Genomic analyses revealed low genetic variation in the intron-exon boundary of the doublesex gene within the natural populations of An. gambiae s.l. in Burkina Faso

Genomic analyses revealed low genetic variation in the intron-exon boundary of the doublesex gene within the natural populations of An. gambiae s.l. in Burkina Faso | 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 Genomic analyses revealed low genetic variation in the intron-exon boundary of the doublesex gene within the natural populations of An. gambiae s.l. in Burkina Faso Mahamadi Kientega, Ioanna Morianou, Nouhoun Traoré, Nace Kranjc, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4996167/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Dec, 2024 Read the published version in BMC Genomics → Version 1 posted 14 You are reading this latest preprint version Abstract Background The recent success of a population control gene drive targeting the doublesex gene in Anopheles gambiae paved the way for development of self-sustaining and self-limiting genetic control strategies targeting the sex determination pathway to reduce and/or distort the reproductive capacity of insect vectors. However, targeting these genes for genetic control purposes requires a better understanding of their genetic variation in natural populations to ensure effective gene drive spread. Using whole genome sequencing data from the Ag1000G project (Ag3.0, 3.4 and 3.8), and Illumina pooled amplicon sequencing, we investigated the genetic polymorphism of the intron-4–exon-5 boundary of the doublesex gene in the natural populations of An. gambiae s.l. Results The analyses showed a very low variant density at the gRNA target sequence of the Ag(QFS)1 gene drive (previously called dsxF CRISPRh ) within the populations of West and East Africa. However, populations from the forest area in Central Africa exhibited four SNP at frequencies ranging from 0.011 to 0.26. The SNP (2R:48714641[C > T]) at high frequencies, i.e. 0.26 is identified within the An. coluzzii population form Angola. The analyses also identified 90 low frequency (1% − 5%) SNPs in the genomic region around the gRNA target sequence (intron-4–exon-5 boundary). Three of these SNPs (2R:48714472 A > T; 2R:48714486 C > A; 2R:48714516 C > T) were observed at frequencies higher than 5% in the UTR region of the doublesex gene. The results also showed a very low variant density and constant nucleotide diversity over a five-year survey in natural An. gambiae s.l. populations of Burkina Faso. Conclusion These findings will guide the implementation of doublesex -targeted gene drives to support the current control tools in malaria elimination efforts. Our methods can be applied to efficiently monitor the evolution of any sequence of interest in a natural population via pooled amplicon sequencing, surpassing the need of WGS. gene drive doublesex gene genetic polymorphism An. gambiae s.l Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Background Malaria is the deadliest vector-borne disease in the world. The disease is caused by Plasmodium parasites, and transmitted to humans by female Anopheles mosquitoes. According to the WHO, deaths attributed to malaria have increased from ~ 400,000 (2018) to ~ 600,000 (2022) in the past 5 years [ 1 ]. This has been largely attributed to the spread of insecticide resistance across sub-Saharan Africa, where the disease is endemic. In response to these challenges, innovative strategies based upon genetic control are being developed, to strengthen current tools and accelerate malaria elimination [ 2 ]. Homing-based gene drives are engineered selfish genetic elements that propagate their own inheritance by use of a homing endonuclease [ 3 ]. Gene drives can be used for vector control, by being engineered to spread desirable genetic traits in the population, such as parasite refractoriness (population replacement), or by spreading negative fitness traits, such as infertility, amongst vector populations (population suppression). The CRISPR/Cas9 gene editing system has been adapted to act as an RNA-guided homing endonuclease for use in gene drive strategies, by inserting the Cas9 and gRNA cassettes within their own recognition sequence [ 4 ] (Fig. S1 ). CRISPR-based gene drives have shown great promise as tools to combat malaria, both as population replacement [ 5 ], and population suppression genetic control strategies [ 6 ]. Most population replacement strategies are focused on introducing genetic modifications or novel transgenes in the population, to make the new population pathogen-refractory [ 7 , 8 ]. Previous studies have shown that FREP1 mediates Plasmodium invasion of the Anopheles midgut epithelium [ 9 ]. CRISPR knock-outs of the FREP1 gene showed resistance to both human and rodent malaria parasites [ 10 ]. Similarly, expression of small antimicrobial peptides in the mosquito midgut delayed sporogonic development of the malaria parasite, and could be propagated in a single generation experiment through gene drive homing [ 5 ]. Conversely, population suppression strategies aim to alter key parameters of population growth, such as sex ratio and fitness. The sex-chromosomes of An. gambiae were targeted by a synthetic sex-distorter I-PpoI, which is not a gene drive, and designed to induce a strong negative bias toward X chromosome–carrying spermatozoa resulting in 95% male offspring [ 11 ]. Similar rates were also achieved using a synthetic CRISPR/Cas9-based sex distorter [ 12 ]. Recent studies have shown additional benefit in targeting the sex determination pathway to disrupt the sex-ratio in the offspring for vector control purposes [ 13 , 14 ]. In most insect species, the sex determination pathway starts by a primary central gene that stimulates the molecular cascade leading to the alternative splicing of the Doublesex ( dsx ) and Fruitless ( fru ) genes [ 15 ], making these two genes the endpoint of the sex determination mechanisms [ 16 ]. The architecture and function of the dsx gene have been well characterized in various insects [ 17 , 18 ], including Anopheles mosquitoes [ 19 ]. In An. gambiae , the dsx gene spans an 88.598 kb (2R:48703664–48792262) sequence on chromosome 2R and consists of 7 exons, with exon 5 being female-specific and exon 6 male-specific. Alternative splicing of the dsx gene during embryonic development produces two isoforms based on the sex-specific exons, which control the expression of endpoint genes required for exhibition of physical sexual dimorphism [ 20 ]. Using CRISPR/Cas9, the disruption of the female-specific intron-4-exon-5 boundary of the dsx gene in An. gambiae resulted in morphological abnormalities in homozygous knock-out females, including in the development of the proboscis, which caused knockout females to be unable to draw a blood-meal, mate, and produce offspring [ 6 ]. A CRISPR-based gene drive built against the female isoform of dsx (Fig. S1 B), termed Ag(QFS)1 (previously called dsxF CRISPRh ) was able to rapidly spread in caged laboratory populations, causing a swift reduction of vector population density, and ultimately eliminating the populations within a year [ 6 , 14 , 21 ]. This success made the dsx gene a relevant target for the development of genetic control strategies to reduce population density of malaria mosquitoes as well as other harmful insects, including agricultural pests [ 22 ]. Genetic variation is an essential component of evolution, allowing natural populations to face ecological challenges. The presence of genetic polymorphisms in a gene drive target sequence could inhibit gRNA recognition and subsequent Cas9 cleavage, limiting the spread of the homing and consequently reducing the efficacy of the genetic tool [ 23 – 25 ]. Therefore, the success of most CRISPR/Cas9-based genetic control tools, requires the target sequence to show limited genetic variation in natural populations. In this study, we investigate the genetic variation within the intron-4-exon-5 boundary (2R: 48714420–48714720) of the Doublesex gene within wild An. gambiae s.l. by analysing existing population genomics data generated by the Anopheles gambiae 1000 genomes project (Ag1000G), as well as newly generated data through targeted pooled amplicon sequencing of wild-caught mosquito populations from Burkina Faso. We also investigate the evolution and spatial distribution of the genetic polymorphisms discovered. We reveal limited variation of the female-specific intron-exon boundary of the doublesex gene in natural populations. These results are valuable in guiding the implementation of gene drive tools to supplement malaria elimination efforts in Africa. Results Spatial distribution of genetic variants in the Ag(QFS)1 gene drive target sequence We first investigated the distribution of variants across a sequence spanning the intron-4–exon-5 boundary of the doublesex gene (2R: 48714420–48714720), including the Ag(QFS)1 gene drive target sequence (2R: 48714637–48714660) using an existing genomics dataset generated by the Anopheles gambiae 1000 genomes Consortium (Ag1000G). This dataset includes SNP calls from 4200 wild-caught An. gambiae s.l. ( An. coluzzii, An. gambiae s.s. and An. arabiensis ) mosquitoes from 19 African countries (Fig. 1 ). We identified 143 single nucleotide polymorphisms (SNPs) at varying frequencies from 0.000609 to 0.59 in the intron-4–exon-5 boundary of the doublesex gene within vector populations collected in 17 African countries (Table S1 ). The SNP 2R:48714486[C > A], located in the UTR sequence of the exon 5, is found at relatively high frequencies (freq. ~ 0.12–0.59) in all An. coluzzii populations. The third allele of 2R:48714486 ([C > G]) is only identified in An. arabiensis populations (Freq. ~ 0.006–0.15). The target sequence of the Ag(QFS)1 gene drive is located in the boundary of the intron 4 and the exon 5 spanning 23 bp from the nucleotide in the 2R chromosome. This sequence contained five SNPs at relatively low frequencies (freq. ~ 0.00–0.26) in the vector populations. Most of these SNPs were identified at very low frequencies (Freq. T], was found at frequencies higher than 0.05 (0.011–0.26) in the An. gambiae populations of central Africa. Specifically, the 2R: 48714641[C > T] SNP was found at frequencies of 0.26 in Angola, 0.07 in the DRC, 0.06 in Cameroon, and 0.01 in Gabon. This was previously reported as a G > A SNP, as read on the antisense DNA strand [ 6 , 26 ]. This position was found to be triallelic (1 reference and 2 alternative alleles) and its third allele, 2R: 48714641[C > A] was found in An. arabiensis populations from Burkina Faso at frequency lower than 0.01 (Fig. 2 ). The presence of this SNP (2R: 48714641[C > T]) at frequencies higher than 5% in gRNA target sequence within the Central African populations raised concern about the spread of the Ag(QFS)1 gene drive in these populations. However, it was found to be cleavable by the Cas9/gRNA ribonucleoprotein in vitro [ 6 ], and by the Ag(QFS)1 gene drive in vivo [ 26 ], so the gene drive should be able to spread in its presence. No SNPs were identified in the other An. gambiae populations of West and East Africa. Time series variation of the gene drive target sequence in Burkina Faso Using the Ag1000G dataset we also investigated the year-to-year genetic variation in the intron-4–exon-5 boundary of the doublesex gene within wild An. gambiae s.l. populations collected from 2012 to 2019 in three villages (Bana, Souroukoudinga and Pala) of Burkina Faso. The analyses identified 90 SNPs at frequencies ranging from 0.001 to 0.54 in the intron-4–exon-5 boundary of the doublesex gene over the seven-year survey (mostly in the untranslated region, UTR, of doublesex ) (Table S2 ). The 2R:48714486[C > A] SNP, present in the dsx exon 5 UTR, was the most abundant SNP identified, present at frequencies of 0.39–0.54. All other SNPs were found at frequencies lower than 0.01. Most of the SNPs (except 2R:48714445[C > A,T], 2R:48714453[G > A], 2R:48714692[T > A] in An. gambiae s.s. and 2R:48714486[C > A] in An. coluzzii ) were non-constant over the years, i.e. they appeared and disappeared the following year, presumably removed by purifying selection or drift. The Ag(QFS)1 gene drive target sequence (2R:48714637–2R:48714660) displayed four non-constant SNPs at very low frequencies (~ 0.0057–0.05) from 2012 to 2019 in the vector populations. These SNPs were non-constant over the seven-year survey and each of them was removed in the following year after its apparition in the populations (Fig. 3 ). The nucleotide diversity ( θ π ) ranged from 0 to 0.04 in the female-specific intron-exon boundary of the doublesex gene. Interestingly, the nucleotide diversity was shown to be constant over the years (from 2012 to 2017). The highest nucleotide diversity ( θ π ~ 0.04 ) was recorded in the UTR region of the female-specific intron-exon boundary of the doublesex gene. In the target sequence of the Ag(QFS)1 gene drive, the nucleotide diversity was close to 0 in 2012 and remained so until 2017 (Fig. 4 ). The analyses showed a constant nucleotide diversity in the gRNA target sequence in the An. gambiae population of the sampling area. One of the challenges of genetic control remains the potential emergence of new genetic variants at the target sequence, over the time, through evolutionary processes. Our results showed no change in the genetic variation of the gRNA target sequence within the natural populations of the three villages (Bana, Pala and Souroukoudinga) over the seven-year survey, which is promising for the long term efficacy and spread of a gene drive strategy targeting this sequence. Pooled amplicon sequencing to detect genetic variants in the Ag(QFS)1 gene drive target sequence Application of cost-effective and efficient methods are essential for monitoring the evolution of the Ag(QFS)1 gene drive target sequence in the natural population. Here, we employed pooled amplicon sequencing to investigate the genetic polymorphism of the Ag(QFS)1 gene drive target sequence in natural populations from Burkina Faso. We genotyped more than 600 individual mosquitoes belonging to three species of the An. gambiae species complex ( An. gambiae s.s., An coluzzii and An. arabiensis ) sampled in Bana, Pala, Souroukoudinga, Soni, Moara-Grand, Toma-Ile, Toson, Saran and from laboratory colony (Fig. 5 ). An. coluzzii was the most predominant species in almost all the villages except Pala, where An. gambiae s.s. and An. arabiensis were most prevalent. This allowed the formation of 12 pools (9 pools of An. coluzzii , 2 pools of An. gambiae s.s. and 1 pool of An. arabiensis ) of 50 individuals for gDNA extraction and pooled amplicon sequencing of a 365 bp region encompassing the intron4-exon5 boundary of doublesex , as well as the whole exon 5 CDS, including the Ag(QFS)1 target site. The genomic data analyses identified 60 variants, compared to 67 variants detected in the Ag1000G data in the same 365 bp region. Of the 60 SNPs, 17 positions were found to be triallelic. Importantly, no indels were identified in the dataset. Most of the variants (n = 49 SNPs) were identified in the An. coluzzii populations followed by An. gambiae s.s. (n = 34 SNPs) and An. arabiensis (n = 16 SNPs), which showed the lowest degree of genetic diversity in this region. The SNP density was approximately 0.2 bp − 1 [60/300], indicating the presence of 1 SNP per 5 bp. Most of these SNPs were identified in the UTR of exon 5 and intron 4 which are expected to show higher variation as they constitute non-coding regions. The exon 5 CDS showed a varying number of SNPs between the populations from 1 SNP (0.011 bp − 1 [1/91]) in the laboratory colony to 7 SNPs (0.08 bp − 1 [7/91]) in the An. gambiae s.s. samples of Pala. Only two of the SNPs (2R:48714617(C > T) and 2R:48714592(C > T)) identified in the exon 5 CDS were constant and present in all wild-caught populations. Almost all SNPs were identified at low frequencies (Freq A) identified in the UTR sequence of the exon 5 at relatively high frequencies (freq = 0.31–0.45) in the An. coluzzii populations, whilst in An. arabiensis and An. gambiae s.s. it was present at low frequencies (freq. ~ 0.01). A third allele of this position, the SNP 2R:48714486(C > G) was identified at low frequencies (freq. = 0.024) in An. arabiensis populations. In the laboratory samples, this SNP is found at low frequencies and seems to be replaced by the SNP 2R:48714472 (A > T), which is also identified in the UTR sequence of the exon 5 at a frequency of 0.17. Figure 6 shows the allele frequencies of the SNPs whose maximum frequencies were higher than 0.01 in at least one population. The Ag(QFS)1 gene drive target sequence exhibited one SNP at low frequencies, i.e. less than 0.05. The analyses showed most of the SNPs in the UTR sequence of the exon 5 of the dsx gene, especially those with relatively high frequencies (Fig. 7 ). Interestingly, the distribution of the SNPs along the target sequence of the dsx gene and their allele frequencies were similar to those found in the time series Ag1000G data in Burkina Faso. Discussion Malaria control remains a major challenge in Sub-Saharan Africa. Ongoing studies in genetic engineering and biocontrol through endosymbionts intend to develop effective and sustainable control tools to reduce malaria transmission [ 27 , 28 ]. By targeting the sexual determination pathway [ 6 ], CRISPR-based gene drive technologies demonstrated their potential advantages to be the basis of the development of effective and sustainable tools to support malaria elimination efforts [ 7 ]. However, the success of a CRISPR-based gene drive for vector control requires its target sequence to show as little polymorphism as possible in natural populations, to ensure efficient homing rates. Another possible prerequisite is the target sequence to be highly conserved over the years to ensure the continued matching of the gRNA to the target sequence in natural populations several years after the release [ 29 ]. This study investigated the genetic polymorphism of the intron-4-exon-5 boundary of the doublesex gene within the natural populations of An. gambiae s.l. These insights may contribute for the future deployment of gene drive control strategies targeting the doublesex gene [ 6 , 14 ]. The results showed a variable genetic polymorphism among the vector populations with a global variant density of one SNP every 5 bp. This density was shown to be low compared to what was previously found in the whole dsx gene and in the An. gambiae genome that was about one SNP every two nucleotides [ 30 , 31 ]. Interestingly, most of the variants were identified in the untranslated region of exon 5 of the gene. The analysis displayed a very low amount of polymorphism in the Ag(QFS)1 target sequence (2R: 48714637–48714660). Analyses of time series data in Burkina showed four non-constant SNPs over a seven-year survey in the Ag(QFS)1 target sequence. Each of these SNPs appeared in different years and were removed in the following year after their occurrence in the same population, presumably removed by purifying selection or drift. This pattern of genetic variation in the target sequence is expected to be sufficient for the rapid spread of the Ag(QFS)1 gene drive in natural populations. However, given that natural populations are dynamic, the genetic variation might be different in space over time. This situation is observed in the forest area of Central Africa where An. gambiae s.l. populations exhibited a SNP 2R:48714641[C > T] at frequencies reaching 0.26 in the gRNA target sequence in Angola, Democratic Republic of Congo, Cameroon and Gabon but not in Central African Republic. The third allele of this position, the 2R:48714641[C > A] was found in An. arabianesis populations of Burkina Faso. The SNP 2R:48714641[C > T] was identified in previous studies at Hardy-Weinberg equilibrium in An. coluzzii and An. gambiae populations of Angola [ 6 , 26 ]. The emergence pattern of this SNP suggests its ubiquitous presence at evolving frequencies in the forest area of Central Africa and its presence could reduce the spread of the dsx-drive in vector populations. Recent studies showed a complex genetic structure of An. gambiae populations in Africa with important variants flow between populations from Western, Central and Eastern Africa [ 30 , 31 ]. The kdr mutations ( kdr-L995F and kdr-L995S ) constitute an example of continent-widespread genetic variants in the An. gambiae population [ 32 ]. Seeing the high connectivity of An. gambiae populations [ 30 , 31 ], the spread of the Cas9-based dsx-drive could be reduced if the SNP 2R:48714641[C > A] is positively selected and increased in frequency in the populations. Thus, the evolutionary processes driving this variant need to be investigated to understand the extent and the evolution of this SNP within the vector populations in Central Africa. The emergence of resistance alleles remains a great challenge in the development of Cas9-based vector control tools [ 25 , 33 ]. In addition to the existing variants, new alleles resulting from de novo mutations, could be selected and then threaten the efficacy of CRISPR-based gene drives in natural populations. Resistance variants may be introduced by gene drive activity, upon error-prone NHEJ repair of a Cas9-induced double-stranded break in the DNA. This can be exacerbated if the gene drive allows Cas9 deposition into the early embryo, where NHEJ occurs preferentially over HDR [ 25 , 34 , 35 ]. Several strategies are being explored to reduce the potential emergence of resistance variants in natural populations. The use of alternative promoters to contain Cas9 spatiotemporal expression in germline tissues was shown to reduce the rate of resistant allele creation [ 25 ]. Moreover, the use of multiplexed gRNAs targeting neighboring sequences simultaneously constitutes a promising strategy [ 26 , 36 , 37 ]. In this case, if a resistant allele gets created or is pre-existing in the population at one of the target sites, it will be removed as long as at least one other target site is still cleavable, and able to induce homing [ 26 ]. Resistant alleles would need to co-exist at all target sites to prevent gene drive homing, and they will need to cooperatively encode a functional gene product to prevent long-term gene drive spread. Nevertheless, this strategy remains complex in practice because of the difficulties in finding many gRNAs targeting neighboring sites and exhibiting good parameters in a single gene [ 38 ]. Another strategy to prevent the emergence of resistance could be the use of engineered endonucleases or alternative nucleases such as dCas9-FokI and Cpf1/Cas12a. These nucleases showed an ability to tolerate target sequence variation so that existing polymorphisms at the cut site or outside this site are less likely to prevent cleavage and homing [ 39 , 40 ]. Recently, a Cas12a split gene drive was developed in Drosophila melanogaster [ 41 ]. Combining a homing gene drive with an autosomal sex-distorter would also reduce the likelihood, for which a homing-resistant allele will be selected [ 14 ]. Ultimately, all alternative strategies that have been developed to mitigate resistance would only be successful if the gene drive is targeting a highly conserved sequence that shows minimal tolerance for polymorphisms. In the present study we set out a framework for in-depth investigation of the natural variation that is pre-existing in a natural population, to inform gene drive implementation. Conclusion In this study, we investigated the genetic polymorphism of the intron-4-exon-5 boundary of the doublesex gene, a potential target for the development of Cas9-based control tools. The results showed very low polymorphism at the target sequence of the Ag(QFS)1 gene drive in the anopheline populations of Burkina Faso. No SNP was found under positive selection in this region over a seven-year (from 2012 to 2019) survey in An. gambiae s.l. populations from Burkina. Interestingly, most of the vector populations in West and East Africa showed a very low variant density in the gRNA target sequence. Understanding the genetic variation of gene drive target sequences in natural populations is useful and valuable to guide the implementation of gene drive tools to support current control tools for malaria elimination. Material and methods Mosquito sampling Mosquito sampling was carried out in 8 villages (Bana, Pala, Souroukoudinga, Toma-Ile, Soni, Moara-Grand, Saran and Tosson) in the western part of Burkina Faso (Fig. 5 ) during 2019. The environmental conditions of these villages are almost similar with an annual rainfall ranging from 700 mm in the north to 1200 mm in the south between May and October. The average annual temperature sits at approximately 27°C. The sampling area is dominated by agricultural practices especially cereals and cotton cultivation creating suitable environmental niches for the proliferation of mosquito populations. Mosquito samples were collected using pyrethrum spraying catches (PSC) in the rainy season of 2019. Indoor resting mosquitoes were collected early in the morning in 20 randomly selected houses in each village. After collection, mosquitoes were morphologically identified using morphological keys [ 42 , 43 ] and stored in 80° alcohol for further analyses. Molecular analyses The An. gambiae s.l. samples were individually genotyped using the protocol described by Wilkins and collaborators [ 44 ] and grouped per species/location. After pooling, the genomic DNA was extracted from the pools using the DNAzol protocol and stored at -20°C. The extracted DNA was then used to perform PCR for the amplification of the intron 4 – exon 5 boundary (2R: 48714420–48714720) of the dsx gene. PCR was performed using KAPA HiFi Hotstart Ready Mix according to the manufacturer’s instructions and the following primers (illumina adapters underlined) previously designed by Hammond and collaborators [ 21 ]: Forward: 5’- ACACTCTTTCCCTACACGACGCTCTTCCGATCT ACTTATCGGCATCAGTTGCG-3’ Reverse: 5’- GACTGGAGTTCAGACGTGTGCTCTTCCGATCTGT GAATTCCGTCAGCCAGCA-3’ A small amount of the PCR product was migrated in an electrophoresis gel and visualized under UV to check the quality of the PCR. The remaining PCR products were purified using the Promega Wizard® SV Gel & PCR clean-up system according to the manufacturer’s instructions. The purity and the amount of DNA in each sample were checked in a Nanodrop. The purity threshold was set between 1.8 and 2 (OD260/280) and the DNA samples whose purity was outside this threshold were removed. The purified DNA samples were adjusted to a concentration of 20 ng/ml in a 25 µl of a nuclease-free water and then shipped for sequencing at Genewiz Amplicon-EZ sequencing. Data analyses Amplicon sequencing data Genomic data were received in FASTQ format from the sequencing company. Various tools including fastp, bwa, samtools, IGV and lofreq were used to handle the data for quality checking, trimming, mapping to the reference genome and variant calling. Quality checking and trimming were performed using fastp v0.23.2 [ 45 ]. During this step, the DNA sequences whose length is less than 50 bp and phred score less than 30 were removed from the data. After quality checking and trimming, the genomic data were mapped to the An. gambiae reference genome (AgamP4) [ 46 ] using bwa 0.7.17-r1188 [ 47 ] and samtools v1.16.6 [ 48 ] and stored in bam format. The bam files were indexed and visualized using IGV v17.0.3-internal [ 49 ] to check the quality of the mapping to the reference genome. The genetic variants were called from these bam files using the lofreq v2.1.6 program (Wilm et al. , 2012) and stored in VCF format [ 50 ]. The VCF files were handled in the Jupyter Notebook [ 51 ] to analyze the allele frequencies of the identified variants. The Anopheles gambiae 1000 Genomes Consortium data We also analyzed the spatiotemporal dynamics of the gene drive target sequence variants using the genomic data of the Ag1000G Consortium project phase 3.0, 3.4 and 3.8. These data were from wild caught An. gambiae ( An. coluzzii, An. gambiae s.s. and An. arabiensis ) mosquitoes in 19 African countries (Fig. 1 ). The dataset of Burkina Faso was from time series sampling of wild An. gambiae mosquitoes spanning from 2012 to 2019 collected