Mitochondrial genome of Bactrocera fruit flies (Tephritidae: Dacini): Features, Structure, and significance for Diagnosis

Mitochondrial genome of Bactrocera fruit flies (Tephritidae: Dacini): Features, Structure, and significance for Diagnosis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Mitochondrial genome of Bactrocera fruit flies (Tephritidae: Dacini): Features, Structure, and significance for Diagnosis Nathaly Lara Castellanos, Disna N. Gunawardana, Bede McCarthy, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6459370/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Jul, 2025 Read the published version in BMC Genomics → Version 1 posted 9 You are reading this latest preprint version Abstract Background True fruit flies (Diptera: Tephritidae) are among the most destructive pests of fruit and vegetables worldwide and are on the top of quarantine pest lists. To respond effectively to a fruit fly invasion, we need to identify the species rapidly and reliably to understand its biological features and guide response decisions. Molecular techniques have been used to improve the diagnostic ability circumventing many difficulties of morphological identification. However, the commonly used Cytochrome Oxidase I ( COI ) gene lacks sufficient variation to distinguish species within Bactrocera species complexes. Here we conducted mitochondrial genome sequencing to identify additional genetic markers that could aid diagnosis of Bactrocera fruit fly species. Results We assembled 82 complete mitochondrial genomes from 16 Bactrocera species, including 13 species for which no mitochondrial genome data were previously available, as well as one specie each from Dacus aneuvittatus, Dirioxa pornia and Zeugodacus gracilis . Phylogenetic analysis of the Tephritidae family confirmed the monophyly of the Bactrocera genus but could not properly resolve species within species complexes. Comparative mitochondrial genome analysis revealed that intergenic spacer and NADH dehydrogenase genes, specifically ND2 and ND6 , harbour enough variations for new specific real-time PCR assays. Based on these findings, six TaqMan-based real-time PCR assays targeting ND2, COI , and CO3 genes were successfully designed and assessed for their specificity and sensitivity in detecting Bactrocera curvipennis , a member of the B. tryoni complex. Of these, one real-time PCR assay targeting the ND2 gene proved to be the most specific and sensitive. It detects B. curvipennis specifically at the level of 1 copy/µL of target DNA. Conclusions Mitochondrial sequence analysis and comparative studies indicate that mitochondrial genomes offer valuable genetic markers for accurate diagnosis of Bactrocera fruit flies. The successful development of the B. curvipennis real-time PCR assay highlights the importance of having additional genetic markers to advance the molecular diagnostics in economically important Bactrocera species. Molecular identification Species-specific real-time PCR Intergenic spacer DNA barcoding Biosecurity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 BACKGROUND Biological invasions can lead to biodiversity loss, disruption of ecosystem services, reduced agricultural productivity [ 1 – 3 ]. The economic losses are estimated at $ 1,208 billion globally between 1980 to 2019, with a 702% increase in reported losses from 1980–1999 to 2000–2019 [ 1 ]. The unintentional transport of living organisms through global trade and international tourism increase the risk of biological invasions [ 4 , 5 ]. True fruit flies (Diptera: Tephritidae) are on top of the quarantine pest list because they pose a major threat to horticultural crops and high potential of anthropogenic spread [ 2 , 3 ]. Out of the 5,000 species within this family, only 250 highly polyphagous species are considered potential invasive species [ 2 , 3 , 6 ]. The most destructive fruit fly pests to global production of fresh fruits and vegetables belong to the genus Bactrocera , along with Anastrepha, Ceratitis and Rhagoletis [ 7 , 8 ]. The economic impacts of these invasive fruit flies include substantial crop losses, increased cost due to pest management, eradication programs and biosecurity measures, and quarantine bans imposed by importing countries [ 9 – 11 ]. Accurate, rapid, and reliable identification of fruit flies to species level is essential for the adoption of effective quarantine actions to prevent the spread of invasive species. Inaccurate identification of specimens can lead to unnecessary quarantine measures or allow harmful species to establish in new regions [ 12 ]. Morphological differentiation between invasive and non-invasive species can be difficult especially for immature stages due to limited morphological characters and large intra-specific morphological variation [ 10 , 13 ]. Various molecular techniques have been developed to overcome these challenges, including DNA barcoding, specific real-time PCR, and loop-mediated isothermal amplification (LAMP) [ 14 – 19 ]. Among them, DNA barcoding can discriminate most species [ 9 , 20 , 21 ], but it is time-consuming (taking up to four days) and depends on the quality of reference sequences [ 22 – 25 ]. In contrast, molecular identification using real-time PCR assays offer higher sensitivity, higher specificity and can considerably reduce the diagnostic time by eliminating post-PCR electrophoresis and amplicon sequencing [ 16 , 17 ]. Molecular tools have significantly improved the diagnosis of fruit flies, but the selection of genetic markers for specific PCR assays or DNA barcoding purposes remains challenging. Mitochondrial genes, in particular the cytochrome oxidase I ( COI ), are the preferred genetic markers for molecular identifications due to their high mutation rates, rare gene recombination, maternal acquisition, and ease of sequencing using Sanger technique [ 26 – 28 ]. However, COI sequences do not allow distinction between closely related species within the same species complex, leaving at least 11.3% of Dacini fruit flies species non-identifiable [ 12 , 24 ]. High similarity of COI sequences have been observed within invasive Bactrocera complexes [ 29 – 31 ], such as B. dorsalis ( B. dorsalis, B. carambolae, B. musae, B. borneoensis, B. incognita, B. cacuminata, B. verbascifoliae, B. caryecae, B. parafroggati, B. pallida, B. commensurata, B. occipitalis, B. kandiensis and B. raiensis [ 30 , 32 ]), B. tryoni ( B. tryoni, B. neohumeralis, B. erubescentis, B. mutabilis, B. curvipennis and B. ustulata [ 31 ]), and B. frauenfeldi ( B. frauenfeldi, B. albistigrata, B. trilineola, B. kirki, B. caledoniensis, B. psidii and B. parafrauenfeldi [ 29 ]). This high sequence similarity makes species identification using real-time PCR challenging [ 14 , 20 , 31 , 33 , 34 ]. Complete mitochondrial genomes sequencing can reveal alternative genetic markers exhibiting greater interspecific variation, offering greater primer design flexibility and enable more accurate species-specific assays critical for biosecurity [ 11 , 35 ]. Recent advances in high-throughput sequencing (HTS) technologies have accelerated the expansion of fruit fly mitochondrial genomes [ 36 , 37 ], facilitating the discovery of more informative genetic markers that can enhance molecular diagnostics [ 11 ]. This study analysed 82 mitochondrial genomes from 19 species of Bactrocera and related genera to identify molecular markers that can be used to enhance the reliability of fruit fly diagnosis. Specifically, we aim to (i) address phylogenetic gaps in species resolution within species complexes, focusing on the Bactrocera genus, (ii) characterize mitogenomic features useful for species discrimination within this genus, and (iii) develop a real-time PCR assay for the rapid and accurate identification of B. curvipennis. RESULTS Mitochondrial genome sequencing Adult fruit fly specimens were morphologically identified, and their identities were confirmed through COI barcoding following standard protocols (Table S1 , Supporting Information). Mitochondrial genomes from 82 specimens, representing diverse COI haplotypes, were sequenced using the Illumina NovaSeq 6000 platform, generating an average of 20.0 ± 6.8 million reads per sample, totalling 2.70 ± 1.55 Gbp (Table S2). The average sequencing depth was 1,193.44x, ranging from 230 to 7,435x (Table S2). Despite some DNA degradation due to the specimens collected dry from fruit-fly traps and long-time storage, the obtained data quality was high [ 36 ]. Complete circular mitochondrial genomes were assembled for 18 species across four genera within two tribes: 16 Bactrocera species, as well Zeugodacus gracilis from the Dacini tribe, and Dirioxa pornia from the Acanthoneurini tribe. A partial mitochondrial genome for Dacus anevittatus (Dacini) was recovered with only a missing section in the control region. The arrangement of the 37 genes was identical to their order and transcription directions as in other Tephritid fruit flies [ 36 – 38 ] and follows the ancestral insect mitogenome arrangement. The majority strand (J-strand) encodes 23 genes, including 9 protein coding genes (PCGs)— ATP6, ATP8, CYTB, COI, CO2, CO3, ND2, ND3 and ND6 , and 14 tRNA genes. The minority strand (N-strand) encodes 14 genes, comprising 4 PCGs— ND1, ND4, ND4L and ND5 , 8 tRNA genes and 2 rRNA genes— 12S and 16S . Phylogenetic analysis Maximum likelihood analyses (ML) using 13 PCGs and the entire mitochondrial genome from 479 Tephritidae individuals produced trees with nearly identical topologies featuring few unstable branches (Fig. 1 , Figure S1 –3, Supporting Information). The resulting phylogeny was well-supported for subfamily and tribe relationships, with most major nodes showing high posterior probability (> 0.95) (Figure S1 ). Two primary clades emerged, grouping Dacinae, Tripetinae, and Tephritinae, while Phytalmidae formed the basal group of Tephritidae. Although support for the monophyly of Dacinae was low, three monophyletic tribes, (Ceratidini + Gastrozonini) + Dacini, were strongly supported. Within the Dacini tribe, Dacus and Zeugodacus identified as sister groups to Bactrocera , and the genera Bactrocera and Dacus were monophyletic. The genus Zeugodacus was recovered as paraphyletic based on the 13 PCGs reconstruction but monophyletic according the entire mitogenome (Figure S1 ). Within Bactrocera , the subgenera Bactrocera , Daculus , Notodacus , and Tetradacus showed strong monophyly, whereas Afrodacus was polyphyletic. None of the phylogenetic reconstructions were able to resolve certain species within the species complexes as monophyletic, including B. dorsalis, B. carambolae, B. tryoni, B. neohumeralis and B. trilineola , with supported nodes containing mixed samples from different species (Fig. 1 ). The B. tryoni complex was divided into two subclades, one of which included B. curvipennis , with B. neohumeralis and B. tryoni specimens mixed in both clades. Although not yet recognized as a species complex, B. facialis was polyphyletic, consisting of two clades, one of which nested within B. passiflorae clade, referred as B. facialis clade 2 in the tree. These discordances between molecular and morphological data across clades in the complex, as previously reported [ 10 , 29 , 32 , 37 ], highlights the need for more stable morphological and molecular markers to reliably define these species. Features of Bactrocera mitochondrial genomes: Insights into species differentiation The structural analysis of all available mitochondrial genomes of economically important species revealed that Bactrocera mitochondrial genomes remained stable across major clades and provided enough taxonomic resolution for species-level identification (Fig. 2 ). The nucleotide composition of fruit fly mitochondrial genomes showed a high AT bias, the AT contents varied from 66.55% in B. tsuneomus to 74.09% in the B. frauelfeldi complex. The length range of the complete mitochondrial genome was from 15,815.9 ± 2.0 bp in B. oleae to 15,977 bp in B. latifrons (Fig. 2 A). The length variations of 13 PCGs, 22 tRNAs and 2 rRNAs were very minor, while the length variation among different specimens was mainly caused by the variation in noncoding regions especially the control region (size range from 923.8 ± 0.8 bp in B. xanthodes to 953 bp in B. latifrons ; Fig. 2 A). The three longest mitochondrial intergenic spacers (IGS) were between trnQ-trnM with average length of 129.2 ± 22.3 bp, trnC-trnY with 98.2 ± 15.2 bp and trnR-trnN with 21.6 ± 11.5 bp (Fig. 2 B). These intergenic regions usually evolve faster than PCGs, indeed length variation between species was observed (Fig. 2 A). Therefore, these regions should be explored as species-specific markers for the accurate identification of fruit flies. Only minor variations in stop codons were observed across some clades, with the vast majority remaining conserved (Table S3). Evolutionary analysis of mitochondrial protein-coding genes Complete mitochondrial genomes serve as valuable genetic markers, therefore, the availability and abundance of these genomes is essential for designing specific PCR assays to avoid cross-reaction with non-represented species [ 11 ]. Therefore, we analysed the evolutionary patterns of the mitochondrial PCGs in Bactrocera fruit flies, which are more readily accessible across taxa due to higher nucleotide conservation in coding regions compared to intergenic spaces. Our evolutionary patterns analysis of mitochondrial genomes evaluated key parameters such as nucleotide diversity (Pi), synonymous and nonsynonymous substitution rates (Ka/Ks ratio), barcoding gaps, and codon usage bias (Fig. 3 , Figure S3-S4). The average Pi of the individual genes ranged from 0.056 for ND4L and 0.109 for ND2 , the COI gene showed intermediary nucleotide diversity (0.079; Fig. 3 A). Along with ND2 , the genes ND6, ND3 and CYTB had the highest nucleotide diversity, 0.108, 0.094 and 0.093, respectively. The Ka/Ks ratio is a widely used metric in comparative genomics to estimate selection pressure and the evolutionary rate [ 39 ]. For the 13 PCGs, ND5 had the highest average Ka/Ks ratio across all available Bactrocera mitogenomes, followed by ND2 and ND1 , whereas COI and ATP8 had the lowest average values (Fig. 3 B). Even though the ATP8 gene showed a high rate of non-synonymous substitutions, its Ka/Ks ratio remained low due to the large number of synonymous substitutions (Figure S3). The average Ka/Ks values across all available Bactrocera mitogenomes varied from 0.029 ( COI ) to 0.124 ( ND5 ) and consistently remained below 0.213 (below 1.0 the threshold for purifying selection), indicating that all PCGs have undergone purifying selection. Variation in the Ka/Ks ratio between different Bactrocera clades can be observed for genes ND4L and ND6 , with the B. dorsalis complex showing the lowest Ka/Ks values. Intra- and inter-specific genetic distances based on the data of 13 PCGs were significantly overlapped regardless of the marker, providing evidence of the lack of adequate barcoding gaps for mitochondrial genes (Fig. 3 C). For interspecific genetic distance, the highest values were observed for ND2 (mean 13.93 ± 5.53%), followed by ND6 (mean 13.85 ± 5.78%). Although the mean