Insights from transcriptomic profiling identify the CYP6Z gene family as key drivers of pyrethroid resistance escalation in Anopheles gambiae from Cameroon

Insights from transcriptomic profiling identify the CYP6Z gene family as key drivers of pyrethroid resistance escalation in Anopheles gambiae from Cameroon | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Insights from transcriptomic profiling identify the CYP6Z gene family as key drivers of pyrethroid resistance escalation in Anopheles gambiae from Cameroon Arnaud Tepa, Mersimine Kouamo, Jonas A. Kengne-Ouafo, Magellan Tchouakui, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7592244/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Mar, 2026 Read the published version in BMC Genomics → Version 1 posted 13 You are reading this latest preprint version Abstract Malaria control efforts are stagnating, primarily due to increasing pyrethroid resistance in malaria vectors. Elucidating its molecular basis is essential for effective vector control. Here, via RNA-seq-based transcriptomic analyses, we elucidated the molecular basis of permethrin super-resistance in Anopheles gambiae from an agricultural setting in Cameroon with intense pesticide usage. The present study revealed significant overexpression of detoxification genes, including cytochrome P450, carboxylesterases, glutathione S-transferases, glycosyltransferases, and ABC-type xenobiotic transporter genes associated with insecticide resistance. Among the genes consistently overexpressed when surviving (R) and unexposed (C) mosquitoes were compared with the susceptible Kisumu strain (S), the glycosyltransferase UGT308G1 (AGAP007990) gene presented the greatest fold change [FC105.1 (C-S); 50.8 (R-S)]. Similarly, three CY6Z P450 genes were highly expressed [CYP6Z3 41 < FC < 63; CYP6Z2 17 < FC < 29; CYP6Z1 6 < FC < 12)]. Evidence of cuticular resistance was observed with the overexpression of several cuticular protein genes and related P450s, including CYP4G16/17. Signatures of selective sweeps were detected around the L1014F-kdr and E205D-CYP6P3 mutations. Moreover, the expression of key genes, including the protease, sensory appendage protein (SAP2), argininosuccinate lyase, P450 (CYP6Z, CYP6M2), and GST epsilon genes and cuticular proteins, was significantly upregulated in mosquitoes that survived at a 10-fold diagnostic concentration. RNA interference–induced knockdown supported the contribution of the CYP6Z gene family to resistance escalation through significant restoration of susceptibility when mosquitoes were exposed to increasing insecticide doses following gene silencing. Overall, this study provides valuable insights into the genetic mechanisms driving the aggravation of resistance in An. gambiae , highlighting the CYP6Z gene family as one of the key contributors. Anopheles gambiae malaria pyrethroids resistance escalation glycosyltransferases transcriptomic analysis CYP6Zs Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Malaria remains a major public health burden in the tropical world, with 249 million cases reported in 2022 [ 1 ], with most cases concentrated in Africa, the region most affected by malaria. After a significant reduction in the disease burden from 2000–2015 [ 2 ], progress has stalled partly owing to rising resistance to pyrethroids, the main insecticide class used for vector control through insecticide-treated nets (ITNs) or indoor residual spraying (IRS) [ 3 , 4 ]. Indeed, growing reports of pyrethroid resistance have been made for major malaria vectors in Africa, including Anopheles gambiae [ 5 – 9 ] and Anopheles funestus [ 7 , 10 ], with evidence that this resistance has reduced the efficacy of insecticide-based control tools such as long-lasting insecticidal nets (LLINs) [ 11 – 13 ]. Moreover, multiple studies have documented an increasing trend of pyrethroid resistance, with mosquitoes increasingly surviving exposure to concentrations exceeding ten times the diagnostic dose[ 6 , 7 , 14 ] or enduring prolonged contact times[ 15 ]. Previous research has also suggested that high levels of agricultural activity in rural areas may impose selection pressures on these populations [ 16 ]. The exacerbation of pyrethroid resistance has caused further loss of efficacy of control tools, notably for pyrethroid-only nets, which are now less effective in most places, as shown by randomized control trials [ 17 , 18 ]. Pyrethroid resistance is broadly conferred by target-site resistance and metabolic resistance mechanisms. Recent research has confirmed the essential roles of cytochrome P450s, specifically CYP6P3, CYP6P4 , and CYP6M2 , which have been validated as contributors to insecticide resistance in An. gambiae [ 19 – 21 ]. Similarly, in An. funestus , P450s also play key roles, including CYP6P9a/b, CYP6P4a/b, CYP325A , and CYP9K1 [ 22 – 27 ]. In addition to the P450 enzymes associated with phase 1 xenobiotic metabolism, other classes of enzymes are significantly linked to phase 2 insecticide metabolism. Specifically, glutathione S-transferases (GSTs), including GSTe2 in both Anopheles gambiae and Anopheles funestus , as well as other GSTe clusters (3–8) in An. funestus [ 28 , 29 ]. In addition to these established roles of GSTs in phase 2 metabolism, it has been suggested that sulfotransferases and UDP-glycosyltransferases (UGTs) may also contribute [ 30 , 31 ]. However, the detailed evolutionary mechanisms driving the ability to survive prolonged exposure to pyrethroids or to greater doses remain unclear. Initial evidence in An. gambiae have mentioned the possible increased expression of multiple detoxification genes [ 15 , 31 ], but the key genetic changes allowing these mosquitoes to survive 10 times the diagnostic doses of pyrethroids remain to be established, particularly as mosquito populations have shown great variation in their adaptation according to their geographical locations, with different resistance mechanisms observed in different regions [ 32 ]. In this study, we investigated the transcriptomic signature of resistance escalation to permethrin in An. gambiae populations from an Agricultural hotspot of Cameroon, where mosquitoes have elevated levels of resistance to all four classes of insecticides and survive 10x the diagnostic doses of pyrethroids. We revealed that key resistance escalation mechanisms include increased expression of detoxification genes, cuticular resistance, novel gene families, and epigenetic signatures. Moreover, via functional validation, we highlight the role of highly overexpressed CYP6Z P450s in increasing resistance. Results and discussion Results Gene expression profiling The alignment of the sequenced data to the An. gambiae PEST reference genome (version 61) yielded approximately 78 to 170 million reads per sample across the forty-eight sequenced samples (see Supplementary File 1, Figure S1 .1). The analysis of sequence quality is presented in Supplementary File 2 (Table S2 .1). Genes commonly overexpressed across conditions The number of differentially expressed transcripts per comparison is illustrated in Fig. 1 . In total, 2,971 genes were upregulated, whereas 2,311 genes were downregulated in at least one comparison. Analysis of the commonly upregulated genes revealed that 28, 518, and 585 genes were upregulated in the R-S vs. C-S vs. R-C, R-S vs. R-C, and R-S vs. C-S comparisons, respectively. These genes belong to several families associated with insecticide resistance, including detoxification genes such as cytochrome P450s, glycosyltransferases, and ABC transporters. Among the detoxification genes consistently overexpressed across all three comparisons (R-S/R-C/C-S), we identified ATP-binding cassette subfamily A member 2 (ABCA2), which presented fold changes of 9.2, 1.5, and 6.1 in the R-S, R-C, and C-S comparisons, respectively (see Table I). The list of genes commonly upregulated in both R-S and C-S includes several detoxification genes, including cytochrome P450s and glycosyltransferases. Notably, the gene UGT308G1 (AGAP007990) was among the most significantly overexpressed genes, with fold changes of 50.8 and 105.1 in the R-S and C-S comparisons, respectively. The other highly expressed genes included P450s, among which three CY6Z genes presented the greatest fold changes, notably, CYP6Z3 (FC: 41.4 and 68.8), CYP6Z2 (FC: 17.2 and 29.0), and CYP6Z1 (FC: 6.0 and 12.3). The expression of the CYP9K1 gene, a member of the P450 family, was significantly upregulated (FC: 4.7 and 11.3), with read counts exceeding 100,000. Overall, approximately 26 P450 genes, including the CYP6M2 gene (FC: 2.8 and 8.5), were overexpressed. The expression of CYP4G17, previously associated with the production of cuticular hydrocarbons [ 33 ], was 2.5-fold greater than that associated with the overexpression of several cuticular proteins (28), suggesting the contribution of the reduced penetration resistance mechanism in this population. A set of carboxylesterase genes, including COEAE80 (AGAP006700), was significantly upregulated. Furthermore, several other UDP glucosyltransferases, including UGT36C2, UGT49A3, UGT302A2, UGT314A2, UGT306A2 , and UGT302A1, were equally upregulated . Moreover, several olfactory and gustatory genes, including many odorant binding proteins, ionotropic receptors, and odorant receptors (ORs; 46 upregulated in R-S), were significantly upregulated in Mangoum, suggesting that olfaction mechanisms contribute to the observed resistance (see Supplementary File 2, Table S2 .1). Among the ORs, 3 had a fold change > 15 in R-S, including AGAP006077 (FC: 23.7), AGAP002905 (FC: 21.4), and AGAP011368 (FC: 18.01). A set of chemosensory genes was equally upregulated, including the chemosensory proteins (CSP1 and 3) and 3 sensory appendage proteins (SAP1, 2 and 3), with SAP2 (AGAP008052) exhibiting the highest fold change among them at 11-fold overexpression. Additionally, several genes associated with epigenetic regulation, including histone H3/4 and histone deacetylase, are also overexpressed. Notably, argininosuccinate lyase was recently shown to confer resistance to pyrethroids in An. funestus [ 34 ], was also commonly upregulated in both the R-S and C-S comparisons. Table I: Differential expression of candidate resistance genes in Mangoum mosquitoes between different comparisons. Gene ID Gene Name Chr. R-S C-S R-C Annotation AGAP006380 ABCA2 2L 9.2 6.1 1.5 ABC Transporter AGAP008059 CSP1 3R 2.4 2.7 Chemosensory protein AGAP008055 CSP3 3R 14.1 10.3 Chemosensory protein AGAP001789 AGAP001789 2R 3.2 2.6 Cytochrome P450 AGAP008218 CYP6Z2 3R 17.2 29.0 Cytochrome P450 AGAP008209 CYP6M1 3R 3.9 3.8 Cytochrome P450 AGAP009375 CYP9M2 3R 3.3 2.2 Cytochrome P450 AGAP012292 CYP9J4 3L 2.0 2.6 Cytochrome P450 AGAP013128 CYP6AA2 2R 2.4 3.1 Cytochrome P450 AGAP008206 CYP6N2 3R 2.0 2.2 Cytochrome P450 AGAP003067 CYP304C1 2R 2.6 2.8 Cytochrome P450 AGAP010077 CYP303A1 3R 3.6 2.3 Cytochrome P450 AGAP008217 CYP6Z3 3R 41.4 63.8 Cytochrome P450 AGAP012291 CYP9J3 3L 3.0 3.2 Cytochrome P450 AGAP008210 CYP6N1 3R 6.4 8.2 Cytochrome P450 AGAP013241 CYP4D16 2R 3.3 7.4 Cytochrome P450 AGAP008020 CYP12F2 3R 2.0 3.5 Cytochrome P450 AGAP008213 CYP6M3 3R 4.0 6.7 Cytochrome P450 AGAP002867 CYP6P4 2R 3.5 4.5 Cytochrome P450 AGAP000500 Cpr X 2.8 3.0 Cytochrome P450 Reductase AGAP012850 AGAP012850 UNKN 4.3 3.5 Cytochrome P450 AGAP010966 AGAP010966 3L 2.9 3.1 Cytochrome P450 AGAP007589 UGT306A2 2L 2.2 2.4 Glucosyltransferase AGAP007990 UGT308G1 3R 50.8 105.1 Glucosyltransferase AGAP006223 UGT302A2 2L 2.9 4.2 Glucosyltransferase AGAP005754 UGT308F1 2L 2.7 3.7 Glucosyltransferase AGAP005751 UGT308H1 2L 4.1 4.3 Glucosyltransferase AGAP007920 UGT36C2 3R 5.9 7.4 Glucosyltransferase AGAP007374 UGT49A3 2L 3.7 4.4 Glucosyltransferase AGAP002783 UGT314A2 2R 2.9 2.7 Glucosyltransferase AGAP004165 GSTD2 2R 7.7 7.8 Glutathione S-transferase AGAP009342 GSTU3 3R 2.9 2.5 Glutathione S-transferase AGAP012839 AGAP012839 UNKN 55.5 33.5 Glutathione S-transferase AGAP012156 ABCA5 3L 2.2 2.2 ABC Transporter AGAP008437 ABCC9 3R 7.7 9.0 ABC Transporter AGAP012005 ABCF3 3L 3.4 3.6 ABC Transporter AGAP028013 CPLCP25 3L 4.3 8.3 Cuticular AGAP010887 CPR113 3L 3.9 4.8 Cuticular AGAP006369 CPR144 2L 11.9 7.0 Cuticular AGAP000877 CYP4G17 X 2.5 2.9 Cuticular AGAP008141 AGAP008141 3R 2.5 5.1 Argininosuccinate lyase AGAP002207 CYP325C1 2R 3.4 12.5 Cytochrome P450 AGAP000818 CYP9K1 X 4.7 11.3 Cytochrome P450 AGAP008219 CYP6Z1 3R 6.0 12.3 Cytochrome P450 AGAP008212 CYP6M2 3R 3.8 8.5 Cytochrome P450 AGAP008358 CYP4H17 3R 8.4 20.8 Cytochrome P450 AGAP005752 UGT308A2 2L 2.1 3.6 Glucosyltransferase AGAP006222 UGT302A1 2L 7.4 16.0 Glucosyltransferase AGAP003568 COE22933 2R 3.0 2.4 Carboxylesterase AGAP011916 COEB6582 3L 2.9 2.2 Carboxylesterase AGAP006956 COE10O 2L 3.6 3.1 Carboxylesterase AGAP002090 COE16738 2R 2.4 2.0 Carboxylesterase AGAP002863 COEAE6O 2R 2.0 2.1 Carboxylesterase AGAP006700 COEAE8O 2L 5.0 2.6 Carboxylesterase AGAP005835 COEJHE3E 2L 9.7 37.2 Carboxylesterase AGAP005836 COEJHE4E 2L 8.1 20.9 Carboxylesterase AGAP008052 SAP2 3R 11.9 5.8 Chemosensory protein AGAP008054 SAP3 3R 2.8 1.9 Chemosensory protein AGAP001039 CYP307A1 X 2.7 2.7 Cytochrome P450 AGAP003065 CYP11179 2R 3.6 2.4 Cytochrome P450 AGAP002870 CYP6AD1 2R 3.6 3.3 Cytochrome P450 AGAP008356 CYP4H16 3R 6.5 3.4 Cytochrome P450 AGAP000088 CYP4H19 X 2.2 2.1 Cytochrome P450 AGAP004383 GSTD10 2R 7.4 3.6 Glutathione S-transferase AGAP013465 CPLCP1 2R 4.5 6.6 Cuticular AGAP008817 CPLCP3 3R 15.9 15.5 Cuticular AGAP003382 CPR120 2R 2.6 2.2 Cuticular AGAP003385 CPR123 2R 2.9 3.2 Cuticular AGAP003379 AGAP003379 2R 2.3 2.2 Cuticular R-C, Permethrin-resistant vs. control mosquitoes; C-S, control vs. susceptible KISUMU strain mosquitoes; R-S, Permethrin-resistant vs. susceptible KISUMU strain mosquitoes. Genes commonly upregulated in response to the three permethrin doses (1X, 5X and 10X DCs) To identify candidate genes potentially linked to resistance escalation, we analyzed gene expression profiles in resistant mosquitoes exposed to increasing doses of permethrin (1x, 5x, and 10x). The comparison between the 10x and 1x doses was prioritized to pinpoint genes (2,677 upregulated) associated with enhanced survival at higher insecticide concentrations (see Fig. 1 ). Additionally, 10x-5x and 5x-1x comparisons were conducted to validate these findings. A special focus was placed on genes with the highest fold change in the 10x-1x comparison, ensuring that they had at least 100 reads in the 10x condition to improve reliability and minimize false positives caused by low sequencing depth or stochastic sampling. This analysis revealed several top candidate genes, including chymotrypsinogen B (AGAP006385), which presented the greatest fold change (FC: 29.0) in the 10x-1x comparison (see Figs. 1 and 2 ) and was also commonly overexpressed in the 10x-5x and 5x-1x comparisons, similar to other protease-related genes, such as chymotrypsin 1 and 2 (AGAP006709 and AGAP006710), which presented FC values ranging from 9.6–11.1. Endopeptidases such as chymotrypsinogen and chymotrypsin are known for their key role in maintaining healthy cells and in responding to stressors such as pesticides [ 35 ]. Other proteases that play the same role, including the CLIP-domain serine protease CLIPB12 (AGAP009217), were also among the top candidates (FC7.1) in the 10x-1x comparison. Additionally, the sensory appendage protein SAP2, known for its role in insecticide sequestration [ 36 ], presented FC values of 8.3 and 6.7 in the 10x-1x and 5x-1x comparisons, respectively. Moreover, the oxidative stress genes argininosuccinate lyase AGAP008141 (FC6.2 and 4.1) and argininosuccinate synthase AGAP003015 (FC5.3 and 2.7) were overexpressed 10-fold compared with 1- and 5-fold, respectively. These genes have been reported to be involved