 3884 3809 3880 72 72 GAT +-2 -2
tRNA-Gln 3883 3953 3879 3949 71 71 TTG --1 -1
tRNA-Met 3953 4022 3949 4018 70 70 CAT + 0 0
ND2 4023 5067 4019 5063 1045 1045 ATG AT G T T + 0 0
tRNA-Trp 5068 5139 5064 5134 72 71 TCA + 1 1
tRNA-Ala 5141 5209 5136 5204 69 69 TGC - 1 1
tRNA-Asn 5211 5283 5206 5278 73 73 GTT -30 31
tRNA-Cys 5314 5380 5310 5377 67 68 GCA --1 -1
tRNA-Tyr 5380 5449 5377 5446 70 70 GTA - 1 1
COI 5451 7010 5448 7007 1560 1560 GTG GTG AGG AGG +-13 -13
tRNA-Ser 6998 7068 6995 7065 71 71 TGA - 4 4
tRNA-Asp 7073 7141 7070 7138 69 69 GTC + 4 6
COII 7146 7836 7145 7835 691 691 ATG ATG T T + 0 0
tRNA-Lys 7837 7910 7836 7909 74 74 TTT + 1 1
ATPase 8 7912 8079 7911 8078 168 168 ATG ATG TA A TAA +-10 -10
ATPase 6 8070 8753 8069 8752 684 684 ATG ATG TA A TAA +17 21
COIII 8771 9554 8774 9557 784 784 AT G ATG T T + 0 0
tRNA-Gly 9555 9626 9558 9629 72 72 TCC + 0 0
ND3 9627 9975 9630 9978 349 349 AT G ATG T T + 0 0
tRNA-Arg 9976 10045 9979 10048 70 70 TCG + 0 0
ND4L 10046 10342 10049 10345 297 297 ATG ATG TAA TA A +-7 -7
ND4 10336 11716 10339 11719 1381 1381 ATG ATG T T + 0 0
tRNA-His 11717 11786 11720 11789 70 70 GTG + 0 0
tRNA-Ser 11787 11853 11790 11856 67 67 GCT + 1 1
tRNA-Leu 11855 11927 11858 11930 73 73 TA G + 0 0
ND5 11928 13754 11931 13757 1827 1827 ATG ATG TAA TA A +-4 -4
ND6 13751 14266 13754 14269 516 516 ATG AT G TA A TAA - 0 0
tRNA-Glu 14267 14335 14270 14337 69 68 TTC - 2 3
Cyt b 14338 15475 14341 15478 1138 1138 ATG ATG T T + 0 0
tRNA-r 15476 15548 15479 15550 73 72 TGT +-2 -2
tRNA-Pro 15547 15616 15549 15618 70 70 TGG - 0 0
D-loop 15617 16604 15619 16593 988 975 0 0
Cheng-He Sun et al. / ZooKeys 1083: 89–107 (2022)
96
Magalhães et al. 2017; Parente et al. 2017; Parente et al. 2018; Pereira et al. 2019; Ren
et al. 2019; Rocha-Reis et al. 2020; Meng et al. 2021; Zhang et al. 2021) used in this
study were compared, and the gene composition and order were consistent (Suppl.
material 1: Fig. S1). e nucleotide composition of the two entire mitogenomes was
as follows: C. aeneus A = 5417 (32.63%), T = 4299 (25.89%), G = 2451 (14.76%), C
= 4437 (26.72%) and C. paleatus A = 5380 (32.42%), T = 4282 (25.81%), G = 2481
(14.95%), C = 4450 (26.82%). e two mitogenomes (values for C. aeneus followed
by values for C. paleatus) had high A+T contents of 58.52% and 58.23% (Suppl. ma-
terial 1: Table S2), including 58.08% and 57.67% in PCGs, 56.97% and 57.04% in
tRNA genes, 59.70% and 59.10% in 16S rRNA, 55.30% in 12S rRNA, and 67.51%
and 68.21% in the D-loop, respectively, which agrees with the typical base bias of
sh mitogenomes (Gadaleta et al. 1989; Manchado et al. 2007; Xu et al. 2011). e
overall AT and GC skew values in the entire mitogenome of C. aeneus were 0.115 and
-0.288 and in C. paleatus were 0.114 and -0.284, respectively. e GC skew value of
the eleven mitogenomes of Corydoras, except for tRNA, was slightly negative (-0.014
to -0.288), showing a higher occurrence of C than of G. In contrast, AT skew value,
except for the second codon position, was slightly positive (0.028 to 0.379), showing a
higher content of A than of T. e K2P genetic distances of the eleven mitogenomes of
Corydoras were all less than 0.12 (Suppl. material 1: Table S3). C. nattereri and C. ster-
bai and C. nattereri and C. trilineatus showed the largest K2P genetic distances among
the eleven species of Corydoras.
