Next Article in Journal
Analysis of Crop Consumption Using Scatological Samples from the Red-Crowned Crane Grus japonensis in Eastern Hokkaido, Japan
Previous Article in Journal
Novel Heredity Basis of the Four-Horn Phenotype in Sheep Using Genome-Wide Sequence Data
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Analysis of the Complete Mitochondrial Genome of Pteronura brasiliensis and Lontra canadensis

1
Jiangsu Key Laboratory for Biodiversity and Biotechnology, College of Life Sciences, Nanjing Normal University, Nanjing 210023, China
2
College of Life Sciences, Qufu Normal University, Qufu 273165, China
*
Author to whom correspondence should be addressed.
Animals 2023, 13(20), 3165; https://doi.org/10.3390/ani13203165
Submission received: 21 July 2023 / Revised: 25 September 2023 / Accepted: 8 October 2023 / Published: 10 October 2023
(This article belongs to the Section Aquatic Animals)

Abstract

:

Simple Summary

Mitochondria, the energy metabolism center, provide most of the energy required for life processes through oxidative phosphorylation (OXPHOS). Mitochondrial genomes, a useful type of genetic marker, are widely used in phylogenetics and evolutionary and ecological research. Herein, the full-length mitochondrial genome sequences of two otter species, Pteronura brasiliensis (P. brasiliensis) and Lontra canadensis (L. canadensis), were constructed for the first time. Comparative mitochondrial genome, selection pressure, and phylogenetic independent contrasts (PICs) analyses were employed to unveil the structure and evolutionary characteristics of their mitochondrial genomes. Additionally, phylogenetic analysis confirmed the phylogenetic positions of these otter species.

Abstract

P. brasiliensis and L. canadensis are two otter species, which successfully occupied semi-aquatic habitats and diverged from other Mustelidae. Herein, the full-length mitochondrial genome sequences were constructed for these two otter species for the first time. Comparative mitochondrial genome, selection pressure, and phylogenetic independent contrasts (PICs) analyses were conducted to determine the structure and evolutionary characteristics of their mitochondrial genomes. Phylogenetic analyses were also conducted to confirm these two otter species’ phylogenetic position. The results demonstrated that the mitochondrial genome structure of P. brasiliensis and L. canadensis were consistent across Mustelidae. However, selection pressure analyses demonstrated that the evolutionary rates of mitochondrial genome protein-coding genes (PCGs) ND1, ND4, and ND4L were higher in otters than in terrestrial Mustelidae, whereas the evolutionary rates of ND2, ND6, and COX1 were lower in otters. Additionally, PIC analysis demonstrated that the evolutionary rates of ND2, ND4, and ND4L markedly correlated with a niche type. Phylogenetic analysis showed that P. brasiliensis is situated at the base of the evolutionary tree of otters, and then L. canadensis diverged from it. This study suggests a divergent evolutionary pattern of Mustelidae mitochondrial genome PCGs, prompting the otters’ adaptation to semi-aquatic habitats.

1. Introduction

Otters belong to the subfamily Lutrinae within the family Mustelidae and have recently radiated from terrestrial weasel-like ancestors and successfully thrive in semi-aquatic habitats. P. brasiliensis and L. canadensis are two otter species. The giant otter (P. brasiliensis), which belongs to the genus Pteronura, is the largest and unique species of otter living in freshwater habitats [1]. The North American river otter (L. canadensis), belonging to the genus Lontra, is mainly distributed in the North American watersheds [2] and can occupy different habitats from the sea to freshwater habitats, mountain streams, and desert canyons [2]. These two species were often identified by their morphological characteristics [2,3,4]. Their phylogenetic status was also determined using their morphological characteristics or partial fragments of the mitochondrial genome [4,5,6], as their mitochondrial genomes are still unknown up to now. Therefore, we assembled the mitochondrial genomes of these two species for the first time to provide important genetic resources for future study, and to determine their phylogenetic status. Additionally, these mitochondrial genomes may be important genetic resources for protecting these two species in the future.
The mitochondrion is the center of energy metabolism [7], and its genome is independent of the nuclear genome. Mammalian mitochondrial genomes are double-stranded circular molecules approximately 16 kB nucleotide in length [8]. One strand of the mitochondrial genome is the heavy (H) strand, which is rich in guanine. The other strand, called the light (L) strand, is cytosine-rich [8,9]. The mammalian mitochondrial genome usually contains 22 transfer RNA (tRNA) genes, 13 protein-coding genes (PCGs), 2 ribosomal RNA (rRNA) genes, and a major non-coding control region (D-loop) [8,10].
The mitochondrial genome is maternally inherited; its molecular size is small, the sequencing procedures are simple, and the recombination rate is low. Therefore, they are often used to analyze phylogenetic relationships, genetic diversity, and evolutionary adaptations [11,12]. Mitochondrial genome evolution has been previously shown to be related to niche adaptation in animals. The Mustelidae mitochondrial genome had undergone divergent evolution among animals adapted to different niches [13]. The Cetartiodactyla mitochondrial genome also displayed divergent evolutionary patterns during the process of niche adaptation [14]. Positive selection signals in the mitochondrial genomes of Vesicomyidae species had revealed evidence for their adaptive to deep-sea environments [15]. Studies on the mitochondrial genome of domesticated animals, such as dogs, cattle, and yaks had shown relaxed selection patterns when compared with their wild relatives under the adaptation to domesticated environments [16,17,18]. Additionally, the mitochondrial genome of Tibetan loaches displayed more non-synonymous mutations than that of non-Tibetan loaches in order to adapt to the environment of the Tibetan plateau [19]. Based on this previous research, this study hypothesized that the mitochondrial genome of otters might have divergent evolutionary patterns in order to adapt to semi-aquatic environments when compared to their terrestrial Mustelidae close relatives.
In this study, we assembled and annotated the complete mitochondrial genome of P. brasiliensis and L. canadensis based on high-quality raw genome sequencing data for the first time to explore their structural characteristics and provide genetic resources for protecting these two important species. The mitochondrial genomes of the two genera of otters have not been previously studied. Contrastingly, in order to investigate how the otters adapt to the semi-aquatic habitats, nine otters representing all the seven genera of Lutrinae and ten Mustela who were the closest relatives of otters in Mustelidae were selected to explore the evolutionary characteristics of otters’ mitochondrial genome. Therefore, this study aimed to reveal the adaptive evolution of otters to semi-aquatic niches from the mitogenomics perspective and provide genetic resources for the protection of these two otter species.

