Signature of resistance gene evolution and pyrethroid resistance escalation in the major malaria vector Anopheles funestus across the Kenyan Rift Valley

Signature of resistance gene evolution and pyrethroid resistance escalation in the major malaria vector Anopheles funestus across the Kenyan Rift Valley | 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 Signature of resistance gene evolution and pyrethroid resistance escalation in the major malaria vector Anopheles funestus across the Kenyan Rift Valley David P. Tchouassi, Amine M. Mustapha, Gilbert Rotich, Trizah K. Milugo, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8224847/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Background Landscape features such as the Rift Valley (RV) can restrict gene flow in malaria vectors and influence resistance patterns. Here, we assessed resistance alleles and profiles in Anopheles funestus s.s. populations across Kenyan malaria-endemic regions separated by the RV. Methods Anopheles funestus s.s. populations in western, coastal and Kerio Valley (KV, within the RV) were assessed for key resistance markers and their association with Plasmodium sporozoite infection. Phenotypic resistance using F1 progeny was also assessed. Results The 4.3Kb-SV and G454A-Cyp9k1 alleles were nearly fixed in western Kenya but declined towards the RV and coast, whereas L119F-GSTe2 increased across a west-KV-coast gradient with a novel haplotype distinct from known African variants detected at the coast. There were lower odds of Plasmodium infection in mosquitoes with L119F-GSTe2-RR than RS genotype (OR = 0.2, p = 0.046). Likewise, mosquitoes harboring the R allele of the 4.3kb marker had higher Plasmodium infection rates than the S allele (OR = 5.7, p = 0.049). An. funestus populations exhibited a high degree of pyrethroid resistance with intensity higher in KV compared to western Kenya, a traditional malaria hotspot. Pre-exposure to PBO increased mortality for type II (deltamethrin, alpha-cypermethrin), than I (permethrin) pyrethroids, yet recovery remained lower in KV, suggesting non-P450-mediated resistance. Coastal mosquitoes showed extreme permethrin resistance (< 10% mortality at 10× dose). DDT resistance was widespread, while all populations remained fully susceptible to bendiocarb, pirimiphos-methyl, clothianidin, and chlorfenapyr. Conclusions Region-specific selection may drive varying resistance profiles in An. funestus across the RV, with implications for malaria transmission and insecticide resistance management. Malaria transmission pyrethroid resistance escalation An. funestus Rift Valley metabolic resistance resistance markers geographic barriers Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Background Malaria remains a major vector-borne disease of significant medical and public health importance across much of sub-Saharan Africa (SSA). In 2023, the region accounted for 94% of the 263 million malaria cases reported [ 1 ]. Despite the widespread implementation of integrated control measures, including the use of insecticide-treated nets (ITNs), clinical case management such as proper diagnosis and treatment using artemisinin-based combination therapies (ACTs) [ 2 ], malaria incidence remains high in most African countries [ 1 ]. In Kenya alone, over three million cases were reported in 2023, representing a modest decline of 3.6% from the 3.42 million cases recorded in 2022 [ 1 ]. This persistently high burden highlights gaps in the understanding of the drivers of sustained transmission. Changes in vector behavior and ecological heterogeneity may be contributing to ongoing transmission dynamics and undermining the effectiveness of current control measures. Humans become infected with malaria parasites through the bites of parasite-infected female Anopheles mosquitoes, which vary in their vectorial capacity – potential to transmit pathogens. Among the major malaria vectors in SSA is Anopheles funestus s.s. (referred herein as An. funestus ), a species characterized by high susceptibility to Plasmodium parasites, a strong anthropophilic (human-biting) preference and prolonged adult longevity [ 3 , 4 ]. In the wake of declining An. gambiae populations likely due to up-scale of ITNs, An. funestus has emerged as a dominant malaria vector across much of East Africa, including several regions in Kenya [ 3 – 5 ]. Furthermore, An. funestus has the tendency to alter behavior, highly adaptive – breeding throughout the year and can rapidly develop resistance to insecticides [ 6 – 8 ]. Insecticide resistance poses a major challenge to the long-term effectiveness of current vector control tools and malaria control strategies [ 9 ]. Across much of Africa, An. funestus populations have developed resistance to pyrethroids, the primary class of insecticides used in public health [ 10 – 12 ]. Moreover, there is growing evidence of resistance extending to other insecticide classes recommended by the World Health Organisation (WHO) [ 13 ]. The worsening situation of insecticide resistance is currently further exacerbated by the growing threat of resistance escalation - characterized by the ability of mosquito populations to survive very high doses of insecticides, thereby reducing the efficacy of control interventions[ 10 – 12 ]. In An. funestus , resistance is predominantly mediated by overexpression of key metabolic genes, particularly those encoding cytochrome P450s enzymes (CYPs), including CYP6P9a/b, C YP6P4a/b , and CYP9K1, as well as glutathione-transferase epsilon 2 ( GSTe2 ) [ 14 – 16 ]. Moreover, genetic variants within key metabolic genes and structural variants (SVs) such as the 6.5-KbSV and 4.3-Kb SV have also been implicated [ 17 , 18 ]. Additionally, recent studies have identified target site mutations, conferring localized knockdown resistance ( kdr) [ 19 ] and non-coding RNAs[ 20 ] in resistance mechanisms. In Kenya, reduced susceptibility of An. funestus to pyrethroids have been documented in malaria endemic regions, such as coastal and western regions [ 5 , 21 , 22 ]. However, the underlying molecular mechanisms remain poorly understood, underscoring the need for comprehensive investigations into the evolution and spread of insecticide resistance An. funestus populations across diverse ecological settings. In Africa, Anopheles funestus populations are widely distributed and exhibit significant local genetic variability, which may underlie their high adaptive traits [ 23 ]. Environmental pressures- both climatic and anthropogenic, including the widespread use of insecticides, are likely contributors to this adaptability, as has been documented in An. gambiae s.l. [ 24 ]. Moreover, geographical and ecological factors that can affect gene flow, such as physical barriers and spatial distance may limit mosquito dispersal, leading to distinct population structure and hence, response to vector control interventions. Previous studies have proposed that the East African Rift Valley may function as a geographic barrier influencing gene flow and genetic