Protein-coding genes
e 13 PCGs of the two new mitogenomes and those of the previously published
nine mitogenomes of Corydoras contained COI–COIII, ND1–ND6, ND4L, two AT-
Pases, and one Cyt-b, similar to that in other Siluriformes (Nakatani et al. 2011; Liu
et al. 2016; Moreira et al. 2016b; Resende et al. 2016; Magalhães et al. 2017; Parente
et al. 2017; Moreira 2018; Parente et al. 2018; Pereira et al. 2019; Ren et al. 2019;
Rocha-Reis et al. 2020; Meng et al. 2021; Zhang et al. 2021). e total lengths of
PCGs in the eleven mitogenomes of Corydoras were 11,400–11,414 bp, accounting for
67.84–69.24% of the entire mitogenome. Similar to the mitogenomes of other species
of Loricarioidei, ND5 and ATPase 8 were largest (1,827 bp) and smallest (168bp), re-
spectively. Most PCGs stringently start with an ATG start codon, except for all COIs,
which start with GTG, C. nattereri COIII (Moreira et al. 2016a) which starts with
GCA, and C. schwartzi COII (Moreira et al. 2017), which starts with CCA (Suppl.
material 1: Table S4). Most PCGs are stringently terminated by the stop codon TAR
(TAA/TAG) or an incomplete stop codon T, except for all COIs, which terminate with
AGG and C. schwartzi ATPase 6 and C. nattereri ND3, which terminate with TA. e
presence of a truncated stop codon is common among vertebrate mitochondrial genes
and is thought to be introduced by posttranscriptional poly-adenylation.
Similar to most previously sequenced members of Loricarioidei, the AT-skews
(0.033 to 0.052) and GC-skews (-0.268 to -0.299) of the PCGs were similar among
Comparative analysis of the mitogenomes of eleven Corydoras 97
the eleven species of Corydoras (Suppl. material 1: Table S2). Summaries of the rela-
tive synonymous codon usage and the number of amino acids in the annotated PCGs
are presented in Suppl. material 1: Figs S2, S3. e PCGs of the eleven mitogenomes
of Corydoras (Saitoh et al. 2003; Moreira et al. 2016a, 2017; Liu et al. 2019a, b, c, d;
Chen et al. 2020; Lv et al. 2020) translate into 3,798–3,802 codons and showed very
similar codon usage, excluding the stop codons (26–28 bp). Ile (310.82 ± 2.69 co-
dons), r (312.64 ± 2.27 codons), Ala (312.73 ± 3.08 codons), and Leu1 (CUN)
(475.45 ± 12.89 codons) were the four most predominant codon families and may
be associated with the coding function of the chondriosome. In contrast, Cys (24.91
± 0.79 codons) and Ser1 (AGN) (52.18 ± 0.83 codons) had the smallest number of
codons. A/T rather than G/C bias was observed in the third position, as almost all
frequently used codons ended with A/T. e synonymous codon preferences for the
eleven species of Corydoras were conserved, possibly because of the close relationships
among members of this genus.
To reveal the evolutionary pattern of the PCGs, the Ka/Ks, nucleotide diversity,
and K2P genetic distance across all mitogenomes of Corydoras were calculated for each
aligned PCG. e K2P genetic distances of 13 PCGs were all less than 0.12 (Fig. 2a).
Among the PCGs detected, ND4 and ATPase 8 showed the largest K2P genetic dis-
tance among the eleven species of Corydoras, followed by ND2 and ND3. e nucleo-
tide diversity of the 13 PCGs was less than 0.11 (Fig. 2b). ND4 showed the highest nu-
cleotide diversity, whereas COII showed the lowest diversity. To investigate the selective
pressure across species of Corydoras, the Ka/Ks ratios of the PCGs of each mitogenome
were estimated (Fig. 2c). e Ka/Ks value was highest for ND6, followed by ND2; the
lowest values were observed for COI, COIII, ND1, and ND4L. All 13 PCGs showed
Ka/Ks << 1, suggesting that all PCGs of Corydoras evolved under purifying selection.
tRNAs, ribosomal RNAs, and control region
e total lengths of the 22 tRNA genes ranged from 1,438 (C. schwartzi) to 1,561bp
(C. arcuatus and C. panda), whereas individual tRNA genes typically ranged from 58
to 75 bp. All tRNA genes displayed the expected cloverleaf secondary structures with
normal base pairing, except for tRNA-Ser(GCT), which lacked the DHU stem (Suppl.
material 1: Fig. S4), forming a loop commonly found in other vertebrates (Ojala et al.