2. Materials and Methods

2.1. Mitochondrial Genome Assembly and Annotation

High-quality raw sequencing data of otter genomes (P. brasiliensis: SRR12437585, L. canadensis: SRR10409165) were downloaded from the SRA database (https://www.ncbi.nlm.nih.gov/sra/, accessed on 30 April 2022), and then NOVOPlasty 4.1 [20] was used to assemble the two complete mitogenomes with the raw genome sequencing data and the seed sequence (EF491181.1 for P. brasiliensis and JF443249.1 for L. canadensis). Then, the assembled sequences were validated and revised with seed sequences and genome data. The validated mitochondrial genome sequences were annotated with the online software MITOS2 [21] (http://mitos2.bioinf.uni-leipzig.de/index.py, accessed on 1 June 2022). The annotation results were revised by comparing them with the mitochondrial genomes of Enhydra lutris (NC_009692.1) through the method of BLAST. The two mitochondrial genomes were submitted to GenBank under the accession numbers OP056177.1 (L. canadensis) and OP056176.1 (P. brasiliensis). Structure maps of these two mitochondrial genomes were drawn with the online software OGDRAW 1.3.1 [22] (https://chlorobox.mpimp-golm.mpg.de/OGDraw.html, accessed on 3 March 2023). The relative synonymous codon usage (RSCU) of the two mitochondrial genomes was calculated using MEGA X [23] and visualized with R software (V 4.1.3; package, ggplot2).

2.2. tRNA Gene Structure Analyses

The online software tRNAScan-SE 2.0 was used to predict the tRNA gene structure [24] (http://lowelab.ucsc.edu/tRNAscan-SE, accessed on 30 July 2022).

2.3. Comparative Mitochondrial Genome Analyses

The complete mitochondrial genomes of 21 Mustelidae species from 2 different habitats (terrestrial and semi-aquatic) were selected for comparison. The accession numbers of the 21 mitochondrial genomes are shown in Table S1. The nucleotide compositional bias were measured through AT skew [(A − T)/(A + T)] and GC skew [(G − C)/(G + C)] [25]. Furthermore, the synteny analysis was conducted with Mauve software (2.3.1) [26] based on the 11 mitochondrial genomes selected from the above 21 species.

2.4. Phylogenetic Analyses

A total of 13 different protein-coding genes (PCGs) from the 21 mitochondrial genomes were retrieved and combined into a single sequence, named 13PCG. We chose only 13PCG for phylogenetic analyses because the PCG evolutionary rate was more suitable for phylogenetic analyses than that of the other regions of the mitochondrial genome sequences. The combined sequences 13PCG were aligned with MUSCLE v3.8.31 [27]. The optimal model (GTR + G + I) was selected using the model finder function of PhyloSuite software (V 1.2.2) [28]. Bayesian inference (BI) was subsequently used to infer the phylogenetic relationships of these 21 species (Table S1). BI was conducted with MrBayes [29] using the Markov Chain Monte Carlo (MCMC) algorithm, running for 2,000,000 cycles (sampling one tree every 1000 generations). Vormela peregusna and Galictis vittata were selected as the outgroups. Using the Interactive Tree of Life (ITOL) website, the derived BI tree was visualized [30].

2.5. Selection Analyses

To assess the molecular evolution rate of mitochondrial genome 13 PCGs and 13PCG, we constructed 14 datasets and calculated the ratio of the non-synonymous to synonymous substitution rate (ω = dN/dS) using the codon-based maximum likelihood software (CodeML 4.9j), which was implemented in PAML 4.9j [31]. The root-to-tip ω values (average ω value from the last universal common ancestor of all species on the species tree to each terminal branch) were assessed based on the free ratio model in CodeML. Subsequently, the otters’ root-to-tip ω values were compared with those of the terrestrial Mustelidae. The Mann–Whitney–Wilcoxon test was used for this analysis. The branch model (two-ratio model, model = 2, NSsites = 0; one-ratio model, model = 0, NSsites = 0) was used to detect rapidly evolving genes in the otter branches. The tree used in this analysis was based on the traditional otter classification and the BI tree constructed above (Figure S1).

2.6. Phylogenetic Independent Contrast (PIC) Analysis

We performed a PIC analysis [32] on 13 PCGs and 13PCG with the ape package implemented in R 3.6.2 software to explore the relationship between habitat type and the dN/dS of mitochondrial genome PCGs. The tree in selection analyses was used as the input tree. The root-to-tip ω values were transformed by log10 and used as the dN/dS. According to the habitat, the otters and the other species were classified into the semi-aquatic and terrestrial groups, respectively. We subsequently coded them as 1 and 0, respectively.

3. Results

3.1. Mitochondrial Genome Structure and Annotation

The lengths of the complete mitochondrial genome of P. brasiliensis and L. canadensis were 16,395 and 16,500 bp, respectively. They both consisted of 37 genes (13 PCGs, 22 tRNA genes, and 2 rRNA genes) and a control region (D-loop) (Table 1 and Table S3; Figure 1 and Figure S2), in which 9 genes (ND6, tRNAGLN, tRNAALA, tRNAASN, tRNACYS, tRNATYR, tRNASER2, tRNAGLU, and tRNAPRO) were distributed in the light strand, and the other 28 genes were located on the heavy strand. The initiation codon for most PCGs was ATG (number (N) = 9) and the remaining 4 were ATC, ATA, GTG, and ATT in P. brasiliensis, whereas the termination codons were TAA (N = 7), TAG (N = 2), TA- (N = 2), T-- (N = 1), and AGA (N = 1). Contrastingly, the common initiation codon in L. canadensis was also ATG (N = 9), and the remaining 4 were ATC, ATA, GTG, and ATT, while the termination codons were TAA (N = 8), TAG (N = 2), TA- (N = 1), T-- (N = 1), and AGA (N = 1). Qverlaps of 77 and 76 bp existed across the mitochondrial genomes of P. brasiliensis and L. canadensis, respectively. The longest overlap was observed between ATP8 and ATP6 (43 bp in P. brasiliensis and 40 bp in L. canadensis).
GC nucleotide proportions were 39.5% in the complete mitochondrial genome of P. brasiliensis and 42.8% in that of L. canadensis, respectively, which were lower than those of the AT nucleotide proportions. The AT skew for the complete mitochondrial genome of P. brasiliensis and L. canadensis were 0.074 and 0.094, respectively, whereas the GC skew were −0.274 and −0.274, respectively. For both of the two species, the composition proportions of AT nucleotide were also higher than that of the GC nucleotide in the PCGs, tRNAs, rRNAs, and D-loops individually. The AT skew and GC skew were slightly positive and negative, respectively (Table 2 and Table 3). This suggested that the proportion of A was higher than that of T, while the proportion of C was higher than that of G.
According to the codon usage analysis, the two species exhibited a strong preference for eight codon families (Leu1, Val, Ala, Arg, Pro, Thr, Gly, and Ser2) (Figure 2).

3.2. tRNA Gene Structure

Based on the tRNA gene sequences identified in the annotation, their secondary structures were determined using tRNAScan-SE 2.0. Except for tRNASER2(GCT), all of the remaining 21 tRNAs had a canonical cloverleaf structure. However, the tRNASER2(GCT) lacked the dihydrouridine hairpin structure (Figure 3 and Figure S3).