differentiation in malaria vectors such as An. gambiae [ 25 ] and An. funestus [ 26 , 27 ]. This landscape features may similarly affect the spatial dynamics of resistance alleles, potentially restricting their spread across regions [ 28 ]. Consequently, such barriers could influence the efficacy of insecticide-based interventions, local malaria transmission patterns, and overall control outcomes. This study investigated whether the Rift Valley - as a known geographic barrier - impacts gene flow and contributes to variation in resistance genotypes and phenotypes among An. funestus populations in Kenya, compared to other malaria transmission zones. Methods Study sites Adult anophelines were surveyed in selected sites within the Rift Valley (RV): Kerio Valley comprising Kapluk and Kapnarok in Baringo County; west of the RV: Ahero, Busia, Bungoma in western Kenya; and east of the RV in the coastal sites of Taveta, Marigiza (Kwale County) and Sihu/Jaribuni (Kilifi County). These sites encompass the major malaria risk zones in Kenya (Fig 1). Western Kenya in the Lake malaria-endemic zone has some of the highest prevalence of malaria followed by the coastal sites in the Coast malaria-endemic zone [2]. Both western and coastal Kenya experience year-round malaria transmission with peaks linked to the short (October – December) and long (March – May) rainy seasons. In contrast, the KV sites categorised as seasonal malaria-epidemic zones are generally considered low risk with intense transmission in the rainy season [29]. An. funestus are among primary malaria vectors in these localities encompassing the major malaria risk zones in Kenya [3,29]. Host-seeking mosquitoes In each of the sites, surveillance of night-active host-seeking adult anophelines included simultaneous trapping inside and outside of randomly consenting households using CDC light traps (model 512, John W Hock Co, USA). In each household, one was placed indoors and another outdoors between February 2021 and September 2022. The outdoor traps were additionally baited with dry ice. In each site, ten light traps were set daily between 18:00 – 06:00h during each session for 3 – 5 consecutive nights, targeting different households. Inside each household, the trap was set near a bed (foot side of an occupant). The collected mosquitoes were transported in liquid N 2 to the laboratory at the icipe Duduville Campus and later -80 o C freezer until processing. Mosquitoes were sorted and Anopheles funestus s.l. morphologically identified using keys [30]. Genomic DNA was extracted from head/thorax and abdomen separately in each individually processed An. funestus s.l. mosquitoes using the Livak protocol [31]. Extracted DNA from the abdomen was used to identify the sibling species of An. funestus group via species-spe­cific polymerase chain reaction (PCR) [32], as well as genotyping of resistance markers focused on An. funestus s.s. only (hereafter as An. funestus ). DNA from the head/thorax ( An. funestus only) was processed to detect the presence of Plas­modium sporozoite infections via real-time TaqMan PCR assay [33], with further confirmation of positive specimens using the Nested PCR based of [34], as described [35]. Genotyping of resistance markers and sequencing the GSTe2 gene in An. funestus Individual Fo An. funestus specimens (n=139–445) were genotyped for four selected validated markers Cyp6P9a_R , L119F-Gste2 (DDT/permethrin), 4.3kb-SV , and G454A-Cyp9k1 . This was achieved by allele-specific PCR (AS-PCR) method and/or restriction fragment length polymorphism as described [14,16,18,35]. The PCR products of L119F- GSTe2 only, corresponding to different genotypes, were purified using Exo Sap (Thermo Fisher Scientific) and Sanger sequenced using the forward primer only. The obtained sequences ( GSTe-2 ) were cleaned and aligned using MEGA v7 software. The sequences were compared with reference GSTe-2 sequences [14] deposited in GenBank. Maximum likelihood trees were inferred using the best-fit model of sequence evolution, with nodal support for different groupings evaluated through 1000 bootstrap replications. Resting mosquito collection and F1 rearing Indoor resting, blood-fed female An . funestus were collected on walls and roofs of selected houses using battery-powered prokopack aspirators in sites representative of the broad ecological areas: western (Busia), Coast (Kilifi and Kwale), and KV (Kapnarok/Kapluk). The collection was carried out between 3:00 and 6:00 h, for 3 consecutive days, following verbal consent from the chief of the district and the household owners. The mosquito collection was conducted between June and October 2024. Aspirated mosquitoes were kept in cages and then morphologically identified using keys by [30] and separated into An. funestus s.l , from other anophelines or culicines with provision of 10% sucrose solution and kept for 3–5 days until gravid. Eggs obtained using the forced-egg laying method (i.e., placed individually in 1.5 mL Eppendorf tubes) were reared to F 1 generation at icipe Duduville Campus in Nairobi and used for insecticide exposure assays. All mosquitoes were reared under standard insectary conditions at a temperature of between 26±2 °C with 65–85% relative humidity and under a 12:12 photoperiod of natural light. Mosquito larvae were reared in larval trays and fed on Tetramine ad libitum. Larval water (mineral water, Mount Kenya Ltd) was changed every three days until pupation. Emerged adults were kept in Bugdorm cages while being given 10% sugar solution before bioassays. Insecticide susceptibility tests To fully characterise the resistance of An. funestus populations, insecticide susceptibility assays were carried out using 2–5-day-old non-blood-fed F 1 adults (WHO tube or bottle assays protocol) [36] to a range of insecticides (Table S1). This included type I (permethrin) and II pyrethroids (deltamethrin and alpha-cypermethrin), the carbamate bendiocarb, fenitrothion and the newly approved WHO neonicotinoid clothianidin and pyrrole chlorfenapyr. Insecticide resistance was examined not only against standard diagnostic concentrations, but also intensity assays for pyrethroid insecticides only. Two - five replicates of around 15–25 mosquitoes per tube were exposed to insecticide impregnated filter papers for 1h and then transferred to a clean holding tube supplied with 10% sugar. Mortalities were determined 24h after exposure. Additionally, cytochrome P450 genes involvement in metabolic resistance was assessed using PBO (piperonyl butoxide), an inhibitor of P450 activity. Mosquitoes exposed to non-impregnated papers were included as controls. These bioassays were conducted at 26±2˚C and 70±10% relative humidity. Polymorphism analysis of GSTe 2 gene in An. funestus across Kenya Genetic polymorphisms were determined through manual examination of GSTe2 coding sequences using BioEdit version 7.2.3.0 [37] and sequence differences in multiple alignments using ClustalW sequence analyser. Construction of a phylogenetic maximum likelihood tree was done using MEGA