in three villages (Bana, Pala and Souroukoudinga) (Fig. 5 ). Full details about the mosquito sampling, sequencing technology, the raw data clean-up and quality control, variant calling, storage of the data and the rights to access these data, have been described on the homepage of MalariaGEN [ 52 ]. Python packages such as Malariagen-data [ 53 ] and scikit-allel [ 54 ] were used to analyze the spatiotemporal distribution of the variants in the gene drive target sequence. We also analyzed the time series variation of the nucleotide diversity (i.e. the average nucleotide differences per site between two randomly selected individuals in the same population) in the gene drive target sequence within the An. gambiae populations from 2012 to 2017. Declarations Ethics approval and consent to participate All methods in this paper have been implemented in accordance with the relevant guidelines/regulations/legislation in Burkina Faso. No ethics approval was required to run all the activities related to this paper. Consent for publication Not applicable. Availability of data and materials The raw amplicon sequencing data generated and analyzed during the current study are available from the corresponding author upon reasonable request. Jupyter Notebooks and scripts to reproduce all the analyses, tables and figures are available in the GitHub repository: https://github.com/mkient/dsx_works.git. The SNP and haplotype data from the Ag1000G project are available on the MalariaGEN website and can be accessed using the malariagen_data package. The raw sequences in FASTQ format and the aligned sequences in BAM format were stored in the European Nucleotide Archive (ENA, Study Accession n° PRJEB42254). Competing interests All the authors have read and accepted this version of the manuscript. The authors also declare there are no conflicts of interest. Funding This work was supported by Target Malaria and the Institut de Recherche en Sciences de la Santé. Supported by a grant from the Bill & Melinda Gates Foundation and Open Philanthropy. Authors’ contributions M.K., N.K. and I.M. conceived the study. A.B. and A.D. provided the resources. A.M.G.B., A.B. and A.D. supervised the study. N.T., H.K., I.M. and M.K. designed methodology and carried out the molecular analyses. A.M., P.S.E., O.N.Z. and F.A.Y. carried out samples collection in the field. N.K. and M.K. carried out data analysis and visualization. I.M. and M.K. drafted the manuscript. All authors have read, edited, and approved the final version of the manuscript. Acknowledgements The authors would like to acknowledge the Target Malaria, the Wellcome Trust project and their funders for supporting this work. We also acknowledge the Ag1000G Consortium, the MalariaGEN team at the Wellcome Sanger Institute, and their partners in Africa for the production of the An. gambiae genomic data. The authors are grateful to the populations of the sampling sites for their sincere cooperation during the mosquito sampling. References WHO. World malaria report 2023. Geneva; 2023. McLean KJ, Jacobs-Lorena M. Genetic Control Of Malaria Mosquitoes. Trends Parasitol. 2016;32:174–6. Burt A. Site-specific selfish genes as tools for the control and genetic engineering of natural populations. Proc R Soc London Ser B Biol Sci. 2003;270:921–8. Hammond A, Galizi R, Kyrou K, Simoni A, Siniscalchi C, Katsanos D, et al. A CRISPR-Cas9 gene drive system targeting female reproduction in the malaria mosquito vector Anopheles gambiae. Nat Biotechnol. 2016;34:78–83. Hoermann A, Habtewold T, Selvaraj P, Del Corsano G, Capriotti P, Inghilterra MG, et al. Gene drive mosquitoes can aid malaria elimination by retarding Plasmodium sporogonic development. Sci Adv. 2022;8. Kyrou K, Hammond AM, Galizi R, Kranjc N, Burt A, Beaghton AK, et al. A CRISPR–Cas9 gene drive targeting doublesex causes complete population suppression in caged Anopheles gambiae mosquitoes. Nat Biotechnol. 2018;36:1062–6. Adelman ZN, Tu Z. Control of Mosquito-Borne Infectious Diseases: Sex and Gene Drive. Trends Parasitol. 2016;32:219–29. Hammond AM, Galizi R. Gene drives to fight malaria: current state and future directions. Pathog Glob Health. 2017;111:412–23. Zhang G, Niu G, Franca CM, Dong Y, Wang X, Butler NS, et al. Anopheles midgut FREP1 mediates plasmodium invasion. J Biol Chem. 2015;290:16490–501. Dong Y, Simões ML, Marois E, Dimopoulos G. CRISPR/Cas9 -mediated gene knockout of Anopheles gambiae FREP1 suppresses malaria parasite infection. PLOS Pathog. 2018;14:e1006898. Galizi R, Doyle LA, Menichelli M, Bernardini F, Deredec A, Burt A, et al. A synthetic sex ratio distortion system for the control of the human malaria mosquito. Nat Commun. 2014;5:3977. Galizi R, Hammond A, Kyrou K, Taxiarchi C, Bernardini F, O’Loughlin SM, et al. A CRISPR-Cas9 sex-ratio distortion system for genetic control. Sci Rep. 2016;6:31139. Akbari O, Li M, Kandul N, Sun R, Yang T, Benetta ED, et al. Targeting Sex Determination to Suppress Mosquito Populations. Res Sq. 2023;12. Simoni A, Hammond AM, Beaghton AK, Galizi R, Taxiarchi C, Kyrou K, et al. A male-biased sex-distorter gene drive for the human malaria vector Anopheles gambiae. Nat Biotechnol. 2020;38:1054–60. Kientega M, Kranjc N, Traoré N, Kaboré H, Soma DD, Morianou I, et al. Analysis of the Genetic Variation of the Fruitless Gene within the Anopheles gambiae (Diptera: Culicidae) Complex Populations in Africa. Insects. 2022;13:1048. Biedler JK, Tu Z. Sex Determination in Mosquitoes. In: Advances in Insect Physiology. 2016. p. 37–66. Oliveira DCSG, Werren JH, Verhulst EC, Giebel JD, Kamping A, Beukeboom LW, et al. Identification and characterization of the doublesex gene of Nasonia. Insect Mol Biol. 2009;18:315–24. Salvemini M, Mauro U, Lombardo F, Milano A, Zazzaro V, Arcà B, et al. Genomic organization and splicing evolution of the doublesex gene, a Drosophila regulator of sexual differentiation, in the dengue and yellow fever mosquito Aedes aegypti. BMC Evol Biol. 2011;11:41. Scali C, Catteruccia F, Li Q, Crisanti A. Identification of sex-specific transcripts of the Anopheles gambiae doublesex gene. J Exp Biol. 2005;208:3701–9. Dennison NJ. Sex-specific expression during embryonic development of Anopheles gambiae. University of Liverpool; 2012. Hammond A, Pollegioni P, Persampieri T, North A, Minuz R, Trusso A, et al. Gene-drive suppression of mosquito populations in large cages as a bridge between lab and field. Nat Commun. 2021;12:4589. Yadav B, Majhi A, Phagna K, Meena MK, Ram H. Negative regulators of grain yield and mineral contents in rice: potential targets for CRISPR-Cas9-mediated genome editing. Funct Integr Genomics. 2023;23:317. Unckless RL, Clark AG, Messer PW. Evolution of Resistance Against CRISPR/Cas9 Gene Drive. Genetics. 2017;205:827–41. Beaghton AK, Hammond A, Nolan T, Crisanti A, Burt A. Gene drive for population genetic control: non-functional resistance and parental effects. Proc R Soc B Biol Sci. 2019;286:20191586. Hammond A, Karlsson X, Morianou I, Kyrou K, Beaghton A, Gribble M, et al. Regulating the expression of gene drives is key to increasing their invasive potential and the mitigation of resistance. PLOS Genet. 2021;17:e1009321. Morianou I. Determining the landscape of resistance to gene drives in the malaria mosquito. Imperial College London; 2023. Wang G-H, Du J, Chu CY, Madhav M, Hughes GL, Champer J. Symbionts and gene drive: two strategies to combat vector-borne disease. Trends Genet. 2022;38:708–23. Bier E. Gene drives gaining speed. Nat Rev Genet. 2022;23:5–22. Lanzaro GC, Campos M, Crepeau M, Cornel A, Estrada A, Gripkey H, et al. Field Trial Site Selection for Mosquitoes with Gene Drive: Geographic, Ecological, and Population Genetic Considerations. In: Mosquito Gene Drives and the Malaria Eradication Agenda. Jenny Stanford Publishing; 2023. p. 141–96. The Anopheles gambiae 1000 Genomes Consortium. Genetic diversity of the African malaria vector Anopheles gambiae. Nature. 2017;552:96–100. The Anopheles gambiae 1000 Genomes Consortium. Genome variation and population structure among 1142 mosquitoes of the African malaria vector species Anopheles gambiae and Anopheles coluzzii. Genome Res. 2020;30:1533–46. Clarkson CS, Miles A, Harding NJ, O’Reilly AO, Weetman D, Kwiatkowski D, et al. The genetic architecture of target-site resistance to pyrethroid insecticides in the African malaria vectors Anopheles gambiae and Anopheles coluzzii. Mol Ecol. 2021;30:5303–17. Fuchs S, Garrood WT, Beber A, Hammond A, Galizi R, Gribble M, et al. Resistance to a CRISPR-based gene drive at an evolutionarily conserved site is revealed by mimicking genotype fixation. PLOS Genet. 2021;17:e1009740. Gantz VM, Bier E. The mutagenic chain reaction: A method for converting heterozygous to homozygous mutations. Science (80- ). 2015;348:442–4. Hammond AM, Kyrou K, Bruttini M, North A, Galizi R, Karlsson X, et al. The creation and selection of mutations resistant to a gene drive over multiple generations in the malaria mosquito. PLOS Genet. 2017;13:e1007039. Oberhofer G, Ivy T, Hay BA. Behavior of homing endonuclease gene drives targeting genes required for viability or female fertility with multiplexed guide RNAs. Proc Natl Acad Sci. 2018;115. Champer J, Liu J, Oh SY, Reeves R, Luthra A, Oakes N, et al. Reducing resistance allele formation in CRISPR gene drive. Proc Natl Acad Sci. 2018;115:5522–7. Nissim L, Perli SD, Fridkin A, Perez-Pinera P, Lu TK. Multiplexed and Programmable Regulation of Gene Networks with an Integrated RNA and CRISPR/Cas Toolkit in Human Cells. Mol Cell. 2014;54:698–710. Tsai SQ, Wyvekens N, Khayter C, Foden JA, Thapar V, Reyon D, et al. Dimeric CRISPR RNA-guided FokI nucleases for highly specific genome editing. Nat Biotechnol. 2014;32:569–76. Zetsche B, Gootenberg JS, Abudayyeh OO, Slaymaker IM, Makarova KS, Essletzbichler P, et al. Cpf1 Is a Single RNA-Guided Endonuclease of a Class 2 CRISPR-Cas System. Cell. 2015;163:759–71. Sanz Juste S, Okamoto EM, Nguyen C, Feng X, López Del Amo V. Next-generation CRISPR gene-drive systems using Cas12a nuclease. Nat Commun. 2023;14:6388. Gillies MT, Coetzee M. A SUPPLEMENT TO THE ANOPHELINAE OF AFRICA SOUTH OF THE SAHARA (AFROTROPICAL REGION). Johannesburg; 1987. Coetzee M. Key to the females of Afrotropical Anopheles mosquitoes (Diptera: Culicidae). Malar J. 2020;19:70. Wilkins EE, Howell PI, Benedict MQ. IMP PCR primers detect single nucleotide polymorphisms for Anopheles gambiae species identification, Mopti and Savanna rDNA types, and resistance to dieldrin in Anopheles arabiensis. Malar J. 2006;5:125. Chen S, Zhou Y, Chen Y, Gu J. Fastp: An ultra-fast all-in-one FASTQ preprocessor. In: Bioinformatics. Oxford Academic; 2018. p. i884–90. VectorBase. Data Set: Anopheles gambiae PEST Genome Sequence and Annotation. The genome sequence of the malaria mosquito Anopheles gambiae. 2022. https://vectorbase.org/vectorbase/app/record/dataset/NCBITAXON_180454. Accessed 7 Jan 2021. Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009;25:1754–60. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009;25:2078–9. Robinson JT, Thorvaldsdóttir H, Winckler W, Guttman M, Lander ES, Getz G, et al. Integrative genomics viewer. Nature Biotechnology. 2011;29:24–6. GATK. VCF : Variant Call Format. GATK/Technical Doc. 2012;:1–14. Kluyver T, Ragan-Kelley B, Pérez F, Granger B, Bussonnier M, Frederic J, et al. Jupyter Notebooks—a publishing format for reproducible computational workflows. In: Positioning and Power in Academic Publishing: Players, Agents and Agendas - Proceedings of the 20th International Conference on Electronic Publishing, ELPUB 2016. IOS Press BV; 2016. p. 87–90. The Anopheles gambiae 1000 Genomes Consortium. Ag3.0 cloud data access — MalariaGEN vector data user guide. 2021. https://malariagen.github.io/vector-data/ag3/cloud.html. Accessed 30 Apr 2022. Miles A. GitHub - malariagen/malariagen-data-python: A Python package providing functions for accessing and analysing MalariaGEN data. malariagen / malariagen-data-python. 2022. https://github.com/malariagen/malariagen-data-python. Accessed 6 May 2022. Miles A, Bot P i., R. M, Ralph P, Harding N, Pisupati R, et al. Scikit-allel - Explore and analyse genetic variation. Zenodo. 2021. https://doi.org/10.5281/ZENODO.4759368. Additional Declarations No competing interests reported. Supplementary Files Fig.S1.png Fig. S1 . Simplified CRISPR-based homing gene drive designs for vector population replacement vs. vector population suppression. In both cases, a germline-expressed Cas9 and ubiquitously-expressed gRNA will be integrated within their own recognition sequence to catalyse cleavage of the wild-type homologous chromosome. Subsequent homologous directed repair (HDR) of this cut will use the gene drive chromosome as a template for repair, resulting in the copying over of the CRISPR cassette into the site of the cut, thereby causing the conversion of germline tissues from gene drive heterozygosity (HET) to a homozygosity (HOM), in a process called homing. (A) Under this design, the population replacement gene drive is integrated within a neutral locus and comprises: a Cas9; a gRNA complementary to the region within which the gene drive is integrated; a selectable marker; and an anti-parasitic effector (APE) gene, which can be ubiquitously or constitutively expressed (e.g. under a gut-specific, blood-meal(BM)-induced or salivary gland-specific promoter, etc.). (B) Under this design, the population suppression gene drive is knocking out an essential gene, such as one required for female fertility, and comprises: a Cas9; a gRNA complementary to the region within which the gene drive is integrated; and a selectable marker. Ideally, for autonomous gene drive spread, the knockout of the fertility gene will be recessive and sex-specific, whilst the target gene will be highly conserved and required for function in somatic but not in germline tissues (where homing occurs). Figure created in Biorender. TableS1.csv Table S1 . Spatial distribution of the SNPs and their frequencies in the intron-4-exon-5 boundary of the doublesex gene (2R: 48714420 – 48714720) within An. gambiae s.l. populations in Africa. TableS2.csv Table S2 . Time series (2012 to 2019) distribution of the SNPs and their frequencies in the intron-4-exon-5 boundary of the doublesex gene (2R: 48714420 – 48714720) within An. gambiae s.l. populations in Burkina Faso. Cite Share Download PDF Status: Published Journal Publication published 18 Dec, 2024 Read the published version in BMC Genomics → Version 1 posted Editorial decision: Revision requested 16 Oct, 2024 Reviews received at journal 15 Oct, 2024 Reviewers agreed at journal 15 Oct, 2024 Reviews received at journal 15 Oct, 2024 Reviews received at journal 08 Oct, 2024 Reviewers agreed at journal 06 Oct, 2024 Reviewers agreed at journal 06 Oct, 2024 Reviewers agreed at journal 26 Sep, 2024 Reviewers agreed at journal 25 Sep, 2024 Reviewers invited by journal 23 Sep, 2024 Editor invited by journal 23 Sep, 2024 Editor assigned by journal 02 Sep, 2024 Submission checks completed at journal 02 Sep, 2024 First submitted to journal 29 Aug, 2024 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4996167","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":359508352,"identity":"fbc1c915-55c9-48be-b604-be41d33b8104","order_by":0,"name":"Mahamadi Kientega","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYDACHsYGIPmPmR/ESSggWkvCAXZJkM4EA6K0gE0/wG9wAMQgRotuz+HGx4U/7kgbn1+d+OGBAYM8v9gB/FrMzjY2G89IeGZsduPtZgmgwwxnzk4goOU8Y5s0TwJzstmNsxtAWhIMbhPW0v4bqKV+84yzm38Qp+VsYxszT8JhZgP+3m1E2nLmYLM0T1oas8QN3m0WCQYSRPjlTPrDzzw2Nsz8/Wc33/xRYSPPL01ACwJIgFVKEKscBPgPkKJ6FIyCUTAKRhIAAOFuRaCDB8R7AAAAAElFTkSuQmCC","orcid":"","institution":"Institut de Recherche en Sciences de la Santé","correspondingAuthor":true,"prefix":"","firstName":"Mahamadi","middleName":"","lastName":"Kientega","suffix":""},{"id":359508353,"identity":"a930464f-9410-4a1f-bc80-f0f81a163255","order_by":1,"name":"Ioanna Morianou","email":"","orcid":"","institution":"University of East Anglia","correspondingAuthor":false,"prefix":"","firstName":"Ioanna","middleName":"","lastName":"Morianou","suffix":""},{"id":359508354,"identity":"10247aae-8d29-423c-b6b2-d7dcc3d12236","order_by":2,"name":"Nouhoun Traoré","email":"","orcid":"","institution":"Institut de Recherche en Sciences de la Santé","correspondingAuthor":false,"prefix":"","firstName":"Nouhoun","middleName":"","lastName":"Traoré","suffix":""},{"id":359508355,"identity":"0b7e3a03-458d-4a58-a0d2-087f1eaadb4b","order_by":3,"name":"Nace Kranjc","email":"","orcid":"","institution":"Imperial College London","correspondingAuthor":false,"prefix":"","firstName":"Nace","middleName":"","lastName":"Kranjc","suffix":""},{"id":359508356,"identity":"8b1a07ab-1e2f-4e76-b421-3c031759965d","order_by":4,"name":"Honorine Kaboré","email":"","orcid":"","institution":"Institut de Recherche en Sciences de la Santé","correspondingAuthor":false,"prefix":"","firstName":"Honorine","middleName":"","lastName":"Kaboré","suffix":""},{"id":359508357,"identity":"02ba856b-6790-4da1-9aed-119c559379f6","order_by":5,"name":"Odette N Zongo","email":"","orcid":"","institution":"Institut de Recherche en Sciences de la Santé","correspondingAuthor":false,"prefix":"","firstName":"Odette","middleName":"N","lastName":"Zongo","suffix":""},{"id":359508358,"identity":"6a7a3732-553e-41fe-846c-27f40d0725c8","order_by":6,"name":"Abdoul-Azize Millogo","email":"","orcid":"","institution":"Institut de Recherche en Sciences de la Santé","correspondingAuthor":false,"prefix":"","firstName":"Abdoul-Azize","middleName":"","lastName":"Millogo","suffix":""},{"id":359508359,"identity":"241afbcd-9000-4ad0-998b-639e8f4245a1","order_by":7,"name":"Patric Stephane Epopa","email":"","orcid":"","institution":"Institut de Recherche en Sciences de la Santé","correspondingAuthor":false,"prefix":"","firstName":"Patric","middleName":"Stephane","lastName":"Epopa","suffix":""},{"id":359508360,"identity":"7cfddf6b-3bd4-42a4-9499-7596a87f5288","order_by":8,"name":"Franck A. Yao","email":"","orcid":"","institution":"Institut de Recherche en Sciences de la Santé","correspondingAuthor":false,"prefix":"","firstName":"Franck","middleName":"A.","lastName":"Yao","suffix":""},{"id":359508361,"identity":"74d4a5fc-6922-4c8d-b9e0-6453b0069741","order_by":9,"name":"Adrien M G Belem","email":"","orcid":"","institution":"Université Nazi BONI","correspondingAuthor":false,"prefix":"","firstName":"Adrien","middleName":"M G","lastName":"Belem","suffix":""},{"id":359508363,"identity":"80de1af7-850d-4c44-8aa0-bb7e441d8d93","order_by":10,"name":"Austin Burt","email":"","orcid":"","institution":"Imperial College London","correspondingAuthor":false,"prefix":"","firstName":"Austin","middleName":"","lastName":"Burt","suffix":""},{"id":359508365,"identity":"a235ef55-c815-469a-85ee-8e29ef924404","order_by":11,"name":"Abdoulaye Diabaté","email":"","orcid":"","institution":"Institut de Recherche en Sciences de la Santé","correspondingAuthor":false,"prefix":"","firstName":"Abdoulaye","middleName":"","lastName":"Diabaté","suffix":""}],"badges":[],"createdAt":"2024-08-29 08:55:46","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4996167/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4996167/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12864-024-11127-y","type":"published","date":"2024-12-18T15:57:48+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":65818062,"identity":"7a2b6492-9cfd-4f9d-b6d4-f58f4c3e49e4","added_by":"auto","created_at":"2024-10-03 06:53:27","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2759152,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of sampling sites in the 19 African countries where \u003cem\u003eAnopheles\u003c/em\u003emosquitoes were collected for the \u003cem\u003eAnopheles gambiae\u003c/em\u003e 1000 Genomes Project.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-4996167/v1/9b116167da717788de39deec.png"},{"id":65818060,"identity":"eafc845b-a593-418b-8ab3-5945fd7d30f5","added_by":"auto","created_at":"2024-10-03 06:53:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":674782,"visible":true,"origin":"","legend":"\u003cp\u003eHeat map showing the allele frequencies of the variants in the Ag(QFS)1 gene drive target sequence (2R: 48714637 – 48714660) within the \u003cem\u003eAn. gambiae\u003c/em\u003e s.l. populations collected in 19 African countries; the y-axis of the heat map shows the non-synonymous variant positions in the 2R chromosome while the x-axis shows the populations of \u003cem\u003eAn. gambiae\u003c/em\u003e s.l. and number of individual mosquitoes processed (n); the gradient color bar shows the distribution of the allele frequencies; MLI: Mali; BFA: Burkina Faso; AGO: Angola; CAF: Central African Republic; CIV: Côte d’Ivoire; CMR: Cameroon; COD: Democratic Republic of Congo; MYA: Mayotte Island; GAB: Gabon; GHA: Ghana; GIN: Guinea; GMB: Gambia; GNB: Guinea-Bissau; KEN: Kenya, MOZ: Mozambique; MWI: Malawi; TZA: Tanzania; UGA: Uganda.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-4996167/v1/b4a20dd332950f69a0b1dece.png"},{"id":65817354,"identity":"d67e0bb2-3712-44b2-aff9-7321eb76c4e0","added_by":"auto","created_at":"2024-10-03 06:37:27","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":542133,"visible":true,"origin":"","legend":"\u003cp\u003eAllele frequencies of the SNPs in the gene drive target sequence (2R: 48714637 – 48714660) within the \u003cem\u003eAn. gambiae\u003c/em\u003e s.l. populations collected in 3 villages (Bana, Pala and Souroukoudinga) from 2012 to 2019 in Burkina Faso; A: Heat map showing the evolution of the allelic frequencies of the SNPs in the gRNA target sequence; the y-axis of the heat map shows the positions of non-synonymous variants in the 2R chromosome while the x-axis shows the populations of \u003cem\u003eAn. gambiae\u003c/em\u003e s.l. and number of individual mosquitoes processed (n); The gradient color bar shows the distribution of the allele frequencies. BN: Bana; SK: Souroukoudinga; PL: Pala. B: mapping of the SNPs positions alongside the reference genome in the different populations; PAM: protospacer adjacent motif.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-4996167/v1/fd8a4a3d7b5c17c78435522c.png"},{"id":65817356,"identity":"4c55c6cd-3965-447b-90d1-9cb4a0c603e9","added_by":"auto","created_at":"2024-10-03 06:37:27","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":346616,"visible":true,"origin":"","legend":"\u003cp\u003eEvolution of the nucleotide diversity in the \u003cem\u003eAn. gambiae\u003c/em\u003e s.s. and \u003cem\u003eAn. coluzzii\u003c/em\u003e populations collected in 3 villages (Bana, Pala and Souroukoudinga) from 2012 to 2017 in Burkina Faso; the figure on top represents samples collected in 2012 while the figure below represents samples collected in 2017; the rectangles on bottom shows the intron-4-exon-5 boundary of the \u003cem\u003edoublesex\u003c/em\u003e gene (dark red: exon-5 untranslated sequence; gray: exon-5 translated sequence and simple gray line: intron-4); the two vertical pink-filled spans are delimiting the Ag(QFS)1 or CRISPR/Cas9 target sequence; the y-axis of the figures shows the nucleotide diversity (\u003cem\u003eθ\u003c/em\u003e\u003csub\u003e\u003cem\u003eπ\u003c/em\u003e\u003c/sub\u003e) while the x-axis shows the 2R chromosome positions; the nucleotide diversity was constant over the five years and the high values were recorded in the UTR region but remained constant over the five years.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-4996167/v1/bec5e8ebe3cb88afa6e95985.png"},{"id":65817811,"identity":"c980e3c3-eec5-4b5b-be37-576293efb609","added_by":"auto","created_at":"2024-10-03 06:45:27","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1443269,"visible":true,"origin":"","legend":"\u003cp\u003eMosquito sampling sites\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-4996167/v1/dcb96e534474bb2c15a53447.png"},{"id":65818838,"identity":"61736d15-4b62-4e56-a2a8-e3188b970bf1","added_by":"auto","created_at":"2024-10-03 07:01:27","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1147035,"visible":true,"origin":"","legend":"\u003cp\u003eHeat map showing the allele frequencies of the SNPs (whose max frequencies higher than 1%) identified in the female-specific intron-exon boundary of the \u003cem\u003edoublesex\u003c/em\u003e gene identified via pooled amplicon sequencing from laboratory (Lab) and wild-caught mosquito samples (BN: Bana village, SK: Souroukoudinga, PL: Pala, SN: Soni, MG: Moara-Grand, TI: Toma-île, SR: Saran, TS: Toson, Ac: \u003cem\u003eAn. coluzzii\u003c/em\u003e, Ag: \u003cem\u003eAn. gambiae\u003c/em\u003e s.s., Aa: \u003cem\u003eAn. arabiensis\u003c/em\u003e); SNPs were called using lofreq software; the y-axis of the heat map shows the positions of the non-synonymous variant in the 2R chromosome while the x-axis shows the populations of \u003cem\u003eAn. gambiae\u003c/em\u003e s.l. and number of individual mosquitoes processed (n); the gradient color bar shows the distribution of the allele frequencies; the sky blue band shows the frequencies of the SNPs identified in the Ag(QFS)1 or CRISPR/Cas9 target sequence; UTR: the untranslated region (2R:48714420-48714556) of the female-specific exon5; CDS: the coding sequence (2R:48714557-48714648) of the female-specific exon5; Intron: a part (2R:48714649-48714720) of the female-specific intron4; Ag1000G: \u003cem\u003eAnopheles gambiae\u003c/em\u003e 1000 genomes.\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-4996167/v1/4690211ab160edcefada4e82.png"},{"id":65817363,"identity":"95d0e92d-aa6e-4f26-b018-7b7034b96945","added_by":"auto","created_at":"2024-10-03 06:37:27","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":305647,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of the SNPs and their frequencies alongside the intron-4–exon-5 boundary of the \u003cem\u003edoublesex\u003c/em\u003e gene; the rectangle on bottom shows the intron-4-exon-5 boundary of the \u003cem\u003edoublesex\u003c/em\u003egene (dark red: exon-5 untranslated sequence, gray: exon-5 translated sequence and simple gray line: intron-4); the y-axis shows the allele frequencies of the SNPs and the x-axis shows the positions of the SNPs in the 2R chromosome; the two vertical dashed lines are delimiting the Ag(QFS)1 or CRISPR/Cas9 target sequence; The DNA sequences at the bottom shows the the SNPs positions alongside the reference genome in the gRNA target sequence within different populations. GD: « Gene Drive »; Amplicon seq. data: Amplicon sequencing data from laboratory (Lab) and wild-caught mosquito samples from 8 villages in Burkina Faso; Ag1000G data: Whole genome sequencing data of \u003cem\u003eAnopheles gambiae\u003c/em\u003e 1000 genomes Consortium from wild caught \u003cem\u003eAn. gambiae\u003c/em\u003es.l. mosquitoes collected in 3 villages from Burkina Faso.\u003c/p\u003e","description":"","filename":"Fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-4996167/v1/602321623cfd911051135c38.png"},{"id":72201899,"identity":"45478776-8b53-4200-a6c7-f713a66bdb08","added_by":"auto","created_at":"2024-12-23 16:11:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7416217,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4996167/v1/d40d4819-37ed-4dcc-91dd-f95ee29316e2.pdf"},{"id":65817813,"identity":"eb5d1e21-f735-4bb3-a7be-d8928cfe5ff0","added_by":"auto","created_at":"2024-10-03 06:45:27","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":788753,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cu\u003e\u003cstrong\u003eFig. S1\u003c/strong\u003e\u003c/u\u003e. Simplified CRISPR-based homing gene drive designs for vector population replacement vs. vector population suppression. In both cases, a germline-expressed Cas9 and ubiquitously-expressed gRNA will be integrated within their own recognition sequence to catalyse cleavage of the wild-type homologous chromosome. Subsequent homologous directed repair (HDR) of this cut will use the gene drive chromosome as a template for repair, resulting in the copying over of the CRISPR cassette into the site of the cut, thereby causing the conversion of germline tissues from gene drive heterozygosity (HET) to a homozygosity (HOM), in a process called homing. (A) Under this design, the population replacement gene drive is integrated within a neutral locus and comprises: a Cas9; a gRNA complementary to the region within which the gene drive is integrated; a selectable marker; and an anti-parasitic effector (APE) gene, which can be ubiquitously or constitutively expressed (e.g. under a gut-specific, blood-meal(BM)-induced or salivary gland-specific promoter, etc.). (B) Under this design, the population suppression gene drive is knocking out an essential gene, such as one required for female fertility, and comprises: a Cas9; a gRNA complementary to the region within which the gene drive is integrated; and a selectable marker. Ideally, for autonomous gene drive spread, the knockout of the fertility gene will be recessive and sex-specific, whilst the target gene will be highly conserved and required for function in somatic but not in germline tissues (where homing occurs). Figure created in Biorender.\u003c/p\u003e","description":"","filename":"Fig.S1.png","url":"https://assets-eu.researchsquare.com/files/rs-4996167/v1/3e63eb75b8b4df5b1e50a8b3.png"},{"id":65818057,"identity":"aa798ab8-b472-4297-9d6d-b07cc56b99ee","added_by":"auto","created_at":"2024-10-03 06:53:27","extension":"csv","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":18465,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cu\u003e\u003cstrong\u003eTable S1\u003c/strong\u003e\u003c/u\u003e. Spatial distribution of the SNPs and their frequencies in the intron-4-exon-5 boundary of the \u003cem\u003edoublesex\u003c/em\u003e gene (2R: 48714420 – 48714720) within \u003cem\u003eAn. gambiae\u003c/em\u003e s.l. populations in Africa.\u003c/p\u003e","description":"","filename":"TableS1.csv","url":"https://assets-eu.researchsquare.com/files/rs-4996167/v1/59f29faa54b879174a40f398.csv"},{"id":65818059,"identity":"1f8295f4-58f0-405a-82a5-8f2cc420b43f","added_by":"auto","created_at":"2024-10-03 06:53:27","extension":"csv","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":12713,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cu\u003e\u003cstrong\u003eTable S2\u003c/strong\u003e\u003c/u\u003e. Time series (2012 to 2019) distribution of the SNPs and their frequencies in the intron-4-exon-5 boundary of the \u003cem\u003edoublesex\u003c/em\u003e gene (2R: 48714420 – 48714720) within \u003cem\u003eAn. gambiae\u003c/em\u003e s.l. populations in Burkina Faso.\u003c/p\u003e","description":"","filename":"TableS2.csv","url":"https://assets-eu.researchsquare.com/files/rs-4996167/v1/703a2a1fa2973a917f362da9.csv"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genomic analyses revealed low genetic variation in the intron-exon boundary of the doublesex gene within the natural populations of An. gambiae s.l. in Burkina Faso","fulltext":[{"header":"Background","content":"\u003cp\u003eMalaria is the deadliest vector-borne disease in the world. The disease is caused by \u003cem\u003ePlasmodium\u003c/em\u003e parasites, and transmitted to humans by female \u003cem\u003eAnopheles\u003c/em\u003e mosquitoes. According to the WHO, deaths attributed to malaria have increased from ~\u0026thinsp;400,000 (2018) to ~\u0026thinsp;600,000 (2022) in the past 5 years [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. This has been largely attributed to the spread of insecticide resistance across sub-Saharan Africa, where the disease is endemic. In response to these challenges, innovative strategies based upon genetic control are being developed, to strengthen current tools and accelerate malaria elimination [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Homing-based gene drives are engineered selfish genetic elements that propagate their own inheritance by use of a homing endonuclease [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Gene drives can be used for vector control, by being engineered to spread desirable genetic traits in the population, such as parasite refractoriness (population replacement), or by spreading negative fitness traits, such as infertility, amongst vector populations (population suppression). The CRISPR/Cas9 gene editing system has been adapted to act as an RNA-guided homing endonuclease for use in gene drive strategies, by inserting the Cas9 and gRNA cassettes within their own recognition sequence [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). CRISPR-based gene drives have shown great promise as tools to combat malaria, both as population replacement [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], and population suppression genetic control strategies [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMost population replacement strategies are focused on introducing genetic modifications or novel transgenes in the population, to make the new population pathogen-refractory [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Previous studies have shown that FREP1 mediates \u003cem\u003ePlasmodium\u003c/em\u003e invasion of the \u003cem\u003eAnopheles\u003c/em\u003e midgut epithelium [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. CRISPR knock-outs of the \u003cem\u003eFREP1\u003c/em\u003e gene showed resistance to both human and rodent malaria parasites [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Similarly, expression of small antimicrobial peptides in the mosquito midgut delayed sporogonic development of the malaria parasite, and could be propagated in a single generation experiment through gene drive homing [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Conversely, population suppression strategies aim to alter key parameters of population growth, such as sex ratio and fitness. The sex-chromosomes of \u003cem\u003eAn. gambiae\u003c/em\u003e were targeted by a synthetic sex-distorter I-PpoI, which is not a gene drive, and designed to induce a strong negative bias toward X chromosome\u0026ndash;carrying spermatozoa resulting in 95% male offspring [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Similar rates were also achieved using a synthetic CRISPR/Cas9-based sex distorter [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Recent studies have shown additional benefit in targeting the sex determination pathway to disrupt the sex-ratio in the offspring for vector control purposes [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn most insect species, the sex determination pathway starts by a primary central gene that stimulates the molecular cascade leading to the alternative splicing of the \u003cem\u003eDoublesex\u003c/em\u003e (\u003cem\u003edsx\u003c/em\u003e) and \u003cem\u003eFruitless\u003c/em\u003e (\u003cem\u003efru\u003c/em\u003e) genes [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], making these two genes the endpoint of the sex determination mechanisms [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The architecture and function of the \u003cem\u003edsx\u003c/em\u003e gene have been well characterized in various insects [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], including \u003cem\u003eAnopheles\u003c/em\u003e mosquitoes [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In \u003cem\u003eAn. gambiae\u003c/em\u003e, the \u003cem\u003edsx\u003c/em\u003e gene spans an 88.598 kb (2R:48703664\u0026ndash;48792262) sequence on chromosome 2R and consists of 7 exons, with exon 5 being female-specific and exon 6 male-specific. Alternative splicing of the \u003cem\u003edsx\u003c/em\u003e gene during embryonic development produces two isoforms based on the sex-specific exons, which control the expression of endpoint genes required for exhibition of physical sexual dimorphism [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eUsing CRISPR/Cas9, the disruption of the female-specific intron-4-exon-5 boundary of the \u003cem\u003edsx\u003c/em\u003e gene in \u003cem\u003eAn. gambiae\u003c/em\u003e resulted in morphological abnormalities in homozygous knock-out females, including in the development of the proboscis, which caused knockout females to be unable to draw a blood-meal, mate, and produce offspring [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. A CRISPR-based gene drive built against the female isoform of \u003cem\u003edsx\u003c/em\u003e (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eB), termed Ag(QFS)1 (previously called \u003cem\u003edsxF\u003c/em\u003e\u003csup\u003e\u003cem\u003eCRISPRh\u003c/em\u003e\u003c/sup\u003e) was able to rapidly spread in caged laboratory populations, causing a swift reduction of vector population density, and ultimately eliminating the populations within a year [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. This success made the \u003cem\u003edsx\u003c/em\u003e gene a relevant target for the development of genetic control strategies to reduce population density of malaria mosquitoes as well as other harmful insects, including agricultural pests [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGenetic variation is an essential component of evolution, allowing natural populations to face ecological challenges. The presence of genetic polymorphisms in a gene drive target sequence could inhibit gRNA recognition and subsequent Cas9 cleavage, limiting the spread of the homing and consequently reducing the efficacy of the genetic tool [\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Therefore, the success of most CRISPR/Cas9-based genetic control tools, requires the target sequence to show limited genetic variation in natural populations. In this study, we investigate the genetic variation within the intron-4-exon-5 boundary (2R: 48714420\u0026ndash;48714720) of the \u003cem\u003eDoublesex\u003c/em\u003e gene within wild \u003cem\u003eAn. gambiae\u003c/em\u003e s.l. by analysing existing population genomics data generated by the \u003cem\u003eAnopheles gambiae\u003c/em\u003e 1000 genomes project (Ag1000G), as well as newly generated data through targeted pooled amplicon sequencing of wild-caught mosquito populations from Burkina Faso. We also investigate the evolution and spatial distribution of the genetic polymorphisms discovered. We reveal limited variation of the female-specific intron-exon boundary of the \u003cem\u003edoublesex\u003c/em\u003e gene in natural populations. These results are valuable in guiding the implementation of gene drive tools to supplement malaria elimination efforts in Africa.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSpatial distribution of genetic variants in the Ag(QFS)1 gene drive target sequence\u003c/h2\u003e \u003cp\u003eWe first investigated the distribution of variants across a sequence spanning the intron-4\u0026ndash;exon-5 boundary of the \u003cem\u003edoublesex\u003c/em\u003e gene (2R: 48714420\u0026ndash;48714720), including the Ag(QFS)1 gene drive target sequence (2R: 48714637\u0026ndash;48714660) using an existing genomics dataset generated by the \u003cem\u003eAnopheles gambiae\u003c/em\u003e 1000 genomes Consortium (Ag1000G). This dataset includes SNP calls from 4200 wild-caught \u003cem\u003eAn. gambiae\u003c/em\u003e s.l. (\u003cem\u003eAn. coluzzii, An. gambiae\u003c/em\u003e s.s. and \u003cem\u003eAn. arabiensis\u003c/em\u003e) mosquitoes from 19 African countries (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe identified 143 single nucleotide polymorphisms (SNPs) at varying frequencies from 0.000609 to 0.59 in the intron-4\u0026ndash;exon-5 boundary of the \u003cem\u003edoublesex\u003c/em\u003e gene within vector populations collected in 17 African countries (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The SNP 2R:48714486[C\u0026thinsp;\u0026gt;\u0026thinsp;A], located in the UTR sequence of the exon 5, is found at relatively high frequencies (freq.\u0026nbsp;~ 0.12\u0026ndash;0.59) in all \u003cem\u003eAn. coluzzii\u003c/em\u003e populations. The third allele of 2R:48714486 ([C\u0026thinsp;\u0026gt;\u0026thinsp;G]) is only identified in \u003cem\u003eAn. arabiensis\u003c/em\u003e populations (Freq.\u0026nbsp;~ 0.006\u0026ndash;0.15).\u003c/p\u003e \u003cp\u003eThe target sequence of the Ag(QFS)1 gene drive is located in the boundary of the intron 4 and the exon 5 spanning 23 bp from the nucleotide in the 2R chromosome. This sequence contained five SNPs at relatively low frequencies (freq.\u0026nbsp;~ 0.00\u0026ndash;0.26) in the vector populations. Most of these SNPs were identified at very low frequencies (Freq.\u0026nbsp;\u0026lt; 0.01) in West Africa and the \u003cem\u003eAn. gambiae\u003c/em\u003e populations of Burkina Faso, whilst no SNP was identified in the populations of East Africa (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The most abundant target site SNP, 2R: 48714641[C\u0026thinsp;\u0026gt;\u0026thinsp;T], was found at frequencies higher than 0.05 (0.011\u0026ndash;0.26) in the \u003cem\u003eAn. gambiae\u003c/em\u003e populations of central Africa. Specifically, the 2R: 48714641[C\u0026thinsp;\u0026gt;\u0026thinsp;T] SNP was found at frequencies of 0.26 in Angola, 0.07 in the DRC, 0.06 in Cameroon, and 0.01 in Gabon. This was previously reported as a G\u0026thinsp;\u0026gt;\u0026thinsp;A SNP, as read on the antisense DNA strand [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. This position was found to be triallelic (1 reference and 2 alternative alleles) and its third allele, 2R: 48714641[C\u0026thinsp;\u0026gt;\u0026thinsp;A] was found in \u003cem\u003eAn. arabiensis\u003c/em\u003e populations from Burkina Faso at frequency lower than 0.01 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The presence of this SNP (2R: 48714641[C\u0026thinsp;\u0026gt;\u0026thinsp;T]) at frequencies higher than 5% in gRNA target sequence within the Central African populations raised concern about the spread of the Ag(QFS)1 gene drive in these populations. However, it was found to be cleavable by the Cas9/gRNA ribonucleoprotein \u003cem\u003ein vitro\u003c/em\u003e [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], and by the Ag(QFS)1 gene drive \u003cem\u003ein vivo\u003c/em\u003e [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], so the gene drive should be able to spread in its presence. No SNPs were identified in the other \u003cem\u003eAn. gambiae\u003c/em\u003e populations of West and East Africa.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eTime series variation of the gene drive target sequence in Burkina Faso\u003c/h2\u003e \u003cp\u003eUsing the Ag1000G dataset we also investigated the year-to-year genetic variation in the intron-4\u0026ndash;exon-5 boundary of the \u003cem\u003edoublesex\u003c/em\u003e gene within wild \u003cem\u003eAn. gambiae\u003c/em\u003e s.l. populations collected from 2012 to 2019 in three villages (Bana, Souroukoudinga and Pala) of Burkina Faso.\u003c/p\u003e \u003cp\u003eThe analyses identified 90 SNPs at frequencies ranging from 0.001 to 0.54 in the intron-4\u0026ndash;exon-5 boundary of the \u003cem\u003edoublesex\u003c/em\u003e gene over the seven-year survey (mostly in the untranslated region, UTR, of \u003cem\u003edoublesex\u003c/em\u003e) (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). The 2R:48714486[C\u0026thinsp;\u0026gt;\u0026thinsp;A] SNP, present in the \u003cem\u003edsx\u003c/em\u003e exon 5 UTR, was the most abundant SNP identified, present at frequencies of 0.39\u0026ndash;0.54. All other SNPs were found at frequencies lower than 0.01. Most of the SNPs (except 2R:48714445[C\u0026thinsp;\u0026gt;\u0026thinsp;A,T], 2R:48714453[G\u0026thinsp;\u0026gt;\u0026thinsp;A], 2R:48714692[T\u0026thinsp;\u0026gt;\u0026thinsp;A] in \u003cem\u003eAn. gambiae\u003c/em\u003e s.s. and 2R:48714486[C\u0026thinsp;\u0026gt;\u0026thinsp;A] in \u003cem\u003eAn. coluzzii\u003c/em\u003e) were non-constant over the years, i.e. they appeared and disappeared the following year, presumably removed by purifying selection or drift.\u003c/p\u003e \u003cp\u003eThe Ag(QFS)1 gene drive target sequence (2R:48714637\u0026ndash;2R:48714660) displayed four non-constant SNPs at very low frequencies (~\u0026thinsp;0.0057\u0026ndash;0.05) from 2012 to 2019 in the vector populations. These SNPs were non-constant over the seven-year survey and each of them was removed in the following year after its apparition in the populations (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe nucleotide diversity (\u003cem\u003eθ\u003c/em\u003e\u003csub\u003e\u003cem\u003eπ\u003c/em\u003e\u003c/sub\u003e) ranged from 0 to 0.04 in the female-specific intron-exon boundary of the \u003cem\u003edoublesex\u003c/em\u003e gene. Interestingly, the nucleotide diversity was shown to be constant over the years (from 2012 to 2017). The highest nucleotide diversity (\u003cem\u003eθ\u003c/em\u003e\u003csub\u003e\u003cem\u003eπ\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e~\u0026thinsp;0.04\u003c/em\u003e) was recorded in the UTR region of the female-specific intron-exon boundary of the \u003cem\u003edoublesex\u003c/em\u003e gene. In the target sequence of the Ag(QFS)1 gene drive, the nucleotide diversity was close to 0 in 2012 and remained so until 2017 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The analyses showed a constant nucleotide diversity in the gRNA target sequence in the \u003cem\u003eAn. gambiae\u003c/em\u003e population of the sampling area. One of the challenges of genetic control remains the potential emergence of new genetic variants at the target sequence, over the time, through evolutionary processes. Our results showed no change in the genetic variation of the gRNA target sequence within the natural populations of the three villages (Bana, Pala and Souroukoudinga) over the seven-year survey, which is promising for the long term efficacy and spread of a gene drive strategy targeting this sequence.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003ePooled amplicon sequencing to detect genetic variants in the Ag(QFS)1 gene drive target sequence\u003c/h2\u003e \u003cp\u003eApplication of cost-effective and efficient methods are essential for monitoring the evolution of the Ag(QFS)1 gene drive target sequence in the natural population. Here, we employed pooled amplicon sequencing to investigate the genetic polymorphism of the Ag(QFS)1 gene drive target sequence in natural populations from Burkina Faso.\u003c/p\u003e \u003cp\u003eWe genotyped more than 600 individual mosquitoes belonging to three species of the \u003cem\u003eAn. gambiae species\u003c/em\u003e complex (\u003cem\u003eAn. gambiae s.s., An coluzzii\u003c/em\u003e and \u003cem\u003eAn. arabiensis\u003c/em\u003e) sampled in Bana, Pala, Souroukoudinga, Soni, Moara-Grand, Toma-Ile, Toson, Saran and from laboratory colony (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e). \u003cem\u003eAn. coluzzii\u003c/em\u003e was the most predominant species in almost all the villages except Pala, where \u003cem\u003eAn. gambiae\u003c/em\u003e s.s. and \u003cem\u003eAn. arabiensis\u003c/em\u003e were most prevalent. This allowed the formation of 12 pools (9 pools of \u003cem\u003eAn. coluzzii\u003c/em\u003e, 2 pools of \u003cem\u003eAn. gambiae\u003c/em\u003e s.s. and 1 pool of \u003cem\u003eAn. arabiensis\u003c/em\u003e) of 50 individuals for gDNA extraction and pooled amplicon sequencing of a 365 bp region encompassing the intron4-exon5 boundary of \u003cem\u003edoublesex\u003c/em\u003e, as well as the whole exon 5 CDS, including the Ag(QFS)1 target site. The genomic data analyses identified 60 variants, compared to 67 variants detected in the Ag1000G data in the same 365 bp region. Of the 60 SNPs, 17 positions were found to be triallelic. Importantly, no indels were identified in the dataset. Most of the variants (n\u0026thinsp;=\u0026thinsp;49 SNPs) were identified in the \u003cem\u003eAn. coluzzii\u003c/em\u003e populations followed by \u003cem\u003eAn. gambiae\u003c/em\u003e s.s. (n\u0026thinsp;=\u0026thinsp;34 SNPs) and \u003cem\u003eAn. arabiensis\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;16 SNPs), which showed the lowest degree of genetic diversity in this region. The SNP density was approximately 0.2 bp\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e [60/300], indicating the presence of 1 SNP per 5 bp. Most of these SNPs were identified in the UTR of exon 5 and intron 4 which are expected to show higher variation as they constitute non-coding regions. The exon 5 CDS showed a varying number of SNPs between the populations from 1 SNP (0.011 bp\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e [1/91]) in the laboratory colony to 7 SNPs (0.08 bp\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e [7/91]) in the \u003cem\u003eAn. gambiae\u003c/em\u003e s.s. samples of Pala. Only two of the SNPs (2R:48714617(C\u0026thinsp;\u0026gt;\u0026thinsp;T) and 2R:48714592(C\u0026thinsp;\u0026gt;\u0026thinsp;T)) identified in the exon 5 CDS were constant and present in all wild-caught populations.\u003c/p\u003e \u003cp\u003eAlmost all SNPs were identified at low frequencies (Freq\u0026thinsp;\u0026lt;\u0026thinsp;0.05) except the SNP 2R:48714486(C\u0026thinsp;\u0026gt;\u0026thinsp;A) identified in the UTR sequence of the exon 5 at relatively high frequencies (freq\u0026thinsp;=\u0026thinsp;0.31\u0026ndash;0.45) in the \u003cem\u003eAn. coluzzii\u003c/em\u003e populations, whilst in \u003cem\u003eAn. arabiensis\u003c/em\u003e and \u003cem\u003eAn. gambiae\u003c/em\u003e s.s. it was present at low frequencies (freq.\u0026nbsp;~ 0.01). A third allele of this position, the SNP 2R:48714486(C\u0026thinsp;\u0026gt;\u0026thinsp;G) was identified at low frequencies (freq.\u0026nbsp;= 0.024) in \u003cem\u003eAn. arabiensis\u003c/em\u003e populations. In the laboratory samples, this SNP is found at low frequencies and seems to be replaced by the SNP 2R:48714472 (A\u0026thinsp;\u0026gt;\u0026thinsp;T), which is also identified in the UTR sequence of the exon 5 at a frequency of 0.17. Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows the allele frequencies of the SNPs whose maximum frequencies were higher than 0.01 in at least one population. The Ag(QFS)1 gene drive target sequence exhibited one SNP at low frequencies, i.e. less than 0.05. The analyses showed most of the SNPs in the UTR sequence of the exon 5 of the \u003cem\u003edsx\u003c/em\u003e gene, especially those with relatively high frequencies (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Interestingly, the distribution of the SNPs along the target sequence of the \u003cem\u003edsx\u003c/em\u003e gene and their allele frequencies were similar to those found in the time series Ag1000G data in Burkina Faso.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eMalaria control remains a major challenge in Sub-Saharan Africa. Ongoing studies in genetic engineering and biocontrol through endosymbionts intend to develop effective and sustainable control tools to reduce malaria transmission [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. By targeting the sexual determination pathway [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], CRISPR-based gene drive technologies demonstrated their potential advantages to be the basis of the development of effective and sustainable tools to support malaria elimination efforts [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, the success of a CRISPR-based gene drive for vector control requires its target sequence to show as little polymorphism as possible in natural populations, to ensure efficient homing rates. Another possible prerequisite is the target sequence to be highly conserved over the years to ensure the continued matching of the gRNA to the target sequence in natural populations several years after the release [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. This study investigated the genetic polymorphism of the intron-4-exon-5 boundary of the \u003cem\u003edoublesex\u003c/em\u003e gene within the natural populations of \u003cem\u003eAn. gambiae\u003c/em\u003e s.l. These insights may contribute for the future deployment of gene drive control strategies targeting the \u003cem\u003edoublesex\u003c/em\u003e gene [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe results showed a variable genetic polymorphism among the vector populations with a global variant density of one SNP every 5 bp. This density was shown to be low compared to what was previously found in the whole \u003cem\u003edsx\u003c/em\u003e gene and in the \u003cem\u003eAn. gambiae\u003c/em\u003e genome that was about one SNP every two nucleotides [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Interestingly, most of the variants were identified in the untranslated region of exon 5 of the gene. The analysis displayed a very low amount of polymorphism in the Ag(QFS)1 target sequence (2R: 48714637\u0026ndash;48714660). Analyses of time series data in Burkina showed four non-constant SNPs over a seven-year survey in the Ag(QFS)1 target sequence. Each of these SNPs appeared in different years and were removed in the following year after their occurrence in the same population, presumably removed by purifying selection or drift. This pattern of genetic variation in the target sequence is expected to be sufficient for the rapid spread of the Ag(QFS)1 gene drive in natural populations. However, given that natural populations are dynamic, the genetic variation might be different in space over time. This situation is observed in the forest area of Central Africa where \u003cem\u003eAn. gambiae\u003c/em\u003e s.l. populations exhibited a SNP 2R:48714641[C\u0026thinsp;\u0026gt;\u0026thinsp;T] at frequencies reaching 0.26 in the gRNA target sequence in Angola, Democratic Republic of Congo, Cameroon and Gabon but not in Central African Republic. The third allele of this position, the 2R:48714641[C\u0026thinsp;\u0026gt;\u0026thinsp;A] was found in \u003cem\u003eAn. arabianesis\u003c/em\u003e populations of Burkina Faso. The SNP 2R:48714641[C\u0026thinsp;\u0026gt;\u0026thinsp;T] was identified in previous studies at Hardy-Weinberg equilibrium in \u003cem\u003eAn. coluzzii\u003c/em\u003e and \u003cem\u003eAn. gambiae\u003c/em\u003e populations of Angola [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The emergence pattern of this SNP suggests its ubiquitous presence at evolving frequencies in the forest area of Central Africa and its presence could reduce the spread of the \u003cem\u003edsx-drive\u003c/em\u003e in vector populations. Recent studies showed a complex genetic structure of \u003cem\u003eAn. gambiae\u003c/em\u003e populations in Africa with important variants flow between populations from Western, Central and Eastern Africa [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The \u003cem\u003ekdr\u003c/em\u003e mutations (\u003cem\u003ekdr-L995F\u003c/em\u003e and \u003cem\u003ekdr-L995S\u003c/em\u003e) constitute an example of continent-widespread genetic variants in the \u003cem\u003eAn. gambiae\u003c/em\u003e population [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Seeing the high connectivity of \u003cem\u003eAn. gambiae\u003c/em\u003e populations [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], the spread of the Cas9-based \u003cem\u003edsx-drive\u003c/em\u003e could be reduced if the SNP 2R:48714641[C\u0026thinsp;\u0026gt;\u0026thinsp;A] is positively selected and increased in frequency in the populations. Thus, the evolutionary processes driving this variant need to be investigated to understand the extent and the evolution of this SNP within the vector populations in Central Africa.\u003c/p\u003e \u003cp\u003eThe emergence of resistance alleles remains a great challenge in the development of Cas9-based vector control tools [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. In addition to the existing variants, new alleles resulting from de novo mutations, could be selected and then threaten the efficacy of CRISPR-based gene drives in natural populations. Resistance variants may be introduced by gene drive activity, upon error-prone NHEJ repair of a Cas9-induced double-stranded break in the DNA. This can be exacerbated if the gene drive allows Cas9 deposition into the early embryo, where NHEJ occurs preferentially over HDR [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Several strategies are being explored to reduce the potential emergence of resistance variants in natural populations. The use of alternative promoters to contain Cas9 spatiotemporal expression in germline tissues was shown to reduce the rate of resistant allele creation [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Moreover, the use of multiplexed gRNAs targeting neighboring sequences simultaneously constitutes a promising strategy [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In this case, if a resistant allele gets created or is pre-existing in the population at one of the target sites, it will be removed as long as at least one other target site is still cleavable, and able to induce homing [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Resistant alleles would need to co-exist at all target sites to prevent gene drive homing, and they will need to cooperatively encode a functional gene product to prevent long-term gene drive spread. Nevertheless, this strategy remains complex in practice because of the difficulties in finding many gRNAs targeting neighboring sites and exhibiting good parameters in a single gene [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Another strategy to prevent the emergence of resistance could be the use of engineered endonucleases or alternative nucleases such as dCas9-FokI and Cpf1/Cas12a. These nucleases showed an ability to tolerate target sequence variation so that existing polymorphisms at the cut site or outside this site are less likely to prevent cleavage and homing [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Recently, a Cas12a split gene drive was developed in \u003cem\u003eDrosophila melanogaster\u003c/em\u003e [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Combining a homing gene drive with an autosomal sex-distorter would also reduce the likelihood, for which a homing-resistant allele will be selected [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Ultimately, all alternative strategies that have been developed to mitigate resistance would only be successful if the gene drive is targeting a highly conserved sequence that shows minimal tolerance for polymorphisms. In the present study we set out a framework for in-depth investigation of the natural variation that is pre-existing in a natural population, to inform gene drive implementation.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, we investigated the genetic polymorphism of the intron-4-exon-5 boundary of the \u003cem\u003edoublesex\u003c/em\u003e gene, a potential target for the development of Cas9-based control tools. The results showed very low polymorphism at the target sequence of the Ag(QFS)1 gene drive in the anopheline populations of Burkina Faso. No SNP was found under positive selection in this region over a seven-year (from 2012 to 2019) survey in \u003cem\u003eAn. gambiae\u003c/em\u003e s.l. populations from Burkina. Interestingly, most of the vector populations in West and East Africa showed a very low variant density in the gRNA target sequence. Understanding the genetic variation of gene drive target sequences in natural populations is useful and valuable to guide the implementation of gene drive tools to support current control tools for malaria elimination.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eMosquito sampling\u003c/h2\u003e \u003cp\u003eMosquito sampling was carried out in 8 villages (Bana, Pala, Souroukoudinga, Toma-Ile, Soni, Moara-Grand, Saran and Tosson) in the western part of Burkina Faso (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e) during 2019. The environmental conditions of these villages are almost similar with an annual rainfall ranging from 700 mm in the north to 1200 mm in the south between May and October. The average annual temperature sits at approximately 27\u0026deg;C. The sampling area is dominated by agricultural practices especially cereals and cotton cultivation creating suitable environmental niches for the proliferation of mosquito populations. Mosquito samples were collected using pyrethrum spraying catches (PSC) in the rainy season of 2019. Indoor resting mosquitoes were collected early in the morning in 20 randomly selected houses in each village. After collection, mosquitoes were morphologically identified using morphological keys [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] and stored in 80\u0026deg; alcohol for further analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eMolecular analyses\u003c/h2\u003e \u003cp\u003eThe \u003cem\u003eAn. gambiae\u003c/em\u003e s.l. samples were individually genotyped using the protocol described by Wilkins and collaborators [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] and grouped per species/location. After pooling, the genomic DNA was extracted from the pools using the DNAzol protocol and stored at -20\u0026deg;C. The extracted DNA was then used to perform PCR for the amplification of the intron 4 \u0026ndash; exon 5 boundary (2R: 48714420\u0026ndash;48714720) of the \u003cem\u003edsx\u003c/em\u003e gene. PCR was performed using KAPA HiFi Hotstart Ready Mix according to the manufacturer\u0026rsquo;s instructions and the following primers (illumina adapters underlined) previously designed by Hammond and collaborators [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]:\u003c/p\u003e \u003cp\u003eForward: 5\u0026rsquo;-\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eACACTCTTTCCCTACACGACGCTCTTCCGATCT\u003c/span\u003eACTTATCGGCATCAGTTGCG-3\u0026rsquo;\u003c/p\u003e \u003cp\u003eReverse: 5\u0026rsquo;-\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eGACTGGAGTTCAGACGTGTGCTCTTCCGATCTGT\u003c/span\u003eGAATTCCGTCAGCCAGCA-3\u0026rsquo;\u003c/p\u003e \u003cp\u003eA small amount of the PCR product was migrated in an electrophoresis gel and visualized under UV to check the quality of the PCR. The remaining PCR products were purified using the Promega Wizard\u0026reg; SV Gel \u0026amp; PCR clean-up system according to the manufacturer\u0026rsquo;s instructions. The purity and the amount of DNA in each sample were checked in a Nanodrop. The purity threshold was set between 1.8 and 2 (OD260/280) and the DNA samples whose purity was outside this threshold were removed. The purified DNA samples were adjusted to a concentration of 20 ng/ml in a 25 \u0026micro;l of a nuclease-free water and then shipped for sequencing at Genewiz Amplicon-EZ sequencing.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eData analyses\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003eAmplicon sequencing data\u003c/h2\u003e \u003cp\u003eGenomic data were received in FASTQ format from the sequencing company. Various tools including fastp, bwa, samtools, IGV and lofreq were used to handle the data for quality checking, trimming, mapping to the reference genome and variant calling. Quality checking and trimming were performed using fastp v0.23.2 [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. During this step, the DNA sequences whose length is less than 50 bp and phred score less than 30 were removed from the data. After quality checking and trimming, the genomic data were mapped to the \u003cem\u003eAn. gambiae\u003c/em\u003e reference genome (AgamP4) [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] using bwa 0.7.17-r1188 [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] and samtools v1.16.6 [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] and stored in bam format. The bam files were indexed and visualized using IGV v17.0.3-internal [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] to check the quality of the mapping to the reference genome. The genetic variants were called from these bam files using the lofreq v2.1.6 program (Wilm \u003cem\u003eet al.\u003c/em\u003e, 2012) and stored in VCF format [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. The VCF files were handled in the Jupyter Notebook [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] to analyze the allele frequencies of the identified variants.\u003c/p\u003e \u003cp\u003e \u003cb\u003eThe\u003c/b\u003e \u003cb\u003eAnopheles gambiae 1000\u003c/b\u003e \u003cb\u003eGenomes Consortium data\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWe also analyzed the spatiotemporal dynamics of the gene drive target sequence variants using the genomic data of the Ag1000G Consortium project phase 3.0, 3.4 and 3.8. These data were from wild caught \u003cem\u003eAn. gambiae\u003c/em\u003e (\u003cem\u003eAn. coluzzii, An. gambiae\u003c/em\u003e s.s. and \u003cem\u003eAn. arabiensis\u003c/em\u003e) mosquitoes in 19 African countries (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The dataset of Burkina Faso was from time series sampling of wild \u003cem\u003eAn. gambiae\u003c/em\u003e mosquitoes spanning from 2012 to 2019 collected in three villages (Bana, Pala and Souroukoudinga) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Full details about the mosquito sampling, sequencing technology, the raw data clean-up and quality control, variant calling, storage of the data and the rights to access these data, have been described on the homepage of MalariaGEN [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Python packages such as Malariagen-data [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e] and scikit-allel [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e] were used to analyze the spatiotemporal distribution of the variants in the gene drive target sequence. We also analyzed the time series variation of the nucleotide diversity (i.e. the average nucleotide differences per site between two randomly selected individuals in the same population) in the gene drive target sequence within the \u003cem\u003eAn. gambiae\u003c/em\u003e populations from 2012 to 2017.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll methods in this paper have been implemented in accordance with the relevant guidelines/regulations/legislation in Burkina Faso. No ethics approval was required to run all the activities related to this paper. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw amplicon sequencing data generated and analyzed during the current study are available from the corresponding author upon reasonable request. Jupyter Notebooks and scripts to reproduce all the analyses, tables and figures are available in the GitHub repository: https://github.com/mkient/dsx_works.git.\u003c/p\u003e\n\u003cp\u003eThe SNP and haplotype data from the Ag1000G project are available on the MalariaGEN website and can be accessed using the malariagen_data package. The raw sequences in FASTQ format and the aligned sequences in BAM format were stored in the European Nucleotide Archive (ENA, Study Accession n\u0026deg; PRJEB42254).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the authors have read and accepted this version of the manuscript. The authors also declare there are no conflicts of interest. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Target Malaria and the Institut de Recherche en Sciences de la Sant\u0026eacute;. Supported by a grant from the Bill \u0026amp; Melinda Gates Foundation and Open Philanthropy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eM.K., N.K. and I.M. conceived the study. A.B. and A.D. provided the resources. A.M.G.B., A.B. and A.D. supervised the study. N.T., H.K., I.M. and M.K. designed methodology and carried out the molecular analyses. A.M., P.S.E., O.N.Z. and F.A.Y. carried out samples collection in the field. N.K. and M.K. carried out data analysis and visualization. I.M. and M.K. drafted the manuscript. All authors have read, edited, and approved the final version of the manuscript. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to acknowledge the Target Malaria, the Wellcome Trust project and their funders for supporting this work. We also acknowledge the Ag1000G Consortium, the MalariaGEN team at the Wellcome Sanger Institute, and their partners in Africa for the production of the \u003cem\u003eAn. gambiae\u003c/em\u003e genomic data. The authors are grateful to the populations of the sampling sites for their sincere cooperation during the mosquito sampling.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWHO. World malaria report 2023. Geneva; 2023.\u003c/li\u003e\n\u003cli\u003eMcLean KJ, Jacobs-Lorena M. Genetic Control Of Malaria Mosquitoes. Trends Parasitol. 2016;32:174\u0026ndash;6.\u003c/li\u003e\n\u003cli\u003eBurt A. Site-specific selfish genes as tools for the control and genetic engineering of natural populations. Proc R Soc London Ser B Biol Sci. 2003;270:921\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eHammond A, Galizi R, Kyrou K, Simoni A, Siniscalchi C, Katsanos D, et al. A CRISPR-Cas9 gene drive system targeting female reproduction in the malaria mosquito vector Anopheles gambiae. Nat Biotechnol. 2016;34:78\u0026ndash;83.\u003c/li\u003e\n\u003cli\u003eHoermann A, Habtewold T, Selvaraj P, Del Corsano G, Capriotti P, Inghilterra MG, et al. Gene drive mosquitoes can aid malaria elimination by retarding Plasmodium sporogonic development. Sci Adv. 2022;8.\u003c/li\u003e\n\u003cli\u003eKyrou K, Hammond AM, Galizi R, Kranjc N, Burt A, Beaghton AK, et al. A CRISPR\u0026ndash;Cas9 gene drive targeting doublesex causes complete population suppression in caged Anopheles gambiae mosquitoes. Nat Biotechnol. 2018;36:1062\u0026ndash;6.\u003c/li\u003e\n\u003cli\u003eAdelman ZN, Tu Z. Control of Mosquito-Borne Infectious Diseases: Sex and Gene Drive. Trends Parasitol. 2016;32:219\u0026ndash;29.\u003c/li\u003e\n\u003cli\u003eHammond AM, Galizi R. Gene drives to fight malaria: current state and future directions. Pathog Glob Health. 2017;111:412\u0026ndash;23.\u003c/li\u003e\n\u003cli\u003eZhang G, Niu G, Franca CM, Dong Y, Wang X, Butler NS, et al. Anopheles midgut FREP1 mediates plasmodium invasion. J Biol Chem. 2015;290:16490\u0026ndash;501.\u003c/li\u003e\n\u003cli\u003eDong Y, Sim\u0026otilde;es ML, Marois E, Dimopoulos G. CRISPR/Cas9 -mediated gene knockout of Anopheles gambiae FREP1 suppresses malaria parasite infection. PLOS Pathog. 2018;14:e1006898.\u003c/li\u003e\n\u003cli\u003eGalizi R, Doyle LA, Menichelli M, Bernardini F, Deredec A, Burt A, et al. A synthetic sex ratio distortion system for the control of the human malaria mosquito. Nat Commun. 2014;5:3977.\u003c/li\u003e\n\u003cli\u003eGalizi R, Hammond A, Kyrou K, Taxiarchi C, Bernardini F, O\u0026rsquo;Loughlin SM, et al. A CRISPR-Cas9 sex-ratio distortion system for genetic control. Sci Rep. 2016;6:31139.\u003c/li\u003e\n\u003cli\u003eAkbari O, Li M, Kandul N, Sun R, Yang T, Benetta ED, et al. Targeting Sex Determination to Suppress Mosquito Populations. Res Sq. 2023;12.\u003c/li\u003e\n\u003cli\u003eSimoni A, Hammond AM, Beaghton AK, Galizi R, Taxiarchi C, Kyrou K, et al. A male-biased sex-distorter gene drive for the human malaria vector Anopheles gambiae. Nat Biotechnol. 2020;38:1054\u0026ndash;60.\u003c/li\u003e\n\u003cli\u003eKientega M, Kranjc N, Traor\u0026eacute; N, Kabor\u0026eacute; H, Soma DD, Morianou I, et al. Analysis of the Genetic Variation of the Fruitless Gene within the Anopheles gambiae (Diptera: Culicidae) Complex Populations in Africa. Insects. 2022;13:1048.\u003c/li\u003e\n\u003cli\u003eBiedler JK, Tu Z. Sex Determination in Mosquitoes. In: Advances in Insect Physiology. 2016. p. 37\u0026ndash;66.\u003c/li\u003e\n\u003cli\u003eOliveira DCSG, Werren JH, Verhulst EC, Giebel JD, Kamping A, Beukeboom LW, et al. Identification and characterization of the doublesex gene of Nasonia. Insect Mol Biol. 2009;18:315\u0026ndash;24.\u003c/li\u003e\n\u003cli\u003eSalvemini M, Mauro U, Lombardo F, Milano A, Zazzaro V, Arc\u0026agrave; B, et al. Genomic organization and splicing evolution of the doublesex gene, a Drosophila regulator of sexual differentiation, in the dengue and yellow fever mosquito Aedes aegypti. BMC Evol Biol. 2011;11:41.\u003c/li\u003e\n\u003cli\u003eScali C, Catteruccia F, Li Q, Crisanti A. Identification of sex-specific transcripts of the Anopheles gambiae doublesex gene. J Exp Biol. 2005;208:3701\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eDennison NJ. Sex-specific expression during embryonic development of Anopheles gambiae. University of Liverpool; 2012.\u003c/li\u003e\n\u003cli\u003eHammond A, Pollegioni P, Persampieri T, North A, Minuz R, Trusso A, et al. Gene-drive suppression of mosquito populations in large cages as a bridge between lab and field. Nat Commun. 2021;12:4589.\u003c/li\u003e\n\u003cli\u003eYadav B, Majhi A, Phagna K, Meena MK, Ram H. Negative regulators of grain yield and mineral contents in rice: potential targets for CRISPR-Cas9-mediated genome editing. Funct Integr Genomics. 2023;23:317.\u003c/li\u003e\n\u003cli\u003eUnckless RL, Clark AG, Messer PW. Evolution of Resistance Against CRISPR/Cas9 Gene Drive. Genetics. 2017;205:827\u0026ndash;41.\u003c/li\u003e\n\u003cli\u003eBeaghton AK, Hammond A, Nolan T, Crisanti A, Burt A. Gene drive for population genetic control: non-functional resistance and parental effects. Proc R Soc B Biol Sci. 2019;286:20191586.\u003c/li\u003e\n\u003cli\u003eHammond A, Karlsson X, Morianou I, Kyrou K, Beaghton A, Gribble M, et al. Regulating the expression of gene drives is key to increasing their invasive potential and the mitigation of resistance. PLOS Genet. 2021;17:e1009321.\u003c/li\u003e\n\u003cli\u003eMorianou I. Determining the landscape of resistance to gene drives in the malaria mosquito. Imperial College London; 2023.\u003c/li\u003e\n\u003cli\u003eWang G-H, Du J, Chu CY, Madhav M, Hughes GL, Champer J. Symbionts and gene drive: two strategies to combat vector-borne disease. Trends Genet. 2022;38:708\u0026ndash;23.\u003c/li\u003e\n\u003cli\u003eBier E. Gene drives gaining speed. Nat Rev Genet. 2022;23:5\u0026ndash;22.\u003c/li\u003e\n\u003cli\u003eLanzaro GC, Campos M, Crepeau M, Cornel A, Estrada A, Gripkey H, et al. Field Trial Site Selection for Mosquitoes with Gene Drive: Geographic, Ecological, and Population Genetic Considerations. In: Mosquito Gene Drives and the Malaria Eradication Agenda. Jenny Stanford Publishing; 2023. p. 141\u0026ndash;96.\u003c/li\u003e\n\u003cli\u003eThe Anopheles gambiae 1000 Genomes Consortium. Genetic diversity of the African malaria vector Anopheles gambiae. Nature. 2017;552:96\u0026ndash;100.\u003c/li\u003e\n\u003cli\u003eThe Anopheles gambiae 1000 Genomes Consortium. Genome variation and population structure among 1142 mosquitoes of the African malaria vector species Anopheles gambiae and Anopheles coluzzii. Genome Res. 2020;30:1533\u0026ndash;46.\u003c/li\u003e\n\u003cli\u003eClarkson CS, Miles A, Harding NJ, O\u0026rsquo;Reilly AO, Weetman D, Kwiatkowski D, et al. The genetic architecture of target-site resistance to pyrethroid insecticides in the African malaria vectors Anopheles gambiae and Anopheles coluzzii. Mol Ecol. 2021;30:5303\u0026ndash;17.\u003c/li\u003e\n\u003cli\u003eFuchs S, Garrood WT, Beber A, Hammond A, Galizi R, Gribble M, et al. Resistance to a CRISPR-based gene drive at an evolutionarily conserved site is revealed by mimicking genotype fixation. PLOS Genet. 2021;17:e1009740.\u003c/li\u003e\n\u003cli\u003eGantz VM, Bier E. The mutagenic chain reaction: A method for converting heterozygous to homozygous mutations. Science (80- ). 2015;348:442\u0026ndash;4.\u003c/li\u003e\n\u003cli\u003eHammond AM, Kyrou K, Bruttini M, North A, Galizi R, Karlsson X, et al. The creation and selection of mutations resistant to a gene drive over multiple generations in the malaria mosquito. PLOS Genet. 2017;13:e1007039.\u003c/li\u003e\n\u003cli\u003eOberhofer G, Ivy T, Hay BA. Behavior of homing endonuclease gene drives targeting genes required for viability or female fertility with multiplexed guide RNAs. Proc Natl Acad Sci. 2018;115.\u003c/li\u003e\n\u003cli\u003eChamper J, Liu J, Oh SY, Reeves R, Luthra A, Oakes N, et al. Reducing resistance allele formation in CRISPR gene drive. Proc Natl Acad Sci. 2018;115:5522\u0026ndash;7.\u003c/li\u003e\n\u003cli\u003eNissim L, Perli SD, Fridkin A, Perez-Pinera P, Lu TK. Multiplexed and Programmable Regulation of Gene Networks with an Integrated RNA and CRISPR/Cas Toolkit in Human Cells. Mol Cell. 2014;54:698\u0026ndash;710.\u003c/li\u003e\n\u003cli\u003eTsai SQ, Wyvekens N, Khayter C, Foden JA, Thapar V, Reyon D, et al. Dimeric CRISPR RNA-guided FokI nucleases for highly specific genome editing. Nat Biotechnol. 2014;32:569\u0026ndash;76.\u003c/li\u003e\n\u003cli\u003eZetsche B, Gootenberg JS, Abudayyeh OO, Slaymaker IM, Makarova KS, Essletzbichler P, et al. Cpf1 Is a Single RNA-Guided Endonuclease of a Class 2 CRISPR-Cas System. Cell. 2015;163:759\u0026ndash;71.\u003c/li\u003e\n\u003cli\u003eSanz Juste S, Okamoto EM, Nguyen C, Feng X, L\u0026oacute;pez Del Amo V. Next-generation CRISPR gene-drive systems using Cas12a nuclease. Nat Commun. 2023;14:6388.\u003c/li\u003e\n\u003cli\u003eGillies MT, Coetzee M. A SUPPLEMENT TO THE ANOPHELINAE OF AFRICA SOUTH OF THE SAHARA (AFROTROPICAL REGION). Johannesburg; 1987.\u003c/li\u003e\n\u003cli\u003eCoetzee M. Key to the females of Afrotropical Anopheles mosquitoes (Diptera: Culicidae). Malar J. 2020;19:70.\u003c/li\u003e\n\u003cli\u003eWilkins EE, Howell PI, Benedict MQ. IMP PCR primers detect single nucleotide polymorphisms for Anopheles gambiae species identification, Mopti and Savanna rDNA types, and resistance to dieldrin in Anopheles arabiensis. Malar J. 2006;5:125.\u003c/li\u003e\n\u003cli\u003eChen S, Zhou Y, Chen Y, Gu J. Fastp: An ultra-fast all-in-one FASTQ preprocessor. In: Bioinformatics. Oxford Academic; 2018. p. i884\u0026ndash;90.\u003c/li\u003e\n\u003cli\u003eVectorBase. Data Set: Anopheles gambiae PEST Genome Sequence and Annotation. The genome sequence of the malaria mosquito Anopheles gambiae. 2022. https://vectorbase.org/vectorbase/app/record/dataset/NCBITAXON_180454. Accessed 7 Jan 2021.\u003c/li\u003e\n\u003cli\u003eLi H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009;25:1754\u0026ndash;60.\u003c/li\u003e\n\u003cli\u003eLi H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009;25:2078\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eRobinson JT, Thorvaldsd\u0026oacute;ttir H, Winckler W, Guttman M, Lander ES, Getz G, et al. Integrative genomics viewer. Nature Biotechnology. 2011;29:24\u0026ndash;6.\u003c/li\u003e\n\u003cli\u003eGATK. VCF : Variant Call Format. GATK/Technical Doc. 2012;:1\u0026ndash;14.\u003c/li\u003e\n\u003cli\u003eKluyver T, Ragan-Kelley B, P\u0026eacute;rez F, Granger B, Bussonnier M, Frederic J, et al. Jupyter Notebooks\u0026mdash;a publishing format for reproducible computational workflows. In: Positioning and Power in Academic Publishing: Players, Agents and Agendas - Proceedings of the 20th International Conference on Electronic Publishing, ELPUB 2016. IOS Press BV; 2016. p. 87\u0026ndash;90.\u003c/li\u003e\n\u003cli\u003eThe Anopheles gambiae 1000 Genomes Consortium. Ag3.0 cloud data access \u0026mdash; MalariaGEN vector data user guide. 2021. https://malariagen.github.io/vector-data/ag3/cloud.html. Accessed 30 Apr 2022.\u003c/li\u003e\n\u003cli\u003eMiles A. GitHub - malariagen/malariagen-data-python: A Python package providing functions for accessing and analysing MalariaGEN data. malariagen / malariagen-data-python. 2022. https://github.com/malariagen/malariagen-data-python. Accessed 6 May 2022.\u003c/li\u003e\n\u003cli\u003eMiles A, Bot P i., R. M, Ralph P, Harding N, Pisupati R, et al. Scikit-allel - Explore and analyse genetic variation. Zenodo. 2021. https://doi.org/10.5281/ZENODO.4759368.\u003c/li\u003e\n\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":"gene drive, doublesex gene, genetic polymorphism, An. gambiae s.l","lastPublishedDoi":"10.21203/rs.3.rs-4996167/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4996167/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe recent success of a population control gene drive targeting the \u003cem\u003edoublesex\u003c/em\u003e gene in \u003cem\u003eAnopheles gambiae\u003c/em\u003e paved the way for development of self-sustaining and self-limiting genetic control strategies targeting the sex determination pathway to reduce and/or distort the reproductive capacity of insect vectors. However, targeting these genes for genetic control purposes requires a better understanding of their genetic variation in natural populations to ensure effective gene drive spread. Using whole genome sequencing data from the Ag1000G project (Ag3.0, 3.4 and 3.8), and Illumina pooled amplicon sequencing, we investigated the genetic polymorphism of the intron-4\u0026ndash;exon-5 boundary of the \u003cem\u003edoublesex\u003c/em\u003e gene in the natural populations of \u003cem\u003eAn. gambiae\u003c/em\u003e s.l.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe analyses showed a very low variant density at the gRNA target sequence of the Ag(QFS)1 gene drive (previously called \u003cem\u003edsxF\u003c/em\u003e\u003csup\u003e\u003cem\u003eCRISPRh\u003c/em\u003e\u003c/sup\u003e) within the populations of West and East Africa. However, populations from the forest area in Central Africa exhibited four SNP at frequencies ranging from 0.011 to 0.26. The SNP (2R:48714641[C\u0026thinsp;\u0026gt;\u0026thinsp;T]) at high frequencies, i.e. 0.26 is identified within the \u003cem\u003eAn. coluzzii\u003c/em\u003e population form Angola. The analyses also identified 90 low frequency (1% \u0026minus;\u0026thinsp;5%) SNPs in the genomic region around the gRNA target sequence (intron-4\u0026ndash;exon-5 boundary). Three of these SNPs (2R:48714472 A\u0026thinsp;\u0026gt;\u0026thinsp;T; 2R:48714486 C\u0026thinsp;\u0026gt;\u0026thinsp;A; 2R:48714516 C\u0026thinsp;\u0026gt;\u0026thinsp;T) were observed at frequencies higher than 5% in the UTR region of the \u003cem\u003edoublesex\u003c/em\u003e gene. The results also showed a very low variant density and constant nucleotide diversity over a five-year survey in natural \u003cem\u003eAn. gambiae\u003c/em\u003e s.l. populations of Burkina Faso.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThese findings will guide the implementation of \u003cem\u003edoublesex\u003c/em\u003e-targeted gene drives to support the current control tools in malaria elimination efforts. Our methods can be applied to efficiently monitor the evolution of any sequence of interest in a natural population via pooled amplicon sequencing, surpassing the need of WGS.\u003c/p\u003e","manuscriptTitle":"Genomic analyses revealed low genetic variation in the intron-exon boundary of the doublesex gene within the natural populations of An. gambiae s.l. in Burkina Faso","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-03 06:37:22","doi":"10.21203/rs.3.rs-4996167/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-16T10:48:06+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-15T18:10:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"254719993604665895687979946555309120931","date":"2024-10-15T17:12:59+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-15T10:03:25+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-08T09:04:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"181872391394905957204577837592950514995","date":"2024-10-06T13:37:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"104228691049228748866250886413975913762","date":"2024-10-06T06:17:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"118094262471255202680770450647433940689","date":"2024-09-26T20:12:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"153828803027949716998260003333828555364","date":"2024-09-25T22:49:46+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-09-23T22:26:22+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-09-23T17:24:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-09-03T01:32:21+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-09-03T01:32:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Genomics","date":"2024-08-29T08:54:34+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":"c269343f-3d70-4ac3-9ef9-9e9d0e9a50eb","owner":[],"postedDate":"October 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-12-23T16:03:26+00:00","versionOfRecord":{"articleIdentity":"rs-4996167","link":"https://doi.org/10.1186/s12864-024-11127-y","journal":{"identity":"bmc-genomics","isVorOnly":false,"title":"BMC Genomics"},"publishedOn":"2024-12-18 15:57:48","publishedOnDateReadable":"December 18th, 2024"},"versionCreatedAt":"2024-10-03 06:37:22","video":"","vorDoi":"10.1186/s12864-024-11127-y","vorDoiUrl":"https://doi.org/10.1186/s12864-024-11127-y","workflowStages":[]},"version":"v1","identity":"rs-4996167","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4996167","identity":"rs-4996167","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}