in the urea cycle, which may be critical for managing nitrogen waste [ 37 ]. Genes potentially associated with epigenetic gene regulation were equally upregulated, including the glycine N-methyltransferase AGAP002198 (FC: 5.1, 2.5 and 2.1, respectively, at 10x compared with 1x and 5x and 5x vs 1x), which has been shown to play a major role in methylation regulation in mammals by catalyzing the direct transfer of a methyl group from S-adenosylmethionine to glycine (forming sarcosine) [ 38 ]. Only a single detoxification gene, the carboxylesterase COE13O (AGAP011507), was commonly overexpressed in the 3 comparisons. Compared with the 1x dose, a substantial number of glutathione S-transferases (GSTs) were upregulated at both the 5x and 10x doses. These findings suggest that the expression of these enzymes is induced by relatively high insecticide concentrations, highlighting their role in increasing resistance (see Supplementary File 2, Table S2 .2). Notably, GSTD11 (FC: 5.5 and 4.1), GSTD3 (FC: 1.5 and 1.8), GSTE5 (FC: 1.9 and 1.6), GSTE4 (FC: 2.9 and 2.0), and GSTE3 (FC: 2.8 and 1.8) presented increased expression at 10x and 5x compared with that at 1x magnification. Conversely, GSTE2 (AGAP009194) was only upregulated in the 10x–1x comparison (FC: 1.9). In addition, other detoxification genes, including cytochrome P450 enzymes such as CYP6Z1 (FC: 2.1 and 2.0), CYP305A1 (FC: 1.8 and 2.0), CYP6Y1 (FC: 2.2 and 1.5), CYP6AG2 (FC: 1.8 and 1.6), and CYP6AH1 (FC: 2.2 and 1.7), were consistently overexpressed at 5x and 10x doses, indicating potential candidates for genes associated with super-resistance. Among the glucosyltransferases, only two, UGT301C1 (AGAP008404) and UGT36B2 (AGAP028055), with FC values of 4.4 and 1.9, respectively, were differentially expressed at 10x compared with 1x. Genes related to reduced penetration resistance, specifically the cuticular proteins CPR133 (AGAP010123) and CPR76 (AGAP009874) and the cuticle precursor CYP4G16 (AGAP001076), were also overexpressed at 10x (8.5, 3.2 and 2.9, respectively) and 5x (3.2, 6.6 and 3.0, respectively) doses compared with 1x, indicating their possible roles in enhanced resistance mechanisms. Several genes related to immune responses, including numerous C-lectin proteins (CTLMA1 and 2), leucine-rich immune proteins (LRIM 1, 3, 6 and 17), and thioester-containing proteins (TEP1, 12, 18…), were upregulated in the 10x and 5x groups compared with the 1x groups. Additionally, many upregulated genes, including several histone genes (H2A, H2B, H3 and H4), are related to epigenetics. A significant signature of the potential role of noncoding RNA posttranscriptional regulation was detected, with several microRNAs upregulated, especially AGAP013569, AGAP003144, AGAP013659, AGAP028760 and AGAP013705 (see Supplementary 2, Table S2 .3). Gene ontology analysis Gene Ontology (GO) enrichment analysis was performed to assess the biological processes and molecular functions associated with resistance. Using a list of 2,971 transcripts differentially expressed between R, C, and S, we observed a predominance of biological processes related to xenobiotic and insecticide metabolism (GO:2001242, GO:0031125), followed by steroid metabolism (GO:1905609) and chemosensory behavior (GO:2000024) (see Supplementary 2, Table S2 .4–S2.5). Significant enrichment (gene ratio > 0.3) was noted in molecular functions related to voltage-gated monoatomic ion channel activity impacting presynaptic membrane potential regulation (GO:0140662), medium-chain fatty acid omega-hydroxylase activity (GO:0140981), alkane 1-monooxygenase activity (GO:0005504), ABC-type xenobiotic transporter activity (GO:0008559), hexosyltransferase activity (GO:0016758), ATP-dependent protein folding chaperone activity (GO:0022857), alcohol-forming very long-chain fatty acyl-CoA reductase activity (GO:0080019), and UDP-glycosyltransferase activity (GO:0008194). GO enrichment analysis of 1,474 candidate genes identified from comparisons between 1×, 5×, and 10× revealed notable involvement in biological processes related to chemical reactions and pathways leading to protein catabolism (GO:0006412, GO:0032543, GO:0140242, GO:0140236, GO:0002181, GO:0006362, GO:0033539, and GO:0006367), which is pivotal for the rapid adaptation of organisms to various environmental stresses [ 39 ] (see Supplementary 2, Tables S2.6–S2.7). Moreover, resistance escalation involves processes associated with adenosine triphosphate (ATP) synthesis (GO:0042776), responses to oxidative stress involving mitochondrial respiratory chain complex I assembly (GO:0006979), and electron transfer from NADH to ubiquinone during oxidative phosphorylation (GO:0006120), which are crucial for maintaining cellular integrity under stress conditions by enhancing energy metabolism and redox balance [ 40 ]. qPCR expression of candidate detoxification genes For normalization, ten (10) detoxification genes, including cytochrome P450s ( CYP6M2, CYP6M3, CYP6P1, CYP6P3, CYP6P4, CYP6Z1, CYP6Z2, and CYP9K1 ), glutathione S-transferase GSTe2 , and glycosyltransferase UGT308G1 , were assessed via two housekeeping genes (Elongation Factor - EF and ribosomal protein S7 - RSP7 ). The gene expression results are described in Supplementary File 1, Figure S1 .2. The overall transcriptomic profile obtained through quantitative reverse transcription PCR (qRT‒PCR) supported the RNA sequencing results, revealing a significant positive correlation (r = 0.909, p = 0.0003) between the two methods. Transcriptional signatures of selective sweeps associated with increased insecticide resistance in An. gambiae The signatures of the selective sweeps were detected via transcriptome-wide association analysis, which identified candidate nonsynonymous mutations associated with resistance escalation in An. gambiae . Given the low level of differentiation (see Supplementary file 1, Figure S1 .3) between field mosquitoes irrespective of their phenotype, owing to their similar genetic background [ 21 ], a hybrid line was generated by crossing Mangoum mosquitoes with susceptible Kisumu strains to allow better segregation of resistance alleles according to phenotype. Five pairwise fixation index (F ST ) comparisons in 30,000 SNP windows revealed several peaks of genetic divergence, including the voltage-gated sodium channel (VGSC) and glutathione S-transferases (GSTs), which presented elevated peaks with F ST >0.4 (Fig. 3 ). Other minor peaks were detected around the CYP6 P450 cluster, resistance to pyrethroid 1 (rp1) loci (including CYP6P3) on the 2R chromosome, and CYP9K1 P450 on the X chromosome. We focused our analysis on previous candidate resistance loci in An. gambiae [ 21 , 41 ], Tajima’s D and nucleotide diversity were calculated to pinpoint selective sweeps. In Mangoum, resistance escalation was linked to a decline in Tajima’s D and reduced genetic diversity at the VGSC and rp1 loci, respectively (Fig. 4 ), indicating that strong positive selection was associated with increasing resistance to pyrethroids (Tajima’s D < -1). The rp2 locus, which includes UGT308G1, sensory appendage protein, chemosensory proteins and some cytochrome P450s (CYP6Z and CYP6M), was not subjected to strong selection. Polymorphisms associated with resistance escalation in Mangoum Our analysis focused on candidate gene families associated with pyrethroid resistance, including cytochrome P450s, carboxylesterases, glucosyltransferases, glutathione S-transferases, ABC transporters, cuticular, odorant binding, and voltage-gated sodium channels. Within the rp1 locus, three nonsynonymous mutations were identified, all of which presented similar allele frequencies across phenotypes: Cys282Tyr in CYP6P1, Glu205Asp in CYP6P3, and Thr288Ser in CYP6AA1 (Fig. 5 ). Moreover, a significant SNP (Thr128Ala) was detected in CYP12F2 on chromosome 3R, showing fixation in both field and resistant hybrid strains compared with the Kisumu laboratory strain and unexposed hybrids, where the allele frequencies were 6% and 21%, respectively (p value < 0.05). Similarly, the Leu571Phe and Arg1173Gln mutations in the cuticular gene AGAP010302 (located on chromosome 3R) were fixed in the field populations. Several candidate resistance-related mutations were also identified on chromosome 2L, including Ala116Thr and Thr84Ala in the glucosyltransferase UGT302J1 and Arg425Lys in the carboxylesterase COEAE2E. Functional validation of the role of the CYP6Z gene family in pyrethroid resistance via RNA interference The effect of the knockdown of the CYP6Z gene family on the mortality of An. gambiae strains exposed to pyrethroids were examined via RNA interference (RNAi) (Fig. 6 ). The efficiency of the dsRNA was assessed by qPCR, which revealed successful reductions in the expression levels of the CYP6Z1, CYP6Z2, CYP6Z3, and CYP6Z4 genes of 90.17% (p = 0.005), 94.73% (p = 0.001), 79.59% (p = 0.03), and 60.97% (p = 0.09), respectively (Fig. 6 A). RNAi-mediated knockdown of the CYP6Z genes confirmed their role in resistance escalation in An. gambiae mosquitoes. Notably, mortality rates were significantly greater in mosquitoes injected with dsCYP6Z than in noninjected controls across various exposure doses. Bioassays revealed that, after 24 hours of exposure to permethrin or alphacypermethrin, mortality was significantly greater in mosquitoes treated with dsZ2 (22.7% ± 1.4; p = 0.004 and 22.5% ± 7.2; p = 0.04, respectively) than in noninjected mosquitoes (9.5% ± 2.6) (Fig. 6 B). Increasing the pyrethroid dose to 5x and 10x the diagnostic dose emphasized the impact of CYP6Z gene knockdown on resistance reduction to permethrin and alphacypermethrin. Significant differences in mortality were observed among mosquitoes injected with dsCYP6Z1, dsCYP6Z2, and dsCYP6Z3 when exposed to both 5x and 10x doses of the insecticides compared with noninjected mosquitoes subjected to the same insecticide concentrations (Fig. 6 B). For dsCYP6Z4, a significant difference was detected only in mosquitoes exposed to alphacypermethrin at a 10x dosage, with mortality rates of 90.3% ± 1.9% in injected mosquitoes versus 79.9% ± 1.4% in noninjected controls. Discussion Emerging evidence indicates significant pyrethroid resistance among malaria vectors, particularly An. gambiae , resulting in reduced effectiveness of pyrethroid-treated nets [ 6 , 7 ]. The increasing levels of resistance across endemic areas highlight the need to strengthen vector control strategies, especially in agricultural hotspots [ 8 , 14 ]. Understanding the transcriptomic profile associated with increasing permethrin resistance in An. gambiae populations from such hotspots, along with a description of the main resistance mechanisms, is crucial for developing effective malaria vector control interventions. Key Mechanisms Underpinning Pyrethroid Resistance in Anopheles gambiae in Cameroon Transcriptomic analysis of resistant An. gambiae mosquitoes from Mangoum revealed the primary mechanisms driving pyrethroid resistance. Notably, detoxification genes such as cytochrome P450s, carboxylesterases, glutathione S-transferases, glycosyltransferases, and ABC-type xenobiotic transporters were overexpressed, which aligns with prior reports [ 14 , 31 , 42 – 44 ]. In particular, the overexpression of the gene UGT308G1 emerged as a crucial factor linked to pyrethroid resistance. The orthologs of this gene have also been shown to sustain high expression levels after insecticide exposure in An. coluzzii [ 45 ] and An. funestus [ 46 ]. However, recent studies have indicated that knocking down this gene may not significantly impact susceptibility to pyrethroids [ 43 ]. Transcriptomic Insights into Superresistant Mosquitoes from Mangoum Our study identified numerous differentially expressed genes in superresistant mosquitoes, including members of the CYP6Z family, particularly CYP6Z1, CYP6Z2, CYP6Z3, and CYP6Z4, which are integral to phase 1 detoxification processes [ 47 ]. The increased susceptibility to pyrethroids following CYP6Z knockdown through RNA interference-induced knockdown experiments underscores their role in increased resistance. Consistent with previous research, elevated CYP6Z1 levels are associated with improved detoxification capabilities, enabling mosquitoes to neutralize toxic compounds more effectively [ 48 ]. Although it was recognized in 2013 that CYP6 enzymes such as CYP6Z1 and CYP6Z2 contribute secondarily to the metabolism of pyrethroid metabolites and other xenobiotics [ 49 , 50 ], CYP6Z1 has recently been shown to play a significant role in cross-resistance to both carbamates and pyrethroids in Anopheles funestus [ 47 ]. In addition to the genes encoding detoxification enzymes, our study revealed that the SAP2 gene was significantly upregulated in mosquitoes surviving 10× pyrethroid exposure compared with those exposed to 1× pyrethroid, indicating a potential role in enhancing insecticide resistance. This observation is corroborated by Gadji et al. (2025), who reported elevated expression of chemosensory proteins (CSPs) and sensory appendage proteins (SAPs) in resistant populations from Ghana and Uganda, as well as in mosquitoes surviving 10× pyrethroid exposure across Africa. Our findings also parallel those of Ingham et al. (2020), who described similar expression trends in An. gambiae and An. coluzzii populations in West Africa [ 36 ]. These findings suggest that SAPs may play a critical role in driving behavioral and physiological adaptations that increase insecticide resistance in Anopheles mosquitoes, particularly under high-intensity pyrethroid exposure. Protein synthesis and the oxidative stress response can exacerbate resistance in An. gambiae Our findings reveal a robust connection to protein synthesis pathways and the ability of An. gambiae . This pathway has been established to be essential for rapid adaptation to stress conditions [ 39 ]. Moreover, the increased expression levels of arginosuccinate lyase highlight significant alterations in the urea cycle, which may be critical for managing nitrogen waste, especially under conditions of increased metabolic throughput [ 37 ]. These findings are consistent with previous results reported by Riveron and colleagues (2014), who reported a significant increase in the expression of argininosuccinate lyase ( ASL ) in populations of An. funestus in Zambia and Malawi, raising concerns regarding resistance to insecticides used in malaria control [ 51 ]. The role of this gene in resistance escalation in An. funestus across Africa [ 34 ]. Signatures of selective sweeps are linked to increases in resistance These findings suggest that resistance escalation is driven by strong positive selection, as evidenced by the decline in Tajima's D and reduced genetic diversity at the VGSC and rp1 loci, respectively, indicating that an excess of rare alleles are potentially linked to resistance in these regions, which is consistent with previous reports [ 21 , 52 , 53 ]. This pattern reflects a rapid increase in resistance-conferring mutations within the population, highlighting the intensity of the selective pressure exerted by pyrethroid use in the field, leading to the fixation of markers such as L1014F- kdr [ 54 ] and E205D- CYP6P3 [ 21 ]. Despite a slight peak of genetic divergence observed around the CYP9K1 cluster, no signs of selective sweeps were detected in this region in An. gambiae from Mangoum. Although allelic variation at CYP9K1 appears to have a minimal effect on pyrethroid resistance in An. gambiae. Some studies performed in An. funestus [ 22 , 55 ] have shown that this locus is strongly linked to allelic variant-potentiated resistance. A previous work identified a nearly fixed X-linked haplotype containing CYP9K1 in An. coluzzii from Mali [ 41 ], suggesting that this locus could also be involved in this species. Further