v7 [38]. A best-fit substitution model was tested based on Bayesian information criteria using Tamura-2 parameter which best described the sequence dataset. The model was then used with 1000 bootstrap replicates and a maximum likelihood tree generated. Haplotype network analysis was plotted using the Templeton Crandall Singleton (TCS) and TCS beautifier to beautify the generated haplotype network [39]. Data analysis Data was entered into an Excel sheet to plot counts, proportions and frequencies. The distribution of mutations for each marker was assessed by determining allelic frequencies. GraphPad Prism (version 10.6.1) and/or R v 4.1.0 software were used for data analysis at 95% confidence limit. Plasmodium infection rates among the genotypes for each marker were compared using the Fisher’s Exact Test/Pearson’s Chi-square tests. Results Variation in densities of host-seeking An. funestus s.l. Geographic regions along the Kenyan Rift Valley were sampled for adult anopheline mosquitoes both indoors and outdoors using CDC light traps. Sampling sites included the Kerio Valley (Kapluk and Kapnarok), western Kenya (Busia, Ahero, and Bungoma), and coastal areas (Kilifi—Sihu and Jaribuni—and Kwale—Marigiza and Taveta) (Figure 1). Only the outdoor traps were baited with dry ice. A total of 1,967 An. funestus s.l. specimens were processed by PCR, with An. funestus emerging as the predominant sibling species, accounting for 66.6% (1310/1967) of the total. In contrast, captures of An. rivulorum , An. leesoni , An. parensis , and An. longipalpis C were comparatively low. Notably, An. longipalpis C was detected exclusively at Kapluk. A considerable proportion (23.7%) of mosquitoes captured in Taveta (coastal site) failed to amplify during molecular analysis. Interestingly, An. funestus was the dominant species captured indoors across western sites (Ahero, Busia, and Bungoma) and Kerio Valley locations (Kapluk and Kapnarok) and Coast (Kilifi, Kwale) except Taveta. The species was predominant outdoors across all the sites except in Kapluk in the RV and Taveta at the coast. (Figure 2). Interestingly, An. funestus was the dominant species captured indoors across western sites (Ahero, Busia, and Bungoma) and Kerio Valley locations (Kapluk and Kapnarok) and Coast (Kilifi, Kwale) except Taveta. The species was predominant outdoors across all the sites except in Kapluk in the RV and Taveta at the coast (Figure 2). Malaria parasite infection and association with allele frequency of resistance markers A subset of An. funestus (n=463) was analysed for infection with Plasmodium sporozoite and genotyped for metabolic resistance gene markers; L119F-Gste2 and G454A-Cyp9k1 and one structural variant 4.3Kb-SV . Of these, 8.2% (range: 3.3 – 44.4%) tested positive for Plasmodium sporozoite infection (95% P. falciparum; 5% P. ovale ) with variation among sites ( Table 1 ). Cumulative Plasmodium infection rates were highest among locations in western (12.7%; 21/165; range: 5.7 – 44.4%), followed by KV (7.1%; 7/99; range 5.5 – 9.1) and then coast (5.1%; 10/196; range 3.3 -9.5%). Table 1 : Plasmodium infection rates in An. funestus Region Site Plasmodium infection rates (proportion) Western Busia 10.6 (10/94) Bungoma 44.4 (8/18) Ahero 5.7 (3/53) Kerio Valley Kapnarok 5.5 (3/55) Kapluk 9.1 (4/44) Coast Marigiza-Kwale 7.1 (4/56) Taveta 9.5 (2/21) Jaribuni-Kilifi 3.3 (4/122) Total 8.2 (38/463) Genotype frequencies for three key metabolic resistance markers; L119F-GSTe2 , G45A-CYP9K1 , and 4.3-Kb SV transposon insertion, were assessed in An. funestus populations from western, Rift Valley and coastal regions. The results revealed striking regional contrasts (Figure 3), indicating heterogeneous selection pressures across ecological zones, likely reflecting differences in insecticide exposure and local transmission dynamics. The L119F-GSTe2 , successfully assessed in 392 specimens, yielded an overall allele frequency of 0.33 (range: 0.16–0.74). The resistant 119F-GSTe2 allele was most prevalent along the coast (55/138; 39.9%), at lower frequency in KV (7/95; 7.4%), and least frequent in western Kenya (7/159; 4.4%) (Figure 3a). By contrast, genotyping of the G454A-CYP9K1 (n = 445) and 4.3-Kb SV (n = 336) revealed much higher frequencies of the RR genotype, approaching fixation in western Kenya (~98.8%), followed by the Rift Valley (~91%), and the coastal sites of Kwale and Kilifi (~82%) (Figure 3b–c). For the CYP6P9a marker (n = 139), the SS genotype was detected only sporadically in KV and western Kenya and was entirely absent from the coast. We next compared the distribution of genotypes between Plasmodium -infected and non-infected specimens across the detected resistance markers. For both G454A-CYP9K1 and 4.3kb-SV , infection was more frequently observed among individuals carrying the RR genotype (Fig. 4a; Figure S1) but this association was not significant between the genotypes for 4.3kb-SV and G454A- CYP9K1 (Fig. 4b). Further analysis at the allelic level revealed that by combining all samples from different regions hence increasing sample size for phenotype-genotype association, only the R individuals with the 4.3Kb-SV significantly carried higher infection than S (OR = 5.7, p = 0.049) (Figure S1). In contrast, for the L119F-Gste2 marker, revealed an opposite trend, with a higher proportion of infections occurring in individuals carrying the SS genotype (Figure 4a). There was a 5-fold significant likelihood of parasite infection in mosquitoes carrying the RS than RR genotype for L119F-GSTe2 (Figure S2). Nonetheless, there was no difference in infection prevalence between the genotypes and alleles for this marker (Figure 4b, Figure S1 and S2). Although not statistically significant, this suggests a potential negative association between the resistant 119F-GSte2 allele and parasite infection. Sequence analysis of the GSTe -2 gene The negative relationship between resistant allele of GSTe -2 gene and parasite infectivity contrasts with previous literature [35,40], prompted further investigations. A segment of the GSTe-2 gene (666 bp) was sequenced among selected Kenyan genotypes to infer the evolutionary relationship with reference sequences generated across Africa. A pronounced genetic differentiation was evident between the Coastal Kenyan populations and rest of other African regions (Fig. 5). The analysis identified two major clades, a primary clade that included sequences from several African countries, including Benin, Uganda, Malawi, Mozambique, Ghana, Cameroon, and sequences from Rift Valley of Kenya (Figure 5a). A second clade contained sequences exclusively from Coastal site of Kenya. Further, genetic differentiation was demonstrated by the haplotype network analysis which revealed two distinct networks, one containing two dominant haplotypes (H 1 and H 2 ) comprising sequences from various geographical regions in Africa, including West Africa (Ghana and Benin), southern Africa (Malawi and Mozambique), Central Africa (Cameroon), and eastern Africa (Uganda and Rift Valley