intra-specific genetic distance was very low for individual markers, maximum values overlap with the interspecific genetic distance (Fig. 3 C). For instance, the commonly used COI barcode marker showed an interspecific genetic distance of 10.21 ± 4.18% with a maximum intra-specific genetic distance of 19.25%. Very weak codon bias was observed on the Bactrocera PCGs (Figure S4). In summary, although the COI gene has been extensively used for molecular identification of fruit flies, this gene exhibited lower evolutionary rates and moderate nucleotide diversity resulting in small differences among closely related species. Based in our results, the ND2 , and ND6 genes can provide better species resolution for distinguishing closely related species at the species level. Performance Comparison of Mitochondrial Genes for Species-Specific Assays Six Taqman-based real-time PCR assays targeting the ND2 , COI , and CO3 genes were designed (Table S4, Fig. 4 A–B) and used for screening to evaluate potential cross-reactions and the amplification efficiency for known variants of B. curvipennis (Fig. 4 C–H, Figure S5). The initial screening of three ND2, one COI and two CO3 assays (Table S4) showed that the ND2 assay (ND2_1138F_BC, ND2_1251R_BC and ND2_1192P_BC+) performed the best, with no cross-reactivity with non-target species, producing lower Cq values, and generating higher relative fluorescence (RFU > 1000) for all target species haplotypes (Fig. 4 C, F). The COI assay showed cross-reactivity with other members of the B. tryoni complex, although lower RFU was observed for non-target species (Fig. 4 D, F). Both CO3 assays exhibited cross-reactivity not only with the B. tryoni complex but also with additional Bactrocera species, showing non-specific amplification with higher Cq values (Fig. 4 E, H and Figure S5C, F). The ND2 assay's higher specificity was further confirmed with additional primer combinations (Figure S5A, D and S5B, E). Optimization and validation of the ND2 species-specific real-time PCR assay Real-time PCR conditions for the ND2 assay (ND2_1138F_BC, ND2_1192P_BC + and ND2_1251R_BC) was optimized to increase sensitivity while still being specific. The optimized master mix composition and cycling conditions for the B. curvipennis duplex assay with 18S internal control targeting the 18S rRNA gene [ 17 , 40 , 41 ] for evaluation of the DNA quality are provided in Table S5 and S6. The annealing temperature was set at 60°C since higher temperatures resulted in increased Cq values and reduced florescence for certain B. curvipennis variants (Figure S7). The linear dynamic range for the singleplex assay was assessed with serial dilutions of gBlock containing the ND2 primer and probe binding sites (Figure S8). The limit of detection (LOD) the singleplex assay was 1 copy/µL of synthetic DNA template with an average Cq of 36.86 for all replicates detected (Fig. 5 ). The amplification efficiency was 101.1% (Fig. 5 ), an thereby the analytical cut-off was stablished at 36 cycles based on the LOD [ 42 ]. The ND2 assay could also amplify 10 fg/µL of genomic DNA with Cq values around 35 cycles (Figure S8A, B) and the LOD was unaffected by the addition of the 18S internal control (Figure S9C). The diagnostic specificity the ND2 assay was validated with 31 fruit fly samples in the specificity tests and 21 additional samples in the blind panel (a complete list of the specimens tested is provided in Table S7). All the tested samples of B. curvipennis were successfully amplified the ND2 assay with a Cq lower than 25 cycles (Fig. 6 ). A single replicate of Dirioxa pornia DPNC2 resulted in a very weak amplification (Cq = 39.61), above the negative threshold of Cq > 36. The blind test produced consistent results and independently matched to the original identities of the samples provided. High assay reproducibility was demonstrated by low variation in %CV values for each of the samples tested within individual and between different runs (Table S8). Overall, these results demonstrated the ND2 assay robustness and reliability. DISCUSSION This study generated 82 complete mitochondrial genomes from 19 fruit fly species, 16 of which were previously unavailable. Among these are highly polyphagous species with elevated risk of spreading to new locations, such as B. curvipennis, B. facialis, B. kirki, B. melanotus, B. passiflorae, B. psidii, B. trilineola and B. xanthodes [ 8 ]. Analysis of complete mitochondrial genomes provide useful molecular markers for both taxonomic and diagnostic studies [ 43 ]. Features like mitochondrial genome size, GC content, and control region length showed great variation across different Bactrocera clades, but their diagnostic utility is not widely applied due to the high cost and time required for whole genome sequencing. In contrast, the variability on the IGS lengths, specifically the trnQ-trnM, trnC-trnY and trnR-trnN , can be easily implemented as diagnostic markers to differentiate major clades as previously reported [ 43 ]. However, the IGS length seems to be conserved in Bactrocera species complexes. To differentiate species within these complexes, genes with higher substitution rates, nucleotide diversity and interspecific genetic distance, such as ND5, ND2 and ND1 , can be better candidates for the development of species-specific diagnostic tools. This was exemplified by an increased specificity of the ND2 real-time PCR assay for the identification of B. curvipennis as compared with the real-time PCR assays for genes COI and CO3 tested. Overall, these findings highlight that other mitochondrial genes and features offer higher diagnostic specificity than universal COI barcode, particularly for distinguishing species within complexes [ 12 ]. Our phylogenetic analyses of 479 mitochondrial genomes covering the major linages in the Tephritidae family provided a robust reconstruction, which was largely congruent with previous analyses and classifications schemes [ 10 , 31 , 36 , 37 , 44 – 46 ]. Monophyletic clades were observed for the genera of Bactrocera, Dacus, Ceratitis, Ragholetis, Carpomya and Anastrepha , while Zeugodacus was resolved as paraphyletic with Z. rubellus and Z. cilifer positioned external to the Zeugodacus main clade (Fig. 1 ). The fact that mitochondrial genome data failed to distinguish closely related species within the well-known B. dorsalis, B. tryoni , and B. frauenfeldi species complexes, as well as Ceratitis FAR complex (frugivorous flies - Ceratitis fasciventris, C. anonae, and C. rosa ), is not surprising. Notably, the inability to discriminate B. facialis and B. passifloreae using either the COI barcode or the entire mitochondrial genome is a novel finding, indicating that B. facialis and B. passifloreae are belonging to a species complex. Our results support expanding the B. tryoni complex to include the polyphagous fruit pest B. curvipennis from New Caledonia, consistent with previous molecular studies [ 31 , 46 ]. Recent speciation events, hybridization, mitochondrial introgression, inter-specific gene flow likely contribute to the discrepancies between mitochondrial genetic diversity and morphology [ 10 , 30 , 32 ]. To address challenges posed by recent or ongoing speciation, employing nuclear genome wide data, such as RADseq (Restriction-site Associated DNA Sequencing) and HiMAP (Highly Multiplexed Amplicon-based Phylogenomics) can provide more accurate insights into species delimitation [ 29 , 30 , 32 ]. However, the continuous post-speciation gene flow [ 32 ] and the presence of additional, unrecognized species complexes among less studied fruit fly pests with more restricted distribution, highlights significant gaps in our knowledge which can hinder efforts to prevent new invasions or prioritize pest control measures [ 30 ]. Improving the accuracy of species delimitation and resolving taxonomic discrepancies in fruit flies are crucial for strengthening biosecurity measures [ 30 , 31 ]. Our findings demonstrate that mitochondrial genome features like the IGS and other rapid-evolving mitochondrial genes can be valuable to enhance species-detection resolution. In the battle against the spread of fruit fly pests, molecular methods such as DNA barcoding or species-specific assays (e.g., PCR, real-time PCR and LAMP) targeting the COI gene have been developed [ 12 , 16 , 17 , 19 , 47 – 49 ]. However, the COI-5P barcoding fragment that is widely used to develop these assays may not be the most informative for the molecular identification of Bactrocera fruit flies. Despite extensive BOLD records have been generated for this fragment (up to 18,283 on 03/12/2024), its utility for diagnosis is compromised by (i) misidentifications in the reference databases, (ii) underrepresentation of non-pest species that do not respond to lures (only 272 of 750 species in Bactrocera genus), and (iii) considerable overlap of intra- and inter-specific genetic divergences within species complexes [ 12 , 22 , 24 ]. For example, some assays targeting the COI gene are unable to distinguish B. tryoni from other closely related B. tryoni complex members [ 14 , 47 ], B. dorsalis from other B. dorsalis complex species [ 14 , 15 ], B. latifrons from B. correcta [ 50 ], B. minax from B. tsuneomis [ 49 ] and C. capitata from C. cosyra [ 51 ]. Remarkably, the whole length of IGS is relatively consistent within each Bactrocera clade and significantly varied among the other clades, suggesting that the IGS length can serve as a useful diagnostic tool for distinguishing clades. Mitochondrial IGS are usually limited in number and size, and represent the most variable part of insect mitochondrial genomes [ 52 – 55 ]. For instance, the trnL2-CO2 IGS has been effective in characterizing genetic diversity within honey bee populations [ 56 ], while the trnI-trmQ and trnQ-trnM IGS gene region has been successfully used to differentiate the five most common fruit flies in South Africa ( C. capitata, C. cosyra, C. rosa, C. quilicii , and B. dorsalis ) [ 43 ]. Similarly, sequences covering the t rnQ-trnM, trnC-trnY and trnR-trnN IGS gene regions can be used for accurate differentiation of the main Bactrocera clades without downstream analysis and sequencing. However, the high AT content (> 80%) of IGS regions poses a challenge for developing species-specific assays, since AT-rich sequences reduce primer annealing temperatures, compromise the probe binding, and lower the amplification efficiency [ 57 ]. Additionally, the high variability of IGS regions increases the chances of mutations within primer and probe binding sites can lead to false negative results. Mitochondrial PCGs are widely used in animal species-specific assays due to higher temporal stability, moderate AT content (~ 70% in Bactrocera ), and large cellular copy number, which increases the likelihood of detection from the fragmented DNA frequently found in fruit-fly traps [ 58 , 59 ]. Selecting the proper gene is critical for ensuring the analytical specificity and sensibility required for border diagnostics. The nucleotide diversity, evolutionary rates and interspecific genetic distance indicates that the ND5, ND2 and ND1 genes are the least conserved genes among PCGs. While the COI gene was the most conserved gene as reported for other insects, including fruit flies [ 37 ], mosquitoes [ 60 ], beetles [ 55 , 61 , 62 ], parasitoid wasps [ 63 ], and soft scales [ 64 ]. Recent studies suggest that genes with rapid-evolving rates might be more suitable for the design of species-specific assays and capable of distinguishing closely related species [ 61 ]. A comparison of the analytical specificity of real-time PCR assays targeting the COI, CO2 and ND2 genes to distinguish B. curvipennis found that only the ND2 assay did not show cross-reactions with other B. tryoni complex members or other Bactrocera species. Subsequent test for sensitivity, diagnostic specificity, and reproducibility further validated the suitability of the ND2 assay. The COI representation of non-pest Bactrocera species is often lacking in the public databases, and the representation of other genetic markers that can be more suitable for the differentiation of closely related species is even less robust. Using genetic markers with limited representation in public databases can lead to cross-reaction with non-represented species. Therefore, the situations represent a trade-off dilemma between the representation in the databases and the diagnostic capability. Fortunately, advances in sequence technologies have facilitated the rapid expansion of mitochondrial genome databases [ 61 ], mitigating this limitation and enabling the broader implementation of alternative genetic makers for the diagnosis of invasive fruit flies. The advent of high-throughput sequencing (HTS) offers a promising solution by enabling the cost-effective sequencing of entire mitochondrial genome, due to smaller size and high copy number of this organelle [ 58 , 65 – 67 ]. HTS is not only more affordable, but also more technically robust as eliminates primer biases and non-standard amplification [ 65 ]. For instance, the cost of generating the entire mitochondrial genome using Illumina NovaSeq is currently less than US $ 100 for 10 million reads, which is more than sufficient to get the entire mitochondrial genome. Similar pricing and read depth are achievable using portable sequencing technologies, such as Oxford Nanopore Technologies (ONT), with the advantages of in situ sequencing, shorter timeframes and even covering highly AT rich sequences in the control region that are often missing by Illumina [ 68 – 72 ]. Despite these advancements, the accuracy of identifications at species-level based on the entire mitochondrial genome remains dependent on the availability and the quality of the curated sequences in DNA repositories [ 71 ]. Therefore, expanding mitochondrial sequences databases, particularly for fruit flies and other quarantine pests, is essential for employing these technologies for species identification and biosecurity applications. CONCLUSIONS This study addresses significant gaps in sequence databases that have hindered the molecular identification of fruit flies, particularly in South Pacific Countries. By generating 82 complete mitochondrial genomes from 19 species across three genera of Tepritidae, this study provides critical genetic resources for diagnostic and taxonomic studies. Our data also highlight the potential of alternative mitochondrial gene markers, such as ND2, ND5 and IGS gene regions to improve the specificity and sensitivity of species-specific assays for closely related species. The evolutionary analysis of mitochondrial protein-coding genes (PCGs) showed that genes like ND2 and ND6 exhibited higher nucleotide diversity compared to COI . While ND2 and ND5 genes provide better species resolution, making them particularly useful for distinguishing closely related species at the species level. Additionally, the intergenic spacers, such as trnQ-trnM and trnC-trnY , displayed significant length variation between species, making them potential candidates for species-specific markers. Despite the success in screening potential genetic markers based on evolutionary rates, limited