All genomic DNA extraction experiments were conducted in molecular identification experimental unit, in the Dengue Mosquito Experimental Station (DMES), belonging to the Department of Biological Sciences, Faculty of Sciences, King Abdul-Aziz University, Jeddah, Saudi Arabia. As (30) explained, DNA can be extracted from preserved mosquito specimens by immersing the entire body of each specimen in absolute ethanol. The insect lysis buffer involved is prepared using the following chemical components: 16.5 grams of GuSCN (Sigma), 12ml of 0.5 M EDTA pH 8.0, 6ml of 1 M Tris-HCL pH 8.0, 1ml of Triton X-100 (Sigma), and 10ml Tween-20 (Fluka). In order to conduct the experiment in the present study, mosquito samples were placed in Eppendorf tubes, and a combination of proteinase K and insect lysis buffer was added to each tube. After vortexing and incubating the samples at 56 °C for 10 minutes, the samples were centrifuged before the supernatant was transferred to a tube for further analysis. The next step was to gently mix the supernatant with 100 litres of ethanol, and then to add the ethanol to the supernatant. Thereafter, the mixture was placed onto a silica gel-based spin column for further separation. In order to remove any possible remaining liquid, we spun the solution at 12,000 RPM for two minutes at the highest speed. The liquid was removed from the collection tube by hand.

In the same experiment, 500 litres of 70% ethanol were used in the process, and the results were the same. Afterwards, the samples were centrifuged again at 12,000 RPM on the spin column for two more minutes before they were removed from the centrifuge for further processing. It was then rewashed with ethanol to remove any remnants of the liquid that remained. To ensure that the spin column had dried completely when the washing was finished, we centrifuged it for three minutes at 13,000 RPM. In order to move the upper part of the spin column to the Eppendorf tube, the upper part of the column was removed. During the release of the DNA that was trapped within the silica gel, it was first necessary to add 20 litres of nuclease water to the centre of the gel and to leave it for about 10 minutes, so that the DNA could be released. A one-minute spin at 12,000 RPM was then applied to the sample, and it was then dried. This was repeated after another 50 litres of nuclease water was added to the solution. The DNA was then eluted from the silica gel and transferred to the Eppendorf tube, after the gel had been turned (30). Using Thermo Fisher Scientific’s NanoDrop 7000 instrument from Waltham, MA, USA, we assessed the quantity and quality of the DNA samples using the A260/Ratio, which was calculated using the NanoDrop 7000. In addition, an electrophoresis of DNA in 1% gel of agarose was conducted to assure the integrity of the DNA. Following the extraction and preservation of the DNA at a temperature of 20 °C for a short period of time, it was then stored at a temperature of -20 °C for a period, after which it could be used for experiments following preservation.

A microwaveable flask was filled with 70mL 1xTBE buffer, into which was dissolved 0.5% of agarose. The agarose was boiled for 1–2 minutes by swirling the flask occasionally until the agarose was completely dissolved. In order to accomplish this process, ethidium bromide (EtBr) was added to the agarose solution at a final concentration of approximately 0.2-5g/mL after the solvent had warmed up to around 50 °C for about 2–3 minutes. As the gel melted, it was poured into the tray of mini-gel, with the well comb positioned slowly in the middle, in order to avoid causing bubbles in the gel. After 20–30 minutes, once the gel had completely solidified, the comb was removed carefully, and the gel was prepared for use. In addition to covering the gel in the gel box, 1xTBE buffer was also used to load the DNA into each well. One Kbp DNA ladder was also added to each well.

The mitochondrial cytochrome oxidase subunit I (COI) gene was utilized to identify polymorphism among the different populations used in this study. Gene amplification was preformed using a PCR with the universal barcoding primer set, LCO1490 (5’-GGTCAACAAATCATAAAGATATTGG-3’) and HCO2198 (5’-TAAACTTCAGGGTGACCAAAAAATC-3’) (31), generating amplicons ranging between 600–700 base pairs. Each 25 μl reaction contained 15.3 μl of 1× bovine serum albumin (BSA), 2.5 μl of 10× Reaction Buffer, 2 μl of dNTPs (2.5 μM), 1.25μl of each primer (10 μM/L), 0.2 μl Taq DNA Polymerase (1.0 U), and 2.5 μl of template DNA (>50 ng). The PCR conditions consisted of an initial denaturation at 94 °C for two minutes, followed by 40 cycles of denaturation at 94 °C for 30 seconds, annealing at 49 °C for 45 seconds and extension at 72 °C for 45 seconds, with a final extraction step at 72 °C for one minute. The PCR products were verified by electrophoresis on 1.5% agarose gel and the gel was then analysed under UV light. The amplified bands were subsequently purified from the gel and submitted for sequencing to Macrogen Company, Korea, using an applied Biosystems 3730Xl DNA Analyzer.

For sequencing, the samples were sent to a Macrogen company in Seoul, South Korea. A QIA quick PCR purification kit (Promega, Madison, WI, USA) was used to purify the amplified DNA for sequencing, and a Big Dye terminator cycle sequencing Ready Reaction kit (Applied Biosystems, Forster City, CA, USA) was used to perform the sequencing on an ABI Prism^® 310 Genetic Analyzer (Applied Biosystems) to measure the sequence of the amplified DNA.

In order to compare the nucleotide information that was extracted using the sequences discussed above within the Gene Bank database, we used the Basic Local Alignment Search Tool (BLAST) (https://blast.ncbi.nlm.nih.gov/Blast.cgi), which can be found at http://blast.ncbi.nlm.nih.gov/Blast.cgi. A nucleotide sequence alignment was conducted by utilizing the Clustal W tool in MEGA to align the raw nucleotide sequences. In order to construct the phylogenetic tree of each locus, a multiple sequence alignment was conducted using MEGA11 software18, and the sequences were then matched with those in the GenBank database of NCBI (http://www.ncbi.nlm.nih.gov), and the reference sequences were obtained to perform a phylogenetic analysis on the sequences retrieved. A phylogenetic tree of the populations was constructed based on the dissimilarity values computed using the Maximum-Likelihood method with the Tamura-Nei Model2. The distance matrix determined via this approach was utilized to infer the evolutionary relationships across the populations. In addition to the bootstrap analysis, a maximum of 1,000 replicates of the machine learning analysis was also conducted.

The DNA samples employed for this study were collected from two different regions, Jeddah and Jazan, and consisted of 29 samples from each region, with a total of 58 samples for both regions. By using the same protocol and procedure as that employed by (30), namely lysis buffers, DNA was extracted from the insects and analysed on a 1% agarose gel ( Figure 1 ). As a preliminary step towards identification of the gene encoding, Cytochrome c oxidase subunit, I (COX1), genomic DNA was extracted from 58 randomly selected mosquito specimens taken from the two geographical regions in question. As a result of the PCR amplification using universal primers targeting the mTCOX-1 gene, 600–700 bp fragments were generated, which were then subjected to 1-5% agarose gel electrophoresis and then analysed ( Figures 1 , 2 ).

Phylogenetic trees ( Tables 1 , 2 ) show the convergence and divergence of the samples ( Figure 2 ). Correlations were made between the samples and the gene bank samples ( Table 1 ). In the results obtained from the Jeddah region, the Ae. aegypti types MN299016.1 and KU495081.1 demonstrated a complete match (100%) with the respective populations found in Argentina and Australia, with 93.1% (27/29) and 6.9% (2/29), respectively ( Table 1 ; Figure 3 ). Meanwhile, in the samples from the Jazan region, the samples were 100% and 99.6% like the Ae. aegypti types MN298998.1, MK300226.1, PP892777.1, and MF043259.1 found in Canada, Kenya, India, and England ( Table 1 ; Figure 3 ), with proportions of 58.6% (17/29), 20.7% (6/29), 13.8% (4/29), and 6.9% (2/29), respectively. As shown in Figure 3 , the evolutionary distances between the taxa examined were compared with each other, and with the sequences related to the NCBI, in order to determine where the samples evolved from each other. A phylogenetic tree created with MEGA11 represented the convergence and divergence among the samples analysed. The phylogenetic analysis of the mTCOX-1 gene revealed that the species from Jeddah and Jazan did not match each other; instead, they formed two different clusters, according to the results of the analysis, which showed that the Ae. aegypti species formed two major groups.

This study found that samples from Jazan demonstrated genetic similarity with those from Canada, Kenya, England, and India, which constituted the first cluster of samples. As a result of a further subdivision within this cluster, it was observed that there were two distinct sections, the first of which contained only the samples from Canada and Kenya, and the second contained two elements. In the first clade, the samples that matched those found in England were exclusively located, while the second clade contained samples that matched those found in India. As a result of the differentiation between the two clades, subclades were identified. The samples collected from the city of Jeddah formed part of the second cluster, as they demonstrated a genetic similarity with those from Argentina and Australia. In addition to the subdivision within this cluster, two distinct sections were identified within the cluster. Those sampled in the first section could only be compared with those from Argentina, whereas those from the second section were comparable with those from Australia. Several genetic mutations were observed in the study samples, which could be considered the cause of the differences observed between them.

This study aimed to molecularly identify Ae. aegypti mosquitoes from two distinct regions in Saudi Arabia: Jeddah and Jazan. By utilizing the mt COX1 gene as a marker, phylogenetic and sequence analyses revealed significant genetic variability between the mosquito populations from these regions. The results contributed valuable insights into the genetic structure of Ae. aegypti, providing data that may influence vector control programmes, given the importance of genetic diversity in disease transmission and control strategies.

The molecular identification of mosquito samples using the COX1 gene marker conducted by this study proved effective for distinguishing Ae. aegypti populations in Jeddah and Jazan. In both regions, all of the samples were identified as Ae. aegypti, confirming the species’ presence in these areas, as expected, due to its widespread distribution in tropical and subtropical regions (32, 33). The genetic variation observed between the two regions underscored the species’ adaptability to different environmental conditions.

The DNA extraction and PCR amplification conducted yielded fragments of 600–700 bp, corresponding to the COX1 gene, which was sequenced successfully. The sequences obtained were compared with those available in the NCBI Gene Bank, confirming the samples’ species as Ae. aegypti ( Tables 1 , 2 ). These findings corroborated previous reports regarding the utility of the COX1 gene as a molecular marker for species-level identification in mosquitoes (30).

A phylogenetic analysis using the Maximum Likelihood (ML) method was conducted, which revealed that there were two distinct genetic clusters; these corresponded to the mosquito populations from Jeddah and Jazan ( Figure 3 ). This genetic divergence could be attributed to geographic isolation or to differences in selective pressures between the two regions, such as environmental conditions and human activity. The samples from Jeddah showed a complete match (100%) with Ae. aegypti strains from Argentina and Australia (MN299016.1 and KU495081.1, respectively). These samples formed a separate cluster in the phylogenetic tree, suggesting that the Jeddah population shared a closer genetic relationship with the Ae. aegypti populations from these regions. This may indicate a common origin or recent introduction, potentially due to human-mediated movement, such as international trade or travel (32). In contrast, the samples from Jazan displayed a more complex pattern of genetic similarity, matching Ae. aegypti strains from Canada, Kenya, India, and England (MN298998.1, MK300226.1, PP892777.1, and MF043259.1, respectively). This population formed two distinct sub-clusters, suggesting greater genetic diversity within the Jazan population. This may be the result of genetic introgression from multiple sources, or historical population expansion and contraction in response to environmental changes (34).

The genetic divergence observed between the Jeddah and Jazan populations has important implications for mosquito control programmes. Genetic variation within mosquito populations can affect their susceptibility to insecticides and their ability to transmit diseases, such as dengue, Zika, and chikungunya. For example, populations with distinct genetic backgrounds may respond differently to chemical interventions, requiring tailored approaches for effective control (33). The divergence between the Jazan and Jeddah clusters suggested that control strategies may be required to be region-specific.

Although the present study did not analyse specific insecticide resistance genes, previous studies have documented resistance associated kdr mutations in Ae. aegypti populations from Jeddah and other regions of Saudi Arabia (35–37). These mutations, especially those in the voltage-gated sodium channel (Vssc) gene, have been strongly linked to pyrethroid resistance. A broader regional context displays the emergence of “super-resistance” due to combined kdr mutations in parts of Asia, posing serious challenges for vector control (38). These findings underscore the importance of incorporating insecticide resistance markers into future genetic analyses. This will help align population dynamic with resistance patterns, enhancing the accuracy and impact of region-specific vector control efforts.

For instance, the Jazan population’s genetic similarity to Ae. aegypti from geographically distant regions, such as Canada and Kenya may reflect a broader genetic pool that could influence vector competence or insecticide resistance. This highlighted the need for ongoing surveillance and molecular monitoring to track the genetic changes in mosquito populations and the need to adjust control strategies accordingly (39).

The differences in genetic clustering between the Jeddah and Jazan populations may also reflect the geographic and environmental conditions of these regions. Jeddah, which is a coastal city, experiences higher humidity and temperature levels than Jazan, which may influence selective pressures on mosquito populations. Environmental factors, such as temperature, humidity, and vegetation, have been shown to affect mosquito breeding, survival, and behaviour, which in turn can shape genetic diversity (40). Furthermore, the human population density and urbanization in Jeddah may play a role in the genetic similarity of its mosquito population to those in Argentina and Australia, as urbanization tends to homogenize genetic diversity, due to the increased movement of people and goods. In contrast, the more rural setting of Jazan, with its agricultural activities, may provide a more heterogeneous environment, contributing to the greater genetic diversity observed in the mosquito population in this region.