Kenya) (Fig 5b). Conversely, the second haplotype network included sequences exclusively from Coastal Kenya, featuring two major haplotypes (H 3 and H 4 ) specific to this Kenyan region. Overall, the analyses revealed high genetic divergence in GSTe2 sequences between most An. funestus populations from Coastal Kenya and those from the rest of Kenya. Nucleotide polymorphism analysis within the GSTe2 gene in An. funestus across Kenya Nucleotide polymorphism analysis was performed to investigate the nucleotide changes encompassing the different haplotypes of the GSTe2 gene across Africa. The analysis shows that primary dominant haplotype (H 1 ) common to all African samples (Figure 6a) is characterized by the absence of single nucleotide polymorphism (SNP) across the GSTe2 open reading frame (ORF). Twenty-three (23) out of 98 sequences examined harboured this haplotype. The secondary dominant haplotype (H 2 ) found at high frequency in Benin (15 sequences), moderate frequencies in Cameroon (4 sequences) and Ghana (5 sequences) is characterized by a single nucleotide change: a cytosine (C) to thymine nucleotide transition at position 355, resulting in an amino acid change from leucine (L) to phenylalanine (F) on codon 119. The third and fourth haplotype (H 3 and H 4 ), which are exclusively found in Coastal Kenya, have multiple SNPs that produce a unique protein sequence. This protein sequence is marked by 10 amino acid changes in linkage disequilibrium: asparagine (N) to thymine (T) at codon 33, a glycine (G) to alanine (A) at codon 80, lysine (K) changed to valine (V) at codon 146, aspartate (D) to asparagine (N) at codon 147, serine (S) to alanine (A) at codon 153, glutamate (E) to aspartate (D) at codon 176, histidine (H) to tyrosine (Y) at codon 180, arginine (R) to glutamine (Q) at codon 182, glutamate (E) to glycine (G) at codon 185 and aspartate (D) to asparagine (N) at codon 188 (T 33 A 80 V 146 N 147 A 153 D 176 Y 180 Q 182 G 185 N 188 ). This Coastal Kenyan haplotype is present at a moderate frequency, with 10 out of 30 sequences harbouring this specific haplotype. Insecticide resistance profile Insecticide resistance and intensity were higher for type 1 (permethrin) than type II (deltamethrin, alpha-cypermethrin) pyrethroids and was more pronounced for An. funestus in RV than western Kenya, a traditional malaria hotspot (Figure 7 a,b). Increasing dosage only had a modest effect on mortality with permethrin in KV (average mortality: 1× = 3%, 5× = 7.1% and 10× = 11.2%) and Busia (average mortality: 1× = 0%, 5× = 5.4% and 10× = 24%). Mortality increased with an increasing dosage of α-cypermethrin in Busia: (average mortality; 1× = 20.4%, 5× = 87.4.1% and 10× = 100%) but not so much in KV (average mortality; KV: 1× = 3.2%, 5× = 5.2% and 10× = 23.2%). With increasing concentration, the mortality greatly increased with deltamethrin in Busia (average mortality: 1× = 68.3%, 5× = 84.6% and 10× = 86.5%) and KV: average mortality; 1× = 2.7%, 5× = 64.5% and 10× = 71.5%) (Figure 7a-c). Pre-exposure to PBO notably increased the mortality with alpha-cypermethrin (mean mortality=39.5% and 65.3% in KV and Busia, respectively) and deltamethrin (mean mortality= 64.2% and 89.6% in KV and Busia, respectively). However, only a modest increase in mortality with PBO was observed with permethrin in KV (mean mortality=5%), lower than in Busia (mean mortality=10.8%). Coastal population exhibited intense resistance to permethrin only (1× = 6.7%, 5× = 7.5% and 10× = 9.1%) and similar low mortality in PBO synergist assay with this insecticide (mean mortality= 30.8%). Both Busia and KV populations were resistant to DDT (mean mortality; KV: 36.8%; Busia=68.3%) except coast that was susceptible (mean mortality= 98.4%). By contrast, all three populations were fully susceptible (mortality =100%) to bendiocarb, pirimiphos-methyl, clothianidin and chlorfenapyr. Oviposited F 0 An. funestus s.l. mosquitoes (from which F1 progeny used for resistance exposure was derived) identified by cocktail PCR revealed 99% (129/130) and 99% (125/126) were An. funestus from KV and Busia, respectively. In contrast, only a minor proportion of An. funestus s.l. from the coast was An. funestus (5%), with the majority being An. rivulorum (80%, 72/90). Discussion Investigating the spread of resistance markers is critical for insecticide resistance monitoring in malaria vectors, gauging insight into associated mechanisms and informing control strategies. In this study, vector bionomics, distribution of key metabolic resistance markers and resistance profiles to several insecticides were investigated among An. funestus populations along the Rift Valley divide spanning major malaria risk zones of Kenya. Spatial distribution analysis revealed two distinct patterns; that in both KV and western sites, An. funestus is the predominant sibling species in the Funestus group and mostly encountered indoors. The coast, also dominated by An. funestus in some sites which were mostly encountered outdoors, consistent with previous results [ 3 , 41 , 42 ]. The result might indicate divergence in the biting and resting behavior of this mosquito at the coast from western/KV areas. High outdoor biting behavior of malaria vectors could be associated with low exposure to ITNs consequently affecting expression of insecticide resistance differently among the vector populations. The divergence could be ascribed to a combination of factors, including vector adaptation to human behavior, climate, and even genetics [ 41 ]. Previous studies found genetic divergence of coastal An. funestus populations from other areas in Kenya[ 27 , 43 ] but genetic heterogeneities that underpin indoor versus outdoor habits and perhaps biting times should be investigated further. The study also revealed a high Plasmodium sporozoite infection rate in An. funestus , comparable or even higher than values observed for this species in Central [ 44 ] and East Africa [ 3 , 10 , 12 , 45 , 46 ]. Notably, study sites in western Kenya displayed the overall highest Plasmodium infection rates, followed by KV and then coast. The results suggest underlying difference in competence of vector populations or levels of usage or protection provided by ITNs and/or IRS. Evidence of sustained high Plasmodial transmission and infection resurgence in western Kenya despite intensified malaria control interventions since 2006 [ 47 ], indicate the need to investigate this trend further. The vector infection results confirm the active role this mosquito species plays in contemporary and persistent malaria transmission [ 3 , 4 ]. The other sibling species or unknown species were most prevalent at the coast. Because these other sibling species, including cryptic species [ 3 , 29 , 42 , 48 , 49 ]are equally important in transmission, characterization of their resistance profiles remains primordial. Further longitudinal surveys will address seasonal dynamics of these species and role in malaria transmission. Our study reveals distinct spatial gradients in metabolic resistance marker