Skip to main content
Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Comparative mitogenomic analysis provides evolutionary insights into Formica (Hymenoptera: Formicidae)

  • Min Liu ,

    Contributed equally to this work with: Min Liu, Shi-Yun Hu

    Roles Formal analysis, Investigation, Methodology, Visualization, Writing – original draft

    Affiliations State Key Laboratory of Herbage Improvement and Grassland Agro-ecosystems, Lanzhou University, Lanzhou, Gansu, China, Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs, Lanzhou, Gansu, China, National Demonstration Center for Experimental Grassland Science Education, Lanzhou University, Lanzhou, Gansu, China, College of Pastoral Agricultural Science and Technology, Lanzhou University, Lanzhou, Gansu, China

  • Shi-Yun Hu ,

    Contributed equally to this work with: Min Liu, Shi-Yun Hu

    Roles Formal analysis, Investigation, Software, Visualization, Writing – original draft

    Affiliations State Key Laboratory of Herbage Improvement and Grassland Agro-ecosystems, Lanzhou University, Lanzhou, Gansu, China, Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs, Lanzhou, Gansu, China, National Demonstration Center for Experimental Grassland Science Education, Lanzhou University, Lanzhou, Gansu, China, College of Pastoral Agricultural Science and Technology, Lanzhou University, Lanzhou, Gansu, China

  • Min Li,

    Roles Formal analysis, Methodology, Visualization, Writing – review & editing

    Affiliations State Key Laboratory of Herbage Improvement and Grassland Agro-ecosystems, Lanzhou University, Lanzhou, Gansu, China, Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs, Lanzhou, Gansu, China, College of Pastoral Agricultural Science and Technology, Lanzhou University, Lanzhou, Gansu, China

  • Hao Sun,

    Roles Formal analysis, Methodology, Visualization, Writing – review & editing

    Affiliations State Key Laboratory of Herbage Improvement and Grassland Agro-ecosystems, Lanzhou University, Lanzhou, Gansu, China, Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs, Lanzhou, Gansu, China, National Demonstration Center for Experimental Grassland Science Education, Lanzhou University, Lanzhou, Gansu, China, College of Pastoral Agricultural Science and Technology, Lanzhou University, Lanzhou, Gansu, China

  • Ming-Long Yuan

    Roles Conceptualization, Funding acquisition, Supervision, Writing – review & editing

    yuanml@lzu.edu.cn

    Affiliations State Key Laboratory of Herbage Improvement and Grassland Agro-ecosystems, Lanzhou University, Lanzhou, Gansu, China, Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs, Lanzhou, Gansu, China, National Demonstration Center for Experimental Grassland Science Education, Lanzhou University, Lanzhou, Gansu, China, College of Pastoral Agricultural Science and Technology, Lanzhou University, Lanzhou, Gansu, China

Abstract

Formica is a large genus in the family Formicidae with high diversity in its distribution, morphology, and physiology. To better understand evolutionary characteristics of Formica, the complete mitochondrial genomes (mitogenomes) of two Formica species were determined and a comparative mitogenomic analysis for this genus was performed. The two newly sequenced Formica mitogenomes each included 37 typical mitochondrial genes and a large non-coding region (putative control region), as observed in other Formica mitogenomes. Base composition, gene order, codon usage, and tRNA secondary structure were well conserved among Formica species, whereas diversity in sequence size and structural characteristics was observed in control regions. We also observed several conserved motifs in the intergenic spacer regions. These conserved genomic features may be related to mitochondrial function and their highly conserved physiological constraints, while the diversity of the control regions may be associated with adaptive evolution among heterogenous habitats. A negative AT-skew value on the majority chain was presented in each of Formica mitogenomes, indicating a reversal of strand asymmetry in base composition. Strong codon usage bias was observed in Formica mitogenomes, which was predominantly determined by nucleotide composition. All 13 mitochondrial protein-coding genes of Formica species exhibited molecular signatures of purifying selection, as indicated by the ratio of non-synonymous substitutions to synonymous substitutions being less than 1 for each protein-coding gene. Phylogenetic analyses based on mitogenomic data obtained fairly consistent phylogenetic relationships, except for two Formica species that had unstable phylogenetic positions, indicating mitogenomic data are useful for constructing phylogenies of ants. Beyond characterizing two additional Formica mitogenomes, this study also provided some key evolutionary insights into Formica.

1. Introduction

Ants (Hymenoptera: Formicidae) are highly ecologically dominant organisms that typically nest underground and play key roles in symbiotic interactions, soil aeration, and nutrient cycling [1, 2]. There are currently over 14,106 extant ant species described worldwide, belonging to 346 genera in 16 subfamilies (AntWeb, 2023). Formica, as a large genus in Formicidae, is widely distributed and likely originated in Eurasia [3] Presently, 179 extant Formica species are known, which are mainly distributed in Europe, Asia, most of North America, the Canary Islands, and Morocco (AntWeb, 2023). Many species of Formica are widely used as biological control agents, as they are characterized by their rapid reproduction and ease of introduction and release [4, 5]. Ant phylogenetic relationships have been widely studied at various taxonomic levels, and these previous studies consistently supported the monophyly of the Formicinae; however, the species relationships within Formicinae have remained controversial in previous studies [68].

To adapt to more extreme habitats, such as low temperature, high altitude, and low oxygen content, ants can evolve corresponding physiological mechanisms [9, 10]. Extreme habitat conditions promote biodiversity, as beneficial alleles will be fixed by strong positive selection and overwhelmed signatures of historical purifying selection [11, 12]. In addition to adaptive physiological traits, there are also highly conserved ones, but the balance between the two is not yet known [13]. In insects, highly conserved coding regions of mitochondrial genomes (mitogenomes) may be important for ATP production, and their adaptation to different habitats may mainly be reflected in the diversity of control regions (CRs) [14]. Insect mitogenomes are approximately 16 kb in length and encode 37 genes: 2 rRNAs (rrnL and rrnS), 13 protein-coding genes (PCGs), 22 transfer RNAs (tRNAs) [15]. The sequencing of complete mitogenomes is very important in the study of mitogenome architecture, evolutionary processes, phylogenetics, species identification, and management of invasive species [7, 16, 17]. The comparative analysis of mitogenomes is a method commonly used to clarify the evolutionary relationships among animals [18]. In recent years, advances in sequencing technology have promoted the further development of ant mitogenomes. Up to now, mitogenomes have been sequenced for 59 Formicidae species (GenBank, as of Mar 2023), but the sequenced Formica mitogenomes in particular are still limited. This has also limited our understanding of mitogenomic features and evolution within Formica.

To further explore the phylogeny and evolutionary characteristics of Formica, we sequenced and annotated complete mitogenomes of Formica candida and F. glauca, and performed a comparative mitogenomic analysis for 12 Formica species, focusing on general mitogenomic features, codon usage, evolutionary characteristics of PCGs, base composition, tRNA structures, and conserved elements within both large CRs and small intergenic regions. Through comparative analysis, we found many conserved mitochondrial features within Formica. We also reconstructed species-level Formica phylogeny based on mitogenomic data using three analytical methods (maximum likelihood [ML], neighbor-joining [NJ], and Bayesian inference [BI]). By linking the relationship between the mitogenomic characteristics of Formica with the results of previous studies and phylogenetic analysis, the present study shows the effectiveness of mitogenomic approaches to phylogenetics.

2. Materials and methods

2.1. Sampling and DNA extraction

Adult specimens of F. candida and F. glauca were collected from Qumalai County, Qinghai Province, China and Altay City, Xinjiang Uygur Autonomous Region, China, respectively (S1 Table). The two Formica species used in this study are not included in the “List of Protected Animals in China” and no ethical permissions were required for field samping. All samples were initially preserved in 100% ethanol at the sampling site and then stored at -80°C. Total genomic DNA was extracted from a single specimen of each species using a DNeasy Tissue Kit (Qiagen, Germany). We evaluated the quality of the extracted genomic DNA by using 1.5% agarose gel electrophoresis and the NanoDrop spectrophotometer (Thermo Scientific, Waltham, MA, USA).

2.2. Mitogenome sequencing, assembly, and annotation

The entire mitogenome sequences of the two Formica species were sequenced by using the Illumina NovaSeq 6,000 platform (Illumina, San Diego, CA, USA) with 150-bp paired-end reads, conducted by Wuhan Benagen Tech Solutions Co., Ltd. (Wuhan, China). We removed low-quality reads by using SOAPnuke 2.1.0 [19], and the remaining reads (high-quality reads) were used to assemble the mitogenomes by using SPAdes 3.13.0 [20]. The two assembled mitogenomes were annotated by using MITOS (http://mitos2.bioinf.uni-leipzig.de) [21] to identify each of the 37 mitochondiral genes by using the mitogenomes of Formica available in GenBank as references. Tandem repeats within the CRs were detected by using the Tandem Repeats Finder web (https://tandem.bu.edu/trf/trf.html). We used Mfold (http://www.mfold.org/) to construct potential secondary structures of larger gene intervals. The two Formica mitogenomes newly sequenced in this study have been deposited in NCBI (GenBank accession numbers ON408245-46).

2.3. Comparative mitogenomic analysis

We used MEGA X [22] to analyze the mitochondrial nucleotide composition and codon usage of 12 Formica species. Strand asymmetry was evaluated by calculating AT-skew and GC-skew values with the method: AT skew = [A − T]/[A + T] and GC skew = [G − C]/[G + C] [23]. We calculated the codon bias index (CBI) and the effective number of codons (ENC) for the 13 PCGs of each Formica mitogenome by using DnaSP [24]. We also calculated nucleotide composision of the first, second, and third codon positions of 13 PCGs using CUSP (https://www.bioinformatics.nl/emboss-explorer/). To further investigate the codon usage bias among the 12 Formica species, we analyzed relationships between ENC, CBI, G + C content of all codon positions, and G + C content of the third codon positions. We used the ENC curve to determine the dominant evolutionary force for shaping the codon usage bias of the mitochondrial PCGs. The actual ENC values are all below an ENC curve, indicating that the dominant factor of variation is natural selection; otherwise, mutation is the dominant factor [25]. The values of nonsynonymous substitutions per nonsynonymous site (Ka) and synonymous substitutions per synonymous site (Ks) for each PCG were calculated by using MEGA X [26].

2.4. Phylogenetic analysis

Phylogenetic analyses were performed using mitogenomic data from 12 Formica species and species from two other Formicinae genera (S2 Table). The species Myrmica scabrinodis (NC_026133) from Myrmicinae was used as the outgroup. The sequences of PCGs were aligned using Clustal W (Codon) in MEGA X [22], and PCGs were translated employing the standard invertebrate mitochondrial genetic code. Two mitogenomic datasets were used for phylogenetic analyses: i.e. the P123 dataset (nucleotide sequences, all codon sites of 13 PCGs, including 11,184 nucleotides in total) and the P123AA dataset (inferred amino acid sequences of 13 PCGs, including 3,728 amino acids in total). Potential sequence saturation in our alignments were evaluated by using a substitution saturation test, impletemented in DAMBE 5.3.74 [26], and there was no substantial substitution saturation (S3 Table). The best partitioning schemes and corresponding evolutionary models for the two datasets were selected by IQ-TREE (S4 Table) and used for the following phylogenetic analyses.

We performed ML phylogenetic analyses by using RaxML-HPC2 [27], with the GTR+Γ model and 1,000 bootstraps (BS). BI analyses were conducted with MrBayes 3.2.7 [28], running 1 × 108 generations with sampling every 100 generations. NJ trees were constructed by using MEGA X [22], with the Kimura two-parameter molecular evolutionary model.

3. Results

3.1. General features of Formica mitogenomes

We obtained the complete mitogenomes of F. candida and F. glauca (S1 Table). The two newly sequenced mitogenomes encoded all the 37 typical mitochondrial genes and contained a CR. Twenty-three genes (9 PCGs and 14 tRNAs) were encoded on the majority strand (J-strand), whereas the remaining 14 genes on the minority strand (N-strand). The gene arrangement was conserved within all sequenced Formica species, but differed from that of the ancestral insect mitogenome, with trnM showing a translocated position in each of the seven completely sequenced Formica species.

The complete mitogenomes of seven Formica species displayed difference in size, ranging from 16,492 bp in F. glauca to 17,432 bp in F. sinae (Fig 1). This difference was primarily owing to size variation of the CRs, ranging from 399 bp in F. glauca to 1331 bp in F. neogagates (Fig 1). Of these seven species, the largest intergenic regions were mainly located between trnF and nad5, as the largest gene overlap regions were primarily between atp8 and atp6. All the 22 tRNAs presented a typicall cloverleaf structure (i.e. four arms), except for two tRNAs (trnS1 and trnE). trnS1 lost the dihydrouridine (DHU) arm, whereas trnE lacked the TΨC stem in both F. candida and F. glauca (Fig 2).

thumbnail
Fig 1. The size of protein-coding genes (PCGs), tRNA, rrnL, rrnS, and control region (CR) sequences among Formica mitochondrial genomes.

Ant species names are abbreviated as follows: Formica candida, Fc; Formica fusca, Ff; Formica glauca, Fg; Formica moki, Fm; Formica neogagates, Fn; Formica selysi, Fs; Formica sinae, Fi.

https://doi.org/10.1371/journal.pone.0302371.g001

thumbnail
Fig 2. Putative secondary structures of the 22 tRNA genes found in the Formica candida mitogenome.

All tRNA genes are shown in the order of occurrence in the mitochondrial genome starting from trnL2. Completely conserved sites within the twelve species are shown as white nucleotide abbreviations within red spheres. Bars indicate Watson–Crick base pairings or G and U pairs. Unpaired bases are represented as dots.

https://doi.org/10.1371/journal.pone.0302371.g002

3.2. Nucleotide composition and codon usage

The base composition of the two newly sequenced Formica mitogenomes was enriched in A and T, with the A+T content of 83.8% in F. candida and 83.4% in F. glauca. High A+T content (>81%) was also observed in the other ant mitogenomes (Fig 3A). The Formica mitogenomes exhibited a negative GC-skew value, with a moderate average value (-0.312 ± 0.01) (Fig 3B), while all sequenced Formica mitogenomes exhibited a slightly negative AT-skew, ranging from -0.033 to -0.003 (Fig 3A). Codon numbers of Formica mitogenomes ranged from 3,274 in Formica sp.DM656 to 3,712 in F. rufa (S5 Table). Relative synonymous codon usage (RSCU) analysis revealed that the two Formica species (F. sinae and F. moki) used all the 62 mitochondrial codons, while the remaining 10 Formica species did not use one or two codon (S5 Table). Fifty-six codons were consistently used in all the 12 Formica mitogenomes, and four AT-rich codons (UUU [F], AUU [I], AUA [M], and UUA [L]) were the most commonly used (S5 Table). However, the frequency of GC-rich codons was low, especially for CGC (R), GCG (A), and CGG (R), which were not used in at least one species.

thumbnail
Fig 3. AT% versus AT-skew and GC% versus GC-skew in the 12 Formica mitochondrial genomes.

Measured in bp percentage (y-axis) and level of nucleotide skew (x-axis). Values are calculated for J-strands in full-length mitochondrial genomes. A, A + T% vs AT-skew; B, G + C% vs GC-skew.

https://doi.org/10.1371/journal.pone.0302371.g003

The average ENC value among all the PCGs was 34.19, ranging from 33.04 (in F. candida and F. podzolica) to 35.17 (in F. sinae). We found positive correlations between G + C content for all codon positions and ENC (R2 = 0.79, P < 0.01; Fig 4A), so was between G + C content for the third codon positions and ENC (R2 = 0.99, P < 0.01; Fig 4B). Negative correlations were found between CBI and both G + C content for all codon positions (R2 = 0.96, P < 0.01; Fig 4C) and G + C content of the third codon positions (R2 = 0.97, P < 0.01; Fig 4D), so was CBI and ENC (R2 = 0.98, P < 0.01; Fig 4E). The observed ENC values for all Formica species were below the ENC curve (Fig 5A), and no significant correlation (R2 = 0.03, P > 0.05) was found between the combined GC content of the first and second codon positions and the GC content of the third codon positions (Fig 5B), indicating that codon usage bias in Formica mitogenomes might be influenced by natural selection.

thumbnail
Fig 4. Evaluation of codon bias in the mitochondrial genomes of 12 Formica species.

G + C%, G + C content of all codon positions; (G + C)3%, G + C content of the third codon positions; ENC, effective number of codons; CBI, codon bias index.

https://doi.org/10.1371/journal.pone.0302371.g004

thumbnail
Fig 5. The correlation between effective number of codons (ENC) and G + C content of the third codon positions (GC3) for 12 Formica species.

The colored dots correspond to those in Fig 3. (A) The solid line represents the relationship between ENC and GC3 content. (B) The solid line represents the relationship between GC12 and GC3 content, whereas the dotted line indicates y = x. GC12, G + C content of the first and second positions.

https://doi.org/10.1371/journal.pone.0302371.g005

3.3. Intergenic spacers

The Formica mitogenomes contained intergenic spacers (IGSs) of varying lengths, abundantly dispersed through almost all of the genes and with extremely high A+T contents. The A+T contents of the two newly sequenced mitogenomes were 93.78% (F. glauca) and 94.09% (F. candida). Here, we mainly describe IGSs with conserved sequences or notable structures. Although individual IGSs differed in length among species, their sequences were highly conserved. All of them showed two key characteristics, i.e., AT-enrichment and conserved sequences. In addition, some IGSs also contained microsatellites. Regarding the secondary structure, it was found that some IGSs had stem-loop structures, i.e., the IGSs between trnQ and nad2, cox2 and trnK, atp6 and cox3, cox3 and trnG, trnS1 and trnE, trnF and nad5, nad4L and trnT, and also cob and trnS2 (S1 Fig).

Although the sequences of some IGSs were very short (<20 bp), they were highly conserved, i.e., trnItrnQ (TAADTWA) (S2 Fig), trnHnad4 (WTAWAAA) (S2 Fig), trnS2nad1 (TAAATTAYA) (Fig 6). In addition, a total of 14 relatively long IGSs were present in the Formica mitogenomes (S2A–S2R Fig). Highly conserved regions of the 14 IGSs were found among all Formica species, some of which included microsatellites. We also found that several IGSs (e.g. cox1-trnL2, cox2-trnK, atp6-cox3, cox3-trnG, trnN-trnS1, trnF-nad5; S2 Fig) were more conserved in sequence size and similarity among five Formica species (i.e. glauca, sinae, sp.DM659, sp.DM658, and sp. DM656) which clustered together in phylogenetic tree (see the following section 3.6).

thumbnail
Fig 6. Sequence alignment of the intergenic spacer between trnS2 and nad1 for Formica mitochondrial genomes.

https://doi.org/10.1371/journal.pone.0302371.g006

3.4. Control region

Eight Formica mitogenomes with CR sequences contained only one CR, which was located between rrnS and trnM. The AT contents of the CRs of eight mitogenomes ranged from 82.41% (F. moki) to 92.36% (F. candida). Among these eight mitogenomes of Formica species, all CRs had typically high A+T contents. We observed some essential components among the Formica mitogenomes: (1) AT-rich regions; (2) poly-T sequences (except in F. moki); (3) poly-A sequences (in F. candida, F. fusca, and F. selysi). In addition, we found a large number of TATA motifs as well as large tandem repeat units in CRs. Five tandem repeat units presented in the CR of F. neogagates, whereas the CRs of six Formica species had two or three tandem repeat units.

3.5. Protein-coding genes

All the 13 PCGs began with typical codons (ATN) and terminated with a complete stop codon (TAA) in the mitogenomes of F. candida and F. glauca, whereas other Formica species did often have incomplete T or TA stop codons. Among all of the 13 PCGs, A + T content (89.2–92.1%) of the third codon position was higher than that of the first (76.5–77.7%) and second (73.1–74.0%) codon positions (Fig 7). The average values of both Ks and Ka in the 12 Formica species differed among 13 PCGs (Fig 8), indicating that the mutation rate was relatively low. The Ka/Ks values also varied considerably among the 13 PCGs of the 12 Formica species and were less than 1 (Fig 8). The Ka/Ks values of atp8 (0.296) was the largest, indicating a fastest evolutionary rate of atp8. Two PCGs (nad2 and nad3) also showed more amino acid substitutions, whereas cox1 was the most conserved (Fig 8).

thumbnail
Fig 7. A + T contents of the mitochondrial protein-coding genes in Formica mitochondrial genomes.

https://doi.org/10.1371/journal.pone.0302371.g007

thumbnail
Fig 8. Evolutionary rates of 13 protein-coding genes in the mitochondrial genomes of 12 species of Formica.

The left y-axis shows the substitution rate of mitochondrial genes, while the right y-axis shows the G + C content. Synonymous nucleotide substitutions per synonymous site (Ks) and nonsynonymous nucleotide substitutions per nonsynonymous site (Ka) were calculated using the Kumar method. The standard error estimates were obtained by a bootstrap procedure (1,000 replicates).

https://doi.org/10.1371/journal.pone.0302371.g008

3.6. Mitochondrial phylogeny of Formica

Two datasets (P123 and P123AA) and three methods (ML, BI, and NJ) resulted in six phylogenetic trees with highly similar topologies (Fig 9 and S3 Fig). The two phylogenetic topologies differed only in the phylogenetic position of F. candida and F. rufa. All the six phylogenetic trees consistently supported the monophyly of Formica and Cataglyphis, with high support values (Fig 9 and S3 Fig).

thumbnail
Fig 9. Two phylogenies of 16 Formicinae species from four genera based on two datasets (P123 dataset and P123AA dataset) and three analytical methods (Bayesian inference [BI], neighbor-joining [NJ], and maximum likelihood [ML]).

https://doi.org/10.1371/journal.pone.0302371.g009

All the phylogenetic trees supported of the following phylogenetic relationship: (((Formica + Polyergus) + Cataglyphis) + Myrmica scabrinodis). Among Formica species, F. neogagates was sister to the remaing 11 species which were divided into two phylogenetic groups: group 1 including six species, and group 2 including five species. Within group 1, F. fusca first clustered with F. selysi, which both further clustered with F. podzolica, with F. moki at the base of group 1. The phylogenetic positions of F. candida and F. rufa were unstable. Within group 2, F. glauca was located between F. sinae and the three other Formica species in the group.

4. Discussion

4.1. General features of Formica mitogenomes

The two newly sequenced Formica mitogenomes had typical gene contents that were identical to those of other sequenced ant [29, 30] and insect mitogenomes [15]. The observed gene rearrangement that occurred in Formica has been previously reported in the family Formicidae [25]. The rearrangement of Formicidae is consistent with a duplication/random loss model [31, 32]. Therefore, this rearrangement of the Formica mitogenomes can be explained by the plesiomorphic trnI-trnQ-trnM sequence and tandem duplication, as it may be owing to the tandem duplication of trnI-trnQ-trnM and subsequent loss of the first trnI-trnQ and the second trnM, eventually resulting in the observed trnM-trnI-trnQ sequence. Unlike the anticodons of trnS1 and trnK in most insects, the Formica genus specifically uses TCT and TTT, and it has been found that those abnormal anticodons are related to gene rearrangement [33]. One common feature of insect mitogenomes is that trnS1 lacks a DHU arm [34, 35]. However, trnE lacking the TΨC stem is not common in insects, but is found in other arthropods, such as spiders [36, 37]. In addition, we analyzed the conservation of the secondary structure of tRNAs in 12 Formica species. We found these structural nucleotides of the tRNAs were comparatively conserved, and the stem was more conserved than the ring of corresponding tRNAs apart from the difference in the anticodon loop. These conserved regions may be association with the structure and function of tRNAs [38].

4.2. Nucleotide composition and codon usage

Insect mitogenomes generally show a positive value for AT-skew and a negative value for GC-skew on the J-strand. However, our sequenced Formica mitogenomes presented a slightly negative AT-skew, which indicated that the incidence of Ts was higher than that of As, and similar results have been reported in insects such as Galleriinae [39], Apostictopterus fuliginosus [40], and leaf hopper [41]. This negative AT-skew may be associated with codon positions, gene direction, and replication, e.g., probabilities of nucleotide repair differing during the replication process following damage to single-standard DNA [42, 43].

Codon usage bias is an important evolutionary phenomenon commonly found in many animals. Codon usage bias is mainly driven by the frequency of synonymous codons used in the coding region of the mitogenomes differing. Other many factors also could affect codon usage bias, e.g., selection for optimized translation, gene expression, codon location within genes, and the secondary structural of DNA [44]. Generally, mutation pressure and natural selection are considered to be two main factors affecting codon usage [45, 46]. As was found in other insects [46, 47], our results of the RSCU analyses also indicated that the third codon positions had a higher frequency in the usage of A and T relative to G and C, which may have led to the high codon bias observed. The negative correlation between CBI and ENC indicated that the reduced ENC could lead to high codon usage bias [48]. We proposed that differences in codon usage bias of Formica mitogenomes might be influenced by both natural selection and mutation pressure, as has also been reported in other insects [46, 48].

4.3. Evolutionary rates of protein-coding genes

Estimating the Ka/Ks value of PCGs has been widely used to indicate how natural selection affect sequence evolution in various animals [49], i.e. Ka = Ks indicating neutral mutation, Ka/Ks < 1 indicating purifying selection, and Ka/Ks>1 indicating positive selection [50]. The values of Ka/Ks for all the 13 PCGs in Formica mitogenomes were less than 1, indicating that these mitochondrial genes might be evolving under purifying selection [51, 52]. As purifying selection eliminates harmful that arise mutations, it may thus dominate the evolution of mitogenomes [53]. There was a negative correlation between the Ka/Ks values of the 13 PCGs and the G + C content (R2 = 0.73, P < 0.01), indicating that differences in G + C content may lead to different evolutionary patterns of PCGs in Formica mitogenomes. Strong purifying selection and low mutation rates dominate mitochondrial genome evolution [54]. The Ka/Ks value of cox1 presented a slowest evolutionary rate, as has been reported in many insect mitogenomes [55], suggesting fewer changes in amino acids and the conservation of this gene [56].

Most ants build nests underground, which can provide protection from predators and extreme weather, but reduces oxygen concentrations and causes and have high levels of carbon dioxide accumulation [10, 57]. This hypoxic environment may impose relatively stronger purifying selection pressure of subterranean lineages [10]. Most genes have greater Ka/Ks values in subterranean lineages than in non-subterranean lineages [58]. However, the Ka/Ks values of Formica were lower than that of other hymenopterans [59, 60]. This may be a highly conserved physiological defense character that has evolved in Formica ants as an adaptation to this hypoxic environment, thus ensuring the normal functioning of mitogenome [13].

4.4. Non-coding region

The lack of conservation of repeat units among these Formica mitogenomes may be associated with the size variation of CRs and functional lack of these repeat units [61]. The CR of insect mitogenomes plays a key role in both transcription initiation and replication process of mitochondrial genes [62, 63]. The position of the CR between rrnS and trnM in Formica mitogenomes was consistent with that of other ants [17, 63], indicating conservation in the number and location of the Formica CRs. In eight Formica species, all CRs had typically high A+T contents, and the types of base substitutions that can occur are limited compared to the those that can occur in other regions [64, 65]. We observed some essential components among the Formica mitogenomes, as has been reported [66]. However, tandem repeat sequences and poly-T and poly-A regions were not found in the CRs of some Formica, and tandem repeat sequences differed in Formica species. These characteristics indicate the diversity in CR structures in the Formica mitogenomes, and the variation in CR length may be the result of variable numbers of tandem repeats [67]. We also found many stem-loop structures in the CRs, and some stem-loop structures may be associated with the initiation of replication and transcription.

4.5. Formica phylogeny and intergenic spacers

The use of mitogenomic data is a common approach to exploring phylogenetic relationships among different insect groups [68, 69]. The sister relationship between Formica and Polyergus ants confirmed in the present work was largely congruent with the results of previous studies [6]. Among the two major groups inferred for Formica ants, the consistent phylogenetic relationship between the four species (F. fusca, F. selysi, F. rufa and F. candida) in group 1 has also been supported by other research [70]. However, another phylogenetic relationships of the five species within group 1 was inferred in previous studies, supporting a sister relationship of F. rufa and F. candida [8, 71]. Mitogenomic sequences have been extensively used for reconstructing phylogene in many animals [7275]. In the present analyses, two different tree topologies were obtained, indicating that phylogenetic results can be potentially influenced by both the mitogenomic datasets and inference methods. This unstable phylogenetic relationships of Formica were also reported in previous studies. Although there were a few different relationships within Formica based on different datasets, all analyses supported the relationship of (((Formica + Polyergus) + Cataglyphis) among the three different ant genera. Considering the limited species sampled in this study, sequencing more Formica mitogenomes is needed to improve our understanding of Formica.

The IGSs of Formica mitogenomes varied in size, lacked repeat units, and were abundantly dispersed between genes, and changes in the size of IGSs are considered to be a shared derived trait of social insects [76]. However, the individual IGSs had one or more conserved regions among species, and the nucleotide composition of these IGSs was similar to that of adjacent genes. For example, regarding the nucleotide composition of the IGS between nad6 and cob, the G + C content of this IGS was 18.3%, while the G + C content of nad6 was 30.2%, suggesting that this sequence may have been derived from nad6. The evolutionary mechanism of IGSs may be explained by the slipped-strand mispairing and the duplication/random loss model [35, 77]. A conserved motif was located in the IGS of trnS2-nad1, which has been predicted to be the binding site of a mitochondrial transcription termination factor (DmTTF) [78]. The similar conserved motif has been widely reported in various insect mitogenomes [73, 79]. A 7-bp conserved motif (TAAATTA) presented in Formica mitogenomes was higly similar to the conserved motif (THACWW) in Hymenoptera [80].

In addition, we have linked the sequences and structures of the IGSs with phylogenetic relationships in Formica, demonstrating that this feature contributes to a phylogenetic understanding of the genus Formica [81, 82]. The following three examples are rather illustrative. The IGS between atp6 and cox3 genes was relatively conserved, except in F. neogagates. This indicated that this species had a distant genetic relationship with other Formica species, corresponding to inferred phylogenetic relationships. There was only one conserved sequence between trnF and nad5 in the 12 species analyzed. When comparing F. glauca, F. sinae, Formica sp.DM659, Formica sp.DM658, and Formica sp.DM656, we found that the sequence of this IGS was almost completely conserved (S2O Fig), while this same IGS was also relatively conserved between F. candida, F. fusca, F. moki, F. podzolica, F. rufa, and F. selysi (S2P Fig). Compared to other Formica species, F. fusca and F. selysi had a larger IGS (>50 bp) between trnM and trnI, and the two species had the closest genetic relationship in the phylogenetic tree. Thus, we find the distinctive feature of the IGS regions and phylogenetic relationships to be valuable for a systematic understanding of the genus Formica.

5. Conclusion

We sequenced the complete mitogenomes of F. candida and F. glauca, further expanding the number of sequenced Formicidae mitogenomes. These two mitogenomes were similar in size to those of other ants. Formica mitogenomes were highly conserved in gene arrangement, gene content, nucleotide composition, codon usage, and PCG evolutionary patterns. Phylogenetic relationships within Formicinae obtained here were similar to previously inferred relationships, suggesting that mitogenomic data could be usefull for resolving the ant phylogeny. This study provides valuable insights into the phylogenetic relationships of Formica. Sequencing more mitogenomes across various taxonomic levels will greatly improve our understanding of both phylogenetic relationships and key subjects relevant to ants, such as the evolution of their strategies in behavior and life history.

Supporting information

S1 Fig. Stem-loop structures of intergenic spacers in Formica mitochondrial genomes.

(A) The intergenic spacer between trnQ and nad2. (B) The intergenic spacer between cox2 and trnK. (C) The intergenic spacer between atp6 and cox3. (D) The intergenic spacer between cox3 and trnG. (E) The intergenic spacer between trnS1 and trnE. (F) The intergenic spacer between trnF and nad5. (G) The intergenic spacer between nad4L and trnT. (H) The intergenic spacer between cob and trnS2.

https://doi.org/10.1371/journal.pone.0302371.s001

(TIF)

S2 Fig. Sequence alignments of intergenic spacers in Formica mitochondrial genomes.

(A) The intergenic spacer between trnI and trnQ. (B) The intergenic spacer between trnH and nad4. (C) The intergenic spacer between trnQ and nad2. (D) The intergenic spacer between trnC and trnY. (E) The intergenic spacer between trnY and cox1. (F) The intergenic spacer between cox1 and trnL2. (G) The intergenic spacer between cox2 and trnK. (H) The intergenic spacer between atp6 and cox3. (I) The intergenic spacer between atp6 and cox3, except in F. neogagates. (J) The intergenic spacer between cox3 and trnG. (K) The intergenic spacer between trnR and trnN. (L) The intergenic spacer between trnN and trnS1. (M) The intergenic spacer between trnS1 and trnE. (N) The intergenic spacer between trnF and nad5. (O) The intergenic spacer between trnF and nad5 in F. glauca, F. sinae, Formica sp.DM659, Formica sp.DM658, and Formica sp.DM656. (P) The intergenic spacer between trnF and nad5 in F. candida, F. fusca, F. moki, F. podzolica, F. rufa, and F. selysi. (Q) The intergenic spacer between nad4 and nad4L. (R) The intergenic spacer between cob and trnS2.

https://doi.org/10.1371/journal.pone.0302371.s002

(TIF)

S3 Fig. Phylogenetic relationships among 16 Formicinae species in four genera based on two datasets (P123 and P123AA) and three analytical methods (Bayesian inference [BI], neighbor-joining method [NJ], and maximum likelihood [MJ]).

https://doi.org/10.1371/journal.pone.0302371.s003

(TIF)

S1 Table. Sampling information of the two Formica species that were newly sequenced in this study.

https://doi.org/10.1371/journal.pone.0302371.s004

(DOCX)

S2 Table. Characteristics of mitogenomes of 16 Formicidae species analyzed in this study.

Species that were newly sequenced in this study are labelled with an asterisk.

https://doi.org/10.1371/journal.pone.0302371.s005

(DOCX)

S3 Table. Saturation test for each of the 13 protein-coding genes (PCGs) sequences, concatenated sequences of 13 PCGs and 2 rRNAs, and three codon positions of 13 PCGs as implemented in DAMBE.

https://doi.org/10.1371/journal.pone.0302371.s006

(DOCX)

S4 Table. The best partitioning schemes and substitution models selected by IQ-TREE for the two datasets.

https://doi.org/10.1371/journal.pone.0302371.s007

(DOCX)

S5 Table. Codon usage for the 13 mitochondrial protein-coding genes in 12 Formica mitochondrial genomes.

https://doi.org/10.1371/journal.pone.0302371.s008

(XLSX)

References

  1. 1. Moreau CS, Bell CD, Vila R, Archibald SB, Pierce NE. Phylogeny of the ants: Diversification in the age of angiosperms. Science. 2006;312(5770):101–4. pmid:16601190
  2. 2. Moreau CS, Bell CD. Testing the museum versus cradle tropical biological diversity hypothesis: Phylogeny, diversification, and ancestral biogeographic range evolution of the ants. Evolution. 2013;67(8):2240–57. pmid:23888848
  3. 3. Zachos J, Pagani M, Sloan L, Thomas E, Billups K. Trends, rhythms, and aberrations in global climate 65 Ma to present. Science. 2001;292(5517):686–93. pmid:11326091
  4. 4. Sarikaya O. The role of Formica rufa L. (Hym.: Formicidae) (southern wood ant) in biological control. International Forestry Students Symposium. 2003:96–8.
  5. 5. Frizzi F, Masoni A, Quilghini G, Ciampelli P, Santini G. Chronicle of an impact foretold: the fate and effect of the introduced Formica paralugubris ant. Biol Invasions. 2018;20(12):3575–89.
  6. 6. Hasegawa E, Tinaut A, Ruano F. Molecular phylogeny of two slave-making ants: Rossomyrmex and Polyergus (Hymenoptera: Formicidae). Ann Zool Fenn. 2002;3(3):267–71.
  7. 7. Gotzek D, Clarke J, Shoemaker D. Mitochondrial genome evolution in fire ants (Hymenoptera: Formicidae). BMC Evol Biol. 2010;10:300. pmid:20929580
  8. 8. Borowiec ML, Cover SP, Rabeling C. The evolution of social parasitism in Formica ants revealed by a global phylogeny. PNAS. 2020;118(38):e2026029118.
  9. 9. Martin RA, Chick LD, Garvin ML, Diamond SE. In a nutshell, a reciprocal transplant experiment reveals local adaptation and fitness trade-offs in response to urban evolution in an acorn-dwelling ant. Evolution. 2021;75(4):876–87. pmid:33586171
  10. 10. Bondareva O, Genelt-Yanovskiy E, Petrova T, Bodrov S, Smorkatcheva A, Abramson N. Signatures of adaptation in mitochondrial genomes of Palearctic subterranean voles (Arvicolinae, Rodentia). Genes. 2021;12(12):1945. pmid:34946894
  11. 11. Wallis GP, Waters JM, Upton P, Craw D. Transverse alpine speciation driven by glaciation. Trends Ecol Evol. 2016;31(12):916–26. pmid:27640783
  12. 12. Cicconardi F, Krapf P, D’Annessa I, Gamisch A, Wagner HC, Nguyen AD, et al. Genomic signature of shifts in selection in a subalpine ant and its physiological adaptations. Mol Biol Evol. 2020;37(8):2211–27. pmid:32181804
  13. 13. Hochachka PW, Lutz PL. Mechanism, origin, and evolution of anoxia tolerance in animals. Comp Biochem Physiol B-Biochem Mol Biol. 2001;130(4):435–59. pmid:11691622
  14. 14. Zhang QL, Yang XZ, Zhang L, Feng RQ, Zhu QH, Chen JY, et al. Adaptive evidence of mitochondrial genomes in Dolycoris baccarum (Hemiptera: Pentatomidae) to divergent altitude environments. Mitochondrial DNA Part A. 2019;30(1):9–15. pmid:29521177
  15. 15. Cameron SL. Insect mitochondrial genomics: implications for evolution and phylogeny. Annu Rev Entomol. 2014;59:95–117. pmid:24160435
  16. 16. Hasegawa E, Kobayashi K, Yagi N, Tsuji K. Complete mitochondrial genomes of normal and cheater morphs in the parthenogenetic ant Pristomyrmex punctatus (Hymenoptera: Formicidae). Myrmecol News. 2011;15:85–90.
  17. 17. Park J, Park J. Complete mitochondrial genome of the jet ant Lasius spathepus Wheeler, WM, 1910 (Formicidae; Hymenoptera). Mitochondrial DNA Part B-Resour. 2021;6(2):505–7. pmid:33628905
  18. 18. Lee YS, Oh J, Kim YU, Kim N, Yang S, Hwang UW. Mitome: dynamic and interactive database for comparative mitochondrial genomics in metazoan animals. Nucleic Acids Res. 2008;36(Database issue):D938–D42. pmid:17940090
  19. 19. Chen YX, Chen YS, Shi CM, Huang ZB, Zhang Y, Li SK, et al. SOAPnuke: a MapReduce acceleration-supported software for integrated quality control and preprocessing of high-throughput sequencing data. GigaScience. 2017;7(1):1298–9. pmid:29220494
  20. 20. Dierckxsens N, Smits PMG. NOVOPlasty: de novo assembly of organelle genomes from whole genome data. Nucleic Acids Res. 2016;45(4):e18. pmid:28204566
  21. 21. Bernt M, Donath A, Juhling F, Externbrink F, Florentz C, Fritzsch G, et al. MITOS: improved de novo metazoan mitochondrial genome annotation. Mol Phylogenet Evol. 2013;69(2):313–9. pmid:22982435
  22. 22. Kumar S, Stecher G, Li M, Knyaz C, Tamura K. MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol Biol Evol. 2018;35(6):1547–9. pmid:29722887
  23. 23. Perna NT, Kocher TD. Patterns of nucleotide composition at fourfold degenerate sites of animal mitochondrial genomes. J Mol Evol. 1995;41(3):353–8. pmid:7563121
  24. 24. Rozas J, Ferrer-Mata A, Sánchez-DelBarrio JC, Guirao-Rico S, Librado P, Ramos-Onsins SE, et al. DnaSP 6: DNA sequence polymorphism analysis of large data sets. Mol Biol Evol. 2017;34(12):3299–302. pmid:29029172
  25. 25. Zhang XM, Li T, Liu X, Xu ZH. Characterization and phylogenetic implication of complete mitochondrial genome of the medicinal ant Formica sinae (Hymenoptera: Formicidae): genomic comparisons in Formicidae. J Med Entomol. 2022;59(6):1971–9. pmid:36209399
  26. 26. Xia XH. DAMBE5: a comprehensive software package for data analysis in molecular biology and evolution. Mol Biol Evol. 2013;30(7):1720–8. pmid:23564938
  27. 27. S Alexandros. RAxML Version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics. 2014;30(9):1312–3. pmid:24451623
  28. 28. Ronquist F, Teslenko M, Mark Pvd, Ayres DL, Darling A, Höhna S, et al. MrBayes 3.2: efficient bayesian phylogenetic inference and model choice across a large model space. Syst Biol. 2012;61(3):539–42. pmid:22357727
  29. 29. Berman M, Austin CM, Miller AD. Characterisation of the complete mitochondrial genome and 13 microsatellite loci through next-generation sequencing for the New Caledonian spider-ant Leptomyrmex pallens. Mol Biol Rep. 2014;41(3):1179–87. pmid:24496854
  30. 30. Barbosa J, Barbosa MS, Morais S, Santana A, Almeida C. Mitochondrial genomes of genus Atta (Formicidae: Myrmicinae) reveal high gene organization and giant intergenic spacers. Genet Mol Biol. 2019;42(4):e20180055 pmid:31188925
  31. 31. Kim MJ, Hong EJ, Kim I. Complete mitochondrial genome of Camponotus atrox (Hymenoptera: Formicidae): a new tRNA arrangement in Hymenoptera. Genome. 2016;59(1):59–74. pmid:26731510
  32. 32. Lavrov DV, Boore JL, Brown WM. Complete mtDNA sequences of two millipedes suggest a new model for mitochondrial gene rearrangements: duplication and nonrandom loss. Mol Biol Evol. 2002;19(2):163–9. pmid:11801744
  33. 33. Wei SJ, Shi M, He JH, Sharkey M, Chen XX. The complete mitochondrial genome of Diadegma semiclausum (Hymenoptera: Ichneumonidae) indicates extensive independent evolutionary events. Genome. 2009;52(4):308–19. pmid:19370087
  34. 34. Sun YX, Zhu YS, Chen C, Zhu QS, Zhu QQ, Zhou YY, et al. The complete mitochondrial genome of Dysgonia stuposa (Lepidoptera: Erebidae) and phylogenetic relationships within Noctuoidea. PeerJ. 2020;8:e8780. pmid:32211241
  35. 35. Du C, Zhang LF, Lu T, Ma JN, Zeng CJ, Yue BS, et al. Mitochondrial genomes of blister beetles (Coleoptera, Meloidae) and two large intergenic spacers in Hycleus genera. Bmc Genomics. 2017;18:698. pmid:28874137
  36. 36. Yan SY, Xu KK, Yang DX, Li C, Yang WJ. Complete mitochondrial genome of Pisaura bicornis (Araneae: Pisauridae) and its phylogenetic implications. Mitochondrial DNA Part B-Resour. 2019;4(2):3171–2. pmid:33365904
  37. 37. Yang WJ, Xu KK, Liu Y, Yang DX, Li C. Complete mitochondrial genome and phylogenetic analysis of Argiope perforata (Araneae: Araneidae). Mitochondrial DNA Part B-Resour. 2019;4(1):1963–4.
  38. 38. Cao JJ, Wang Y, Li WH. Comparative mitogenomic analysis of species in the subfamily Amphinemurinae (Plecoptera: Nemouridae) reveal conserved mitochondrial genome organization. Int J Biol Macromol. 2019;138:292–301. pmid:31319083
  39. 39. Roh SJ, Jeon JH, Kim DS, Byun BK. The complete mitochondrial genome of unique snout moth, Cathayia obliquella (Pyralidae: Galleriinae) with its phylogenetic implications and a redescription of external morphology. J Asia-Pac Biodivers. 2020;13(4):613–24.
  40. 40. Chen XX, Yuan ZW, Li C, Dietrich CH, Song YH. Structural features and phylogenetic implications of Cicadellidae subfamily and two new mitogenomes leafhoppers. PLoS ONE. 2021;16(5):e0251207. pmid:33989310
  41. 41. Han YK, Huang ZF, Tang J, Chiba H, Fan XL. The complete mitochondrial genomes of two skipper genera (Lepidoptera: Hesperiidae) and their associated phylogenetic analysis. Sci Rep. 2018;8:15762. pmid:30361496
  42. 42. Wei SJ, Shi M, Chen XX, Sharkey MJ, van Achterberg C, Ye GY, et al. New views on strand asymmetry in insect mitochondrial genomes. PLoS ONE. 2010;5(9):e12708. pmid:20856815
  43. 43. Francino MP, Ochman H. Strand asymmetries in DNA evolution. Trends Genet. 1997;13(6):240–5. pmid:9196330
  44. 44. Salim HMW, Cavalcanti ARO. Factors influencing codon usage bias in genomes. J Braz Chem Soc. 2008;19(2):257–62.
  45. 45. Uddin A, Chakraborty S. Analysis of mitochondrial protein-coding genes of Antheraea assamensis: Muga silkworm of Assam. Arch Insect Biochem Physiol. 2021;106(1):e21750. pmid:33075174
  46. 46. Abdoli R, Mazumder TH, Nematollahian S, Zanjani RS, Mesbah RA, Uddin A. Gaining insights into the compositional constraints and molecular phylogeny of five silkworms mitochondrial genome. Int J Biol Macromol. 2022;206:543–52. pmid:35245576
  47. 47. Ward PS. The phylogeny and evolution of ants. Annu Rev Ecol Evol S. 2014;45(1):23–43.
  48. 48. Kabiraj D, Chetia H, Nath A, Sharma P, Mosahari PV, Singh D, et al. Mitogenome-wise codon usage pattern from comparative analysis of the first mitogenome of Blepharipa sp. (Muga uzifly) with other oestroid flies. Sci Rep. 2022;12(1):7028–61. pmid:35487927
  49. 49. Ohta T. The nearly neutral theory of molecular evolution. Annu Rev Ecol Syst. 1992;23(1):263–86.
  50. 50. Meiklejohn CD, Montooth KL, Rand DM. Positive and negative selection on the mitochondrial genome. Trends Genet. 2007;23(6):259–63. pmid:17418445
  51. 51. Roques S, Fox CJ, Villasana MI, Rico C. The complete mitochondrial genome of the whiting, Merlangius merlangus and the haddock, Melanogrammus aeglefinus: a detailed genomic comparison among closely related species of the Gadidae family. Gene. 2006;383:12–23. pmid:16996701
  52. 52. Oliveira DCSG, Raychoudhury R, Lavrov DV, Werren JH. Rapidly evolving mitochondrial genome and directional selection in mitochondrial genes in the parasitic wasp Nasonia (Hymenoptera: Pteromalidae). Mol Biol Evol. 2008;25(10):2167–80. pmid:18653734
  53. 53. Oteo-García G, Oteo JA. Evolutionary distances corrected for purifying selection and ancestral polymorphisms. J Theor Biol. 2019;483:110004. pmid:31513800
  54. 54. Freely KC, Friedrich A, Hou J, Schacherer J. Population genomic analysis reveals highly conserved mitochondrial genomes in the yeast species Lachancea thermotolerans. Genome Biol Evol. 2014;6(10):2586–94. pmid:25212859
  55. 55. Chakraborty R, Tyagi K, Kundu S, Rahaman I, Singha D, Chandra K, et al. The complete mitochondrial genome of Melon thrips, Thrips palmi (Thripinae): Comparative analysis. PLoS ONE. 2018;13(10)(10):1–17. pmid:30379813
  56. 56. Tyagi K, Kumar V, Singha D, Chandra K, Laskar BA, Kundu S, et al. DNA barcoding studies on thrips in india: cryptic species and species complexes. Sci Rep. 2017;7(1):4898–912. pmid:28687754
  57. 57. Yang X, Shao MA, Li TC, Jia YH, Jia XX, Huang LM. Structure characteristics of Camponotus japonicus nests in northern part of loess plateau and influencing factors. Acta Pedologica Sinica 2018;55(04):868–78.
  58. 58. Tomasco IH, Lessa EP. The evolution of mitochondrial genomes in subterranean caviomorph rodents: adaptation against a background of purifying selection. Mol Phylogenet Evol. 2011;61(1):64–70. pmid:21723951
  59. 59. Song SN, Tang P, Wei SJ, Chen XX. Comparative and phylogenetic analysis of the mitochondrial genomes in basal hymenopterans. Sci Rep. 2016;6:20972. pmid:26879745
  60. 60. Xing ZP, Liang X, Wang X, Hu HY, Huang YX. Novel gene rearrangement pattern in mitochondrial genome of Ooencyrtus plautus Huang & Noyes, 1994: new gene order in Encyrtidae (Hymenoptera, Chalcidoidea). Zookeys. 2022;1124:1–21. pmid:36762364
  61. 61. Li H, L Hui, Shi A, Štys P, Zhou X, Cai WZ. The complete mitochondrial genome and novel gene arrangement of the unique-headed bug Stenopirates sp. (Hemiptera: Enicocephalidae) PLoS ONE. 2012;7(1):e29419. pmid:22235294
  62. 62. Saito S, Tamura K, Aotsuka T. Replication origin of mitochondrial DNA in insects. Genetics. 2005;171(4):1695–705. pmid:16118189
  63. 63. Babbucci M, Basso A, Scupola A, Patarnello T, Negrisolo E. Is it an ant or a butterfly? Convergent evolution in the mitochondrial gene order of Hymenoptera and Lepidoptera. Genome Biol Evol. 2014;6(12):3326–43. pmid:25480682
  64. 64. Zhang DX, Hewitt GM. Insect mitochondrial control region: a review of its structure, evolution and usefulness in evolutionary studies. Biochem Syst Ecol. 1997;25(2):99–120.
  65. 65. Liu L, Wu Y, Chen F, Wang QX, Qian ZQ. Characterization of the complete mitochondrial genome of the invasive tramp ant Cardiocondyla Obscurior (Hymenoptera: Formicidae: Myrmicinae). Mitochondrial DNA Part B-Resour. 2019;4(1):1496–8.
  66. 66. Hua JM, Li M, Dong PZ, Cui Y, Xie Q, Bu WJ. Comparative and phylogenomic studies on the mitochondrial genomes of Pentatomomorpha (Insecta: Hemiptera: Heteroptera). Bmc Genomics. 2008;9(1):1–15. pmid:19091056
  67. 67. Lunt DH, Whipple LE, Hyman BC. Mitochondrial DNA variable number tandem repeats (VNTRs): utility and problems in molecular ecology. Mol Ecol. 1998;7(11):1441–55. pmid:9819900
  68. 68. Zheng FY, Shi QY, Ling Y, Chen JY, Zhang BF, Li XJ. Comparative analysis of mitogenomes among five species of Filchnerella (Orthoptera: Acridoidea: Pamphagidae) and their phylogenetic and taxonomic implications. Insects. 2021;12(7):605. pmid:34357265
  69. 69. Ren YF, Lu HH, Chen LY, Sabatelli S, Wang CJ, Xie GL, et al. Comparative mitogenomic analysis of two longhorn beetles (Coleoptera: Cerambycidae: Lamiinae) with preliminary investigation into phylogenetic relationships of tribes of lamiinae. Insects. 2021;12(9). pmid:34564260
  70. 70. Goropashnaya AV, Fedorov VB, Seifert B, Pamilo P. Phylogenetic relationships of palaearctic Formica species (Hymenoptera, Formicidae) based on mitochondrial cytochrome b sequences. PLoS ONE. 2012;7(7):e41697. pmid:22911845
  71. 71. Chen Y, Zhou SY. Phylogenetic relationships based on DNA barcoding among 16 species of the ant genus Formica (Hymenoptera: Formicidae) from China. J Insect Sci. 2017;17(6):117–24.
  72. 72. Li H, Leavengood JM, Chapman EG, Burkhardt D, Song F, Jiang P, et al. Mitochondrial phylogenomics of Hemiptera reveals adaptive innovations driving the diversification of true bugs. P Roy Soc B-biol Sci. 2017;284(1862):20171223–33. pmid:28878063
  73. 73. Chen WT, Li M, Hu SY, Wang SH, Yuan ML. Comparative mitogenomic and evolutionary analysis of Lycaenidae (Insecta: Lepidoptera): potential association with high-altitude adaptation. Front Genet. 2023;14:1137588. pmid:37144132
  74. 74. Yuan ML, Zhang QL, Zhang L, Jia CL, Li XP, Yang XZ, et al. Mitochondrial phylogeny, divergence history and high-altitude adaptation of grassland caterpillars (Lepidoptera: Lymantriinae: Gynaephora) inhabiting the Tibetan Plateau. Mol Phylogenet Evol. 2018;122:116–24. pmid:29408286
  75. 75. Li M, Chen WT, Zhang QL, Liu M, Xing CW, Cao Y, et al. Mitochondrial phylogenomics provides insights into the phylogeny and evolution of spiders (Arthropoda: Araneae). Zool Res. 2022;43(4):566–84. pmid:35638362
  76. 76. Rodovalho CdM, Lyra ML, Ferro M, Bacci M Jr. The mitochondrial genome of the leaf-cutter ant Atta laevigata: a mitogenome with a large number of intergenic spacers. PLoS ONE. 2014;9(5):e97117. pmid:24828084
  77. 77. Levinson G, Gutman GA. Slipped-strand mispairing—a major mechanism for DNA-sequence evolution. Mol Biol Evol. 1987;4(3):203–21. pmid:3328815
  78. 78. Roberti M, Polosa PL, Bruni F, Musicco C, Gadaleta MN, Cantatore P. DmTTF, a novel mitochondrial transcription termination factor that recognises two sequences of Drosophila melanogaster mitochondrial DNA. Nucleic Acids Res. 2003;31(6):1597–604. pmid:12626700
  79. 79. Yuan ML, Zhang QL, Zhang L, Guo ZL, Liu YJ, Shen YY, et al. High-level phylogeny of the Coleoptera inferred with mitochondrial genome sequences. Mol Phylogenet Evol. 2016;104:99–111. pmid:27497607
  80. 80. Wei SJ, Tang P, Zheng LH, Shi M, Chen XX. The complete mitochondrial genome of Evania appendigaster (Hymenoptera: Evaniidae) has low A plus T content and a long intergenic spacer between atp8 and atp6. Mol Biol Rep. 2010;37(4):1931–42. pmid:19655273
  81. 81. Chen YJ, Kim S, Wan X. Mitochondrial genomes of the Dorcus velutinus complex (Coleoptera: Lucanidae) with the large intergenic spacer showing unique short sequence repeats and their implications for systematics. J Asia-Pac Entomol. 2021;24(1):493–501.
  82. 82. Papaioannou IA, Dimopoulou CD, Typas MA. Structural and phylogenetic analysis of the rDNA intergenic spacer region of Verticillium dahliae. Fems Microbiol Lett. 2013;347(1):23–32. pmid:23841495