3.3. Comparison of Mitochondrial Genomes among Species

The nucleotide composition of the heavy strand of the mitochondrial genome was generally consistent in otters and Mustelidae. The proportion of A was higher than that of T, whereas the proportion of G was lower than that of C. The AT skew of the mitochondrial genome’s heavy strand was slightly positive, whereas the GC skew was slightly negative. Contrastingly, the AT proportion was higher than that of GC in all of the 21 species (Table 4).
Comparative alignment of the 11 mitochondrial genomes showed that the gene order was conserved among otters and their close Mustela relatives (Figure 4).

3.4. Phylogenetic Analyses

All otters were clustered in one clade, whereas the Mustela species were clustered in another clade with high Bayesian posterior probabilities (Figure 5). P. brasiliensis was the earliest otter species to diverge from the Mustela species. Subsequently, L. canadensis appeared. Lutra lutra and Lutra sumatrana were clustered into one clade; Aonyx cinerea, Lutrogale perspicillata, and Aonyx capensis were clustered into another clade. The remaining Enhydra lutris and Hydrictis maculicollis individually formed single clades each.

3.5. Selection Analyses

Based on the root-to-tip ω values, the mitochondrial genome PCGs of 21 Mustelidae species were mainly under purifying selection (Table S2). The result of comparing the mitochondrial genome PCGs ω values of otters with that of other Mustelidae species demonstrated that the ω values of ND1, ND4, and ND4L were higher in otters than in terrestrial Mustelidae, whereas ND2, ND6, and COX1 had lower ω values in otters (Figure 6). The result also indicated that ATP8 had the highest ω values in all of the 13 PCGs in these 21 species (Figure 7). However, the difference between otters and terrestrial Mustelidae’s ω values on ATP8 was insignificant. In the otter clade, 6 of the 13 PCGs of the mitochondrial genomes were rapidly evolving. The rapidly evolving genes were ND1, ND4, ND4L, ND5, COX3, and CYTB (Table 5). Combined with the root-to-tip ω values, we predicted that the three genes ND1, ND4, and ND4L likely evolved more quickly in otters than in terrestrial Mustelidae species.

3.6. PIC Analysis

PIC analysis demonstrated that the correlation between evolutionary rates and habitats was significant for the genes ND2, ND4, and ND4L. This demonstrated that habitat type significantly influenced the evolutionary rate of these three genes in otters and terrestrial Mustelidae species (Figure 8).

4. Discussion

Mitochondria are the key cellular organs that provide energy for the life activities of animals through OXPHOS [33,34,35]. The mammalian mitochondrial genome is independent of the nuclear genome and is a double-stranded circular molecule, approximately 16 kb in length [8]. One strand of the guanine-rich mitochondrial genome is called the heavy (H) strand, and the other cytosine-rich strand is called the light (L) strand [8,9]. The mitochondrial genome is maternally inherited and often used to analyze phylogenetic relationships and evolutionary adaptations [11,12]. Additionally, it is extremely important in conservation genetics research. Therefore, we assembled two mitochondrial genomes for the two otter species that needed to be protected and studied.
Characteristics of the mitochondrial genome may differ among different animal groups. Herein, the mitochondrial genomes of P. brasiliensis and L. canadensis were 16,395 and 16,500 bp, respectively, in length and contained 37 genes (13 PCGs, 22 tRNA genes, and 2 rRNA genes) and a control region (D-loop). Among these, 28 genes were in the heavy strand and 9 other genes were in the light strand. The initiation codon for most PCGs was ATG, and the common termination codon was TAA. These characteristics were consistent with those of the mitochondrial genome’s characteristics of other otters [36,37,38,39,40]. The overall AT content of the heavy strands of these two mitochondrial genomes was higher than the GC content, indicating AT-rich characteristics. The base compositions were skewed similarly to those of other vertebrate mitochondrial genome sequences [41,42,43]. The AT content was higher than the GC content in most of the mitochondrial genomes of otters (Table 4). In bacteria, the GC skew represents the footprint of genome evolution driven by DNA replication [44]. Whether there was a relationship between the GC skew of the mitochondrial genomes and the otter species evolution remains unknown.
Among the 22 tRNAs, the tRNASER2(GCT) lacked the dihydrouridine hairpin structure, and all the remaining 21 tRNAs had a canonical cloverleaf structure. The tRNASER was found to lack a canonical cloverleaf structure in several animals [37,40,42,45]. Several studies have demonstrated that the lack of a dihydrouridine arm or thymidine-pseudouridine-cytidine (TψC) loop in tRNASER might not affect its normal function [46,47]. This suggested that tRNASER2(GCT) was able to perform normal functions in these two otter species.
The results of the phylogenetic analysis showed that all otters were clustered into one clade, whereas the Mustela species were clustered in another clade. P. brasiliensis was the earliest otter species to diverge from the Mustela species, which formed the genus Pteronura. Subsequently, L. canadensis branched out from P. brasiliensis. E. lutris and H. maculicollis individually formed one single clade each, followed by L. canadensis, which formed the genera Enhydra and Hydrictis. L. lutra and L. sumatrana clustered into one clade, belonging to the genus Lutra. These evolutionary relationships were consistent with the results of previous traditional studies on otter classification [48]. L. perspicillata, A. cinerea, and A. capensis were clustered into one clade. Several previous studies had demonstrated that L. perspicillata and A. cinerea clustered into one clade [39,49,50,51,52,53], which was inconsistent with the traditional classification [48]. We inferred that this might be the result of hybridization between L. perspicillata and A. cinerea [49].
Otters are semi-aquatic mammals, with important characteristics that distinguish them from other terrestrial Mustelidae species. Habitat and locomotive styles have been shown to exert a certain influence on the evolution of animal mitochondrial genomes. For example, ecological specialization exerted selective constraints on the mitochondrial genomes of Mustelidae [13]. The mitochondrial genome of some domesticated lineages, such as dogs, cattle, yaks, pigs, and silkworms, and weakly locomotive mollusks, birds, and mammalian lineages showed a relaxed evolutionary selection pattern [16,17,18,54,55,56,57]. Different habitats and lifestyles affect the evolutionary style of fish mitochondrial genomes [43,58]. Additionally, the evolution of the Cetartiodactyla mitochondrial genome displayed divergent patterns during the process of niche adaptation [14]. We analyzed the evolutionary patterns of the otters’ mitochondrial genomes through the method of comparative mitogenomics to clarify the influence of semi-aquatic habitats. The result of root-to-tip ω values demonstrated that the mitochondrial genome PCGs of the nine otters were mainly under purifying selection. This was consistent with the results of previous studies in other animals [59]. Furthermore, the ω values of ND1, ND4, and ND4L were higher in otters than in terrestrial Mustelidae, whereas ND2, ND6, and COX1 had lower ω values in otters. Additionally, the studies on Tibetan loaches found some differences in mitochondrial genome PCGs’ ω values when compared with the plain species [19]. Our results also showed that ATP8 had the highest ω values in all of the 13 PCGs in these 21 species. ATP8 is suggested to have a high evolutionary rate in several animals [13,14,56]. ATP8 plays an important role in metabolism, respiratory electron transport, and heat production [60]. The high evolutionary rate of ATP8 in animals may allow for several further beneficial substitutions, which may be advantageous for animals to adapt to different ecological niches [61,62]. Contrastingly, six (ND1, ND4, ND4L, ND5, COX3, and CYTB) of the thirteen PCGs of the mitochondrial genome were rapidly evolving in the otter branch. This suggested that the otter group accumulated more nonsynonymous mutations than other terrestrial Mustelidae animals in these six genes. The high number of nonsynonymous mutations might result in a few beneficial amino acid changes, which may help otters adapt to semi-aquatic habitats [19,61,62,63]. Studies on galliform birds and loaches found that high-altitude species had large dN/dS for the 13 concatenated mitochondrial PCGs [19,64] because of their high energy demands. Combined with the root-to-tip ω values, we predict that the three genes (ND1, ND4, and ND4L) likely evolved more quickly in otters than in terrestrial Mustelidae species. We inferred that these rapidly evolving genes are related to otters adapting to semi-aquatic habitats.
We conducted a PIC analysis to eliminate the impact of evolutionary relationships on the mitochondrial genome evolution. A significant correlation between evolutionary rates and habitats for ND2, ND4, and ND4L was observed. This suggested that habitat type markedly influenced the evolutionary rate of these three genes in otters and terrestrial Mustelidae species, and mitochondrial gene evolution in otters might be correlated with their adaptation to semi-aquatic habitats.