allele frequencies among Kenyan An. funestus populations, with G454A-CYP9K1 and the 4.3-kb SV increasing from the western regions through the Rift Valley to the coast, while L199F-GSTe2 declines, suggesting region-specific selective pressures. The CYP6P9a mutation was rare, restricted to homozygous susceptible genotypes in western and Rift Valley sites, consistent with the limited geographic spread of the mutant variants of the southern Africa CYP6P9 markers into eastern Africa[ 15 , 50 ] except central west and south to northeastern Tanzania [ 19 ]. Although the GSTe2 RR genotype predominated at the coast, east of the Rift Valley, mosquitoes carrying the susceptible GSTe2 SS genotype were more frequently infected with Plasmodium , contrasting with Central African studies reporting higher parasite prevalence among resistant genotypes [ 35 , 40 ]. The discovery of a novel GSTe2 haplotype unique to coastal populations perhaps further explains this contrast and further supports differential local selection pressures that could impact vector-parasite interaction. Interestingly, we observed a strong positive association between the 4.3-kb SV mutant alleles and parasite infection, opposite to prior reports where infection was higher in mosquitoes lacking this variant [ 18 ]. This discrepancy may reflect regional differences of the impact of this marker on gene expression, vector competence, environmental adaptation, or insecticide-driven selection that could modulate parasite susceptibility. The G454A mutation in CYP9K1, a major driver of type II pyrethroid resistance in East and Central Africa [ 16 ], approached fixation alongside the 4.3-kb SV , consistent with observations in eastern Uganda [ 10 , 12 ]. Presence of multi-locus combinations of L119F-GSTe2 , G454A-CYP9K1 , and 4.3-kb SV alleles did not correlate with infection status. Known antagonistic interactions between CYP6P9a/b and the 4.3-kb SV [ 18 ] and the cumulative impact of multiple resistance factors on extreme pyrethroid resistance (> 1000-fold) highlight the complex adaptive landscape. Collectively, these findings reveal heterogeneous selection acting on key resistance genes across Kenyan populations, shaped by ecological variation and local insecticide exposure, and underscore the need for genomic studies to clarify adaptive signatures, potential fitness costs, and their implications for malaria transmission. This study provides the first evidence of pyrethroid resistance escalation in Kenyan Anopheles funestus , using 5× and 10× discriminating concentrations. Resistance intensity varied markedly across regions, with coastal populations exhibiting lower mortality than those from the Kerio Valley (Rift Valley) and Busia (western Kenya). Species composition likely contributes to this pattern: coastal samples were dominated by An. rivulorum ( 99% An. funestus . Whether these sibling species differ in resistance mechanisms remains unclear, but this may partly explain the lower resistance observed at the coast. Nonetheless, the low mortality to permethrin at 10× concentration is highly concerning and aligns with recent reports of pyrethroid resistance in coastal An. funestus [ 51 ]. Notably, resistance intensity in KV was higher than in Busia, possibly reflecting intensified insecticide selection from agricultural practices such as cotton cultivation, which relies heavily on pyrethroids, compared with public health interventions like ITNs and IRS that dominate the coast and western Kenya [ 52 ]. Synergist assays revealed low mortality following pre-exposure to PBO, particularly with permethrin in KV, suggesting that cytochrome P450–mediated detoxification is an important but not exclusive mechanism driving resistance. The persistence of high resistance despite PBO exposure indicates that other metabolic pathways, cuticular modifications, or additional genetic factors may also contribute. Moreover, the presence of multilocus resistance genotypes, including combinations of CYP9K1, 4.3-kb SV, and GSTe2 , suggests complex interactions that may influence both insecticide tolerance and vectorial capacity. These findings highlight the need for molecular and genomic studies to unravel the adaptive signatures and functional mechanisms underlying resistance escalation in these populations [ 53 ] Encouragingly, full susceptibility was observed to bendiocarb in all populations, in contrast to previous and recent reports in Uganda [ 10 , 54 ], indicating that carbamates remain viable alternatives for IRS in these localities. Full susceptibility to organophosphates and newer chemistries, including pyrimiphos-methyl, clothianidin, and chlorfenapyr, further expands the portfolio of tools for pyrethroid resistance management [ 10 , 55 , 56 ]. The escalation of pyrethroid resistance documented here has clear implications for vector control efficacy, particularly for ITN and IRS strategies [ 11 ] and similar trends have been observed in the neighboring country, Uganda [ 10 , 12 ]. The observed heterogeneity in resistance across species, regions, and selection pressures emphasizes the necessity for locally tailored interventions and sustained resistance monitoring to anticipate shifts in adaptive dynamics and inform effective malaria control programs. Conclusions In summary, the study confirms high malaria parasite infection in An. funestus populations exhibiting pyrethroid resistance escalation and different evolutionary histories in resistance genes (at least in GSTe-2 ) along a west-RV-coast gradient in Kenya. The role of the Rift Valley should be considered in insecticide resistance spread and effective resistance management in Kenya. Further work is needed to elucidate the underlying molecular basis of resistance escalation and the significance of the observed resistance mutations on malaria transmission. Declarations Acknowledgments To Juliet Onditi for designing the study map. We are grateful for the technical support of Irene Wainaina and Josephine Osalla. Special thanks to the local administration and the household heads for access to their homesteads for mosquito trapping. Funding This work was supported by a Wellcome Trust Intermediate Fellowship to D.P.T. (award number 222005/Z/20/Z). Amine M Mustapha is supported by UNESCO-TWAS (The World Academy of Sciences) PhD Scholarship through the icipe ARPPIS Program. The authors gratefully acknowledge the financial support for this research by the following organizations and agencies: the Swedish International Development Cooperation Agency (Sida); the Swiss Agency for Development and Cooperation (SDC); the Australian Centre for International Agricultural Research (ACIAR); the Government of Norway; the German Federal Ministry for Economic Cooperation and Development (BMZ); and the Government of the Republic of Kenya. The views expressed herein do not necessarily reflect the official opinion of the donors. Availability of data and materials All data generated or analyzed during this study are included in this published article and its additional files. Author contributions DPT and WCS designed the study. DPT, AMM, GR, conducted field work. DPT, AMM, TKM, BM, performed the resistance testing in the insectary. DPT performed molecular experiments. DPT, AO, CSDT. analysed the data; DPT, LK, BT and WCS contributed to resources and funding acquisition; DPT, AO wrote the manuscript with contribution from all the authors. All authors read and approved the final manuscript. Ethical Approval and consent to participate The study was approved by the Scientific and Ethical Review Unit (SERU) of the Kenya Medical Research Institute (KEMRI) (Protocol no. NON KEMRI 4592) and adhered to relevant guidelines and regulations. Prior to data collection, the purpose of the study, procedures and associated benefits or risks were provided to the local leadership at county and community levels. 