1981; Gadaleta et al. 1989; Wolstenholme 1992). e A+T contents of these tRNAs
were 56.55–57.58%. All AT-skew and GC-skew values were slightly positive, indicating
a slight bias toward the use of A and G in the tRNAs (Suppl. material 1: Table S2). ese
rRNA genes are between tRNA-Phe and tRNA-Leu(TAA) and are separated by tRNA-
Val. e average total size of the two rRNAs was 2,614 bp, and the average A+T content
was 57.89%. Like the tRNAs, all AT-skew values were positive, whereas all GC-skew
values were negative, indicating that rRNAs favor C compared to tRNAs in Corydoras.
e control region (D-loop), also known as the A+T rich region that contains
hypervariable non-coding sequences and regulates the replication and transcription of
mitochondrial DNA, is the largest non-coding region and is located between tRNA-
Cheng-He Sun et al. / ZooKeys 1083: 89–107 (2022)
98
Pro and tRNA-Phe in these mitogenomes. Compared with PCGs, the D-loop dis-
played a higher mutation rate and the highest variation throughout the mitogenome;
thus, this region is dominant and can be used to evaluate intraspecies variations. e
Figure 2. K2P genetic distance a nucleotide diversity b Ka/Ks ratio c analyses of protein-coding genes
among the eleven Corydoras mitogenomes.
Comparative analysis of the mitogenomes of eleven Corydoras 99
D-loops in the eleven species of Corydoras were 718‒1,218 bp. Compared with the
other four regions (entire genome, PCGs, tRNAs, and rRNAs), the control region
showed the highest A+T content, ranging from 66.77% to 71.87%. Like the rRNAs,
all AT-skew values were positive, and all GC-skew values were negative.
Phylogenetic analysis
To determine the phylogenetic relationships within the suborder Loricarioidei and
family Callichthyidae, we obtained the concatenated nucleotide sequences of 13 PCGs
and two rRNAs from 42 species of Loricarioidei. Phylogenetic analyses based on both
ML and BI methods revealed same topologies, which also generally agreed with those
presented in previous studies (Alexandrou et al. 2011; Lujan et al. 2015; Moreira et al.
2017; Roxo et al. 2019) (Figs 3, 4). ese analyses conrmed that the genus Corydoras
was part of the monophyletic family Callichthyidae.
Both Callichthyidae and Loricariidae were recovered as monophyletic with very
high support values (BI posterior probabilities, PP = 1; ML bootstrap, BS = 100).
e 44 species of Siluriformes were divided into four major clades corresponding to
the families Siluridae Callichthyidae, Trichomycteridae, and Loricariidae. e target
Figure 3. Phylogenetic trees of 44 Siluriformes species using concatenated nucleotide sequences of 13
protein-coding genes and two rRNAs using the maximum likelihood method. Numbers in the ML tree
represent SH-aLRT support/ultrafast bootstrap support values.
Cheng-He Sun et al. / ZooKeys 1083: 89–107 (2022)
100
species C. aeneus and C. paleatus were clustered into two clades (C. aeneus + C. rabauti)
and (C. paleatus + C. nattereri) with a high nodal support value (PP = 1; BS = 100).
e eleven species of the genus Corydoras clustered together quite well [((C. aeneus + C.
rabauti) + (C. schwartzi + C. agassizii)) + (C. arcuatus + (C. panda + (C. duplicareus +
(C. sterbai + C. trilineatus))))] + [(C. paleatus + C. nattereri)]. Corydoras trilineatus and
C. sterbai have short, almost non-existent branch lengths; thus, they are likely the same
species. e K2P genetic distances of these two species are 0.000 (Suppl. material 1:
Table S3), which veries that they are the same species. is may be caused by incorrect
identication, taxonomic problems (these two species are, in fact, synonymous), and/
or introgressive hybridization. Moreover, in the family Loricariidae, the genera Ancis-
trus and Loricariichthys were clustered into monophyletic clades [(A. cryptophthalmus +
A. multispinis) + A. temminckii] and (L. castaneus + L. platymetopon) with a high nodal
support value (PP = 1; BS = 100). ere was a paraphyletic relationship between the
genera Hypostomus and Pterygoplichthys, [H. francisci + (H. ancistroides + H. anis), P.
pardalis + (H. plecostomus + (P. anisitsi + P. disjunctivus))]. Our results demonstrate that
the concatenated nucleotide sequences of the 13 PCGs and two rRNAs were useful
for determining the phylogenetic relationships of the order Siluriformes. ese results
can be used to improve classication of the families Callichthyidae and Loricariidae.
Figure 4. Phylogenetic tree of 44 Siluriformes species using concatenated nucleotide sequences of 13
protein-coding genes and two rRNAs via the Bayesian interference method. Applicable posterior prob-
ability values are shown.
Comparative analysis of the mitogenomes of eleven Corydoras 101
Conclusions
Using next-generation sequencing methods, the complete mitogenomes of the bronze
C. aeneus and peppered C. paleatus were analyzed and compared with those of nine
members of Corydoras. e complete mitogenomes of C. aeneus and C. paleatus com-
prised 16,604 and 16,593 bp, respectively. e two mitogenomes had high A+T con-
tents (58.52% in C. aeneus and 58.23% in C. paleatus), a phenomenon that agrees
with the typical base bias of ichthyic mitogenomes. Our results indicate that the mi-
togenome features, including genome size, gene content, and gene arrangement, in
Corydoras are highly conserved. Phylogenetic analysis was performed with 42 species
of Loricarioidei and two outgroup species. ese analyses conrmed the occurrence
of the genus Corydoras within the monophyletic family Callichthyidae. e complete
mitogenome information, including the gene content, gene orders, genome structure,
base compositions, evolutionary features, codon usage, gene arrangement, and phy-
logenetic analyses, provides a basis for future studies on the population genetic and
evolution of Corydoras and related groups.