5. Conclusions

The mitochondrial genomes of P. brasiliensis and L. canadensis were constructed for the first time, which will be helpful for the protection of these two otter species in the future. The structural characteristics of these two mitochondrial genomes are consistent with those of other otter species. Selective pressure and PIC analyses demonstrated that habitat type markedly influenced the evolutionary rate of otter PCGs of mitochondrial genomes and the evolution of mitochondrial genomes in otters might be correlated with their adaptation to semi-aquatic habitats.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/ani13203165/s1. Table S1: The twenty-one species information selected in this study. Table S2: The root-to-tip ω values of mitochondrial genomes 13PCG and each PCG of twenty-one species. Table S3: Characteristics of the mitochondrial genome of L. canadensis. Figure S1: The tree used in the selection and PIC analyses. Figure S2: Mitochondrial genome structure map of L. canadensis. Figure S3: The predicted secondary structures of 22 tRNAs genes in L. canadensis mitochondrial genome.

Author Contributions

Conceptualization, writing—review and editing, writing—original draft preparation, data curation, formal analysis, and project administration, Q.W.; data curation and data analysis, X.W. (Xibao Wang); data analysis, Y.D., Y.S. and G.S.; project administration, X.W. (Xiaoyang Wu), C.Z. and W.S.; conceptualization and project administration, G.Y.; conceptualization, funding acquisition, and project administration, H.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the China Postdoctoral Science Foundation (2019M661878) and the National Natural Science Foundation of China (32370443, 32070405, 32270444, 32000291, 32001228 and 32170530).

Institutional Review Board Statement

Ethical review and approval were omitted in this study, as only non-invasive samples were collected and analyzed.

Informed Consent Statement

Not applicable.