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Supplementary Files FigS1.pdf FigS2.pdf TableS1Summaryofinsecticidestested.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 05 Jan, 2026 Reviews received at journal 31 Dec, 2025 Reviewers agreed at journal 27 Dec, 2025 Reviews received at journal 13 Dec, 2025 Reviewers agreed at journal 05 Dec, 2025 Reviewers agreed at journal 05 Dec, 2025 Reviewers agreed at journal 03 Dec, 2025 Reviewers invited by journal 02 Dec, 2025 Editor assigned by journal 01 Dec, 2025 Submission checks completed at journal 01 Dec, 2025 First submitted to journal 27 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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09:23:39","extension":"png","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":51073,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8224847/v1/53a75e9d9dc819a32a022560.png"},{"id":97448139,"identity":"d0b9d6c5-0bab-40ea-979c-7218ac3357a5","added_by":"auto","created_at":"2025-12-04 13:08:48","extension":"png","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":22968,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8224847/v1/43b8ac595044d6c1356c31ed.png"},{"id":97448127,"identity":"709461f7-05f5-44c5-aaa4-d2a2f30255b4","added_by":"auto","created_at":"2025-12-04 13:08:47","extension":"xml","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":149957,"visible":true,"origin":"","legend":"","description":"","filename":"4310790146004a24bc028c9e8227094b1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8224847/v1/001d9389e69255b42816bc90.xml"},{"id":97448116,"identity":"32392e3a-a820-4764-8229-747f199dca84","added_by":"auto","created_at":"2025-12-04 13:08:47","extension":"html","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":165601,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8224847/v1/38ae75affe721eff380207fb.html"},{"id":97448133,"identity":"1c7667d5-bb5f-447a-9523-e939ffeefd67","added_by":"auto","created_at":"2025-12-04 13:08:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":362000,"visible":true,"origin":"","legend":"\u003cp\u003eKenyan map indicating the study sites along the Rift Valley divide\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8224847/v1/f25a9cc27b4a4cf2e3bf1bce.png"},{"id":97448120,"identity":"04dd40a4-1ae6-4c49-8d02-ada7c0c2f504","added_by":"auto","created_at":"2025-12-04 13:08:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":84407,"visible":true,"origin":"","legend":"\u003cp\u003eRelative abundance of \u003cem\u003eAn. funestus\u003c/em\u003e to other sibling species and distribution indoors and outdoors\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8224847/v1/827aa1df1db5ee75d688b1fe.png"},{"id":97448112,"identity":"baf55630-3ded-4739-a276-09dfccfa11ad","added_by":"auto","created_at":"2025-12-04 13:08:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":363262,"visible":true,"origin":"","legend":"\u003cp\u003eFrequency of genotypes detected in \u003cem\u003eAn. funestus\u003c/em\u003e for selected resistance markers by region\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8224847/v1/6d60689944d0785e85002260.png"},{"id":97448119,"identity":"09221468-19c4-4775-b505-824d0c41a9e5","added_by":"auto","created_at":"2025-12-04 13:08:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":870673,"visible":true,"origin":"","legend":"\u003cp\u003eFrequency of resistant genotypes among \u003cem\u003ePlasmodium \u003c/em\u003esporozoite-infected \u003cem\u003eAn. funestus\u003c/em\u003e by region a) and genotype-infection association (b).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8224847/v1/5de75657117411aa7a975c46.png"},{"id":97667471,"identity":"96ddf353-0c59-4267-af7d-30e92a85696c","added_by":"auto","created_at":"2025-12-08 09:23:37","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":217618,"visible":true,"origin":"","legend":"\u003cp\u003eAfrica-wide genetic analysis of the coding region of GSTe2 gene. (a) Maximum likelihood phylogenetic tree. (b) haplotype network.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8224847/v1/86fccef42c573cf07a51d26e.png"},{"id":97668290,"identity":"1c95c27e-bb5b-492f-bee9-68563b2b703b","added_by":"auto","created_at":"2025-12-08 09:25:13","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":508013,"visible":true,"origin":"","legend":"\u003cp\u003eNucleotide diversity analysis representation of Africa-wide \u003cem\u003eGSTe2\u003c/em\u003e coding sequence. (A) Nucleic acid sequences. (B) Amino acid sequences, with the \u003cem\u003eL119F-GSTe2\u003c/em\u003emarker highlighted in yellow and changes in Kenyan Coastal samples highlighted in blue.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8224847/v1/12b9304aea71c4401cd02ece.png"},{"id":97448138,"identity":"0c07802b-e9ad-4b33-86da-43f9350df4df","added_by":"auto","created_at":"2025-12-04 13:08:48","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":340608,"visible":true,"origin":"","legend":"\u003cp\u003eInsecticide resistance patterns of \u003cem\u003eAnopheles funestus\u003c/em\u003e populations from Busia, Kerio Valley, and Coast to discriminatory insecticide doses (a), intensity assays (b) and synergist assays with PBO to pyrethroids (c), respectively. Per, permethrin; α-cyp, alpha-cypermethrin; delta, deltamethrin; PBO, piperonyl butoxide.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8224847/v1/dd7433c1146bdb1b6818cb05.png"},{"id":97892566,"identity":"1e32a35f-83a2-40ce-9b13-d4de3cf21dc9","added_by":"auto","created_at":"2025-12-10 15:15:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3697001,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8224847/v1/1249b944-1e27-4345-bb1f-be49d6acd65b.pdf"},{"id":97448131,"identity":"0e4ebe97-1824-4bb3-baa6-fa8ec7e0ebdf","added_by":"auto","created_at":"2025-12-04 13:08:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":63880,"visible":true,"origin":"","legend":"","description":"","filename":"FigS1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8224847/v1/fbf39e4c5f86f79e763d61d2.pdf"},{"id":97448140,"identity":"73774b38-23b1-4afc-bba7-2eb7809efa32","added_by":"auto","created_at":"2025-12-04 13:08:48","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":83093,"visible":true,"origin":"","legend":"","description":"","filename":"FigS2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8224847/v1/8989636ce165b90d90eb3ec7.pdf"},{"id":97668525,"identity":"fbe6dc3b-d722-4fec-85d6-eaebab4a529a","added_by":"auto","created_at":"2025-12-08 09:25:44","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":9835,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1Summaryofinsecticidestested.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8224847/v1/29f42db03e7812daaf234973.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Signature of resistance gene evolution and pyrethroid resistance escalation in the major malaria vector Anopheles funestus across the Kenyan Rift Valley","fulltext":[{"header":"Background","content":"\u003cp\u003eMalaria remains a major vector-borne disease of significant medical and public health importance across much of sub-Saharan Africa (SSA). In 2023, the region accounted for 94% of the 263\u0026nbsp;million malaria cases reported [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Despite the widespread implementation of integrated control measures, including the use of insecticide-treated nets (ITNs), clinical case management such as proper diagnosis and treatment using artemisinin-based combination therapies (ACTs) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], malaria incidence remains high in most African countries [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In Kenya alone, over three million cases were reported in 2023, representing a modest decline of 3.6% from the 3.42\u0026nbsp;million cases recorded in 2022 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. This persistently high burden highlights gaps in the understanding of the drivers of sustained transmission. Changes in vector behavior and ecological heterogeneity may be contributing to ongoing transmission dynamics and undermining the effectiveness of current control measures.\u003c/p\u003e\u003cp\u003eHumans become infected with malaria parasites through the bites of parasite-infected female \u003cem\u003eAnopheles\u003c/em\u003e mosquitoes, which vary in their vectorial capacity \u0026ndash; potential to transmit pathogens. Among the major malaria vectors in SSA is \u003cem\u003eAnopheles funestus\u003c/em\u003e s.s. (referred herein as \u003cem\u003eAn. funestus\u003c/em\u003e), a species characterized by high susceptibility to \u003cem\u003ePlasmodium\u003c/em\u003e parasites, a strong anthropophilic (human-biting) preference and prolonged adult longevity [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In the wake of declining \u003cem\u003eAn. gambiae\u003c/em\u003e populations likely due to up-scale of ITNs, \u003cem\u003eAn. funestus\u003c/em\u003e has emerged as a dominant malaria vector across much of East Africa, including several regions in Kenya [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Furthermore, \u003cem\u003eAn. funestus\u003c/em\u003e has the tendency to alter behavior, highly adaptive \u0026ndash; breeding throughout the year and can rapidly develop resistance to insecticides [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eInsecticide resistance poses a major challenge to the long-term effectiveness of current vector control tools and malaria control strategies [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Across much of Africa, \u003cem\u003eAn. funestus\u003c/em\u003e populations have developed resistance to pyrethroids, the primary class of insecticides used in public health [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Moreover, there is growing evidence of resistance extending to other insecticide classes recommended by the World Health Organisation (WHO) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The worsening situation of insecticide resistance is currently further exacerbated by the growing threat of resistance escalation - characterized by the ability of mosquito populations to survive very high doses of insecticides, thereby reducing the efficacy of control interventions[\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In \u003cem\u003eAn. funestus\u003c/em\u003e, resistance is predominantly mediated by overexpression of key metabolic genes, particularly those encoding cytochrome P450s enzymes (CYPs), including CYP6P9a/b, C\u003cem\u003eYP6P4a/b\u003c/em\u003e, and CYP9K1, as well as glutathione-transferase epsilon 2 (\u003cem\u003eGSTe2\u003c/em\u003e) [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Moreover, genetic variants within key metabolic genes and structural variants (SVs) such as the 6.5-KbSV and 4.3-Kb SV have also been implicated [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Additionally, recent studies have identified target site mutations, conferring localized knockdown resistance (\u003cem\u003ekdr)\u003c/em\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] and non-coding RNAs[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] in resistance mechanisms.\u003c/p\u003e\u003cp\u003eIn Kenya, reduced susceptibility of \u003cem\u003eAn. funestus\u003c/em\u003e to pyrethroids have been documented in malaria endemic regions, such as coastal and western regions [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. However, the underlying molecular mechanisms remain poorly understood, underscoring the need for comprehensive investigations into the evolution and spread of insecticide resistance \u003cem\u003eAn. funestus\u003c/em\u003e populations across diverse ecological settings.\u003c/p\u003e\u003cp\u003eIn Africa, \u003cem\u003eAnopheles funestus\u003c/em\u003e populations are widely distributed and exhibit significant local genetic variability, which may underlie their high adaptive traits [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Environmental pressures- both climatic and anthropogenic, including the widespread use of insecticides, are likely contributors to this adaptability, as has been documented in \u003cem\u003eAn. gambiae\u003c/em\u003e s.l. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Moreover, geographical and ecological factors that can affect gene flow, such as physical barriers and spatial distance may limit mosquito dispersal, leading to distinct population structure and hence, response to vector control interventions.\u003c/p\u003e\u003cp\u003ePrevious studies have proposed that the East African Rift Valley may function as a geographic barrier influencing gene flow and genetic differentiation in malaria vectors such as \u003cem\u003eAn. gambiae\u003c/em\u003e [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] and \u003cem\u003eAn. funestus\u003c/em\u003e [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. This landscape features may similarly affect the spatial dynamics of resistance alleles, potentially restricting their spread across regions [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Consequently, such barriers could influence the efficacy of insecticide-based interventions, local malaria transmission patterns, and overall control outcomes.