Acknowledgements
is work was supported by the National Key R&D Program of China (Grant number
2018YFD0900802); Director’s Fund of the Hubei Key Laboratory of ree Gorges
Project for Conservation of Fishes, China ree Gorges Corporation (0704157); Out-
standing Innovative Talents Cultivation Funded Programs for Doctoral Students of
Jinan University (Project No: 2021CXB022) and Priority Academic Program Devel-
opment of Jiangsu Higher Education Institutions (PAPD). We gratefully acknowledge
two reviewers for their constructive comments and would like to thank Editage (www.
editage.com) for their support with language editing.
References
Alexandrou MA, Oliveira C, Maillard M, McGill RA, Newton J, Creer S, Taylor MI (2011)
Competition and phylogeny determine community structure in Müllerian co-mimics.
Nature 469: 84–88. https://doi.org/10.1038/nature09660
Anderson S, Bankier AT, Barrell BG, de Bruijn MH, Coulson AR, Drouin J, Eperon IC, Nier-
lich DP, Roe BA, Sanger F, Schreier PH, Smith AJ, Staden R, Young IG (1981) Sequence
and organization of the human mitochondrial genome. Nature 290: 457–465. https://doi.
org/10.1038/290457a0
Avise JC, Arnold J, Ball RM, Bermingham E, Lamb T, Neigel JE, Reeb CA, Saunders NC
(1987) Intraspecic phylogeography: e mitochondrial DNA bridge between population
genetics and systematics. Annual Review of Ecology and Systematics 18: 489–522. https://
doi.org/10.1146/annurev.es.18.110187.002421
Cheng-He Sun et al. / ZooKeys 1083: 89–107 (2022)
102
Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, Lesin VM, Niko-
lenko SI, Pham S, Prjibelski AD, Pyshkin AV, Sirotkin AV, Vyahhi N, Tesler G, Alekseyev
MA, Pevzner PA (2012) SPAdes: A new genome assembly algorithm and its applications
to single-cell sequencing. Journal of Computational Biology 19: 455–477. https://doi.
org/10.1089/cmb.2012.0021
Bartlett SE, Davidson WS (1991) Identication of unnus tuna species by the polymerase
chain reaction and direct sequence analysis of their mitochondrial cytochrome b genes.
Canadian Journal of Fisheries and Aquatic Sciences 48: 309–317. https://doi.org/10.1139/
f91-043
Beheregaray LB, Sunnucks P (2001) Fine‐scale genetic structure, estuarine colonization and in-
cipient speciation in the marine silverside sh Odontesthes argentinensis. Molecular Ecology
10: 2849–2866. https://doi.org/10.1046/j.1365-294X.2001.t01-1-01406.x
Bermingham E, Avise JC (1986) Molecular zoogeography of freshwater shes in the southeast-
ern United States. Genetics 113: 939–965. https://doi.org/10.1007/BF00123216
Brown WM, George M, Wilson AC (1979) Rapid evolution of animal mitochondrial DNA.
Proceedings of the National Academy of Sciences 76: 1967–1971. https://doi.org/10.1073/
pnas.76.4.1967
Burland TG (2000) DNASTAR’s Lasergene sequence analysis software. In: Misener S, Krawetz
SA (Eds) Bioinformatics Methods and Protocols. Methods in Molecular Biology, vol. 132.
Humana Press, Totowa, 71–91. https://doi.org/10.1385/1-59259-192-2:71
Chen L, Xu B, Xiao T, Liu Q (2020) Characterization and phylogenetic analysis of Using
MUMmer to identify similar regions in large sequence sets Corydoras trilineatus mitochon-
drial genome. Mitochondrial DNA Part B Resources 5: 3017–3018. https://doi.org/10.1
080/23802359.2020.1797551
Delcher AL, Salzberg SL, Phillippy AM (2003) Using MUMmer to identify similar regions in
large sequence sets. Current Protocols in Bioinformatics 00: 10.3.1–10.3.18. https://doi.
org/10.1002/0471250953.bi1003s00
De Rijk P, Van de Peer Y, Van den Broeck I, De Wachter R (1995) Evolution according to
large ribosomal subunit RNA. Journal of Molecular Evolution 41: 366–375. https://doi.
