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Research Article
Phylogenetic relationships of Culex (Diptera: Culicidae) based on mitogenomes
expand article infoYang Liu‡, Ruoqian Sun‡, Cong Li‡, Ruyue Zhang‡, Liming Wang‡, Ding Yang§, Yuyu Wang‡
‡ College of Plant Protection, Hebei Agricultural University, Baoding, China
§ Department of Entomology, China Agricultural University, Beijing, China

Corresponding author: Liming Wang ( wanglm1990@126.com )

Corresponding author: Ding Yang ( dyangcau@126.com )

Corresponding author: Yuyu Wang ( wangyy_amy@126.com )

Academic editor: Brian Wiegmann
© 2026 Yang Liu, Ruoqian Sun, Cong Li, Ruyue Zhang, Liming Wang, Ding Yang, Yuyu Wang.
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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ZooBank: urn:lsid:zoobank.org:pub:054AC4F3-DBE0-4048-BA43-FD5BFE95F1BD
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Abstract

Mosquitoes rank among the most deadly organisms worldwide, facilitating >700,000 human deaths annually through transmission of vector-borne pathogens. Culex are famous as vectors of multiple pathogens affecting both animals and humans. This study presents the first mitogenome sequencing and comparative analysis of seven species within Culex. Our findings demonstrated conserved structural features and nucleotide composition across the mitogenomes of these species. This study performed phylogenetic analysis of Culex based on mitochondrial genome data under both homogeneous and heterogeneous models separately, and estimated the divergence times. Phylogenetic analyses revealed that Culex is paraphyletic, with Lutzia nested within it. Both Cx. (Neoculex) and Cx. (Culex) were non-monophyletic. The two species of Cx. (Neoculex) were placed in separate lineages, with Cx. fergusoni as the sister group to all other Culex. Meanwhile, Cx. (Culex) was rendered paraphyletic by the inclusion of Cx. (Culiciomyia) and Cx. (Oculeomyia) within its clade. Divergence time estimation placed the basal split of Culicidae in Late Triassic, followed by the Culicinae-Anophelinae divergence in Late Jurassic (~147 Mya), with all speciation events within Culex postdating these splits and clustering in Neogene. This study provides a fundamental basis for understanding the mitogenomic architecture and phylogenetic relationships within the genus Culex, and also establishes a theoretical foundation for transmission mechanisms and control strategies of common mosquito-borne diseases.

Keywords

Divergence time, Mitochondrial DNA, Molecular evolution, Culicidae

1. Background

Mosquitoes are one of the deadliest organisms on Earth, facilitating over 700,000 deaths annually due to the transmission of various pathogens, such as malaria parasites and viruses that cause dengue, Zika, and yellow fever (Bhatt et al. 2013). Culex mosquitoes are known to be vectors of multiple pathogens that affect both animals and humans. Some examples of viral diseases transmitted by Culex mosquitoes include Usutu virus (USUV) (Busquets et al. 2008; Cook et al. 2018), West Nile virus (WNV) (Khan et al. 2017; Leggewie et al. 2016), and Japanese encephalitis virus (JEV) (Faizah et al. 2020). Japanese encephalitis is primarily prevalent in Asia and the Pacific region, putting over 3 billion people at risk of infection, the mortality rate of those infected with JEV is estimated to be approximately 20–30% (Campbell et al. 2011; Mulvey et al. 2021). After first emerging in New York City in the 1990s, West Nile Virus (WNV) rapidly spread across North America within just three years and has now become one of the most widely distributed mosquito-borne viruses globally (Karim and Bai 2023). The virus causes a considerable number of infections worldwide each year, and its post-infection mortality rate (6%) ranks among the highest for infectious diseases (Tao et al. 2024). In addition, Culex mosquitoes can also transmit parasites such as nematodes that cause lymphatic filariasis and protozoans responsible for avian malaria (Ferraguti et al. 2021; Nchoutpouen et al. 2019; Samy et al. 2016).

Culex is the largest genus within Culicinae, with 26 subgenera and 772 known species worldwide (Fu and Chen 2018). In China, eight subgenera (Barraudius, Culex, Culiciomyia, Eumelanomyia, Lophoceraomyia, Maillotia, Neoculex, and Oculeomyia) comprising 77 species have been recorded, all of which are distributed across the country (Fu and Chen 2018). Based on mitogenome studies, Culex has been demonstrated to be monophyletic and the sister group to Lutzia (Sun et al. 2019). Phylogenetic analyses based on ITS-1 and ITS-2 also yielded the same results (Miller et al. 1996). Phylogenetic analyses based on protein coding genes (PCGs) from 149 mitogenomes demonstrated that Culex was the sister group to other examined taxa within Culicinae (Chen et al. 2024). Tian et al. 2020 demonstrated that Culex and Mansonia are sister groups to each other based on complete mitogenomes (Tian et al. 2020). Although some phylogenetic studies have been conducted on certain Culex species (Demari-Silva et al. 2015; Harbach 2012; Luo 2016; Miller et al. 1996; Sun et al. 2017), little is known about the phylogenetic relationships across the entire genus. Besides, the phylogenetic relationships at the subgenus level within Culex remain unclear.

With the rapid development of molecular systematics, molecular biology techniques have provided new opportunities for taxonomy and have gradually become indispensable methods in the study of mosquito systematics. The insect mitogenome is usually a circular double-stranded DNA molecule ranging from 15 to 18 kb (Cameron et al. 2011). It has been widely used in research on phylogenetics and population genetics because of its characteristics such as stable gene structure, conserved coding components, maternal inheritance, rapid evolutionary rate, absence of repetition, and high copy numbers (Cheung et al. 2024; Tian et al. 2023; Zhu et al. 2024).

In this study, we report the sequence and annotation of mitogenomes for seven species of Culex: Cx. bicornutus, Cx. harrisoni, Cx. nigropunctatus, Cx. torrentium, Cx. huangae, Cx. pseudovishnui and Cx. bitaeniorhynchus. The nucleotide composition and codon usage are analyzed, the secondary structures of transfer RNAs (tRNAs) and ribosomal RNAs (rRNAs) are predicted, and a comparative analysis of the composition and structure of published Culex mitogenomes is performed. Additionally, the phylogenetic relationships are explored by incorporating 30 previously published Culex mitogenomes. This research contributes new mitogenome data for Culex and provides a foundation for further phylogenetic studies of Culicidae.

2. Materials and methods

2.1. Sampling and genomic DNA ­extraction

Adult specimens were sampled during 2012–2022 from Yunnan, Chongqing, Guangxi, Jilin and Hubei Province (Table S1). The specimens were preserved in 95% alcohol and at –20°C before the DNA extraction. Genomic DNA was extracted using the DNeasy Blood and Tissue kit (QIAGEN, Hilden, Germany) from the thoracic muscle tissue. The DNA concentration was measured using a nucleic acid protein analyzer (Thermo Scientific, Waltham, MA, USA).

2.2. Genome sequencing and analysis

Whole genomes were sequenced on the Illumina HiSeq 2500 and Illumina X plus. Raw reads were checked by FastQC 0.11.9 (Brown et al. 2017) and lo w-quality reads were filtered using Trimmomatic 0.32 (Bolger et al. 2014) with an average quality threshold of Q15. The mitogenomes were assembled by IDBA-UD 1.1.3 (Peng et al. 2012). Annotations were conducted using MitoZ 2.4 (Meng et al. 2019) with the “invertebrate mitochondrial code 5” as genetic code and “Arthropoda” as clade. Then, the sequence was checked by manual proofreading according to related species. The circular maps of mitogenomes were drawn using OGDRAW (Greiner et al. 2019), and the base composition and codon usage were analyzed using MEGA 7.0 (Kumar et al. 2016). The calculation formulas of base composition asymmetry were AT-skew = (A−T)/(A+T) and GC-skew = (G−C)/(G+C) (Perna and Kocher 1995). The relative synonymous codon usage (RSCU) of PCGs were calculated using MEGA 7.0 (Kumar et al. 2016). Sequences of 13 PCGs of all Culex (including downloaded from Genbank and the seven newly sequenced mitogenomes) were individually aligned via the L-INS-i algorithm using MAFFT 7.313 (Katoh and Standley 2013) with the “invertebrate mitochondrial” as code table, “codon” as alignment mode and “auto” as strategy. Then, nucleotide diversity (Pi) and non-synonymous/synonymous substitution ratios (Ka/Ks) were calculated using DnaSP 6 (Rozas et al. 2017) with the “first site” as protein coding regions and the “mtDNA Drosophila” as genetic code. The secondary structures of tRNAs were predicted using the MITOS Web Server (http://mitos.bioinf.uni-leipzig.de/index.py) and checked through manual proofreading (Bernt et al. 2013), and the secondary structures of rrnS and rrnL were predicted using RNA Structure (http://rna.urmc.rochester.edu/RNAstructureWeb).

2.3. Phylogenetic analysis

There were 50 specimens of Culicidae in the ingroup and two specimens of Simuliidae were used as outgroup representatives to reconstruct the phylogeny in this study (Table S2). Sequences of PCGs and rRNAs were aligned using MAFFT 7.313 (Katoh and Standley 2013). Each rRNA gene alignment was conducted using the G-INS-i algorithm by MAFFT 7.313 (Katoh and Standley 2013) with the “invertebrate mitochondrial” as code table, “normal” as alignment mode and “auto” as strategy. The ambiguous regions in the alignments of PCGs and rRNAs were removed using Gblocks (Talavera and Castresana 2007). Maximum Likelihood (ML) trees were inferred using IQ-TREE v.1.6.10 (Nguyen et al. 2015) with 1000 ultrafast bootstraps. Bayesian Inference (BI) analyses were conducted using MrBayes v.3.2.2 (Ronquist and Huelsenbeck 2003) under the best substitution models selected by ModelFinder 2.1.10 (Kalyaanamoorthy et al. 2017). Finally, the phylogenetic trees were visualized using FigTree 1.4.4 (Rambaut 2009).

2.4. Divergence time estimation

Divergence times in Culicidae were estimated using MCMCTree v4.9e (Yang 2007). The topology obtained through BI analysis of the PCGAA dataset was incorporated as the input file. Three fossil calibrations were selected: Toxorhynchites mexicanus (Zavortink and Poinar, 2000) (16 million years ago Mya) as the earliest fossil record of Toxorhynchites; Cx. winchesteri Cockerell, 1908 (34 Mya) as the earliest fossil of Culex; Priscoculex burmanicus, Zavortink & Brown, 2009 (89 Mya) as the earliest fossil of Anophelinae (https://mosquito-taxonomic inventory.myspecies.info). The independent rate clock model (clock = 2) was used for further calculation. Two independent MCMC chains were conducted for 20 million generations with 25% discarded as burn-in. The convergence of the MCMC chains was evaluated using Tracer v1.7.2 (Rambaut et al. 2018), ensuring that the ESS values exceeded 200.

3. Results

3.1. Genome organization and base composition

The complete mitogenomes of the seven newly sequenced species (Fig. S1) range from 15,564 to 15,971 bp in length, exhibiting the typical gene content for the genus Culex: 13 PCGs, 22 tRNAs, two rRNAs, and a control region (CR). Gene strandedness were conserved, with 23 genes (nine PCGs, 14 tRNAs) encoded on the heavy strand (J-strand) and 14 genes (four PCGs, eight tRNAs, and two rRNAs) on the light strand (N-strand) (Tables S3–S9).

There are 34 complete mitogenomes of Culex including those published on GenBank (https://www.ncbi.nlm.nih.gov/genbank), ranging from 15,564 bp (Cx. bitaeniorhynchus) to 16,052 bp (Cx. chidesteri) in length. Across these mitogenomes, the content of C is higher than G and the content of A is higher than T, with A + T content ranging from 77.98% to 79.57% (Fig. 1, Table S10). The length of complete PCGs ranges from 11,199 bp (Cx. bitaeniorhynchus) to 11,232 bp (Cx. huangae). AT content in PCGs ranges from 78.17% (Cx. lygrus) to 76.11% (Cx. fergusoni) (Fig. 1, Table S11).

Figure 1. 

AT% vs. AT-Skew and GC% vs. GC-Skew in complete mitogenomes and PCGs of Culex.

For complete mitogenomes, AT-skew values range from 0.00 to 0.01, and GC-skew values range from –0.19 to –0.15. In PCGs specifically, AT-skew ranges from –0.17 to –0.15, and GC-skew ranges from 0.03 to 0.07 (Fig. 1, Tables S10, S11), a pattern consistent with previous reports in mosquitoes (Krzywinski et al. 2011; Martinez-Villegas et al. 2019). Notably, base composition analysis at the three codon positions reveals that variation is most pronounced at the third position (Table S12). Due to codon degeneracy, this nucleotide variation doesn’t alter the encoded amino acid sequences (Table S13; Reetz et al. 2008).

This pattern indicates that purifying selection acts primarily at the protein level, while synonymous substitutions accumulate at the nucleotide level – a feature that makes mitochondrial PCGs informative for phylogenetic studies across different taxonomic scales (Zardoya and Meyer 1996; Simon et al. 2006).

3.2. Protein-coding genes and codon usage

Start and stop codon usage across the 34 Culex mitogenomes is largely conserved, with some gene- and species-specific exceptions (Tables S14, S15). While most PCGs initiate with the canonical ATN start codon, two notable deviations are observed: the ND1 gene initiates with TTG in 11 species, and the ATP6 gene in Cx. vishnui also utilizes TTG. The recurrent use of the non-canonical TTG start codon for ND1 across multiple lineages suggests either a shared ancestral feature or a conserved mechanism of translational initiation within the genus (similar non-canonical starts have been reported in other insect mitogenomes) (Cameron 2014). Furthermore, COX1 consistently employs TCG as the start codon across all 34 species. Stop codon usage also varies. Most PCGs (ATP6, ATP8, ND1, ND2, ND4L, ND5, ND6) use the complete TAA stop codons in all species examined, with the exceptions of ND2 in Cx. harrisoni (TAG) and ND6 in Cx. vishnui (TA-tRNA). Incomplete stop codons (T-- or TA-), completed via post-transcriptional polyadenylation, are observed in COX1, COX2, COX3, CYTB, ND3, and ND4, with the specific type varying by species.

Relative synonymous codon usages (RSCUs) in the seven newly sequenced mitogenomes are presented in Figure S2 (Tables S16–S22). The most frequently used codons are UUU (Phe), UAU (Tyr), AUU (Ile), AUA (Met), AAU (Asn) and AAA (Lys). Notably, CAA (Gln) is not used in these seven mitogenomes. This codon bias reflects the high A + T content of the genomes, favoring codons rich in A and T.

Nucleotide diversity (Pi) and the rate of nucleotide substitution (Ka/Ks) are calculated for the 13 PCGs (Fig. 2). Pi values range from 0.041 (ND4L) to 0.080 (CYTB). The Ka/Ks ratios for all 13 PCGs are less than 1, indicating that these genes evolve under purifying selection (Hurst 2002; Hurst 2009; Mori and Matsunami 2018). Among them, COX1 exhibits the lowest Ka/Ks ratio (0.170), suggesting the strongest purifying selection and the slowest evolutionary rate, while ATP8 shows the highest ratio (0.170), indicating a relatively faster evolutionary rate.

Figure 2. 

The nucleotide diversity (Pi) and non-synonymous (Ka) to synonymous (Ks) substitution rates of 13 protein-coding genes of the mitochondrial genomes of Culex.

The strong purifying selection on COX1 reinforces its suitability as a reliable marker for deep phylogenetic relationships (Folmer et al. 1994; Hebert et al. 2003), while the elevated evolutionary rate of ATP8 may provide greater resolution for separating closely related species (Gissi et al. 2010).

3.3. The control region and overlapping regions

The CR, located between tRNAVal and tRNAIle in all seven newly sequenced mitogenomes, is the most variable region of the Culex mitogenome. Across the 34 species, CR length ranges from 580 to 1195 bp (Table S23), with A + T content varying from 87.89% to 91.56%. AT-skew ranges from –0.13 to 0.02, and GC-skew from –0.46 to –0.24. Two long tandem repeats (>200 bp) are identified in the CR of Cx. bicornutus, and their predicted secondary structures are shown in Figure 3. Additionally, numerous microsatellite-like sequences, such as (TA)n repeats, are present in the CR of all 34 species (Table S24). These sequences may facilitate the completion of DNA replication and transcription, and may serve as useful markers for geographical studies (Tian et al. 2023; Zhao et al. 2017). The extensive length polymorphism and presence of variable microsatellite motifs in the CR suggest this region may contain sufficient population-level variation to resolve phylogeographic structure within widely distributed Culex species complexes (Simon et al. 2006).

Figure 3. 

The structure and likely secondary structures of the control region in the mitogenome of Culex bicornutus.

3.4. Transfer RNAs and ribosomal RNAs

Secondary structures of all 22 tRNAs are predicted and compared for the seven newly sequenced mitogenomes (Fig. 4). Most tRNAs are highly conserved. The greatest difference is observed in trnD (11 variable sites), while trnQ, trnH, trnI, trnL1, trnK, trnM, trnS1, and trnM each exhibit only one variable site. Almost all tRNAs fold into typical cloverleaf structures, except trnS2, in which the dihydrouridine (DHU) arm forms a simple loop. This feature is widely documented in metazoan mitogenomes (Wolstenholme 1992).

Figure 4. 

Inferred secondary structures of 22 tRNAs of the complete mitochondrial genomes of Culex.

The rrnL is located between trnL1 and trnV, with length ranging from 1,279 to 1,360 bp across the 34 species. A + T content varies from 82.49% to 83.76%, and G + C content from 16.24% to 17.51%. The content of T is higher than A, and the content of G is higher than C (Table S25). The predicted secondary structures of rrnL contain four canonical domains (I–II, IV–V) and 46 helices (Fig. 5), with domains III and VI absent, a typical structure in arthropods (Cannone et al. 2002).

Figure 5. 

Inferred secondary structures of rrnL of the complete mitochondrial genomes of Culex.

The rrnS is located between trnV and the CR, with lengths ranging from 783 to 819 bp and A + T content from 80.46% to 81.89%. The content of T is higher than A, and the content of G was higher than C (Table S26). The predicted secondary structure of rrnS contains three domains and 34 helices (Fig. 6).

Figure 6. 

Inferred secondary structures of rrnS of the complete mitochondrial genomes of Culex.

3.5. Phylogenetic analyses

There ar e five different types of datasets used in the phylogenetic analysis (Fig. 7, Figs S3–S11). The PCG123RNA dataset (13,222 sites) incorporates all codon positions of 13 PCGs and two rRNAs; the PCG123 dataset (11,160 sites) contains three codon positions for all 13 PCGs; the PCG12RNA dataset (9,502 sites) includes only the first and second codon positions of 13 PCGs along with two rRNAs; the PCG12 dataset (7,440 sites) comprises only the first two codon positions from all 13 PCGs, while the dataset PCGAA contains 3,720 sites including all amino acids of 13 PCGs.

Figure 7. 

Phylogeny of Culex inferred based on the dataset PCGAA. a The topological structure of Bayesian Inference and Maximum Likelihood trees. Subsequent Bayesian probabilities (BP) (before slash) and values of support for bootstrapping (BPP) (after slash) shown at each node. b The parts of the ML tree that differ from the BI tree.

There are 10 phylogenetic trees reconstructed based on BI and ML analyses in this study (Figs 7, S3–S11). The phylogenetic results demonstrate that both the subfamily Anopheline and Culicinae are monophyletic.

Across all phylogenetic topologies examined, the genus Culex is consistently recovered non-monophyletic, with Lutzia fuscana invariably neste within it. With the exception of the ML analysis using the PCG123 dataset, all topologies support the monophyly of Cx. (Lophoceraomyia) and Cx. (Culiciomyia). In contrast, Cx. (Neoculex) and Cx. (Culex) are recovered as paraphyletic. Notably, the BI and ML analyses based on the PCGAA dataset reveal that subgenera Cx. (Oculeomyia) and Cx. (Culiciomyia) are nested within Cx. (Culex), which is consistent with ML analyses of all datasets except PCG12RNA. Based on the PCGAA dataset, both BI and ML phylogenetic analyses consistently support the monophyly of Cx. (Lophoceraomyia) and its position as the sister group to a clade containing Lutzia, Cx. (Culex), and several other subgenera nested within Cx. (Culex). This result is consistent with the ML analyses based on the PCG12, PCG123, and PCG123RNA datasets. The BI analysis of PCG12 dataset and both BI and ML analyses of PCG12RNA demonstrate that Cx. (Oculeomyia) is the sister group to Cx. (Culex) with Cx. (Culiciomyia) nested within Cx. (Culex), while BI analyses of PCG123 and PCG123RNA datasets consistently recover Cx. (Culiciomyia) being the sister group to Cx. (Culex) with both Lutzia and Cx. (Oculeomyia) nested within Cx. (Culex) clade.

3.6. Divergence time estimation

The divergence time is estimated based on the phylogeny of dataset PCGAA and the chronogram is shown in Figure 8. Mean age values and 95% high posterior density (HPD) intervals for each node are presented in Table S27.

Figure 8. 

Divergence time estimation of Culicidae based on the phylogeny of dataset PCGAA.

The family Culicidae diverged during Late Triassic (~219.34 Mya; 95% HPD = 129.57–305.38 Mya). Within this family, Culicinae diverged from Anophelinae in Late Jurassic (~147.45 Mya; 95% HPD = 87.03–216.38 Mya). The clade Sabethini + Mansoniini split from other lineages during mid-Cretaceous (~119.90 Mya; 95% HPD = 69.43–177.94 Mya), followed by the divergence of Mansoniini from Sabethini later in mid-Cretaceous (~98.67 Mya; 95% HPD = 49.99–153.91 Mya). The lineage comprising Aedeomyiini + Toxorhynchitini emerged during mid-Cretaceous (~106.60 Mya; 95% HPD = 61.96–161.42 Mya), while Culisetini + Culicini diverged from Aedini later in Cretaceous (~86.42 Mya; 95% HPD = 47.41–131.23 Mya). Significant Neogene divergences include the separation of Toxorhynchitini from Aedeomyiini in the Early Neogene (~22.00 Mya; 95% HPD = 14.23–33.20 Mya). Additionally, the split between Culicini and Culisetini occurred during the Late Cretaceous (~67.12 Mya; 95% HPD = 35.79–102.36 Mya).

Within Culex, the clade Cx. (Culex) + Lutzia diverged from Cx. (Lophoceraomyia) at ~28.62 Mya (95% HPD = 15.07–44.73 Mya), with Cx. (Culex) differentiating from Lutzia at ~22.69 Mya (95% HPD = 13.13–35.15 Mya). The subgenera Cx. (Oculeomyia) and Cx. (Culiciomyia) diverged at ~11.09 Mya (95% HPD = 4.54–18.23 Mya) and ~11.65 Mya (95% HPD = 6.61–17.71 Mya), respectively.

4. Discussion

Gene composition and structural characteristics of the seven Culex mitogenomes were consistent with other published Culicidae species (Aragao et al. 2019; da Silva et al. 2020; Hao et al. 2017; Lorenz et al. 2019; Sun et al. 2019) and other reported insect groups (Dai et al. 2018; Jiang et al. 2016; Wang et al. 2017). In the complete mitogenomes of Culex, the content of A + C was higher than that of G + T. This is consistent with previous studies based on the genera Anopheles and Culex (Demari-Silva et al. 2015; Martinez-Villegas et al. 2019; Oliveira et al. 2016). Similar results have also been observed in other groups (Jiang et al. 2016; Wang et al. 2016; Wei et al. 2010). The use of ATN as the start codon is common in Culicidae (Demari-Silva et al. 2015; Hao et al. 2017; Sun et al. 2019) and has also been observed in studies of other groups (Ji et al. 2024; Liu et al. 2024; Tian et al. 2023; Xu et al. 2020; Zhang et al. 2024).

The phylogenetic status of Lutzia has long been controversial. Initially classified as a subgenus within Culex, Lutzia was proposed for elevation to generic rank as early as 1932 due to its distinctive biological characteristic of larval predation on other mosquito species (Edwards 1932). Subsequently, Sun et al. reached the same conclusion based on mitochondrial gene evidence (Sun et al. 2019). However, subsequent phylogenetic analyses have consistently recovered Lutzia was nested within Culex (Chen et al. 2024; Zadra et al. 2021; Pierce et al. 2025), which is congruent with our results. Within Culex, the Cx. (Neoculex) was found to be non-monophyletic. Our results revealed that Cx. (Culex) is non-monophyletic, with Cx. (Oculeomyia) and Cx. (Culiciomyia) nested within it. These fin dings are consistent with previous studies employing both mitogenome and nuclear gene datasets (Han et al. 2024; Koh et al. 2023; Li et al. 2023). However, a recent whole-genome-based study showed that Cx. (Culiciomyia) was the sister group to Cx. (Culex), which included Lutzia (Pierce et al. 2025). Given the instability observed at deeper nodes and the well-known limitations of mitochondrial data for resolving ancient rapid radiations (Rubinoff and Holland 2005), the phylogenetic relationships presented here should be regarded as a working hypothesis that requires further testing with additional data, particularly from nuclear genomes.

Our divergence time estimates based on the PCGAA dataset indicated that the family Culicidae originated in Late Triassic (~219 Mya), which was consistent with the known fossil evidence demonstrating the existence of Culicoidea as early as Late Triassic (210 Mya) (Chen et al. 2024). This esti mate is also broadly congruent with recent phylogenomic analyses based on whole-genome data, which placed the crown age of Culicidae at approximately 188–250 Mya (Late Triassic to Early Jurassic) (Soghigian et al. 2023). The subfamily Culicinae diverged from Anophelinae during Late Jurassic (~147 Mya), this aligns with the results of Hao et al. from the mitochondrial PCG123 dataset (~145.03 Mya) and is corroborated by Krzywinski et al.’s divergence time estimates based on mitochondrial data (rRNA, protein-coding genes, and J-strand protein genes) (~145–200 Mya) (Hao et al. 2017; Krzywinski et al. 2006). Soghigian et al. (2023) similarly estimated the Anophelinae-Culicinae split at approximately 147–213 Mya, further supporting the robustness of this divergence timing across different data types and analytical approaches. The Cretaceous period marked the primary diversification of major mosquito lineages at both the family and tribe levels, coinciding with three pivotal evolutionary developments, i.e: the origin of flowering plants (angiosperms), the diversification of birds (Aves), and the ecological expansion of mammals (Mammalia). This temporal overlap suggests an ecological interdependence, in which mammals became key vertebrate hosts for hematophagous mosquito species, while contemporaneously evolving angiosperms provided essential nutritional resources (e.g., plant exudates) and suitable microhabitats for the immature stages of mosquitoes (Chen et al. 2024; Lyimo and Ferguson 2009). The consi stency between our mitogenome-based estimates and those derived from phylogenomic analyses (Soghigian et al. 2023) demonstrates that mitochondrial data, despite representing a smaller fraction of the genome, can recover reliable divergence patterns when combined with appropriate fossil calibrations. To better understand the phylogenetic relationships within Culex as well as subgenera and to estimate their divergence times, more comprehensive samplings and the whole genomic data will be needed in the future studies.

5. Conclusions

This study provides the first comprehensive mitogenome characterization and comparative analysis of seven Culex species, offering new insights into the structural conservation, nucleotide composition, and phylogenetic relationships within the genus. The mitochondrial genomic features are largely conserved across the analyzed species, supporting their close evolutionary affinities. Phylogenetic analyses revealed a non-monophyletic Culex, with Lutzia nested within the clade, as well as paraphyletic relationships among several traditionally recognized subgenera. Divergence time estimation suggests that the major splits in the Culicidae family occurred during Late Triassic and Late Jurassic, preceding most speciation events within Culex, which were concentrated in the Neogene. These findings corroborate previous nuclear gene-based studies but also highlight the complex evolutionary history and unresolved systematics within the genus. To better resolve these relationships and refine divergence time estimates, future studies should incorporate more comprehensive sampling and integrate nuclear and mitochondrial genomic data. This work lays a foundational framework for further investigations into the molecular evolution, systematics, and evolutionary history of the medically important Culex mosquitoes.

6. Declarations

Availability of data and materials. The genome sequencing data of Cx. bicornutus (PQ213471), Cx. bitaeniorhynchus (PQ213468), Cx. harrisoni (PP818790), Cx. huangae (OR074506), Cx. nigropunctatus (PQ213469), Cx. pseudovishnui (PQ213470) and Cx. torrentium (OR127146) that support the findings of this study are openly available in the GenBank of NCBI at https://www.ncbi.nlm.nih.gov (accessed on 6 October 2024).

Competing Interests. The authors declare that they have no competing interests.

Funding. This work was supported by the National Natural Science Foundation of China (32170451), the Natural Science Foundation of Hebei Province (C2025204080), the Basic Research Project of Shijiazhuang for University in Hebei Province (241791137A), the Science and Technology Planning Project of Baoding (2472P018), and the Earmarked Fund for CARS-27.

Authors’ Contributions. Yang Liu: Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft preparation, Writing – review and editing, Visualization. Ruoqian Sun: Validation, Formal analysis, Writing – original draft preparation, Visualization. Cong Li: Investigation, Resources. Ruyue Zhang: Methodology, Software. Zimeng Zhang: Formal analysis, Data curation. Liming Wang: Conceptualization, Writing – review and editing, Supervision. Ding Yang: Conceptualization, Methodology, Writing – review and editing, Supervision. Yuyu Wang: Conceptualization, Methodology, Validation, Writing – original draft preparation, Writing – review and editing, Supervision, Project administration, Funding acquisition. All authors have read and agreed to the published version of the manuscript.

7. Acknowledgements

We are grateful to the Entomological Museum of China Agriculture University for the loan of specimens. We thank Xulong Chen, Qicheng Yang, Liang Wang and Zhifei Li for collecting and providing the specimens.

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Supplementary materials

Supplementary material 1 

Figures S1–S10

Liu Y, Sun RQ, Li C, Zhang RY, Zhang ZM, Wang LM, Yang D, Wang YY (2026)

Data type: .zip

Explanation notes: Figure S1. Mitochondrial maps. Genes outside the map are transcribed counterclockwise, whereas those inside are transcribed clockwise. — Figure S2. Relative synonymous codon usages (RSCUs) of protein-coding genes in the complete mitochondrial genomes of Culex. — Figure S3. Phylogenetic tree inferred from Bayesian Inference analysis based on the PCG12 dataset. — Figure S4. Phylogenetic tree inferred from Maximum Likelihood analysis based on the PCG12 dataset. — Figure S5. Phylogenetic tree inferred from Bayesian Inference analysis based on the PCG12RNA dataset. — Figure S6. Phylogenetic tree inferred from Maximum Likelihood analysis based on the PCG12RNA dataset. — Figure S7. Phylogenetic tree inferred from Bayesian Inference analysis based on the PCG123 dataset. — Figure S8. Phylogenetic tree inferred from Maximum Likelihood analysis based on the PCG123 dataset. — Figure S9. Phylogenetic tree inferred from Bayesian Inference analysis based on the PCG123RNA dataset. — Figure S10. Phylogenetic tree inferred from Maximum Likelihood analysis based on the PCG123RNA dataset.

This dataset is made available under the Open Database License (http://opendatacommons.org/licenses/odbl/1.0). The Open Database License (ODbL) is a license agreement intended to allow users to freely share, modify, and use this dataset while maintaining this same freedom for others, provided that the original source and author(s) are credited.
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Supplementary material 2 

Tables S1–S27

Liu Y, Sun RQ, Li C, Zhang RY, Zhang ZM, Wang LM, Yang D, Wang YY (2026)

Data type: .zip

Explanation notes: Table S1. Mitogenome-sequenced Culex specimens in this study. — Table S2. List of taxonomic groups used for the phylogenetic analyses in this study. — Table S3. Organization of Culex bicornutus mitogenome. — Table S4. Organization of Culex bitaeniorhynchus mitogenome. — Table S5. Organization of Culex harrisoni mitogenome. — Table S6. Organization of Culex huangae mitogenome. — Table S7. Organization of Culex nigropunctatus mitogenome. — Table S8. Organization of Culex pseudovishnui mitogenome. — Table S9. Organization of Culex torrentium mitogenome. — Table S10. Nucleotide composition and skews in the complete mitogenomes of Culex. — Table S11. Nucleotide composition and skews in the PCGs of complete Culex mitogenomes. — Table S12. Nucleotide composition at the three codon sites in the complete PCGs of Culex. — Table S13. Encoded amino acids composition in the complete PCGs of Culex. — Table S14. Start codons of PCGs in Culex. — Table S15. Stop codons of PCGs in Culex. — Table S16. Relative synonymous codon usages (RSCUs) of PCGs of Culex bitaeniorhynchus. — Table S17. Relative synonymous codon usages (RSCUs) of PCGs of Culex harrisoni. — Table S18. Relative synonymous codon usages (RSCUs) of PCGs of Culex nigropunctatus. — Table S19. Relative synonymous codon usages (RSCUs) of PCGs of Culex huangae. — Table S20. Relative synonymous codon usages (RSCUs) of PCGs of Culex torrentium. — Table S21. Relative synonymous codon usages (RSCUs) of PCGs of Culex pseudovishnui. — Table S22. Relative synonymous codon usages (RSCUs) of PCGs of Culex bicornutus. — Table S23. Nucleotide composition and skews in the complete control regions of Culex. — Table S24. The count of microsatellite-like sequence in the complete control regions of Culex. — Table S25. Nucleotide composition and skews in the rrnL. — Table S26. Nucleotide composition and skews in the rrnS. — Table S27. Mean divergence times and 95% high posterior density (HPD) intervals for each node of the topology presented in Figure 8.

This dataset is made available under the Open Database License (http://opendatacommons.org/licenses/odbl/1.0). The Open Database License (ODbL) is a license agreement intended to allow users to freely share, modify, and use this dataset while maintaining this same freedom for others, provided that the original source and author(s) are credited.
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