Data Availability Statement

All the mitochondria genome sequences used in this study were downloaded from the GenBank database using the accession numbers in Table S1.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Duplaix, N. Observations on the Ecology and Behavior of the Giant River Otter Pteronura brasiliensis in Suriname. Rev. D’écologie 1980, 34, 495–620. [Google Scholar] [CrossRef]
  2. Lariviere, S.; Walton, L.R. Lontra canadensis. Mamm. Species 1998, 587, 1–8. [Google Scholar] [CrossRef]
  3. Noonan, P.; Prout, S.; Hayssen, V. Pteronura brasiliensis (Carnivora: Mustelidae). Mamm. Species 2017, 49, 97–108. [Google Scholar] [CrossRef]
  4. Chadwick, E. Otters: Ecology, Behaviour and Conservation. Freshw. Biol. 2010, 53, 1914–1915. [Google Scholar] [CrossRef]
  5. Timm-Davis, L.L.; DeWitt, T.J.; Marshall, C.D. Divergent Skull Morphology Supports Two Trophic Specializations in Otters (Lutrinae). PLoS ONE 2015, 10, e0143236. [Google Scholar] [CrossRef]
  6. Waku, D.; Segawa, T.; Yonezawa, T.; Akiyoshi, A.; Ishige, T.; Ueda, M.; Ogawa, H.; Sasaki, H.; Ando, M.; Kohno, N.; et al. Evaluating the Phylogenetic Status of the Extinct Japanese Otter on the Basis of Mitochondrial Genome Analysis. PLoS ONE 2016, 11, e0149341. [Google Scholar] [CrossRef]
  7. Das, J. The role of mitochondrial respiration in physiological and evolutionary adaptation. Bioessays 2010, 28, 890–901. [Google Scholar] [CrossRef]
  8. Boore, J.L. Animal mitochondrial genomes. Nucleic Acids Res. 1999, 27, 1767–1780. [Google Scholar] [CrossRef]
  9. Macey, J.R.; Schulte, J.A.; Larson, A.; Papenfuss, T.J. Tandem duplication via light-strand synthesis may provide a precursor for mitochondrial genomic rearrangement. Mol. Biol. Evol. 1998, 15, 71–75. [Google Scholar] [CrossRef]
  10. Pereira, S.L. Mitochondrial genome organization and vertebrate phylogenetics. Genet. Mol. Biol. 2000, 23, 745–752. [Google Scholar] [CrossRef]
  11. Gissi, C.; Iannelli, F.; Pesole, G. Evolution of the mitochondrial genome of Metazoa as exemplified by comparison of congeneric species. Heredity 2008, 101, 301–320. [Google Scholar] [CrossRef]
  12. Saccone, C.; De Giorgi, C.; Gissi, C.; Pesole, G.; Reyes, A. Evolutionary genomics in Metazoa: The mitochondrial DNA as a model system. Gene 1999, 238, 195–209. [Google Scholar] [CrossRef] [PubMed]
  13. Wei, Q.; Zhang, H.; Wu, X.; Sha, W. The selective constraints of ecological specialization in mustelidae on mitochondrial genomes. Mammal Res. 2019, 65, 85–92. [Google Scholar] [CrossRef]
  14. Wang, X.; Shang, Y.; Wu, X.; Wei, Q.; Zhou, S.; Sun, G.; Mei, X.; Dong, Y.; Sha, W.; Zhang, H. Divergent evolution of mitogenomics in Cetartiodactyla niche adaptation. Org. Divers. Evol. 2023, 23, 243–259. [Google Scholar] [CrossRef]
  15. Yang, M.; Gong, L.; Sui, J.; Li, X. The complete mitochondrial genome of Calyptogena marissinica (Heterodonta: Veneroida: Vesicomyidae): Insight into the deep-sea adaptive evolution of vesicomyids. PLoS ONE 2019, 14, e0217952. [Google Scholar] [CrossRef] [PubMed]
  16. Bjornerfeldt, S.; Webster, M.T.; Vila, C. Relaxation of selective constraint on dog mitochondrial DNA following domestication. Genome Res. 2006, 16, 990–994. [Google Scholar] [CrossRef]
  17. Maceachern, S.; Mcewan, J.; Mcculloch, A.; Mather, A.; Savin, K.; Goddard, M. Molecular evolution of the Bovini tribe (Bovidae, Bovinae): Is there evidence of rapid evolution or reduced selective constraint in Domestic cattle? BMC Genom. 2009, 10, 179–193. [Google Scholar] [CrossRef]
  18. Wang, Z.; Yonezawa, T.; Bin Liu, B.; Ma, T.; Shen, X.; Su, J.; Guo, S.; Hasegawa, M.; Liu, J. Domestication relaxed selective constraints on the yak mitochondrial genome. Mol. Biol. Evol. 2011, 28, 1553–1556. [Google Scholar] [CrossRef]
  19. Wang, Y.; Shen, Y.; Feng, C.; Zhao, K.; Song, Z.; Zhang, Y.; Yang, L.; He, S. Mitogenomic perspectives on the origin of Tibetan loaches and their adaptation to high altitude. Sci. Rep. 2016, 6, 29690–29700. [Google Scholar] [CrossRef]
  20. Dierckxsens, N.; Mardulyn, P.; Smits, G. NOVOPlasty: De novo assembly of organelle genomes from whole genome data. Nucleic Acids Res. 2017, 45, e18. [Google Scholar]
  21. Donath, A.; Jühling, F.; Al-Arab, M.; Bernhart, S.H.; Reinhardt, F.; Stadler, P.F.; Middendorf, M.; Bernt, M. Improved annotation of protein-coding genes boundaries in metazoan mitochondrial genomes. Nucleic Acids Res. 2019, 47, 10543–10552. [Google Scholar] [CrossRef]
  22. Lohse, M.; Drechsel, O.; Bock, R. OrganellarGenomeDRAW (OGDRAW): A tool for the easy generation of high-quality custom graphical maps of plastid and mitochondrial genomes. Curr. Genet. 2007, 52, 267–274. [Google Scholar] [CrossRef] [PubMed]
  23. Kumar, S.; Stecher, G.; Li, M.; Knyaz, C.; Tamura, K. MEGA X: Molecular Evolutionary Genetics Analysis across Computing Platforms. Mol. Biol. Evol. 2018, 35, 1547–1549. [Google Scholar] [CrossRef] [PubMed]
  24. Lowe, T.M.; Chan, P.P. tRNAscan-SE On-line: Integrating search and context for analysis of transfer RNA genes. Nucleic Acids Res. 2016, 44, W54–W57. [Google Scholar] [CrossRef] [PubMed]
  25. Perna, N.T.; Kocher, T.D. Patterns of nucleotide composition at fourfold degenerate sites of animal mitochondrial genomes. J. Mol. Evol. 1995, 41, 353–358. [Google Scholar] [CrossRef] [PubMed]
  26. Darling, A.C.; Mau, B.; Blattner, F.R.; Perna, N.T. Mauve: Multiple alignment of conserved genomic sequence with rearrangements. Genome Res. 2004, 14, 1394–1403. [Google Scholar] [CrossRef] [PubMed]
  27. Edgar, R.C. MUSCLE: Multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004, 32, 1792–1797. [Google Scholar] [CrossRef]
  28. Zhang, D.; Gao, F.; Jakovli, I.; Zou, H.; Wang, G.T. PhyloSuite: An integrated and scalable desktop platform for streamlined molecular sequence data management and evolutionary phylogenetics studies. Mol. Ecol. Resour. 2020, 20, 348–355. [Google Scholar] [CrossRef]