org/10.1007/BF01215184
Gadaleta G, Pepe G, De Candia G, Quagliariello C, Sbisà E, Saccone C (1989) e complete
nucleotide sequence of the Rattus norvegicus mitochondrial genome: Cryptic signals re-
vealed by comparative analysis between vertebrates. Journal of Molecular Evolution 28:
497–516. https://doi.org/10.1007/BF02602930
Griths-Jones S, Bateman A, Marshall M, Khanna A, Eddy SR (2003) Rfam: An RNA family
database. Nucleic Acids Research 31: 439–441. https://doi.org/10.1093/nar/gkg006
Hrbek T, Seckinger J, Meyer A (2007) A phylogenetic and biogeographic perspective on the
evolution of poeciliid shes. Molecular Phylogenetics and Evolution 43: 986–998. https://
doi.org/10.1016/j.ympev.2006.06.009
Huelsenbeck JP, Ronquist F (2001) MRBAYES: Bayesian inference of phylogenetic trees.
Bioinformatics 17: 754–755. https://doi.org/10.1093/bioinformatics/17.8.754
Huysentruyt F, Adriaens D (2005a) Descriptive osteology of Corydoras aeneus (Siluriformes:
Callichthyidae). Cybium 29: 261–73.
Comparative analysis of the mitogenomes of eleven Corydoras 103
Huysentruyt F, Adriaens D (2005b) Adhesive structures in the eggs of Corydoras aeneus (Gill
1858; Callichthyidae). Journal of Fish Biology 66: 871–876. https://doi.org/10.1111/
j.0022-1112.2005.00647.x
Ingram S, Munzner T, Olano M (2009) Glimmer: Multilevel MDS on the GPU. IEEE Trans-
actions on Visualization and Computer Graphics 15: 249–261. https://doi.org/10.1109/
TVCG.2008.85
Iwasaki W, Fukunaga T, Isagozawa R, Yamada K, Maeda Y, Satoh TP, Sado T, Mabuchi K,
Takeshima H, Miya M, Nishida M (2013) MitoFish and MitoAnnotator: A mitochondrial
genome database of sh with an accurate and automatic annotation pipeline. Molecular
Biology Evolution 30: 2531–2540. https://doi.org/10.1093/molbev/mst141
Kai Y, Nakayama K, Nakabo T (2002) Genetic dierences among three colour morphotypes of
the black rocksh, Sebastes inermis, inferred from mtDNA and AFLP analyses. Molecular
Ecology 11: 2591–2598. https://doi.org/10.1046/j.1365-294X.2002.01628.x
Kalyaanamoorthy S, Minh BQ, Wong TKF, von Haeseler A, Jermiin LS (2017) ModelFinder:
Fast model selection for accurate phylogenetic estimates. Nature Methods 14: 587–589.
https://doi.org/10.1038/nmeth.4285
Katoh K, Standley DM (2013) MAFFT multiple sequence alignment software version 7: Im-
provements in performance and usability. Molecular Biology Evolution 30: 772–780.
https://doi.org/10.1093/molbev/mst010
Kumar S, Tamura K, Nei M (1994) MEGA: Molecular evolutionary genetics analysis software
for microcomputers. Computer Applications in the Biosciences 10: 189–191. https://doi.
org/10.1093/bioinformatics/10.2.189
Letunic I, Bork P (2016) Interactive tree of life (iTOL) v3: An online tool for the display and
annotation of phylogenetic and other trees. Nucleic Acids Research 44: W242–W245.
https://doi.org/10.1093/nar/gkw290
Librado P, Rozas J (2009) DnaSP v5: A software for comprehensive analysis of DNA polymor-
phism data. Bioinformatics 25: 1451–1452. https://doi.org/10.1093/bioinformatics/btp187
Lima FCT, Britto MR (2020) A new Corydoras (Ostariophysi: Siluriformes: Callichthyidae)
with an unusual sexual dimorphism from the Rio Juruena basin, Brazil. Zootaxa 4742:
518–530. https://doi.org/10.11646/zootaxa.4742.3.6
Liu H, Tzeng CS, Teng HY (2002) Sequence variations in the mitochondrial DNA control
region and their implications for the phylogeny of the Cypriniformes. Canadian Journal of
Zoology 80: 569–581. https://doi.org/10.1139/z02-035
Liu Q, Liu Y, Xiao T, Xu B (2019a) Complete mitochondrial genome of Corydoras panda (Tele-
ostei, Siluriformes, Callichthyidae, Corydoradinae). Mitochondrial DNA Part B Resources
4: 2878–2879. https://doi.org/10.1080/23802359.2019.1660253
Liu Q, Liu Y, Xu B, Xiao T (2019b) Next-generation sequencing yields the complete mito-
chondrial genome of Corydoras sterbai (Teleostei, Siluriformes, Callichthyidae, Corydora-
dinae). Mitochondrial DNA Part B Resources 4: 2880–2881. https://doi.org/10.1080/23
802359.2019.1660255
Liu Q, Xu B, Xiao T (2019c) Complete mitochondrial genome of Corydoras duplicareus (Tele-
ostei, Siluriformes, Callichthyidae). Mitochondrial DNA Part B Resources 4: 1832–1833.