representation of many mitochondrial genes in public databases remains a challenge for their widespread use in fruit fly diagnostics. The ND2 real-time PCR assay developed in this study demonstrates the practical application of these findings. The assay showed high sensitivity and specificity, with a limit of detection (LOD) of 1 copy/µL of the synthetic DNA, making it a reliable tool for the identification of B. curvipennis . When compared with the assays targeting COI and CO3 genes, the ND2 assays demonstrated superior specificity in distinguishing B. curpipennis from other B. tryoni complex members without cross-reactions. This research provide supports to improve surveillance and management of fruit flies, enhance the diagnostic accuracy and aid border security agencies in rapid decision-making to safeguard international trade; ultimately improving biosecurity response capability. The continuous expansion of genetic databases and innovations in sequencing technologies present further opportunities to refine molecular diagnosis and strengthen biosecurity responses. Additionally, these innovations can reduce the cost and time of fruit fly detection, benefiting the biosecurity agents and agricultural sectors that rely heavily on fruit production. METHODS Specimen selection Fruit fly species, representing five genera were obtained from interceptions in New Zealand and fruit fly traps from Australia, Cook Islands, Fiji, French Polynesia, New Caledonia, Tonga, and Vanuatu (Table S2 and S7). All adult specimens were identified using taxonomic keys and DNA barcoding (Supplementary Notes). DNA extraction For mitochondrial genome sequencing, total DNA from individual whole fruit fly specimens of 82 individuals of three genera (Table S2) was extracted using the DNeasy for Blood and Tissue kit (Qiagen, Germany) as per the manufacturer’s instructions. The purified genomic DNA was quantified using the Qubit dsDNA Quantification Assay Kits (Invitrogen, Waltham, MA, USA) and normalized to 300 ng per sample. For real-time PCR validation, additional DNA (Table S7) was extracted either a leg of adult, pieces of larva/pupa or an egg with DNeasy for Blood and Tissue kit (Qiagen, Germany) or PrepGEM universal kit (ZyGEM, New Zealand) according to manufacturers’ instructions. Mitochondrial genome sequencing, assembly, and annotation Fragment length, Illumina library preparation and sequencing were performed by Novogene on the Agilent 5400 Fragment Analyzer System and Illumina NovaSeq 6000 platform paired-end 150 to attain at least 2.0–3.0 Gbp raw data per sample. The quality of sequences was evaluated using the FastQC software (Table S2). All ambiguous reads with an average quality value lower than Q20 and adaptor sequences were excluded from further analysis using Trimmomatic. De novo assembly was performed with MitoZ [ 73 ], MitoFlex [ 74 ] and GetOrganelle [ 75 ]. MitoFlex and MitoZ are modular pipelines specifically designed for mitochondrial genome assembly and annotation. MitoFlex and MitoZ represent a modified version of the assembler MEGAHIT to better assemble mitochondrial sequences, while GetOrganalle use Spades for de novo assembly of organelle genomes. For the MitoZ assembly module, it was specified that the reads stemmed from a specimen in the clade Arthropoda and all other default parameters were used. For the MitoFlex and GetOrganelle default parameter to assembly animal mitochondria were used. The final accessions were generated with consensus sequences from an alignment of the assembly methods that obtained circular sequence. After that, majority consensus sequences were generated using Samtools and coverage calculated using BEDtools [ 76 ]. Consensus sequences were then annotated using Geneious Primer 2021.1.1 ( www.geneious.com/ ) and Mitos2 with the appropriate parameters: Reference= “RefSeq 63 Metazoa” and Genetic Code = “5 Invertebrate” [ 77 ]. The two different annotation tools were compared and adjusted using the available fruit fly mitochondrial genomes. Phylogenetic analysis The complete mitochondrial genome sequences of all 397 Tephritidae fruit fly specimens available in October 2024 were downloaded from NCBI (Table S9). Along with 82 from this study, 479 Tepthitidae mitochondrial genomes available representing 4 subfamilies, 8 tribes, 14 genera and 116 species were used for mitochondrial phylogenetic analyses, using Drosophila yakuba NC_001322.1 as the outgroup. Additionally, 13 complete PCGs were extracted from the 473 annotated mitochondrial genome sequences and concatenated into a single dataset using Geneious Prime 2021.1.1. ( www.geneious.com ). Multiple sequence alignment was performed using MAFFT version 7.505 with default parameters [ 78 ]. Possible incongruences in the alignment of protein-coding genes (PCGs) were verified using Geneious Prime. The alignments based on the entire mitochondrial genome and the concatenated PCGs were used for the phylogenetic analyses. Two different methodologies were used for the reconstruction of the phylogenetic relationships: (i) leaving the data unpartitioned with the same substitution model applied to all the sites in the alignment; and (ii) using a partition scheme based on genes and codon positions. For the first method, Maximum Likelihood (ML) tree was inferred for each of the three alignments by searching for the best substitution model using ModelFinder and calculating the bootstrap support with 10,000 replicates in IQ-TREE. PartitionFinder2 [ 79 ] was used to select the optimal partitioning schemes and substitution models for phylogenetic analyses using the “greedy” algorithm and branch lengths estimated as “unlinked” under the AICC model selection. The best partitioning scheme was used for phylogenetic reconstruction using 10,000 replicates in IQ-TREE. Phylogenetic analyses were also reconstructed using ML performed with RAxML 8.2.12 [ 80 ] using the GTRCAT model and a full ML bootstrap analysis with 100 replicates. Mitochondrial genome comparative analysis Comparative analysis of the Bactrocera mitochondrial genomes was performed using the concatenated PCGs dataset that includes the 325 annotated mitochondrial genome sequences distributed in 46 species. Nucleotide diversity (Pi) values of each PCG from 4SPE were determined using sliding window analyses of a window size of 100 bp and step size of 20 bp in DnaSP v6 [ 81 ]. The nonsynonymous substitution 184 rate (Ka), synonymous substitution rate (Ks), and Ka/Ks were calculated by the seqinr package [ 82 ]. The Ka/Ks ratio for each PCG, and each taxon was measured in comparison with the outgroup Drosophila yakuba. Genetic distances among fruit fly species were calculated using K80 model of evolution in the dist.dna function of ape for R. Barcoding gap analysis and the calculation of maximum intra-specific distances and the distance to nearest neighbour were performed employing the spider package in R [ 83 ]. The indices of codon usage and synonymous codon usage bias were measured using CodonW ( https://codonw.sourceforge.net/ ). In order to identify more effective genetic markers for the molecular identification of Bactrocera fruit flies, we analysed the structure of all available mitochondrial genomes, with a focus on economically important species [according to 8]. The selected clades included the complexes B. dorsalis ( B. dorsalis, B. carambolae, B. occipitalis ), B. tryoni ( B. tryoni, B. neohumeralis, B. curvipennis ), B. frauenfeldi ( B. frauenfeldi, B. albistigrata, B. kirki, B. trilineola, B. psidii, B. caledoniensis ), B. zonata ( B. zonata, B. correcta ), B. facialis ( B. facialis, B. passiflorae ), B. latrifrons, B. oleae, B. melanotus, B. tsuneomis, B. xanthodes, B. distincta , and B. umbrosa. Development and validation of the qPCR assay Species-specific primer design The entire mitochondrial genome alignment was used to design qPCR assay for B. curvipennis . This alignment includes seven B. curvipennis , nine B. neohumeralis and twenty B. tryoni mitochondrial sequences. Additionally, all 591 available COI sequences from the B. tryoni complex defined by [ 31 ], namely B. curvipennis, B. tryoni, B. neohumeralis, B. aquilonis, B. erubescentis, B. mutabilis and B. ustulata , were also included in the aligment. Regions that showed differences specific for B. curvipennis were selected manually in the alignment and used for the design of specific probes and primers manually. The optimal condition for melting temperatures, GC content, dimer formation, and secondary structure formation were analysed through Oligo Analyzer Tool (IDT) and Geneious Prime 2021.1.1. The BHQplus stabilising technology was used to increase the melting temperature (Tm) of the probe, mismatch discrimination and binding to the AT rich regions in ND2 gene [ 84 ]. Optimization of the assay conditions All real-time PCR reactions were set up on a CFX96™ Touch Real-time platform (BioRad, Hercules, CA, USA). For each PCR assay, 2 µL of the DNA extract was added to a 20 µL final volume of reaction mix, containing 10 µL of the 2X mastermix, variable concentrations of primers and probes and DEPC water. Each reaction mix was performed in duplicate wells and all runs included a positive control and a non-template control (NTC). Newly designed primers and probe for each assay (Table S4) were preliminary tested in different combinations using the target species and closely related species. Based on results from the preliminary specificity assessment, the best-performing primer-probe combination was selected for further optimisation. The selected primers and probes for each species was first tested using different annealing temperatures (58.1, 59.7, 61.6, 63.2°C). Then, the assay was further optimised using mastermixes [PerfeCTa® qPCR ToughMix® (Quanta Bioscience, Beverly, MA, USA) and SsoAdvanced™ Universal Probes Supermix (BioRad, Hercules, CA, USA)], different primer concentrations (200–400 nM), probe gradients (100–300 nM), and with or without the addition of Bovine Serum Albumin (BSA, Sigma-Aldrich Co., Massachusetts, USA; 0.1–0.5 µg/µL). To coamplify the 18S ribosomal RNA (rRNA) gene as an internal control, the TaqMan™ 18S internal control (Applied Biosystems ™, CA, USA) primers and probe were incorporated with the B. curvipennis specific assay in duplex format. Sensitivity and Specificity of the species-specific real-time assay A synthetic control (Integrated DNA Technologies gBlocks™ Gene Fragment) was used to determine the sensitivity of the qPCR assay. The synthetic control is a 283 bp template for different qPCR assay targets used in the detections of B. curvipennis and B. tryoni complex. The concatenated primers - probe sequence of the all-in-one synthetic control, is given in Figure S8. Connecting strings of Cs and Gs nucleotides were added before, between and after the primers/probes to increase CG content to meet the IDT manufacturer’s requirement of minimum of 32% of GC content for a gBlock fragment. Extra bases at 3’ and 5’ ends were added to ensure primer binding. Sequences in between the primer and probe binding sites were not included for better differentiation of the COI or ND2 sequences of the target specimens from the gBlock positive control. Analytical sensitivity of the assay was determined using the dilution series of the “all-in-one” template prepared in TE buffer ranging from 1 copy/µL to 1 × 10 7 copies/µL with each concentration in quintuplicate per reaction. Additionally, sensitivity was tested with ten-fold serial dilutions of genomic DNA from the target specimens (concentration ranging from 1 fg/µL to 10 ng/µL). Standard curve, PCR efficiency and R 2 were calculated automatically by the CFX manager software. In addition to the specificity implied through the design process, the specificity of real-time PCR assay was tested using singleplex format against DNA extracted from target and the non-target species. In total, 31 fruit fly specimens were tested, including target species, closely related non-target species as well as species commonly intercepted at New Zealand’s borders (Supplementary table S7). For the blind panel test, total of 21 specimens and 3 dilutions of the synthetic control were provided to the operators with no knowledge of the sample origin and identity (Supplementary table S7). The samples were tested with the ND2 qPCR assay in singleplex format. For each run, positive and non‐template controls were included, and all the reactions were performed in triplicate wells. Abbreviations ML: Maximum likelihood LAMP: Loop-mediated isothermal amplification Gbp: Gigabase pair PCG: Protein Coding Gene ATP6: ATP synthase membrane subunit 6 ATP8: ATP synthase membrane subunit 8 CYTB: Cytochrome b COI: Cytochrome c oxidase I CO2: Cytochrome c oxidase 2 CO3: Cytochrome c oxidase 3 ND1: NADH dehydrogenase subunit 1 ND2: NADH dehydrogenase subunit 2 ND3: NADH dehydrogenase subunit 3 ND4: NADH dehydrogenase subunit 4 ND5: NADH dehydrogenase subunit 5 ND6: NADH dehydrogenase subunit 6 tRNA: transfer RNA rRNA: Ribosomal RNA Pi: Average pairwise differences between all possible pairs of individuals Ka/Ks ratio: The ratio of the number of nonsynonymous substitutions per non-synonymous site (K a ), in a given period of time, to the number of synonymous substitutions per synonymous site (K s ), in the same period. IGS: Intergenic Spacer region HTS: High-Throughput Sequencing ONT: Oxford Nanopore Technologies HiMAP: Highly Multiplexed Amplicon-based Phylogenomics RADseq: Restriction-site Associated DNA sequencing Declarations Ethics approval and consent to participate No specific permits were required for all the sample collected for this study. No samples were collected in national parks and no endangered or threatened insects were included in this study, thus collection permits were not required for all the collections. All specimens imported into New Zealand were in accordance with the Import Health Standard, Section 22 of the Biosecurity Act 1993. Ethics approval was not required as insects are not classified as animals for the purposes of the Animal Welfare Act, 1999, New Zealand Legislation. Consent for publication Not applicable. Availability of data and materials All the relevant data are included in the manuscript and additional data are in the supplementary information files. All genome sequences reported here were deposited in GenBank database under accession numbers from PV604183 - PV604264. Competing interests The authors declare no competing interests. Funding The research was funded by the Operational Research programme of the Ministry for Primary Industries (MPI), New Zealand, grant number 406793. Authors’ contributions DL, DG, BM and SG conceived the study. SP provided financial and project administration. DL and NLC designed the study. DG sourced all colony and trap-collected fruit flies and performed morphological identification. NLC conducted all laboratory experiments, primer design, HTS data analysis and mitochondrial genome assembly. DL supervised the genomic work, validation of the real-time PCR assay and data analysis, and co-wrote the manuscript. SP made a major contribution in revising the manuscript. All authors reviewed and approved the final manuscript. Acknowledgements We would like to thank Dr. Catia Delmiglio from Plant Health and Environment Laboratory (PHEL), MPI for the critically reviewing the manuscript. We would like to thank Zhidong Yu and Claire McDonald from the Operational Research Team, MPI for their support. We also acknowledge the use of New Zealand eScience Infrastructure (NeSI) high-performance computing facilities. Our special thanks go to the Entomology team members of the PHEL, MPI, specially for Yan Chen, for assisting in morphological identification of the specimens. 