  29. Huelsenbeck, J.P. MrBayes 3.2: Efficient Bayesian Phylogenetic Inference and Model Choice Across a Large Model Space. Syst. Biol. 2012, 61, 539–542. [Google Scholar]
  30. Letunic, I.; Bork, P. Interactive Tree Of Life (iTOL) v4: Recent updates and new developments. Nucleic Acids Res. 2019, 47, W256–W259. [Google Scholar] [CrossRef]
  31. Yang, Z.H. PAML 4: Phylogenetic analysis by maximum likelihood. Mol. Biol. Evol. 2007, 24, 1586–1591. [Google Scholar] [CrossRef] [PubMed]
  32. Felsenstein, J. Phylogenies and the Comparative Method. Am. Nat. 1985, 125, 1–15. [Google Scholar] [CrossRef]
  33. Maes, D.; Collins, D.; Declercq, L.; Foyouzi-Yousseffi, R.; Gan, D.; Mammone, T.; Pelle, E.; Marenus, K.; Gedeon, H. Improving cellular function through modulation of energy metabolism. Int. J. Cosmet. 2004, 26, 268–269. [Google Scholar] [CrossRef]
  34. Huttemann, M.; Lee, I.; Samavati, L.; Yu, H.; Doan, J.W. Regulation of mitochondrial oxidative phosphorylation through cell signaling. Biochim. Biophys. Acta 2007, 1773, 1701–1720. [Google Scholar] [CrossRef]
  35. Koch, R.E.; Buchanan, K.L.; Casagrande, S.; Crino, O.; Dowling, D.K.; Hill, G.E.; Hood, W.R.; McKenzie, M.; Mariette, M.M.; Noble, D.W.; et al. Integrating Mitochondrial Aerobic Metabolism into Ecology and Evolution. Trends Ecol. Evol. 2021, 36, 321–332. [Google Scholar] [CrossRef]
  36. Yonezawa, T.; Nikaido, M.; Kohno, N.; Fukumoto, Y.; Okada, N.; Hasegawa, M. Molecular phylogenetic study on the origin and evolution of Mustelidae. Gene 2007, 396, 1–12. [Google Scholar] [CrossRef]
  37. Ki, J.S.; Hwang, D.S.; Park, T.J.; Han, S.H.; Lee, J.S. A comparative analysis of the complete mitochondrial genome of the Eurasian otter Lutra lutra (Carnivora; Mustelidae). Mol. Biol. Rep. 2010, 37, 1943–1955. [Google Scholar] [CrossRef]
  38. Salleh, F.M.; Ramos-Madrigal, J.; Peñaloza, F.; Liu, S.; Mikkel-Holger, S.S.; Riddhi, P.P.; Martins, R.; Lenz, D.; Fickel, J.; Roos, C.; et al. An expanded mammal mitogenome dataset from Southeast Asia. GigaScience 2017, 6, gix053. [Google Scholar]
  39. Madisha, M.T.; du Plessis, M.; Kotze, A.; Dalton, D.L. Complete mitochondrial genomes of the African clawless (Aonyx capensis) and spotted necked (Hydrictis maculicollis) otter: Structure, annotation, and interspecies variation. Mitochondrial DNA B 2019, 4, 1556–1557. [Google Scholar] [CrossRef]
  40. Baeza, J.A.; Macdonald-Shedd, A.; Latorre-Cárdenas, M.C.; Griffin, E.; Gutiérrez-Rodríguez, C. The first genomic resource for the ‘near threatened’ Neotropical otter Lontra longicaudis (Carnivora: Mustelidae): Mitochondrial genome characterisation and insights into phylomitogenomic relationships in the family Mustelidae. J. Nat. Hist. 2023, 57, 408–425. [Google Scholar] [CrossRef]
  41. Saccone, C.; Gissi, C.; Reyes, A.; Larizza, A.; Pesole, G. Mitochondrial DNA in metazoa: Degree of freedom in a frozen event. Gene 2002, 286, 3–12. [Google Scholar] [CrossRef] [PubMed]
  42. Sun, G.; Zhao, C.; Xia, T.; Wei, Q.; Yang, X.; Feng, S.; Sha, W.; Zhang, H. Sequence and organisation of the mitochondrial genome of Japanese Grosbeak (Eophona personata), and the phylogenetic relationships of Fringillidae. Zookeys 2020, 995, 67–80. [Google Scholar] [CrossRef] [PubMed]
  43. Shang, Y.; Wang, X.; Liu, G.; Wu, X.; Wei, Q.; Sun, G.; Mei, X.; Dong, Y.; Sha, W.; Zhang, H. Adaptability and Evolution of Gobiidae: A Genetic Exploration. Animals 2022, 12, 1741. [Google Scholar] [CrossRef] [PubMed]
  44. Kono, N.; Tomita, M.; Arakawa, K. Accelerated Laboratory Evolution Reveals the Influence of Replication on the GC Skew in Escherichia coli. Genome Biol. Evol. 2018, 10, 3110–3117. [Google Scholar] [CrossRef]
  45. Ma, B.; Li, Z.; Lv, Y.; E, Z.; Fang, J.; Ren, C.; Luo, P.; Hu, C. Analysis of Complete Mitochondrial Genome of Bohadschia argus (Jaeger, 1833) (Aspidochirotida, Holothuriidae). Animals 2022, 12, 1437. [Google Scholar] [CrossRef] [PubMed]
  46. Fourdrilis, S.; de Frias Martins, A.M.; Backeljau, T. Relation between mitochondrial DNA hyperdiversity, mutation rate and mitochondrial genome evolution in Melarhaphe neritoides (Gastropoda: Littorinidae) and other Caenogastropoda. Sci. Rep. 2018, 8, 17964. [Google Scholar] [CrossRef]
  47. Watanabe, Y.; Suematsu, T.; Ohtsuki, T. Losing the stem-loop structure from metazoan mitochondrial tRNAs and co-evolution of interacting factors. Front. Genet. 2014, 5, 109. [Google Scholar] [CrossRef]
  48. Schoch, C.L.; Ciufo, S.; Domrachev, M.; Hotton, C.L.; Kannan, S.; Khovanskaya, R.; Leipe, D.; Mcveigh, R.; O’Neill, K.; Robbertse, B.; et al. NCBI Taxonomy: A comprehensive update on curation, resources and tools. Database 2020, 2020, baaa062. [Google Scholar] [CrossRef]
  49. Moretti, B.; Al-Sheikhly, O.F.; Guerrini, M.; Theng, M.; Gupta, B.K.; Haba, M.K.; Khan, W.A.; Khan, A.A.; Barbanera, F. Phylogeography of the smooth-coated otter (Lutrogale perspicillata): Distinct evolutionary lineages and hybridization with the Asian small-clawed otter (Aonyx cinereus). Sci. Rep. 2017, 7, 41611. [Google Scholar] [CrossRef]
  50. Park, H.-C.; Kurihara, N.; Kim, K.S.; Min, M.-S.; Han, S.; Lee, H.; Kimura, J. What is the taxonomic status of East Asian otter species based on molecular evidence?: Focus on the position of the Japanese otter holotype specimen from museum. Anim. Cells Syst. 2019, 23, 228–234. [Google Scholar] [CrossRef]
  51. Kim, H.; Jo, Y. Complete mitochondrial genome sequencing of Lutra lutra (Linnaeus, 1758) (Carnivora: Mustelidae) and its phylogenetic status in Mustelidae. Mitochondrial DNA Part B Resour. 2021, 6, 2066–2068. [Google Scholar] [CrossRef]