https://doi.org/10.1080/23802359.2019.1612714
Cheng-He Sun et al. / ZooKeys 1083: 89–107 (2022)
104
Liu S, Zhang J, Yao J, Liu Z (2016) e complete mitochondrial genome of the armored
catsh, Hypostomus plecostomus (Siluriformes: Loricariidae). Mitochondrial DNA Part A
DNA Mapping, Sequencing, and Analysis 27: 1908–1909. https://doi.org/10.3109/1940
1736.2014.971281
Liu Y, Xu B, Xiao T, Liu Q (2019d) Characterization and phylogenetic analysis of Corydoras
arcuatus mitochondrial genome. Mitochondrial DNA Part B Resources 4: 2876–2877.
https://doi.org/10.1080/23802359.2019.1660251
Lowe TM, Eddy SR (1997) TRNAscan-SE: A program for improved detection of transfer RNA
genes in genomic sequence. Nucleic Acids Research 25: 955–964. https://doi.org/10.1093/
nar/25.5.955
Lujan NK, Armbruster JW, Lovejoy NR, López-Fernández H (2015) Multilocus molecular
phylogeny of the suckermouth armored catshes (Siluriformes: Loricariidae) with a fo-
cus on subfamily Hypostominae. Molecular phylogenetics and evolution 82: 269–288.
https://doi.org/10.1016/j.ympev.2014.08.020
Luo R, Liu B, Xie Y, Li Z, Huang W, Yuan J, He G, Chen Y, Pan Q, Liu Y, Tang J, Wu G,
Zhang H, Shi Y, Liu Y, Yu C, Wang B, Lu Y, Han C, Wang J (2012) SOAPdenovo2: An
empirically improved memory-ecient short-read de novo assembler. GigaScience 1: 18.
https://doi.org/10.1186/2047-217X-1-18
Lv L, Su H, Xu B, Liu Q, Xiao T (2020) Complete mitochondrial genome of Corydoras agas-
sizii. Mitochondrial DNA Part B Resources 5: 727–728. https://doi.org/10.1080/238023
59.2020.1715277
Magalhães MGP, Moreira DA, Furtado C, Parente TE (2017) e mitochondrial genome of
Hypancistrus zebra (Isbrücker Nijssen 1991) (Siluriformes: Loricariidae), an endangered
ornamental sh from the Brazilian Amazon. Conservation Genetics Resources 9: 319–324.
https://doi.org/10.1007/s12686-016-0645-5
Manchado M, Catanese G, Ponce M, Funes V, Infante C (2007) e complete mitochon-
drial genome of the Senegal sole, Solea senegalensis Kaup. Comparative analysis of tan-
dem repeats in the control region among soles. DNA Sequence 18: 169–175. https://doi.
org/10.1080/10425170701308956
Meng F, Yin X, Zhang T, Zhao C, Xue X, Xia X, Zhu X, Duan Z, Liu B, Liu Y (2021) e rst
determination and analysis of the complete mitochondrial genome of Ancistrus temmincki
(Siluriformes: Loricariidae). Mitochondrial DNA Part B Resources 6: 1583–1585. https://
doi.org/10.1080/23802359.2020.1866446
Meyer A (1993) Evolution of mitochondrial DNA in shes. In: Hochachka PW, Mommsen TP
(Eds) Biochemistry and Molecular Biology of Fishes. Elsevier Press, [xxxx,] 1–38.
Michot B, Qu LH, Bachellerie JP (1990) Evolution of large‐subunit rRNA structure: e di-
versication of divergent D3 domain among major phylogenetic groups. European Journal
of Biochemistry 188: 219–229. https://doi.org/10.1111/j.1432-1033.1990.tb15393.x
Moreira DA (2018) O que Dados transcriptômicos revelam sobre a biodiversidade e evolução
de Loricarioidei (Siluriformes) [Doctoral Dissertation, Instituto Oswaldo Cruz, Fundação
Oswaldo Cruz, Rio de Janeiro].