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Supplementary Files 406793BMCGenomicsSupplementarymaterialMitochondrialgenomeofBactrocerafruitfliesFeaturesStructureandsignificanceforDiagnosis.docx Cite Share Download PDF Status: Published Journal Publication published 29 Jul, 2025 Read the published version in BMC Genomics → Version 1 posted Editorial decision: Revision requested 17 Jun, 2025 Reviews received at journal 16 Jun, 2025 Reviews received at journal 04 Jun, 2025 Reviewers agreed at journal 18 May, 2025 Reviewers agreed at journal 15 May, 2025 Reviewers invited by journal 13 May, 2025 Editor assigned by journal 09 May, 2025 Submission checks completed at journal 07 May, 2025 First submitted to journal 07 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6459370","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":457215103,"identity":"00ed470c-8817-4f24-b4fa-cd09e0906e3c","order_by":0,"name":"Nathaly Lara Castellanos","email":"","orcid":"","institution":"Ministry for Primary Industries","correspondingAuthor":false,"prefix":"","firstName":"Nathaly","middleName":"Lara","lastName":"Castellanos","suffix":""},{"id":457215104,"identity":"db21b587-ddc4-482d-a53c-506070d08cf3","order_by":1,"name":"Disna N. Gunawardana","email":"","orcid":"","institution":"Ministry for Primary Industries","correspondingAuthor":false,"prefix":"","firstName":"Disna","middleName":"N.","lastName":"Gunawardana","suffix":""},{"id":457215105,"identity":"a73956df-71b0-41b7-bb10-a1cc3c019aa6","order_by":2,"name":"Bede McCarthy","email":"","orcid":"","institution":"Ministry for Primary Industries","correspondingAuthor":false,"prefix":"","firstName":"Bede","middleName":"","lastName":"McCarthy","suffix":""},{"id":457215106,"identity":"888279d6-81e1-46b0-9795-5e9f1472e809","order_by":3,"name":"Puthigae Sathish","email":"","orcid":"","institution":"Ministry for Primary Industries","correspondingAuthor":false,"prefix":"","firstName":"Puthigae","middleName":"","lastName":"Sathish","suffix":""},{"id":457215107,"identity":"5e9f1110-6ec0-4f96-b84b-b0bf131c29c2","order_by":4,"name":"Sherly George","email":"","orcid":"","institution":"Ministry for Primary Industries","correspondingAuthor":false,"prefix":"","firstName":"Sherly","middleName":"","lastName":"George","suffix":""},{"id":457215108,"identity":"e676d552-0ebd-47e3-b758-122aa72cfd69","order_by":5,"name":"Dongmei Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYBACewglwWDAzHyAgcGACC2GDXAtbAlQLcz4tRgcgDEYeGBWENJyvPfwqxsVFgwG7DxfNxcUMNj1S+QfYC74g0fLmXNp1jlnQA7j3XZ7hgFD8swZyQzMM3jwaLmRY2ac2wbVAnRbssGZwwzMPBKEtPwDaeF5hqQFT9ABtRg/zm0Aa2EDabEzON4M1JKAW4thzxkz5pxjEjzAQDYD+kUiQbK92eAwzwHcWuzZe4w/59TUydn3H352u+CPjT0/M+PDxzx4QgwI2EBeBYcQMEIkEhuADDx2gAHzBziLAZ6CRsEoGAWjYBQgAADoR0Xrjf0u5QAAAABJRU5ErkJggg==","orcid":"","institution":"Ministry for Primary Industries","correspondingAuthor":true,"prefix":"","firstName":"Dongmei","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-04-16 04:23:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6459370/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6459370/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12864-025-11872-8","type":"published","date":"2025-07-29T16:29:27+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":82880039,"identity":"2d8c8a7f-038d-4cb6-9330-094bf84bcc84","added_by":"auto","created_at":"2025-05-16 10:36:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1162035,"visible":true,"origin":"","legend":"\u003cp\u003eReconstructed phylogenetic tree of Tephritidae fruit flies inferred from the PCGs dataset with partitions using RAxML. Maximum likelihood (ML) bootstrap support values are shown at each node. Species and genera clades have been collapsed to improve visualization of the tree’s topology. The \u003cem\u003eBactrocera tryoni\u003c/em\u003e complex includes \u003cem\u003eB. tryoni\u003c/em\u003e and \u003cem\u003eB. neohumeralis\u003c/em\u003e, while the \u003cem\u003eB. dorsalis\u003c/em\u003e complex comprises primarily \u003cem\u003eB. dorsalis\u003c/em\u003e and three \u003cem\u003eB. carambolae\u003c/em\u003e specimens. The number of specimens used in each collapsed clade is specified. Boxes highlight subfamilies and brackets denote well-supported \u003cem\u003eBactrocera\u003c/em\u003eclades selected for further analyses.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6459370/v1/6f763a3af73b30520644a83f.png"},{"id":82879651,"identity":"6efa4370-7571-449b-96d4-e3cdec2d9f02","added_by":"auto","created_at":"2025-05-16 10:28:34","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":275202,"visible":true,"origin":"","legend":"\u003cp\u003eMitochondrial genome comparative analysis of \u003cem\u003eBactrocera\u003c/em\u003e fruit flies. (\u003cstrong\u003eA\u003c/strong\u003e) Schematic representation of mitochondrial gene arrangement. Protein-coding genes (PCGs), rRNAs, tRNAs, and the control region (CR) are shown in green, cyan, pink, and orange, respectively. Box plots of the genome length, GC content, and control region length are provided for selected \u003cem\u003eBactrocera\u003c/em\u003e clades. (\u003cstrong\u003eB\u003c/strong\u003e) Detailed schematic of major intergenic spaces (IGS) within \u003cem\u003eBactrocera\u003c/em\u003e, showing the boxplots of the lengths of the \u003cem\u003etrnQ-trnM, trnC-trnY, \u003c/em\u003eand \u003cem\u003etrnR-trnN\u003c/em\u003e IGS for selected clades. Each data point represents individual \u003cem\u003eBactrocera\u003c/em\u003especimens, with point colours indicating different \u003cem\u003eBactrocera\u003c/em\u003e clades shown on Figure 1. The number of specimens used for each clade is indicated in the pruned phylogenetic tree.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6459370/v1/23a2e1bf251d5d946d441262.png"},{"id":82879652,"identity":"6ddb309d-f211-44e6-a9c0-4c581fb4c55c","added_by":"auto","created_at":"2025-05-16 10:28:34","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":277830,"visible":true,"origin":"","legend":"\u003cp\u003eEvolutionary analysis of mitochondrial protein-coding genes (PCGs) in \u003cem\u003eBactrocera\u003c/em\u003e fruit flies. (\u003cstrong\u003eA\u003c/strong\u003e) Sliding window analysis of nucleotide diversity (Pi) across the PCGs, using a 100 bp window with a 20 bp step. Gene names and Pi values are indicated above or below the schematic representation of the genes. (\u003cstrong\u003eB\u003c/strong\u003e) Boxplots showing synonymous (Ks) and nonsynonymous (Ka) substitution rates for each PCG, with each data point represents an individual \u003cem\u003eBactrocera\u003c/em\u003e specimen. Point colours correspond to different \u003cem\u003eBactrocera\u003c/em\u003e clades shown on Figure 1. (\u003cstrong\u003eC\u003c/strong\u003e) Boxplots of intra-specific and inter-specific pairwise genetic distances across PCGs to assess the barcoding gap. The median values for nucleotide diversity, Ka/Ks ratio, and interspecific genetic distances for the \u003cem\u003eCOI\u003c/em\u003e gene are indicated by the blue dotted line.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6459370/v1/718e0a62356fba8aca146f19.png"},{"id":82879654,"identity":"3be3f824-6d26-48e1-85ca-0ca1ce5b342b","added_by":"auto","created_at":"2025-05-16 10:28:34","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":261071,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePerformance comparison of the specificity of real-time PCR assays designed for \u003c/strong\u003e\u003cem\u003eBactrocera curvipennis\u003c/em\u003e\u003cstrong\u003e detection.\u003c/strong\u003e (\u003cstrong\u003eA\u003c/strong\u003e) Primer and probe sequences for each of the three assays targeting the \u003cem\u003eND2\u003c/em\u003e, \u003cem\u003eCOI\u003c/em\u003e, and \u003cem\u003eCO3\u003c/em\u003e genes, represented by color-coded boxes. (\u003cstrong\u003eB\u003c/strong\u003e) Schematic overview of amplicon positions of each assay on the \u003cem\u003eB. curvipennis\u003c/em\u003e mitochondrial genome (BCNC1). (\u003cstrong\u003eC–E\u003c/strong\u003e) Boxplot of Cq values for specific and non-specific amplifications across different samples, cut-off set at 36 cycles (grey dotted line). Neg: no amplification within 40 cycles. (\u003cstrong\u003eF-H\u003c/strong\u003e) Boxplot of relative fluorescence unit (RFU) for specific and non-specific amplifications, fluorescence threshold set at 100 RFU (grey dotted line). Primers (F: forward; R: reverse) and probes (P) sequences of each assay are shown in the same colour of boxes. Each data point represents samples from different \u003cem\u003eBactrocera\u003c/em\u003e clades shown on Figure 1.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6459370/v1/d0b16c538915cc78602fa797.png"},{"id":82879653,"identity":"7713a737-8800-4d5f-b623-408f71f5ef58","added_by":"auto","created_at":"2025-05-16 10:28:34","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":12121,"visible":true,"origin":"","legend":"\u003cp\u003eSensitivity of the \u003cem\u003eND2\u003c/em\u003ereal-time PCR assay for \u003cem\u003eBactrocera curvipennis\u003c/em\u003e detection. The gBlock synthetic template (Bac_TC_allin_control), containing concatenated primer and probe binding sites, was serially diluted, and tested with the singleplex real-time PCR assay. Linear regression of gBlock concentrations against Cq values generated a standard curve, plotting Cq values against the log copy number (range = 10⁷–1 copies) of the gBlock.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6459370/v1/60b4a9c50727e87cd4e7d35f.png"},{"id":82880987,"identity":"31cf95db-e201-4266-8962-8fbf57b33247","added_by":"auto","created_at":"2025-05-16 10:52:35","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":292336,"visible":true,"origin":"","legend":"\u003cp\u003eSpecificity of the \u003cem\u003eND2 \u003c/em\u003ereal-time PCR assay for \u003cem\u003eBactrocera curvipennis\u003c/em\u003e detection. Reconstructed phylogenetic tree of the samples used in the development of the assay, inferred from the COI alignment using IQ-TREE. The boxplot of Cq values and bars representing the number of specimens displayed on the tree. Positive results with early amplification (Cq \u0026lt; 30) are indicated between the green dotted lines, while the grey dotted line represents the assay cut-off at 36 cycles.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6459370/v1/9613486f1761fd519e5ee153.png"},{"id":88268401,"identity":"1a642014-34ec-44c5-bc26-7beea8261b69","added_by":"auto","created_at":"2025-08-04 16:51:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3253368,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6459370/v1/eeb09304-a65e-4c06-bde5-3910e6ed64e0.pdf"},{"id":82879670,"identity":"c797736a-6c66-4683-ab0d-1b11bb4a16a6","added_by":"auto","created_at":"2025-05-16 10:28:35","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":3849571,"visible":true,"origin":"","legend":"","description":"","filename":"406793BMCGenomicsSupplementarymaterialMitochondrialgenomeofBactrocerafruitfliesFeaturesStructureandsignificanceforDiagnosis.docx","url":"https://assets-eu.researchsquare.com/files/rs-6459370/v1/ee90f5be34d105699615a656.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Mitochondrial genome of Bactrocera fruit flies (Tephritidae: Dacini): Features, Structure, and significance for Diagnosis","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eBiological invasions can lead to biodiversity loss, disruption of ecosystem services, reduced agricultural productivity [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The economic losses are estimated at \u003cspan\u003e$\u003c/span\u003e1,208\u0026nbsp;billion globally between 1980 to 2019, with a 702% increase in reported losses from 1980\u0026ndash;1999 to 2000\u0026ndash;2019 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The unintentional transport of living organisms through global trade and international tourism increase the risk of biological invasions [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. True fruit flies (Diptera: Tephritidae) are on top of the quarantine pest list because they pose a major threat to horticultural crops and high potential of anthropogenic spread [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Out of the 5,000 species within this family, only 250 highly polyphagous species are considered potential invasive species [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The most destructive fruit fly pests to global production of fresh fruits and vegetables belong to the genus \u003cem\u003eBactrocera\u003c/em\u003e, along with \u003cem\u003eAnastrepha, Ceratitis\u003c/em\u003e and \u003cem\u003eRhagoletis\u003c/em\u003e [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The economic impacts of these invasive fruit flies include substantial crop losses, increased cost due to pest management, eradication programs and biosecurity measures, and quarantine bans imposed by importing countries [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAccurate, rapid, and reliable identification of fruit flies to species level is essential for the adoption of effective quarantine actions to prevent the spread of invasive species. Inaccurate identification of specimens can lead to unnecessary quarantine measures or allow harmful species to establish in new regions [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Morphological differentiation between invasive and non-invasive species can be difficult especially for immature stages due to limited morphological characters and large intra-specific morphological variation [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Various molecular techniques have been developed to overcome these challenges, including DNA barcoding, specific real-time PCR, and loop-mediated isothermal amplification (LAMP) [\u003cspan additionalcitationids=\"CR15 CR16 CR17 CR18\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Among them, DNA barcoding can discriminate most species [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], but it is time-consuming (taking up to four days) and depends on the quality of reference sequences [\u003cspan additionalcitationids=\"CR23 CR24\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In contrast, molecular identification using real-time PCR assays offer higher sensitivity, higher specificity and can considerably reduce the diagnostic time by eliminating post-PCR electrophoresis and amplicon sequencing [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMolecular tools have significantly improved the diagnosis of fruit flies, but the selection of genetic markers for specific PCR assays or DNA barcoding purposes remains challenging. Mitochondrial genes, in particular