The argininosuccinate lyase gene exacerbates pyrethroid resistance in the major African vectors Anopheles funestus

The argininosuccinate lyase gene exacerbates pyrethroid resistance in the major African vectors Anopheles funestus | 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 Article The argininosuccinate lyase gene exacerbates pyrethroid resistance in the major African vectors Anopheles funestus Charles Wondji, Vanessa NGANNANG-FEZEU, Mersimine Mangoua, Mahamat Gadji, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6063665/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Escalating pyrethroid resistance in malaria vectors is jeopardizing malaria control. Deciphering its complex evolutionary mechanisms is paramount to mitigate its impact. Here, we demonstrate that over-expression and allelic variation of the argininosuccinate lyase ( ASL) gene exacerbate pyrethroid resistance in Anopheles funestus . Multi-omics analyses revealed that ASL is upregulated Africa-wide and detected a strong signal of genetic differentiation around ASL in resistant populations exhibiting copy number variation (CNV). A predominant resistant haplotype harboring the R125H and T-277A mutations was detected in resistant mosquitoes. Transgenic expression of ASL and RNAi confirm its ability to confer pyrethroid resistance. A DNA-based assay confirmed its association with super-resistance, revealing a marked temporal increase in Uganda (2010–2023) but with a drastic reduction observed in 2024 after deployment of chlorfenapyr-based bed nets. This study elucidates a novel resistance pathway driven by ASL and introduces a new DNA-based diagnostic tool to monitor its spread and impact in Africa. Biological sciences/Genetics/Genetic markers Biological sciences/Evolution/Evolutionary genetics Malaria insecticide resistance Anopheles funestus s.s argininosuccinate lyase gene expression allelic variation transgenic expression DNA-based assay Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 INTRODUCTION Malaria prevention still relies on insecticide-based vector control interventions, notably Long-Lasting Insecticidal Nets (LLINs) and Indoor Residual Spraying (IRS) (1). Massive use of insecticides in vector control, coupled with the pesticide's use in agriculture are the main selective pressure of insecticide resistance in malaria vectors (2, 3). Understanding the evolution and genetic adaptation of malaria vectors to these control interventions is capital to maximise the effectiveness of malaria control and elimination strategies. The mechanisms driving insecticide resistance in major malaria vectors include (1) overexpression of detoxification enzymes responsible for metabolic resistance, (2) modification of insecticide targets, (3) reduction of insecticide penetration through the cuticle, and (4) insecticide avoidance behaviour (4–6). Microbiota also has recently emerged as a contributing factor to insecticide resistance (7). In major African malaria vectors such as Anopheles funestus and Anopheles gambiae , insecticide resistance has become a significant concern for vector control with the escalation of the pyrethroid resistance increasingly observed across Africa (8–11). It is likely that beyond classical resistance drivers, that mosquitoes are selecting novel routes of resistance to acquire the now observed ability to survive exposure to higher doses of insecticides (9). The detection of the role of chemosensory proteins (Sensory appendage proteins (SAP)) in An. gambiae is an indication that resistance aggravation could be driven by unknown factors (Ingham et al 2020). In An. funestus , metabolic resistance is the primary mechanism of resistance conferred by detoxification enzymes primarily cytochrome P450s, glutathione s-transferases (GST) and carboxylesterases, whose overexpression, or protein structural modification, results in the alteration or sequestration of the insecticide ingested by the insect (12). However, several other gene families have exhibited increased expression in resistant mosquitoes but with no evidence yet of their contribution to the resistance phenotype. Moreover, the molecular basis of resistance to insecticides is heterogeneous across Africa with populations from different regions showing overlapping or contrasting resistance patterns (13–16). Despite the recent significant progress in detecting candidate genes driving resistance and identifying key genetic variants in some cases (17–21), the genetic factors responsible for the aggravation of pyrethroid resistance are yet to be elucidated and no DNA-based tool has been designed for field surveillance of resistance escalation. The argininosuccinate lyase gene is a primary such candidate gene as it has been previously found overexpressed in resistant mosquitoes compared to their susceptible counterparts (20, 22–25). However, its contribution to resistance and/or its escalation (super-resistance) and its impact on vector control measures remain unknown. Argininosuccinate lyase ( ASL ), is involved in converting citrulline to arginine and belongs to a superfamily of tetrameric enzymes known to catalyse the reactions involving fumarate production (26). Its primary function is the reversible hydrolysis of argininosuccinate to arginine and fumarate. This reaction is crucial for ammonia detoxification and arginine biosynthesis (27). Arginine, produced by ASL , is a building block for many proteins synthesis, and the immediate precursor of nitric oxide (NO) a critical signaling molecule (28). Additionally, arginine serves as the precursor of many other amino acids as ornithine, proline, and glutamate (29). It plays a vital role in the mosquito's immune defence, antioxidant system, gene expression, and overall growth and development (30). In An. funestus , the ASL gene ( AFUN004002 ) resides on chromosome 3 and encodes a conserved protein with numerous orthologs across various species https://vectorbase.org/vectorbase/app/record/gene/AFUN004002#GeneLocation . It remains to establish if ASL involvement in these many biological processes could also interfere and/or contribute to resistance escalation in malaria vector such as An. funestus . Here, using a multi-omics approach, we uncovered a novel resistance mechanism by demonstrating that over-expression and allelic variation of ASL confer and exacerbate pyrethroid resistance in An. funestus . The detection of a signature of selective sweep at this gene in resistant populations facilitated the design of a simple, field-applicable DNA-based resistant assay, enabling the detection and to survey the spread of ASL resistant variant while assessing its impact on current control strategies. RESULTS Expression pattern of ASL An. funestus across Africa using RNAseq in An. funestus population RNA-seq data analysis revealed the overexpression of several gene families with significantly increased expression across various comparisons in An. funestus populations across Africa. Among the top overexpressed gene families were cytochrome P450s, glutathione S-transferases (GSTs), carboxylesterases, gustatory receptors, odorant proteins, cuticular proteins and ATP-binding cassette (ABC) transporters (DataSet 1). Additionally, we observed elevated expression of several digestive enzymes, including trypsin, chymotrypsin, and the argininosuccinate lyase gene ( ASL , AFUN004002 ). The heightened expression and consistent read counts of ASL , in particular, drew our attention for further investigation (DataSet 2). A comparative RNA-seq analysis of ASL expression across different An. funestus populations in Africa (four countries) confirmed its consistent upregulation in multiple conditions (Table. S1) particularly when comparing Uganda and Malawi populations. Indeed, ASL gene expression showed a significant increase in Ugandan mosquitoes (unexposed to insecticide) compared to the fully susceptible Fang strain (FC: 2.25, FDR < 0.00001), and not in the others population, where expression was lower. Similarly, ASL expression was significantly higher in resistant mosquitoes from Malawi when compared to Fang and unexposed population (FC: 2.85, FDR < 0.0001 and FC: 2.62, FDR < 0.0001 respectively), but not in other regions. Suggesting that ASL may contribute to permethrin resistance through its increased expression. The Gene Ontology (GO) enrichment analysis of the list of 142 transcripts with similarly expressed genes to ASL in the same comparison revealed an over-representation of 20 GO terms enriched around this gene (Table. S2). This enrichment reflects the function of those transcripts including heme binding GO:0020037, tetrapyrrole binding GO:0046906, iron ion bind GO:0005506, monooxygenase activity GO:0004497 and oxidoreductase activity GO:0016705 which were the main GO enriched (Fig. S1 ). Other groups of GO terms were found related to argininosuccinate lyase activity including amidine-lyase activity GO:0016842, arginase activity GO:0004053 and carbon-nitrogen lyase activity GO:0016840. Transcriptional profiling expression profile of ASL across Africa using qPCR To validate the RNAseq transcription profile, the expression of argininosuccinate lyase was assessed between deltamethrin-resistant mosquitoes after 24h of exposure (alive) and unexposed mosquitoes (control) using laboratory strain FANG as a reference. Overall, argininosuccinate lyase was significantly overexpressed in all studied populations, with a fold-change greater than 2 (Fig. 1 ). A notable increase in ASL expression was observed between 2014 and 2021 across all locations with high expression in 2021 (Cameroon: alive FC = 9.1 ± 4.8 vs unexposed FC = 5.3 ± 1.7; Ghana: alive FC = 19.9 ± 26 vs unexposed FC = 50.4 ± 47.9; Uganda: alive FC = 33.1 ± 6.3 vs unexposed FC = 14.3 ± 5.7; Malawi: alive FC = 14.9 ± 3.43 vs unexposed FC = 15.99 ± 0.09 and in resistant Lab strain FUMOZ-R FC = 4.9 ± 1.4) (Fig. 2 B). Compared to the 2014 collection where the expression level was moderate (Cameroon: alive FC = 3.8 ± 1.9 vs unexposed FC = 1.01 ± 0.7; Ghana: alive FC = 1.92 ± 0.9 vs unexposed FC = 1.64 ± 0.7; Uganda: alive FC = 8.3 ± 3.3 vs unexposed FC = 3.5 ± 4; Malawi: alive FC = 6.37 ± 1.1 vs unexposed FC = 6.4 ± 4.1) (Fig. 1 A). The most significant increase in ASL expression was observed in Uganda, where resistant mosquitoes showed higher levels compared to unexposed individuals; similarly, in Cameroon, ASL expression was elevated in resistant populations relative to unexposed groups, particularly in 2021. Genome-wide F ST differentiation and selection analysis across African populations around the argininosuccinate lyase ( ASL ) locus Genome-wide F ST differentiation analysis, conducted in non-overlapping windows of 10,000 SNPs across African samples (Cameroon, Ghana, Uganda, and Malawi), revealed a strong signal of differentiation on chromosome 3RL directly centered on the argininosuccinate lyase gene ( AFUN004002 ) with background F ST values superior to 0.2 (Fig. 2 ). This signal was observed in all comparisons involving samples from Tororo and Mayuge (Uganda) collected in 2009 and 2014, respectively, compared to other populations. (Fig. 2 A). Individual whole genome sequencing data also indicated a strong signal of genetic differentiation directly centered on the ASL gene in all comparisons involving the Uganda population (Fig. 2 B). To further investigate possible evidence selection between 2009 and 2014 Uganda population, we compute H 12 and H 1X across the 2L chromosome, identifying no signal of positive selection centered directly on the ASL gene ( LOC125762180 equivalent to AFUN004002 FUMOZ reference genome annotation) in any of the populations. Similarly, H 1X analysis on chromosome 2L revealed no shared signals of selection in this genomic region between populations, suggesting an absence of common selective sweeps at this locus between 2009 and 2014. Selective sweep at ASL locus in Anopheles funestus across Africa Genome-wide Tajima’s D and nucleotide diversity analyses in non-overlapping windows of 10,000 SNPs identified a major sweep spanning the ASL locus across all populations, including FANG and FUMOZ laboratory strains (Fig. 3 ). Reduced Tajima’s D values, ranging from − 3.5 to -4.5, are observed starting around 40Mb, with the sweep continuing through the ASL locus (marked by a blue dashed line) at 45Mb, displaying a consistent pattern across all populations (Fig. 3 A). A major valley forms beyond the ASL locus before the Tajima’s D values increase back to approximately − 3.5. The nucleotide diversity pattern aligns perfectly with the Tajima’s D results, indicating reduced nucleotide diversity across populations (Fig. 3 B). This suggests a strong selective sweep in the genomic region around the ASL locus, consistent across diverse populations, highlighting the importance of this locus in insecticide resistance in agreement with the genetic differentiation observed earlier (Fig. 2 A). Negative Tajima's D values typically indicate population expansion or purifying selection, while the lack of detection of selection using the H 12 test could suggest a hard sweep as it is sensitive to soft sweep. These findings suggest that the ASL locus has undergone strong selection, leading to reduced genetic diversity and the emergence to high frequency of advantageous alleles across populations. The sweep observed in FANG could potentially be the result of genetic drift, as we wouldn't expect a fully susceptible laboratory strain to be under strong selective pressure. Africa-wide polymorphism analysis of ASL gene between 2014 and 2021 An. funestus samples Polymorphism analysis and genetic diversity at the ASL ( LOC125762180 ) gene using sure-select and iWGS data Polymorphism analysis of the ASL gene using SureSelect data, generated with mosquitoes collected in 2014, revealed high genetic diversity across all populations (Fig. S2A and S2B). In a cohort of 68 mosquitoes, we identified 64 substitutions and 52 haplotypes within the 1.4 kb gene body of ASL across different countries. When mosquitoes were analysed by country, a similar pattern was observed across all populations, though Uganda exhibited lower polymorphism compared to other countries. This was evident in several parameters, most notably the lower number of substitution sites in Uganda (11 overall) compared to Cameroon (28) and Malawi (39). A similar scarcity of haplotypes was observed in Uganda (h = 11) and Cameroon (h = 10), while Malawi had a higher number of haplotypes (h = 22) (Table. S3). Consequently, haplotype diversity was lower but similar in Uganda (0.88) compared to Cameroon (1.0) and Malawi (0.98). This pattern was consistent for other metrics, such as nucleotide diversity (π), which was higher in Cameroon (0.004) and Malawi (0.004), while Uganda samples displayed lower nucleotide diversity (0.001), even when compared to reference strains such as FANG and FUMOZ. Both dead and alive mosquitoes from Uganda exhibited similarly lower nucleotide diversity (0.001) compared to Cameroon (0.004) and Malawi (0.004) (Table. S3). A similar pattern of high polymorphism was noted when analysing the non-coding regions (1kb upstream of the transcription start site (promoter)) of the ASL gene (Fig. S2C, S2D and Table. S4). Similarly, the genetic diversity analysis of the ASL gene ( LOC125762180 ) from 2014 iWGS samples reveals that the median nucleotide diversity (θπ) and Watterson’s theta (θw) were lower in the populations from Cameroon, Ghana, and Uganda compared to the Malawi population, which exhibited higher genetic diversity (Fig. S3A, S3C, S3D). This diversity was lower in Uganda compared to Cameroon aligning with the SureSelect data. This lower diversity in Cameroon, Ghana, and Uganda likely reflects similar evolutionary histories of this gene among these populations. In contrast, the Malawi population may have a distinct evolutionary history, contributing to its higher genetic diversity. Additionally, the median Tajima's D values for the ASL gene were near the equilibrium across all populations in line with SureSelect and H 12 selection scan reinforcing no evidence of selection of this gene in 2014 (Fig. S3B). Allelic variation at LOC125762180/ASL in An. funestus across Africa : Variant calling around the ASL differentiated region using PoolSeq data revealed replacement polymorphisms with low to moderate allele frequencies (DataSet 3). The most significant SNP was identified at position 3, where arginine (N) is substituted with threonine (Thr). In Cameroon, this SNP's frequency decreased from 44% in 2014 to 27%, while in Malawi, it dropped from 11.1–6.4%. Conversely, in Ghana, the frequency temporarily increased from 9–27%. In the Uganda Tororo population, no significant SNPs were found, with the highest frequency at only 3.9%. However, several low-frequency SNPs (< 20%) were detected in Uganda Mayuge. Notably, most SNP frequencies were null in the FANG fully susceptible laboratory strain. Individual whole-genome sequencing confirmed the N3T SNP at low to moderate frequencies: 28% in Cameroon, 19% in Ghana, 11% in Malawi, and 1% in Uganda. Overall, these results suggest that allelic variation in ASL SNPs is not the primary mechanism of pyrethroid resistance in An. funestus populations across Africa in 2014, indicating a need to investigate other mechanisms such as copy number variations (CNVs). Diplotype clustering analyses of LOC125762180 in An. funestus across Africa Applying diplotype calling method to the ASL ( LOC125762180 ) across 2014 samples from Cameroon, Ghana, Malawi, and Uganda, we identified a cluster, termed Cluster B (Fig. S4), characterised by a CNV that spans most of the Uganda population. This CNV is associated with genetically identical haplotypes displaying low heterozygosity. Notably, apart from this CNV, there were no SNPs linked to this selective sweep in the Uganda population, and no other amino acid variants were present except for N3T. However, N3T is absent in Uganda and is unlikely to be causative due to its physical location away from the gene's active site or the substrate binding pocket (Fig. S4). These findings suggest that the increased expression in Uganda's population is potentially driven by the CNV that has been present since 2014. We also observed another cluster, Cluster A, which includes samples from Uganda with moderate heterozygosity. In this cluster, the CNV spans only the Uganda population, while the N3T SNP is present in other populations lacking the CNV. A third cluster, Cluster C, comprises samples from Cameroon and Ghana, with some Cameroon samples harbouring the CNV. The LOC125762180 gene was found to increase expression in 2021 RNA-seq data, a result confirmed by RT-qPCR in Uganda samples. Therefore, we hypothesise that by increasing the expression of the ASL gene, this CNV could enhance the mosquito's ability to detoxify insecticides. However, this hypothesis still requires functional validation. Interestingly, the CNV appears to exist at variable copy numbers within the population. Evidence of ASL -based CNV allele in An. funestus from Uganda population PoolSeq data provided potential evidence of gene duplication spanning the ASL gene in 2014, which was subsequently confirmed using individual whole-genome sequencing (iWGS). Copy number variations (CNVs) were detected specifically in the Uganda population, with some samples showing more than three additional copies of the ASL gene (Fig. S5A). Copy number allele frequency analysis revealed that 86% of the 2014 Uganda samples (approaching fixation) possess this CNV allele (Fig. S5A, S5B). In contrast, only 11% of the 2014 samples from Cameroon exhibit this CNV (Fig. S5A), while it was absent in the populations from Ghana and Malawi. The findings reinforced previous observations suggesting that the overexpression of the ASL gene in the Uganda population could be associated with gene duplication. However, individual WGS data from 2021 samples is required to confirm the persistence and evidence of this CNV. Genetic variability of ASL Africa-wide by SANGER sequencing Genetic diversity of ASL using coding region of the gene across Africa Genetic variability analysis of ASL coding region (1437 bp) in a total of 50 field-resistant An. funestus samples across Africa revealed an overall high diversity of this gene using cloning, generating a total of 71 substitution sites (S), 33 haplotypes (h) and a high haplotype diversity (H d = 0.977) (Table. S5). However, comparative polymorphism analysis revealed the lowest diversity level in samples from Uganda (Eastern Africa), generating 8 substitution sites versus 45, 13 and 11, respectively for Mozambique (Southern Africa), Ghana (West Africa) and Cameroon (Central Africa). Similarly, laboratory samples FANG (N = 14) and FUMOZ (N = 10) respectively generated 18 and 21 polymorphic sites (S). Moreover, Ugandan samples (N = 17) generated 9 haplotypes with low nucleotide diversity (Pi = 0.00119), while samples from Mozambique (N = 12), Cameroon (N = 11) and Ghana (N = 10) respectively generated 11, 7 and 8 haplotypes (h) with higher nucleotide diversity values of 0.00804, 0.00187 and 0.00306 respectively for the three countries (Table. S5). Furthermore, computing Tajima’s (D) and the Fu and Li’s (D*) test statistics revealed negative values for Uganda, Mozambique, Cameroon and Ghana, this could indicate positive selection of this gene though at different rates in all these different populations (Table. S5). Phylogenetic tree analysis of the coding sequences ASL gene in 2021 revealed the clustering of Ugandan samples to form major dominant clades, different from the samples from other African regions (Mozambique, Ghana, Cameroon and FUMOZ) that clustered together, forming different minor clades (Fig. 3 C) this can suggest a possible selection of the gene in Uganda. Also, laboratory susceptible samples FANG have clustered together to form a dominant clade distinct from other clusters (Fig. 3 C). Similarly, haplotype networking of the ASL coding sequence revealed dominant haplotypes, 3 major haplotypes specific to Ugandan samples and 1 haplotype specific to FANG laboratory samples, while other African populations clustered together forming other minor clades (Fig. 3 D). Sequence analysis of the ASL gene detected a point mutation guanine (G) to adenine (A) nucleotide at position 374 in the open reading frame (ORF) leading to amino acid replacement arginine (R) to histidine (H) on codon 125 (R125H). This mutation was detected only in Ugandan samples at 17.6% (3/17) and completely absent in other African populations and we have used it for the functional validation. No other mutation was detected in the other three countries with a higher frequency than the one from the Ugandan population. Polymorphism analysis of 1kb putative promoter region To identify cis-regulatory variants associated with ASL -based pyrethroid resistance, we analysed a 1000-bp sequence upstream of the ASL gene in Ugandan mosquito samples, comparing F1 alive and dead individuals’ post-permethrin exposure. We also examined mosquitoes from Fang/Uganda and Fang/Cameroon crosses alongside the laboratory-susceptible strain FANG. Our polymorphism analysis indicated lower genetic diversity in the 5’UTR region of alive Ugandan samples, with 10 polymorphic sites and 7 haplotypes compared to 30 sites and 8 haplotypes in dead samples (Table. S6). Maximum likelihood tree plotting revealed distinct clustering of alive ASL sequences, forming a major clade separate from dead sequences (Fig. 3 E, 4 F). Key mutations were identified in the ASL promoter alleles of alive and dead Ugandan An. funestus mosquitoes, including C/G, T/C, G/A, T/A, and C/T transition at various positions upstream of the translation start site. These mutations occurred more frequently in alive samples (e.g., C/G at 84.6% vs. 33.3% in dead). An ACAT insertion was also noted, with a higher prevalence in alive samples (100% vs. 50%). The T/A SNP introduced a new binding site for transcription factor IIB (TFIIB), while the ACAT insertion created additional transcription factor binding sites such as TATA box. This T/A SNP was selected to develop a DNA-based diagnostic assay to track ASL -based resistance in the field. Transgenic expression of ASL increases resistance to insecticide in Drosophila To establish whether ASL overexpression and allelic variation can independently confer resistance to insecticides using the GAL4/UAS expression system, transgenic Drosophila melanogaster strains expressing each of the alleles (125H- ASL and R125- ASL ), transgenes were successfully generated under the control of the GAL4-Act5C driver. Confirmation of overexpression of transgenes by qRT-PCR To confirm the overexpression of ASL alleles in the F 1 progeny of the GAL4/UAS crosses (GAL4-125H- ASL and GAL4-R125- ASL ) after qRT-PCR we compared the generated crossing to the control the UAS line without the gene. The expression levels of the GAL4-125H- ASL flies (Fold change = 17.89) and the GAL4-R125- ASL flies (Fold change = 13.82) did not differ significantly (t-test = 0.09), (Fig. 4 A). Which revealed a higher expression in the progeny generated with the ubiquitous GAL4-Act5C driver. Expression of ASL in Drosophila flies confers higher resistance to pyrethroids From the exposure of transgenic flies to pyrethroids, it was observed that drosophila expressing the mutant-type 125H- ASL allele survived pyrethroid exposure better than control flies (not expressing An. funestus ASL ) and the wild-type R125- ASL allele for the three insecticides (Fig. 4 ). Indeed, significantly lower mortality rates were obtained with transgenic Drosophila expressing the 125H- ASL allele. For permethrin (4%), the GAL4-125H- ASL flies exhibited a significant low mortality rate of (4.3 ± 6.4%; 5.6 ± 6.5%; and 10.8 ± 6.4%; P˂0.001) than the GAL4-R125- ASL flies (34.9 ± 4.7%; 54.7 ± 8.9% and 92.9 ± 4.9%; P˃0.05) compared to the control not expressing ASL (43 ± 3.5%; 61.7 ± 2.6% and 91.7 ± 2%) at 6h, 12h, 24h respectively for each group (Fig. 4 B). For deltamethrin (0.2%), the GAL4-125H- ASL flies show a reduced average mortality of (13.2 ± 6.4%; 45.4 ± 5.5%; and 67.3 ± 6.4%; P˂0.05) than the GAL4-R125- ASL flies (32.3 ± 4.7%; 48.6 ± 8.9%; and 82.5 ± 4.9%; P˃0.05) compared to the control not expressing ASL (46.9 ± 3.4; 79.5 ± 2.6 and 93.5 ± 2.03) at 6h, 12h, 24h respectively (Fig. 4 C). For alpha-cypermethrin (0.0007%), a similar pattern was observed, the GAL4-125H- ASL flies had significantly lower mortality rates of (34.9 ± 2.9%; and 35.8 ± 1.9%; P˂0.001) than the GAL4-R125- ASL flies (63.3 ± 6.3% and 80 ± 5.7%; P˃0.05) compared to the control not expressing ASL (78.9 ± 4.8% and 82.2 ± 4.8%) at 12h, 24h respectively (Fig. 4 D). Averagely, significantly higher resistance was observed in the experimental group compared to the control group and GAL4-125H- ASL flies throughout the 24h while revealing no difference in mean mortality between GAL4-R125- ASL and control group (Fig. 4 E). This implies that at any given time of exposure, the experimental group was more resistant than the non-transgenic group and the overexpression of the GAL4-R125- ASL allele confers no significant resistance to permethrin and alpha-cypermethrin (Fig. 4 E). Overall, these observations suggest that overexpression of GAL4-125H- ASL would increase pyrethroid resistance in Drosophila , and consequently, overexpression of this gene would be involved in pyrethroid resistance in An. funestus . Impact of the knockdown of ASL on the susceptibility profile of Anopheles funestus Confirmation of knockdown effect of ASL by RT-qPCR To verify if the injection of ds ASL effectively suppressed the expression of the ASL gene in mosquitoes, the RT-qPCR analysis on cDNA derived from both injected and non-injected mosquitoes, using specific primers for ASL was conducted. Housekeeping genes Actin5C and RSP7 were employed as reference genes. Figure 4 F shows a noteworthy decrease in ASL gene expression in the mosquitoes injected with ds ASL (No-Ct) compared to the non-injected mosquitoes, with a p-value < 0.00001 4-day post-injection. This significant reduction in the expression of ASL in the injected mosquitoes compared to the non-injected group supports the conclusion that in vivo dsRNA of ASL injected effectively decreases the expression of the ASL gene in Mibellon An. funestus mosquitoes. Knockdown of Argininosuccinate lyase increased susceptibility to pyrethroids After the test, the mortality rate did not differ significantly between non-injected mosquitoes and those injected with ds GFP , indicating that the injection itself did not affect the mosquito survival. Bioassay carried out with mosquitoes injected with ds ASL significantly showed higher mortality rates when exposed to permethrin (50.8% ± 7.1; P < 0.001) compared to the non-injected (19% ± 2.5) and the injected with ds GFP (mortality rate 21.8% ± 5.5) 24h post-exposure (Fig. 4 G). After exposure of ds ASL -injected mosquito to deltamethrin the result revealed that the mortality rate was higher (25.3% ± 3.3; P < 0.05) compared to the non-injected (9.43% ± 2.03) and the injected with ds GFP (17.4% ± 2.6) with a significant difference between the non-injected and the injected with ds ASL (Fig. 4 H). By exposing ds ASL -injected mosquito to alpha-cypermethrin (Fig. 4 I), we observed a significant difference between the mortality rates of ds ASL (13.6% ± 1.6; P < 0.05) compared to the non-injected (10.4% ± 3.9) and then injected with ds GFP (6.3% ± 2.2). The T-277A ASL -resistant marker is associated with pyrethroid resistance Correlation of T-277A ASL marker with permethrin resistance To assess the efficacy of the T-277A- ASL diagnostic assay, the genotyping was performed using 40 F 1 mosquitoes (Alive and Dead each after 1h exposure to permethrin 0.75%) from Uganda 2021 collection. This result revealed a high prevalence (85%) of the resistant allele in the population. Genotyping of T-277A- ASL in the resistant mosquitoes revealed a predominance of the homozygote RR 72.5% (29/40) and moderate for the heterozygote 25% (10/40) with one bearing the homozygote susceptibility genotype (Fig. S6A). From the 40 dead mosquitoes 62.5% (25/40) were homozygous (RR) resistant, and 33% (13/40) were heterozygous (RS), with 5% (2/40) homozygous (SS) susceptible. A significant difference was not observed in the distribution of the three genotypes between mosquitoes that survived (alive) and those that died (dead) (Chi 2 = 3.3; P = 0.19, Chi-square). This corresponds to an allelic frequency distribution of 85% and 15% for R and S alleles in alive populations. For dead population, the distribution is 79% and 21% for R and S alleles respectively (Fig. S6B). In contrast, all FANG samples were homozygous SS for the marker. It was not possible to establish an association between the alive and dead samples from the field (F1) as the ASL -resistant (RR) marker was almost fixed in the population without significant difference in all comparisons (P = 0.13, Fisher’s exact test) (Fig. S6C). To clearly assess the association between the mutation and pyrethroid resistance, the T-277A- ASL was genotyped in the crossing Fang/Uganda F3 generation exposure to permethrin 0.75%, after 60 mins for alive and 30 mins for dead. A significant difference was observed in the distribution of the three genotypes between mosquitoes that survived (alive) and those that died (dead) (Chi 2 = 13.2; P < 0.001, Chi-square) (Fig. 5 C). Assessing the correlation between genotype and permethrin resistance revealed that homozygote resistant RR mosquitoes were significantly more able to survive exposure to permethrin than homozygote susceptible SS (OR = 5.7; CI = 2.1 to 15.4; P = 0.0007, Fisher’s exact test) (Table. S7). A similar significant correlation was observed when comparing RS vs SS (OR = 3.1; CI = 1.5 to 6.2; P = 0.0016, Fisher’s exact test) (Fig. 5 E). Moreover, possessing one R allele significantly increases the ability to survive compared to having the S allele (OR = 1.9; CI = 1.1 to 3.411; P = 0.023, Fisher’s exact test) (Fig. 5 D). T-277A- ASL correlates with reduced efficacy of LLINs in experiment huts The impact of the ASL resistance allele on the effectiveness of next-generation nets, PBO-based (PermaNet 3.0 and Olyset Plus) nets and chlorfenapyr-based nets (Interceptor G2) was assessed in semi-field condition using experimental huts. Genotyping of alive and dead collected in the room with PermaNet 3.0, Olyset Plus and Interceptor G2 nets revealed a significant difference in the distribution of the three genotypes between phenotypes (chi-square = 11.33; P < 0.001, chi-square) for PermaNet 3.0 (Fig. 5 F) and not for Olyset Plus due to the complete absence of the SS (Fig. 5 H). A comparison of genotypes based on mortality outcomes revealed that T-277A- ASL homozygote resistant (RR) mosquitoes had a significantly higher survival rate against PermaNet 3.0 than heterozygote (RS) mosquitoes (OR = 2.2; CI = 1.1164 to 3.6; P = 0.01) (Fig. 5 G). The presence of the R allele notably enhanced survival compared to the S allele (OR = 2.2; CI = 1.05 to 4.3; P = 0.03) (Fig. S6D, Table. S7). In the case of Olyset Plus nets, RR mosquitoes again demonstrated a significantly greater survival rate than RS mosquitoes (OR = 2; CI = 1.04 to 3.3; P = 0.03) (Fig. 5 H). However, no significant differences were observed between S and R alleles in this context (Fig. S6E). The Interceptor G2 (IG2) nets showed a significant negative association between T-277A- ASL genotypes and survival rates (chi-square = 13.1; P < 0.001) (Fig. 5 J). Comparisons revealed strong negative associations for RR vs SS and RS vs SS (both OR = Infinity; P < 0.005) (Fig. 5 K). Regarding blood-feeding ability, RR mosquitoes exhibited a significant advantage over RS mosquitoes with PermaNet 3.0 (OR = 11.96; P < 0.0001), while no significant differences were noted for Olyset Plus or IG2 nets, where strong negative associations were also observed (chi-square = 124.4; P < 0.0001) (Fig. S7). Africa-wide distribution of ASL-resistant marker The 277A_ ASL resistance allele ( ASLR ) was predominantly found in East Africa with a frequency of 84% in Uganda (2021) and 52% in Tanzania (2018). This high frequency is driven by the high proportion of the homozygote resistant genotype RR (67% and 35 respectively). The allele was also detected in southern Africa but at a more moderate level of 35% in Malawi and 25% in Mozambique (Fig. S8A) and with only a low frequency of the RR genotypes (11 and 10% respectively). West and Central Africa exhibited a lower frequency of the ASLR allele with 19% in Ghana, 6.25% in Cameroon (Mibellon) and 7% in Central Africa Republic (CAR) and low frequency of RR in Ghana and absence in CAR and in Mibellon (Fig. 6 A). A nation-wide distribution of ASLR in Cameroon confirmed its low frequency in this country with the RR detected only in a single location (Gounougou) out of 7 screened (Fig. 6 A, Fig. S8B). Altogether, this distribution range suggests that East Africa is the focal region of ASLR resistance allele especially Uganda. Africa-wide spatio-temporal distribution of T-277A_ ASL marker Temporal analysis of Ugandan samples (Eastern Africa) revealed a gradual increase in the frequency of the homozygote mutant genotype (RR) from 2010 (48%), 2016 (61%), 2021 (67%), 2022 (75%) to 2023 where it is reaching near fixation (92%) (Fig. 7B). This change is translated at the allele frequency level with an increase from 70–98% between 2010 and 2023. But surprisingly we found in 2024 a reduction of the RR genotype in the population and the resurgence of the SS in the population at 54% for the RR and 6% for the SS (Fig. 6 B). With the increase of the S allele and the reduction of the R allele in the population (Fig. S8C). This reduction coincides with the deployment of Interceptor G2 nets in this Ugandan location (Mayuge) since end of 2023. Impact of T-277A_ ASL on resistance escalation The analysis revealed a significant difference in the distribution of the three genotypes between dead and live mosquitoes (chi-square = 40.2; P < 0.0001, chi-square). Comparing all the alive alone for 1X, 5X 10X we noticed a significant difference (chi-square = 128.9; P < 0.0001, chi-square). The RR genotype is more present in mosquitoes resistant to 10X than 1X, with a significant difference, when we compared RR vs RS (OR = 0.4; CI = 0.22 to 0.72; P = 0.002, Fisher’s exact test). The allele distribution also revealed that mosquitoes carrying the R allele survive significantly more than the mosquitoes with the S allele (OR = 0.36; CI = 0.138 to 1; P = 0.04, Fisher’s exact test). When comparing the 5X and 10X in the resistant mosquitoes, the genotyping shows that RR has more chance to survive exposure than RS (OR = 0.34; CI = 0.18 to 0.6; P = 0.0003, Fisher’s exact test). This finding shows that the T-277A_ ASL marker is linked to resistance escalation when compared to 1X vs 10X and 5X vs 10X in alive mosquitoes (Fig. 6 C, Fig. S8D). However, there was no significant difference using the dead mosquitoes (Fig. 6 C, Fig. S8E). DISCUSSION Elucidating the complex molecular processes driving the exacerbation of insecticide resistance in vectors is paramount to improve vector control programs and inform on the design of novel insecticides. This study has uncovered and elucidated the contribution of a biochemical enzyme, argininosuccinate lyase to pyrethroid resistance in a major Afrotropical malaria vector, An. funestus and provides a DNA-based tool to not only track pyrethroid resistance escalation but also assess its impact on current and future insecticide-based vector control tools. Over-expression of argininosuccinate lyase correlates with increased pyrethroid resistance in An. funestus across the continent Recent transcriptomic studies have identified that pyrethroid resistant mosquitoes are expressing a suite of novel gene families beyond classical detoxification genes including ASL , a non-detoxification enzyme not previously explored in An. funestus (23, 24). Transcriptomic analyses revealed differential expression of ASL in resistant mosquitoes across various regions of Africa supporting previous reports (20, 23). This is further supported by Ibrahim et al. (2016) who observed similar overexpression in Malawi after exposure to carbamate/pyrethroid insecticides (31). Uganda exhibited the highest initial ASL overexpression in 2014 (no significant difference between resistant and susceptible mosquitoes). However, by 2021, expression levels had increased significantly in all locations, with the most pronounced rise observed in Uganda. This coincides with a reported aggravation of resistance within the same region (11) suggesting a potential involvement of ASL overexpression in resistance aggravation. In this study, we observed a significant induced overexpression of ASL in deltamethrin exposed mosquitoes. The overexpression of ASL was likely induced by a combination of stress response signaling, transcriptional regulation and metabolic adaption mechanisms. Insecticides especially at higher doses generate reactive oxygen species (ROS) as part of their toxic effects (32). Reactive oxygen species act as signaling molecules, activating transcription factors or stress response regulators which subsequently upregulate genes involved in metabolic adaptation, antioxidant defense and cellular repair such as ASL (29). High diversity observed and signature of selective sweep around argininosuccinate lyase The analysis of polymorphism of ASL highlights that this gene is under two contrasting evolutionary processes which is positive selection in Uganda linked with pyrethroid resistance but no selection and high diversity in other regions. However, this pattern could change with time and ASL in Uganda could migrate in other localities as seen for G454A-CYP9K1 which was so low in 2014 in Central Africa (Cameroon) but has now become fixed spreading from East Africa (Uganda) (17). The high genetic diversity observed at ASL is similar to the high polymorphism seen in some cytochrome P450s genes conferring pyrethroid resistance in malaria vectors such as CYP6M7 in An. funestus (33) but contrasts with strong evidence of positive selection seen in other major resistance genes in An. funestus (17, 21, 23), An. gambiae (3) or even in Drosophila (34). This study detected a major differentiation occurring in the ASL region within the Uganda populations in the absence of recent positive selection which is likely to be masked by low genetic diversity background in the population. Looking at the SureSelect and the cloned sequences, we noticed a low diversity in Uganda's population compared to Cameroon and Malawi, suggesting a different selection process for these populations. Furthermore, detection of a cluster of haplotypes in Uganda including one bearing the R125H is further proof of selection with amino acid change likely linked with greater ability to confer resistance to pyrethroid as seen for L119F (35), or M220I in CYP6P4a (21). Genetic differentiation from Poolseq spatio-temporally indicates that Uganda is under selection. The analysis of polymorphism patterns in the promoter region suggested a potential selection happening specifically in Uganda. Many new transcription factors binding sites and elements were found in Uganda alive after exposure to permethrin compared to the dead mosquitoes. However, further investigation is required to validate the extent of this selection and determine its effects on the cis -regulation found in the ASL gene which can lead to the increased expression of the gene. This study identified a candidate marker (T-277) in the promoter region of ASL gene that strongly associates with pyrethroid resistance similar to reported contribution of cis-regulatory factors to the up-regulation of key detoxification genes such as CYP6P9a/b in An. funestus (20, 23). PoolSeq data analysis revealed evidence of gene duplication in the ASL gene in 2014, CNV was detected in the Uganda population with some samples showing more than three copies of the ASL gene. This CNV likely contributes to the increased expression of ASL as reported in other detoxification genes such as Coeae1f and Coeae2f in An. gambiae (36) and carboxylesterase in Culex pipiens (37). ASL confers and exacerbates pyrethroid resistance: a challenge for resistance management In this study, using GAL4/UAS transgenic expression in Drosophila , we demonstrated that ASL over-expression is sufficient to confer resistance independently to both pyrethroids type I (permethrin) and moderate resistance to type II (deltamethrin and alpha-cypermethrin). It is the first time that the ASL gene has been functionally validated as conferring insecticide resistance and correlates with the signature of selection detected around this gene in Uganda. This resistance observed in ASL -flies supports that ASL over-expression contributes to pyrethroid resistance in field populations of mosquitoes (31). This validation of ASL is another indication that besides standard detoxification enzymes families such as P450s, GSTs and esterases, other enzymes are playing a key role as was shown for the sensory appendage proteins in An. gambiae (38). The difference in mortality observed between flies expressing the mutant and wild alleles reveals that the allelic variation impacts the ability of ASL to confer this resistance suggesting that ASL contribution to pyrethroid resistance is mediated through an up-regulation combined to allelic variation of coding region similar to cases reported for some key resistance genes including P450 (17, 21) for An. funestus , for (3) An. gambiae , (34) for Drosophila and GSTe in An. funestus (19) but also epsilon in Aedes aegypti (39). Moreover, the increase susceptibility to pyrethroids observed after ASL knockdown using RNAi further supports that ASL can confer resistance to pyrethroids. The role of ASL in insecticide resistance is not clear but we hypothesise that, unlike P450s, ASL indirectly confers insecticide resistance by fuelling metabolic and cellular pathways that are essential for managing the toxic effects of insecticides. We hypothesise that arginine, one of the products of ASL supports the synthesis of polyamines, proline and ammonia detoxification which are critical for cellular repair, membrane stability, stress response and energy production for instance during insecticide exposure. In humans ASL is a critical enzyme of the urea cycle acting as an intermediate enzyme in the synthesis pathway of urea with a defect in ASL leading to the accumulation of ammonia in the blood causing serious impairments (40). We hypothesise that in insects ASL could also be contributing to helping eliminate ammonia or other products of the xenobiotic metabolism pathway including for pyrethroids. Therefore, increased expression of ASL combined with selection of a variant boosting its activities could work to not only confer resistance to pyrethroids but even exacerbate it as seen with the greater frequency of RR observed in mosquitoes surviving 10X permethrin (41). A novel DNA-based assay allows tracking the spread of the ASL -based resistance and its impact on control tools The newly designed DNA-based assay was shown to be robust to detect and track the spread of ASL -based resistance across Africa. This is another evidence that metabolic-based resistance could be detected with simple DNA-based assays as shown previously for GSTs (35) and P450s (17, 20, 21, 23). The mutation associated with resistance here is located in the upstream promoter region as previously established for others metabolic resistance genes including P450s such as CYP6P9a/b in An. funestus (20), for previous observation in the mosquitoes Culex quinquefasciatus (42) and Drosophila (43). This newly developed DNA-based assay will complement other recently designed diagnostic assays for metabolic resistance which so far were only detoxification-based on P450s or GSTs. The CYP6P9a/b in An. funestus is currently used to monitor resistance in Southern Africa (20, 23). Other genes, such as CYP6P4a/b in West Africa (21) and CYP9K1 and 4.3kb transposon-containing structural variant in East/Central Africa (17, 44) have been recently detected and validated to track resistance in field populations. The higher frequency of ASL homozygote resistant mosquitoes surviving 10 times the diagnostic concentration is a strong indication that this gene also drives the aggravation of pyrethroid resistance in the field which is different from results obtained with other detoxication markers such as L119F- GSTe2 which although associated with deltamethrin resistance at 1X in a Ghanaian An. funestus population, was not at 10X (9). Similarly, no association was seen between CYP6P9a/b markers and escalation in Malawi although this was in a background of near fixation of these markers in the field (8). Therefore, ASL appears as one of the first marker that could allow to assess the impact of pyrethroid escalation in the field. Use of this ASL DNA-based marker in experimental hut study revealing that ASL is associated with the reduced efficacy of bed nets, particularly PBO-based nets (PermaNet 3.0 and Olyset Plus) which is different from results obtained with P450s which tend to mainly reduce efficacy of pyrethroid-only nets (8, 20). Therefore, in region of high ASL -based resistance, PBO should not be deployed as supported by the continuous increased frequency of this resistance allele in Uganda where PBO-based nets were deployed (11, 45). Chlorfenapyr-based nets (IG2), in contrast showed greater efficacy against ASL -based resistance with higher frequency of RR among the dead than the alive mosquitoes similar to the effect on P450-based resistance as recently reported in An. funestus (46). Therefore, this study highlights the efficacy of IG2 against both P450s and ASL -based resistance likely explaining the higher efficacy of this net in recent randomised-control trials in either East (47) or West (48) Africa. The marked reduction of the frequency of ASL from 92% in 2023 to just 54% in 2024 after the deployment of IG 2 LLINs suggests that ASL -based resistance could be managed by switching from PBO-based nets to chlorfenapyr-based nets such as IG2. It would be interesting to continue monitoring the frequency of ASL in areas of IG2-deployment to further assess the evolution of ASL allele and inform the design of resistance management strategies. CONCLUSION A comprehensive understanding of the molecular mechanisms driving insecticide resistance in malaria vectors is crucial for effective resistance management. Our study has identified a novel non-detoxification gene, argininosuccinate lyase ( ASL ), as a key factor in pyrethroid resistance escalation in Anopheles funestus across Africa. We have demonstrated that ASL overexpression in resistant populations, particularly in East Africa, is a key mechanism of aggravation of pyrethroid resistance. This finding led to the development of a simple DNA-based diagnostic assay to track the spread of ASL -mediated resistance in field populations showing that although ASL -based resistance is a threat to PBO-based net, this can be mitigated by rather deploying Chlorfenapyr-based net such as IG2. MATERIALS AND METHODS Study sites An. funestus s.s mosquitoes were collected between 2018 to 2024 from different countries across the four sub-Sahara African regions and where resistance escalation has been reported. This includes Mibellon in Cameroon (6°4′60′′N, 11°70′0′′E) (49), Obuasi in Ghana (6°17.377″N, 1°27.545″W) (9), Mayugue in Uganda (0°23′10.8′′N, 33°37′16.5′′E) (50), Chikwawa in Malawi (16°2′8′′S, 34°50′21′′N) (8) and Palmeira in Mozambique (25°15′19′′S, 32°52′22′′E) (10) (Fig. S9). The laboratory strains FANG (susceptible colony originated from Angola) and FUMOZ (resistant colony originated from Mozambique) (51) were also used in this study. Comparative transcriptomic profiling of argininosuccinate lyase ( ASL ) gene in Anopheles funestus across Africa A comparative transcriptomic analysis of the ASL gene ( AFUN004002 ) from four African countries, Mibellon (Cameroon), Obuasi (Ghana), Chikwawa (Malawi), and Mayuge (Uganda) was performed to examine its contribution in pyrethroid resistance in An . funestus . To this effect, previous RNAseq data (20, 23) was analysed following the methodology described by (24). Differentially expressed genes (DEGs) were analyzed by comparing the transcriptomes of mosquitoes that survived permethrin 1X exposure to those of unexposed populations from Ghana, Malawi, Uganda, and Cameroon to investigate the ASL response to permethrin exposure. Additionally, DEGs were identified by contrasting the transcription profiles of permethrin-resistant populations from these countries and a laboratory-resistant colony (FUMOZ) with the laboratory-susceptible strain (FANG). In each comparison, DEGs were determined globally, with a focus on detoxification-related genes, digestive enzymes and the ASL gene. DEG analysis was performed using DESeq2 (52), with overexpressed genes defined as those having a corrected p-value 1. REVIGO was used to perform gene ontology (GO) enrichment analysis on differentially expressed gene sets. A more extensive study was undertaken on all of the genes over-expressed in all four countries to identify groups of transcripts that are comparable to the ASL gene and may be interacting together to impart pyrethroid resistance to An. funestus . This was accomplished by first picking a list of transcripts that were considerably overexpressed in the same comparison as the ASL gene, followed by selecting the transcript of interest. Validation of RNA-Sequencing result by RT-qPCR Quantitative reverse transcription PCR (qRT-PCR) assay was performed to validate the expression profile of the candidate gene across Africa through RNAseq data results obtained in different countries comparatively in 2014 and 2021. RNA was extracted from three biological replicates of 10 mosquitoes, in the resistant (alive after exposure to pyrethroid), control (unexposed), FUMOZ (resistant Lab strain) and FANG (susceptible Lab strain). The Arcturus PicoPure RNA Isolation Kit (Life Technologies, Carlsbad, CA, USA) was used according to the manufacturer’s instructions. One (1) µg of each RNA sample was used as a template for complementary DNA (cDNA) synthesis using the superscript III (Invitrogen) with 1µl of oligo-dT20 and 1µl of RNase H, as the manufacturer’s guide. The qRT-PCR was carried out as previously described (53, 54). A standard curve of ASL was established using 5 dilutions of cDNA to assess PCR efficiency and quantify the differences between each dilution. The quantitative PCR (qPCR) amplification was carried out in an MX-PRO 3005 real-time PCR system (Agilent) using Brilliant III Ultra-Fast SYBR Green qPCR Master Mix (Agilent). The primers used for qPCR are listed in the supplemental file (Table. S8). A total of 1 ng/µl of cDNA from each sample was used as a template in a three-step program involving a denaturation for 3 minutes at 95°C followed by 40 cycles of 10s at 95°C and 10s at 60°C and a last step of 1 minute at 95°C, 30s at 55°C, and 30s at 95°C (total time of 1h12min54s). The relative expression level of each experimental group was compared to the reference susceptible strain FANG according to the 2- ΔΔCT method (55). Expression of the gene was normalised with the housekeeping genes ribosomal protein RSP7 ( AFUN007153 ) and Actin5C ( AFUN006819 ). Detection of genomic differentiation and selection ( F ST , H 1X , H 12 ) around ASL gene in An. funestus across Africa We performed genetic differentiation and selection analyses of An. funestus across Africa using Genome-Wide Association Studies of PoolSeq data (GWAS-PoolSeq) and individual whole-genome sequencing (iWGS) from MalariaGen data ( https://www.malariagen.net/project/anopheles-funestus-genomic-surveillance-project/ ). A windowed F ST analysis of chromosome 3RL where the ASL gene is located, using PoolSeq data collected between 2014 and 2021, was conducted with PoPoolation2, as previously described (56). For the iWGS, data available were collected in 2014, pairwise F ST values were estimated using Hudson’s method (57). We computed and plotted F ST values for multiple pairwise comparisons across our four populations (Cameroon, Ghana, Malawi, and Uganda) using the plot_fst_gwss function from the MalariaGEN package ( https://malariagen.github.io/malariagen-data-python/latest/Af1.html ). To detect evidence of recent positive selection around the ASL gene in our populations, we applied Garud's H 12 scans and plotted the results using the plot_h12_gwss function. Additionally, we computed and visualized H 1X scores with the plot_h1x_gwss function to identify shared selective sweeps between populations. All the selection analyses were calculated in windows of 1000 Single Nucleotide Polymorphisms ( SNPs ). CNV and diplotype calling around ASL gene using iWGS in An. funestus across Africa Increased gene dosage has been linked with gene overexpression in An. gambiae sl across its range (36, 58–60). To understand the genetic factors behind the overexpression of the ASL gene in some of our populations, copy number variation (CNV) in the 2RL:80–100 Mbp region surrounding the ASL locus was analyzed using iWGS data with the plot_cnv_hmm_coverage function, following the method described in (58). In brief, read counts for each individual were recorded in 300 bp non-overlapping windows and normalized by the per-individual mean number of reads in genome-wide autosomal 300 bp windows, stratified by GC content. These normalized coverage values were then used as input for a Gaussian Hidden Markov Model (HMM), with copy number states treated as hidden variables. CNVs were defined as regions with at least five consecutive 300 bp windows showing elevated HMM-predicted copy number states. A CNV allele was considered present in an individual if at least two diagnostic reads supported it. For diplotype calling, we utilized a recently implemented function in the MalariaGEN package to visualize diplotype (plot_diplotype_clustering_advanced), which combines two haplotypes, one from each chromosome, enabling us to focus on the gene of interest ( LOC125762180 ). This approach allowed us to identify selective sweeps, assess their extent, detect potential gene flow between countries, and investigate whether these sweeps are driven by copy number variants (CNVs), amino acid mutations, or both. Polymorphism of ASL gene in relation to pyrethroids resistance across Africa Analysis of polymorphism of ASL generated from SureSelect target-enrichment data To investigate whether allelic variation is associated with the increased expression of the ASL gene ( AFUN004002 , based on the FUMOZ reference annotation) in An. funestus populations across Africa, we analyzed previously generated SureSelect data (61). Initial processing and quality assessment of the sequencing data were performed using Strand NGS 3.4 (Strand Life Sciences). Alignment and mapping were conducted using the "DNA alignment" option against the whole genome (AfunF1 version), which was organized into three chromosomes through synteny with An. gambiae (23, 62). Comprehensive variant calling was carried out using VarScan for PoolSeq and Freebayes for SureSelect data, as previously described (61). The resulting VCF (Variants Calling Format) files were annotated, and gene variant effects were predicted using SnpEff (63) for both PoolSeq and SureSelect datasets. From the filtered VCF generated from the SureSelect data, sequences from the ASL gene, including both promoter and coding regions, were extracted using custom scripts. These sequences were analyzed to identify potential SNPs linked with ASL overexpression. Polymorphism analysis was conducted in dnaSP 6.1 (64) following multiple sequence alignment (MSA) with the ClustalW Multiple Alignment tool integrated in BioEdit (65). Haplotype network analysis was performed using the TCS package (Templeton, Crandall, and Sing) (66). For phylogenetic tree construction, FASTA sequences were aligned using MAFFT version 7.029 with the auto option. Phylogenetic trees were inferred using MEGA 11 (67) with maximum likelihood and 1000 bootstrap replicates. Additionally, we interrogated the recent MalariaGEN dataset on An. funestus genetic diversity across Africa to perform a similar analysis, focusing on the ASL gene ( LOC125762180 based on the AFUNGA1 reference annotation) in the four countries. Variant calling for this data was done with GATK. Diversity estimates were obtained using the plot_diversity_stats function, calculated in windows of 1,000 SNPs. We visualized nucleotide diversity (π), theta diversity (θ), and Tajima’s D to assess patterns of genetic variation within populations. Analysis of cDNA polymorphism of ASL The full coding sequence of ASL was amplified from mosquitoes cDNAs (alive after exposure to deltamethrin (0.05%), and unexposed across 4 countries (Cameroon, Ghana, Uganda, and Mozambique), the susceptible FANG and the resistant laboratory colony FUMOZ. The Phusion Taq Kit (Fermentas, Burlington, Ontario, Canada) was used as the enzyme for amplification with the two pairs of primer listed in Table S8. PCR reaction was performed using a total of 15µL, containing: 3µL of 5x HF buffer (1.5mM MgCl2), 0.12µL of 25Mm dNTPs, 0.51µL of 10mM forward and reverse primers, 0.15µL of Phusion Taq polymerase, 9.71 µL of deionized water and 1 µL of DNA template. The amplification uses the following conditions: an initial denaturation at 98°C for 1 min, followed by 35 cycles of denaturation at 98°C for 30s, annealing at 60°C for 30s and extension at 72°C for 90s. A final elongation step was performed at 72°C for 10 min. PCR products were gel-purified using the QIAquick Gel Extraction Kit and ligated into the pJET1.2/blunt cloning vector using the CloneJET PCR Cloning Kit. Recombinant ASL -pJET1.2 plasmids were transformed into E. coli DH5α cells. Plasmid DNA was miniprepped using the QIAprep Spin Miniprep Kit and sequenced using the pJET1.2-specific primers pJET1.2F and pJET1.2R. Sequence analysis was carried out using the same software as previously in the Sureselect section multiple alignment, haplotype network using TCS and phylogenetic trees using Mega. Polymorphism analysis of 1 kb putative promoter in Uganda We comparatively analysed the polymorphism of ASL gene between pyrethroid resistance mosquitoes from Uganda, focusing on the gene's promoter region. This was done by amplification and direct sequencing of 1,080 bp fragment of the promoter region of ASL located upstream of the gene. Five (05) F1 samples from Uganda dead after 60 min exposure to permethrin (susceptible mosquitoes) were analysed comparatively to five alive after 60 min exposure (resistant mosquitoes). We also analysed the crossing Fang/UGA F3 alive and dead after exposure to permethrin 60 and 30 min respectively generated in the insectary by crossing the female Fang and male from Uganda population collected in 2022. The ASL promoter region was amplified using the following conditions: an initial denaturation at 95°C for 5 min, followed by 35 cycles of denaturation at 94°C for 30s, annealing at 60°C for 30s and extension at 72°C for 90s. A final elongation step was performed at 72°C for 5 min. Amplified products were cleaned individually, cloned and purified. The polymorphic position was identified by manually analysing sequence traces using the same software as above. To detect cis-trans-regulatory element and transcription factor binding site, PROMO and TFBIND (68) software were used. GAL4/UAS transgenic expression of ASL in Drosophila melanogaster Due to the overexpression of ASL in resistant mosquitoes to pyrethroid in the African population, transgenic expression of this gene of interest was achieved using the Gal4-UAS system (54, 69). The mutant-type (Uganda) and the wild-type (FANG) alleles of ASL were independently expressed in D. melanogaster to determine if the expression of ASL alone and/or the presence of allelic variation can significantly contribute to the increased resistance level. Cloning and construction of transgenic Drosophila expressing An. funestus ASL gene Two Plasmids containing a mutant type ASL -resistant (125H- ASL ) allele from the Uganda population, and a wild-type ASL -susceptible (R125- ASL ) allele from Fang laboratory strain. The recombinant ASL -plasmids were then digested with the EcoRI and Xbal (Fermentas, Burlington, Ontario, Canada) restriction enzymes using the High-Fidelity enzyme kit from NEB-cutter (Fermentas, Burlington, Ontario, Canada) to extract the alleles of interest in the pJET1.2/blunt vector previously cloned in the genetic diversity analysis section. These candidate alleles were then cloned into the Drosophila expression vector pUASattB, with the same restriction enzymes. Following colony PCR, the resulting construct pUAS::125H- ASL , and pUAS::R125- ASL , were purified using Qiagen's midiprep kit according to the manufacturer's instructions. The midipreps were sequenced to ensure the ligation of alleles on pUAS vector. The construct, pUAS::125H- ASL and pUAS::R125- ASL were injected into the D. melanogaster germline carrying the attP40 binding site on chromosome 2 (y1w 67c23; P (CaryP) attP40,1;2) as previously described (33, 70). The Fly Facility ( https://www.flyfacility.gen.cam.acuk/ ) conducted the microinjection process and the balancing. Control Drosophila was injected with a gene-free vector. Expression of ASL in drosophila were active after crossing of pUAS::125H- ASL and the pUAS::R125- ASL males flies cary Actin II with virgin female from GAL4 flies line. After crossing, 2 to 5 days females from F1 generation with red eyes and curly wings expressing the 125H- ASL and R125- ASL were selected for insecticide contract assays. Validation of gene expression of ASL in transgenic Drosophila and insecticide susceptibility contact assay Confirmation of ASL expression in transgenic Drosophila and their absence in the control was done using quantitative real-time PCR (qPCR) using the Sybr Green as previously described with ASL -specific primers for flies (Table. S8), following the method described by Riveron in 2014 (Riveron et al., 2014b). To achieve this aim, total RNA was extracted from three pools of five (05) females Drosophila from the first generation of the pUAS::125H- ASL and the pUAS::R125- ASL and the control line after crossing, and the different cDNA were synthesized. Comparative gene expression levels between experimental and control Drosophila were normalized using the RPL11 housekeeping gene. To assess whether expression of ASL alone can confer insecticide resistance independently of other resistant mechanisms, first-generation of transgenic females expressing ASL were selected as experimental groups for insecticide exposure. In practice, 5 replicates of 20 to 25 transgenic females of Drosophila aged 2 to 5 days from the experimental and control groups were exposed to pyrethroid (permethrin (4%), deltamethrin (0.2%), and alpha-cypermethrin (0.0007%)). Mortalities were recorded after 1h, 2h, 3h, 6h, 12h and 24h at a temperature of 25–27°C and a relative humidity of 70–80%. A comparison of the cumulative mortality rates during the six time points was done between the experimental flies line expressing 125H- ASL and R125- ASL alleles and control groups using student t-test statistical analysis. Investigation of the role of the knockdown of ASL on mosquitoes’ susceptibility to insecticide through RNAi silencing Double-stranded RNAs (dsRNA) specific to ASL were synthesized for gene-silencing experiments as previously described (19). Double-strand ASL oligonucleotide primers (Table. S8) were designed using the E-RNAi web application ( http://e-rnai.dkfz.de/ ) with a product length of 450bp. Specific ASL fragment was amplified from pJET::ASL plasmid using KAPA Taq Kit (Kapa Biosystems, Wilmington, MA USA) following this step: initial denaturation step of 5 minutes at 95°C, followed by 35 cycles of 30s at 94°C, 30s at 65°C, and 60s at 72°C with 10 minutes at 72°C for final extension. PCR products were purified using Quiagen QIAquick PCR purification kit following the manufacturer’s instructions. dsRNA was synthesized using in vitro transcription MEGAscript® T7 Kit (Thermo Fisher Scientific, UK) and purified using MEGAclear columns (Thermo Fisher Scientific, UK) with an incubation of 37°C during 16h. The resultant dsRNA product was analysed using a nanodrop spectrometer (Nanodrop Technologies, UK) and subsequently concentrated to 3µg/µl by using ethanol precipitation, the dsRNA was resuspended in nuclease-free water and stored at − 20°C. Briefly, 69 nl of ds ASL or dsGFP (control) were injected directly into the thorax of mosquitoes An. funestus population from Mibellon, 3–5 day old after sleeping with CO 2 , and were kept 72h in insectary for the silencing effect of the ASL gene to be effective. To confirm ASL expression knockdown, RNA of ds ASL injected and non-injected mosquitoes was extracted from 3 pools of 10 mosquitoes using the Arcturus PicoPure RNA Isolation Kit (Life Technologies, Carlsbad, CA, USA) and cDNA synthesized using the Super-Script III (Invitrogen, Carlsbad, CA, USA) according to the manufacturer’s instructions. To assess the knockdown efficiency after injection and the quantitative difference in the level of ASL expression between injected and non-injected mosquitoes. The relative expression and fold-change of ASL were calculated according to the 2- ΔΔCT Livak method, comparing the expression level ASL in ds ASL -injected and non-injected mosquitoes, after normalization with the housekeeping genes, RPS7 (AFUN007153) and actin5C (AFUN006819), as described above. Four days after injection, four replicates of 20–25 mosquitoes for each dsRNA were exposed to permethrin (0.75%), deltamethrin (0.05%) for 1h and alpha-cypermethrin (0.05%) for 1h30minutes following the WHO testing protocol (71). Mosquitoes were transferred to holding tubes after exposure, supplemented with sugar and mortalities were counted 24 h after the exposure. The susceptibility test was comparatively performed in triplicates between mosquitoes with ds ASL , ds GFP and those not injected. Design of DNA-based assay to detect ASL -mediated pyrethroid resistance The ARMS-PCR (amplification refractory mutation system) tool was used to design a simple Allele Specific PCR that could discriminate between the ASL_ T-277A promoter of the resistant (Uganda) and the susceptible (FANG) strains. Four sets of primers were designed two outers and two inners (Table. S8) to amplify the gene with the different variants. PCR reaction was performed with a final volume of 15µl containing 1× buffer A, 25 mM MgCl 2 , 25 mM dNTPs, and 10 mM of each primer and 1U of KAPA Taq polymerase (Kapa Biosystems, Boston, MA, USA). The following PCR amplification conditions were used initial denaturation step of 5 minutes at 95°C, followed by 35 cycles of 30s at 94°C, 30s at 57°C, and 90s at 72°C with 10 minutes at 72°C for the final extension according to the KAPA kit instructions. The PCR products were visualized using 1.5% agarose gel electrophoresis to confirm the product sizes. Two bands were found at 654 bp for the common allele, 381 bp for the resistant allele, AA (RR) and 272 bp for the susceptible allele, TT (SS). To validate the robustness of the allele-specific PCR to detect the pyrethroid resistance in the field population, F3 progeny from a cross between highly resistant (Uganda) and highly susceptible (FANG) strains were genotyped and correlated with the resistance phenotype established using the odds ratio and Fisher’s exact test. Furthermore, a Locked Nucleic Acid (LNA) assay b-based diagnostic tool was also developed for the ASL _T-277A SNP with a primer set flanking each primer (Table. S9) and covering the region with the mutation. Two locked nucleic acid (LNA) based probes conjugated to FAM for the mutant and HEX for the wild-type alleles were commercially designed and acquired from IDT ( http://biophysics.idtdna.com/ ) (Table. S9). PCR amplification was carried out in a final volume of 10µl containing 1µmole of each probe, 2µmole of each primer in 1x PrimeTime Master Mix (IDT) or 1x Luna Universal qPCR Master Mix (NEB) and 1µl of genomic DNA. The reactions were set up in optical PCR tubes and conducted on an AriaMX Real-Time qPCR cycler (Agilent, USA) using Fam and Hex filters. The ASL _T-277A-LNA PCR test included 10 minutes of denaturation at 95°C; (Segment 1) and 40 cycles of denaturation for 10s at 95°C, annealing for 45s at 60°C; (Segment 2). Association between the ASL marker and the pyrethroid resistance The high level of pyrethroid resistance, coupled with the high frequency of the resistance alleles in the Ugandan field population made the establishment of the link between the genotype and the resistance phenotype difficult. To address this challenge, a mosquito genetic cross between female FANG bearing the homozygote susceptible (SS) ASL T-277/T-277 genotype and male field mosquitoes from Uganda bearing the homozygote resistant (RR) ASL − 277A/-277A genotype was established as done for the development of CYP6P4a/b and CYP9K1 markers (17, 21). The crosses were maintained through to the third generation to allow the three genotypes (RR, RS, and SS) to segregate. WHO tube bioassays were carried out on three- to five-day-old hybrid female mosquitoes using pyrethroid insecticide papers following WHO protocol (71) and the correlation between resistance phenotype and genotypes was established using odds ratio. Impact of T-277A- ASL marker on LLINs’ efficacy using experimental hut trials This study was performed in Mayuge a village in Uganda where 12 experimental huts have recently been built with concrete bricks following the specific design for the experimental hut from the West Africa region (71). This part of the work was done by a colleague from Uganda. Africa-wide spatio-temporal assessment of the spread of T-277A- ASL marker To assess the spread and temporal changes in the frequency of the T-277A- ASL marker in Africa, An. funestus samples previously collected at different time points in the same localities across Africa, involving eastern Africa (Uganda 2010, 2016, 2021,2022 and 2023, and Tanzania 2018), central Africa (Cameroon (Mibellon 2021 and 2023, Elende 2021 and 2024, Obout 2018, Elon 2018, Gounougou 2021, Tibati 2021, Njombe 2021, and Central Africa Republic 2016), southern Africa (Malawi 2021) and western Africa (Ghana 2021) were used for the study (17). Additionally, we checked the distribution of the marker after exposure to a high dose of insecticide we used Ugandan samples from 2021 F1 exposed to permethrin 1X, 5X and 10X alive and dead. Thirty to forty parental mosquitoes were genotyped per locality using the above diagnostic tools. Data analysis Statistical analysis was carried out with Prism 8 (GraphPad Software, San Diego, California USA, www.graphpad.com ), and alpha values for significance were taken at P < 0.05, with all confidence intervals (CI) at 95%. Student's t-test was used to compare two columns of data generated from metabolism assays and contact bioassays with transgenic D. melanogaster flies. Fisher's exact test was performed to assess whether any difference in proportion was found for the genotype contingency tables using MedCalc Software, as we got zero value for some genotypes (Ltd. Odds ratio calculator. https://www.medcalc.org/calc/odds_ratio.php Version 23.0.2; accessed September 3, 2024) (Schoonjans, 2024). The statistical significances indicated by asterisks: P > 0.05, *P < 0.05, **P < 0.01, ***P < 0.001 and ****P < 0.0001. Declarations Funding This work was supported by a Wellcome Trust Senior Research Fellowships in Biomedical Sciences to CSW (217188/Z/19/Z) and a Bill and Melinda Gates Foundation grant to CSW (INV24 006003). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Author Contributions Design and Conceptualization: CSW Methodology: VBNF, MFMK, GM, LMJM, and CSW Sample collection: VBNF, MT, AO, CSW Investigation: VBNF, GM, MFMK, LMJM, MT, CSTD and CSW Visualization: VBNF, GM, LMJM, MFMK, CSW Supervision: MT, LMJM, and CSW Writing—original draft: VBNF Writing—review & editing: VBNF, GM, MFMK, LMJM, MT, CSTD, AO and CSW. Competing interests: The authors declare that they have no competing interests. Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. References Bhatt S, Weiss DJ, Cameron E, Bisanzio D, Mappin B, Dalrymple U, et al. The effect of malaria control on Plasmodium falciparum in Africa between 2000 and 2015. Nature. 2015;526(7572):207-11. Ueda M, Takeuchi N, Kaneko K. Stronger selection can slow down evolution driven by recombination on a smooth fitness landscape. PloS one. 2017;12(8):e0183120. Vontas J, Grigoraki L, Morgan J, Tsakireli D, Fuseini G, Segura L, et al. Rapid selection of a pyrethroid metabolic enzyme CYP9K1 by operational malaria control activities. Proceedings of the National Academy of Sciences. 2018;115(18):4619-24. Liu N. Insecticide resistance in mosquitoes: impact, mechanisms, and research directions. Annu Rev Entomol. 2015;60:537-59. Mulamba C, Riveron JM, Ibrahim SS, Irving H, Barnes KG, Mukwaya LG, et al. Widespread pyrethroid and DDT resistance in the major malaria vector Anopheles funestus in East Africa is driven by metabolic resistance mechanisms. PLoS One. 2014;9(10):e110058. Wood O, Hanrahan S, Coetzee M, Koekemoer L, Brooke B. Cuticle thickening associated with pyrethroid resistance in the major malaria vector Anopheles funestus. Parasites & vectors. 2010;3:1-7. Djondji Kamga FM, Mugenzi LMJ, Tchouakui M, Sandeu MM, Maffo CGT, Nyegue MA, et al. Contrasting Patterns of Asaia Association with Pyrethroid Resistance Escalation between the Malaria Vectors Anopheles funestus and Anopheles gambiae. Microorganisms. 2023;11(3). Menze BD, Tchouakui M, Mugenzi LMJ, Tchapga W, Tchoupo M, Wondji MJ, et al. Marked aggravation of pyrethroid resistance in major malaria vectors in Malawi between 2014 and 2021 is partly linked with increased expression of P450 alleles. BMC Infect Dis. 2022;22(1):660. Mugenzi LMJ, Akosah-Brempong G, Tchouakui M, Menze BD, Tekoh TA, Tchoupo M, et al. Escalating pyrethroid resistance in two major malaria vectors Anopheles funestus and Anopheles gambiae (s.l.) in Atatam, Southern Ghana. BMC Infect Dis. 2022;22(1):799. Riveron JM, Huijben S, Tchapga W, Tchouakui M, Wondji MJ, Tchoupo M, et al. Escalation of pyrethroid resistance in the malaria vector Anopheles funestus induces a loss of efficacy of piperonyl butoxide–based insecticide-treated nets in Mozambique. The Journal of infectious diseases. 2019;220(3):467-75. Tchouakui M, Mugenzi LMJ, B DM, Khaukha JNT, Tchapga W, Tchoupo M, et al. Pyrethroid Resistance Aggravation in Ugandan Malaria Vectors Is Reducing Bednet Efficacy. Pathogens. 2021;10(4). Hemingway J, Hawkes NJ, McCarroll L, Ranson H. The molecular basis of insecticide resistance in mosquitoes. Insect biochemistry and molecular biology. 2004;34(7):653-65. Cuamba N, Morgan JC, Irving H, Steven A, Wondji CS. High level of pyrethroid resistance in an Anopheles funestus population of the Chokwe District in Mozambique. PLoS One. 2010;5(6):e11010. Djouaka R, Irving H, Tukur Z, Wondji CS. Exploring mechanisms of multiple insecticide resistance in a population of the malaria vector Anopheles funestus in Benin. PLoS One. 2011;6(11):e27760. Morgan JC, Irving H, Okedi LM, Steven A, Wondji CS. Pyrethroid resistance in an Anopheles funestus population from Uganda. PLoS One. 2010;5(7):e11872. Wondji CS, Coleman M, Kleinschmidt I, Mzilahowa T, Irving H, Ndula M, et al. Impact of pyrethroid resistance on operational malaria control in Malawi. Proc Natl Acad Sci U S A. 2012;109(47):19063-70. Djoko Tagne CS, Kouamo MF, Tchouakui M, Muhammad A, Mugenzi LJ, Tatchou-Nebangwa NM, et al. A single mutation G454A in the P450 CYP9K1 drives pyrethroid resistance in the major malaria vector Anopheles funestus reducing bed net efficacy. Genetics. 2024:iyae181. Kengne-Ouafo JA, Kouamo M, Muhammad A, Tepa A, Ntadoun S, Mugenzi L, et al. A single E205D allele of a key P450 CYP6P3 is driving metabolic pyrethroid resistance in the major African malaria vector Anopheles gambiae. bioRxiv. 2024:2024.02. 18.580859. Kouamo MFM, Ibrahim SS, Hearn J, Riveron JM, Kusimo M, Tchouakui M, et al. Genome-Wide Transcriptional Analysis and Functional Validation Linked a Cluster of Epsilon Glutathione S-Transferases with Insecticide Resistance in the Major Malaria Vector Anopheles funestus across Africa. Genes (Basel). 2021;12(4). Mugenzi LMJ, Menze BD, Tchouakui M, Wondji MJ, Irving H, Tchoupo M, et al. Cis-regulatory CYP6P9b P450 variants associated with loss of insecticide-treated bed net efficacy against Anopheles funestus. Nat Commun. 2019;10(1):4652. Tatchou-Nebangwa NM, Mugenzi LM, Muhammad A, Nebangwa DN, Kouamo MF, Tagne CSD, et al. Two highly selected mutations in the tandemly duplicated CYP6P4a and CYP6P4b genes drive pyrethroid resistance in Anopheles funestus in West Africa. BMC biology. 2024;22(1):286. Sandeu MM, Mulamba C, Weedall GD, Wondji CS. A differential expression of pyrethroid resistance genes in the malaria vector Anopheles funestus across Uganda is associated with patterns of gene flow. PLoS One. 2020;15(11):e0240743. Weedall GD, Mugenzi LM, Menze BD, Tchouakui M, Ibrahim SS, Amvongo-Adjia N, et al. A cytochrome P450 allele confers pyrethroid resistance on a major African malaria vector, reducing insecticide-treated bednet efficacy. Science translational medicine. 2019;11(484):eaat7386. Wondji CS, Hearn J, Irving H, Wondji MJ, Weedall G. RNAseq-based gene expression profiling of the Anopheles funestus pyrethroid-resistant strain FUMOZ highlights the predominant role of the duplicated CYP6P9a/b cytochrome P450s. G3. 2022;12(1):jkab352. Zhao A, Li Y, Leng C, Wang P, Li Y. Inhibitory effect of protease inhibitors on larval midgut protease activities and the performance of Plutella xylostella (Lepidoptera: Plutellidae). Frontiers in Physiology. 2019;9:1963. Yu B, Howell P. Intragenic complementation and the structure and function of argininosuccinate lyase. Cellular and Molecular Life Sciences CMLS. 2000;57:1637-51. Wan PJ, Yang L, Wang WX, Fan JM, Fu Q, Li GQ. Constructing the major biosynthesis pathways for amino acids in the brown planthopper, N ilaparvata lugens S tål (H emiptera: D elphacidae), based on the transcriptome data. Insect Molecular Biology. 2014;23(2):152-64. Davies S-A. Nitric oxide signalling in insects. Insect Biochemistry and Molecular Biology. 2000;30(12):1123-38. Meireles P, Mendes AM, Aroeira RI, Mounce BC, Vignuzzi M, Staines HM, et al. Uptake and metabolism of arginine impact Plasmodium development in the liver. Scientific Reports. 2017;7(1):4072. Castro FLdS, Kim WK. Secondary functions of arginine and sulfur amino acids in poultry health. Animals. 2020;10(11):2106. Ibrahim SS, Ndula M, Riveron JM, Irving H, Wondji CS. The P450 CYP6Z1 confers carbamate/pyrethroid cross-resistance in a major African malaria vector beside a novel carbamate-insensitive N485I acetylcholinesterase-1 mutation. Mol Ecol. 2016;25(14):3436-52. Otali D, Novak RJ, Wan W, Bu S, Moellering DR, De Luca M. Increased production of mitochondrial reactive oxygen species and reduced adult life span in an insecticide-resistant strain of Anopheles gambiae. Bulletin of entomological research. 2014;104(3):323-33. Riveron JM, Ibrahim SS, Chanda E, Mzilahowa T, Cuamba N, Irving H, et al. The highly polymorphic CYP6M7 cytochrome P450 gene partners with the directionally selected CYP6P9a and CYP6P9b genes to expand the pyrethroid resistance front in the malaria vector Anopheles funestus in Africa. BMC genomics. 2014;15:1-19. Schlenke TA, Begun DJ. Strong selective sweep associated with a transposon insertion in Drosophila simulans. Proceedings of the National Academy of Sciences. 2004;101(6):1626-31. Riveron JM, Yunta C, Ibrahim SS, Djouaka R, Irving H, Menze BD, et al. A single mutation in the GSTe2 gene allows tracking of metabolically based insecticide resistance in a major malaria vector. Genome biology. 2014;15:1-20. Nagi SC, Lucas ER, Egyir-Yawson A, Essandoh J, Dadzie S, Chabi J, et al. Parallel evolution in mosquito vectors - a duplicated esterase locus is associated with resistance to pirimiphos-methyl in An. gambiae. bioRxiv. 2024. Buss D, Callaghan A. Molecular comparisons of the Culex pipiens (L.) complex esterase gene amplicons. Insect biochemistry and molecular biology. 2004;34(5):433-41. Ingham VA, Anthousi A, Douris V, Harding NJ, Lycett G, Morris M, et al. A sensory appendage protein protects malaria vectors from pyrethroids. Nature. 2020;577(7790):376-80. Lumjuan N, Rajatileka S, Changsom D, Wicheer J, Leelapat P, Prapanthadara L-a, et al. The role of the Aedes aegypti Epsilon glutathione transferases in conferring resistance to DDT and pyrethroid insecticides. Insect biochemistry and molecular biology. 2011;41(3):203-9. O'Brien WE, McInnes R, Kalumuck K, Adcock M. Cloning and sequence analysis of cDNA for human argininosuccinate lyase. Proceedings of the National Academy of Sciences. 1986;83(19):7211-5. Ficicioglu C, Mandell R, Shih V. Argininosuccinate lyase deficiency: longterm outcome of 13 patients detected by newborn screening. Molecular genetics and metabolism. 2009;98(3):273-7. Komagata O, Kasai S, Tomita T. Overexpression of cytochrome P450 genes in pyrethroid-resistant Culex quinquefasciatus. Insect biochemistry and molecular biology. 2010;40(2):146-52. Daborn P, Yen J, Bogwitz M, Le Goff G, Feil E, Jeffers S, et al. A single P450 allele associated with insecticide resistance in Drosophila. Science. 2002;297(5590):2253-6. Mugenzi LMJ, Tekoh TA, Ntadoun ST, Chi AD, Gadji M, Menze BD, et al. Association of a rapidly selected 4.3kb transposon-containing structural variation with a P450-based resistance to pyrethroids in the African malaria vector Anopheles funestus. PLoS Genet. 2024;20(7):e1011344. Staedke SG, Gonahasa S, Dorsey G, Kamya MR, Maiteki-Sebuguzi C, Lynd A, et al. Effect of long-lasting insecticidal nets with and without piperonyl butoxide on malaria indicators in Uganda (LLINEUP): a pragmatic, cluster-randomised trial embedded in a national LLIN distribution campaign. The Lancet. 2020;395(10232):1292-303. Tchouakui M, Ibrahim S, Mangoua M, Thiomela R, ASSATSE T, Ngongang-Yipmo S, et al. Substrate promiscuity of key resistance P450s confers clothianidin resistance whilst increasing chlorfenapyr potency in malaria vectors. 2024. Mosha JF, Kulkarni MA, Lukole E, Matowo NS, Pitt C, Messenger LA, et al. Effectiveness and cost-effectiveness against malaria of three types of dual-active-ingredient long-lasting insecticidal nets (LLINs) compared with pyrethroid-only LLINs in Tanzania: a four-arm, cluster-randomised trial. The Lancet. 2022;399(10331):1227-41. Accrombessi M, Cook J, Dangbenon E, Yovogan B, Akpovi H, Sovi A, et al. Efficacy of pyriproxyfen-pyrethroid long-lasting insecticidal nets (LLINs) and chlorfenapyr-pyrethroid LLINs compared with pyrethroid-only LLINs for malaria control in Benin: a cluster-randomised, superiority trial. The Lancet. 2023;401(10375):435-46. Natchema SFB, Tchouakui M, Menze BD, Mugenzi LMJ, Fofie D, Nguiffo-Nguete D, et al. Entomological longitudinal surveys in two contrasted eco-climatic settings in Cameroon reveal a high malaria transmission from Anopheles funestus associated with GSTe2 metabolic resistance. BMC Infect Dis. 2023;23(1):738. Tchouakui M, Mugenzi LMJ, Wondji MJ, Tchoupo M, Njiokou F, Wondji CS. A 6.5kb Intergenic Structural Variation Exacerbates the Fitness Cost of P450-Based Metabolic Resistance in the Major African Malaria Vector Anopheles funestus. Genes (Basel). 2022;13(4). Hunt R, Brooke B, Pillay C, Koekemoer L, Coetzee M. Laboratory selection for and characteristics of pyrethroid resistance in the malaria vector Anopheles funestus. Medical and veterinary entomology. 2005;19(3):271-5. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550. Kwiatkowska RM, Platt N, Poupardin R, Irving H, Dabire RK, Mitchell S, et al. Dissecting the mechanisms responsible for the multiple insecticide resistance phenotype in Anopheles gambiae s.s., M form, from Vallee du Kou, Burkina Faso. Gene. 2013;519(1):98-106. Riveron JM, Irving H, Ndula M, Barnes KG, Ibrahim SS, Paine MJ, et al. Directionally selected cytochrome P450 alleles are driving the spread of pyrethroid resistance in the major malaria vector Anopheles funestus. Proc Natl Acad Sci U S A. 2013;110(1):252-7. Schmittgen TD, Livak KJ. Analyzing real-time PCR data by the comparative CT method. Nature protocols. 2008;3(6):1101-8. Kofler R, Pandey RV, Schlotterer C. PoPoolation2: identifying differentiation between populations using sequencing of pooled DNA samples (Pool-Seq). Bioinformatics. 2011;27(24):3435-6. Hudson RR, Slatkin M, Maddison WP. Estimation of levels of gene flow from DNA sequence data. Genetics. 1992;132(2):583-9. Lucas ER, Miles A, Harding NJ, Clarkson CS, Lawniczak MKN, Kwiatkowski DP, et al. Whole-genome sequencing reveals high complexity of copy number variation at insecticide resistance loci in malaria mosquitoes. Genome Res. 2019;29(8):1250-61. Lucas ER, Nagi SC, Egyir-Yawson A, Essandoh J, Dadzie S, Chabi J, et al. Genome-wide association studies reveal novel loci associated with pyrethroid and organophosphate resistance in Anopheles gambiae and Anopheles coluzzii. Nat Commun. 2023;14(1):4946. Njoroge H, Van't Hof A, Oruni A, Pipini D, Nagi SC, Lynd A, et al. Identification of a rapidly-spreading triple mutant for high-level metabolic insecticide resistance in Anopheles gambiae provides a real-time molecular diagnostic for antimalarial intervention deployment. Mol Ecol. 2022;31(16):4307-18. Hearn J, Djoko Tagne CS, Ibrahim SS, Tene-Fossog B, Mugenzi LMJ, Irving H, et al. Multi-omics analysis identifies a CYP9K1 haplotype conferring pyrethroid resistance in the malaria vector Anopheles funestus in East Africa. Mol Ecol. 2022;31(13):3642-57. Weedall GD, Riveron JM, Hearn J, Irving H, Kamdem C, Fouet C, et al. An Africa-wide genomic evolution of insecticide resistance in the malaria vector Anopheles funestus involves selective sweeps, copy number variations, gene conversion and transposons. PLoS Genet. 2020;16(6):e1008822. Cingolani P, Platts A, Wang le L, Coon M, Nguyen T, Wang L, et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly (Austin). 2012;6(2):80-92. Rozas J, Ferrer-Mata A, Sánchez-DelBarrio JC, Guirao-Rico S, Librado P, Ramos-Onsins SE, et al. DnaSP 6: DNA sequence polymorphism analysis of large data sets. Molecular biology and evolution. 2017;34(12):3299-302. Rozewicki J, Li S, Amada KM, Standley DM, Katoh K. MAFFT-DASH: integrated protein sequence and structural alignment. Nucleic Acids Res. 2019;47(W1):W5-W10. Múrias dos Santos A, Cabezas MP, Tavares AI, Xavier R, Branco M. tcsBU: a tool to extend TCS network layout and visualization. Bioinformatics. 2016;32(4):627-8. Tamura K, Stecher G, Kumar S. MEGA11: Molecular Evolutionary Genetics Analysis Version 11. Mol Biol Evol. 2021;38(7):3022-7. Tsunoda T, Takagi T. Estimating transcription factor bindability on DNA. Bioinformatics (Oxford, England). 1999;15(7):622-30. Daborn PJ, Lumb C, Harrop TW, Blasetti A, Pasricha S, Morin S, et al. Using Drosophila melanogaster to validate metabolism-based insecticide resistance from insect pests. Insect biochemistry and molecular biology. 2012;42(12):918-24. Markstein M, Pitsouli C, Villalta C, Celniker SE, Perrimon N. Exploiting position effects and the gypsy retrovirus insulator to engineer precisely expressed transgenes. Nat Genet. 2008;40(4):476-83. WHO. Test procedures for insecticide resistance monitoring in malaria vector mosquitoes. 2016. Additional Declarations There is NO Competing Interest. Supplementary Files NgannangetalSupplementaryMaterialsNatComSubmitted190225.pdf SUPPLEMENTARY MATERIALS This PDF file includes: Figs. S1 to S9 Tables. S1 to S9 DataSet 1 to 3 Cite Share Download PDF Status: Under Review Version 1 posted 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-6063665","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":427966394,"identity":"940e559b-8177-4e43-8d23-e6a718ddcb52","order_by":0,"name":"Charles Wondji","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0003-0791-3673","institution":"Liverpool School of Tropical Medicine","correspondingAuthor":true,"prefix":"","firstName":"Charles","middleName":"","lastName":"Wondji","suffix":""},{"id":427966395,"identity":"3f98aeed-7944-4848-9d67-70885c58ee93","order_by":1,"name":"Vanessa NGANNANG-FEZEU","email":"","orcid":"https://orcid.org/0000-0002-4400-7795","institution":"Centre for Research in Infectious Diseases","correspondingAuthor":false,"prefix":"","firstName":"Vanessa","middleName":"","lastName":"NGANNANG-FEZEU","suffix":""},{"id":427966396,"identity":"3596aaea-5bde-4e24-ad16-b6a230b1f6e3","order_by":2,"name":"Mersimine Mangoua","email":"","orcid":"","institution":"Centre for Research in Infectious Diseases (CRID)","correspondingAuthor":false,"prefix":"","firstName":"Mersimine","middleName":"","lastName":"Mangoua","suffix":""},{"id":427966397,"identity":"1411a681-20fb-4fb9-a1c4-318bc136922d","order_by":3,"name":"Mahamat Gadji","email":"","orcid":"","institution":"Centre for Research in Infectious Diseases (CRID)","correspondingAuthor":false,"prefix":"","firstName":"Mahamat","middleName":"","lastName":"Gadji","suffix":""},{"id":427966398,"identity":"8d6f8e38-6270-4414-b768-25f9e449a9fe","order_by":4,"name":"Leon Mugenzi","email":"","orcid":"","institution":"Syngenta Crop Protection","correspondingAuthor":false,"prefix":"","firstName":"Leon","middleName":"","lastName":"Mugenzi","suffix":""},{"id":427966399,"identity":"bcd13e89-4d51-421a-b428-370497fb31a5","order_by":5,"name":"Ambrose Oruni","email":"","orcid":"","institution":"Liverpool School of Tropical Medicine","correspondingAuthor":false,"prefix":"","firstName":"Ambrose","middleName":"","lastName":"Oruni","suffix":""},{"id":427966400,"identity":"e8a3c4e5-63d7-4022-b5b5-48aa218b1e9a","order_by":6,"name":"Carlos Djoko","email":"","orcid":"","institution":"Centre for Research in Infectious Diseases","correspondingAuthor":false,"prefix":"","firstName":"Carlos","middleName":"","lastName":"Djoko","suffix":""},{"id":427966401,"identity":"21b6b8f1-dcb4-4587-a1e8-b6ac160e7a0e","order_by":7,"name":"Magellan Tchouakui","email":"","orcid":"https://orcid.org/0000-0002-1683-8057","institution":"Centre for Research in Infectious Diseases","correspondingAuthor":false,"prefix":"","firstName":"Magellan","middleName":"","lastName":"Tchouakui","suffix":""},{"id":427966402,"identity":"e424b2da-4ca1-4f7e-b425-058bd5f7532a","order_by":8,"name":"Sulaiman Ibrahim","email":"","orcid":"","institution":"Bayero University","correspondingAuthor":false,"prefix":"","firstName":"Sulaiman","middleName":"","lastName":"Ibrahim","suffix":""},{"id":427966403,"identity":"670e7b30-edf3-443b-8e53-a80a55ec580c","order_by":9,"name":"Jude Bigoga","email":"","orcid":"","institution":"University of Yaounde I","correspondingAuthor":false,"prefix":"","firstName":"Jude","middleName":"","lastName":"Bigoga","suffix":""}],"badges":[],"createdAt":"2025-02-19 11:30:56","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6063665/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6063665/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78493734,"identity":"849f96ff-51b7-445f-b3bc-912a71d8470d","added_by":"auto","created_at":"2025-03-14 03:28:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":107256,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExpression profile of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eArgininosuccinate Lyase\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e Africa-wide.\u003c/strong\u003e Using qRT-PCR comparison between 2014 \u003cstrong\u003e(A)\u003c/strong\u003e and 2021 \u003cstrong\u003e(B)\u003c/strong\u003esamples collection. Error bars indicate standard error. The significance between values for each experimental group and its respective control was determined with a t-test, * = P \u0026lt; 0.05\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-6063665/v1/857fe63c8631a98f2f68d8a4.png"},{"id":78493735,"identity":"4f377fcb-88f6-487f-9463-4f691b38556e","added_by":"auto","created_at":"2025-03-14 03:28:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":429557,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGenome-wide \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/em\u003e\u003csub\u003e\u003cem\u003e\u003cstrong\u003eST\u003c/strong\u003e\u003c/em\u003e\u003c/sub\u003e\u003cstrong\u003e differentiation around \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eASL\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e. (A)\u003c/strong\u003e \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eST\u003c/em\u003e\u003c/sub\u003e genetic differentiation in \u003cem\u003eAn. funestus\u003c/em\u003e 3RL chromosome using PoolSeq data. The differentiated region in between the dashed red lines contains the \u003cem\u003eASL\u003c/em\u003e gene. \u003cstrong\u003e(B)\u003c/strong\u003e \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eST\u003c/em\u003e\u003c/sub\u003e genetic differentiation on 2RL chromosome in \u003cem\u003eAn. funestus\u003c/em\u003e across Africa using individual whole genome sequence.\u0026nbsp; \u003cstrong\u003e(C)\u003c/strong\u003e Shows the coverage plot computed based on the hidden Markov model (HMM) where the increase in copy number at \u003cem\u003eASL\u003c/em\u003e gene is framed by a red box. The region outlined in red in Fig. 2A represents an inversion event known as 3Rb, while the region outlined in black in Fig. 2B hosts the \u003cem\u003eASL\u003c/em\u003e gene.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-6063665/v1/9071b6c36e756258350b0e39.png"},{"id":78493737,"identity":"c7b72f6d-3678-4c49-b93b-a03610a4beb3","added_by":"auto","created_at":"2025-03-14 03:28:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":432420,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSelective sweep at \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eASL\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e locus. \u003c/strong\u003eGenome-wide Tajima’s D \u003cstrong\u003e(A)\u003c/strong\u003e and nucleotide diversity \u003cstrong\u003e(B)\u003c/strong\u003e estimates in \u003cem\u003eAn. funestus\u003c/em\u003e across Africa. \u003cstrong\u003eSelective sweep and Polymorphism analysis of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eASL.\u003c/strong\u003e\u003c/em\u003e Using cloning sequences. Analysis of the coding part, \u003cstrong\u003e(C)\u003c/strong\u003e haplotype network and \u003cstrong\u003e(D)\u003c/strong\u003e Phylogenetic tree of \u003cem\u003eASL\u003c/em\u003e. Using genomic DNA between Uganda field population F1, crossing Fang/Uganda and Fang/Cameroon exposed to permethrin \u003cstrong\u003e(E)\u003c/strong\u003e haplotype network and \u003cstrong\u003e(F)\u003c/strong\u003ePhylogenetic tree of \u003cem\u003eASL\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-6063665/v1/ff32f1c7bb5d0f44494c01f3.png"},{"id":78493736,"identity":"4e23c49c-eedf-4f7f-bd0b-30d99b35233d","added_by":"auto","created_at":"2025-03-14 03:28:16","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":167125,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional validation of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eASL\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e. \u003c/strong\u003eTransgenic expression of \u003cem\u003eASL\u003c/em\u003ein \u003cem\u003eDrosophila melanogaster\u003c/em\u003e using the GAL4/UAS system conferring pyrethroid resistance. \u003cstrong\u003e(A)\u003c/strong\u003e Relative expression of GAL4-125H-\u003cem\u003eASL \u003c/em\u003eand GAL4-R125-\u003cem\u003eASL\u003c/em\u003e in \u003cem\u003eDrosophila\u003c/em\u003e flies. The observed mortality pattern of GAL4 x UAS-GAL4-125H-\u003cem\u003eASL\u003c/em\u003e and GAL4 x UAS-GAL4-R125-\u003cem\u003eASL\u003c/em\u003e and control strains. Overexpression of \u003cem\u003eASL\u003c/em\u003e alleles exposed to Pyrethroids: \u003cstrong\u003e(B)\u003c/strong\u003e Permethrin 4%; \u003cstrong\u003e(C)\u003c/strong\u003e Deltamethrin 0.2%; \u003cstrong\u003e(D)\u003c/strong\u003eAlpha cypermethrin 0.0007%. \u003cstrong\u003e(E)\u003c/strong\u003e Comparative mortalities of F1 transgenic flies of crosses between Actin5C-GAL4 and UAS-\u003cem\u003eASL. \u003c/em\u003eValidation of Knockdown effect of \u003cem\u003eASL\u003c/em\u003e gene in \u003cem\u003eAn. funestus\u003c/em\u003e. \u003cstrong\u003e(F)\u003c/strong\u003e To confirm the knockdown, quantitative RT-PCR between non-exposed double-strand injected and non-injected mosquitoes of the same age. Bioassay result of a mosquito after injection and exposure to \u003cstrong\u003e(G)\u003c/strong\u003e permethrin; \u003cstrong\u003e(H)\u003c/strong\u003e deltamethrin; \u003cstrong\u003e(I)\u003c/strong\u003e alpha-cypermethrin. Error bars indicate standard error. The significance between values for each experimental group and its respective control was determined with a t-test ns = non-significant; * = P \u0026lt; 0.05; ** = P \u0026lt; 0.01; *** = P \u0026lt; 0.001 and **** = P \u0026lt; 0.0001, n=5 replicates of 20-25 \u003cem\u003eDrosophila\u003c/em\u003e and n=4 replicates of 20-25 mosquitoes.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-6063665/v1/4834a6feb4430ec0091b855b.png"},{"id":78493739,"identity":"61c3640c-6498-4690-b15c-21401e8b6e64","added_by":"auto","created_at":"2025-03-14 03:28:16","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":260545,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDesign of DNA-based diagnostic tools for detection of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eASL\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e-resistance allele and impact on resistance phenotype\u003c/strong\u003e. \u003cstrong\u003e(A),\u003c/strong\u003e AS-PCR assay for genotyping the T-277A-\u003cem\u003eASL\u003c/em\u003emarker; \u003cstrong\u003e(B),\u003c/strong\u003e LNA probe-based assay for genotyping the T-277A-\u003cem\u003eASL\u003c/em\u003emarker. Association of the \u003cem\u003eASL\u003c/em\u003e-T-277A mutation with insecticide resistance phenotype. \u003cstrong\u003e(C),\u003c/strong\u003e Distribution of \u003cem\u003eASL\u003c/em\u003e-T-277A resistance marker among F3 FANGxUGANDA hybrid \u003cem\u003eAn. funestus \u003c/em\u003emosquitoes exposed to permethrin alive and dead. \u003cstrong\u003e(D),\u003c/strong\u003e Percentage allele frequency distribution Fang/Uganda F3 permethrin alive and dead. \u003cstrong\u003e(E),\u003c/strong\u003e Estimation of odds ratio (OR) and associated significance between different \u003cem\u003eASL\u003c/em\u003e genotypes in permethrin alive and dead Fang/Uganda F3 female mosquitoes. \u003cstrong\u003e(F),\u003c/strong\u003e Distribution of \u003cem\u003eASL\u003c/em\u003e-T-277A genotypes between dead and alive mosquitoes after exposure to PermaNet 3.0 net in experimental huts. \u003cstrong\u003e(G),\u003c/strong\u003e Estimation of odds ratio (OR) and associated significance alive and dead. \u003cstrong\u003e(H),\u003c/strong\u003e Distribution of \u003cem\u003eASL\u003c/em\u003e-T-277A genotypes between dead and alive mosquitoes after exposure to Olyset Plus net in experimental huts. \u003cstrong\u003e(I),\u003c/strong\u003e Estimation of odds ratio (OR) and associated significance alive and dead. \u003cstrong\u003e(J),\u003c/strong\u003e Distribution of \u003cem\u003eASL\u003c/em\u003e-T-277A genotypes between dead and alive mosquitoes after exposure to IG2 nets in experimental huts. \u003cstrong\u003e(K),\u003c/strong\u003e Estimation of odds ratio (OR) and associated significance alive and dead showing the negative association. The arrow within the triangle indicates the direction of OR estimation and ORs are given with asterisks indicating level of significance.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-6063665/v1/6a056c71ec7c0926c6ef0b98.png"},{"id":78494380,"identity":"ccca0f09-edf4-4260-a59d-b13bc35cbd19","added_by":"auto","created_at":"2025-03-14 03:44:16","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":207151,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGeographical distribution of molecular markers across Africa\u003c/strong\u003e. (\u003cstrong\u003eA\u003c/strong\u003e) Mapping of the geographical distribution of \u003cem\u003eASL\u003c/em\u003e-T-277A resistance marker among the African populations. \u003cstrong\u003e(B)\u003c/strong\u003eTemporal evolution of \u003cem\u003eASL\u003c/em\u003e allele frequencies in Uganda showing the ongoing fixation of mutant 277A-\u003cem\u003eASL\u003c/em\u003e (RR) genotype with a big variation in 2024. \u003cstrong\u003e(C)\u003c/strong\u003e Implication of the marker in resistance escalation using Permethrin 1X, 5X and 10X alive and dead.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-6063665/v1/a4c601ccfd958d0ab3bfabbc.png"},{"id":78494935,"identity":"7bab7327-e7ad-4020-95d2-71a79eee4e42","added_by":"auto","created_at":"2025-03-14 03:52:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3897779,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6063665/v1/6bff2610-4b55-4190-889f-eab279bd7cdd.pdf"},{"id":78493741,"identity":"fa06efc3-9475-4b9d-bf2c-e91ee0ca5f63","added_by":"auto","created_at":"2025-03-14 03:28:16","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1332566,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSUPPLEMENTARY MATERIALS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThis PDF file includes:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigs. S1 to S9\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTables. S1 to S9\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDataSet 1 to 3\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"NgannangetalSupplementaryMaterialsNatComSubmitted190225.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6063665/v1/5aaf4415c98ef86f2d0041ba.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"The argininosuccinate lyase gene exacerbates pyrethroid resistance in the major African vectors Anopheles funestus","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eMalaria prevention still relies on insecticide-based vector control interventions, notably Long-Lasting Insecticidal Nets (LLINs) and Indoor Residual Spraying (IRS) (1). Massive use of insecticides in vector control, coupled with the pesticide's use in agriculture are the main selective pressure of insecticide resistance in malaria vectors (2, 3). Understanding the evolution and genetic adaptation of malaria vectors to these control interventions is capital to maximise the effectiveness of malaria control and elimination strategies. The mechanisms driving insecticide resistance in major malaria vectors include (1) overexpression of detoxification enzymes responsible for metabolic resistance, (2) modification of insecticide targets, (3) reduction of insecticide penetration through the cuticle, and (4) insecticide avoidance behaviour (4\u0026ndash;6). Microbiota also has recently emerged as a contributing factor to insecticide resistance (7). In major African malaria vectors such as \u003cem\u003eAnopheles funestus and Anopheles gambiae\u003c/em\u003e, insecticide resistance has become a significant concern for vector control with the escalation of the pyrethroid resistance increasingly observed across Africa (8\u0026ndash;11). It is likely that beyond classical resistance drivers, that mosquitoes are selecting novel routes of resistance to acquire the now observed ability to survive exposure to higher doses of insecticides (9). The detection of the role of chemosensory proteins (Sensory appendage proteins (SAP)) in \u003cem\u003eAn. gambiae\u003c/em\u003e is an indication that resistance aggravation could be driven by unknown factors (Ingham et al 2020). In \u003cem\u003eAn. funestus\u003c/em\u003e, metabolic resistance is the primary mechanism of resistance conferred by detoxification enzymes primarily cytochrome P450s, glutathione s-transferases (GST) and carboxylesterases, whose overexpression, or protein structural modification, results in the alteration or sequestration of the insecticide ingested by the insect (12). However, several other gene families have exhibited increased expression in resistant mosquitoes but with no evidence yet of their contribution to the resistance phenotype.\u003c/p\u003e \u003cp\u003eMoreover, the molecular basis of resistance to insecticides is heterogeneous across Africa with populations from different regions showing overlapping or contrasting resistance patterns (13\u0026ndash;16). Despite the recent significant progress in detecting candidate genes driving resistance and identifying key genetic variants in some cases (17\u0026ndash;21), the genetic factors responsible for the aggravation of pyrethroid resistance are yet to be elucidated and no DNA-based tool has been designed for field surveillance of resistance escalation. The \u003cem\u003eargininosuccinate lyase\u003c/em\u003e gene is a primary such candidate gene as it has been previously found overexpressed in resistant mosquitoes compared to their susceptible counterparts (20, 22\u0026ndash;25). However, its contribution to resistance and/or its escalation (super-resistance) and its impact on vector control measures remain unknown.\u003c/p\u003e \u003cp\u003e \u003cem\u003eArgininosuccinate lyase\u003c/em\u003e (\u003cem\u003eASL\u003c/em\u003e), is involved in converting citrulline to arginine and belongs to a superfamily of tetrameric enzymes known to catalyse the reactions involving fumarate production (26). Its primary function is the reversible hydrolysis of argininosuccinate to arginine and fumarate. This reaction is crucial for ammonia detoxification and arginine biosynthesis (27). Arginine, produced by \u003cem\u003eASL\u003c/em\u003e, is a building block for many proteins synthesis, and the immediate precursor of nitric oxide (NO) a critical signaling molecule (28). Additionally, arginine serves as the precursor of many other amino acids as ornithine, proline, and glutamate (29). It plays a vital role in the mosquito's immune defence, antioxidant system, gene expression, and overall growth and development (30). In \u003cem\u003eAn. funestus\u003c/em\u003e, the \u003cem\u003eASL\u003c/em\u003e gene (\u003cem\u003eAFUN004002\u003c/em\u003e) resides on chromosome 3 and encodes a conserved protein with numerous orthologs across various species \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://vectorbase.org/vectorbase/app/record/gene/AFUN004002#GeneLocation\u003c/span\u003e\u003cspan address=\"https://vectorbase.org/vectorbase/app/record/gene/AFUN004002#GeneLocation\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. It remains to establish if \u003cem\u003eASL\u003c/em\u003e involvement in these many biological processes could also interfere and/or contribute to resistance escalation in malaria vector such as \u003cem\u003eAn. funestus\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eHere, using a multi-omics approach, we uncovered a novel resistance mechanism by demonstrating that over-expression and allelic variation of \u003cem\u003eASL\u003c/em\u003e confer and exacerbate pyrethroid resistance in \u003cem\u003eAn. funestus\u003c/em\u003e. The detection of a signature of selective sweep at this gene in resistant populations facilitated the design of a simple, field-applicable DNA-based resistant assay, enabling the detection and to survey the spread of \u003cem\u003eASL\u003c/em\u003e resistant variant while assessing its impact on current control strategies.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cb\u003eExpression pattern of\u003c/b\u003e \u003cb\u003eASL An. funestus\u003c/b\u003e \u003cb\u003eacross Africa using RNAseq in\u003c/b\u003e \u003cb\u003eAn. funestus\u003c/b\u003e \u003cb\u003epopulation\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eRNA-seq data analysis revealed the overexpression of several gene families with significantly increased expression across various comparisons in \u003cem\u003eAn. funestus\u003c/em\u003e populations across Africa. Among the top overexpressed gene families were cytochrome P450s, glutathione S-transferases (GSTs), carboxylesterases, gustatory receptors, odorant proteins, cuticular proteins and ATP-binding cassette (ABC) transporters (DataSet 1). Additionally, we observed elevated expression of several digestive enzymes, including trypsin, chymotrypsin, and the \u003cem\u003eargininosuccinate lyase gene\u003c/em\u003e (\u003cem\u003eASL\u003c/em\u003e, \u003cem\u003eAFUN004002\u003c/em\u003e). The heightened expression and consistent read counts of \u003cem\u003eASL\u003c/em\u003e, in particular, drew our attention for further investigation (DataSet 2). A comparative RNA-seq analysis of \u003cem\u003eASL\u003c/em\u003e expression across different \u003cem\u003eAn. funestus\u003c/em\u003e populations in Africa (four countries) confirmed its consistent upregulation in multiple conditions (Table. S1) particularly when comparing Uganda and Malawi populations. Indeed, \u003cem\u003eASL\u003c/em\u003e gene expression showed a significant increase in Ugandan mosquitoes (unexposed to insecticide) compared to the fully susceptible Fang strain (FC: 2.25, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.00001), and not in the others population, where expression was lower. Similarly, \u003cem\u003eASL\u003c/em\u003e expression was significantly higher in resistant mosquitoes from Malawi when compared to Fang and unexposed population (FC: 2.85, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.0001 and FC: 2.62, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.0001 respectively), but not in other regions. Suggesting that \u003cem\u003eASL\u003c/em\u003e may contribute to permethrin resistance through its increased expression.\u003c/p\u003e \u003cp\u003eThe Gene Ontology (GO) enrichment analysis of the list of 142 transcripts with similarly expressed genes to \u003cem\u003eASL\u003c/em\u003e in the same comparison revealed an over-representation of 20 GO terms enriched around this gene (Table. S2). This enrichment reflects the function of those transcripts including heme binding GO:0020037, tetrapyrrole binding GO:0046906, iron ion bind GO:0005506, monooxygenase activity GO:0004497 and oxidoreductase activity GO:0016705 which were the main GO enriched (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Other groups of GO terms were found related to \u003cem\u003eargininosuccinate lyase\u003c/em\u003e activity including amidine-lyase activity GO:0016842, arginase activity GO:0004053 and carbon-nitrogen lyase activity GO:0016840.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cb\u003eTranscriptional profiling expression profile of\u003c/b\u003e \u003cb\u003eASL\u003c/b\u003e \u003cb\u003eacross Africa using qPCR\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eTo validate the RNAseq transcription profile, the expression of \u003cem\u003eargininosuccinate lyase\u003c/em\u003e was assessed between deltamethrin-resistant mosquitoes after 24h of exposure (alive) and unexposed mosquitoes (control) using laboratory strain FANG as a reference. Overall, \u003cem\u003eargininosuccinate lyase\u003c/em\u003e was significantly overexpressed in all studied populations, with a fold-change greater than 2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). A notable increase in \u003cem\u003eASL\u003c/em\u003e expression was observed between 2014 and 2021 across all locations with high expression in 2021 (Cameroon: alive FC\u0026thinsp;=\u0026thinsp;9.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8 vs unexposed FC\u0026thinsp;=\u0026thinsp;5.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7; Ghana: alive FC\u0026thinsp;=\u0026thinsp;19.9\u0026thinsp;\u0026plusmn;\u0026thinsp;26 vs unexposed FC\u0026thinsp;=\u0026thinsp;50.4\u0026thinsp;\u0026plusmn;\u0026thinsp;47.9; Uganda: alive FC\u0026thinsp;=\u0026thinsp;33.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.3 vs unexposed FC\u0026thinsp;=\u0026thinsp;14.3\u0026thinsp;\u0026plusmn;\u0026thinsp;5.7; Malawi: alive FC\u0026thinsp;=\u0026thinsp;14.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.43 vs unexposed FC\u0026thinsp;=\u0026thinsp;15.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09 and in resistant Lab strain FUMOZ-R FC\u0026thinsp;=\u0026thinsp;4.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Compared to the 2014 collection where the expression level was moderate (Cameroon: alive FC\u0026thinsp;=\u0026thinsp;3.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9 vs unexposed FC\u0026thinsp;=\u0026thinsp;1.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7; Ghana: alive FC\u0026thinsp;=\u0026thinsp;1.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9 vs unexposed FC\u0026thinsp;=\u0026thinsp;1.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7; Uganda: alive FC\u0026thinsp;=\u0026thinsp;8.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3 vs unexposed FC\u0026thinsp;=\u0026thinsp;3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4; Malawi: alive FC\u0026thinsp;=\u0026thinsp;6.37\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1 vs unexposed FC\u0026thinsp;=\u0026thinsp;6.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). The most significant increase in \u003cem\u003eASL\u003c/em\u003e expression was observed in Uganda, where resistant mosquitoes showed higher levels compared to unexposed individuals; similarly, in Cameroon, \u003cem\u003eASL\u003c/em\u003e expression was elevated in resistant populations relative to unexposed groups, particularly in 2021.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eGenome-wide\u003c/b\u003e \u003cb\u003eF\u003c/b\u003e\u003csub\u003e\u003cb\u003eST\u003c/b\u003e\u003c/sub\u003e \u003cb\u003edifferentiation and selection analysis across African populations around the\u003c/b\u003e \u003cb\u003eargininosuccinate lyase\u003c/b\u003e \u003cb\u003e(\u003c/b\u003e\u003cb\u003eASL\u003c/b\u003e\u003cb\u003e) locus\u003c/b\u003e\u003c/p\u003e \u003cp\u003eGenome-wide \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eST\u003c/em\u003e\u003c/sub\u003e differentiation analysis, conducted in non-overlapping windows of 10,000 SNPs across African samples (Cameroon, Ghana, Uganda, and Malawi), revealed a strong signal of differentiation on chromosome 3RL directly centered on the \u003cem\u003eargininosuccinate lyase\u003c/em\u003e gene (\u003cem\u003eAFUN004002\u003c/em\u003e) with background \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eST\u003c/em\u003e\u003c/sub\u003e values superior to 0.2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This signal was observed in all comparisons involving samples from Tororo and Mayuge (Uganda) collected in 2009 and 2014, respectively, compared to other populations. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Individual whole genome sequencing data also indicated a strong signal of genetic differentiation directly centered on the \u003cem\u003eASL\u003c/em\u003e gene in all comparisons involving the Uganda population (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). To further investigate possible evidence selection between 2009 and 2014 Uganda population, we compute H\u003csub\u003e12\u003c/sub\u003e and H\u003csub\u003e1X\u003c/sub\u003e across the 2L chromosome, identifying no signal of positive selection centered directly on the \u003cem\u003eASL\u003c/em\u003e gene (\u003cem\u003eLOC125762180\u003c/em\u003e equivalent to \u003cem\u003eAFUN004002\u003c/em\u003e FUMOZ reference genome annotation) in any of the populations. Similarly, H\u003csub\u003e1X\u003c/sub\u003e analysis on chromosome 2L revealed no shared signals of selection in this genomic region between populations, suggesting an absence of common selective sweeps at this locus between 2009 and 2014.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cb\u003eSelective sweep at\u003c/b\u003e \u003cb\u003eASL\u003c/b\u003e \u003cb\u003elocus in\u003c/b\u003e \u003cb\u003eAnopheles funestus\u003c/b\u003e \u003cb\u003eacross Africa\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eGenome-wide Tajima\u0026rsquo;s D and nucleotide diversity analyses in non-overlapping windows of 10,000 SNPs identified a major sweep spanning the \u003cem\u003eASL\u003c/em\u003e locus across all populations, including FANG and FUMOZ laboratory strains (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Reduced Tajima\u0026rsquo;s D values, ranging from \u0026minus;\u0026thinsp;3.5 to -4.5, are observed starting around 40Mb, with the sweep continuing through the \u003cem\u003eASL\u003c/em\u003e locus (marked by a blue dashed line) at 45Mb, displaying a consistent pattern across all populations (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). A major valley forms beyond the \u003cem\u003eASL\u003c/em\u003e locus before the Tajima\u0026rsquo;s D values increase back to approximately \u0026minus;\u0026thinsp;3.5. The nucleotide diversity pattern aligns perfectly with the Tajima\u0026rsquo;s D results, indicating reduced nucleotide diversity across populations (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). This suggests a strong selective sweep in the genomic region around the \u003cem\u003eASL\u003c/em\u003e locus, consistent across diverse populations, highlighting the importance of this locus in insecticide resistance in agreement with the genetic differentiation observed earlier (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Negative Tajima's D values typically indicate population expansion or purifying selection, while the lack of detection of selection using the H\u003csub\u003e12\u003c/sub\u003e test could suggest a hard sweep as it is sensitive to soft sweep. These findings suggest that the \u003cem\u003eASL\u003c/em\u003e locus has undergone strong selection, leading to reduced genetic diversity and the emergence to high frequency of advantageous alleles across populations. The sweep observed in FANG could potentially be the result of genetic drift, as we wouldn't expect a fully susceptible laboratory strain to be under strong selective pressure.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cb\u003eAfrica-wide polymorphism analysis of\u003c/b\u003e \u003cb\u003eASL\u003c/b\u003e \u003cb\u003egene between 2014 and 2021\u003c/b\u003e \u003cb\u003eAn. funestus\u003c/b\u003e \u003cb\u003esamples\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cstrong\u003ePolymorphism analysis and genetic diversity at the \u003cem\u003eASL\u003c/em\u003e (\u003cem\u003eLOC125762180\u003c/em\u003e) gene using sure-select and iWGS data\u003c/strong\u003e \u003cp\u003ePolymorphism analysis of the \u003cem\u003eASL\u003c/em\u003e gene using SureSelect data, generated with mosquitoes collected in 2014, revealed high genetic diversity across all populations (Fig. S2A and S2B). In a cohort of 68 mosquitoes, we identified 64 substitutions and 52 haplotypes within the 1.4 kb gene body of \u003cem\u003eASL\u003c/em\u003e across different countries. When mosquitoes were analysed by country, a similar pattern was observed across all populations, though Uganda exhibited lower polymorphism compared to other countries. This was evident in several parameters, most notably the lower number of substitution sites in Uganda (11 overall) compared to Cameroon (28) and Malawi (39). A similar scarcity of haplotypes was observed in Uganda (h\u0026thinsp;=\u0026thinsp;11) and Cameroon (h\u0026thinsp;=\u0026thinsp;10), while Malawi had a higher number of haplotypes (h\u0026thinsp;=\u0026thinsp;22) (Table. S3). Consequently, haplotype diversity was lower but similar in Uganda (0.88) compared to Cameroon (1.0) and Malawi (0.98). This pattern was consistent for other metrics, such as nucleotide diversity (π), which was higher in Cameroon (0.004) and Malawi (0.004), while Uganda samples displayed lower nucleotide diversity (0.001), even when compared to reference strains such as FANG and FUMOZ. Both dead and alive mosquitoes from Uganda exhibited similarly lower nucleotide diversity (0.001) compared to Cameroon (0.004) and Malawi (0.004) (Table. S3). A similar pattern of high polymorphism was noted when analysing the non-coding regions (1kb upstream of the transcription start site (promoter)) of the \u003cem\u003eASL\u003c/em\u003e gene (Fig. S2C, S2D and Table. S4).\u003c/p\u003e \u003c/p\u003e \u003cp\u003eSimilarly, the genetic diversity analysis of the \u003cem\u003eASL\u003c/em\u003e gene (\u003cem\u003eLOC125762180\u003c/em\u003e) from 2014 iWGS samples reveals that the median nucleotide diversity (θπ) and Watterson\u0026rsquo;s theta (θw) were lower in the populations from Cameroon, Ghana, and Uganda compared to the Malawi population, which exhibited higher genetic diversity (Fig. S3A, S3C, S3D). This diversity was lower in Uganda compared to Cameroon aligning with the SureSelect data. This lower diversity in Cameroon, Ghana, and Uganda likely reflects similar evolutionary histories of this gene among these populations. In contrast, the Malawi population may have a distinct evolutionary history, contributing to its higher genetic diversity. Additionally, the median Tajima's D values for the \u003cem\u003eASL\u003c/em\u003e gene were near the equilibrium across all populations in line with SureSelect and H\u003csub\u003e12\u003c/sub\u003e selection scan reinforcing no evidence of selection of this gene in 2014 (Fig. S3B).\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cb\u003eAllelic variation at\u003c/b\u003e \u003cb\u003eLOC125762180/ASL\u003c/b\u003e \u003cb\u003ein\u003c/b\u003e \u003cb\u003eAn. funestus\u003c/b\u003e \u003cb\u003eacross Africa\u003c/b\u003e: Variant calling around the \u003cem\u003eASL\u003c/em\u003e differentiated region using PoolSeq data revealed replacement polymorphisms with low to moderate allele frequencies (DataSet 3). The most significant SNP was identified at position 3, where arginine (N) is substituted with threonine (Thr). In Cameroon, this SNP's frequency decreased from 44% in 2014 to 27%, while in Malawi, it dropped from 11.1\u0026ndash;6.4%. Conversely, in Ghana, the frequency temporarily increased from 9\u0026ndash;27%. In the Uganda Tororo population, no significant SNPs were found, with the highest frequency at only 3.9%. However, several low-frequency SNPs (\u0026lt;\u0026thinsp;20%) were detected in Uganda Mayuge. Notably, most SNP frequencies were null in the FANG fully susceptible laboratory strain. Individual whole-genome sequencing confirmed the N3T SNP at low to moderate frequencies: 28% in Cameroon, 19% in Ghana, 11% in Malawi, and 1% in Uganda. Overall, these results suggest that allelic variation in \u003cem\u003eASL\u003c/em\u003e SNPs is not the primary mechanism of pyrethroid resistance in \u003cem\u003eAn. funestus\u003c/em\u003e populations across Africa in 2014, indicating a need to investigate other mechanisms such as copy number variations (CNVs).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eDiplotype clustering analyses of \u003cem\u003eLOC125762180\u003c/em\u003e in \u003cem\u003eAn. funestus\u003c/em\u003e across Africa\u003c/strong\u003e \u003cp\u003eApplying diplotype calling method to the \u003cem\u003eASL\u003c/em\u003e (\u003cem\u003eLOC125762180\u003c/em\u003e) across 2014 samples from Cameroon, Ghana, Malawi, and Uganda, we identified a cluster, termed Cluster B (Fig. S4), characterised by a CNV that spans most of the Uganda population. This CNV is associated with genetically identical haplotypes displaying low heterozygosity. Notably, apart from this CNV, there were no SNPs linked to this selective sweep in the Uganda population, and no other amino acid variants were present except for N3T. However, N3T is absent in Uganda and is unlikely to be causative due to its physical location away from the gene's active site or the substrate binding pocket (Fig. S4). These findings suggest that the increased expression in Uganda's population is potentially driven by the CNV that has been present since 2014. We also observed another cluster, Cluster A, which includes samples from Uganda with moderate heterozygosity. In this cluster, the CNV spans only the Uganda population, while the N3T SNP is present in other populations lacking the CNV. A third cluster, Cluster C, comprises samples from Cameroon and Ghana, with some Cameroon samples harbouring the CNV. The \u003cem\u003eLOC125762180\u003c/em\u003e gene was found to increase expression in 2021 RNA-seq data, a result confirmed by RT-qPCR in Uganda samples. Therefore, we hypothesise that by increasing the expression of the \u003cem\u003eASL\u003c/em\u003e gene, this CNV could enhance the mosquito's ability to detoxify insecticides. However, this hypothesis still requires functional validation. Interestingly, the CNV appears to exist at variable copy numbers within the population.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEvidence of \u003cem\u003eASL\u003c/em\u003e-based CNV allele in \u003cem\u003eAn. funestus\u003c/em\u003e from Uganda population\u003c/strong\u003e \u003cp\u003ePoolSeq data provided potential evidence of gene duplication spanning the \u003cem\u003eASL\u003c/em\u003e gene in 2014, which was subsequently confirmed using individual whole-genome sequencing (iWGS). Copy number variations (CNVs) were detected specifically in the Uganda population, with some samples showing more than three additional copies of the \u003cem\u003eASL\u003c/em\u003e gene (Fig. S5A). Copy number allele frequency analysis revealed that 86% of the 2014 Uganda samples (approaching fixation) possess this CNV allele (Fig. S5A, S5B). In contrast, only 11% of the 2014 samples from Cameroon exhibit this CNV (Fig. S5A), while it was absent in the populations from Ghana and Malawi. The findings reinforced previous observations suggesting that the overexpression of the \u003cem\u003eASL\u003c/em\u003e gene in the Uganda population could be associated with gene duplication. However, individual WGS data from 2021 samples is required to confirm the persistence and evidence of this CNV.\u003c/p\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eGenetic variability of ASL Africa-wide by SANGER sequencing\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eGenetic diversity of \u003cem\u003eASL\u003c/em\u003e using coding region of the gene across Africa\u003c/strong\u003e \u003cp\u003eGenetic variability analysis of \u003cem\u003eASL\u003c/em\u003e coding region (1437 bp) in a total of 50 field-resistant \u003cem\u003eAn. funestus\u003c/em\u003e samples across Africa revealed an overall high diversity of this gene using cloning, generating a total of 71 substitution sites (S), 33 haplotypes (h) and a high haplotype diversity (H\u003csub\u003ed\u003c/sub\u003e = 0.977) (Table. S5). However, comparative polymorphism analysis revealed the lowest diversity level in samples from Uganda (Eastern Africa), generating 8 substitution sites versus 45, 13 and 11, respectively for Mozambique (Southern Africa), Ghana (West Africa) and Cameroon (Central Africa). Similarly, laboratory samples FANG (N\u0026thinsp;=\u0026thinsp;14) and FUMOZ (N\u0026thinsp;=\u0026thinsp;10) respectively generated 18 and 21 polymorphic sites (S). Moreover, Ugandan samples (N\u0026thinsp;=\u0026thinsp;17) generated 9 haplotypes with low nucleotide diversity (Pi\u0026thinsp;=\u0026thinsp;0.00119), while samples from Mozambique (N\u0026thinsp;=\u0026thinsp;12), Cameroon (N\u0026thinsp;=\u0026thinsp;11) and Ghana (N\u0026thinsp;=\u0026thinsp;10) respectively generated 11, 7 and 8 haplotypes (h) with higher nucleotide diversity values of 0.00804, 0.00187 and 0.00306 respectively for the three countries (Table. S5). Furthermore, computing Tajima\u0026rsquo;s (D) and the Fu and Li\u0026rsquo;s (D*) test statistics revealed negative values for Uganda, Mozambique, Cameroon and Ghana, this could indicate positive selection of this gene though at different rates in all these different populations (Table. S5).\u003c/p\u003e \u003c/p\u003e \u003cp\u003ePhylogenetic tree analysis of the coding sequences \u003cem\u003eASL\u003c/em\u003e gene in 2021 revealed the clustering of Ugandan samples to form major dominant clades, different from the samples from other African regions (Mozambique, Ghana, Cameroon and FUMOZ) that clustered together, forming different minor clades (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC) this can suggest a possible selection of the gene in Uganda. Also, laboratory susceptible samples FANG have clustered together to form a dominant clade distinct from other clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Similarly, haplotype networking of the \u003cem\u003eASL\u003c/em\u003e coding sequence revealed dominant haplotypes, 3 major haplotypes specific to Ugandan samples and 1 haplotype specific to FANG laboratory samples, while other African populations clustered together forming other minor clades (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Sequence analysis of the \u003cem\u003eASL\u003c/em\u003e gene detected a point mutation guanine (G) to adenine (A) nucleotide at position 374 in the open reading frame (ORF) leading to amino acid replacement arginine (R) to histidine (H) on codon 125 (R125H). This mutation was detected only in Ugandan samples at 17.6% (3/17) and completely absent in other African populations and we have used it for the functional validation. No other mutation was detected in the other three countries with a higher frequency than the one from the Ugandan population.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003ePolymorphism analysis of 1kb putative promoter region\u003c/strong\u003e \u003cp\u003eTo identify cis-regulatory variants associated with \u003cem\u003eASL\u003c/em\u003e-based pyrethroid resistance, we analysed a 1000-bp sequence upstream of the \u003cem\u003eASL\u003c/em\u003e gene in Ugandan mosquito samples, comparing F1 alive and dead individuals\u0026rsquo; post-permethrin exposure. We also examined mosquitoes from Fang/Uganda and Fang/Cameroon crosses alongside the laboratory-susceptible strain FANG. Our polymorphism analysis indicated lower genetic diversity in the 5\u0026rsquo;UTR region of alive Ugandan samples, with 10 polymorphic sites and 7 haplotypes compared to 30 sites and 8 haplotypes in dead samples (Table. S6).\u003c/p\u003e \u003c/p\u003e \u003cp\u003eMaximum likelihood tree plotting revealed distinct clustering of alive \u003cem\u003eASL\u003c/em\u003e sequences, forming a major clade separate from dead sequences (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF). Key mutations were identified in the \u003cem\u003eASL\u003c/em\u003e promoter alleles of alive and dead Ugandan \u003cem\u003eAn. funestus\u003c/em\u003e mosquitoes, including C/G, T/C, G/A, T/A, and C/T transition at various positions upstream of the translation start site. These mutations occurred more frequently in alive samples (e.g., C/G at 84.6% vs. 33.3% in dead). An ACAT insertion was also noted, with a higher prevalence in alive samples (100% vs. 50%). The T/A SNP introduced a new binding site for transcription factor IIB (TFIIB), while the ACAT insertion created additional transcription factor binding sites such as TATA box. This T/A SNP was selected to develop a DNA-based diagnostic assay to track \u003cem\u003eASL\u003c/em\u003e-based resistance in the field.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cb\u003eTransgenic expression of\u003c/b\u003e \u003cb\u003eASL\u003c/b\u003e \u003cb\u003eincreases resistance to insecticide in\u003c/b\u003e \u003cb\u003eDrosophila\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo establish whether \u003cem\u003eASL\u003c/em\u003e overexpression and allelic variation can independently confer resistance to insecticides using the GAL4/UAS expression system, transgenic \u003cem\u003eDrosophila melanogaster\u003c/em\u003e strains expressing each of the alleles (125H-\u003cem\u003eASL\u003c/em\u003e and R125-\u003cem\u003eASL\u003c/em\u003e), transgenes were successfully generated under the control of the GAL4-Act5C driver.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConfirmation of overexpression of transgenes by qRT-PCR\u003c/strong\u003e \u003cp\u003eTo confirm the overexpression of \u003cem\u003eASL\u003c/em\u003e alleles in the F\u003csub\u003e1\u003c/sub\u003e progeny of the GAL4/UAS crosses (GAL4-125H-\u003cem\u003eASL\u003c/em\u003e and GAL4-R125-\u003cem\u003eASL\u003c/em\u003e) after qRT-PCR we compared the generated crossing to the control the UAS line without the gene. The expression levels of the GAL4-125H-\u003cem\u003eASL\u003c/em\u003e flies (Fold change\u0026thinsp;=\u0026thinsp;17.89) and the GAL4-R125-\u003cem\u003eASL\u003c/em\u003e flies (Fold change\u0026thinsp;=\u0026thinsp;13.82) did not differ significantly (t-test\u0026thinsp;=\u0026thinsp;0.09), (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Which revealed a higher expression in the progeny generated with the ubiquitous GAL4-Act5C driver.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eExpression of \u003cem\u003eASL\u003c/em\u003e in \u003cem\u003eDrosophila\u003c/em\u003e flies confers higher resistance to pyrethroids\u003c/strong\u003e \u003cp\u003eFrom the exposure of transgenic flies to pyrethroids, it was observed that drosophila expressing the mutant-type 125H-\u003cem\u003eASL\u003c/em\u003e allele survived pyrethroid exposure better than control flies (not expressing \u003cem\u003eAn. funestus ASL\u003c/em\u003e) and the wild-type R125-\u003cem\u003eASL\u003c/em\u003e allele for the three insecticides (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Indeed, significantly lower mortality rates were obtained with transgenic Drosophila expressing the 125H-\u003cem\u003eASL\u003c/em\u003e allele.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eFor permethrin (4%), the GAL4-125H-\u003cem\u003eASL\u003c/em\u003e flies exhibited a significant low mortality rate of (4.3\u0026thinsp;\u0026plusmn;\u0026thinsp;6.4%; 5.6\u0026thinsp;\u0026plusmn;\u0026thinsp;6.5%; and 10.8\u0026thinsp;\u0026plusmn;\u0026thinsp;6.4%; P˂0.001) than the GAL4-R125-\u003cem\u003eASL\u003c/em\u003e flies (34.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7%; 54.7\u0026thinsp;\u0026plusmn;\u0026thinsp;8.9% and 92.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9%; P˃0.05) compared to the control not expressing \u003cem\u003eASL\u003c/em\u003e (43\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5%; 61.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6% and 91.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2%) at 6h, 12h, 24h respectively for each group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eFor deltamethrin (0.2%), the GAL4-125H-\u003cem\u003eASL\u003c/em\u003e flies show a reduced average mortality of (13.2\u0026thinsp;\u0026plusmn;\u0026thinsp;6.4%; 45.4\u0026thinsp;\u0026plusmn;\u0026thinsp;5.5%; and 67.3\u0026thinsp;\u0026plusmn;\u0026thinsp;6.4%; P˂0.05) than the GAL4-R125-\u003cem\u003eASL\u003c/em\u003e flies (32.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7%; 48.6\u0026thinsp;\u0026plusmn;\u0026thinsp;8.9%; and 82.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9%; P˃0.05) compared to the control not expressing \u003cem\u003eASL\u003c/em\u003e (46.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4; 79.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6 and 93.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.03) at 6h, 12h, 24h respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eFor alpha-cypermethrin (0.0007%), a similar pattern was observed, the GAL4-125H-\u003cem\u003eASL\u003c/em\u003e flies had significantly lower mortality rates of (34.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9%; and 35.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9%; P˂0.001) than the GAL4-R125-\u003cem\u003eASL\u003c/em\u003e flies (63.3\u0026thinsp;\u0026plusmn;\u0026thinsp;6.3% and 80\u0026thinsp;\u0026plusmn;\u0026thinsp;5.7%; P˃0.05) compared to the control not expressing \u003cem\u003eASL\u003c/em\u003e (78.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8% and 82.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8%) at 12h, 24h respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003eAveragely, significantly higher resistance was observed in the experimental group compared to the control group and GAL4-125H-\u003cem\u003eASL\u003c/em\u003e flies throughout the 24h while revealing no difference in mean mortality between GAL4-R125-\u003cem\u003eASL\u003c/em\u003e and control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). This implies that at any given time of exposure, the experimental group was more resistant than the non-transgenic group and the overexpression of the GAL4-R125-\u003cem\u003eASL\u003c/em\u003e allele confers no significant resistance to permethrin and alpha-cypermethrin (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). Overall, these observations suggest that overexpression of GAL4-125H-\u003cem\u003eASL\u003c/em\u003e would increase pyrethroid resistance in \u003cem\u003eDrosophila\u003c/em\u003e, and consequently, overexpression of this gene would be involved in pyrethroid resistance in \u003cem\u003eAn. funestus\u003c/em\u003e.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cb\u003eImpact of the knockdown of\u003c/b\u003e \u003cb\u003eASL\u003c/b\u003e \u003cb\u003eon the susceptibility profile of\u003c/b\u003e \u003cb\u003eAnopheles funestus\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConfirmation of knockdown effect of \u003cem\u003eASL\u003c/em\u003e by RT-qPCR\u003c/strong\u003e \u003cp\u003eTo verify if the injection of ds\u003cem\u003eASL\u003c/em\u003e effectively suppressed the expression of the \u003cem\u003eASL\u003c/em\u003e gene in mosquitoes, the RT-qPCR analysis on cDNA derived from both injected and non-injected mosquitoes, using specific primers for \u003cem\u003eASL\u003c/em\u003e was conducted. Housekeeping genes \u003cem\u003eActin5C\u003c/em\u003e and \u003cem\u003eRSP7\u003c/em\u003e were employed as reference genes. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF shows a noteworthy decrease in \u003cem\u003eASL\u003c/em\u003e gene expression in the mosquitoes injected with ds\u003cem\u003eASL\u003c/em\u003e (No-Ct) compared to the non-injected mosquitoes, with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.00001 4-day post-injection. This significant reduction in the expression of \u003cem\u003eASL\u003c/em\u003e in the injected mosquitoes compared to the non-injected group supports the conclusion that in vivo dsRNA of \u003cem\u003eASL\u003c/em\u003e injected effectively decreases the expression of the \u003cem\u003eASL\u003c/em\u003e gene in Mibellon \u003cem\u003eAn. funestus\u003c/em\u003e mosquitoes.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eKnockdown of \u003cem\u003eArgininosuccinate lyase\u003c/em\u003e increased susceptibility to pyrethroids\u003c/strong\u003e \u003cp\u003eAfter the test, the mortality rate did not differ significantly between non-injected mosquitoes and those injected with ds\u003cem\u003eGFP\u003c/em\u003e, indicating that the injection itself did not affect the mosquito survival. Bioassay carried out with mosquitoes injected with ds\u003cem\u003eASL\u003c/em\u003e significantly showed higher mortality rates when exposed to permethrin (50.8% \u0026plusmn; 7.1; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared to the non-injected (19% \u0026plusmn; 2.5) and the injected with ds\u003cem\u003eGFP\u003c/em\u003e (mortality rate 21.8% \u0026plusmn; 5.5) 24h post-exposure (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG). After exposure of ds\u003cem\u003eASL\u003c/em\u003e-injected mosquito to deltamethrin the result revealed that the mortality rate was higher (25.3% \u0026plusmn; 3.3; P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) compared to the non-injected (9.43% \u0026plusmn; 2.03) and the injected with ds\u003cem\u003eGFP\u003c/em\u003e (17.4% \u0026plusmn; 2.6) with a significant difference between the non-injected and the injected with ds\u003cem\u003eASL\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eH). By exposing ds\u003cem\u003eASL\u003c/em\u003e-injected mosquito to alpha-cypermethrin (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eI), we observed a significant difference between the mortality rates of ds\u003cem\u003eASL\u003c/em\u003e (13.6% \u0026plusmn; 1.6; P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) compared to the non-injected (10.4% \u0026plusmn; 3.9) and then injected with ds\u003cem\u003eGFP\u003c/em\u003e (6.3% \u0026plusmn; 2.2).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cb\u003eThe T-277A\u003c/b\u003e \u003cb\u003eASL\u003c/b\u003e\u003cb\u003e-resistant marker is associated with pyrethroid resistance\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCorrelation of T-277A \u003cem\u003eASL\u003c/em\u003e marker with permethrin resistance\u003c/strong\u003e \u003cp\u003eTo assess the efficacy of the T-277A-\u003cem\u003eASL\u003c/em\u003e diagnostic assay, the genotyping was performed using 40 F\u003csub\u003e1\u003c/sub\u003e mosquitoes (Alive and Dead each after 1h exposure to permethrin 0.75%) from Uganda 2021 collection. This result revealed a high prevalence (85%) of the resistant allele in the population. Genotyping of T-277A-\u003cem\u003eASL\u003c/em\u003e in the resistant mosquitoes revealed a predominance of the homozygote RR 72.5% (29/40) and moderate for the heterozygote 25% (10/40) with one bearing the homozygote susceptibility genotype (Fig. S6A). From the 40 dead mosquitoes 62.5% (25/40) were homozygous (RR) resistant, and 33% (13/40) were heterozygous (RS), with 5% (2/40) homozygous (SS) susceptible. A significant difference was not observed in the distribution of the three genotypes between mosquitoes that survived (alive) and those that died (dead) (Chi\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;3.3; P\u0026thinsp;=\u0026thinsp;0.19, Chi-square). This corresponds to an allelic frequency distribution of 85% and 15% for R and S alleles in alive populations. For dead population, the distribution is 79% and 21% for R and S alleles respectively (Fig. S6B). In contrast, all FANG samples were homozygous SS for the marker. It was not possible to establish an association between the alive and dead samples from the field (F1) as the \u003cem\u003eASL\u003c/em\u003e-resistant (RR) marker was almost fixed in the population without significant difference in all comparisons (P\u0026thinsp;=\u0026thinsp;0.13, Fisher\u0026rsquo;s exact test) (Fig. S6C).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo clearly assess the association between the mutation and pyrethroid resistance, the T-277A-\u003cem\u003eASL\u003c/em\u003e was genotyped in the crossing Fang/Uganda F3 generation exposure to permethrin 0.75%, after 60 mins for alive and 30 mins for dead. A significant difference was observed in the distribution of the three genotypes between mosquitoes that survived (alive) and those that died (dead) (Chi\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;13.2; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Chi-square) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Assessing the correlation between genotype and permethrin resistance revealed that homozygote resistant RR mosquitoes were significantly more able to survive exposure to permethrin than homozygote susceptible SS (OR\u0026thinsp;=\u0026thinsp;5.7; CI\u0026thinsp;=\u0026thinsp;2.1 to 15.4; P\u0026thinsp;=\u0026thinsp;0.0007, Fisher\u0026rsquo;s exact test) (Table. S7). A similar significant correlation was observed when comparing RS vs SS (OR\u0026thinsp;=\u0026thinsp;3.1; CI\u0026thinsp;=\u0026thinsp;1.5 to 6.2; P\u0026thinsp;=\u0026thinsp;0.0016, Fisher\u0026rsquo;s exact test) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). Moreover, possessing one R allele significantly increases the ability to survive compared to having the S allele (OR\u0026thinsp;=\u0026thinsp;1.9; CI\u0026thinsp;=\u0026thinsp;1.1 to 3.411; P\u0026thinsp;=\u0026thinsp;0.023, Fisher\u0026rsquo;s exact test) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eT-277A-\u003cem\u003eASL\u003c/em\u003e correlates with reduced efficacy of LLINs in experiment huts\u003c/strong\u003e \u003cp\u003eThe impact of the \u003cem\u003eASL\u003c/em\u003e resistance allele on the effectiveness of next-generation nets, PBO-based (PermaNet 3.0 and Olyset Plus) nets and chlorfenapyr-based nets (Interceptor G2) was assessed in semi-field condition using experimental huts. Genotyping of alive and dead collected in the room with PermaNet 3.0, Olyset Plus and Interceptor G2 nets revealed a significant difference in the distribution of the three genotypes between phenotypes (chi-square\u0026thinsp;=\u0026thinsp;11.33; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, chi-square) for PermaNet 3.0 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF) and not for Olyset Plus due to the complete absence of the SS (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eH). A comparison of genotypes based on mortality outcomes revealed that T-277A-\u003cem\u003eASL\u003c/em\u003e homozygote resistant (RR) mosquitoes had a significantly higher survival rate against PermaNet 3.0 than heterozygote (RS) mosquitoes (OR\u0026thinsp;=\u0026thinsp;2.2; CI\u0026thinsp;=\u0026thinsp;1.1164 to 3.6; P\u0026thinsp;=\u0026thinsp;0.01) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG). The presence of the R allele notably enhanced survival compared to the S allele (OR\u0026thinsp;=\u0026thinsp;2.2; CI\u0026thinsp;=\u0026thinsp;1.05 to 4.3; P\u0026thinsp;=\u0026thinsp;0.03) (Fig. S6D, Table. S7). In the case of Olyset Plus nets, RR mosquitoes again demonstrated a significantly greater survival rate than RS mosquitoes (OR\u0026thinsp;=\u0026thinsp;2; CI\u0026thinsp;=\u0026thinsp;1.04 to 3.3; P\u0026thinsp;=\u0026thinsp;0.03) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eH). However, no significant differences were observed between S and R alleles in this context (Fig. S6E). The Interceptor G2 (IG2) nets showed a significant negative association between T-277A-\u003cem\u003eASL\u003c/em\u003e genotypes and survival rates (chi-square\u0026thinsp;=\u0026thinsp;13.1; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eJ). Comparisons revealed strong negative associations for RR vs SS and RS vs SS (both OR\u0026thinsp;=\u0026thinsp;Infinity; P\u0026thinsp;\u0026lt;\u0026thinsp;0.005) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eK). Regarding blood-feeding ability, RR mosquitoes exhibited a significant advantage over RS mosquitoes with PermaNet 3.0 (OR\u0026thinsp;=\u0026thinsp;11.96; P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), while no significant differences were noted for Olyset Plus or IG2 nets, where strong negative associations were also observed (chi-square\u0026thinsp;=\u0026thinsp;124.4; P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Fig. S7).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eAfrica-wide distribution of \u003cem\u003eASL-resistant\u003c/em\u003e marker\u003c/strong\u003e \u003cp\u003eThe 277A_\u003cem\u003eASL\u003c/em\u003e resistance allele (\u003cem\u003eASLR\u003c/em\u003e) was predominantly found in East Africa with a frequency of 84% in Uganda (2021) and 52% in Tanzania (2018). This high frequency is driven by the high proportion of the homozygote resistant genotype RR (67% and 35 respectively). The allele was also detected in southern Africa but at a more moderate level of 35% in Malawi and 25% in Mozambique (Fig. S8A) and with only a low frequency of the RR genotypes (11 and 10% respectively). West and Central Africa exhibited a lower frequency of the \u003cem\u003eASLR\u003c/em\u003e allele with 19% in Ghana, 6.25% in Cameroon (Mibellon) and 7% in Central Africa Republic (CAR) and low frequency of RR in Ghana and absence in CAR and in Mibellon (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). A nation-wide distribution of \u003cem\u003eASLR\u003c/em\u003e in Cameroon confirmed its low frequency in this country with the RR detected only in a single location (Gounougou) out of 7 screened (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, Fig. S8B). Altogether, this distribution range suggests that East Africa is the focal region of \u003cem\u003eASLR\u003c/em\u003e resistance allele especially Uganda.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eAfrica-wide spatio-temporal distribution of T-277A_\u003cem\u003eASL\u003c/em\u003e marker\u003c/strong\u003e \u003cp\u003eTemporal analysis of Ugandan samples (Eastern Africa) revealed a gradual increase in the frequency of the homozygote mutant genotype (RR) from 2010 (48%), 2016 (61%), 2021 (67%), 2022 (75%) to 2023 where it is reaching near fixation (92%) (Fig.\u0026nbsp;7B). This change is translated at the allele frequency level with an increase from 70\u0026ndash;98% between 2010 and 2023. But surprisingly we found in 2024 a reduction of the RR genotype in the population and the resurgence of the SS in the population at 54% for the RR and 6% for the SS (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). With the increase of the S allele and the reduction of the R allele in the population (Fig. S8C). This reduction coincides with the deployment of Interceptor G2 nets in this Ugandan location (Mayuge) since end of 2023.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eImpact of T-277A_\u003cem\u003eASL\u003c/em\u003e on resistance escalation\u003c/strong\u003e \u003cp\u003eThe analysis revealed a significant difference in the distribution of the three genotypes between dead and live mosquitoes (chi-square\u0026thinsp;=\u0026thinsp;40.2; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, chi-square). Comparing all the alive alone for 1X, 5X 10X we noticed a significant difference (chi-square\u0026thinsp;=\u0026thinsp;128.9; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, chi-square). The RR genotype is more present in mosquitoes resistant to 10X than 1X, with a significant difference, when we compared RR vs RS (OR\u0026thinsp;=\u0026thinsp;0.4; CI\u0026thinsp;=\u0026thinsp;0.22 to 0.72; P\u0026thinsp;=\u0026thinsp;0.002, Fisher\u0026rsquo;s exact test). The allele distribution also revealed that mosquitoes carrying the R allele survive significantly more than the mosquitoes with the S allele (OR\u0026thinsp;=\u0026thinsp;0.36; CI\u0026thinsp;=\u0026thinsp;0.138 to 1; P\u0026thinsp;=\u0026thinsp;0.04, Fisher\u0026rsquo;s exact test). When comparing the 5X and 10X in the resistant mosquitoes, the genotyping shows that RR has more chance to survive exposure than RS (OR\u0026thinsp;=\u0026thinsp;0.34; CI\u0026thinsp;=\u0026thinsp;0.18 to 0.6; P\u0026thinsp;=\u0026thinsp;0.0003, Fisher\u0026rsquo;s exact test). This finding shows that the T-277A_\u003cem\u003eASL\u003c/em\u003e marker is linked to resistance escalation when compared to 1X vs 10X and 5X vs 10X in alive mosquitoes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC, Fig. S8D). However, there was no significant difference using the dead mosquitoes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC, Fig. S8E).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eElucidating the complex molecular processes driving the exacerbation of insecticide resistance in vectors is paramount to improve vector control programs and inform on the design of novel insecticides. This study has uncovered and elucidated the contribution of a biochemical enzyme, \u003cem\u003eargininosuccinate lyase\u003c/em\u003e to pyrethroid resistance in a major Afrotropical malaria vector, \u003cem\u003eAn. funestus\u003c/em\u003e and provides a DNA-based tool to not only track pyrethroid resistance escalation but also assess its impact on current and future insecticide-based vector control tools.\u003c/p\u003e \u003cp\u003e \u003cb\u003eOver-expression of\u003c/b\u003e \u003cb\u003eargininosuccinate lyase\u003c/b\u003e \u003cb\u003ecorrelates with increased pyrethroid resistance in\u003c/b\u003e \u003cb\u003eAn. funestus\u003c/b\u003e \u003cb\u003eacross the continent\u003c/b\u003e\u003c/p\u003e \u003cp\u003eRecent transcriptomic studies have identified that pyrethroid resistant mosquitoes are expressing a suite of novel gene families beyond classical detoxification genes including \u003cem\u003eASL\u003c/em\u003e, a non-detoxification enzyme not previously explored in \u003cem\u003eAn. funestus\u003c/em\u003e (23, 24). Transcriptomic analyses revealed differential expression of \u003cem\u003eASL\u003c/em\u003e in resistant mosquitoes across various regions of Africa supporting previous reports (20, 23). This is further supported by Ibrahim et al. (2016) who observed similar overexpression in Malawi after exposure to carbamate/pyrethroid insecticides (31). Uganda exhibited the highest initial \u003cem\u003eASL\u003c/em\u003e overexpression in 2014 (no significant difference between resistant and susceptible mosquitoes). However, by 2021, expression levels had increased significantly in all locations, with the most pronounced rise observed in Uganda. This coincides with a reported aggravation of resistance within the same region (11) suggesting a potential involvement of \u003cem\u003eASL\u003c/em\u003e overexpression in resistance aggravation. In this study, we observed a significant induced overexpression of \u003cem\u003eASL\u003c/em\u003e in deltamethrin exposed mosquitoes. The overexpression of \u003cem\u003eASL\u003c/em\u003e was likely induced by a combination of stress response signaling, transcriptional regulation and metabolic adaption mechanisms. Insecticides especially at higher doses generate reactive oxygen species (ROS) as part of their toxic effects (32). Reactive oxygen species act as signaling molecules, activating transcription factors or stress response regulators which subsequently upregulate genes involved in metabolic adaptation, antioxidant defense and cellular repair such as \u003cem\u003eASL\u003c/em\u003e (29).\u003c/p\u003e \u003cp\u003e \u003cb\u003eHigh diversity observed and signature of selective sweep around\u003c/b\u003e \u003cb\u003eargininosuccinate lyase\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe analysis of polymorphism of \u003cem\u003eASL\u003c/em\u003e highlights that this gene is under two contrasting evolutionary processes which is positive selection in Uganda linked with pyrethroid resistance but no selection and high diversity in other regions. However, this pattern could change with time and \u003cem\u003eASL\u003c/em\u003e in Uganda could migrate in other localities as seen for \u003cem\u003eG454A-CYP9K1\u003c/em\u003e which was so low in 2014 in Central Africa (Cameroon) but has now become fixed spreading from East Africa (Uganda) (17). The high genetic diversity observed at \u003cem\u003eASL\u003c/em\u003e is similar to the high polymorphism seen in some cytochrome P450s genes conferring pyrethroid resistance in malaria vectors such as \u003cem\u003eCYP6M7\u003c/em\u003e in \u003cem\u003eAn. funestus\u003c/em\u003e (33) but contrasts with strong evidence of positive selection seen in other major resistance genes in \u003cem\u003eAn. funestus\u003c/em\u003e (17, 21, 23), \u003cem\u003eAn. gambiae\u003c/em\u003e (3) or even in \u003cem\u003eDrosophila\u003c/em\u003e (34).\u003c/p\u003e \u003cp\u003eThis study detected a major differentiation occurring in the \u003cem\u003eASL\u003c/em\u003e region within the Uganda populations in the absence of recent positive selection which is likely to be masked by low genetic diversity background in the population. Looking at the SureSelect and the cloned sequences, we noticed a low diversity in Uganda's population compared to Cameroon and Malawi, suggesting a different selection process for these populations. Furthermore, detection of a cluster of haplotypes in Uganda including one bearing the R125H is further proof of selection with amino acid change likely linked with greater ability to confer resistance to pyrethroid as seen for L119F (35), or M220I in \u003cem\u003eCYP6P4a\u003c/em\u003e (21). Genetic differentiation from Poolseq spatio-temporally indicates that Uganda is under selection.\u003c/p\u003e \u003cp\u003eThe analysis of polymorphism patterns in the promoter region suggested a potential selection happening specifically in Uganda. Many new transcription factors binding sites and elements were found in Uganda alive after exposure to permethrin compared to the dead mosquitoes. However, further investigation is required to validate the extent of this selection and determine its effects on the \u003cem\u003ecis\u003c/em\u003e-regulation found in the \u003cem\u003eASL\u003c/em\u003e gene which can lead to the increased expression of the gene. This study identified a candidate marker (T-277) in the promoter region of \u003cem\u003eASL\u003c/em\u003e gene that strongly associates with pyrethroid resistance similar to reported contribution of cis-regulatory factors to the up-regulation of key detoxification genes such as \u003cem\u003eCYP6P9a/b\u003c/em\u003e in \u003cem\u003eAn. funestus\u003c/em\u003e (20, 23). PoolSeq data analysis revealed evidence of gene duplication in the \u003cem\u003eASL\u003c/em\u003e gene in 2014, CNV was detected in the Uganda population with some samples showing more than three copies of the \u003cem\u003eASL\u003c/em\u003e gene. This CNV likely contributes to the increased expression of \u003cem\u003eASL\u003c/em\u003e as reported in other detoxification genes such as \u003cem\u003eCoeae1f\u003c/em\u003e and \u003cem\u003eCoeae2f\u003c/em\u003e in \u003cem\u003eAn. gambiae\u003c/em\u003e (36) and carboxylesterase in \u003cem\u003eCulex pipiens\u003c/em\u003e (37).\u003c/p\u003e \u003cp\u003e \u003cb\u003eASL\u003c/b\u003e \u003cb\u003econfers and exacerbates pyrethroid resistance: a challenge for resistance management\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIn this study, using GAL4/UAS transgenic expression in \u003cem\u003eDrosophila\u003c/em\u003e, we demonstrated that \u003cem\u003eASL\u003c/em\u003e over-expression is sufficient to confer resistance independently to both pyrethroids type I (permethrin) and moderate resistance to type II (deltamethrin and alpha-cypermethrin). It is the first time that the \u003cem\u003eASL\u003c/em\u003e gene has been functionally validated as conferring insecticide resistance and correlates with the signature of selection detected around this gene in Uganda. This resistance observed in \u003cem\u003eASL\u003c/em\u003e-flies supports that \u003cem\u003eASL\u003c/em\u003e over-expression contributes to pyrethroid resistance in field populations of mosquitoes (31). This validation of \u003cem\u003eASL\u003c/em\u003e is another indication that besides standard detoxification enzymes families such as P450s, GSTs and esterases, other enzymes are playing a key role as was shown for the sensory appendage proteins in \u003cem\u003eAn. gambiae\u003c/em\u003e (38). The difference in mortality observed between flies expressing the mutant and wild alleles reveals that the allelic variation impacts the ability of \u003cem\u003eASL\u003c/em\u003e to confer this resistance suggesting that \u003cem\u003eASL\u003c/em\u003e contribution to pyrethroid resistance is mediated through an up-regulation combined to allelic variation of coding region similar to cases reported for some key resistance genes including P450 (17, 21) for \u003cem\u003eAn. funestus\u003c/em\u003e, for (3) \u003cem\u003eAn. gambiae\u003c/em\u003e, (34) for \u003cem\u003eDrosophila\u003c/em\u003e and \u003cem\u003eGSTe\u003c/em\u003e in \u003cem\u003eAn. funestus\u003c/em\u003e (19) but also epsilon in \u003cem\u003eAedes aegypti\u003c/em\u003e (39). Moreover, the increase susceptibility to pyrethroids observed after ASL knockdown using RNAi further supports that \u003cem\u003eASL\u003c/em\u003e can confer resistance to pyrethroids.\u003c/p\u003e \u003cp\u003eThe role of \u003cem\u003eASL\u003c/em\u003e in insecticide resistance is not clear but we hypothesise that, unlike P450s, \u003cem\u003eASL\u003c/em\u003e indirectly confers insecticide resistance by fuelling metabolic and cellular pathways that are essential for managing the toxic effects of insecticides. We hypothesise that arginine, one of the products of \u003cem\u003eASL\u003c/em\u003e supports the synthesis of polyamines, proline and ammonia detoxification which are critical for cellular repair, membrane stability, stress response and energy production for instance during insecticide exposure. In humans \u003cem\u003eASL\u003c/em\u003e is a critical enzyme of the urea cycle acting as an intermediate enzyme in the synthesis pathway of urea with a defect in \u003cem\u003eASL\u003c/em\u003e leading to the accumulation of ammonia in the blood causing serious impairments (40). We hypothesise that in insects \u003cem\u003eASL\u003c/em\u003e could also be contributing to helping eliminate ammonia or other products of the xenobiotic metabolism pathway including for pyrethroids. Therefore, increased expression of \u003cem\u003eASL\u003c/em\u003e combined with selection of a variant boosting its activities could work to not only confer resistance to pyrethroids but even exacerbate it as seen with the greater frequency of RR observed in mosquitoes surviving 10X permethrin (41).\u003c/p\u003e \u003cp\u003e \u003cb\u003eA novel DNA-based assay allows tracking the spread of the\u003c/b\u003e \u003cb\u003eASL\u003c/b\u003e\u003cb\u003e-based resistance and its impact on control tools\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe newly designed DNA-based assay was shown to be robust to detect and track the spread of \u003cem\u003eASL\u003c/em\u003e-based resistance across Africa. This is another evidence that metabolic-based resistance could be detected with simple DNA-based assays as shown previously for GSTs (35) and P450s (17, 20, 21, 23). The mutation associated with resistance here is located in the upstream promoter region as previously established for others metabolic resistance genes including P450s such as \u003cem\u003eCYP6P9a/b\u003c/em\u003e in \u003cem\u003eAn. funestus\u003c/em\u003e (20), for previous observation in the mosquitoes \u003cem\u003eCulex quinquefasciatus\u003c/em\u003e (42) and \u003cem\u003eDrosophila\u003c/em\u003e (43). This newly developed DNA-based assay will complement other recently designed diagnostic assays for metabolic resistance which so far were only detoxification-based on P450s or GSTs. The \u003cem\u003eCYP6P9a/b\u003c/em\u003e in \u003cem\u003eAn. funestus\u003c/em\u003e is currently used to monitor resistance in Southern Africa (20, 23). Other genes, such as \u003cem\u003eCYP6P4a/b\u003c/em\u003e in West Africa (21) and \u003cem\u003eCYP9K1\u003c/em\u003e and 4.3kb transposon-containing structural variant in East/Central Africa (17, 44) have been recently detected and validated to track resistance in field populations. The higher frequency of \u003cem\u003eASL\u003c/em\u003e homozygote resistant mosquitoes surviving 10 times the diagnostic concentration is a strong indication that this gene also drives the aggravation of pyrethroid resistance in the field which is different from results obtained with other detoxication markers such as L119F-\u003cem\u003eGSTe2\u003c/em\u003e which although associated with deltamethrin resistance at 1X in a Ghanaian \u003cem\u003eAn. funestus\u003c/em\u003e population, was not at 10X (9). Similarly, no association was seen between \u003cem\u003eCYP6P9a/b\u003c/em\u003e markers and escalation in Malawi although this was in a background of near fixation of these markers in the field (8). Therefore, \u003cem\u003eASL\u003c/em\u003e appears as one of the first marker that could allow to assess the impact of pyrethroid escalation in the field.\u003c/p\u003e \u003cp\u003eUse of this \u003cem\u003eASL\u003c/em\u003e DNA-based marker in experimental hut study revealing that \u003cem\u003eASL\u003c/em\u003e is associated with the reduced efficacy of bed nets, particularly PBO-based nets (PermaNet 3.0 and Olyset Plus) which is different from results obtained with P450s which tend to mainly reduce efficacy of pyrethroid-only nets (8, 20). Therefore, in region of high \u003cem\u003eASL\u003c/em\u003e-based resistance, PBO should not be deployed as supported by the continuous increased frequency of this resistance allele in Uganda where PBO-based nets were deployed (11, 45). Chlorfenapyr-based nets (IG2), in contrast showed greater efficacy against \u003cem\u003eASL\u003c/em\u003e-based resistance with higher frequency of RR among the dead than the alive mosquitoes similar to the effect on P450-based resistance as recently reported in \u003cem\u003eAn. funestus\u003c/em\u003e (46). Therefore, this study highlights the efficacy of IG2 against both P450s and \u003cem\u003eASL\u003c/em\u003e-based resistance likely explaining the higher efficacy of this net in recent randomised-control trials in either East (47) or West (48) Africa.\u003c/p\u003e \u003cp\u003eThe marked reduction of the frequency of \u003cem\u003eASL\u003c/em\u003e from 92% in 2023 to just 54% in 2024 after the deployment of IG 2 LLINs suggests that \u003cem\u003eASL\u003c/em\u003e-based resistance could be managed by switching from PBO-based nets to chlorfenapyr-based nets such as IG2. It would be interesting to continue monitoring the frequency of \u003cem\u003eASL\u003c/em\u003e in areas of IG2-deployment to further assess the evolution of \u003cem\u003eASL\u003c/em\u003e allele and inform the design of resistance management strategies.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eA comprehensive understanding of the molecular mechanisms driving insecticide resistance in malaria vectors is crucial for effective resistance management. Our study has identified a novel non-detoxification gene, \u003cem\u003eargininosuccinate lyase\u003c/em\u003e (\u003cem\u003eASL\u003c/em\u003e), as a key factor in pyrethroid resistance escalation in \u003cem\u003eAnopheles funestus\u003c/em\u003e across Africa. We have demonstrated that \u003cem\u003eASL\u003c/em\u003e overexpression in resistant populations, particularly in East Africa, is a key mechanism of aggravation of pyrethroid resistance. This finding led to the development of a simple DNA-based diagnostic assay to track the spread of \u003cem\u003eASL\u003c/em\u003e-mediated resistance in field populations showing that although \u003cem\u003eASL\u003c/em\u003e-based resistance is a threat to PBO-based net, this can be mitigated by rather deploying Chlorfenapyr-based net such as IG2.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStudy sites\u003c/h2\u003e \u003cp\u003e \u003cem\u003eAn. funestus s.s\u003c/em\u003e mosquitoes were collected between 2018 to 2024 from different countries across the four sub-Sahara African regions and where resistance escalation has been reported. This includes Mibellon in Cameroon (6\u0026deg;4\u0026prime;60\u0026prime;\u0026prime;N, 11\u0026deg;70\u0026prime;0\u0026prime;\u0026prime;E) (49), Obuasi in Ghana (6\u0026deg;17.377\u0026Prime;N, 1\u0026deg;27.545\u0026Prime;W) (9), Mayugue in Uganda (0\u0026deg;23\u0026prime;10.8\u0026prime;\u0026prime;N, 33\u0026deg;37\u0026prime;16.5\u0026prime;\u0026prime;E) (50), Chikwawa in Malawi (16\u0026deg;2\u0026prime;8\u0026prime;\u0026prime;S, 34\u0026deg;50\u0026prime;21\u0026prime;\u0026prime;N) (8) and Palmeira in Mozambique (25\u0026deg;15\u0026prime;19\u0026prime;\u0026prime;S, 32\u0026deg;52\u0026prime;22\u0026prime;\u0026prime;E) (10) (Fig. S9). The laboratory strains FANG (susceptible colony originated from Angola) and FUMOZ (resistant colony originated from Mozambique) (51) were also used in this study.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cb\u003eComparative transcriptomic profiling of\u003c/b\u003e \u003cb\u003eargininosuccinate lyase\u003c/b\u003e \u003cb\u003e(\u003c/b\u003e\u003cb\u003eASL\u003c/b\u003e\u003cb\u003e) gene in\u003c/b\u003e \u003cb\u003eAnopheles funestus\u003c/b\u003e \u003cb\u003eacross Africa\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eA comparative transcriptomic analysis of the \u003cem\u003eASL\u003c/em\u003e gene (\u003cem\u003eAFUN004002\u003c/em\u003e) from four African countries, Mibellon (Cameroon), Obuasi (Ghana), Chikwawa (Malawi), and Mayuge (Uganda) was performed to examine its contribution in pyrethroid resistance in \u003cem\u003eAn\u003c/em\u003e. \u003cem\u003efunestus\u003c/em\u003e. To this effect, previous RNAseq data (20, 23) was analysed following the methodology described by (24). Differentially expressed genes (DEGs) were analyzed by comparing the transcriptomes of mosquitoes that survived permethrin 1X exposure to those of unexposed populations from Ghana, Malawi, Uganda, and Cameroon to investigate the \u003cem\u003eASL\u003c/em\u003e response to permethrin exposure. Additionally, DEGs were identified by contrasting the transcription profiles of permethrin-resistant populations from these countries and a laboratory-resistant colony (FUMOZ) with the laboratory-susceptible strain (FANG). In each comparison, DEGs were determined globally, with a focus on detoxification-related genes, digestive enzymes and the \u003cem\u003eASL\u003c/em\u003e gene. DEG analysis was performed using DESeq2 (52), with overexpressed genes defined as those having a corrected p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and a log2 fold change\u0026thinsp;\u0026gt;\u0026thinsp;1.\u003c/p\u003e \u003cp\u003eREVIGO was used to perform gene ontology (GO) enrichment analysis on differentially expressed gene sets. A more extensive study was undertaken on all of the genes over-expressed in all four countries to identify groups of transcripts that are comparable to the \u003cem\u003eASL\u003c/em\u003e gene and may be interacting together to impart pyrethroid resistance to \u003cem\u003eAn. funestus\u003c/em\u003e. This was accomplished by first picking a list of transcripts that were considerably overexpressed in the same comparison as the \u003cem\u003eASL\u003c/em\u003e gene, followed by selecting the transcript of interest.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eValidation of RNA-Sequencing result by RT-qPCR\u003c/h2\u003e \u003cp\u003eQuantitative reverse transcription PCR (qRT-PCR) assay was performed to validate the expression profile of the candidate gene across Africa through RNAseq data results obtained in different countries comparatively in 2014 and 2021. RNA was extracted from three biological replicates of 10 mosquitoes, in the resistant (alive after exposure to pyrethroid), control (unexposed), FUMOZ (resistant Lab strain) and FANG (susceptible Lab strain). The Arcturus PicoPure RNA Isolation Kit (Life Technologies, Carlsbad, CA, USA) was used according to the manufacturer\u0026rsquo;s instructions. One (1) \u0026micro;g of each RNA sample was used as a template for complementary DNA (cDNA) synthesis using the superscript III (Invitrogen) with 1\u0026micro;l of oligo-dT20 and 1\u0026micro;l of RNase H, as the manufacturer\u0026rsquo;s guide. The qRT-PCR was carried out as previously described (53, 54). A standard curve of \u003cem\u003eASL\u003c/em\u003e was established using 5 dilutions of cDNA to assess PCR efficiency and quantify the differences between each dilution. The quantitative PCR (qPCR) amplification was carried out in an MX-PRO 3005 real-time PCR system (Agilent) using Brilliant III Ultra-Fast SYBR Green qPCR Master Mix (Agilent). The primers used for qPCR are listed in the supplemental file (Table. S8). A total of 1 ng/\u0026micro;l of cDNA from each sample was used as a template in a three-step program involving a denaturation for 3 minutes at 95\u0026deg;C followed by 40 cycles of 10s at 95\u0026deg;C and 10s at 60\u0026deg;C and a last step of 1 minute at 95\u0026deg;C, 30s at 55\u0026deg;C, and 30s at 95\u0026deg;C (total time of 1h12min54s). The relative expression level of each experimental group was compared to the reference susceptible strain FANG according to the 2-\u003csup\u003eΔΔCT\u003c/sup\u003e method (55). Expression of the gene was normalised with the housekeeping genes ribosomal protein \u003cem\u003eRSP7\u003c/em\u003e (\u003cem\u003eAFUN007153\u003c/em\u003e) and \u003cem\u003eActin5C\u003c/em\u003e (\u003cem\u003eAFUN006819\u003c/em\u003e).\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cb\u003eDetection of genomic differentiation and selection (\u003c/b\u003e \u003cb\u003eF\u003c/b\u003e \u003csub\u003e \u003cb\u003eST\u003c/b\u003e \u003c/sub\u003e, \u003cb\u003eH\u003c/b\u003e\u003csub\u003e\u003cb\u003e1X\u003c/b\u003e\u003c/sub\u003e, \u003cb\u003eH\u003c/b\u003e\u003csub\u003e\u003cb\u003e12\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e) around\u003c/b\u003e \u003cb\u003eASL\u003c/b\u003e \u003cb\u003egene in\u003c/b\u003e \u003cb\u003eAn. funestus\u003c/b\u003e \u003cb\u003eacross Africa\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWe performed genetic differentiation and selection analyses of \u003cem\u003eAn. funestus\u003c/em\u003e across Africa using Genome-Wide Association Studies of PoolSeq data (GWAS-PoolSeq) and individual whole-genome sequencing (iWGS) from MalariaGen data (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.malariagen.net/project/anopheles-funestus-genomic-surveillance-project/\u003c/span\u003e\u003cspan address=\"https://www.malariagen.net/project/anopheles-funestus-genomic-surveillance-project/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e A windowed \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eST\u003c/em\u003e\u003c/sub\u003e analysis of chromosome 3RL where the \u003cem\u003eASL\u003c/em\u003e gene is located, using PoolSeq data collected between 2014 and 2021, was conducted with PoPoolation2, as previously described (56). For the iWGS, data available were collected in 2014, pairwise \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eST\u003c/em\u003e\u003c/sub\u003e values were estimated using Hudson\u0026rsquo;s method (57). We computed and plotted \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eST\u003c/em\u003e\u003c/sub\u003e values for multiple pairwise comparisons across our four populations (Cameroon, Ghana, Malawi, and Uganda) using the plot_fst_gwss function from the MalariaGEN package (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://malariagen.github.io/malariagen-data-python/latest/Af1.html\u003c/span\u003e\u003cspan address=\"https://malariagen.github.io/malariagen-data-python/latest/Af1.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e To detect evidence of recent positive selection around the \u003cem\u003eASL\u003c/em\u003e gene in our populations, we applied Garud's H\u003csub\u003e12\u003c/sub\u003e scans and plotted the results using the plot_h12_gwss function. Additionally, we computed and visualized H\u003csub\u003e1X\u003c/sub\u003e scores with the plot_h1x_gwss function to identify shared selective sweeps between populations. All the selection analyses were calculated in windows of 1000 Single Nucleotide Polymorphisms (\u003cem\u003eSNPs\u003c/em\u003e).\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cb\u003eCNV and diplotype calling around\u003c/b\u003e \u003cb\u003eASL\u003c/b\u003e \u003cb\u003egene using iWGS in\u003c/b\u003e \u003cb\u003eAn. funestus\u003c/b\u003e \u003cb\u003eacross Africa\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIncreased gene dosage has been linked with gene overexpression in \u003cem\u003eAn. gambiae sl\u003c/em\u003e across its range (36, 58\u0026ndash;60). To understand the genetic factors behind the overexpression of the \u003cem\u003eASL\u003c/em\u003e gene in some of our populations, copy number variation (CNV) in the 2RL:80\u0026ndash;100 Mbp region surrounding the \u003cem\u003eASL\u003c/em\u003e locus was analyzed using iWGS data with the plot_cnv_hmm_coverage function, following the method described in (58). In brief, read counts for each individual were recorded in 300 bp non-overlapping windows and normalized by the per-individual mean number of reads in genome-wide autosomal 300 bp windows, stratified by GC content. These normalized coverage values were then used as input for a Gaussian Hidden Markov Model (HMM), with copy number states treated as hidden variables. CNVs were defined as regions with at least five consecutive 300 bp windows showing elevated HMM-predicted copy number states. A CNV allele was considered present in an individual if at least two diagnostic reads supported it. For diplotype calling, we utilized a recently implemented function in the MalariaGEN package to visualize diplotype (plot_diplotype_clustering_advanced), which combines two haplotypes, one from each chromosome, enabling us to focus on the gene of interest (\u003cem\u003eLOC125762180\u003c/em\u003e). This approach allowed us to identify selective sweeps, assess their extent, detect potential gene flow between countries, and investigate whether these sweeps are driven by copy number variants (CNVs), amino acid mutations, or both.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cb\u003ePolymorphism of\u003c/b\u003e \u003cb\u003eASL\u003c/b\u003e \u003cb\u003egene in relation to pyrethroids resistance across Africa\u003c/b\u003e\u003c/p\u003e\u003cp\u003e \u003cb\u003eAnalysis of polymorphism of\u003c/b\u003e \u003cb\u003eASL\u003c/b\u003e \u003cb\u003egenerated from SureSelect target-enrichment data\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eTo investigate whether allelic variation is associated with the increased expression of the \u003cem\u003eASL\u003c/em\u003e gene (\u003cem\u003eAFUN004002\u003c/em\u003e, based on the FUMOZ reference annotation) in \u003cem\u003eAn. funestus\u003c/em\u003e populations across Africa, we analyzed previously generated SureSelect data (61). Initial processing and quality assessment of the sequencing data were performed using Strand NGS 3.4 (Strand Life Sciences). Alignment and mapping were conducted using the \"DNA alignment\" option against the whole genome (AfunF1 version), which was organized into three chromosomes through synteny with \u003cem\u003eAn. gambiae\u003c/em\u003e (23, 62). Comprehensive variant calling was carried out using VarScan for PoolSeq and Freebayes for SureSelect data, as previously described (61). The resulting VCF (Variants Calling Format) files were annotated, and gene variant effects were predicted using SnpEff (63) for both PoolSeq and SureSelect datasets. From the filtered VCF generated from the SureSelect data, sequences from the \u003cem\u003eASL\u003c/em\u003e gene, including both promoter and coding regions, were extracted using custom scripts. These sequences were analyzed to identify potential SNPs linked with \u003cem\u003eASL\u003c/em\u003e overexpression. Polymorphism analysis was conducted in dnaSP 6.1 (64) following multiple sequence alignment (MSA) with the ClustalW Multiple Alignment tool integrated in BioEdit (65). Haplotype network analysis was performed using the TCS package (Templeton, Crandall, and Sing) (66). For phylogenetic tree construction, FASTA sequences were aligned using MAFFT version 7.029 with the auto option. Phylogenetic trees were inferred using MEGA 11 (67) with maximum likelihood and 1000 bootstrap replicates.\u003c/p\u003e \u003cp\u003eAdditionally, we interrogated the recent MalariaGEN dataset on \u003cem\u003eAn. funestus\u003c/em\u003e genetic diversity across Africa to perform a similar analysis, focusing on the \u003cem\u003eASL\u003c/em\u003e gene (\u003cem\u003eLOC125762180\u003c/em\u003e based on the AFUNGA1 reference annotation) in the four countries. Variant calling for this data was done with GATK. Diversity estimates were obtained using the plot_diversity_stats function, calculated in windows of 1,000 SNPs. We visualized nucleotide diversity (π), theta diversity (θ), and Tajima\u0026rsquo;s D to assess patterns of genetic variation within populations.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cb\u003eAnalysis of cDNA polymorphism of\u003c/b\u003e \u003cb\u003eASL\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe full coding sequence of \u003cem\u003eASL\u003c/em\u003e was amplified from mosquitoes cDNAs (alive after exposure to deltamethrin (0.05%), and unexposed across 4 countries (Cameroon, Ghana, Uganda, and Mozambique), the susceptible FANG and the resistant laboratory colony FUMOZ. The Phusion Taq Kit (Fermentas, Burlington, Ontario, Canada) was used as the enzyme for amplification with the two pairs of primer listed in Table S8. PCR reaction was performed using a total of 15\u0026micro;L, containing: 3\u0026micro;L of 5x HF buffer (1.5mM MgCl2), 0.12\u0026micro;L of 25Mm dNTPs, 0.51\u0026micro;L of 10mM forward and reverse primers, 0.15\u0026micro;L of Phusion Taq polymerase, 9.71 \u0026micro;L of deionized water and 1 \u0026micro;L of DNA template. The amplification uses the following conditions: an initial denaturation at 98\u0026deg;C for 1 min, followed by 35 cycles of denaturation at 98\u0026deg;C for 30s, annealing at 60\u0026deg;C for 30s and extension at 72\u0026deg;C for 90s. A final elongation step was performed at 72\u0026deg;C for 10 min. PCR products were gel-purified using the QIAquick Gel Extraction Kit and ligated into the pJET1.2/blunt cloning vector using the CloneJET PCR Cloning Kit. Recombinant \u003cem\u003eASL\u003c/em\u003e-pJET1.2 plasmids were transformed into \u003cem\u003eE. coli\u003c/em\u003e DH5α cells. Plasmid DNA was miniprepped using the QIAprep Spin Miniprep Kit and sequenced using the pJET1.2-specific primers pJET1.2F and pJET1.2R. Sequence analysis was carried out using the same software as previously in the Sureselect section multiple alignment, haplotype network using TCS and phylogenetic trees using Mega.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePolymorphism analysis of 1 kb putative promoter in Uganda\u003c/h3\u003e\n\u003cp\u003eWe comparatively analysed the polymorphism of \u003cem\u003eASL\u003c/em\u003e gene between pyrethroid resistance mosquitoes from Uganda, focusing on the gene's promoter region. This was done by amplification and direct sequencing of 1,080 bp fragment of the promoter region of \u003cem\u003eASL\u003c/em\u003e located upstream of the gene. Five (05) F1 samples from Uganda dead after 60 min exposure to permethrin (susceptible mosquitoes) were analysed comparatively to five alive after 60 min exposure (resistant mosquitoes). We also analysed the crossing Fang/UGA F3 alive and dead after exposure to permethrin 60 and 30 min respectively generated in the insectary by crossing the female Fang and male from Uganda population collected in 2022. The \u003cem\u003eASL\u003c/em\u003e promoter region was amplified using the following conditions: an initial denaturation at 95\u0026deg;C for 5 min, followed by 35 cycles of denaturation at 94\u0026deg;C for 30s, annealing at 60\u0026deg;C for 30s and extension at 72\u0026deg;C for 90s. A final elongation step was performed at 72\u0026deg;C for 5 min. Amplified products were cleaned individually, cloned and purified. The polymorphic position was identified by manually analysing sequence traces using the same software as above. To detect cis-trans-regulatory element and transcription factor binding site, PROMO and TFBIND (68) software were used.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cb\u003eGAL4/UAS transgenic expression of\u003c/b\u003e \u003cb\u003eASL\u003c/b\u003e \u003cb\u003ein\u003c/b\u003e \u003cb\u003eDrosophila melanogaster\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eDue to the overexpression of \u003cem\u003eASL\u003c/em\u003e in resistant mosquitoes to pyrethroid in the African population, transgenic expression of this gene of interest was achieved using the Gal4-UAS system (54, 69). The mutant-type (Uganda) and the wild-type (FANG) alleles of \u003cem\u003eASL\u003c/em\u003e were independently expressed in \u003cem\u003eD. melanogaster\u003c/em\u003e to determine if the expression of \u003cem\u003eASL\u003c/em\u003e alone and/or the presence of allelic variation can significantly contribute to the increased resistance level.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cb\u003eCloning and construction of transgenic Drosophila expressing\u003c/b\u003e \u003cb\u003eAn. funestus ASL\u003c/b\u003e \u003cb\u003egene\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eTwo Plasmids containing a mutant type \u003cem\u003eASL\u003c/em\u003e-resistant (125H-\u003cem\u003eASL\u003c/em\u003e) allele from the Uganda population, and a wild-type \u003cem\u003eASL\u003c/em\u003e-susceptible (R125-\u003cem\u003eASL\u003c/em\u003e) allele from Fang laboratory strain. The recombinant \u003cem\u003eASL\u003c/em\u003e-plasmids were then digested with the \u003cem\u003eEcoRI\u003c/em\u003e and \u003cem\u003eXbal\u003c/em\u003e (Fermentas, Burlington, Ontario, Canada) restriction enzymes using the High-Fidelity enzyme kit from NEB-cutter (Fermentas, Burlington, Ontario, Canada) to extract the alleles of interest in the pJET1.2/blunt vector previously cloned in the genetic diversity analysis section. These candidate alleles were then cloned into the Drosophila expression vector pUASattB, with the same restriction enzymes. Following colony PCR, the resulting construct pUAS::125H-\u003cem\u003eASL\u003c/em\u003e, and pUAS::R125-\u003cem\u003eASL\u003c/em\u003e, were purified using Qiagen's midiprep kit according to the manufacturer's instructions. The midipreps were sequenced to ensure the ligation of alleles on pUAS vector. The construct, pUAS::125H-\u003cem\u003eASL\u003c/em\u003e and pUAS::R125-\u003cem\u003eASL\u003c/em\u003e were injected into the \u003cem\u003eD. melanogaster\u003c/em\u003e germline carrying the attP40 binding site on chromosome 2 (y1w 67c23; P (CaryP) attP40,1;2) as previously described (33, 70). The Fly Facility (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.flyfacility.gen.cam.acuk/\u003c/span\u003e\u003cspan address=\"https://www.flyfacility.gen.cam.acuk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e conducted the microinjection process and the balancing. Control Drosophila was injected with a gene-free vector. Expression of \u003cem\u003eASL\u003c/em\u003e in drosophila were active after crossing of pUAS::125H-\u003cem\u003eASL\u003c/em\u003e and the pUAS::R125-\u003cem\u003eASL\u003c/em\u003e males flies cary Actin II with virgin female from GAL4 flies line. After crossing, 2 to 5 days females from F1 generation with red eyes and curly wings expressing the 125H-\u003cem\u003eASL\u003c/em\u003e and R125-\u003cem\u003eASL\u003c/em\u003e were selected for insecticide contract assays.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cb\u003eValidation of gene expression of\u003c/b\u003e \u003cb\u003eASL\u003c/b\u003e \u003cb\u003ein transgenic Drosophila and insecticide susceptibility contact assay\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eConfirmation of \u003cem\u003eASL\u003c/em\u003e expression in transgenic Drosophila and their absence in the control was done using quantitative real-time PCR (qPCR) using the Sybr Green as previously described with \u003cem\u003eASL\u003c/em\u003e-specific primers for flies (Table. S8), following the method described by Riveron in 2014 (Riveron et al., 2014b). To achieve this aim, total RNA was extracted from three pools of five (05) females Drosophila from the first generation of the pUAS::125H-\u003cem\u003eASL\u003c/em\u003e and the pUAS::R125-\u003cem\u003eASL\u003c/em\u003e and the control line after crossing, and the different cDNA were synthesized. Comparative gene expression levels between experimental and control Drosophila were normalized using the \u003cem\u003eRPL11\u003c/em\u003e housekeeping gene.\u003c/p\u003e \u003cp\u003eTo assess whether expression of \u003cem\u003eASL\u003c/em\u003e alone can confer insecticide resistance independently of other resistant mechanisms, first-generation of transgenic females expressing \u003cem\u003eASL\u003c/em\u003e were selected as experimental groups for insecticide exposure. In practice, 5 replicates of 20 to 25 transgenic females of Drosophila aged 2 to 5 days from the experimental and control groups were exposed to pyrethroid (permethrin (4%), deltamethrin (0.2%), and alpha-cypermethrin (0.0007%)). Mortalities were recorded after 1h, 2h, 3h, 6h, 12h and 24h at a temperature of 25\u0026ndash;27\u0026deg;C and a relative humidity of 70\u0026ndash;80%. A comparison of the cumulative mortality rates during the six time points was done between the experimental flies line expressing 125H-\u003cem\u003eASL\u003c/em\u003e and R125-\u003cem\u003eASL\u003c/em\u003e alleles and control groups using student t-test statistical analysis.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cb\u003eInvestigation of the role of the knockdown of\u003c/b\u003e \u003cb\u003eASL\u003c/b\u003e \u003cb\u003eon mosquitoes\u0026rsquo; susceptibility to insecticide through RNAi silencing\u003c/b\u003e\u003c/p\u003e\u003cp\u003eDouble-stranded RNAs (dsRNA) specific to \u003cem\u003eASL\u003c/em\u003e were synthesized for gene-silencing experiments as previously described (19). Double-strand \u003cem\u003eASL\u003c/em\u003e oligonucleotide primers (Table. S8) were designed using the E-RNAi web application (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://e-rnai.dkfz.de/\u003c/span\u003e\u003cspan address=\"http://e-rnai.dkfz.de/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e with a product length of 450bp. Specific \u003cem\u003eASL\u003c/em\u003e fragment was amplified from pJET::ASL plasmid using KAPA Taq Kit (Kapa Biosystems, Wilmington, MA USA) following this step: initial denaturation step of 5 minutes at 95\u0026deg;C, followed by 35 cycles of 30s at 94\u0026deg;C, 30s at 65\u0026deg;C, and 60s at 72\u0026deg;C with 10 minutes at 72\u0026deg;C for final extension. PCR products were purified using Quiagen QIAquick PCR purification kit following the manufacturer\u0026rsquo;s instructions. dsRNA was synthesized using in vitro transcription MEGAscript\u0026reg; T7 Kit (Thermo Fisher Scientific, UK) and purified using MEGAclear columns (Thermo Fisher Scientific, UK) with an incubation of 37\u0026deg;C during 16h. The resultant dsRNA product was analysed using a nanodrop spectrometer (Nanodrop Technologies, UK) and subsequently concentrated to 3\u0026micro;g/\u0026micro;l by using ethanol precipitation, the dsRNA was resuspended in nuclease-free water and stored at \u0026minus;\u0026thinsp;20\u0026deg;C. Briefly, 69 nl of ds\u003cem\u003eASL\u003c/em\u003e or dsGFP (control) were injected directly into the thorax of mosquitoes \u003cem\u003eAn. funestus\u003c/em\u003e population from Mibellon, 3\u0026ndash;5 day old after sleeping with CO\u003csub\u003e2\u003c/sub\u003e, and were kept 72h in insectary for the silencing effect of the \u003cem\u003eASL\u003c/em\u003e gene to be effective. To confirm \u003cem\u003eASL\u003c/em\u003e expression knockdown, RNA of ds\u003cem\u003eASL\u003c/em\u003e injected and non-injected mosquitoes was extracted from 3 pools of 10 mosquitoes using the Arcturus PicoPure RNA Isolation Kit (Life Technologies, Carlsbad, CA, USA) and cDNA synthesized using the Super-Script III (Invitrogen, Carlsbad, CA, USA) according to the manufacturer\u0026rsquo;s instructions. To assess the knockdown efficiency after injection and the quantitative difference in the level of \u003cem\u003eASL\u003c/em\u003e expression between injected and non-injected mosquitoes. The relative expression and fold-change of \u003cem\u003eASL\u003c/em\u003e were calculated according to the 2-\u003csup\u003eΔΔCT\u003c/sup\u003e Livak method, comparing the expression level \u003cem\u003eASL\u003c/em\u003e in ds\u003cem\u003eASL\u003c/em\u003e-injected and non-injected mosquitoes, after normalization with the housekeeping genes, RPS7 (AFUN007153) and actin5C (AFUN006819), as described above. Four days after injection, four replicates of 20\u0026ndash;25 mosquitoes for each dsRNA were exposed to permethrin (0.75%), deltamethrin (0.05%) for 1h and alpha-cypermethrin (0.05%) for 1h30minutes following the WHO testing protocol (71). Mosquitoes were transferred to holding tubes after exposure, supplemented with sugar and mortalities were counted 24 h after the exposure. The susceptibility test was comparatively performed in triplicates between mosquitoes with ds\u003cem\u003eASL\u003c/em\u003e, ds\u003cem\u003eGFP\u003c/em\u003e and those not injected.\u003c/p\u003e\u003cp\u003e \u003cb\u003eDesign of DNA-based assay to detect\u003c/b\u003e \u003cb\u003eASL\u003c/b\u003e\u003cb\u003e-mediated pyrethroid resistance\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe ARMS-PCR (amplification refractory mutation system) tool was used to design a simple Allele Specific PCR that could discriminate between the \u003cem\u003eASL_\u003c/em\u003eT-277A promoter of the resistant (Uganda) and the susceptible (FANG) strains. Four sets of primers were designed two outers and two inners (Table. S8) to amplify the gene with the different variants. PCR reaction was performed with a final volume of 15\u0026micro;l containing 1\u0026times; buffer A, 25 mM MgCl\u003csub\u003e2\u003c/sub\u003e, 25 mM dNTPs, and 10 mM of each primer and 1U of KAPA Taq polymerase (Kapa Biosystems, Boston, MA, USA). The following PCR amplification conditions were used initial denaturation step of 5 minutes at 95\u0026deg;C, followed by 35 cycles of 30s at 94\u0026deg;C, 30s at 57\u0026deg;C, and 90s at 72\u0026deg;C with 10 minutes at 72\u0026deg;C for the final extension according to the KAPA kit instructions. The PCR products were visualized using 1.5% agarose gel electrophoresis to confirm the product sizes. Two bands were found at 654 bp for the common allele, 381 bp for the resistant allele, AA (RR) and 272 bp for the susceptible allele, TT (SS). To validate the robustness of the allele-specific PCR to detect the pyrethroid resistance in the field population, F3 progeny from a cross between highly resistant (Uganda) and highly susceptible (FANG) strains were genotyped and correlated with the resistance phenotype established using the odds ratio and Fisher\u0026rsquo;s exact test. Furthermore, a Locked Nucleic Acid (LNA) assay b-based diagnostic tool was also developed for the \u003cem\u003eASL\u003c/em\u003e_T-277A SNP with a primer set flanking each primer (Table. S9) and covering the region with the mutation. Two locked nucleic acid (LNA) based probes conjugated to FAM for the mutant and HEX for the wild-type alleles were commercially designed and acquired from IDT (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://biophysics.idtdna.com/\u003c/span\u003e\u003cspan address=\"http://biophysics.idtdna.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e (Table. S9). PCR amplification was carried out in a final volume of 10\u0026micro;l containing 1\u0026micro;mole of each probe, 2\u0026micro;mole of each primer in 1x PrimeTime Master Mix (IDT) or 1x Luna Universal qPCR Master Mix (NEB) and 1\u0026micro;l of genomic DNA. The reactions were set up in optical PCR tubes and conducted on an AriaMX Real-Time qPCR cycler (Agilent, USA) using Fam and Hex filters. The \u003cem\u003eASL\u003c/em\u003e_T-277A-LNA PCR test included 10 minutes of denaturation at 95\u0026deg;C; (Segment 1) and 40 cycles of denaturation for 10s at 95\u0026deg;C, annealing for 45s at 60\u0026deg;C; (Segment 2).\u003c/p\u003e \u003cp\u003e \u003cb\u003eAssociation between the\u003c/b\u003e \u003cb\u003eASL\u003c/b\u003e \u003cb\u003emarker and the pyrethroid resistance\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe high level of pyrethroid resistance, coupled with the high frequency of the resistance alleles in the Ugandan field population made the establishment of the link between the genotype and the resistance phenotype difficult. To address this challenge, a mosquito genetic cross between female FANG bearing the homozygote susceptible (SS) \u003cem\u003eASL\u003c/em\u003e T-277/T-277 genotype and male field mosquitoes from Uganda bearing the homozygote resistant (RR) \u003cem\u003eASL\u003c/em\u003e \u0026minus;\u0026thinsp;277A/-277A genotype was established as done for the development of \u003cem\u003eCYP6P4a/b\u003c/em\u003e and \u003cem\u003eCYP9K1\u003c/em\u003e markers (17, 21). The crosses were maintained through to the third generation to allow the three genotypes (RR, RS, and SS) to segregate. WHO tube bioassays were carried out on three- to five-day-old hybrid female mosquitoes using pyrethroid insecticide papers following WHO protocol (71) and the correlation between resistance phenotype and genotypes was established using odds ratio.\u003c/p\u003e \u003cp\u003e \u003cb\u003eImpact of T-277A-\u003c/b\u003e \u003cb\u003eASL\u003c/b\u003e \u003cb\u003emarker on LLINs\u0026rsquo; efficacy using experimental hut trials\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThis study was performed in Mayuge a village in Uganda where 12 experimental huts have recently been built with concrete bricks following the specific design for the experimental hut from the West Africa region (71). This part of the work was done by a colleague from Uganda.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAfrica-wide spatio-temporal assessment of the spread of T-277A-\u003c/b\u003e \u003cb\u003eASL\u003c/b\u003e \u003cb\u003emarker\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo assess the spread and temporal changes in the frequency of the T-277A-\u003cem\u003eASL\u003c/em\u003e marker in Africa, \u003cem\u003eAn. funestus\u003c/em\u003e samples previously collected at different time points in the same localities across Africa, involving eastern Africa (Uganda 2010, 2016, 2021,2022 and 2023, and Tanzania 2018), central Africa (Cameroon (Mibellon 2021 and 2023, Elende 2021 and 2024, Obout 2018, Elon 2018, Gounougou 2021, Tibati 2021, Njombe 2021, and Central Africa Republic 2016), southern Africa (Malawi 2021) and western Africa (Ghana 2021) were used for the study (17). Additionally, we checked the distribution of the marker after exposure to a high dose of insecticide we used Ugandan samples from 2021 F1 exposed to permethrin 1X, 5X and 10X alive and dead. Thirty to forty parental mosquitoes were genotyped per locality using the above diagnostic tools.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis was carried out with Prism 8 (GraphPad Software, San Diego, California USA, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"https://vectorbase.org/vectorbase/app/record/gene/AFUN004002#GeneLocation\" target=\"_blank\"\u003ewww.graphpad.com\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.graphpad.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and alpha values for significance were taken at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, with all confidence intervals (CI) at 95%. Student's t-test was used to compare two columns of data generated from metabolism assays and contact bioassays with transgenic \u003cem\u003eD. melanogaster\u003c/em\u003e flies. Fisher's exact test was performed to assess whether any difference in proportion was found for the genotype contingency tables using MedCalc Software, as we got zero value for some genotypes (Ltd. Odds ratio calculator. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.medcalc.org/calc/odds_ratio.php\u003c/span\u003e\u003cspan address=\"https://www.medcalc.org/calc/odds_ratio.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e Version 23.0.2; accessed September 3, 2024) (Schoonjans, 2024). The statistical significances indicated by asterisks: P\u0026thinsp;\u0026gt;\u0026thinsp;0.05, *P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and ****P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by a Wellcome Trust Senior Research Fellowships in Biomedical Sciences to CSW (217188/Z/19/Z) and a Bill and Melinda Gates Foundation grant to CSW (INV24 006003). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDesign and Conceptualization: CSW\u003c/p\u003e\n\u003cp\u003eMethodology: VBNF, MFMK, GM, LMJM, and CSW\u003c/p\u003e\n\u003cp\u003eSample collection: VBNF, MT, AO, CSW\u003c/p\u003e\n\u003cp\u003eInvestigation: VBNF, GM, MFMK, LMJM, MT, CSTD and CSW\u003c/p\u003e\n\u003cp\u003eVisualization: VBNF, GM, LMJM, MFMK, CSW\u003c/p\u003e\n\u003cp\u003eSupervision: MT, LMJM, and CSW\u003c/p\u003e\n\u003cp\u003eWriting—original draft: VBNF\u003c/p\u003e\n\u003cp\u003eWriting—review \u0026amp; editing: VBNF, GM, MFMK, LMJM, MT, CSTD, AO and CSW.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData and materials availability:\u003c/strong\u003e All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBhatt S, Weiss DJ, Cameron E, Bisanzio D, Mappin B, Dalrymple U, et al. The effect of malaria control on Plasmodium falciparum in Africa between 2000 and 2015. Nature. 2015;526(7572):207-11.\u003c/li\u003e\n\u003cli\u003eUeda M, Takeuchi N, Kaneko K. Stronger selection can slow down evolution driven by recombination on a smooth fitness landscape. PloS one. 2017;12(8):e0183120.\u003c/li\u003e\n\u003cli\u003eVontas J, Grigoraki L, Morgan J, Tsakireli D, Fuseini G, Segura L, et al. Rapid selection of a pyrethroid metabolic enzyme CYP9K1 by operational malaria control activities. Proceedings of the National Academy of Sciences. 2018;115(18):4619-24.\u003c/li\u003e\n\u003cli\u003eLiu N. Insecticide resistance in mosquitoes: impact, mechanisms, and research directions. Annu Rev Entomol. 2015;60:537-59.\u003c/li\u003e\n\u003cli\u003eMulamba C, Riveron JM, Ibrahim SS, Irving H, Barnes KG, Mukwaya LG, et al. Widespread pyrethroid and DDT resistance in the major malaria vector Anopheles funestus in East Africa is driven by metabolic resistance mechanisms. PLoS One. 2014;9(10):e110058.\u003c/li\u003e\n\u003cli\u003eWood O, Hanrahan S, Coetzee M, Koekemoer L, Brooke B. Cuticle thickening associated with pyrethroid resistance in the major malaria vector Anopheles funestus. Parasites \u0026amp; vectors. 2010;3:1-7.\u003c/li\u003e\n\u003cli\u003eDjondji Kamga FM, Mugenzi LMJ, Tchouakui M, Sandeu MM, Maffo CGT, Nyegue MA, et al. Contrasting Patterns of Asaia Association with Pyrethroid Resistance Escalation between the Malaria Vectors Anopheles funestus and Anopheles gambiae. Microorganisms. 2023;11(3).\u003c/li\u003e\n\u003cli\u003eMenze BD, Tchouakui M, Mugenzi LMJ, Tchapga W, Tchoupo M, Wondji MJ, et al. Marked aggravation of pyrethroid resistance in major malaria vectors in Malawi between 2014 and 2021 is partly linked with increased expression of P450 alleles. BMC Infect Dis. 2022;22(1):660.\u003c/li\u003e\n\u003cli\u003eMugenzi LMJ, Akosah-Brempong G, Tchouakui M, Menze BD, Tekoh TA, Tchoupo M, et al. Escalating pyrethroid resistance in two major malaria vectors Anopheles funestus and Anopheles gambiae (s.l.) in Atatam, Southern Ghana. BMC Infect Dis. 2022;22(1):799.\u003c/li\u003e\n\u003cli\u003eRiveron JM, Huijben S, Tchapga W, Tchouakui M, Wondji MJ, Tchoupo M, et al. Escalation of pyrethroid resistance in the malaria vector Anopheles funestus induces a loss of efficacy of piperonyl butoxide\u0026ndash;based insecticide-treated nets in Mozambique. The Journal of infectious diseases. 2019;220(3):467-75.\u003c/li\u003e\n\u003cli\u003eTchouakui M, Mugenzi LMJ, B DM, Khaukha JNT, Tchapga W, Tchoupo M, et al. Pyrethroid Resistance Aggravation in Ugandan Malaria Vectors Is Reducing Bednet Efficacy. Pathogens. 2021;10(4).\u003c/li\u003e\n\u003cli\u003eHemingway J, Hawkes NJ, McCarroll L, Ranson H. The molecular basis of insecticide resistance in mosquitoes. Insect biochemistry and molecular biology. 2004;34(7):653-65.\u003c/li\u003e\n\u003cli\u003eCuamba N, Morgan JC, Irving H, Steven A, Wondji CS. High level of pyrethroid resistance in an Anopheles funestus population of the Chokwe District in Mozambique. PLoS One. 2010;5(6):e11010.\u003c/li\u003e\n\u003cli\u003eDjouaka R, Irving H, Tukur Z, Wondji CS. Exploring mechanisms of multiple insecticide resistance in a population of the malaria vector Anopheles funestus in Benin. PLoS One. 2011;6(11):e27760.\u003c/li\u003e\n\u003cli\u003eMorgan JC, Irving H, Okedi LM, Steven A, Wondji CS. Pyrethroid resistance in an Anopheles funestus population from Uganda. PLoS One. 2010;5(7):e11872.\u003c/li\u003e\n\u003cli\u003eWondji CS, Coleman M, Kleinschmidt I, Mzilahowa T, Irving H, Ndula M, et al. Impact of pyrethroid resistance on operational malaria control in Malawi. Proc Natl Acad Sci U S A. 2012;109(47):19063-70.\u003c/li\u003e\n\u003cli\u003eDjoko Tagne CS, Kouamo MF, Tchouakui M, Muhammad A, Mugenzi LJ, Tatchou-Nebangwa NM, et al. A single mutation G454A in the P450 CYP9K1 drives pyrethroid resistance in the major malaria vector Anopheles funestus reducing bed net efficacy. Genetics. 2024:iyae181.\u003c/li\u003e\n\u003cli\u003eKengne-Ouafo JA, Kouamo M, Muhammad A, Tepa A, Ntadoun S, Mugenzi L, et al. A single E205D allele of a key P450 CYP6P3 is driving metabolic pyrethroid resistance in the major African malaria vector Anopheles gambiae. bioRxiv. 2024:2024.02. 18.580859.\u003c/li\u003e\n\u003cli\u003eKouamo MFM, Ibrahim SS, Hearn J, Riveron JM, Kusimo M, Tchouakui M, et al. Genome-Wide Transcriptional Analysis and Functional Validation Linked a Cluster of Epsilon Glutathione S-Transferases with Insecticide Resistance in the Major Malaria Vector Anopheles funestus across Africa. Genes (Basel). 2021;12(4).\u003c/li\u003e\n\u003cli\u003eMugenzi LMJ, Menze BD, Tchouakui M, Wondji MJ, Irving H, Tchoupo M, et al. Cis-regulatory CYP6P9b P450 variants associated with loss of insecticide-treated bed net efficacy against Anopheles funestus. Nat Commun. 2019;10(1):4652.\u003c/li\u003e\n\u003cli\u003eTatchou-Nebangwa NM, Mugenzi LM, Muhammad A, Nebangwa DN, Kouamo MF, Tagne CSD, et al. Two highly selected mutations in the tandemly duplicated CYP6P4a and CYP6P4b genes drive pyrethroid resistance in Anopheles funestus in West Africa. BMC biology. 2024;22(1):286.\u003c/li\u003e\n\u003cli\u003eSandeu MM, Mulamba C, Weedall GD, Wondji CS. A differential expression of pyrethroid resistance genes in the malaria vector Anopheles funestus across Uganda is associated with patterns of gene flow. PLoS One. 2020;15(11):e0240743.\u003c/li\u003e\n\u003cli\u003eWeedall GD, Mugenzi LM, Menze BD, Tchouakui M, Ibrahim SS, Amvongo-Adjia N, et al. A cytochrome P450 allele confers pyrethroid resistance on a major African malaria vector, reducing insecticide-treated bednet efficacy. Science translational medicine. 2019;11(484):eaat7386.\u003c/li\u003e\n\u003cli\u003eWondji CS, Hearn J, Irving H, Wondji MJ, Weedall G. RNAseq-based gene expression profiling of the Anopheles funestus pyrethroid-resistant strain FUMOZ highlights the predominant role of the duplicated CYP6P9a/b cytochrome P450s. G3. 2022;12(1):jkab352.\u003c/li\u003e\n\u003cli\u003eZhao A, Li Y, Leng C, Wang P, Li Y. Inhibitory effect of protease inhibitors on larval midgut protease activities and the performance of Plutella xylostella (Lepidoptera: Plutellidae). Frontiers in Physiology. 2019;9:1963.\u003c/li\u003e\n\u003cli\u003eYu B, Howell P. Intragenic complementation and the structure and function of argininosuccinate lyase. Cellular and Molecular Life Sciences CMLS. 2000;57:1637-51.\u003c/li\u003e\n\u003cli\u003eWan PJ, Yang L, Wang WX, Fan JM, Fu Q, Li GQ. Constructing the major biosynthesis pathways for amino acids in the brown planthopper, N ilaparvata lugens S t\u0026aring;l (H emiptera: D elphacidae), based on the transcriptome data. Insect Molecular Biology. 2014;23(2):152-64.\u003c/li\u003e\n\u003cli\u003eDavies S-A. Nitric oxide signalling in insects. Insect Biochemistry and Molecular Biology. 2000;30(12):1123-38.\u003c/li\u003e\n\u003cli\u003eMeireles P, Mendes AM, Aroeira RI, Mounce BC, Vignuzzi M, Staines HM, et al. Uptake and metabolism of arginine impact Plasmodium development in the liver. Scientific Reports. 2017;7(1):4072.\u003c/li\u003e\n\u003cli\u003eCastro FLdS, Kim WK. Secondary functions of arginine and sulfur amino acids in poultry health. Animals. 2020;10(11):2106.\u003c/li\u003e\n\u003cli\u003eIbrahim SS, Ndula M, Riveron JM, Irving H, Wondji CS. The P450 CYP6Z1 confers carbamate/pyrethroid cross-resistance in a major African malaria vector beside a novel carbamate-insensitive N485I acetylcholinesterase-1 mutation. Mol Ecol. 2016;25(14):3436-52.\u003c/li\u003e\n\u003cli\u003eOtali D, Novak RJ, Wan W, Bu S, Moellering DR, De Luca M. Increased production of mitochondrial reactive oxygen species and reduced adult life span in an insecticide-resistant strain of Anopheles gambiae. Bulletin of entomological research. 2014;104(3):323-33.\u003c/li\u003e\n\u003cli\u003eRiveron JM, Ibrahim SS, Chanda E, Mzilahowa T, Cuamba N, Irving H, et al. The highly polymorphic CYP6M7 cytochrome P450 gene partners with the directionally selected CYP6P9a and CYP6P9b genes to expand the pyrethroid resistance front in the malaria vector Anopheles funestus in Africa. BMC genomics. 2014;15:1-19.\u003c/li\u003e\n\u003cli\u003eSchlenke TA, Begun DJ. Strong selective sweep associated with a transposon insertion in Drosophila simulans. Proceedings of the National Academy of Sciences. 2004;101(6):1626-31.\u003c/li\u003e\n\u003cli\u003eRiveron JM, Yunta C, Ibrahim SS, Djouaka R, Irving H, Menze BD, et al. A single mutation in the GSTe2 gene allows tracking of metabolically based insecticide resistance in a major malaria vector. Genome biology. 2014;15:1-20.\u003c/li\u003e\n\u003cli\u003eNagi SC, Lucas ER, Egyir-Yawson A, Essandoh J, Dadzie S, Chabi J, et al. Parallel evolution in mosquito vectors - a duplicated esterase locus is associated with resistance to pirimiphos-methyl in An. gambiae. bioRxiv. 2024.\u003c/li\u003e\n\u003cli\u003eBuss D, Callaghan A. Molecular comparisons of the Culex pipiens (L.) complex esterase gene amplicons. Insect biochemistry and molecular biology. 2004;34(5):433-41.\u003c/li\u003e\n\u003cli\u003eIngham VA, Anthousi A, Douris V, Harding NJ, Lycett G, Morris M, et al. A sensory appendage protein protects malaria vectors from pyrethroids. Nature. 2020;577(7790):376-80.\u003c/li\u003e\n\u003cli\u003eLumjuan N, Rajatileka S, Changsom D, Wicheer J, Leelapat P, Prapanthadara L-a, et al. The role of the Aedes aegypti Epsilon glutathione transferases in conferring resistance to DDT and pyrethroid insecticides. Insect biochemistry and molecular biology. 2011;41(3):203-9.\u003c/li\u003e\n\u003cli\u003eO'Brien WE, McInnes R, Kalumuck K, Adcock M. Cloning and sequence analysis of cDNA for human argininosuccinate lyase. Proceedings of the National Academy of Sciences. 1986;83(19):7211-5.\u003c/li\u003e\n\u003cli\u003eFicicioglu C, Mandell R, Shih V. Argininosuccinate lyase deficiency: longterm outcome of 13 patients detected by newborn screening. Molecular genetics and metabolism. 2009;98(3):273-7.\u003c/li\u003e\n\u003cli\u003eKomagata O, Kasai S, Tomita T. Overexpression of cytochrome P450 genes in pyrethroid-resistant Culex quinquefasciatus. Insect biochemistry and molecular biology. 2010;40(2):146-52.\u003c/li\u003e\n\u003cli\u003eDaborn P, Yen J, Bogwitz M, Le Goff G, Feil E, Jeffers S, et al. A single P450 allele associated with insecticide resistance in Drosophila. Science. 2002;297(5590):2253-6.\u003c/li\u003e\n\u003cli\u003eMugenzi LMJ, Tekoh TA, Ntadoun ST, Chi AD, Gadji M, Menze BD, et al. Association of a rapidly selected 4.3kb transposon-containing structural variation with a P450-based resistance to pyrethroids in the African malaria vector Anopheles funestus. PLoS Genet. 2024;20(7):e1011344.\u003c/li\u003e\n\u003cli\u003eStaedke SG, Gonahasa S, Dorsey G, Kamya MR, Maiteki-Sebuguzi C, Lynd A, et al. Effect of long-lasting insecticidal nets with and without piperonyl butoxide on malaria indicators in Uganda (LLINEUP): a pragmatic, cluster-randomised trial embedded in a national LLIN distribution campaign. The Lancet. 2020;395(10232):1292-303.\u003c/li\u003e\n\u003cli\u003eTchouakui M, Ibrahim S, Mangoua M, Thiomela R, ASSATSE T, Ngongang-Yipmo S, et al. Substrate promiscuity of key resistance P450s confers clothianidin resistance whilst increasing chlorfenapyr potency in malaria vectors. 2024.\u003c/li\u003e\n\u003cli\u003eMosha JF, Kulkarni MA, Lukole E, Matowo NS, Pitt C, Messenger LA, et al. Effectiveness and cost-effectiveness against malaria of three types of dual-active-ingredient long-lasting insecticidal nets (LLINs) compared with pyrethroid-only LLINs in Tanzania: a four-arm, cluster-randomised trial. The Lancet. 2022;399(10331):1227-41.\u003c/li\u003e\n\u003cli\u003eAccrombessi M, Cook J, Dangbenon E, Yovogan B, Akpovi H, Sovi A, et al. Efficacy of pyriproxyfen-pyrethroid long-lasting insecticidal nets (LLINs) and chlorfenapyr-pyrethroid LLINs compared with pyrethroid-only LLINs for malaria control in Benin: a cluster-randomised, superiority trial. The Lancet. 2023;401(10375):435-46.\u003c/li\u003e\n\u003cli\u003eNatchema SFB, Tchouakui M, Menze BD, Mugenzi LMJ, Fofie D, Nguiffo-Nguete D, et al. Entomological longitudinal surveys in two contrasted eco-climatic settings in Cameroon reveal a high malaria transmission from Anopheles funestus associated with GSTe2 metabolic resistance. BMC Infect Dis. 2023;23(1):738.\u003c/li\u003e\n\u003cli\u003eTchouakui M, Mugenzi LMJ, Wondji MJ, Tchoupo M, Njiokou F, Wondji CS. A 6.5kb Intergenic Structural Variation Exacerbates the Fitness Cost of P450-Based Metabolic Resistance in the Major African Malaria Vector Anopheles funestus. Genes (Basel). 2022;13(4).\u003c/li\u003e\n\u003cli\u003eHunt R, Brooke B, Pillay C, Koekemoer L, Coetzee M. Laboratory selection for and characteristics of pyrethroid resistance in the malaria vector Anopheles funestus. Medical and veterinary entomology. 2005;19(3):271-5.\u003c/li\u003e\n\u003cli\u003eLove MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550.\u003c/li\u003e\n\u003cli\u003eKwiatkowska RM, Platt N, Poupardin R, Irving H, Dabire RK, Mitchell S, et al. Dissecting the mechanisms responsible for the multiple insecticide resistance phenotype in Anopheles gambiae s.s., M form, from Vallee du Kou, Burkina Faso. Gene. 2013;519(1):98-106.\u003c/li\u003e\n\u003cli\u003eRiveron JM, Irving H, Ndula M, Barnes KG, Ibrahim SS, Paine MJ, et al. Directionally selected cytochrome P450 alleles are driving the spread of pyrethroid resistance in the major malaria vector Anopheles funestus. Proc Natl Acad Sci U S A. 2013;110(1):252-7.\u003c/li\u003e\n\u003cli\u003eSchmittgen TD, Livak KJ. Analyzing real-time PCR data by the comparative CT method. Nature protocols. 2008;3(6):1101-8.\u003c/li\u003e\n\u003cli\u003eKofler R, Pandey RV, Schlotterer C. PoPoolation2: identifying differentiation between populations using sequencing of pooled DNA samples (Pool-Seq). Bioinformatics. 2011;27(24):3435-6.\u003c/li\u003e\n\u003cli\u003eHudson RR, Slatkin M, Maddison WP. Estimation of levels of gene flow from DNA sequence data. Genetics. 1992;132(2):583-9.\u003c/li\u003e\n\u003cli\u003eLucas ER, Miles A, Harding NJ, Clarkson CS, Lawniczak MKN, Kwiatkowski DP, et al. Whole-genome sequencing reveals high complexity of copy number variation at insecticide resistance loci in malaria mosquitoes. Genome Res. 2019;29(8):1250-61.\u003c/li\u003e\n\u003cli\u003eLucas ER, Nagi SC, Egyir-Yawson A, Essandoh J, Dadzie S, Chabi J, et al. Genome-wide association studies reveal novel loci associated with pyrethroid and organophosphate resistance in Anopheles gambiae and Anopheles coluzzii. Nat Commun. 2023;14(1):4946.\u003c/li\u003e\n\u003cli\u003eNjoroge H, Van't Hof A, Oruni A, Pipini D, Nagi SC, Lynd A, et al. Identification of a rapidly-spreading triple mutant for high-level metabolic insecticide resistance in Anopheles gambiae provides a real-time molecular diagnostic for antimalarial intervention deployment. Mol Ecol. 2022;31(16):4307-18.\u003c/li\u003e\n\u003cli\u003eHearn J, Djoko Tagne CS, Ibrahim SS, Tene-Fossog B, Mugenzi LMJ, Irving H, et al. Multi-omics analysis identifies a CYP9K1 haplotype conferring pyrethroid resistance in the malaria vector Anopheles funestus in East Africa. Mol Ecol. 2022;31(13):3642-57.\u003c/li\u003e\n\u003cli\u003eWeedall GD, Riveron JM, Hearn J, Irving H, Kamdem C, Fouet C, et al. An Africa-wide genomic evolution of insecticide resistance in the malaria vector Anopheles funestus involves selective sweeps, copy number variations, gene conversion and transposons. PLoS Genet. 2020;16(6):e1008822.\u003c/li\u003e\n\u003cli\u003eCingolani P, Platts A, Wang le L, Coon M, Nguyen T, Wang L, et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly (Austin). 2012;6(2):80-92.\u003c/li\u003e\n\u003cli\u003eRozas J, Ferrer-Mata A, S\u0026aacute;nchez-DelBarrio JC, Guirao-Rico S, Librado P, Ramos-Onsins SE, et al. DnaSP 6: DNA sequence polymorphism analysis of large data sets. Molecular biology and evolution. 2017;34(12):3299-302.\u003c/li\u003e\n\u003cli\u003eRozewicki J, Li S, Amada KM, Standley DM, Katoh K. MAFFT-DASH: integrated protein sequence and structural alignment. Nucleic Acids Res. 2019;47(W1):W5-W10.\u003c/li\u003e\n\u003cli\u003eM\u0026uacute;rias dos Santos A, Cabezas MP, Tavares AI, Xavier R, Branco M. tcsBU: a tool to extend TCS network layout and visualization. Bioinformatics. 2016;32(4):627-8.\u003c/li\u003e\n\u003cli\u003eTamura K, Stecher G, Kumar S. MEGA11: Molecular Evolutionary Genetics Analysis Version 11. Mol Biol Evol. 2021;38(7):3022-7.\u003c/li\u003e\n\u003cli\u003eTsunoda T, Takagi T. Estimating transcription factor bindability on DNA. Bioinformatics (Oxford, England). 1999;15(7):622-30.\u003c/li\u003e\n\u003cli\u003eDaborn PJ, Lumb C, Harrop TW, Blasetti A, Pasricha S, Morin S, et al. Using Drosophila melanogaster to validate metabolism-based insecticide resistance from insect pests. Insect biochemistry and molecular biology. 2012;42(12):918-24.\u003c/li\u003e\n\u003cli\u003eMarkstein M, Pitsouli C, Villalta C, Celniker SE, Perrimon N. Exploiting position effects and the gypsy retrovirus insulator to engineer precisely expressed transgenes. Nat Genet. 2008;40(4):476-83.\u003c/li\u003e\n\u003cli\u003eWHO. Test procedures for insecticide resistance monitoring in malaria vector mosquitoes. 2016.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Malaria, insecticide resistance, Anopheles funestus s.s, argininosuccinate lyase, gene expression, allelic variation, transgenic expression, DNA-based assay","lastPublishedDoi":"10.21203/rs.3.rs-6063665/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6063665/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEscalating pyrethroid resistance in malaria vectors is jeopardizing malaria control. Deciphering its complex evolutionary mechanisms is paramount to mitigate its impact. Here, we demonstrate that over-expression and allelic variation of the argininosuccinate lyase (\u003cem\u003eASL)\u003c/em\u003e gene exacerbate pyrethroid resistance in \u003cem\u003eAnopheles funestus\u003c/em\u003e. Multi-omics analyses revealed that \u003cem\u003eASL\u003c/em\u003e is upregulated Africa-wide and detected a strong signal of genetic differentiation around \u003cem\u003eASL\u003c/em\u003e in resistant populations exhibiting copy number variation (CNV). A predominant resistant haplotype harboring the R125H and T-277A mutations was detected in resistant mosquitoes. Transgenic expression of \u003cem\u003eASL\u003c/em\u003e and RNAi confirm its ability to confer pyrethroid resistance. A DNA-based assay confirmed its association with super-resistance, revealing a marked temporal increase in Uganda (2010\u0026ndash;2023) but with a drastic reduction observed in 2024 after deployment of chlorfenapyr-based bed nets. This study elucidates a novel resistance pathway driven by \u003cem\u003eASL\u003c/em\u003e and introduces a new DNA-based diagnostic tool to monitor its spread and impact in Africa.\u003c/p\u003e","manuscriptTitle":"The argininosuccinate lyase gene exacerbates pyrethroid resistance in the major African vectors Anopheles funestus","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-14 03:28:11","doi":"10.21203/rs.3.rs-6063665/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"9653a066-a855-486b-a57d-46fc8dcb1373","owner":[],"postedDate":"March 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":45598456,"name":"Biological sciences/Genetics/Genetic markers"},{"id":45598457,"name":"Biological sciences/Evolution/Evolutionary genetics"}],"tags":[],"updatedAt":"2026-03-17T09:38:11+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-14 03:28:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6063665","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6063665","identity":"rs-6063665","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}