Moreira DA, Buckup PA, Britto MR, Magalhães MGP, de Andrade PCC Furtado C, Parente TE
(2016a) e complete mitochondrial genome of Corydoras nattereri (Callichthyidae: Cory-
Comparative analysis of the mitogenomes of eleven Corydoras 105
doradinae). Neotropical Ichthyology 14(01): e1501670. https://doi.org/10.1590/1982-
0224-20150167
Moreira DA, Buckup PA, Furtado C, Val AL., Schama R, Parente TE (2017) Reducing the
information gap on Loricarioidei (Siluriformes) mitochondrial genomics. BMC Genomics
18: 1–13. https://doi.org/10.1186/s12864-017-3709-3
Moreira DA, Magalhães MGP, de Andrade PCC, Furtado C, Val AL, Parente TE (2016b)
An RNA-based approach to sequence the mitogenome of Hypoptopoma incognitum (Si-
luriformes: Loricariidae). Mitochondrial DNA Part A DNA Mapping, Sequencing, and
Analysis 27: 3784–3786. https://doi.org/10.3109/19401736.2015.1079903
Nakatani M, Miya M, Mabuchi K, Saitoh K, Nishida M (2011) Evolutionary history of Otophy-
si (Teleostei), a major clade of the modern freshwater shes: Pangaean origin and Mesozoic
radiation. BMC Evolutionary Biology 11: 177. https://doi.org/10.1186/1471-2148-11-177
Nawrocki EP, Eddy SR (2013) Infernal 1.1: 100-Fold faster RNA homology searches. Bioinfor-
matics 29: 2933–2935. https://doi.org/10.1093/bioinformatics/btt509
O’Brien TW (1971) e general occurrence of 55 S ribosomes in mammalian liver mitochon-
dria. Journal of Biological Chemistry 246: 3409–3417. https://doi.org/10.1016/S0021-
9258(18)62239-2
Ojala D, Montoya J, Attardi G (1981) tRNA punctuation model of RNA processing in human
mitochondria. Nature 290: 470–474. https://doi.org/10.1038/290470a0
Parente TE, Moreira DA, Buckup PA, de Andrade PCC, Magalhães MGP, Furtado C, Britto
MR, Val AL (2018) Remarkable genetic homogeneity supports a single widespread species
of Hoplosternum littorale (Siluriformes, Callichthyidae) in South America. Conservation
Genetics Resources 10: 563–569. https://doi.org/10.1007/s12686-017-0831-0
Parente TE, Moreira DA, Magalhães MGP, de Andrade PCC, Furtado C, Haas BJ, Stegeman
JJ, Hahn ME (2017) e liver transcriptome of suckermouth armoured catsh (Pterygopli-
chthys anisitsi, Loricariidae): Identication of expansions in defensome gene families. Ma-
rine Pollution Bulletin 115: 352–361. https://doi.org/10.1016/j.marpolbul.2016.12.012
Pereira AH, Facchin S, Oliveira do Carmo A, Núñez Rodriguez D, Cardoso Resende LC, Kala-
pothakis Y, Brandão Dias Ferreira Pinto P, Mascarenhas Alves CB, Henrique Zawadzki C,
Kalapothakis E (2019) Complete mitochondrial genome sequence of Hypostomus francisci
(Siluriformes: Loricariidae). Mitochondrial DNA Part B Resources 4: 155–157. https://
doi.org/10.1080/23802359.2018.1544860
Pesole G, Gissi C, De Chirico A, Saccone C (1999) Nucleotide substitution rate of mam-
malian mitochondrial genomes. Journal of molecular evolution 48: 427–434. https://doi.
org/10.1007/PL00006487
Popazoglo F, Boeger WA (2000) Neotropical Monogenoidea 37. Redescription of Gyrodacty-
lus superbus (Szidat 1973) comb. n. and description of two new species of Gyrodactylus
(Gyrodactylidea: Gyrodactylidae) from Corydoras paleatus and C. ehrhardti (Teleostei: Silu-
riformes: Callichthyidae) of southern Brazil. Folia Parasitologica 47(2): 105–110. https://
doi.org/10.14411/fp.2000.022
Ranwez V, Douzery EJP, Cambon C, Chantret N, Delsuc F (2018) MACSE v2: toolkit for
the alignment of coding sequences accounting for frameshifts and stop codons. Molecular
Biology Evolution 35: 2582–2584. https://doi.org/10.1093/molbev/msy159
Cheng-He Sun et al. / ZooKeys 1083: 89–107 (2022)
106
Ren F, Chen D, Ma X (2019) e complete mitochondrial genome of Sturisomatichthys pana-
mense (Siluriformes: Loricariidae) analysed by next-generation sequencing and phylogeny
of the catsh subfamily Loricariinae (Siluriformes: Loricariidae). Biologia 75: 1365–1372.
https://doi.org/10.2478/s11756-019-00389-7
Resende LC, Carmo AOd, Núñez-Rodriguez D, Pimentel JdSM, Bedore AG, Leal HG, Kala-
pothakis E (2016) Pimelodus maculatus (Siluriformes, Pimelodidae): Complete mtDNA
sequence of an economically important sh from the São Francisco river basin. Mitochon-
drial DNA Part B Resources 1: 806–808. https://doi.org/10.1080/23802359.2016.1219
646
Rocha-Reis DA, Pasa R, Menegidio FB, Heslop-Harrison JS, Schwarzacher T, Kavalco KF
(2020) e complete mitochondrial genome of two armored catsh populations of the
genus Hypostomus (Siluriformes, Loricariidae, Hypostominae). Frontiers in Ecology and
Evolution 8: e579965. https://doi.org/10.3389/fevo.2020.579965
Roxo FF, Ochoa LE, Sabaj MH, Lujan NK, Covain R, Silva GS, Oliveira C (2019) Phylog-
enomic reappraisal of the Neotropical catsh family Loricariidae (Teleostei: Siluriformes)
using ultraconserved elements. Molecular Phylogenetics and Evolution 135: 148–165.