the cytochrome oxidase I (\u003cem\u003eCOI\u003c/em\u003e), are the preferred genetic markers for molecular identifications due to their high mutation rates, rare gene recombination, maternal acquisition, and ease of sequencing using Sanger technique [\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. However, COI sequences do not allow distinction between closely related species within the same species complex, leaving at least 11.3% of Dacini fruit flies species non-identifiable [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. High similarity of COI sequences have been observed within invasive \u003cem\u003eBactrocera\u003c/em\u003e complexes [\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], such as \u003cem\u003eB. dorsalis\u003c/em\u003e (\u003cem\u003eB. dorsalis, B. carambolae, B. musae, B. borneoensis, B. incognita, B. cacuminata, B. verbascifoliae, B. caryecae, B. parafroggati, B. pallida, B. commensurata, B. occipitalis, B. kandiensis\u003c/em\u003e and \u003cem\u003eB. raiensis\u003c/em\u003e [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]), \u003cem\u003eB. tryoni\u003c/em\u003e (\u003cem\u003eB. tryoni, B. neohumeralis, B. erubescentis, B. mutabilis, B. curvipennis\u003c/em\u003e and \u003cem\u003eB. ustulata\u003c/em\u003e [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]), and \u003cem\u003eB. frauenfeldi\u003c/em\u003e (\u003cem\u003eB. frauenfeldi, B. albistigrata, B. trilineola, B. kirki, B. caledoniensis, B. psidii\u003c/em\u003e and \u003cem\u003eB. parafrauenfeldi\u003c/em\u003e [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]). This high sequence similarity makes species identification using real-time PCR challenging [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Complete mitochondrial genomes sequencing can reveal alternative genetic markers exhibiting greater interspecific variation, offering greater primer design flexibility and enable more accurate species-specific assays critical for biosecurity [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Recent advances in high-throughput sequencing (HTS) technologies have accelerated the expansion of fruit fly mitochondrial genomes [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], facilitating the discovery of more informative genetic markers that can enhance molecular diagnostics [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study analysed 82 mitochondrial genomes from 19 species of \u003cem\u003eBactrocera\u003c/em\u003e and related genera to identify molecular markers that can be used to enhance the reliability of fruit fly diagnosis. Specifically, we aim to (i) address phylogenetic gaps in species resolution within species complexes, focusing on the \u003cem\u003eBactrocera\u003c/em\u003e genus, (ii) characterize mitogenomic features useful for species discrimination within this genus, and (iii) develop a real-time PCR assay for the rapid and accurate identification of \u003cem\u003eB. curvipennis.\u003c/em\u003e\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eMitochondrial genome sequencing\u003c/h2\u003e \u003cp\u003eAdult fruit fly specimens were morphologically identified, and their identities were confirmed through COI barcoding following standard protocols (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, Supporting Information). Mitochondrial genomes from 82 specimens, representing diverse COI haplotypes, were sequenced using the Illumina NovaSeq 6000 platform, generating an average of 20.0\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8\u0026nbsp;million reads per sample, totalling 2.70\u0026thinsp;\u0026plusmn;\u0026thinsp;1.55 Gbp (Table S2). The average sequencing depth was 1,193.44x, ranging from 230 to 7,435x (Table S2). Despite some DNA degradation due to the specimens collected dry from fruit-fly traps and long-time storage, the obtained data quality was high [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Complete circular mitochondrial genomes were assembled for 18 species across four genera within two tribes: 16 \u003cem\u003eBactrocera\u003c/em\u003e species, as well \u003cem\u003eZeugodacus gracilis\u003c/em\u003e from the Dacini tribe, and \u003cem\u003eDirioxa pornia\u003c/em\u003e from the Acanthoneurini tribe. A partial mitochondrial genome for \u003cem\u003eDacus anevittatus\u003c/em\u003e (Dacini) was recovered with only a missing section in the control region.\u003c/p\u003e \u003cp\u003eThe arrangement of the 37 genes was identical to their order and transcription directions as in other Tephritid fruit flies [\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] and follows the ancestral insect mitogenome arrangement. The majority strand (J-strand) encodes 23 genes, including 9 protein coding genes (PCGs)\u0026mdash;\u003cem\u003eATP6, ATP8, CYTB, COI, CO2, CO3, ND2, ND3\u003c/em\u003e and \u003cem\u003eND6\u003c/em\u003e, and 14 tRNA genes. The minority strand (N-strand) encodes 14 genes, comprising 4 PCGs\u0026mdash;\u003cem\u003eND1, ND4, ND4L\u003c/em\u003e and \u003cem\u003eND5\u003c/em\u003e, 8 tRNA genes and 2 rRNA genes\u0026mdash;\u003cem\u003e12S\u003c/em\u003e and \u003cem\u003e16S\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePhylogenetic analysis\u003c/h3\u003e\n\u003cp\u003eMaximum likelihood analyses (ML) using 13 PCGs and the entire mitochondrial genome from 479 Tephritidae individuals produced trees with nearly identical topologies featuring few unstable branches (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u0026ndash;3, Supporting Information). The resulting phylogeny was well-supported for subfamily and tribe relationships, with most major nodes showing high posterior probability (\u0026gt;\u0026thinsp;0.95) (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Two primary clades emerged, grouping Dacinae, Tripetinae, and Tephritinae, while Phytalmidae formed the basal group of Tephritidae. Although support for the monophyly of Dacinae was low, three monophyletic tribes, (Ceratidini\u0026thinsp;+\u0026thinsp;Gastrozonini)\u0026thinsp;+\u0026thinsp;Dacini, were strongly supported.\u003c/p\u003e \u003cp\u003eWithin the \u003cem\u003eDacini\u003c/em\u003e tribe, \u003cem\u003eDacus\u003c/em\u003e and \u003cem\u003eZeugodacus\u003c/em\u003e identified as sister groups to \u003cem\u003eBactrocera\u003c/em\u003e, and the genera \u003cem\u003eBactrocera\u003c/em\u003e and \u003cem\u003eDacus\u003c/em\u003e were monophyletic. The genus \u003cem\u003eZeugodacus\u003c/em\u003e was recovered as paraphyletic based on the 13 PCGs reconstruction but monophyletic according the entire mitogenome (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Within \u003cem\u003eBactrocera\u003c/em\u003e, the subgenera \u003cem\u003eBactrocera\u003c/em\u003e, \u003cem\u003eDaculus\u003c/em\u003e, \u003cem\u003eNotodacus\u003c/em\u003e, and \u003cem\u003eTetradacus\u003c/em\u003e showed strong monophyly, whereas \u003cem\u003eAfrodacus\u003c/em\u003e was polyphyletic. None of the phylogenetic reconstructions were able to resolve certain species within the species complexes as monophyletic, including \u003cem\u003eB. dorsalis, B. carambolae, B. tryoni, B. neohumeralis\u003c/em\u003e and \u003cem\u003eB. trilineola\u003c/em\u003e, with supported nodes containing mixed samples from different species (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The \u003cem\u003eB. tryoni\u003c/em\u003e complex was divided into two subclades, one of which included \u003cem\u003eB. curvipennis\u003c/em\u003e, with \u003cem\u003eB. neohumeralis\u003c/em\u003e and \u003cem\u003eB. tryoni\u003c/em\u003e specimens mixed in both clades. Although not yet recognized as a species complex, \u003cem\u003eB. facialis\u003c/em\u003e was polyphyletic, consisting of two clades, one of which nested within \u003cem\u003eB. passiflorae\u003c/em\u003e clade, referred as \u003cem\u003eB. facialis\u003c/em\u003e clade 2 in the tree. These discordances between molecular and morphological data across clades in the complex, as previously reported [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], highlights the need for more stable morphological and molecular markers to reliably define these species.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eFeatures of\u003c/b\u003e \u003cb\u003eBactrocera\u003c/b\u003e \u003cb\u003emitochondrial genomes: Insights into species differentiation\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe structural analysis of all available mitochondrial genomes of economically important species revealed that \u003cem\u003eBactrocera\u003c/em\u003e mitochondrial genomes remained stable across major clades and provided enough taxonomic resolution for species-level identification (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The nucleotide composition of fruit fly mitochondrial genomes showed a high AT bias, the AT contents varied from 66.55% in \u003cem\u003eB. tsuneomus\u003c/em\u003e to 74.09% in the \u003cem\u003eB. frauelfeldi\u003c/em\u003e complex. The length range of the complete mitochondrial genome was from 15,815.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0 bp in \u003cem\u003eB. oleae\u003c/em\u003e to 15,977 bp in \u003cem\u003eB. latifrons\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The length variations of 13 PCGs, 22 tRNAs and 2 rRNAs were very minor, while the length variation among different specimens was mainly caused by the variation in noncoding regions especially the control region (size range from 923.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8 bp in \u003cem\u003eB. xanthodes\u003c/em\u003e to 953 bp in \u003cem\u003eB. latifrons\u003c/em\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The three longest mitochondrial intergenic spacers (IGS) were between \u003cem\u003etrnQ-trnM\u003c/em\u003e with average length of 129.2\u0026thinsp;\u0026plusmn;\u0026thinsp;22.3 bp, \u003cem\u003etrnC-trnY\u003c/em\u003e with 98.2\u0026thinsp;\u0026plusmn;\u0026thinsp;15.2 bp and \u003cem\u003etrnR-trnN\u003c/em\u003e with 21.6\u0026thinsp;\u0026plusmn;\u0026thinsp;11.5 bp (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). These intergenic regions usually evolve faster than PCGs, indeed length variation between species was observed (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Therefore, these regions should be explored as species-specific markers for the accurate identification of fruit flies. Only minor variations in stop codons were observed across some clades, with the vast majority remaining conserved (Table S3).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eEvolutionary analysis of mitochondrial protein-coding genes\u003c/h3\u003e\n\u003cp\u003eComplete mitochondrial genomes serve as valuable genetic markers, therefore, the availability and abundance of these genomes is essential for designing specific PCR assays to avoid cross-reaction with non-represented species [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Therefore, we analysed the evolutionary patterns of the mitochondrial PCGs in \u003cem\u003eBactrocera\u003c/em\u003e fruit flies, which are more readily accessible across taxa due to higher nucleotide conservation in coding regions compared to intergenic spaces. Our evolutionary patterns analysis of mitochondrial genomes evaluated key parameters such as nucleotide diversity (Pi), synonymous and nonsynonymous substitution rates (Ka/Ks ratio), barcoding gaps, and codon usage bias (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Figure S3-S4). The average Pi of the individual genes ranged from 0.056 for \u003cem\u003eND4L\u003c/em\u003e and 0.109 for \u003cem\u003eND2\u003c/em\u003e, the \u003cem\u003eCOI\u003c/em\u003e gene showed intermediary nucleotide diversity (0.079; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Along with \u003cem\u003eND2\u003c/em\u003e, the genes \u003cem\u003eND6, ND3\u003c/em\u003e and \u003cem\u003eCYTB\u003c/em\u003e had the highest nucleotide diversity, 0.108, 0.094 and 0.093, respectively. The Ka/Ks ratio is a widely used metric in comparative genomics to estimate selection pressure and the evolutionary rate [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. For the 13 PCGs, \u003cem\u003eND5\u003c/em\u003e had the highest average Ka/Ks ratio across all available \u003cem\u003eBactrocera\u003c/em\u003e mitogenomes, followed by \u003cem\u003eND2\u003c/em\u003e and \u003cem\u003eND1\u003c/em\u003e, whereas \u003cem\u003eCOI\u003c/em\u003e and \u003cem\u003eATP8\u003c/em\u003e had the lowest average values (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Even though the \u003cem\u003eATP8\u003c/em\u003e gene showed a high rate of non-synonymous substitutions, its Ka/Ks ratio remained low due to the large number of synonymous substitutions (Figure S3). The average Ka/Ks values across all available \u003cem\u003eBactrocera\u003c/em\u003e mitogenomes varied from 0.029 (\u003cem\u003eCOI\u003c/em\u003e) to 0.124 (\u003cem\u003eND5\u003c/em\u003e) and consistently remained below 0.213 (below 1.0 the threshold for purifying selection), indicating that all PCGs have undergone purifying selection. Variation in the Ka/Ks ratio between different \u003cem\u003eBactrocera\u003c/em\u003e clades can be observed for genes \u003cem\u003eND4L\u003c/em\u003e and \u003cem\u003eND6\u003c/em\u003e, with the \u003cem\u003eB. dorsalis\u003c/em\u003e complex showing the lowest Ka/Ks values. Intra- and inter-specific genetic distances based on the data of 13 PCGs were significantly overlapped regardless of the marker, providing evidence of the lack of adequate barcoding gaps for mitochondrial genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). For interspecific genetic distance, the highest values were observed for \u003cem\u003eND2\u003c/em\u003e (mean 13.93\u0026thinsp;\u0026plusmn;\u0026thinsp;5.53%), followed by \u003cem\u003eND6\u003c/em\u003e (mean 13.85\u0026thinsp;\u0026plusmn;\u0026thinsp;5.78%). Although the mean intra-specific genetic distance was very low for individual markers, maximum values overlap with the interspecific genetic distance (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). For instance, the commonly used COI barcode marker showed an interspecific genetic distance of 10.21\u0026thinsp;\u0026plusmn;\u0026thinsp;4.18% with a maximum intra-specific genetic distance of 19.25%. Very weak codon bias was observed on the \u003cem\u003eBactrocera\u003c/em\u003e PCGs (Figure S4). In summary, although the \u003cem\u003eCOI\u003c/em\u003e gene has been extensively used for molecular identification of fruit flies, this gene exhibited lower evolutionary rates and moderate nucleotide diversity resulting in small differences among closely related species. Based in our results, the \u003cem\u003eND2\u003c/em\u003e, and \u003cem\u003eND6\u003c/em\u003e genes can provide better species resolution for distinguishing closely related species at the species level.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003ePerformance Comparison of Mitochondrial Genes for Species-Specific Assays\u003c/h3\u003e\n\u003cp\u003eSix Taqman-based real-time PCR assays targeting the \u003cem\u003eND2\u003c/em\u003e, \u003cem\u003eCOI\u003c/em\u003e, and \u003cem\u003eCO3\u003c/em\u003e genes were designed (Table S4, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA\u0026ndash;B) and used for screening to evaluate potential cross-reactions and the amplification efficiency for known variants of \u003cem\u003eB. curvipennis\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC\u0026ndash;H, Figure S5). The initial screening of three ND2, one COI and two CO3 assays (Table S4) showed that the ND2 assay (ND2_1138F_BC, ND2_1251R_BC and ND2_1192P_BC+) performed the best, with no cross-reactivity with non-target species, producing lower Cq values, and generating higher relative fluorescence (RFU\u0026thinsp;\u0026gt;\u0026thinsp;1000) for all target species haplotypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC, F). The COI assay showed cross-reactivity with other members of the \u003cem\u003eB. tryoni\u003c/em\u003e complex, although lower RFU was observed for non-target species (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD, F). Both CO3 assays exhibited cross-reactivity not only with the \u003cem\u003eB. tryoni\u003c/em\u003e complex but also with additional \u003cem\u003eBactrocera\u003c/em\u003e species, showing non-specific amplification with higher Cq values (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE, H and Figure S5C, F). The ND2 assay's higher specificity was further confirmed with additional primer combinations (Figure S5A, D and S5B, E).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eOptimization and validation of the ND2 species-specific real-time PCR assay\u003c/h3\u003e\n\u003cp\u003eReal-time PCR conditions for the ND2 assay (ND2_1138F_BC, ND2_1192P_BC\u0026thinsp;+\u0026thinsp;and ND2_1251R_BC) was optimized to increase sensitivity while still being specific. The optimized master mix composition and cycling conditions for the \u003cem\u003eB. curvipennis\u003c/em\u003e duplex assay with 18S internal control targeting the 18S rRNA gene [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] for evaluation of the DNA quality are provided in Table S5 and S6. The annealing temperature was set at 60\u0026deg;C since higher temperatures resulted in increased Cq values and reduced florescence for certain \u003cem\u003eB. curvipennis\u003c/em\u003e variants (Figure S7).\u003c/p\u003e \u003cp\u003eThe linear dynamic range for the singleplex assay was assessed with serial dilutions of gBlock containing the ND2 primer and probe binding sites (Figure S8). The limit of detection (LOD) the singleplex assay was 1 copy/\u0026micro;L of synthetic DNA template with an average Cq of 36.86 for all replicates detected (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The amplification efficiency was 101.1% (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), an thereby the analytical cut-off was stablished at 36 cycles based on the LOD [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The ND2 assay could also amplify 10 fg/\u0026micro;L of genomic DNA with Cq values around 35 cycles (Figure S8A, B) and the LOD was unaffected by the addition of the 18S internal control (Figure S9C).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe diagnostic specificity the ND2 assay was validated with 31 fruit fly samples in the specificity tests and 21 additional samples in the blind panel (a complete list of the specimens tested is provided in Table S7). All the tested samples of \u003cem\u003eB. curvipennis\u003c/em\u003e were successfully amplified the ND2 assay with a Cq lower than 25 cycles (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). A single replicate of \u003cem\u003eDirioxa pornia\u003c/em\u003e DPNC2 resulted in a very weak amplification (Cq\u0026thinsp;=\u0026thinsp;39.61), above the negative threshold of Cq\u0026thinsp;\u0026gt;\u0026thinsp;36. The blind test produced consistent results and independently matched to the original identities of the samples provided. High assay reproducibility was demonstrated by low variation in %CV values for each of the samples tested within individual and between different runs (Table S8). Overall, these results demonstrated the ND2 assay robustness and reliability.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study generated 82 complete mitochondrial genomes from 19 fruit fly species, 16 of which were previously unavailable. Among these are highly polyphagous species with elevated risk of spreading to new locations, such as \u003cem\u003eB. curvipennis, B. facialis, B. kirki, B. melanotus, B. passiflorae, B. psidii, B. trilineola\u003c/em\u003e and \u003cem\u003eB. xanthodes\u003c/em\u003e [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Analysis of complete mitochondrial genomes provide useful molecular markers for both taxonomic and diagnostic studies [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Features like mitochondrial genome size, GC content, and control region length showed great variation across different \u003cem\u003eBactrocera\u003c/em\u003e clades, but their diagnostic utility is not widely applied due to the high cost and time required for whole genome sequencing. In contrast, the variability on the IGS lengths, specifically the \u003cem\u003etrnQ-trnM, trnC-trnY\u003c/em\u003e and \u003cem\u003etrnR-trnN\u003c/em\u003e, can be easily implemented as diagnostic markers to differentiate major clades as previously reported [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. However, the IGS length seems to be conserved in \u003cem\u003eBactrocera\u003c/em\u003e species complexes. To differentiate species within these complexes, genes with higher substitution rates, nucleotide diversity and interspecific genetic distance, such as \u003cem\u003eND5, ND2\u003c/em\u003e and \u003cem\u003eND1\u003c/em\u003e, can be better candidates for the development of species-specific diagnostic tools. This was exemplified by an increased specificity of the ND2 real-time PCR assay for the identification of \u003cem\u003eB. curvipennis\u003c/em\u003e as compared with the real-time PCR assays for genes \u003cem\u003eCOI\u003c/em\u003e and \u003cem\u003eCO3\u003c/em\u003e tested. Overall, these findings highlight that other mitochondrial genes and features offer higher diagnostic specificity than universal COI barcode, particularly for distinguishing species within complexes [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur phylogenetic analyses of 479 mitochondrial genomes covering the major linages in the Tephritidae family provided a robust reconstruction, which was largely congruent with previous analyses and classifications schemes [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan additionalcitationids=\"CR45\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Monophyletic clades were observed for the genera of \u003cem\u003eBactrocera, Dacus, Ceratitis, Ragholetis, Carpomya\u003c/em\u003e and \u003cem\u003eAnastrepha\u003c/em\u003e, while \u003cem\u003eZeugodacus\u003c/em\u003e was resolved as paraphyletic with \u003cem\u003eZ. rubellus\u003c/em\u003e and \u003cem\u003eZ. cilifer\u003c/em\u003e positioned external to the \u003cem\u003eZeugodacus\u003c/em\u003e main clade (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The fact that mitochondrial genome data failed to distinguish closely related species within the well-known \u003cem\u003eB. dorsalis, B. tryoni\u003c/em\u003e, and \u003cem\u003eB. frauenfeldi\u003c/em\u003e species complexes, as well as \u003cem\u003eCeratitis\u003c/em\u003e FAR complex (frugivorous flies - \u003cem\u003eCeratitis fasciventris, C. anonae, and C. rosa\u003c/em\u003e), is not surprising. Notably, the inability to discriminate \u003cem\u003eB. facialis\u003c/em\u003e and \u003cem\u003eB. passifloreae\u003c/em\u003e using either the COI barcode or the entire mitochondrial genome is a novel finding, indicating that \u003cem\u003eB. facialis\u003c/em\u003e and \u003cem\u003eB. passifloreae\u003c/em\u003e are belonging to a species complex. Our results support expanding the \u003cem\u003eB. tryoni\u003c/em\u003e complex to include the polyphagous fruit pest \u003cem\u003eB. curvipennis\u003c/em\u003e from New Caledonia, consistent with previous molecular studies [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Recent speciation events, hybridization, mitochondrial introgression, inter-specific gene flow likely contribute to the discrepancies between mitochondrial genetic diversity and morphology [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. To address challenges posed by recent or ongoing speciation, employing nuclear genome wide data, such as RADseq (Restriction-site Associated DNA Sequencing) and HiMAP (Highly Multiplexed Amplicon-based Phylogenomics) can provide more accurate insights into species delimitation [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. However, the continuous post-speciation gene flow [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] and the presence of additional, unrecognized species complexes among less studied fruit fly pests with more restricted distribution, highlights significant gaps in our knowledge which can hinder efforts to prevent new invasions or prioritize pest control measures [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Improving the accuracy of species delimitation and resolving taxonomic discrepancies in fruit flies are crucial for strengthening biosecurity measures [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur findings demonstrate that mitochondrial genome features like the IGS and other rapid-evolving mitochondrial genes can be valuable to enhance species-detection resolution. In the battle against the spread of fruit fly pests, molecular methods such as DNA barcoding or species-specific assays (e.g., PCR, real-time PCR and LAMP) targeting the COI gene have been developed [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan additionalcitationids=\"CR48\" citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. However, the COI-5P barcoding fragment that is widely used to develop these assays may not be the most informative for the molecular identification of \u003cem\u003eBactrocera\u003c/em\u003e fruit flies. Despite extensive BOLD records have been generated for this fragment (up to 18,283 on 03/12/2024), its utility for diagnosis is compromised by (i) misidentifications in the reference databases, (ii) underrepresentation of non-pest species that do not respond to lures (only 272 of 750 species in \u003cem\u003eBactrocera\u003c/em\u003e genus), and (iii) considerable overlap of intra- and inter-specific genetic divergences within species complexes [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. For example, some assays targeting the COI gene are unable to distinguish \u003cem\u003eB. tryoni\u003c/em\u003e from other closely related \u003cem\u003eB. tryoni\u003c/em\u003e complex members [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], \u003cem\u003eB. dorsalis\u003c/em\u003e from other \u003cem\u003eB. dorsalis\u003c/em\u003e complex species [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], \u003cem\u003eB. latifrons\u003c/em\u003e from \u003cem\u003eB. correcta\u003c/em\u003e [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], \u003cem\u003eB. minax\u003c/em\u003e from \u003cem\u003eB. tsuneomis\u003c/em\u003e [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] and \u003cem\u003eC. capitata\u003c/em\u003e from \u003cem\u003eC. cosyra\u003c/em\u003e [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRemarkably, the whole length of IGS is relatively consistent within each \u003cem\u003eBactrocera\u003c/em\u003e clade and significantly varied among the other clades, suggesting that the IGS length can serve as a useful diagnostic tool for distinguishing clades. Mitochondrial IGS are usually limited in number and size, and represent the most variable part of insect mitochondrial genomes [\u003cspan additionalcitationids=\"CR53 CR54\" citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. For instance, the \u003cem\u003etrnL2-CO2\u003c/em\u003e IGS has been effective in characterizing genetic diversity within honey bee populations [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e], while the \u003cem\u003etrnI-trmQ\u003c/em\u003e and \u003cem\u003etrnQ-trnM\u003c/em\u003e IGS gene region has been successfully used to differentiate the five most common fruit flies in South Africa (\u003cem\u003eC. capitata, C. cosyra, C. rosa, C. quilicii\u003c/em\u003e, and \u003cem\u003eB. dorsalis\u003c/em\u003e) [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Similarly, sequences covering the t\u003cem\u003ernQ-trnM, trnC-trnY\u003c/em\u003e and \u003cem\u003etrnR-trnN\u003c/em\u003e IGS gene regions can be used for accurate differentiation of the main \u003cem\u003eBactrocera\u003c/em\u003e clades without downstream analysis and sequencing. However, the high AT content (\u0026gt;\u0026thinsp;80%) of IGS regions poses a challenge for developing species-specific assays, since AT-rich sequences reduce primer annealing temperatures, compromise the probe binding, and lower the amplification efficiency [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Additionally, the high variability of IGS regions increases the chances of mutations within primer and probe binding sites can lead to false negative results.\u003c/p\u003e \u003cp\u003eMitochondrial PCGs are widely used in animal species-specific assays due to higher temporal stability, moderate AT content (~\u0026thinsp;70% in \u003cem\u003eBactrocera\u003c/em\u003e), and large cellular copy number, which increases the likelihood of detection from the fragmented DNA frequently found in fruit-fly traps [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Selecting the proper gene is critical for ensuring the analytical specificity and sensibility required for border diagnostics. The nucleotide diversity, evolutionary rates and interspecific genetic distance indicates that the \u003cem\u003eND5, ND2\u003c/em\u003e and \u003cem\u003eND1\u003c/em\u003e genes are the least conserved genes among PCGs. While the \u003cem\u003eCOI\u003c/em\u003e gene was the most conserved gene as reported for other insects, including fruit flies [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], mosquitoes [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e], beetles [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e], parasitoid wasps [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e], and soft scales [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. Recent studies suggest that genes with rapid-evolving rates might be more suitable for the design of species-specific assays and capable of distinguishing closely related species [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. A comparison of the analytical specificity of real-time PCR assays targeting the \u003cem\u003eCOI, CO2\u003c/em\u003e and \u003cem\u003eND2\u003c/em\u003e genes to distinguish \u003cem\u003eB. curvipennis\u003c/em\u003e found that only the ND2 assay did not show cross-reactions with other \u003cem\u003eB. tryoni\u003c/em\u003e complex members or other \u003cem\u003eBactrocera\u003c/em\u003e species. Subsequent test for sensitivity, diagnostic specificity, and reproducibility further validated the suitability of the ND2 assay. The COI representation of non-pest \u003cem\u003eBactrocera\u003c/em\u003e species is often lacking in the public databases, and the representation of other genetic markers that can be more suitable for the differentiation of closely related species is even less robust. Using genetic markers with limited representation in public databases can lead to cross-reaction with non-represented species. Therefore, the situations represent a trade-off dilemma between the representation in the databases and the diagnostic capability. Fortunately, advances in sequence technologies have facilitated the rapid expansion of mitochondrial genome databases [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e], mitigating this limitation and enabling the broader implementation of alternative genetic makers for the diagnosis of invasive fruit flies.\u003c/p\u003e \u003cp\u003eThe advent of high-throughput sequencing (HTS) offers a promising solution by enabling the cost-effective sequencing of entire mitochondrial genome, due to smaller size and high copy number of this organelle [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan additionalcitationids=\"CR66\" citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. HTS is not only more affordable, but also more technically robust as eliminates primer biases and non-standard amplification [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. For instance, the cost of generating the entire mitochondrial genome using Illumina NovaSeq is currently less than US \u003cspan\u003e$\u003c/span\u003e100 for 10\u0026nbsp;million reads, which is more than sufficient to get the entire mitochondrial genome. Similar pricing and read depth are achievable using portable sequencing technologies, such as Oxford Nanopore Technologies (ONT), with the advantages of \u003cem\u003ein situ\u003c/em\u003e sequencing, shorter timeframes and even covering highly AT rich sequences in the control region that are often missing by Illumina [\u003cspan additionalcitationids=\"CR69 CR70 CR71\" citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. Despite these advancements, the accuracy of identifications at species-level based on the entire mitochondrial genome remains dependent on the availability and the quality of the curated sequences in DNA repositories [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. Therefore, expanding mitochondrial sequences databases, particularly for fruit flies and other quarantine pests, is essential for employing these technologies for species identification and biosecurity applications.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eThis study addresses significant gaps in sequence databases that have hindered the molecular identification of fruit flies, particularly in South Pacific Countries. By generating 82 complete mitochondrial genomes from 19 species across three genera of Tepritidae, this study provides critical genetic resources for diagnostic and taxonomic studies.\u003c/p\u003e \u003cp\u003eOur data also highlight the potential of alternative mitochondrial gene markers, such as \u003cem\u003eND2, ND5\u003c/em\u003e and IGS gene regions to improve the specificity and sensitivity of species-specific assays for closely related species. The evolutionary analysis of mitochondrial protein-coding genes (PCGs) showed that genes like \u003cem\u003eND2\u003c/em\u003e and \u003cem\u003eND6\u003c/em\u003e exhibited higher nucleotide diversity compared to \u003cem\u003eCOI\u003c/em\u003e. While \u003cem\u003eND2\u003c/em\u003e and \u003cem\u003eND5\u003c/em\u003e genes provide better species resolution, making them particularly useful for distinguishing closely related species at the species level. Additionally, the intergenic spacers, such as \u003cem\u003etrnQ-trnM\u003c/em\u003e and \u003cem\u003etrnC-trnY\u003c/em\u003e, displayed significant length variation between species, making them potential candidates for species-specific markers. Despite the success in screening potential genetic markers based on evolutionary rates, limited representation of many mitochondrial genes in public databases remains a challenge for their widespread use in fruit fly diagnostics.\u003c/p\u003e \u003cp\u003eThe ND2 real-time PCR assay developed in this study demonstrates the practical application of these findings. The assay showed high sensitivity and specificity, with a limit of detection (LOD) of 1 copy/\u0026micro;L of the synthetic DNA, making it a reliable tool for the identification of \u003cem\u003eB. curvipennis\u003c/em\u003e. When compared with the assays targeting \u003cem\u003eCOI\u003c/em\u003e and \u003cem\u003eCO3\u003c/em\u003e genes, the ND2 assays demonstrated superior specificity in distinguishing \u003cem\u003eB. curpipennis\u003c/em\u003e from other \u003cem\u003eB. tryoni\u003c/em\u003e complex members without cross-reactions.\u003c/p\u003e \u003cp\u003eThis research provide supports to improve surveillance and management of fruit flies, enhance the diagnostic accuracy and aid border security agencies in rapid decision-making to safeguard international trade; ultimately improving biosecurity response capability. The continuous expansion of genetic databases and innovations in sequencing technologies present further opportunities to refine molecular diagnosis and strengthen biosecurity responses. Additionally, these innovations can reduce the cost and time of fruit fly detection, benefiting the biosecurity agents and agricultural sectors that rely heavily on fruit production.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003eSpecimen selection\u003c/h2\u003e\n\u003cp\u003eFruit fly species, representing five genera were obtained from interceptions in New Zealand and fruit fly traps from Australia, Cook Islands, Fiji, French Polynesia, New Caledonia, Tonga, and Vanuatu (Table S2 and S7). All adult specimens were identified using taxonomic keys and DNA barcoding (Supplementary Notes).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003ch2\u003eDNA extraction\u003c/h2\u003e\n\u003cp\u003eFor mitochondrial genome sequencing, total DNA from individual whole fruit fly specimens of 82 individuals of three genera (Table S2) was extracted using the DNeasy for Blood and Tissue kit (Qiagen, Germany) as per the manufacturer\u0026rsquo;s instructions. The purified genomic DNA was quantified using the Qubit dsDNA Quantification Assay Kits (Invitrogen, Waltham, MA, USA) and normalized to 300 ng per sample.\u003c/p\u003e\n\u003cp\u003eFor real-time PCR validation, additional DNA (Table S7) was extracted either a leg of adult, pieces of larva/pupa or an egg with DNeasy for Blood and Tissue kit (Qiagen, Germany) or PrepGEM universal kit (ZyGEM, New Zealand) according to manufacturers\u0026rsquo; instructions.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n\u003ch2\u003eMitochondrial genome sequencing, assembly, and annotation\u003c/h2\u003e\n\u003cp\u003eFragment length, Illumina library preparation and sequencing were performed by Novogene on the Agilent 5400 Fragment Analyzer System and Illumina NovaSeq 6000 platform paired-end 150 to attain at least 2.0\u0026ndash;3.0 Gbp raw data per sample. The quality of sequences was evaluated using the FastQC software (Table S2). All ambiguous reads with an average quality value lower than Q20 and adaptor sequences were excluded from further analysis using Trimmomatic. De novo assembly was performed with MitoZ [\u003cspan class=\"CitationRef\"\u003e73\u003c/span\u003e], MitoFlex [\u003cspan class=\"CitationRef\"\u003e74\u003c/span\u003e] and GetOrganelle [\u003cspan class=\"CitationRef\"\u003e75\u003c/span\u003e]. MitoFlex and MitoZ are modular pipelines specifically designed for mitochondrial genome assembly and annotation. MitoFlex and MitoZ represent a modified version of the assembler MEGAHIT to better assemble mitochondrial sequences, while GetOrganalle use Spades for \u003cem\u003ede novo\u003c/em\u003e assembly of organelle genomes. For the MitoZ assembly module, it was specified that the reads stemmed from a specimen in the clade Arthropoda and all other default parameters were used. For the MitoFlex and GetOrganelle default parameter to assembly animal mitochondria were used. The final accessions were generated with consensus sequences from an alignment of the assembly methods that obtained circular sequence. After that, majority consensus sequences were generated using Samtools and coverage calculated using BEDtools [\u003cspan class=\"CitationRef\"\u003e76\u003c/span\u003e]. Consensus sequences were then annotated using Geneious Primer 2021.1.1 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.geneious.com/\u003c/span\u003e\u003c/span\u003e) and Mitos2 with the appropriate parameters: Reference= \u0026ldquo;RefSeq 63 Metazoa\u0026rdquo; and Genetic Code = \u0026ldquo;5 Invertebrate\u0026rdquo; [\u003cspan class=\"CitationRef\"\u003e77\u003c/span\u003e]. The two different annotation tools were compared and adjusted using the available fruit fly mitochondrial genomes.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n\u003ch2\u003ePhylogenetic analysis\u003c/h2\u003e\n\u003cp\u003eThe complete mitochondrial genome sequences of all 397 Tephritidae fruit fly specimens available in October 2024 were downloaded from NCBI (Table S9). Along with 82 from this study, 479 Tepthitidae mitochondrial genomes available representing 4 subfamilies, 8 tribes, 14 genera and 116 species were used for mitochondrial phylogenetic analyses, using \u003cem\u003eDrosophila yakuba\u003c/em\u003e NC_001322.1 as the outgroup. Additionally, 13 complete PCGs were extracted from the 473 annotated mitochondrial genome sequences and concatenated into a single dataset using Geneious Prime 2021.1.1. (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.geneious.com\u003c/span\u003e\u003c/span\u003e). Multiple sequence alignment was performed using MAFFT version 7.505 with default parameters [\u003cspan class=\"CitationRef\"\u003e78\u003c/span\u003e]. Possible incongruences in the alignment of protein-coding genes (PCGs) were verified using Geneious Prime. The alignments based on the entire mitochondrial genome and the concatenated PCGs were used for the phylogenetic analyses. Two different methodologies were used for the reconstruction of the phylogenetic relationships: (i) leaving the data unpartitioned with the same substitution model applied to all the sites in the alignment; and (ii) using a partition scheme based on genes and codon positions. For the first method, Maximum Likelihood (ML) tree was inferred for each of the three alignments by searching for the best substitution model using ModelFinder and calculating the bootstrap support with 10,000 replicates in IQ-TREE. PartitionFinder2 [\u003cspan class=\"CitationRef\"\u003e79\u003c/span\u003e] was used to select the optimal partitioning schemes and substitution models for phylogenetic analyses using the \u0026ldquo;greedy\u0026rdquo; algorithm and branch lengths estimated as \u0026ldquo;unlinked\u0026rdquo; under the AICC model selection. The best partitioning scheme was used for phylogenetic reconstruction using 10,000 replicates in IQ-TREE. Phylogenetic analyses were also reconstructed using ML performed with RAxML 8.2.12 [\u003cspan class=\"CitationRef\"\u003e80\u003c/span\u003e] using the GTRCAT model and a full ML bootstrap analysis with 100 replicates.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n\u003ch2\u003eMitochondrial genome comparative analysis\u003c/h2\u003e\n\u003cp\u003eComparative analysis of the \u003cem\u003eBactrocera\u003c/em\u003e mitochondrial genomes was performed using the concatenated PCGs dataset that includes the 325 annotated mitochondrial genome sequences distributed in 46 species. Nucleotide diversity (Pi) values of each PCG from 4SPE were determined using sliding window analyses of a window size of 100 bp and step size of 20 bp in DnaSP v6 [\u003cspan class=\"CitationRef\"\u003e81\u003c/span\u003e]. The nonsynonymous substitution 184 rate (Ka), synonymous substitution rate (Ks), and Ka/Ks were calculated by the seqinr package [\u003cspan class=\"CitationRef\"\u003e82\u003c/span\u003e]. The Ka/Ks ratio for each PCG, and each taxon was measured in comparison with the outgroup \u003cem\u003eDrosophila yakuba.\u003c/em\u003e Genetic distances among fruit fly species were calculated using K80 model of evolution in the dist.dna function of ape for R. Barcoding gap analysis and the calculation of maximum intra-specific distances and the distance to nearest neighbour were performed employing the spider package in R [\u003cspan class=\"CitationRef\"\u003e83\u003c/span\u003e]. The indices of codon usage and synonymous codon usage bias were measured using CodonW (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://codonw.sourceforge.net/\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eIn order to identify more effective genetic markers for the molecular identification of \u003cem\u003eBactrocera\u003c/em\u003e fruit flies, we analysed the structure of all available mitochondrial genomes, with a focus on economically important species [according to 8]. The selected clades included the complexes \u003cem\u003eB. dorsalis\u003c/em\u003e (\u003cem\u003eB. dorsalis, B. carambolae, B. occipitalis\u003c/em\u003e), \u003cem\u003eB. tryoni\u003c/em\u003e (\u003cem\u003eB. tryoni, B. neohumeralis, B. curvipennis\u003c/em\u003e), \u003cem\u003eB. frauenfeldi\u003c/em\u003e (\u003cem\u003eB. frauenfeldi, B. albistigrata, B. kirki, B. trilineola, B. psidii, B. caledoniensis\u003c/em\u003e), \u003cem\u003eB. zonata\u003c/em\u003e (\u003cem\u003eB. zonata, B. correcta\u003c/em\u003e), \u003cem\u003eB. facialis\u003c/em\u003e (\u003cem\u003eB. facialis, B. passiflorae\u003c/em\u003e), \u003cem\u003eB. latrifrons, B. oleae, B. melanotus, B. tsuneomis, B. xanthodes, B. distincta\u003c/em\u003e, and \u003cem\u003eB. umbrosa.\u003c/em\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n\u003ch2\u003eDevelopment and validation of the qPCR assay\u003c/h2\u003e\n\u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\n\u003ch2\u003eSpecies-specific primer design\u003c/h2\u003e\n\u003cp\u003eThe entire mitochondrial genome alignment was used to design qPCR assay for \u003cem\u003eB. curvipennis\u003c/em\u003e. This alignment includes seven \u003cem\u003eB. curvipennis\u003c/em\u003e, nine \u003cem\u003eB. neohumeralis\u003c/em\u003e and twenty \u003cem\u003eB. tryoni\u003c/em\u003e mitochondrial sequences. Additionally, all 591 available \u003cem\u003eCOI\u003c/em\u003e sequences from the \u003cem\u003eB. tryoni\u003c/em\u003e complex defined by [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e], namely \u003cem\u003eB. curvipennis, B. tryoni, B. neohumeralis, B. aquilonis, B. erubescentis, B. mutabilis\u003c/em\u003e and \u003cem\u003eB. ustulata\u003c/em\u003e, were also included in the aligment. Regions that showed differences specific for \u003cem\u003eB. curvipennis\u003c/em\u003e were selected manually in the alignment and used for the design of specific probes and primers manually. The optimal condition for melting temperatures, GC content, dimer formation, and secondary structure formation were analysed through Oligo Analyzer Tool (IDT) and Geneious Prime 2021.1.1. The BHQplus stabilising technology was used to increase the melting temperature (Tm) of the probe, mismatch discrimination and binding to the AT rich regions in ND2 gene [\u003cspan class=\"CitationRef\"\u003e84\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n\u003ch2\u003eOptimization of the assay conditions\u003c/h2\u003e\n\u003cp\u003eAll real-time PCR reactions were set up on a CFX96\u0026trade; Touch Real-time platform (BioRad, Hercules, CA, USA). For each PCR assay, 2 \u0026micro;L of the DNA extract was added to a 20 \u0026micro;L final volume of reaction mix, containing 10 \u0026micro;L of the 2X mastermix, variable concentrations of primers and probes and DEPC water. Each reaction mix was performed in duplicate wells and all runs included a positive control and a non-template control (NTC). Newly designed primers and probe for each assay (Table S4) were preliminary tested in different combinations using the target species and closely related species. Based on results from the preliminary specificity assessment, the best-performing primer-probe combination was selected for further optimisation. The selected primers and probes for each species was first tested using different annealing temperatures (58.1, 59.7, 61.6, 63.2\u0026deg;C). Then, the assay was further optimised using mastermixes [PerfeCTa\u0026reg; qPCR ToughMix\u0026reg; (Quanta Bioscience, Beverly, MA, USA) and SsoAdvanced\u0026trade; Universal Probes Supermix (BioRad, Hercules, CA, USA)], different primer concentrations (200\u0026ndash;400 nM), probe gradients (100\u0026ndash;300 nM), and with or without the addition of Bovine Serum Albumin (BSA, Sigma-Aldrich Co., Massachusetts, USA; 0.1\u0026ndash;0.5 \u0026micro;g/\u0026micro;L). To coamplify the 18S ribosomal RNA (rRNA) gene as an internal control, the TaqMan\u0026trade; 18S internal control (Applied Biosystems \u0026trade;, CA, USA) primers and probe were incorporated with the \u003cem\u003eB. curvipennis\u003c/em\u003e specific assay in duplex format.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n\u003ch2\u003eSensitivity and Specificity of the species-specific real-time assay\u003c/h2\u003e\n\u003cp\u003eA synthetic control (Integrated DNA Technologies gBlocks\u0026trade; Gene Fragment) was used to determine the sensitivity of the qPCR assay. The synthetic control is a 283 bp template for different qPCR assay targets used in the detections of \u003cem\u003eB. curvipennis\u003c/em\u003e and \u003cem\u003eB. tryoni\u003c/em\u003e complex. The concatenated primers - probe sequence of the all-in-one synthetic control, is given in Figure S8. Connecting strings of Cs and Gs nucleotides were added before, between and after the primers/probes to increase CG content to meet the IDT manufacturer\u0026rsquo;s requirement of minimum of 32% of GC content for a gBlock fragment. Extra bases at 3\u0026rsquo; and 5\u0026rsquo; ends were added to ensure primer binding. Sequences in between the primer and probe binding sites were not included for better differentiation of the COI or ND2 sequences of the target specimens from the gBlock positive control.\u003c/p\u003e\n\u003cp\u003eAnalytical sensitivity of the assay was determined using the dilution series of the \u0026ldquo;all-in-one\u0026rdquo; template prepared in TE buffer ranging from 1 copy/\u0026micro;L to 1 \u0026times; 10\u003csup\u003e7\u003c/sup\u003e copies/\u0026micro;L with each concentration in quintuplicate per reaction. Additionally, sensitivity was tested with ten-fold serial dilutions of genomic DNA from the target specimens (concentration ranging from 1 fg/\u0026micro;L to 10 ng/\u0026micro;L). Standard curve, PCR efficiency and R\u003csup\u003e2\u003c/sup\u003e were calculated automatically by the CFX manager software.\u003c/p\u003e\n\u003cp\u003eIn addition to the specificity implied through the design process, the specificity of real-time PCR assay was tested using singleplex format against DNA extracted from target and the non-target species. In total, 31 fruit fly specimens were tested, including target species, closely related non-target species as well as species commonly intercepted at New Zealand\u0026rsquo;s borders (Supplementary table S7). For the blind panel test, total of 21 specimens and 3 dilutions of the synthetic control were provided to the operators with no knowledge of the sample origin and identity (Supplementary table S7). The samples were tested with the ND2 qPCR assay in singleplex format. For each run, positive and non‐template controls were included, and all the reactions were performed in triplicate wells.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eML: \u0026nbsp;Maximum likelihood\u003c/p\u003e\n\u003cp\u003eLAMP: Loop-mediated isothermal amplification\u003c/p\u003e\n\u003cp\u003eGbp: Gigabase pair\u003c/p\u003e\n\u003cp\u003ePCG: Protein Coding Gene\u003c/p\u003e\n\u003cp\u003eATP6:\u0026nbsp;ATP synthase membrane subunit 6\u003c/p\u003e\n\u003cp\u003eATP8:\u0026nbsp;ATP synthase membrane subunit 8\u003c/p\u003e\n\u003cp\u003eCYTB:\u0026nbsp;Cytochrome b\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCOI:\u0026nbsp;Cytochrome c oxidase I\u003c/p\u003e\n\u003cp\u003eCO2:\u0026nbsp;Cytochrome c oxidase 2\u003c/p\u003e\n\u003cp\u003eCO3:\u0026nbsp;Cytochrome c oxidase 3\u003c/p\u003e\n\u003cp\u003eND1: NADH dehydrogenase subunit 1\u003c/p\u003e\n\u003cp\u003eND2: NADH dehydrogenase subunit 2\u003c/p\u003e\n\u003cp\u003eND3: NADH dehydrogenase subunit 3\u003c/p\u003e\n\u003cp\u003eND4: NADH dehydrogenase subunit 4\u003c/p\u003e\n\u003cp\u003eND5: NADH dehydrogenase subunit 5\u003c/p\u003e\n\u003cp\u003eND6: NADH dehydrogenase subunit 6\u003c/p\u003e\n\u003cp\u003etRNA: transfer RNA\u0026nbsp;\u003c/p\u003e\n\u003cp\u003erRNA: Ribosomal RNA\u003c/p\u003e\n\u003cp\u003ePi:\u0026nbsp;Average pairwise differences between all possible pairs of individuals\u003c/p\u003e\n\u003cp\u003eKa/Ks ratio: The ratio of the number of nonsynonymous substitutions per non-synonymous site (K\u003csub\u003ea\u003c/sub\u003e), in a given period of time, to the number of synonymous substitutions per synonymous site (K\u003csub\u003es\u003c/sub\u003e), in the same period.\u003c/p\u003e\n\u003cp\u003eIGS: Intergenic Spacer region\u003c/p\u003e\n\u003cp\u003eHTS: High-Throughput Sequencing\u003c/p\u003e\n\u003cp\u003eONT: Oxford Nanopore Technologies\u003c/p\u003e\n\u003cp\u003eHiMAP: Highly Multiplexed Amplicon-based Phylogenomics\u003c/p\u003e\n\u003cp\u003eRADseq: Restriction-site Associated DNA sequencing\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eNo specific permits were required for all the sample collected for this study. No samples were collected in national parks and no endangered or threatened insects were included in this study, thus collection permits were not required for all the collections. All specimens imported into New Zealand were in accordance with the Import Health Standard, Section 22 of the Biosecurity Act 1993. Ethics approval was not required as insects are not classified as animals for the purposes of the Animal Welfare Act, 1999, New Zealand Legislation.\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eAll the relevant data are included in the manuscript and additional data are in the supplementary information files.\u003c/p\u003e\n\u003cp\u003eAll genome sequences reported here were deposited in GenBank database under accession numbers from PV604183 - PV604264.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThe research was funded by the Operational Research programme of the Ministry for Primary Industries (MPI), New Zealand, grant number 406793.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026rsquo; contributions\u003c/h2\u003e\n\u003cp\u003eDL, DG, BM and SG conceived the study. SP provided financial and project administration. DL and NLC designed the study. DG sourced all colony and trap-collected fruit flies and performed morphological identification. NLC conducted all laboratory experiments, primer design, HTS data analysis and mitochondrial genome assembly. DL supervised the genomic work, validation of the real-time PCR assay and data analysis, and co-wrote the manuscript. SP made a major contribution in revising the manuscript. \u0026nbsp;All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eWe would like to thank Dr. Catia Delmiglio from Plant Health and Environment Laboratory (PHEL), MPI for the critically reviewing the manuscript. We would like to thank Zhidong Yu and Claire McDonald from the Operational Research Team, MPI for their support. We also acknowledge the use of New Zealand eScience Infrastructure (NeSI) high-performance computing facilities. Our special thanks go to the Entomology team members of the PHEL, MPI, specially for Yan Chen, for assisting in morphological identification of the specimens. Finally, we would like to thank the following researchers for providing the fruit fly specimens from Pacific countries:\u003c/p\u003e\n\u003cp\u003ePiriaki Maaoo from the Ministry of Agriculture\u0026ndash;Cook Islands; Siutoni Tupou from Ministry of Agriculture, Food and Forests\u0026ndash;Tonga; Jainesh Ram from Biosecurity Authority Fiji; Faalelei Tunupopo from the Ministry of Agriculture and Fisheries\u0026ndash;Samoa; Laura Hartamann from the Bios\u0026eacute;curit\u0026eacute; de la Polyn\u0026eacute;sie Fran\u0026ccedil;aise and Jeffline Tasale from Biosecurity Vanuatu.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTurbelin AJ, Cuthbert RN, Essl F, Haubrock PJ, Ricciardi A, Courchamp F. Biological invasions are as costly as natural hazards. Perspect Ecol Conserv. 2023;21:143\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePapadopoulos NT, Meyer MD, Terblanche JS, Kriticos DJ. 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In: Biosearch Technologies. 2007.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gics","sideBox":"Learn more about [BMC Genomics](http://bmcgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gics","title":"BMC Genomics","twitterHandle":"#BMCGenomics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Molecular identification, Species-specific real-time PCR, Intergenic spacer, DNA barcoding, Biosecurity","lastPublishedDoi":"10.21203/rs.3.rs-6459370/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6459370/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eTrue fruit flies (Diptera: Tephritidae) are among the most destructive pests of fruit and vegetables worldwide and are on the top of quarantine pest lists. To respond effectively to a fruit fly invasion, we need to identify the species rapidly and reliably to understand its biological features and guide response decisions. Molecular techniques have been used to improve the diagnostic ability circumventing many difficulties of morphological identification. However, the commonly used Cytochrome Oxidase I (\u003cem\u003eCOI\u003c/em\u003e) gene lacks sufficient variation to distinguish species within \u003cem\u003eBactrocera\u003c/em\u003e species complexes. Here we conducted mitochondrial genome sequencing to identify additional genetic markers that could aid diagnosis of \u003cem\u003eBactrocera\u003c/em\u003e fruit fly species.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe assembled 82 complete mitochondrial genomes from 16 \u003cem\u003eBactrocera\u003c/em\u003e species, including 13 species for which no mitochondrial genome data were previously available, as well as one specie each from \u003cem\u003eDacus aneuvittatus, Dirioxa pornia\u003c/em\u003e and \u003cem\u003eZeugodacus gracilis\u003c/em\u003e. Phylogenetic analysis of the Tephritidae family confirmed the monophyly of the \u003cem\u003eBactrocera\u003c/em\u003e genus but could not properly resolve species within species complexes. Comparative mitochondrial genome analysis revealed that intergenic spacer and NADH dehydrogenase genes, specifically \u003cem\u003eND2\u003c/em\u003e and \u003cem\u003eND6\u003c/em\u003e, harbour enough variations for new specific real-time PCR assays. Based on these findings, six TaqMan-based real-time PCR assays targeting \u003cem\u003eND2, COI\u003c/em\u003e, and \u003cem\u003eCO3\u003c/em\u003e genes were successfully designed and assessed for their specificity and sensitivity in detecting \u003cem\u003eBactrocera curvipennis\u003c/em\u003e, a member of the \u003cem\u003eB. tryoni\u003c/em\u003e complex. Of these, one real-time PCR assay targeting the ND2 gene proved to be the most specific and sensitive. It detects \u003cem\u003eB. curvipennis\u003c/em\u003e specifically at the level of 1 copy/\u0026micro;L of target DNA.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eMitochondrial sequence analysis and comparative studies indicate that mitochondrial genomes offer valuable genetic markers for accurate diagnosis of \u003cem\u003eBactrocera\u003c/em\u003e fruit flies. The successful development of the \u003cem\u003eB. curvipennis\u003c/em\u003e real-time PCR assay highlights the importance of having additional genetic markers to advance the molecular diagnostics in economically important \u003cem\u003eBactrocera\u003c/em\u003e species.\u003c/p\u003e","manuscriptTitle":"Mitochondrial genome of Bactrocera fruit flies (Tephritidae: Dacini): Features, Structure, and significance for Diagnosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-16 10:28:30","doi":"10.21203/rs.3.rs-6459370/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-17T07:29:03+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-16T06:07:05+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-04T13:51:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"195043207501202136571141929860575426847","date":"2025-05-18T22:02:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"280604543789018857538603747656215590464","date":"2025-05-15T16:13:46+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-13T17:30:47+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-09T21:29:25+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-07T10:22:01+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Genomics","date":"2025-05-07T10:20:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gics","sideBox":"Learn more about [BMC Genomics](http://bmcgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gics","title":"BMC Genomics","twitterHandle":"#BMCGenomics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f2d7f211-c129-4790-9add-a99860888ff8","owner":[],"postedDate":"May 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-08-04T16:48:04+00:00","versionOfRecord":{"articleIdentity":"rs-6459370","link":"https://doi.org/10.1186/s12864-025-11872-8","journal":{"identity":"bmc-genomics","isVorOnly":false,"title":"BMC Genomics"},"publishedOn":"2025-07-29 16:29:27","publishedOnDateReadable":"July 29th, 2025"},"versionCreatedAt":"2025-05-16 10:28:30","video":"","vorDoi":"10.1186/s12864-025-11872-8","vorDoiUrl":"https://doi.org/10.1186/s12864-025-11872-8","workflowStages":[]},"version":"v1","identity":"rs-6459370","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6459370","identity":"rs-6459370","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}