  52. de Ferran, V.; Figueiró, H.V.; de Jesus Trindade, F.; Smith, O.; Sinding, M.H.; Trinca, C.S.; Lazzari, G.Z.; Veron, G.; Vianna, J.A.; Barbanera, F.; et al. Phylogenomics of the world’s otters. Curr. Biol. 2022, 32, 3650–3658.e4. [Google Scholar] [CrossRef]
  53. Koepfli, K.P.; Deere, K.A.; Slater, G.J.; Begg, C.; Begg, K.; Grassman, L.; Lucherini, M.; Veron, G.; Wayne, R.K. Multigene phylogeny of the Mustelidae: Resolving relationships, tempo and biogeographic history of a mammalian adaptive radiation. BMC Biol. 2008, 6, 10. [Google Scholar] [CrossRef]
  54. Hughes, A.L. Accumulation of slightly deleterious mutations in the mitochondrial genome: A hallmark of animal domestication. Gene 2013, 515, 28–33. [Google Scholar] [CrossRef]
  55. Shen, Y.Y.; Shi, P.; Sun, Y.B.; Zhang, Y.P. Relaxation of selective constraints on avian mitochondrial DNA following the degeneration of flight ability. Genome Res. 2009, 19, 1760–1765. [Google Scholar] [CrossRef]
  56. Sun, S.; Li, Q.; Kong, L.; Yu, H. Limited locomotive ability relaxed selective constraints on molluscs mitochondrial genomes. Sci. Rep. 2017, 7, 10628–10636. [Google Scholar] [CrossRef]
  57. Zhang, S.; Han, J.; Zhong, D.; Wang, T. Analysis of selective constraints on mitochondrial DNA, Flight ability and physiological index on avian. In Proceedings of the 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Osaka, Japan, 3–7 July 2013. [Google Scholar]
  58. Sun, Y.B.; Shen, Y.Y.; Irwin, D.M.; Zhang, Y.P. Evaluating the roles of energetic functional constraints on teleost mitochondrial-encoded protein evolution. Mol. Biol. Evol. 2011, 28, 39–44. [Google Scholar] [CrossRef]
  59. Palozzi, J.M.; Jeedigunta, S.P.; Hurd, T.R. Mitochondrial DNA Purifying Selection in Mammals and Invertebrates. J. Mol. Biol. 2018, 430, 4834–4848. [Google Scholar] [CrossRef]
  60. Allen, J.F. Cyclic, pseudocyclic and noncyclic photophosphorylation: New links in the chain. Trends Plant Sci. 2003, 8, 15–19. [Google Scholar] [CrossRef]
  61. Li, F.; Lv, Y.; Wen, Z.; Bian, C.; Zhang, X.; Guo, S.; Shi, Q.; Li, D. The complete mitochondrial genome of the intertidal spider (Desis jiaxiangi) provides novel insights into the adaptive evolution of the mitogenome and the evolution of spiders. BMC Ecol. Evol. 2021, 21, 72. [Google Scholar] [CrossRef]
  62. Chang, H.; Qiu, Z.; Yuan, H.; Wang, X.; Li, X.; Sun, H.; Guo, X.; Lu, Y.; Feng, X.; Majid, M.; et al. Evolutionary rates of and selective constraints on the mitochondrial genomes of Orthoptera insects with different wing types. Mol. Phylogenet. Evol. 2020, 145, 106734. [Google Scholar] [CrossRef]
  63. Wang, X.; Zhou, S.; Wu, X.; Wei, Q.; Shang, Y.; Sun, G.; Mei, X.; Dong, Y.; Sha, W.; Zhang, H. High-altitude adaptation in vertebrates as revealed by mitochondrial genome analyses. Ecol. Evol. 2021, 11, 15077–15084. [Google Scholar] [CrossRef]
  64. Zhou, T.; Shen, X.; Irwin, D.M.; Shen, Y.; Zhang, Y. Mitogenomic analyses propose positive selection in mitochondrial genes for high-altitude adaptation in galliform birds. Mitochondrion 2014, 18, 70–75. [Google Scholar] [CrossRef]
Figure 1. Mitochondrial genome structure map of P. brasiliensis. Genes encoded in the heavy strand are located on the outside of the ring, while genes encoded in the light stand are located on the inside of the ring.
Figure 1. Mitochondrial genome structure map of P. brasiliensis. Genes encoded in the heavy strand are located on the outside of the ring, while genes encoded in the light stand are located on the inside of the ring.
Animals 13 03165 g001
Figure 2. Relative synonymous codon usage (RSCU) of P. brasiliensis (A) and L. canadensis (B). The x-axis represents the amino acids encoded by the codon, which is listed beneath each amino acid. The RSCU values are listed on the y-axis.
Figure 2. Relative synonymous codon usage (RSCU) of P. brasiliensis (A) and L. canadensis (B). The x-axis represents the amino acids encoded by the codon, which is listed beneath each amino acid. The RSCU values are listed on the y-axis.
Animals 13 03165 g002
Figure 3. The predicted secondary structures of 22 tRNAs genes in P. brasiliensis mitochondrial genome.
Figure 3. The predicted secondary structures of 22 tRNAs genes in P. brasiliensis mitochondrial genome.
Animals 13 03165 g003
Figure 4. Gene arrangement comparison of the 11 species selected in this study with Mauve. PCG is in the white block, 12S rRNA and 16S rRNA are in the red block, and tRNA is in the green block.
Figure 4. Gene arrangement comparison of the 11 species selected in this study with Mauve. PCG is in the white block, 12S rRNA and 16S rRNA are in the red block, and tRNA is in the green block.
Animals 13 03165 g004
Figure 5. Phylogenetic relationships of 21 species evaluated in this study based on nucleotide dataset of the 13 mitochondrial protein-coding genes through the method of Bayesian inference.
Figure 5. Phylogenetic relationships of 21 species evaluated in this study based on nucleotide dataset of the 13 mitochondrial protein-coding genes through the method of Bayesian inference.
Animals 13 03165 g005
Figure 6. Comparisons of root-to-tip ω values among 21 selected species between otters and Mustela, based on 13 protein-coding genes (13PCG) and each PCG. The figure showed genes that have significantly different ω values. dN/dS: root to tip ω values; AM: otters, TM: Mustela.
Figure 6. Comparisons of root-to-tip ω values among 21 selected species between otters and Mustela, based on 13 protein-coding genes (13PCG) and each PCG. The figure showed genes that have significantly different ω values. dN/dS: root to tip ω values; AM: otters, TM: Mustela.
Animals 13 03165 g006
Figure 7. The molecular evolution rate (root-to-tip ω values) of 13 protein-coding genes (13PCG) and each PCG.