https://doi.org/10.1016/j.ympev.2019.02.017
Saitoh K, Miya M, Inoue JG, Ishiguro NB, Nishida M (2003) Mitochondrial genomics of
ostariophysan shes: Perspectives on phylogeny and biogeography. Journal of Molecular
Evolution 56: 464–472. https://doi.org/10.1007/s00239-002-2417-y
Simon C, Frati F, Beckenbach A, Crespi B, Liu H, Flook P (1994) Evolution, weighting, and
phylogenetic utility of mitochondrial gene sequences and a compilation of conserved poly-
merase chain reaction primers. Annals of the Entomological Society of America 87: 651–
701. https://doi.org/10.1093/aesa/87.6.651
Tencatt LFC, Dos Santos SA, Evers HG, Britto MR (2021) Corydoras fulleri (Siluriformes:
Callichthyidae), a new catsh species from the Rio Madeira basin, Peru. Journal of Fish
Biology 99(2): 614–628. https://doi.org/10.1111/jfb.14750
Watanabe S (2013) A widely applicable Bayesian information criterion. Journal of Machine
Learning Research 14: 867–897.
Wolstenholme DR (1992) Animal mitochondrial DNA: Structure and evolution. International
Review of Cytology 141: 173–216. https://doi.org/10.1016/S0074-7696(08)62066-5
Xu B, Su H, Liu Q, Lv L, Chen K, Xiao T (2020) Complete mitochondrial genome of Brochis
multiradiatus. Mitochondrial DNA Part B Resources 5: 646–647. https://doi.org/10.108
0/23802359.2019.1711227
Xu TJ, Cheng YZ, XZ, Liu GS, Wang RX (2011) e complete mitochondrial genome of
the marbled rocksh Sebastiscus marmoratus (Scorpaeniformes, Scorpaenidae): Genome
characterization and phylogenetic considerations. Molekulyarnaya Biologiya (Mosk) 45:
392–403. https://doi.org/10.1134/S0026893311020191
Yoshizawa K, Johnson KP (2003) Phylogenetic position of Phthiraptera (Insecta: Paraneoptera)
and elevated rate of evolution in mitochondrial 12S and 16S rDNA. Molecular Phyloge-
netics and Evolution 29: 102–114. https://doi.org/10.1016/S1055-7903(03)00073-3
Zhang D, Gao F, Jakovlić I, Zou H, Zhang J, Li WX, Wang GT (2020) PhyloSuite: An in-
tegrated and scalable desktop platform for streamlined molecular sequence data manage-
Comparative analysis of the mitogenomes of eleven Corydoras 107
ment and evolutionary phylogenetics studies. Molecular Ecology Resources 20: 348–355.
https://doi.org/10.1111/1755-0998.13096
Zhang K, Liu Y, Chen J, Zhang H, Gong L, Jiang L, Liu L, Lü Z, Liu B (2021) Charac-
terization of the complete mitochondrial genome of Macrotocinclus anis (Siluriformes;
Loricariidae) and phylogenetic studies of Siluriformes. Molecular Biology Reports 48:
677–689. https://doi.org/10.1007/s11033-020-06120-z
Supplementary material 1
COI sequences of Corydoras aeneus and C. paleatus Tables S1–S4, Figs S1–S4
Authors: Cheng-He Sun, Qi Huang, Xiao-Shu Zeng, Sha Li, Xiao-Li Zhang, Ya-Nan
Zhang, Jian Liao, Chang-Hu Lu, Bo-Ping Han, Qun Zhang
Data type: docx le
Explanation note: COI sequences of Corydoras aeneus and C. paleatus. Table S1. Best
substitution models for Bayesian inference (BI) and maximum-likelihood (ML)
analyses. Table S2. Summarized mitogenomic characteristics of the eleven Corydoras
species investigated in this study. Table S3. e K2P genetic distances of the eleven
mitogenomes of Corydoras. Table S4. Start and stop codons of protein-coding genes
in the eleven Corydoras mitogenomes. Figure S1. Gene orders of mitogenomes of
the studied species. Figure S2. Relative synonymous codon usage of 13 protein-
coding genes in the mitogenomes of eleven Corydoras species. Figure S3. Codon
usage patterns of eleven Corydoras mitogenomes. Figure S4. Secondary structures
of tRNA-Ser(GCT) in the two newly sequenced Corydoras species.
Copyright notice: is dataset is made available under the Open Database License
(http://opendatacommons.org/licenses/odbl/1.0/). e Open Database License
(ODbL) is a license agreement intended to allow users to freely share, modify, and
use this Dataset while maintaining this same freedom for others, provided that the
original source and author(s) are credited.
Link: https://doi.org/10.3897/zookeys.1083.76887.suppl1
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