research, including studies on An. coluzzii , is needed to better understand the role of CYP9K1 allelic variation in resistance within An. gambiae s.l . Conclusion This study elucidated the transcriptomic drivers of super-resistance to pyrethroids in a hotspot of intense pesticide use for agriculture in Cameroon, revealing multiple resistance routes, including increased detoxification, chemosensory, and cuticular resistance, and evidence of epigenetics. Specifically, the role of a cluster of CYP6Z P450s was validated, and the exceptionally high expression of UGT308G1 was identified as a key driver of the observed resistance, underscoring the contribution of UGTs to pyrethroid resistance alongside P450s and knockdown resistance. Materials and methods Mosquito collection and rearing Mosquitoes, both adults and larvae, were collected in Mangoum (latitude 5°29′09.2′′ N, longitude 10°35′20.8′′ E), a region in western Cameroon known for its extensive agricultural activities and the associated proliferation of Anopheles breeding sites. Blood-fed adult female mosquitoes were collected indoors, with the consent of the property owners, between 06:00 and 10:00 AM via electric aspirators (Prokopack Aspirator, model 1419, John W. Hock Company, Gainesville, FL, USA). Larval samples were obtained from field irrigation reservoirs and stagnant water bodies. This collection effort spans the period from 2020–2023. The collected adult mosquitoes and larvae were then packaged and transported to the insectarium at the Center of Research in Infectious Diseases (CRID), where they were identified following the methodology outlined by Tepa et al. (2022). Mosquito selection F 0 and F 1 female An. gambiae s.s. were used to select for resistant mosquitoes. Susceptibility tests were conducted following the WHO protocol for adults [ 56 ]. All insecticide-impregnated papers were obtained from the WHO/Vector Control Research Unit (VCRU) at the University of Science Malaysia (Penang, Malaysia). Four replicates, each consisting of 20–25 females aged 3–5 days, were used per tube. To identify resistant and super-resistant mosquitoes, bioassays were performed using permethrin and alphacypermethrin at concentrations of 1x (0.75%), 5x (3.75%), and 10x (7.5%). The mosquitoes were exposed to insecticide-impregnated papers for 1 hour, with untreated papers serving as systematic controls. The results of these bioassays have been reported previously [ 14 ]. Given the high level of pyrethroid resistance observed in Mangoum, this study also involved the analysis of candidate genes in F 4 generation offspring from crosses between Mangoum and Kisumu mosquitoes. This approach allowed for the examination of resistance-related genetic variance within a consistent genetic background. The crossing line was established between selected Mangoum-resistant strains and laboratory-susceptible strains (Kisumu) as described by Wondji et al. (2007) and Cattel et al. (2020) [ 26 , 57 ]. To obtain distinct phenotypes, individual F 4 progenies from these crosses were segregated on the basis of their resistance phenotype by exposing 3- to 5-day-old females to 0.75% permethrin or alphacypermethrin at a lethal time of 80% (LT80). After 24 hours, the mosquitoes that remained alive were classified as resistant. Surviving and deceased mosquitoes were subsequently preserved in RNAlater® and silica gel, respectively, for further molecular analysis. RNA extraction, library preparation, and sequencing Total RNA was extracted from three batches of ten mosquitoes each, as well as from the Kisumu-sensitive laboratory strain, via the Arcturus PicoPure RNA isolation kit (Life Technologies, Carlsbad, CA, USA) according to the manufacturer’s protocol. The extracted RNA was sent to Novogene (25 Cambridge Science Park, Milton Road, Cambridge, CB4 0FW, United Kingdom) for further analysis, where library preparation and sequencing were performed in accordance with their established protocols. Pools of eight libraries per line were sequenced via 2 × 150 bp paired-end sequencing via the Illumina NovaSeq 6000 Sequencing System. Data analysis of differential gene expression The workflow started with quality control of each FASTQ file via the FastQC algorithm [ 58 ], and the results were compiled into a report via MultiQC [ 59 ]. HISAT2 was employed to map Illumina reads to the Anopheles gambiae genome. Differential expression analysis among permethrin-resistant mosquitoes, unexposed mosquitoes, and susceptible mosquitoes was conducted via the DESeq2 package [ 60 ] in R on the basis of the counts generated from alignment via the featureCounts tool [ 61 ]. A triangular analysis of the transcriptional profile among mosquito pools was performed to elucidate potential genes responsible for resistance in Mangoum (Man) mosquitoes: resistant versus susceptible (R-S) to highlight differences between resistant and susceptible strains; controls versus susceptible (C-S) to reveal genes constitutively expressed in field mosquitoes; and resistant versus control (R-C) to identify genes induced by insecticide exposure. To further highlight candidate genes associated with exacerbated resistance, gene expression was also analyzed in pools of resistant mosquitoes exposed to 5x or 10x the diagnostic dose of permethrin (0.75%). A similar triangular comparison was conducted: resistant at 10x vs. resistant at 1x (10x_vs_1x), resistant at 5x vs. resistant at 1x (5x_vs_1x), and resistant at 10x vs. resistant at 5x (10x_vs_1x). The significance of gene expression levels was assessed via a moderated t test, a modification of the unpaired t test. P values were adjusted for multiple testing via Story’s false discovery rate (FDR) approach with bootstrapping. Differentially expressed genes were defined as those with an FDR-adjusted p value of less than 5% and an expression level of greater than or equal to 2 for comparisons between field mosquitoes and laboratory strains. For comparisons among field mosquitoes, the threshold for gene expression levels was set at 1.5 because of the generally low degree of differentiation observed in field populations. The expression levels of the candidate genes are displayed via a volcano plot. Functional enrichment analysis was conducted via gene annotations derived from Blast2GO software via the nonredundant database from NCBI. The significance of the results was assessed via Fisher's exact test with adjusted p values, considering a fold enrichment (FE) greater than 1 as indicative of enhanced enrichment in the candidate genes, with significance defined at p values less than 0.05. Quantitative reverse transcription PCR For the validation of gene expression via qRT‒PCR, cDNA was synthesized from RNA purified through reverse transcription‒PCR via the SuperScript III kit (Invitrogen, Waltham, MA, USA) alongside the oligo-dT20 and RNase H kit (New England Biolabs, Ipswich, MA, USA) in a total reaction volume of 20 µL. The expression profiles of 15 genes (see Table S1 ) were assessed via quantitative reverse transcription‒PCR (qRT‒PCR) in mosquitoes that survived exposure to permethrin at concentrations of 1×, 5×, and 10× in comparison with unexposed mosquitoes and the susceptible Kisumu strain. Two housekeeping genes—Elongation Factor and Ribosomal Protein S7—were used for normalization. Amplification was carried out on an Agilent Mx3005 qRT‒PCR thermal cycler (Santa Clara, CA, USA) as previously described [ 14 ]. Relative expression was calculated via the 2^-ΔΔCt method [ 62 ] and visualized via R Studio software. To evaluate the correlation between the RNA sequencing and qRT‒PCR results, a Pearson correlation test was performed [ 63 ]. The relationships between sequencing results, which are based on the number of reads for each gene, and qRT‒PCR results, which are based on the relative abundance of these genes, were assessed. A correlation coefficient close to 1 indicates a strong positive correlation between the two methods. In vivo validation of the role of CYP6Z family genes in increasing insecticide resistance in An. gambiae through RNAi Double-stranded RNA synthesis To investigate the role of the UGT candidate gene in insecticide resistance, RNA silencing was performed in resistant mosquitoes from Mangoum, and the impact of gene knockdown on mosquito susceptibility was evaluated following a previously (Kouamo et al., 2021)described protocol. To achieve this aim, double-strand RNAs specific to each target candidate gene were synthesized after amplification via a KAPA Taq polymerase kit (Kapa Biosystems, Wilmington, MA, USA) from complementary DNA with primers designed via the online RNAi tool ( http://www.dkfz.de/signaling2/rnai/ ). The T7 promoter sequence was subsequently added to both forward and reverse PCR primers (see Table S1 ). Briefly, after amplification, the PCR product was purified via a QIAquick gel extraction kit (QIAGEN, Hilden, Germany) according to the manufacturer's instructions, and the purified PCR amplicons were then used to synthesize double-stranded RNA (dsRNA) with the MEGAscript™ RNAi T7 kit (Thermo Fisher Scientific, Inc., Renfrewshire, UK). The transcription reaction mixture included 6 µl of nuclease-free water, 4 µl each of ATP, GTP, CTP, and UTP, 4 µl of 10x buffer, and the enzyme; 10 µl of the purified PCR product was added, and the mixture was incubated at 37°C for 16 hours in a thermocycler. To remove the DNA template, 1 µl of RNase A-free DNA was added to 40 µl of the reaction mixture and incubated at 37°C. The dsRNA was purified to remove the protein-free nucleotides and dsRNA degradation products via a MEGAclear kit from Thermo Fisher (Thermo Fisher Scientific, Inc., Renfrewshire, UK) according to the manufacturer's protocol. Following purification, the integrity of the synthesized dsRNA was confirmed by electrophoresis on a 1% agarose gel. The dsUGT and dsGFP were precipitated with ethanol, eluted with Sigma water to a final concentration of 3 µg/µl and kept at -20°C. Mosquito Injection and Susceptibility Bioassays To knockdown the expression of CYP6Z1, CYP6Z2 and CYP6Z3 in An. gambiae from Mangoum and evaluated the impact on insecticide susceptibility, dsCYP6Zs were injected into the thorax of 2- to 3-day-old female F 1 mosquitoes via a nanoinjector (Nanoinject; Drummond, Burton, OH, USA) as previously described [ 28 , 64 ]. Briefly, mosquitoes were anesthetized with CO2 and injected with 69 nL of dsCYP6Zs, and dsGFP (green fluorescence protein) was used as a control. Mosquitoes were kept in the insectary to allow gene silencing to occur. Four days post-injection, four replicates of 20–25 mosquitoes were injected with dsCYP6Zs, and the controls were injected with dsGFP. The noninjected mosquitoes were exposed to permethrin (0.75%) or alpha-cypermethrin (0.05%) for 1 hour in accordance with WHO testing guidelines [ 56 ]. After exposure, the mosquitoes were transferred to holding tubes containing sugar, and mortality was recorded 24 hours later. Mosquitoes injected with dsGFP and those that were not injected served as controls. qPCR was performed to validate the knockdown of CYP6Zs via RNA extracted from dsCYP6Z-injected mosquitoes on the day post-injection compared with the control mosquitoes not injected following the protocol described above. Polymorphism Analysis in Pyrethroid-Resistant Mosquito Populations To detect single nucleotide polymorphisms (SNPs) in the main resistance genes of interest potentially associated with resistance, we used Samtools mpileup and Varscan 2 [ 65 ]. The generated variant file was annotated and filtered via the Snpeff tool [ 66 ]. Only chromosomes 2, 3 and X were included in graphical representations, with only the coding regions of the genes considered owing to the nature of the messenger RNA used as the template in RNA-seq. The analysis of sample clustering according to phenotype was performed via Plink 1.9 (Supplementary file 1, Figure S3). Potentially resistance-associated loci were analyzed via the Fixation Index (FST) test, which measures genetic variation between subpopulations relative to the total variation within a dataset. Signs of selective sweeps were assessed via Tajima's D test and nucleotide diversity, with a focus on genes belonging to loci associated with resistance in An. gambiae [ 26 , 53 ]. All the statistical parameters were estimated via Grenedalf software [ 67 ]. To identify SNPs significantly linked to pyrethroid resistance, we applied two distinct methodologies, as outlined by Wondji et al. (2022). The first approach relied on stringent differential allele frequency analysis, where a variant was deemed significant if its allele frequency ranged between 0–10% across all four Kisumu laboratory strains but was 50–100% in unexposed field strains. The second method examined the statistical association of each variant with permethrin resistance, employing an unpaired t test to compare allele frequencies between groups. The resulting –Log10 P value was then used to construct Manhattan plots for each chromosome, visualizing significant genomic associations. The allele frequencies of some SNPs detected in candidate genes potentially involved in insecticide resistance are represented in a heatmap. Declarations Ethics approval and consent to participate Not applicable Consent for publication Not applicable Availability of data and materials Sequence data that support the findings of this study have been deposited in the European Nucleotide Archive with the primary accession code PRJEB97187. The data analyzed in this study are available within the article and its Supplementary Information files. Further details are available from the authors upon request. Competing Interests The authors declare no conflicts of interest. Funding This work was supported by a Wellcome Trust Senior Research Fellowships in Biomedical Sciences to Charles S. Wondji (217188/Z/19/Z) and a Bill and Melinda Gates Foundation grant to CSW (INV-006003). Authors' contributions AT conducted field sampling, sample processing, experimental work, RNA-seq analysis, data interpretation, and manuscript drafting. MK provided technical support and performed functional genomic experiments and analysis. JAKO and MT contributed laboratory support and participated in manuscript writing. CAP contributed to manuscript preparation. CSW acquired funding, conceived and designed the study, led aspects of the RNA-seq analysis, and contributed to manuscript writing. All authors reviewed and approved the final version for submission. Acknowledgments The authors thank Hervé Raoul Tazokong, Mahamat Gadji, Jack Hearn, Cedrique Noutchih, Helen Irving and Murielle Wondji for their valuable collaboration throughout the study, including support with experimental procedures and data interpretation. Their expertise and dedication greatly contributed to the successful completion of this work. The authors also gratefully acknowledge the facilities and logistical support provided by the Center for Research in Infectious Diseases (CRID), which enabled the smooth execution of both field and laboratory activities. We further extend our appreciation to the technical staff and field team whose commitment facilitated sample collection and laboratory analyses. Authors' information 1 Medical Entomology Department, Centre for Research in Infectious Diseases (CRID), Yaoundé, Cameroon; 2 Department of Biochemistry, Faculty of Medicine and Biomedical Sciences University of Yaoundé 1, Yaoundé, Cameroon; 3 International Institute of Tropical Agriculture, Yaoundé, Cameroon; 4 Vector Biology Department, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool L3 5QA, UK; £ Current address: Emerging Pathogens Institute, Department of Infectious Diseases and Immunology, College of Veterinary Medicine, University of Florida, Gainesville, FL 32610, USA. *Correspondence: [email protected] (A.T.); [email protected] (C.S.W.) 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08:20:24","extension":"html","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":240142,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7592244/v1/599b663afd5c2bfd581cd36a.html"},{"id":91965122,"identity":"6e228e3f-0a74-4e69-bb0a-eeb6e928575f","added_by":"auto","created_at":"2025-09-23 08:20:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":461511,"visible":true,"origin":"","legend":"\u003cp\u003eTranscriptomic response to permethrin exposure in \u003cem\u003eAn. gambiae\u003c/em\u003e. \u003cem\u003eA. Venn diagram showing the number of genes differentially expressed in the R-S, R-C and C-S comparisons. B. Venn diagram showing the number of genes differentially expressed in the 10x-1x, 5x-1x and 10x-5x comparisons. C. Differential gene expression between unexposed Mangoum and Kisumu strains reveals baseline differences. D. Compared with Kisumu, permethrin exposure (1x dose) significantly altered gene expression in Mangoum. E. Exposed and unexposed mosquitoes presented reduced differential expression. F. Increased permethrin dose (10x \u003c/em\u003evs\u003cem\u003e 1x) in Mangoum alters the expression of resistance-related genes.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7592244/v1/e25575fc26f3b5bbc4313fd3.png"},{"id":91965153,"identity":"2bbabbcb-c469-446d-aafe-07b298640e93","added_by":"auto","created_at":"2025-09-23 08:20:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":397210,"visible":true,"origin":"","legend":"\u003cp\u003eTop genes consistently overexpressed in Mangoum mosquitoes across the dose‒response assays (1X, 5X, and 10X), with a particular focus on genes upregulated in the 10X versus 1X comparison. \u003cem\u003eThe heatmap presents the fold change (FC) values for each gene, with color coding illustrating the magnitude of expression differences: green indicates low FC, yellow corresponds to moderate to high FC, and red denotes high FC. These results highlight candidate genes potentially involved in high-level pyrethroid resistance in Mangoum mosquitoes.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7592244/v1/b3bbad0dde9f3c0f5cadaec9.png"},{"id":91966329,"identity":"ce2a2d8e-1d78-4ac0-a28d-f3193d71a04a","added_by":"auto","created_at":"2025-09-23 08:28:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":268606,"visible":true,"origin":"","legend":"\u003cp\u003eTranscriptome-wide F\u003csub\u003eST\u003c/sub\u003e comparisons highlighting regions of genetic divergence linked to permethrin resistance in \u003cem\u003eAn. gambiae\u003c/em\u003e. \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eST\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e values were computed across 30,000 SNP windows via Grenedalf, and different resistance phenotypes were compared. Each row represents a different comparison among phenotypes, with lines color-coded by chromosome (2L, 2R, 3L, 3R, X). Peaks in F\u003c/em\u003e\u003csub\u003e\u003cem\u003eST\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e highlight regions of increased genetic differentiation, potentially indicating loci associated with permethrin resistance. The vertical colored bars denote genomic regions or candidate loci of interest, such as VGSC, rp1, rp2, GSTs, and CYP9K1, which may be involved in resistance mechanisms. The x-axis shows the physical position along the genome (in Mbp), whereas the y-axis depicts the FST values, indicating the extent of genetic divergence between the studied phenotypes.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7592244/v1/671226074234b0e578e4122d.png"},{"id":91965128,"identity":"108b7eee-b50c-4174-8c45-6acfa33343d5","added_by":"auto","created_at":"2025-09-23 08:20:22","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1887939,"visible":true,"origin":"","legend":"\u003cp\u003eTranscriptional signatures of selective sweeps at pyrethroid resistance loci in \u003cem\u003eAnopheles gambiae\u003c/em\u003e from Mangoum. \u003cem\u003eThis figure illustrates the patterns of Tajima's D and nucleotide diversity around key loci associated with pyrethroid resistance in An. gambiae populations. The data reveal signatures consistent with selective sweeps, highlighting regions potentially under positive selection due to insecticide pressure, including the voltage-gated sodium channel (VGSC) and pyrethroid resistance 1 (rp1) loci. \"Kis\" refers to the Kisumu susceptible laboratory strain, \"Man\" refers to the Mangoum field strain, and \"MK\" refers to a Mangoum-Kisumu hybrid. Mosquitoes were exposed to increasing doses of insecticide (1x, 5x, or 10x) or not exposed (control, C).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7592244/v1/3f017fb17978ed1ee2295234.png"},{"id":91965130,"identity":"19aa499c-8384-4660-992c-04a451668033","added_by":"auto","created_at":"2025-09-23 08:20:22","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":804876,"visible":true,"origin":"","legend":"\u003cp\u003eNonsynonymous mutations identified within key candidate gene families potentially linked to resistance escalation in \u003cem\u003eAn. gambiae\u003c/em\u003e from Mangoum. \u003cem\u003e\"Kis\" denotes susceptible laboratory strains, whereas \"Man_Unx\" and \"Man_Perm\" (at 1x, 5x, and 10x doses) represent Mangoum mosquitoes that are unexposed or exposed to varying insecticide concentrations, respectively. \"MK_Unx\" and \"MK_Perm1x\" refer to hybrid Kisumu/Mangoum strains, unexposed and exposed, respectively.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7592244/v1/c22b4b0bf4868ebae5726e61.png"},{"id":91965134,"identity":"e60d193c-ab51-4ad4-ad28-3e6bbb8953e3","added_by":"auto","created_at":"2025-09-23 08:20:22","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":417906,"visible":true,"origin":"","legend":"\u003cp\u003eValidation of the role of the CYP6Z1, 6Z2, 6Z3 and 6Z4 genes in resistance escalation. \u003cem\u003eA. Confirmation of the knockdown effect of double-stranded RNA on CYP6Z family genes via qPCR and B. bioassays. Susceptibility was assessed 24 hours after a 1-hour exposure to the insecticide. p value \u0026gt; 0.05 ns, \u0026lt; 0.05 *, \u0026lt; 0.01 **.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7592244/v1/be550b76bc331d1aef2a315f.png"},{"id":105755863,"identity":"398c2409-d1a1-4628-8ae1-bbc079e7aab4","added_by":"auto","created_at":"2026-03-30 16:31:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6011651,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7592244/v1/f6d89473-2b78-4287-a017-6525dbdb7330.pdf"},{"id":91966236,"identity":"aaac8c81-6d17-4f2b-b80a-6eb1045b3c57","added_by":"auto","created_at":"2025-09-23 08:28:21","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":246574,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfiles1ok.docx","url":"https://assets-eu.researchsquare.com/files/rs-7592244/v1/0099e2364823c92ce11bf571.docx"},{"id":91965140,"identity":"1082596d-4e65-4be0-9905-43f1b88bcdf7","added_by":"auto","created_at":"2025-09-23 08:20:22","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1008503,"visible":true,"origin":"","legend":"","description":"","filename":"supplementfile2ok.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7592244/v1/c6e90dc9054cd0a5c2d64718.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Insights from transcriptomic profiling identify the CYP6Z gene family as key drivers of pyrethroid resistance escalation in Anopheles gambiae from Cameroon","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMalaria remains a major public health burden in the tropical world, with 249\u0026nbsp;million cases reported in 2022 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], with most cases concentrated in Africa, the region most affected by malaria. After a significant reduction in the disease burden from 2000\u0026ndash;2015 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], progress has stalled partly owing to rising resistance to pyrethroids, the main insecticide class used for vector control through insecticide-treated nets (ITNs) or indoor residual spraying (IRS) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Indeed, growing reports of pyrethroid resistance have been made for major malaria vectors in Africa, including \u003cem\u003eAnopheles gambiae\u003c/em\u003e [\u003cspan additionalcitationids=\"CR6 CR7 CR8\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] and \u003cem\u003eAnopheles funestus\u003c/em\u003e [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], with evidence that this resistance has reduced the efficacy of insecticide-based control tools such as long-lasting insecticidal nets (LLINs) [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Moreover, multiple studies have documented an increasing trend of pyrethroid resistance, with mosquitoes increasingly surviving exposure to concentrations exceeding ten times the diagnostic dose[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] or enduring prolonged contact times[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Previous research has also suggested that high levels of agricultural activity in rural areas may impose selection pressures on these populations [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe exacerbation of pyrethroid resistance has caused further loss of efficacy of control tools, notably for pyrethroid-only nets, which are now less effective in most places, as shown by randomized control trials [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Pyrethroid resistance is broadly conferred by target-site resistance and metabolic resistance mechanisms. Recent research has confirmed the essential roles of cytochrome P450s, specifically \u003cem\u003eCYP6P3, CYP6P4\u003c/em\u003e, and \u003cem\u003eCYP6M2\u003c/em\u003e, which have been validated as contributors to insecticide resistance in \u003cem\u003eAn. gambiae\u003c/em\u003e [\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Similarly, in \u003cem\u003eAn. funestus\u003c/em\u003e, P450s also play key roles, including \u003cem\u003eCYP6P9a/b, CYP6P4a/b, CYP325A\u003c/em\u003e, and \u003cem\u003eCYP9K1\u003c/em\u003e [\u003cspan additionalcitationids=\"CR23 CR24 CR25 CR26\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In addition to the P450 enzymes associated with phase 1 xenobiotic metabolism, other classes of enzymes are significantly linked to phase 2 insecticide metabolism. Specifically, glutathione S-transferases (GSTs), including GSTe2 in both \u003cem\u003eAnopheles gambiae\u003c/em\u003e and \u003cem\u003eAnopheles funestus\u003c/em\u003e, as well as other GSTe clusters (3\u0026ndash;8) in \u003cem\u003eAn. funestus\u003c/em\u003e [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In addition to these established roles of GSTs in phase 2 metabolism, it has been suggested that sulfotransferases and UDP-glycosyltransferases (UGTs) may also contribute [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. However, the detailed evolutionary mechanisms driving the ability to survive prolonged exposure to pyrethroids or to greater doses remain unclear. Initial evidence in \u003cem\u003eAn. gambiae\u003c/em\u003e have mentioned the possible increased expression of multiple detoxification genes [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], but the key genetic changes allowing these mosquitoes to survive 10 times the diagnostic doses of pyrethroids remain to be established, particularly as mosquito populations have shown great variation in their adaptation according to their geographical locations, with different resistance mechanisms observed in different regions [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn this study, we investigated the transcriptomic signature of resistance escalation to permethrin in \u003cem\u003eAn. gambiae\u003c/em\u003e populations from an Agricultural hotspot of Cameroon, where mosquitoes have elevated levels of resistance to all four classes of insecticides and survive 10x the diagnostic doses of pyrethroids. We revealed that key resistance escalation mechanisms include increased expression of detoxification genes, cuticular resistance, novel gene families, and epigenetic signatures. Moreover, via functional validation, we highlight the role of highly overexpressed CYP6Z P450s in increasing resistance.\u003c/p\u003e"},{"header":"Results and discussion","content":"\n\u003ch3\u003eResults\u003c/h3\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003eGene expression profiling\u003c/h2\u003e\u003cp\u003eThe alignment of the sequenced data to the \u003cem\u003eAn. gambiae\u003c/em\u003e PEST reference genome (version 61) yielded approximately 78 to 170\u0026nbsp;million reads per sample across the forty-eight sequenced samples (see Supplementary File 1, Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.1). The analysis of sequence quality is presented in Supplementary File 2 (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e.1).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eGenes commonly overexpressed across conditions\u003c/h3\u003e\n\u003cp\u003eThe number of differentially expressed transcripts per comparison is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. In total, 2,971 genes were upregulated, whereas 2,311 genes were downregulated in at least one comparison. Analysis of the commonly upregulated genes revealed that 28, 518, and 585 genes were upregulated in the R-S vs. C-S vs. R-C, R-S vs. R-C, and R-S vs. C-S comparisons, respectively. These genes belong to several families associated with insecticide resistance, including detoxification genes such as cytochrome P450s, glycosyltransferases, and ABC transporters.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAmong the detoxification genes consistently overexpressed across all three comparisons (R-S/R-C/C-S), we identified ATP-binding cassette subfamily A member 2 (ABCA2), which presented fold changes of 9.2, 1.5, and 6.1 in the R-S, R-C, and C-S comparisons, respectively (see Table I).\u003c/p\u003e\u003cp\u003eThe list of genes commonly upregulated in both R-S and C-S includes several detoxification genes, including cytochrome P450s and glycosyltransferases. Notably, the gene \u003cem\u003eUGT308G1\u003c/em\u003e (AGAP007990) was among the most significantly overexpressed genes, with fold changes of 50.8 and 105.1 in the R-S and C-S comparisons, respectively. The other highly expressed genes included P450s, among which three CY6Z genes presented the greatest fold changes, notably, \u003cem\u003eCYP6Z3\u003c/em\u003e (FC: 41.4 and 68.8), \u003cem\u003eCYP6Z2\u003c/em\u003e (FC: 17.2 and 29.0), and \u003cem\u003eCYP6Z1\u003c/em\u003e (FC: 6.0 and 12.3). The expression of the CYP9K1 gene, a member of the P450 family, was significantly upregulated (FC: 4.7 and 11.3), with read counts exceeding 100,000. Overall, approximately 26 P450 genes, including the CYP6M2 gene (FC: 2.8 and 8.5), were overexpressed. The expression of CYP4G17, previously associated with the production of cuticular hydrocarbons [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], was 2.5-fold greater than that associated with the overexpression of several cuticular proteins (28), suggesting the contribution of the reduced penetration resistance mechanism in this population. A set of carboxylesterase genes, including COEAE80 (AGAP006700), was significantly upregulated. Furthermore, several other UDP glucosyltransferases, including \u003cem\u003eUGT36C2, UGT49A3, UGT302A2, UGT314A2, UGT306A2\u003c/em\u003e, and \u003cem\u003eUGT302A1, were equally upregulated\u003c/em\u003e. Moreover, several olfactory and gustatory genes, including many odorant binding proteins, ionotropic receptors, and odorant receptors (ORs; 46 upregulated in R-S), were significantly upregulated in Mangoum, suggesting that olfaction mechanisms contribute to the observed resistance (see Supplementary File 2, Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e.1). Among the ORs, 3 had a fold change\u0026thinsp;\u0026gt;\u0026thinsp;15 in R-S, including AGAP006077 (FC: 23.7), AGAP002905 (FC: 21.4), and AGAP011368 (FC: 18.01). A set of chemosensory genes was equally upregulated, including the chemosensory proteins (CSP1 and 3) and 3 sensory appendage proteins (SAP1, 2 and 3), with SAP2 (AGAP008052) exhibiting the highest fold change among them at 11-fold overexpression. Additionally, several genes associated with epigenetic regulation, including histone H3/4 and histone deacetylase, are also overexpressed. Notably, argininosuccinate lyase was recently shown to confer resistance to pyrethroids in \u003cem\u003eAn. funestus\u003c/em\u003e [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], was also commonly upregulated in both the R-S and C-S comparisons.\u003c/p\u003e\u003cp\u003eTable I: Differential expression of candidate resistance genes in Mangoum mosquitoes between different comparisons.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGene ID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGene Name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChr.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eR-S\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eC-S\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eR-C\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAnnotation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP006380\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eABCA2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eABC Transporter\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP008059\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCSP1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eChemosensory protein\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP008055\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCSP3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eChemosensory protein\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP001789\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAGAP001789\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCytochrome P450\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP008218\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCYP6Z2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e17.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e29.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCytochrome P450\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP008209\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCYP6M1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCytochrome P450\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP009375\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCYP9M2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCytochrome P450\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP012292\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCYP9J4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCytochrome P450\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP013128\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCYP6AA2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCytochrome P450\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP008206\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCYP6N2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCytochrome P450\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP003067\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCYP304C1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCytochrome P450\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP010077\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCYP303A1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCytochrome P450\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP008217\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCYP6Z3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e41.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e63.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCytochrome P450\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP012291\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCYP9J3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCytochrome P450\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP008210\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCYP6N1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCytochrome P450\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP013241\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCYP4D16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCytochrome P450\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP008020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCYP12F2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCytochrome P450\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP008213\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCYP6M3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCytochrome P450\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP002867\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCYP6P4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCytochrome P450\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP000500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCpr\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCytochrome P450 Reductase\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP012850\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAGAP012850\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUNKN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCytochrome P450\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP010966\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAGAP010966\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCytochrome P450\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP007589\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUGT306A2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGlucosyltransferase\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP007990\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUGT308G1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e50.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e105.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGlucosyltransferase\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP006223\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUGT302A2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGlucosyltransferase\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP005754\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUGT308F1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGlucosyltransferase\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP005751\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUGT308H1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGlucosyltransferase\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP007920\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUGT36C2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGlucosyltransferase\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP007374\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUGT49A3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGlucosyltransferase\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP002783\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUGT314A2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGlucosyltransferase\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP004165\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGSTD2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGlutathione S-transferase\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP009342\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGSTU3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGlutathione S-transferase\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP012839\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAGAP012839\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUNKN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e55.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e33.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGlutathione S-transferase\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP012156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eABCA5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eABC Transporter\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP008437\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eABCC9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eABC Transporter\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP012005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eABCF3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eABC Transporter\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP028013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCPLCP25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCuticular\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP010887\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCPR113\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCuticular\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP006369\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCPR144\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCuticular\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP000877\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCYP4G17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCuticular\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP008141\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAGAP008141\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eArgininosuccinate lyase\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP002207\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCYP325C1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e12.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCytochrome P450\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP000818\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCYP9K1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e11.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCytochrome P450\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP008219\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCYP6Z1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e12.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCytochrome P450\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP008212\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCYP6M2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCytochrome P450\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP008358\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCYP4H17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e20.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCytochrome P450\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP005752\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUGT308A2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGlucosyltransferase\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP006222\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUGT302A1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e16.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGlucosyltransferase\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP003568\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOE22933\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCarboxylesterase\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP011916\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOEB6582\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCarboxylesterase\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP006956\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOE10O\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCarboxylesterase\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP002090\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOE16738\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCarboxylesterase\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP002863\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOEAE6O\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCarboxylesterase\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP006700\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOEAE8O\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCarboxylesterase\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP005835\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOEJHE3E\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e37.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCarboxylesterase\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP005836\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOEJHE4E\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e20.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCarboxylesterase\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP008052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSAP2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eChemosensory protein\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP008054\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSAP3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eChemosensory protein\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP001039\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCYP307A1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCytochrome P450\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP003065\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCYP11179\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCytochrome P450\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP002870\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCYP6AD1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCytochrome P450\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP008356\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCYP4H16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCytochrome P450\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP000088\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCYP4H19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCytochrome P450\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP004383\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGSTD10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGlutathione S-transferase\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP013465\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCPLCP1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCuticular\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP008817\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCPLCP3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e15.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCuticular\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP003382\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCPR120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCuticular\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP003385\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCPR123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCuticular\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGAP003379\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAGAP003379\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCuticular\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eR-C, Permethrin-resistant\u003c/em\u003e vs. \u003cem\u003econtrol mosquitoes; C-S, control\u003c/em\u003e vs. \u003cem\u003esusceptible KISUMU strain mosquitoes; R-S, Permethrin-resistant\u003c/em\u003e vs. \u003cem\u003esusceptible KISUMU strain mosquitoes.\u003c/em\u003e\u003c/p\u003e\n\u003ch3\u003eGenes commonly upregulated in response to the three permethrin doses (1X, 5X and 10X DCs)\u003c/h3\u003e\n\u003cp\u003eTo identify candidate genes potentially linked to resistance escalation, we analyzed gene expression profiles in resistant mosquitoes exposed to increasing doses of permethrin (1x, 5x, and 10x). The comparison between the 10x and 1x doses was prioritized to pinpoint genes (2,677 upregulated) associated with enhanced survival at higher insecticide concentrations (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Additionally, 10x-5x and 5x-1x comparisons were conducted to validate these findings. A special focus was placed on genes with the highest fold change in the 10x-1x comparison, ensuring that they had at least 100 reads in the 10x condition to improve reliability and minimize false positives caused by low sequencing depth or stochastic sampling.\u003c/p\u003e\u003cp\u003eThis analysis revealed several top candidate genes, including chymotrypsinogen B (AGAP006385), which presented the greatest fold change (FC: 29.0) in the 10x-1x comparison (see Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) and was also commonly overexpressed in the 10x-5x and 5x-1x comparisons, similar to other protease-related genes, such as chymotrypsin 1 and 2 (AGAP006709 and AGAP006710), which presented FC values ranging from 9.6\u0026ndash;11.1. Endopeptidases such as chymotrypsinogen and chymotrypsin are known for their key role in maintaining healthy cells and in responding to stressors such as pesticides [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Other proteases that play the same role, including the CLIP-domain serine protease CLIPB12 (AGAP009217), were also among the top candidates (FC7.1) in the 10x-1x comparison.\u003c/p\u003e\u003cp\u003eAdditionally, the sensory appendage protein SAP2, known for its role in insecticide sequestration [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], presented FC values of 8.3 and 6.7 in the 10x-1x and 5x-1x comparisons, respectively. Moreover, the oxidative stress genes argininosuccinate lyase AGAP008141 (FC6.2 and 4.1) and argininosuccinate synthase AGAP003015 (FC5.3 and 2.7) were overexpressed 10-fold compared with 1- and 5-fold, respectively. These genes have been reported to be involved in the urea cycle, which may be critical for managing nitrogen waste [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGenes potentially associated with epigenetic gene regulation were equally upregulated, including the glycine N-methyltransferase AGAP002198 (FC: 5.1, 2.5 and 2.1, respectively, at 10x compared with 1x and 5x and 5x vs 1x), which has been shown to play a major role in methylation regulation in mammals by catalyzing the direct transfer of a methyl group from S-adenosylmethionine to glycine (forming sarcosine) [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Only a single detoxification gene, the carboxylesterase COE13O (AGAP011507), was commonly overexpressed in the 3 comparisons.\u003c/p\u003e\u003cp\u003eCompared with the 1x dose, a substantial number of glutathione S-transferases (GSTs) were upregulated at both the 5x and 10x doses. These findings suggest that the expression of these enzymes is induced by relatively high insecticide concentrations, highlighting their role in increasing resistance (see Supplementary File 2, Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e.2). Notably, GSTD11 (FC: 5.5 and 4.1), GSTD3 (FC: 1.5 and 1.8), GSTE5 (FC: 1.9 and 1.6), GSTE4 (FC: 2.9 and 2.0), and GSTE3 (FC: 2.8 and 1.8) presented increased expression at 10x and 5x compared with that at 1x magnification. Conversely, GSTE2 (AGAP009194) was only upregulated in the 10x\u0026ndash;1x comparison (FC: 1.9). In addition, other detoxification genes, including cytochrome P450 enzymes such as CYP6Z1 (FC: 2.1 and 2.0), CYP305A1 (FC: 1.8 and 2.0), CYP6Y1 (FC: 2.2 and 1.5), CYP6AG2 (FC: 1.8 and 1.6), and CYP6AH1 (FC: 2.2 and 1.7), were consistently overexpressed at 5x and 10x doses, indicating potential candidates for genes associated with super-resistance. Among the glucosyltransferases, only two, UGT301C1 (AGAP008404) and UGT36B2 (AGAP028055), with FC values of 4.4 and 1.9, respectively, were differentially expressed at 10x compared with 1x.\u003c/p\u003e\u003cp\u003eGenes related to reduced penetration resistance, specifically the cuticular proteins CPR133 (AGAP010123) and CPR76 (AGAP009874) and the cuticle precursor CYP4G16 (AGAP001076), were also overexpressed at 10x (8.5, 3.2 and 2.9, respectively) and 5x (3.2, 6.6 and 3.0, respectively) doses compared with 1x, indicating their possible roles in enhanced resistance mechanisms.\u003c/p\u003e\u003cp\u003eSeveral genes related to immune responses, including numerous C-lectin proteins (CTLMA1 and 2), leucine-rich immune proteins (LRIM 1, 3, 6 and 17), and thioester-containing proteins (TEP1, 12, 18\u0026hellip;), were upregulated in the 10x and 5x groups compared with the 1x groups. Additionally, many upregulated genes, including several histone genes (H2A, H2B, H3 and H4), are related to epigenetics. A significant signature of the potential role of noncoding RNA posttranscriptional regulation was detected, with several microRNAs upregulated, especially AGAP013569, AGAP003144, AGAP013659, AGAP028760 and AGAP013705 (see Supplementary 2, Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e.3).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eGene ontology analysis\u003c/h3\u003e\n\u003cp\u003eGene Ontology (GO) enrichment analysis was performed to assess the biological processes and molecular functions associated with resistance. Using a list of 2,971 transcripts differentially expressed between R, C, and S, we observed a predominance of biological processes related to xenobiotic and insecticide metabolism (GO:2001242, GO:0031125), followed by steroid metabolism (GO:1905609) and chemosensory behavior (GO:2000024) (see Supplementary 2, Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e.4\u0026ndash;S2.5). Significant enrichment (gene ratio\u0026thinsp;\u0026gt;\u0026thinsp;0.3) was noted in molecular functions related to voltage-gated monoatomic ion channel activity impacting presynaptic membrane potential regulation (GO:0140662), medium-chain fatty acid omega-hydroxylase activity (GO:0140981), alkane 1-monooxygenase activity (GO:0005504), ABC-type xenobiotic transporter activity (GO:0008559), hexosyltransferase activity (GO:0016758), ATP-dependent protein folding chaperone activity (GO:0022857), alcohol-forming very long-chain fatty acyl-CoA reductase activity (GO:0080019), and UDP-glycosyltransferase activity (GO:0008194).\u003c/p\u003e\u003cp\u003eGO enrichment analysis of 1,474 candidate genes identified from comparisons between 1\u0026times;, 5\u0026times;, and 10\u0026times; revealed notable involvement in biological processes related to chemical reactions and pathways leading to protein catabolism (GO:0006412, GO:0032543, GO:0140242, GO:0140236, GO:0002181, GO:0006362, GO:0033539, and GO:0006367), which is pivotal for the rapid adaptation of organisms to various environmental stresses [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] (see Supplementary 2, Tables S2.6\u0026ndash;S2.7). Moreover, resistance escalation involves processes associated with adenosine triphosphate (ATP) synthesis (GO:0042776), responses to oxidative stress involving mitochondrial respiratory chain complex I assembly (GO:0006979), and electron transfer from NADH to ubiquinone during oxidative phosphorylation (GO:0006120), which are crucial for maintaining cellular integrity under stress conditions by enhancing energy metabolism and redox balance [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eqPCR expression of candidate detoxification genes\u003c/h2\u003e\u003cp\u003eFor normalization, ten (10) detoxification genes, including cytochrome P450s (\u003cem\u003eCYP6M2, CYP6M3, CYP6P1, CYP6P3, CYP6P4, CYP6Z1, CYP6Z2, and CYP9K1\u003c/em\u003e), glutathione S-transferase \u003cem\u003eGSTe2\u003c/em\u003e, and glycosyltransferase \u003cem\u003eUGT308G1\u003c/em\u003e, were assessed via two housekeeping genes (Elongation Factor - \u003cem\u003eEF\u003c/em\u003e and ribosomal protein S7 - \u003cem\u003eRSP7\u003c/em\u003e). The gene expression results are described in Supplementary File 1, Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.2. The overall transcriptomic profile obtained through quantitative reverse transcription PCR (qRT‒PCR) supported the RNA sequencing results, revealing a significant positive correlation (r\u0026thinsp;=\u0026thinsp;0.909, p\u0026thinsp;=\u0026thinsp;0.0003) between the two methods.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eTranscriptional signatures of selective sweeps associated with increased insecticide resistance in An. gambiae\u003c/h3\u003e\n\u003cp\u003eThe signatures of the selective sweeps were detected via transcriptome-wide association analysis, which identified candidate nonsynonymous mutations associated with resistance escalation in \u003cem\u003eAn. gambiae\u003c/em\u003e. Given the low level of differentiation (see Supplementary file 1, Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.3) between field mosquitoes irrespective of their phenotype, owing to their similar genetic background [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], a hybrid line was generated by crossing Mangoum mosquitoes with susceptible Kisumu strains to allow better segregation of resistance alleles according to phenotype. Five pairwise fixation index (F\u003csub\u003eST\u003c/sub\u003e) comparisons in 30,000 SNP windows revealed several peaks of genetic divergence, including the voltage-gated sodium channel (VGSC) and glutathione S-transferases (GSTs), which presented elevated peaks with F\u003csub\u003eST\u003c/sub\u003e \u0026gt;0.4 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Other minor peaks were detected around the CYP6 P450 cluster, resistance to pyrethroid 1 (rp1) loci (including CYP6P3) on the 2R chromosome, and CYP9K1 P450 on the X chromosome.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWe focused our analysis on previous candidate resistance loci in \u003cem\u003eAn. gambiae\u003c/em\u003e [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], Tajima\u0026rsquo;s D and nucleotide diversity were calculated to pinpoint selective sweeps. In Mangoum, resistance escalation was linked to a decline in Tajima\u0026rsquo;s D and reduced genetic diversity at the VGSC and rp1 loci, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), indicating that strong positive selection was associated with increasing resistance to pyrethroids (Tajima\u0026rsquo;s D \u0026lt; -1). The \u003cem\u003erp2\u003c/em\u003e locus, which includes UGT308G1, sensory appendage protein, chemosensory proteins and some cytochrome P450s (CYP6Z and CYP6M), was not subjected to strong selection.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003ePolymorphisms associated with resistance escalation in Mangoum\u003c/h3\u003e\n\u003cp\u003eOur analysis focused on candidate gene families associated with pyrethroid resistance, including cytochrome P450s, carboxylesterases, glucosyltransferases, glutathione S-transferases, ABC transporters, cuticular, odorant binding, and voltage-gated sodium channels. Within the rp1 locus, three nonsynonymous mutations were identified, all of which presented similar allele frequencies across phenotypes: Cys282Tyr in CYP6P1, Glu205Asp in CYP6P3, and Thr288Ser in CYP6AA1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Moreover, a significant SNP (Thr128Ala) was detected in CYP12F2 on chromosome 3R, showing fixation in both field and resistant hybrid strains compared with the Kisumu laboratory strain and unexposed hybrids, where the allele frequencies were 6% and 21%, respectively (p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Similarly, the Leu571Phe and Arg1173Gln mutations in the cuticular gene AGAP010302 (located on chromosome 3R) were fixed in the field populations. Several candidate resistance-related mutations were also identified on chromosome 2L, including Ala116Thr and Thr84Ala in the glucosyltransferase UGT302J1 and Arg425Lys in the carboxylesterase COEAE2E.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eFunctional validation of the role of the CYP6Z gene family in pyrethroid resistance via RNA interference\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe effect of the knockdown of the CYP6Z gene family on the mortality of \u003cem\u003eAn. gambiae\u003c/em\u003e strains exposed to pyrethroids were examined via RNA interference (RNAi) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The efficiency of the dsRNA was assessed by qPCR, which revealed successful reductions in the expression levels of the CYP6Z1, CYP6Z2, CYP6Z3, and CYP6Z4 genes of 90.17% (p\u0026thinsp;=\u0026thinsp;0.005), 94.73% (p\u0026thinsp;=\u0026thinsp;0.001), 79.59% (p\u0026thinsp;=\u0026thinsp;0.03), and 60.97% (p\u0026thinsp;=\u0026thinsp;0.09), respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA).\u003c/p\u003e\u003cp\u003eRNAi-mediated knockdown of the CYP6Z genes confirmed their role in resistance escalation in \u003cem\u003eAn. gambiae\u003c/em\u003e mosquitoes. Notably, mortality rates were significantly greater in mosquitoes injected with dsCYP6Z than in noninjected controls across various exposure doses. Bioassays revealed that, after 24 hours of exposure to permethrin or alphacypermethrin, mortality was significantly greater in mosquitoes treated with dsZ2 (22.7% \u0026plusmn; 1.4; p\u0026thinsp;=\u0026thinsp;0.004 and 22.5% \u0026plusmn; 7.2; p\u0026thinsp;=\u0026thinsp;0.04, respectively) than in noninjected mosquitoes (9.5% \u0026plusmn; 2.6) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003eIncreasing the pyrethroid dose to 5x and 10x the diagnostic dose emphasized the impact of CYP6Z gene knockdown on resistance reduction to permethrin and alphacypermethrin. Significant differences in mortality were observed among mosquitoes injected with dsCYP6Z1, dsCYP6Z2, and dsCYP6Z3 when exposed to both 5x and 10x doses of the insecticides compared with noninjected mosquitoes subjected to the same insecticide concentrations (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). For dsCYP6Z4, a significant difference was detected only in mosquitoes exposed to alphacypermethrin at a 10x dosage, with mortality rates of 90.3% \u0026plusmn; 1.9% in injected mosquitoes versus 79.9% \u0026plusmn; 1.4% in noninjected controls.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eEmerging evidence indicates significant pyrethroid resistance among malaria vectors, particularly \u003cem\u003eAn. gambiae\u003c/em\u003e, resulting in reduced effectiveness of pyrethroid-treated nets [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The increasing levels of resistance across endemic areas highlight the need to strengthen vector control strategies, especially in agricultural hotspots [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Understanding the transcriptomic profile associated with increasing permethrin resistance in \u003cem\u003eAn. gambiae\u003c/em\u003e populations from such hotspots, along with a description of the main resistance mechanisms, is crucial for developing effective malaria vector control interventions.\u003c/p\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eKey Mechanisms Underpinning Pyrethroid Resistance in Anopheles gambiae in Cameroon\u003c/h2\u003e\u003cp\u003eTranscriptomic analysis of resistant \u003cem\u003eAn. gambiae\u003c/em\u003e mosquitoes from Mangoum revealed the primary mechanisms driving pyrethroid resistance. Notably, detoxification genes such as cytochrome P450s, carboxylesterases, glutathione S-transferases, glycosyltransferases, and ABC-type xenobiotic transporters were overexpressed, which aligns with prior reports [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan additionalcitationids=\"CR43\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. In particular, the overexpression of the gene \u003cem\u003eUGT308G1\u003c/em\u003e emerged as a crucial factor linked to pyrethroid resistance. The orthologs of this gene have also been shown to sustain high expression levels after insecticide exposure in \u003cem\u003eAn. coluzzii\u003c/em\u003e [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] and \u003cem\u003eAn. funestus\u003c/em\u003e [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. However, recent studies have indicated that knocking down this gene may not significantly impact susceptibility to pyrethroids [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eTranscriptomic Insights into Superresistant Mosquitoes from Mangoum\u003c/h2\u003e\u003cp\u003eOur study identified numerous differentially expressed genes in superresistant mosquitoes, including members of the CYP6Z family, particularly CYP6Z1, CYP6Z2, CYP6Z3, and CYP6Z4, which are integral to phase 1 detoxification processes [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. The increased susceptibility to pyrethroids following CYP6Z knockdown through RNA interference-induced knockdown experiments underscores their role in increased resistance. Consistent with previous research, elevated CYP6Z1 levels are associated with improved detoxification capabilities, enabling mosquitoes to neutralize toxic compounds more effectively [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Although it was recognized in 2013 that CYP6 enzymes such as CYP6Z1 and CYP6Z2 contribute secondarily to the metabolism of pyrethroid metabolites and other xenobiotics [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], \u003cem\u003eCYP6Z1\u003c/em\u003e has recently been shown to play a significant role in cross-resistance to both carbamates and pyrethroids in \u003cem\u003eAnopheles funestus\u003c/em\u003e [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn addition to the genes encoding detoxification enzymes, our study revealed that the SAP2 gene was significantly upregulated in mosquitoes surviving 10\u0026times; pyrethroid exposure compared with those exposed to 1\u0026times; pyrethroid, indicating a potential role in enhancing insecticide resistance. This observation is corroborated by Gadji et al. (2025), who reported elevated expression of chemosensory proteins (CSPs) and sensory appendage proteins (SAPs) in resistant populations from Ghana and Uganda, as well as in mosquitoes surviving 10\u0026times; pyrethroid exposure across Africa. Our findings also parallel those of Ingham et al. (2020), who described similar expression trends in \u003cem\u003eAn. gambiae\u003c/em\u003e and \u003cem\u003eAn. coluzzii\u003c/em\u003e populations in West Africa [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. These findings suggest that SAPs may play a critical role in driving behavioral and physiological adaptations that increase insecticide resistance in Anopheles mosquitoes, particularly under high-intensity pyrethroid exposure.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eProtein synthesis and the oxidative stress response can exacerbate resistance in An. gambiae\u003c/h2\u003e\u003cp\u003eOur findings reveal a robust connection to protein synthesis pathways and the ability of \u003cem\u003eAn. gambiae\u003c/em\u003e. This pathway has been established to be essential for rapid adaptation to stress conditions [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Moreover, the increased expression levels of arginosuccinate lyase highlight significant alterations in the urea cycle, which may be critical for managing nitrogen waste, especially under conditions of increased metabolic throughput [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. These findings are consistent with previous results reported by Riveron and colleagues (2014), who reported a significant increase in the expression of argininosuccinate lyase (\u003cem\u003eASL\u003c/em\u003e) in populations of \u003cem\u003eAn. funestus\u003c/em\u003e in Zambia and Malawi, raising concerns regarding resistance to insecticides used in malaria control [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. The role of this gene in resistance escalation in \u003cem\u003eAn. funestus\u003c/em\u003e across Africa [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eSignatures of selective sweeps are linked to increases in resistance\u003c/h2\u003e\u003cp\u003eThese findings suggest that resistance escalation is driven by strong positive selection, as evidenced by the decline in Tajima's D and reduced genetic diversity at the VGSC and rp1 loci, respectively, indicating that an excess of rare alleles are potentially linked to resistance in these regions, which is consistent with previous reports [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. This pattern reflects a rapid increase in resistance-conferring mutations within the population, highlighting the intensity of the selective pressure exerted by pyrethroid use in the field, leading to the fixation of markers such as L1014F-\u003cem\u003ekdr\u003c/em\u003e [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e] and E205D-\u003cem\u003eCYP6P3\u003c/em\u003e [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite a slight peak of genetic divergence observed around the \u003cem\u003eCYP9K1\u003c/em\u003e cluster, no signs of selective sweeps were detected in this region in \u003cem\u003eAn. gambiae\u003c/em\u003e from Mangoum. Although allelic variation at \u003cem\u003eCYP9K1\u003c/em\u003e appears to have a minimal effect on pyrethroid resistance in \u003cem\u003eAn. gambiae.\u003c/em\u003e Some studies performed in \u003cem\u003eAn. funestus\u003c/em\u003e [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e] have shown that this locus is strongly linked to allelic variant-potentiated resistance. A previous work identified a nearly fixed X-linked haplotype containing \u003cem\u003eCYP9K1\u003c/em\u003e in \u003cem\u003eAn. coluzzii\u003c/em\u003e from Mali [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], suggesting that this locus could also be involved in this species. Further research, including studies on \u003cem\u003eAn. coluzzii\u003c/em\u003e, is needed to better understand the role of \u003cem\u003eCYP9K1\u003c/em\u003e allelic variation in resistance within \u003cem\u003eAn. gambiae s.l\u003c/em\u003e.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study elucidated the transcriptomic drivers of super-resistance to pyrethroids in a hotspot of intense pesticide use for agriculture in Cameroon, revealing multiple resistance routes, including increased detoxification, chemosensory, and cuticular resistance, and evidence of epigenetics. Specifically, the role of a cluster of CYP6Z P450s was validated, and the exceptionally high expression of UGT308G1 was identified as a key driver of the observed resistance, underscoring the contribution of UGTs to pyrethroid resistance alongside P450s and knockdown resistance.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eMosquito collection and rearing\u003c/h2\u003e\u003cp\u003eMosquitoes, both adults and larvae, were collected in Mangoum (latitude 5\u0026deg;29\u0026prime;09.2\u0026prime;\u0026prime; N, longitude 10\u0026deg;35\u0026prime;20.8\u0026prime;\u0026prime; E), a region in western Cameroon known for its extensive agricultural activities and the associated proliferation of Anopheles breeding sites. Blood-fed adult female mosquitoes were collected indoors, with the consent of the property owners, between 06:00 and 10:00 AM via electric aspirators (Prokopack Aspirator, model 1419, John W. Hock Company, Gainesville, FL, USA). Larval samples were obtained from field irrigation reservoirs and stagnant water bodies. This collection effort spans the period from 2020\u0026ndash;2023. The collected adult mosquitoes and larvae were then packaged and transported to the insectarium at the Center of Research in Infectious Diseases (CRID), where they were identified following the methodology outlined by Tepa et al. (2022).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eMosquito selection\u003c/h2\u003e\u003cp\u003eF\u003csub\u003e0\u003c/sub\u003e and F\u003csub\u003e1\u003c/sub\u003e female \u003cem\u003eAn. gambiae s.s.\u003c/em\u003e were used to select for resistant mosquitoes. Susceptibility tests were conducted following the WHO protocol for adults [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. All insecticide-impregnated papers were obtained from the WHO/Vector Control Research Unit (VCRU) at the University of Science Malaysia (Penang, Malaysia). Four replicates, each consisting of 20\u0026ndash;25 females aged 3\u0026ndash;5 days, were used per tube. To identify resistant and super-resistant mosquitoes, bioassays were performed using permethrin and alphacypermethrin at concentrations of 1x (0.75%), 5x (3.75%), and 10x (7.5%). The mosquitoes were exposed to insecticide-impregnated papers for 1 hour, with untreated papers serving as systematic controls. The results of these bioassays have been reported previously [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Given the high level of pyrethroid resistance observed in Mangoum, this study also involved the analysis of candidate genes in F\u003csub\u003e4\u003c/sub\u003e generation offspring from crosses between Mangoum and Kisumu mosquitoes. This approach allowed for the examination of resistance-related genetic variance within a consistent genetic background. The crossing line was established between selected Mangoum-resistant strains and laboratory-susceptible strains (Kisumu) as described by Wondji et al. (2007) and Cattel et al. (2020) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. To obtain distinct phenotypes, individual F\u003csub\u003e4\u003c/sub\u003e progenies from these crosses were segregated on the basis of their resistance phenotype by exposing 3- to 5-day-old females to 0.75% permethrin or alphacypermethrin at a lethal time of 80% (LT80). After 24 hours, the mosquitoes that remained alive were classified as resistant. Surviving and deceased mosquitoes were subsequently preserved in RNAlater\u0026reg; and silica gel, respectively, for further molecular analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eRNA extraction, library preparation, and sequencing\u003c/h2\u003e\u003cp\u003eTotal RNA was extracted from three batches of ten mosquitoes each, as well as from the Kisumu-sensitive laboratory strain, via the Arcturus PicoPure RNA isolation kit (Life Technologies, Carlsbad, CA, USA) according to the manufacturer\u0026rsquo;s protocol. The extracted RNA was sent to Novogene (25 Cambridge Science Park, Milton Road, Cambridge, CB4 0FW, United Kingdom) for further analysis, where library preparation and sequencing were performed in accordance with their established protocols. Pools of eight libraries per line were sequenced via 2 \u0026times; 150 bp paired-end sequencing via the Illumina NovaSeq 6000 Sequencing System.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eData analysis of differential gene expression\u003c/h2\u003e\u003cp\u003eThe workflow started with quality control of each FASTQ file via the FastQC algorithm [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e], and the results were compiled into a report via MultiQC [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. HISAT2 was employed to map Illumina reads to the \u003cem\u003eAnopheles gambiae\u003c/em\u003e genome. Differential expression analysis among permethrin-resistant mosquitoes, unexposed mosquitoes, and susceptible mosquitoes was conducted via the DESeq2 package [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e] in R on the basis of the counts generated from alignment via the featureCounts tool [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. A triangular analysis of the transcriptional profile among mosquito pools was performed to elucidate potential genes responsible for resistance in Mangoum (Man) mosquitoes: resistant versus susceptible (R-S) to highlight differences between resistant and susceptible strains; controls versus susceptible (C-S) to reveal genes constitutively expressed in field mosquitoes; and resistant versus control (R-C) to identify genes induced by insecticide exposure.\u003c/p\u003e\u003cp\u003eTo further highlight candidate genes associated with exacerbated resistance, gene expression was also analyzed in pools of resistant mosquitoes exposed to 5x or 10x the diagnostic dose of permethrin (0.75%). A similar triangular comparison was conducted: resistant at 10x vs. resistant at 1x (10x_vs_1x), resistant at 5x vs. resistant at 1x (5x_vs_1x), and resistant at 10x vs. resistant at 5x (10x_vs_1x). The significance of gene expression levels was assessed via a moderated t test, a modification of the unpaired t test. P values were adjusted for multiple testing via Story\u0026rsquo;s false discovery rate (FDR) approach with bootstrapping. Differentially expressed genes were defined as those with an FDR-adjusted p value of less than 5% and an expression level of greater than or equal to 2 for comparisons between field mosquitoes and laboratory strains. For comparisons among field mosquitoes, the threshold for gene expression levels was set at 1.5 because of the generally low degree of differentiation observed in field populations. The expression levels of the candidate genes are displayed via a volcano plot. Functional enrichment analysis was conducted via gene annotations derived from Blast2GO software via the nonredundant database from NCBI. The significance of the results was assessed via Fisher's exact test with adjusted p values, considering a fold enrichment (FE) greater than 1 as indicative of enhanced enrichment in the candidate genes, with significance defined at p values less than 0.05.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003eQuantitative reverse transcription PCR\u003c/h2\u003e\u003cp\u003eFor the validation of gene expression via qRT‒PCR, cDNA was synthesized from RNA purified through reverse transcription‒PCR via the SuperScript III kit (Invitrogen, Waltham, MA, USA) alongside the oligo-dT20 and RNase H kit (New England Biolabs, Ipswich, MA, USA) in a total reaction volume of 20 \u0026micro;L. The expression profiles of 15 genes (see Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) were assessed via quantitative reverse transcription‒PCR (qRT‒PCR) in mosquitoes that survived exposure to permethrin at concentrations of 1\u0026times;, 5\u0026times;, and 10\u0026times; in comparison with unexposed mosquitoes and the susceptible Kisumu strain. Two housekeeping genes\u0026mdash;Elongation Factor and Ribosomal Protein S7\u0026mdash;were used for normalization. Amplification was carried out on an Agilent Mx3005 qRT‒PCR thermal cycler (Santa Clara, CA, USA) as previously described [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Relative expression was calculated via the 2^-ΔΔCt method [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e] and visualized via R Studio software.\u003c/p\u003e\u003cp\u003eTo evaluate the correlation between the RNA sequencing and qRT‒PCR results, a Pearson correlation test was performed [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. The relationships between sequencing results, which are based on the number of reads for each gene, and qRT‒PCR results, which are based on the relative abundance of these genes, were assessed. A correlation coefficient close to 1 indicates a strong positive correlation between the two methods.\u003c/p\u003e\u003cp\u003e\u003cb\u003eIn vivo\u003c/b\u003e \u003cb\u003evalidation of the role of CYP6Z family genes in increasing insecticide resistance in\u003c/b\u003e \u003cb\u003eAn. gambiae\u003c/b\u003e \u003cb\u003ethrough RNAi\u003c/b\u003e\u003c/p\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003eDouble-stranded RNA synthesis\u003c/h2\u003e\u003cp\u003eTo investigate the role of the \u003cem\u003eUGT\u003c/em\u003e candidate gene in insecticide resistance, RNA silencing was performed in resistant mosquitoes from Mangoum, and the impact of gene knockdown on mosquito susceptibility was evaluated following a previously (Kouamo et al., 2021)described protocol. To achieve this aim, double-strand RNAs specific to each target candidate gene were synthesized after amplification via a KAPA Taq polymerase kit (Kapa Biosystems, Wilmington, MA, USA) from complementary DNA with primers designed via the online RNAi tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.dkfz.de/signaling2/rnai/\u003c/span\u003e\u003cspan address=\"http://www.dkfz.de/signaling2/rnai/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The T7 promoter sequence was subsequently added to both forward and reverse PCR primers (see Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Briefly, after amplification, the PCR product was purified via a QIAquick gel extraction kit (QIAGEN, Hilden, Germany) according to the manufacturer's instructions, and the purified PCR amplicons were then used to synthesize double-stranded RNA (dsRNA) with the MEGAscript\u0026trade; RNAi T7 kit (Thermo Fisher Scientific, Inc., Renfrewshire, UK). The transcription reaction mixture included 6 \u0026micro;l of nuclease-free water, 4 \u0026micro;l each of ATP, GTP, CTP, and UTP, 4 \u0026micro;l of 10x buffer, and the enzyme; 10 \u0026micro;l of the purified PCR product was added, and the mixture was incubated at 37\u0026deg;C for 16 hours in a thermocycler. To remove the DNA template, 1 \u0026micro;l of RNase A-free DNA was added to 40 \u0026micro;l of the reaction mixture and incubated at 37\u0026deg;C. The dsRNA was purified to remove the protein-free nucleotides and dsRNA degradation products via a MEGAclear kit from Thermo Fisher (Thermo Fisher Scientific, Inc., Renfrewshire, UK) according to the manufacturer's protocol. Following purification, the integrity of the synthesized dsRNA was confirmed by electrophoresis on a 1% agarose gel. The dsUGT and dsGFP were precipitated with ethanol, eluted with Sigma water to a final concentration of 3 \u0026micro;g/\u0026micro;l and kept at -20\u0026deg;C.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003eMosquito Injection and Susceptibility Bioassays\u003c/h2\u003e\u003cp\u003eTo knockdown the expression of CYP6Z1, CYP6Z2 and CYP6Z3 in \u003cem\u003eAn. gambiae from Mangoum\u003c/em\u003e and evaluated the impact on insecticide susceptibility, dsCYP6Zs were injected into the thorax of 2- to 3-day-old female F\u003csub\u003e1\u003c/sub\u003e mosquitoes via a nanoinjector (Nanoinject; Drummond, Burton, OH, USA) as previously described [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. Briefly, mosquitoes were anesthetized with CO2 and injected with 69 nL of dsCYP6Zs, and dsGFP (green fluorescence protein) was used as a control. Mosquitoes were kept in the insectary to allow gene silencing to occur. Four days post-injection, four replicates of 20\u0026ndash;25 mosquitoes were injected with dsCYP6Zs, and the controls were injected with dsGFP. The noninjected mosquitoes were exposed to permethrin (0.75%) or alpha-cypermethrin (0.05%) for 1 hour in accordance with WHO testing guidelines [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. After exposure, the mosquitoes were transferred to holding tubes containing sugar, and mortality was recorded 24 hours later. Mosquitoes injected with dsGFP and those that were not injected served as controls. qPCR was performed to validate the knockdown of CYP6Zs via RNA extracted from dsCYP6Z-injected mosquitoes on the day post-injection compared with the control mosquitoes not injected following the protocol described above.\u003c/p\u003e\u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\u003ch2\u003ePolymorphism Analysis in Pyrethroid-Resistant Mosquito Populations\u003c/h2\u003e\u003cp\u003eTo detect single nucleotide polymorphisms (SNPs) in the main resistance genes of interest potentially associated with resistance, we used Samtools mpileup and Varscan 2 [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. The generated variant file was annotated and filtered via the Snpeff tool [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. Only chromosomes 2, 3 and X were included in graphical representations, with only the coding regions of the genes considered owing to the nature of the messenger RNA used as the template in RNA-seq.\u003c/p\u003e\u003cp\u003eThe analysis of sample clustering according to phenotype was performed via Plink 1.9 (Supplementary file 1, Figure S3). Potentially resistance-associated loci were analyzed via the Fixation Index (FST) test, which measures genetic variation between subpopulations relative to the total variation within a dataset. Signs of selective sweeps were assessed via Tajima's D test and nucleotide diversity, with a focus on genes belonging to loci associated with resistance in \u003cem\u003eAn. gambiae\u003c/em\u003e [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. All the statistical parameters were estimated via Grenedalf software [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo identify SNPs significantly linked to pyrethroid resistance, we applied two distinct methodologies, as outlined by Wondji et al. (2022). The first approach relied on stringent differential allele frequency analysis, where a variant was deemed significant if its allele frequency ranged between 0\u0026ndash;10% across all four Kisumu laboratory strains but was 50\u0026ndash;100% in unexposed field strains. The second method examined the statistical association of each variant with permethrin resistance, employing an unpaired t test to compare allele frequencies between groups. The resulting \u0026ndash;Log10 P value was then used to construct Manhattan plots for each chromosome, visualizing significant genomic associations. The allele frequencies of some SNPs detected in candidate genes potentially involved in insecticide resistance are represented in a heatmap.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSequence data that support the findings of this study have been deposited in the European Nucleotide Archive with the primary accession code PRJEB97187. The data analyzed in this study are available within the article and its Supplementary Information files. Further details are available from the authors upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\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 Charles S. Wondji (217188/Z/19/Z) and a Bill and Melinda Gates Foundation grant to CSW (INV-006003).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAT conducted field sampling, sample processing, experimental work, RNA-seq analysis, data interpretation, and manuscript drafting. MK provided technical support and performed functional genomic experiments and analysis. JAKO and MT contributed laboratory support and participated in manuscript writing. CAP contributed to manuscript preparation. CSW acquired funding, conceived and designed the study, led aspects of the RNA-seq analysis, and contributed to manuscript writing. All authors reviewed and approved the final version for submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank Herv\u0026eacute; Raoul Tazokong, Mahamat Gadji, Jack Hearn, Cedrique Noutchih, Helen Irving and Murielle Wondji for their valuable collaboration throughout the study, including support with experimental procedures and data interpretation. Their expertise and dedication greatly contributed to the successful completion of this work. The authors also gratefully acknowledge the facilities and logistical support provided by the Center for Research in Infectious Diseases (CRID), which enabled the smooth execution of both field and laboratory activities. We further extend our appreciation to the technical staff and field team whose commitment facilitated sample collection and laboratory analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003e Medical Entomology Department, Centre for Research in Infectious Diseases (CRID), Yaound\u0026eacute;, Cameroon; \u003csup\u003e2 \u003c/sup\u003eDepartment of Biochemistry, Faculty of Medicine and Biomedical Sciences University of Yaound\u0026eacute; 1, Yaound\u0026eacute;, Cameroon; \u003csup\u003e3 \u003c/sup\u003eInternational Institute of Tropical Agriculture, Yaound\u0026eacute;, Cameroon; \u003csup\u003e4\u003c/sup\u003e Vector Biology Department, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool L3 5QA, UK; \u003csup\u003e\u0026pound;\u003c/sup\u003eCurrent address: Emerging Pathogens Institute, Department of Infectious Diseases and Immunology, College of Veterinary Medicine, University of Florida, Gainesville, FL 32610, USA. *Correspondence: [email protected] (A.T.); [email protected] (C.S.W.)\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWHO \u003cem\u003eWorld Malaria Report 2023\u003c/em\u003e; 2023;\u003c/li\u003e\n\u003cli\u003eBhatt, S.; Weiss, D.J.; Cameron, E.; Bisanzio, D.; Mappin, B.; Dalrymple, U.; Battle, K.E.; Moyes, C.L.; Henry, A.; Eckhoff, P.A.; et al. 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Board Andreas Baxevanis Al\u003c/em\u003e \u003cstrong\u003e2013\u003c/strong\u003e, \u003cem\u003e44\u003c/em\u003e, 15.4.1-15.4.17, doi:10.1002/0471250953.bi1504s44.\u003c/li\u003e\n\u003cli\u003eCingolani, P.; Platts, A.; Wang, L.L.; Coon, M.; Nguyen, T.; Wang, L.; Land, S.J.; Lu, X.; Ruden, D.M. A Program for Annotating and Predicting the Effects of Single Nucleotide Polymorphisms, SnpEff. \u003cem\u003eFly (Austin)\u003c/em\u003e \u003cstrong\u003e2012\u003c/strong\u003e, \u003cem\u003e6\u003c/em\u003e, 80\u0026ndash;92, doi:10.4161/fly.19695.\u003c/li\u003e\n\u003cli\u003eCzech, L.; Spence, J.P.; Exp\u0026oacute;sito-Alonso, M. Grenedalf: Population Genetic Statistics for the next Generation of Pool Sequencing. \u003cstrong\u003e2024\u003c/strong\u003e.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gics","sideBox":"Learn more about [BMC Genomics](http://bmcgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gics","title":"BMC Genomics","twitterHandle":"#BMCGenomics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Anopheles gambiae, malaria, pyrethroids, resistance escalation, glycosyltransferases, transcriptomic analysis, CYP6Zs","lastPublishedDoi":"10.21203/rs.3.rs-7592244/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7592244/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMalaria control efforts are stagnating, primarily due to increasing pyrethroid resistance in malaria vectors. Elucidating its molecular basis is essential for effective vector control. Here, via RNA-seq-based transcriptomic analyses, we elucidated the molecular basis of permethrin super-resistance in \u003cem\u003eAnopheles gambiae\u003c/em\u003e from an agricultural setting in Cameroon with intense pesticide usage.\u003c/p\u003e\u003cp\u003eThe present study revealed significant overexpression of detoxification genes, including cytochrome P450, carboxylesterases, glutathione S-transferases, glycosyltransferases, and ABC-type xenobiotic transporter genes associated with insecticide resistance. Among the genes consistently overexpressed when surviving (R) and unexposed (C) mosquitoes were compared with the susceptible Kisumu strain (S), the glycosyltransferase UGT308G1 (AGAP007990) gene presented the greatest fold change [FC105.1 (C-S); 50.8 (R-S)]. Similarly, three CY6Z P450 genes were highly expressed [CYP6Z3 41\u0026thinsp;\u0026lt;\u0026thinsp;FC\u0026thinsp;\u0026lt;\u0026thinsp;63; CYP6Z2 17\u0026thinsp;\u0026lt;\u0026thinsp;FC\u0026thinsp;\u0026lt;\u0026thinsp;29; CYP6Z1 6\u0026thinsp;\u0026lt;\u0026thinsp;FC\u0026thinsp;\u0026lt;\u0026thinsp;12)]. Evidence of cuticular resistance was observed with the overexpression of several cuticular protein genes and related P450s, including CYP4G16/17. Signatures of selective sweeps were detected around the L1014F-kdr and E205D-CYP6P3 mutations. Moreover, the expression of key genes, including the protease, sensory appendage protein (SAP2), argininosuccinate lyase, P450 (CYP6Z, CYP6M2), and GST epsilon genes and cuticular proteins, was significantly upregulated in mosquitoes that survived at a 10-fold diagnostic concentration. RNA interference\u0026ndash;induced knockdown supported the contribution of the CYP6Z gene family to resistance escalation through significant restoration of susceptibility when mosquitoes were exposed to increasing insecticide doses following gene silencing.\u003c/p\u003e\u003cp\u003eOverall, this study provides valuable insights into the genetic mechanisms driving the aggravation of resistance in \u003cem\u003eAn. gambiae\u003c/em\u003e, highlighting the CYP6Z gene family as one of the key contributors.\u003c/p\u003e","manuscriptTitle":"Insights from transcriptomic profiling identify the CYP6Z gene family as key drivers of pyrethroid resistance escalation in Anopheles gambiae from Cameroon","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-23 08:20:01","doi":"10.21203/rs.3.rs-7592244/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-07T06:16:43+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-05T15:24:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-05T11:38:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"7290961305929402714461540505583473728","date":"2025-11-03T18:26:23+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-31T09:33:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"308432565969024721051254614516181110104","date":"2025-10-28T05:38:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"271472393074611964833188067673940206940","date":"2025-10-18T04:54:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"38438963568738156451071098997631703430","date":"2025-10-16T10:15:30+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-06T03:21:09+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-19T05:08:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-17T13:34:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-17T13:34:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Genomics","date":"2025-09-11T12:55:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gics","sideBox":"Learn more about [BMC Genomics](http://bmcgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gics","title":"BMC Genomics","twitterHandle":"#BMCGenomics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6e7ba72e-3939-4c60-9262-a0c95b6f8b85","owner":[],"postedDate":"September 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-30T16:27:51+00:00","versionOfRecord":{"articleIdentity":"rs-7592244","link":"https://doi.org/10.1186/s12864-026-12743-6","journal":{"identity":"bmc-genomics","isVorOnly":false,"title":"BMC Genomics"},"publishedOn":"2026-03-23 16:08:57","publishedOnDateReadable":"March 23rd, 2026"},"versionCreatedAt":"2025-09-23 08:20:01","video":"","vorDoi":"10.1186/s12864-026-12743-6","vorDoiUrl":"https://doi.org/10.1186/s12864-026-12743-6","workflowStages":[]},"version":"v1","identity":"rs-7592244","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7592244","identity":"rs-7592244","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}