Figure 7. The molecular evolution rate (root-to-tip ω values) of 13 protein-coding genes (13PCG) and each PCG.
Animals 13 03165 g007
Figure 8. Phylogenetic independent contrast analysis between different habitats and root-to-tip ω values (Log10-transformed) on ND2, ND4, and ND4L in 21 selected species.
Figure 8. Phylogenetic independent contrast analysis between different habitats and root-to-tip ω values (Log10-transformed) on ND2, ND4, and ND4L in 21 selected species.
Animals 13 03165 g008
Table 1. Characteristics of the mitochondrial genome of P. brasiliensis.
Table 1. Characteristics of the mitochondrial genome of P. brasiliensis.
Gene Nucleotide Positions Size (bp) Stand Intergenic Nucleotide Start Stop
tRNAPHE 1–69 69 +
12s rRNA 72–1033 962 + 2
tRNAVAL 1034–1101 68 + 0
16s rRNA 1102–2670 1569 + 0
tRNALEU 2671–2745 75 + 0
ND1 2748–3704 957 + 2 ATG TAG
tRNAILE 3704–3772 69 + −1
tRNAGLN 3770–3843 74 −3
tRNAMET 3845–3913 69 + 1
ND2 3914–4957 1044 + 0 ATC TAG
tRNATRP 4956–5023 68 + −2
tRNAALA 5033–5101 69 9
tRNAASN 5103–5175 73 1
tRNACYS 5209–5275 67 33
tRNATYR 5276–5343 68 0
COX1 5345–6889 1545 + 1 ATG TAA
tRNASER 6887–6955 69 −3
tRNAASP 6962–7028 67 + 6
COX2 7029–7712 684 + 0 ATG TAA
tRNALYS 7716–7783 68 + 3
ATP8 7785–7988 204 + 1 ATG TAA
ATP6 7946–8626 681 + −43 ATG TAA
COX3 8626–9410 785 + −1 ATG TA-
tRNAGLY 9410–9479 70 + −1
ND3 9480–9827 348 + 0 ATA TAA
tRNAARG 9828–9895 69 + 0
ND4L 9896–10,192 297 + 0 GTG TAA
ND4 10,186–11,563 1378 + −7 ATG T--
tRNAHIS 11,564–11,632 69 + 0
tRNASER 11,633–11,694 62 + 0
tRNALEU 11,695–11,764 70 + 0
ND5 11,765–13,585 1821 + 0 ATT TAA
ND6 13,570–14,102 533 −16 ATG TA-
tRNAGLU 14,103–14,171 69 0
CYTB 14,176–15,315 1140 + 4 ATG AGA
tRNATHR 15,316–15,383 68 + 0
tRNAPRO 15,384–15,449 66 0
Table 2. Nucleotide composition and AT/GC skew of the P. brasiliensis mitochondrial genome.
Table 2. Nucleotide composition and AT/GC skew of the P. brasiliensis mitochondrial genome.
P. brasiliensis Size A% T% G% C% AT% GC% AT Skew GC Skew
mtDNA 16,395.0 32.5 28.0 14.3 25.2 60.5 39.5 0.074 −0.276
PCGs 11,414.0 30.6 29.7 14.0 25.6 60.3 39.7 0.015 −0.293
tRNAs 1515.0 33.1 31.2 18.5 17.2 64.3 35.7 0.030 0.036
rRNAs 2532.0 36.1 24.0 18.2 21.7 60.1 39.9 0.201 −0.088
D-loop 1010.0 29.7 27.9 15.5 26.8 57.6 42.4 0.031 −0.267
Table 3. Nucleotide composition and AT/GC skew of the L. canadensis mitochondrial genome.
Table 3. Nucleotide composition and AT/GC skew of the L. canadensis mitochondrial genome.
L. canadensis Size A% T% G% C% AT% GC% AT Skew GC Skew
mitogenome 16,500.0 31.3 25.9 15.5 27.2 57.2 42.7 0.094 −0.274
PCGs 11,412.0 29.0 27.4 15.5 28.1 56.4 43.6 0.028 −0.289
tRNAs 1512.0 31.9 30.6 19.7 17.8 62.5 37.5 0.021 0.051
rRNAs 2530.0 36.0 22.8 18.5 22.6 58.8 41.1 0.224 −0.100
D-loop 1123.0 30.0 26.0 16.4 27.6 56 44 0.071 −0.255
Table 4. Nucleotide composition and AT/GC skew of 21 species mitogenomes.
Table 4. Nucleotide composition and AT/GC skew of 21 species mitogenomes.
Species T(U)% A% AT% AT Skew C% G% GC% GC Skew
Pteronura brasiliensis 28.0 32.5 60.5 0.074 25.2 14.3 39.5 −0.276
Lontra canadensis 25.9 31.3 57.2 0.094 27.2 15.5 42.8 −0.274
Hydrictis maculicollis 26.9 31.8 58.7 0.083 26.4 14.8 41.3 −0.281
Aonyx cinerea 25.3 31.7 57.0 0.112 28.0 15.0 43.0 −0.303
Aonyx capensis 25.6 31.7 57.3 0.107 27.9 14.8 42.7 −0.307
Enhydra lutris 26.4 32.5 58.9 0.104 26.9 14.2 41.1 −0.308
Lutra lutra 25.8 32.3 58.1 0.112 27.5 14.4 41.9 −0.313
Lutrogale perspicillata 25.5 31.1 56.6 0.100 28.0 15.4 43.4 −0.289
Lutra sumatrana 25.8 32.6 58.3 0.116 27.5 14.2 41.7 −0.318
Mustela frenata 27.4 33.3 60.8 0.096 25.8 13.5 39.2 −0.314
Mustela eversmannii 27.3 32.8 60.0 0.091 26.1 13.9 40.0 −0.305
Mustela itatsi 27.5 33.0 60.5 0.091 25.7 13.7 39.5 −0.304
Mustela nigripes 27.2 32.9 60.1 0.095 26.2 13.8 39.9 −0.310
Mustela putorius 27.4 32.8 60.2 0.091 26.0 13.8 39.8 −0.308
Mustela erminea 26.6 33.4 60.1 0.113 26.5 13.4 39.9 −0.327
Mustela kathiah 27.9 33.3 61.1 0.088 25.3 13.6 38.9 −0.301
Mustela nivalis 27.3 32.6 60.0 0.088 26.0 14.0 40.0 −0.299
Mustela sibirica 27.3 32.9 60.2 0.093 26.0 13.9 39.8 −0.304
Mustela altaica 27.6 32.8 60.3 0.087 25.8 13.9 39.7 −0.301
Vormela peregusna 27.6 33.4 61.0 0.095 26.1 12.9 39.0 −0.338
Galictis vittata 26.8 32.3 59.2 0.093 26.3 14.5 40.8 −0.290
Table 5. The rapid evolution genes in otters’ branches (bold character in the table).
Table 5. The rapid evolution genes in otters’ branches (bold character in the table).
Gene Omega Background Omega Forward Branches 2ΔlnL p Value
ND1 0.0147 0.0233 4.2566 0.0391
ND4 0.0226 0.0355 7.6079 <0.01
ND4L 0.0226 0.0355 7.6079 <0.01
ND5 0.0491 0.0628 4.0296 0.0447
COX3 0.0253 0.0493 15.3550 <0.01
CYTB 0.0253 0.0493 15.3550 <0.01
ND2 0.0632 0.0665 0.1480 0.7004
ND3 0.0332 0.0459 1.3190 0.2508
ND6 0.0182 0.0264 1.5611 0.2115
COX1 0.0121 0.0085 1.9831 0.1591
COX2 0.0145 0.0173 0.3077 0.5791
ATP6 0.0338 0.0506 3.4832 0.0620
ATP8 0.1867 0.2720 1.6266 0.2022
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Wei, Q.; Wang, X.; Dong, Y.; Shang, Y.; Sun, G.; Wu, X.; Zhao, C.; Sha, W.; Yang, G.; Zhang, H. Analysis of the Complete Mitochondrial Genome of Pteronura brasiliensis and Lontra canadensis. Animals 2023, 13, 3165. https://doi.org/10.3390/ani13203165

AMA Style

Wei Q, Wang X, Dong Y, Shang Y, Sun G, Wu X, Zhao C, Sha W, Yang G, Zhang H. Analysis of the Complete Mitochondrial Genome of Pteronura brasiliensis and Lontra canadensis. Animals. 2023; 13(20):3165. https://doi.org/10.3390/ani13203165

Chicago/Turabian Style

Wei, Qinguo, Xibao Wang, Yuehuan Dong, Yongquan Shang, Guolei Sun, Xiaoyang Wu, Chao Zhao, Weilai Sha, Guang Yang, and Honghai Zhang. 2023. "Analysis of the Complete Mitochondrial Genome of Pteronura brasiliensis and Lontra canadensis" Animals 13, no. 20: 3165. https://doi.org/10.3390/ani13203165

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop