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. 2024 Mar 16;24(2):7. doi: 10.1093/jisesa/ieae029

A comprehensive sampling of mitogenomes shows the utility to infer phylogeny of termites (Blattodea: Termitoidae)

Miao-Miao Wang 1, Nan Song 2,✉, Shi-Bao Guo 3, Xin-Ming Yin 4,✉
Editor: Joanna Chiu
PMCID: PMC10944015  PMID: 38491951

Abstract

The mitogenome sequence data have been widely used in inferring the phylogeny of insects. In this study, we determined the complete mitogenome for Macrotermes sp. (Termitidae, Macrotermitinae) using next-generation sequencing. Macrotermes sp. possesses a typical insect mitogenome, displaying an identical gene order and gene content to other existing termite mitogenomes. We present the first prediction of the secondary structure of ribosomal RNA genes in termites. The rRNA secondary structures of Macrotermes sp. exhibit similarities to closely related insects and also feature distinctive characteristics in their helical structures. Together with 321 published mitogenomes of termites as ingroups and 8 cockroach mitogenomes as outgroups, we compiled the most comprehensive mitogenome sequence matrix for Termitoidae to date. Phylogenetic analyses were conducted using datasets employing different data coding strategies and various inference methods. Robust relationships were recovered at the family or subfamily level, demonstrating the utility of comprehensive mitogenome sampling in resolving termite phylogenies. The results supported the monophyly of Termitoidae, and consistent relationships within this group were observed across different analyses. Mastotermitidae was consistently recovered as the sister group to all other termite families. The families Hodotermitidae, Stolotermitidae, and Archotermopsidae formed the second diverging clade, followed by the Kalotermitidae. The Neoisoptera was consistently supported with strong node support, with Stylotermitidae being sister to the remaining families. Rhinotermitidae was found to be non-monophyletic, and Serritermitidae nested within the basal clades of Rhinotermitidae and was sister to Psammotermitinae. Overall, our phylogenetic results are largely consistent with earlier mitogenome studies.

Keywords: Termitoidae, Macrotermes, phylogeny, mitogenome, next-generation sequencing

Introduction

Termites (Blattodea: Termitoidae) are the second largest group of eusocial insects, following the social Hymenoptera. While certain termite species are known to damage human buildings (Su and Scheffrahn 2000) and agricultural crops (Rouland-Lefèvre 2011), most termite species are not considered pests. Instead, these insects are crucial ecosystem engineers (Jouquet et al. 2016). Despite their economic and ecological significance, the evolutionary relationships among termites remain controversial.

Termites belong to the epifamily Termitoidae (former order Isoptera) within the Blattoidea and are a diverse group of insects, comprising around 3,000 described species (Krishna et al. 2013) in approximately 280 genera and 9 extant families (Kambhampati et al. 1996, Engel and Krishna 2004, Engel et al. 2009, Engel 2011, Bucek et al. 2019). The termite species were once divided into 7 families, including Mastotermitidae, Kalotermitidae, Hodotermitidae, Termopsidae, Rhinotermitidae, Serritermidae, and Termitidae (Engel and Krishna 2004). The family Termitidae contains about 80% of termite species worldwide and is the most prominent subgroup within Termitoidae (Bourguignon et al. 2017). Termitidae is often referred to as the ‘higher termites’, while all other non-Termitidae families are classified as ‘lower termites’. Despite several phylogenetic analyses that have produced well-supported trees of termite relationships, the estimation of family or subfamily relationships remains contentious. The widely accepted consensus from previous studies is that Mastotermitidae is the most basal clade in Termitoidae and is the sister group to all remaining termites (Kambhampati et al. 1996, Donovan et al. 2000, Kambhampati and Eggleton 2000, Thompson et al. 2000, Inward et al. 2007, Legendre et al. 2008, Engel et al. 2009, Ware et al. 2010, Lo and Eggleton 2011, Cameron et al. 2012, Bucek et al. 2019). Termitidae is a morphologically derived group within Termitoidae, but the sequence of divergences among the remaining families and the degree of paraphyly of Rhinotermitidae remain to be addressed. Termite systematists have proposed differing hypotheses on the phylogenetic placements of Kalotermitidae, Hodotermitidae, and Serritermidae (Donovan et al. 2000, Kambhampati and Eggleton 2000, Thompson et al. 2000, Legendre et al. 2008, Engel et al. 2009, Lo and Eggleton 2011). In addition, revisions to the higher classification of Termitidae have been made by Engel et al. (2009) and Engel (2011). A new family, Archotermopsidae, was established to include 3 extant genera of Archotermopsis, Zootermopsis, and Hodotermopsis (Engel et al. 2009). The family Hodotermitidae was restricted to include the genera Hodotermes, Anacanthotermes, and Microhodotermes (Engel et al. 2009). Stolotermitidae was resurrected to encompass Stolotermes and Porotermes. Finally, 2 genera, Stylotermes and Parastylotermes (formerly assigned to Porotermitinae in Termopsidae) (Engel et al. 2009), split from Rhinotermitidae to constitute the family Stylotermitidae (Engel et al. 2009). Engel (2011) proposed a total of 9 extant families classified in the infraorder Isoptera. These families include Mastotermitidae, Hodotermitidae, Archotermopsidae, Stolotermitidae, Kalotermitidae, Stylotermitidae, Rhinotermitidae, Serritermitidae, and Termitidae.

Previous studies have highlighted the usefulness of mitogenome sequences in resolving termite phylogenetic relationships (Cameron et al. 2012, Bourguignon et al. 2017). Despite recent advancements in termite mitochondrial genomics, studies utilizing mitogenome data for the entire Termitoidae phylogenetic relationships are lacking. In this study, we utilized next-generation sequencing technology to determine a new mitogenome for Macrotermes. We combined these data with existing mitogenomes to create the most comprehensive mitogenome sequence matrices (including 320 termite species and representing 9 extant families) for Termitoidae. The primary goals of this study are to (i) predict, for the first time, the secondary structure of ribosomal RNA genes (rrnL and rrnS) in termites based on the complete sequencing of rRNA from Macrotermes sp., (ii) examine the phylogenetic placement of Macrotermes, and (iii) explore the phylogenetic relationships of Termitoidae using mitogenome sequences, assessing the efficacy of mitogenomes in resolving family-level relationships within this insect group.

Materials and Methods

Taxon Sampling

An adult insect of Macrotermes sp. was collected in the field of Jigong mountain (E114°05’, N31°50’), Xinyang, China, in July 2017. The specimen was preserved in 100% ethanol and stored at −20 °C at the Entomological Museum of Henan Agricultural University until DNA extraction. The voucher number of the specimen is EMHAU-12K7S451. No specific permits were required for collecting the insect specimens used in this study, and the field studies did not involve endangered or protected species. The sequenced insect is the common insect species in China and is not included in the ‘List of Protected Animals in China’.

A total of 329 species belonging to 11 Blattodea families were analyzed (Supplementary Table S1), following the taxonomic classification by Engel (2011). The Termitoidae ingroup comprised 320 species from 9 families, including 1 species each from Mastotermitidae, Stolotermitidae, Serritermitidae, Archotermopsidae, and Stylotermitidae, 2 from Hodotermitidae, 11 from Kalotermitidae, 60 from Rhinotermitidae, and 242 from Termitidae. The outgroup taxa consisted of 9 species belonging to Blattidae and Cryptocercidae.

Assembling Mitogenome Sequence from NGS Data

Total genomic DNA was extracted from thoracic muscle tissues using a TIANamp Micro DNA Kit (Tiangen Biotech Co., Ltd) according to the manufacturer’s protocol. An Illumina Truseq library was constructed with an average insert size of 350 bp and sequenced on an Illumina HiSeq X Ten sequencing platform (Beijing Novogene Bioinformatics Technology Co., Ltd, China), with 150-bp paired-end reads setting. Raw reads were processed using the NGS QC toolkit (Patel and Jain 2012) to remove adapters, unpaired, short, and low-quality reads, with default settings.

High-quality reads were used for de novo assembly with IDBA-UD v. 1.1.1 (Peng et al. 2012), using a minimum k-mer size of 41 and a maximum k-mer size of 91. Initially, the mitochondrial cox1 gene fragment was sequenced to identify the long mitochondrial contig. In addition, MITObim 1.9.1 (Hahn et al. 2013) was employed to assemble the complete mitogenome sequence, starting from the cox1 gene as an initial seed.

The preliminary annotation was carried out using the MITOS webserver (Donath et al. 2019). The gene boundaries of protein-coding genes (PCGs) and rRNA genes were verified and refined by aligning them with published mitogenome sequences of termites. Transfer RNA genes were identified using both MITOS (Donath et al. 2019) and ARWEN (Laslett and Canbäck 2008). Clean reads were employed for mapping to the mitochondrial contig using Geneious R11 to evaluate the quality of the assembled mitogenome sequence. The new mitogenome sequence of Macrotermes sp. has been deposited at GenBank with the accession number OR287478.

Sequence Alignment

The PCGs were aligned separately using TranslatorX (Abascal et al. 2010) with the “MAFFT” method (Katoh and Standley 2013). Each tRNA and rRNA gene was aligned using the MAFFT program with the iterative refinement method of “E-INS-i.” Alignments were trimmed with TrimAl v.1.4.1 (Capella-Gutiérrez et al. 2009), using the automated1 option. FASconCAT-G_v1.04 (Abascal et al. 2010) was utilized to concatenate alignments and generate an associated gene partition file. Further trimming of nucleotide sequence alignments was performed using BMGE (Block Mapping and Gathering with Entropy) (Criscuolo and Gribaldo 2010) to minimize the potential effect of saturated sites on phylogenetic reconstruction. The concatenated datasets were assembled as follows: PCG_aa: Comprising the protein sequences of 13 PCGs, totaling 3,627 amino acid sites. PCG_nt: Encompassing the nucleotide sequences of 13 PCGs, with a total of 10,770 nucleotide sites. PCG_nt12: Consisting of the nucleotide sequences of 13 PCGs, with third codon positions removed, totaling 7,180 nucleotide sites. PCGRNA: Including all 37 mitochondrial genes, with a total of 13,723 nucleotide sites.

Salichos and Rokas (2013) showed that the use of genes with higher average bootstrap support can lead to more accurate results in phylogenetic estimation. Therefore, we selected genes whose bootstrap consensus trees had higher average bootstrap support to reconstruct the phylogenetic relationships of termites, in addition to conducting concatenation analyses of all 37 mitochondrial genes. IQ-TREE v2.2.0 (Nguyen et al. 2015) was utilized to perform maximum likelihood (ML) searches for constructing individual locus trees. ModelFinder (Kalyaanamoorthy et al. 2017) was employed to automatically select the best-fitting model for each gene alignment. To assess branch support, 10,000 ultrafast bootstrap replicates (Hoang et al. 2018) were conducted, with an average bootstrap value > 70 set as the threshold. Consequently, we selected a total of 20 mitochondrial genes, namely, atp6, atp8, cob, cox1, cox2, cox3, nad1, nad2, nad3, nad4, nad4L, nad5, nad6, rrnL, rrnS, trnA, trnD, trnI, trnR, and trnS2. The concatenated 20 mitochondrial genes (13,163 nucleotide sites) were included in the dataset ABS_70, which was utilized for subsequent phylogenetic analysis.

Phylogenetic Analyses

Prior to conducting phylogenetic analyses, the data were partitioned based on genes, and PCGs were further partitioned by codon position. Partitioned ML searches were conducted using IQ-TREE v2.2.0 (Nguyen et al. 2015), employing the partitioning scheme and corresponding best-fitting models selected by ModelFinder (Kalyaanamoorthy et al. 2017). To evaluate branch support, 10,000 ultrafast bootstraps were performed.

Bayesian inference (BI) was conducted in RevBayes (Höhna et al. 2016). For the nucleotide datasets (PCG_nt, PCG_nt12, PCGRNA, and ABS_70), we implemented the GTR + Gamma4 + I model. For the amino acid dataset (PCG_aa), we used the MtRev model. Each RevBayes run was performed with 2 independent Markov chain Monte Carlo analyses for 50,000 cycles, sampling parameters, and node histories every 100 cycles, and discarding the first 30% cycles as burnin. The posterior distributions were visualized using the program Tracer v1.7.1 (Rambaut et al. 2018) to assess the convergence between 2 independent runs.

We also conducted BI analyses using PhyloBayes MPI v.1.8 (Lartillot et al. 2013) through the CIPRES web portal. To save computational resources, we performed PhyloBayes analyses based only on the concatenated nucleotide datasets PCGRNA and ABS_70. The site-heterogeneous CAT-GTR (Lartillot and Philippe 2004) model was employed in each BI analysis, with 2 parallel chains run for 30,000 generations. Once stationary was reached, the first 5,000 generations were discarded as burn-in (maxdiff < 0.3 or minimum effective size > 50). We calculated the 50% majority rule consensus tree from the remaining 25,000 trees from each chain, and posterior probabilities generated by the bpcomp program in the PhyloBayes package were used to evaluate node support.

A previous study has demonstrated that employing multispecies coalescent analyses with ASTRAL may enhance the accuracy of inferring phylogenies using mitochondrial genomic data (Kim et al. 2020). We employed the coalescent-based method implemented in ASTRAL v 5.7.1 (Mirarab et al. 2014, Zhang et al. 2018) to infer a species tree of termites. To accomplish this, we performed an ASTRAL analysis on gene trees estimated from 37 mitochondrial gene alignments, using IQ-TREE with parameters set as those in the construction of the ABS_70 dataset. We used the local posterior support values (Sayyari and Mirarab 2016) to estimate branch support.

Results

Mitogenome Assembly and Characteristics

Out of 117,968,060 reads, we were able to assemble a total of 73,216 reads to generate the mitogenome sequence of Macrotermes sp. The mean coverage of the new mitogenome sequence was 679, with a minimum of 1 and a maximum of 1,269.

The newly sequenced mitogenome has a length of 15,925 bp. The gene content and order were consistent with published termite mitogenomes. Moreover, we found a pronounced A + T-rich region (AT% = 70.9) located between rrnS and trnI. The overall nucleotide composition revealed a high bias toward A and T, with an A + T content of 67.1%. This bias is consistent with other termite mitogenomes (58.2% in Astratotermes sp. to 71.6% in Hodotermopsis sjostedti), although it was slightly higher than most of the existing Macrotermes mitogenomes (64.6% in Macrotermes muelleri to 66.8% in Macrotermes barneyi).

The majority of PCGs in the newly sequenced mitogenome commence with either ATG or ATT and terminate with TAA or TAG. Notably, the cox1 gene used ATC as the start codon, which is consistent with other published Macrotermes mitogenomes (e.g., Macrotermes barneyi, NC_018599). The nad5 gene, on the other hand, utilized an incomplete TA as the stop codon. The most frequently used amino acid was leucine (Leu: 486), followed by serine (Ser: 359), phenylalanine (Phe: 306), and isoleucine (Ile: 263). We conducted a relative synonymous codon usage (RSCU) analysis, and the corresponding values are presented in Fig. 1. Our RSCU analysis revealed that codons with A or T in the third position were more frequently utilized compared to other synonymous codons.

Fig. 1.

Fig. 1.

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RSCU (relative synonymous codon usage) values calculated for 13 concatenated protein-coding genes.

We employed MITOS to identify 21 out of 22 tRNA genes, except for trnF, which was identified using ARWEN. The lengths of the tRNA genes ranged from 63 bp (trnR) to 78 bp (trnY). Figure 2 displays the secondary structures of the tRNAs, all of which can be inferred as the typical cloverleaf secondary structure, with the exception of trnS1. The secondary structure of trnS1 had an atypical dihydrouridine arm, which was only folded into a simple loop.

Fig. 2.

Fig. 2.

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The secondary structures predicted for 22 tRNA genes in the mitogenome of Macrotermes sp.

We identified 2 typical insect mitochondrial ribosomal RNA genes, rrnL and rrnS, located between trnL1(tag) and trnV(tac), and between trnV(tac) and the AT-rich region, respectively. The lengths of rrnL and rrnS were 1,306 bp and 810 bp, with A + T content of 72.0% and 67.9%, respectively. The secondary structures of both rRNA genes of Macrotermes sp. are presented in Figs. 3 and 4. These rRNA genes are the first representatives from a termite species and their secondary structure models are similar to those proposed for other Polyneoptera insects. The secondary structure of rrnL inferred for Macrotermes sp. consists of 5 domains (I–II and IV–VI) and 44 helices. However, the third domain is missing due to the short sequences between domains II and IV that prevent the formation of helices and loops. The secondary structure of rrnS gene contains 3 domains (I, II, and III) with 27 helices.

Fig. 3.

Fig. 3.

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The secondary structure for the rrnL gene in the mitogenome of Macrotermes sp.

Fig. 4.

Fig. 4.

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The secondary structure predicted for the rrnS gene in the mitogenome of Macrotermes sp.

Phylogeny

Phylogenetic analyses, which included different datasets using the same inference method, as well as the same dataset under different inference methods, produced consistent relationships within Termitoidae. Figure 5 (ML_PCG_aa), Fig. 6 (RevBayes_PCG_nt), and Fig. 7 (ASTRAL-tree) illustrate the family relationships inferred by the concatenated analysis and the coalescent-based species tree analysis, respectively. The full trees can be found in Supplementary Figures S1–S3. In addition, the well-supported clades identified within the topologies (Supplementary Figures S1–S13) were largely congruent with the findings from previous molecular studies (Cameron et al. 2012, Bucek et al. 2019).

Fig. 5.

Fig. 5.

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The phylogenetic tree of Termitoidae constructed by maximum likelihood method based on the amino acid dataset PCG_aa. Lineages were collapsed for clarity with the length of triangles equal to the longest terminal branch. Numbers at nodes indicate bootstrap values (BS > 70). Scale bar represents substitutions/site. Colored lines correspond to different family groups. The full tree is in Supplementary Figure S1.

Fig. 6.

Fig. 6.

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The phylogenetic tree of Termitoidae constructed by Bayesian inference using RevBayes based on the nucleotide dataset PCG_nt. Lineages were collapsed for clarity with the length of triangles equal to the longest terminal branch. Numbers at nodes indicate posterior probabilities (PP > 0.9). Scale bar represents substitutions/site. Colored lines correspond to different family groups. The full tree is in Supplementary Figure S2.

Fig. 7.

Fig. 7.

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The species tree of Termitoidae constructed using ASTRAL. Lineages were collapsed for clarity with the length of triangles equal to the longest terminal branch. Numbers at nodes indicate the local posterior support values (>0.9). Scale bar represents substitutions/site. Colored lines correspond to different family groups. The full tree is in Supplementary Figure S3.

Mastotermitidae was consistently recovered as the sister group of all other termite lineages. The families Hodotermitidae, Stolotermitidae, and Archotermopsidae constituted the second diverging lineage, followed by the monophyletic Kalotermitidae. This branching pattern remained consistent across analyses.

The Neoisoptera, which includes Stylotermitidae, Rhinotermitidae, Serritermitidae, and Termitidae (Engel et al. 2009), received strong and consistent support (BS = 100, PP = 1.0). Within the Neoisoptera, Stylotermitidae consistently appeared as the sister group to all other Neoisoptera lineages. Rhinotermitidae was found to be a non-monophyletic grade, while Termitidae formed a strongly supported clade (BS = 100, PP = 1). Serritermitidae, represented by Serritermes serrifer, was sister to Psammotermitinae, and they formed a clade in 6 out of 8 analyses. At the subfamily level, Heterotermitinae was found to be paraphyletic with respect to Coptotermitinae.

Termitidae was a large clade and was found to be in a derived position. Within Termitidae, Macrotermitinae was consistently recovered as the sister group of all other subfamilies. The subfamilies Apicotermitinae, Nasutitermitinae, and Syntermitinae were found to be monophyletic, while the Macrotermitinae and Termitinae were non-monophyletic.

Discussion

While there is currently a substantial number of termite mitochondrial genomes published in GenBank, to the best of our knowledge, there are no existing reports on predicting the secondary structures of mitochondrial ribosomal RNA in termites. Here, we present, for the first time, the predicted secondary structures of rrnL and rrnS genes in Macrotermes sp., representing the first such data in termites. In contrast to the mantis (Shi et al. 2021), there is a reduction of one helix in domain I of the secondary structure of rrnL in Macrotermes sp. Substantial stability differences are observed in helices H14 and H15 in domain II. In addition, in Macrotermes sp., an extra helix (H21) is present in domain IV, and H26 has a distinct structure compared to the mantis. Both Macrotermes sp. and mantis share a similar structure in domain V. There is a reduction of 1 helix in domain VI in Macrotermes sp. When compared to stick insects (Song et al. 2020), helices H8 and H14 in domain II, as well as H19 and H20 in domain IV, exhibit different structures.

Concerning rrnS secondary structure, Macrotermes sp. exhibits an additional helix (H1) in domain I compared to mantis (Shi et al. 2021). Furthermore, domain II shows notable differences with the absence of 6 helices. However, both share a similar secondary structure in domain III. When compared to stick insects (Song et al. 2020), the secondary structure of rrnS is largely identical, except for minor differences in helices H6 and H7 in domain I. Overall, the mitochondrial ribosomal RNA secondary structures in termites show some resemblance to relatively closely related mantises and stick insects. Nevertheless, they also feature unique characteristics. Confirming these distinctive traits requires additional data from secondary structure predictions. The predicted termite secondary structures are expected to enhance the alignment accuracy of rrnL and rrnS gene sequences in this group, thereby advancing research into the phylogeny of Termitidae.

The newly sequenced Macrotermes sp. consistently falls within a clade that encompasses all other sampled Macrotermes species, forming a monophyletic group (BS = 100, PP = 1). Macrotermes was recovered as the sister group to a clade containing Allodontermes, Synacanthotermes, Protermes, and Odontotermes, exhibiting a branching pattern similar to that observed in Bourguignon et al. (2017). Although our study is basically consistent with Bourguignon et al. (2017), some minor discrepancies exist. In the tree of Bourguignon et al. (2017), Odontotermes is non-monophyletic with respect to Protermes, while our results consistently place Protermes as the sister group to a monophyletic Odontotermes (including Hypotermes makhamensis). Additionally, Bourguignon et al. (2017) identified a Microtermes species as the sister taxon to the clade comprising Allodontermes, Synacanthotermes, Protermes, and Odontotermes. In contrast, our study demonstrates differences in the relative placement of Ancistrotermes to the majority of Microtermes. While Bourguignon et al. (2017) nested Ancistrotermes within Microtermes at the terminal end of the clade, our findings indicate a sister group relationship between Ancistrotermes and Microtermes. Despite these conflicts, both studies agree on a clade consisting of Microtermes and Ancistrotermes as the sister group to the larger clade encompassing Macrotermes, Allodontermes, Synacanthotermes, Protermes, and Odontotermes.

Our data unequivocally support Macrotermes as a close relative of a clade that encompasses Allodontermes, Synacanthotermes, Protermes, and Odontotermes. However, in light of the discussed discrepancies, additional data and broader sampling are essential to clarify relationships within the clade containing Macrotermes and its relatives.

In this study, we employed 3 different inference methods to reconstruct the phylogenetic relationships of termites. All 3 methods yielded nearly identical tree topologies. Even when using the program PhyloBayes, which implements the site-heterogeneous CAT-GTR model (Lartillot and Philippe 2004), the phylogenetic relationships did not change significantly. These results demonstrate that our phylogenetic reconstructions, based on the present mitogenome sequences, are robust across inference methods. We compiled 5 datasets using different data coding strategies. Comparison between the datasets revealed that tree topologies from the amino acid dataset and the nucleotide dataset excluding third codon positions exhibited lower nodal support in some deep nodes. Fossil evidence suggests that termite families radiated within a relatively short geological time span (Simpson 1953). The diversification of termites occurred in the late Jurassic or early Cretaceous, during which time they diverged into various families and subfamilies (Engel et al. 2009, Ware et al. 2010). Time estimates based on transcriptome sequence data indicate that Termitidae subfamilies diverged within 3.0 million years (Bucek et al. 2019). Fossil and molecular evidence suggest that the tempo of Termitoidae anagenesis was rapid. This may explain why nucleotide datasets with a fast-evolving rate are more suitable for resolving the phylogenetic relationships of termites than the amino acid dataset.

Previous studies have indicated that phylogenetic relationships inferred from mitochondrial gene sequences are often different from those from nuclear gene sequences (Hey 1997, Bensch et al. 2006, Wahlberg et al. 2009, Wiens et al. 2010, Platt et al. 2018). Mitogenomes and nuclear genomic regions have differing evolutionary histories. Some authors advocate for assigning greater significance to nuclear DNA in resolving insect phylogeny. In this study, consistent relationships within Termitoidae recovered by different analyses indicated that mitogenome sequences may be suitable for examining the phylogeny of termites, whose cladogenesis occurred over a period of time consistent with the evolutionary rate of mitochondrial gene sequences. The phylogenetic relationships recovered by the current mitogenomes provide a useful framework for understanding the evolution of termites. In comparison to earlier mitogenome studies, our research benefits from a larger sample size and more comprehensive taxon sampling. All 9 extant termite families were included in our phylogenetic analyses, making the findings from the present mitogenome sequences more reliable. Several hypotheses were confirmed. For example, Engel (2011) proposed the name Xylophagodea for a clade that includes Isoptera and Cryptocercidae, a hypothesis supported by our analyses. The sister-group relationship between Termitoidae and Cryptocercidae was consistently recovered across various phylogenetic analyses and datasets.

Mastotermitidae has commonly been regarded as the most primitive living termite family (Kambhampati et al. 1996). The mitogenome sequence data consistently support the basal position of Mastotermitidae, represented by the single species Mastotermes darwiniensis. In previous studies, the branching order of family divergences after Mastotermitidae remained a subject of debate. The Euisoptera, a suprafamilial termite lineage proposed by Engel et al. (2009), consists of the families Mastotermitidae, Hodotermitidae, Archotermopsidae, Stolotermitidae, and Kalotermitidae. However, the present mitogenome data did not support the hypothesis of Euisoptera. Our datasets robustly resolved a clade comprising Stolotermitidae, Hodotermitidae, and Archotermopsidae as the second diverging lineage within Termitoidae. Within this clade, Stolotermitidae consistently appeared as the sister group to Hodotermitidae + Archotermopsidae. Kalotermitidae was supported as the sister group of Neoisoptera (BS > 94, PP = 1). These relationships are similar to those reported by Kambhampati et al. (1996) and Thompson et al. (2000). However, Kambhampati et al. (1996) did not incorporate Hodotermitidae and Stolotermitidae into their analyses. Our findings are consistent with those of Cameron et al. (2012).

The Neoisoptera, another suprafamilial termite lineage, comprises the families Stylotermitidae, Rhinotermitidae, Serritermitidae, and Termitidae (Engel et al. 2009). The present mitogenome sequences consistently support the hypothesis of Neoisoptera with strong nodal support (BS = 100, PP = 1). However, the interrelationships among families within Neoisoptera remain unstable. Stylotermitidae consistently appears as the sister group to all other Neoisoptera lineages (BS = 100, PP = 1), except in the ML analysis using the dataset PCGRNA. The basal position of Stylotermitidae within Neoisoptera was confirmed using the present mitogenome sequence data. Serritermitidae was strongly supported as the sister group of Psammotermitinae and Rhinotermitidae. However, the placement of the clade Serritermitidae + Psammotermitinae varied across analyses.

Rhinotermitidae is composed of 6 subfamilies: Coptotermitinae, Heterotermitinae, Prorhinotermitinae, Psammotermitinae, Termitogetoninae, and Rhinotermitinae (Engel 2011). Our study included all 6 subfamilies, and they formed non-monophyletic groups. Specifically, both Heterotermitinae and Coptotermitinae clustered together in a clade, where partial Heterotermes exemplars were sister to Coptotermes. Hence, Heterotermitinae was paraphyletic with respect to Coptotermitinae. This pattern was also observed in a previous mitogenome study by Cameron et al. (2012). The clade comprising Heterotermitinae and Coptotermitinae was sister to the monophyletic Termitidae. In some analyses, such as ML_PCG_nt12, ML_PCGRNA, ML_PCG_nt12, ASTRAL, and ML_ABS70, Rhinotermitinae formed the first diverging clade among the subfamilies of Rhinotermitidae. The subfamilies Termitogetoninae, Psammotermitinae, and Prorhinotermitinae were placed in intermediate positions.

Legendre et al. (2008) supported Serritermitidae as the sister group of a clade comprising Rhinotermitidae and Termitidae. The same arrangement of Serritermitidae with Rhinotermitidae and Termitidae was also proposed by Lo and Eggleton (2011), Kambhampati and Eggleton (2000), and Donovan et al. (2000). However, Engel et al. (2009) placed Serritermitidae between Rhinotermitidae and Termitidae. In our study, Serritermitidae was most often placed as the sister group of Psammotermitinae, showing a relatively close relationship with Rhinotermitinae. Thus, it is possible that serritermitids are part of Rhinotermitidae. Our result is similar to that of Inward et al. (2007) and Ware et al. (2010).

Our analyses included 6 subfamilies of Termitidae. The relationships among these subfamilies, as recovered by the mitogenome sequences, were more consistent with the findings of Lo and Eggleton (2011). Macrotermitinae consistently formed the sister group of the remaining Termitidae subfamilies, which is congruent with most previous studies (Inward et al. 2007, Engel et al. 2009, Cameron et al. 2012, Bourguignon et al. 2015, 2017). Sphaerotermitinae was placed between partial Termitinae and Apicotermitinae. Bucek et al. (2019) clustered Sphaerotermitinae and Macrotermitinae in a clade, but this arrangement was not supported by our mitogenome sequences. Termitinae was found to be non-monophyletic, consistent with earlier studies (Inward et al. 2007, Bourguignon et al. 2015, 2017, Bucek et al. 2019), while the monophyly of the other 5 subfamilies of Termitidae was supported.

Supplementary Material

ieae029_suppl_Supplementary_Material
ieae029_suppl_supplementary_material.zip (3.8MB, zip)

Acknowledgments

This research was funded by the Earmarked Fund for China Agriculture Research System (NO. CARS-27), National Natural Science Foundation of China (U1904104), Foundation of Central Laboratory of Xinyang Agriculture and Forestry University, Grant/Award Number: FCL202003, and Special Funds for Local Scientific and Technological Development Guided by the Central Government, Grant/Award Number: Z20221341063.

Contributor Information

Miao-Miao Wang, Department of Entomology, Henan International Laboratory for Green Pest Control, Henan Engineering Laboratory of Pest Biological Control, College of Plant Protection, Henan Agricultural University, Zhengzhou 450046, China.

Nan Song, Department of Entomology, Henan International Laboratory for Green Pest Control, Henan Engineering Laboratory of Pest Biological Control, College of Plant Protection, Henan Agricultural University, Zhengzhou 450046, China.

Shi-Bao Guo, Department of Plant Protection, Xinyang Agriculture and Forestry University, Xinyang 464399, China.

Xin-Ming Yin, Department of Entomology, Henan International Laboratory for Green Pest Control, Henan Engineering Laboratory of Pest Biological Control, College of Plant Protection, Henan Agricultural University, Zhengzhou 450046, China.

Author Contributions

Miaomiao Wang (Conceptualization [equal], Data curation [equal], Formal analysis [equal], Investigation [equal], Methodology [equal], Resources [equal], Software [equal], Supervision [equal], Validation [equal], Visualization [equal], Writing—original draft [equal], Writing—review & editing [equal]), Nan Song (Conceptualization [equal], Data curation [equal], Formal analysis [equal], Funding acquisition [equal], Investigation [equal], Methodology [equal], Project administration [equal], Resources [equal], Software [equal], Supervision [equal], Validation [equal], Visualization [equal], Writing—original draft [equal], Writing—review & editing [equal]), Shi-Bao Guo (Conceptualization [equal], Data curation [equal], Formal analysis [equal], Investigation [equal], Software [equal], Validation [equal], Visualization [equal], Writing—review & editing [equal]), Xinming Yin (Data curation [equal], Formal analysis [equal], Funding acquisition [equal], Investigation [equal], Project administration [equal], Resources [equal], Software [equal], Supervision [equal], Validation [equal], Visualization [equal], Writing—review & editing [equal]), and Xiao-Long Liu (Data curation [equal], Formal analysis [equal], Investigation [equal], Methodology [equal], Software [equal], Validation [equal], Writing—review & editing [equal])

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Supplementary Materials

ieae029_suppl_Supplementary_Material
ieae029_suppl_supplementary_material.zip (3.8MB, zip)

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