\u003c/p\u003e\u003cp\u003eThis study investigated whether the Rift Valley - as a known geographic barrier - impacts gene flow and contributes to variation in resistance genotypes and phenotypes among \u003cem\u003eAn. funestus\u003c/em\u003e populations in Kenya, compared to other malaria transmission zones.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy sites\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAdult anophelines were surveyed in selected sites within the Rift Valley (RV): Kerio Valley comprising Kapluk and Kapnarok in Baringo County; west of the RV: Ahero, Busia, Bungoma in western Kenya; and east of the RV in the coastal sites of Taveta, Marigiza (Kwale County) and Sihu/Jaribuni (Kilifi County). These sites encompass the major malaria risk zones in Kenya (Fig 1). Western Kenya in the Lake malaria-endemic zone has some of the highest prevalence of malaria followed by the coastal sites in the Coast malaria-endemic zone [2]. Both western and coastal Kenya experience year-round malaria transmission with peaks linked to the short (October \u0026ndash; December) and long (March \u0026ndash; May) rainy seasons. In contrast, the KV sites categorised as seasonal malaria-epidemic zones are generally considered low risk with intense transmission in the rainy season [29]. \u003cem\u003eAn. funestus\u003c/em\u003e are among primary malaria vectors in these localities encompassing the major malaria risk zones in Kenya [3,29].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHost-seeking\u003cem\u003e\u0026nbsp;\u003c/em\u003emosquitoes\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn each of the sites, surveillance of night-active host-seeking adult anophelines included simultaneous trapping inside and outside of randomly consenting households using CDC light traps (model 512, John W Hock Co, USA). In each household, one was placed indoors and another outdoors between February 2021 and September 2022. The outdoor traps were additionally baited with dry ice. In each site, ten light traps were set daily between 18:00 \u0026ndash; 06:00h during each session for 3 \u0026ndash; 5 consecutive nights, targeting different households. Inside each household, the trap was set near a bed (foot side of an occupant). The collected mosquitoes were transported in liquid N\u003csub\u003e2\u003c/sub\u003e to the laboratory at the \u003cem\u003eicipe\u0026nbsp;\u003c/em\u003eDuduville Campus and later -80\u003csup\u003eo\u003c/sup\u003e C freezer until processing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMosquitoes were sorted and \u003cem\u003eAnopheles funestus\u003c/em\u003e s.l. morphologically identified using keys [30]. Genomic DNA was extracted from head/thorax and abdomen separately in each individually processed \u003cem\u003eAn. funestus\u003c/em\u003e s.l. mosquitoes using the Livak protocol [31]. Extracted DNA from the abdomen was used to identify the sibling species of \u003cem\u003eAn. funestus\u003c/em\u003e group via species-spe\u0026shy;cific polymerase chain reaction (PCR) [32], as well as genotyping of resistance markers focused on \u003cem\u003eAn. funestus\u003c/em\u003e s.s. only (hereafter as \u003cem\u003eAn. funestus\u003c/em\u003e). \u0026nbsp;DNA from the head/thorax (\u003cem\u003eAn. funestus\u003c/em\u003e only) was processed to detect the presence of \u003cem\u003ePlas\u0026shy;modium\u0026nbsp;\u003c/em\u003esporozoite infections via real-time TaqMan PCR assay [33], with further confirmation of positive specimens using the Nested PCR based of [34], as described [35].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenotyping of resistance markers and sequencing the\u003cem\u003e\u0026nbsp;GSTe2\u003c/em\u003e gene in \u003cem\u003eAn. funestus\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIndividual Fo \u003cem\u003eAn. funestus\u003c/em\u003e specimens (n=139\u0026ndash;445) were genotyped for four selected validated markers \u003cem\u003eCyp6P9a_R\u003c/em\u003e, \u003cem\u003eL119F-Gste2\u003c/em\u003e (DDT/permethrin), \u003cem\u003e4.3kb-SV\u003c/em\u003e, and \u003cem\u003eG454A-Cyp9k1\u003c/em\u003e. This was achieved by allele-specific PCR (AS-PCR) method and/or restriction fragment length polymorphism as described [14,16,18,35]. The PCR products of L119F-\u003cem\u003eGSTe2\u0026nbsp;\u003c/em\u003eonly,\u003cem\u003e\u0026nbsp;\u003c/em\u003ecorresponding to different genotypes, were purified using Exo Sap (Thermo Fisher Scientific) and Sanger sequenced using the forward primer only. The obtained sequences (\u003cem\u003eGSTe-2\u003c/em\u003e) were cleaned and aligned using MEGA v7 software. The sequences were compared with reference \u003cem\u003eGSTe-2\u003c/em\u003e sequences [14] deposited in GenBank. Maximum likelihood trees were inferred using the best-fit model of sequence evolution, with nodal support for different groupings evaluated through 1000 bootstrap replications.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResting mosquito collection and F1 rearing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIndoor resting, blood-fed female \u003cem\u003eAn\u003c/em\u003e. \u003cem\u003efunestus\u0026nbsp;\u003c/em\u003ewere collected on walls and roofs of selected houses using battery-powered prokopack aspirators in sites representative of the broad ecological areas: western (Busia), Coast (Kilifi and Kwale), and KV (Kapnarok/Kapluk). The collection was carried out between 3:00 and 6:00 h, for 3 consecutive days, following verbal consent from the chief of the district and the household owners. The mosquito collection was conducted between June and October 2024. Aspirated mosquitoes were kept in cages and then morphologically identified using keys by [30] and separated into \u003cem\u003eAn. funestus s.l\u003c/em\u003e, from other anophelines or culicines with provision of 10% sucrose solution and kept for 3\u0026ndash;5 days until gravid. Eggs obtained using the forced-egg laying method (i.e.,\u0026nbsp;placed individually in 1.5\u0026nbsp;mL Eppendorf tubes) were reared to F\u003csub\u003e1\u003c/sub\u003e generation at \u003cem\u003eicipe\u003c/em\u003e Duduville Campus in Nairobi and used for insecticide exposure assays. All mosquitoes were reared under standard insectary conditions at a temperature of between 26\u0026plusmn;2 \u0026deg;C with 65\u0026ndash;85% relative humidity and under a 12:12 photoperiod of natural light. Mosquito larvae were reared in larval trays and fed on Tetramine ad libitum. Larval water (mineral water, Mount Kenya Ltd) was changed every three days until pupation. Emerged adults were kept in Bugdorm cages while being given 10% sugar solution before bioassays.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInsecticide susceptibility tests\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo fully characterise the resistance of \u003cem\u003eAn. funestus\u003c/em\u003e populations, insecticide susceptibility assays were carried out using 2\u0026ndash;5-day-old non-blood-fed F\u003csub\u003e1\u0026nbsp;\u003c/sub\u003eadults (WHO tube or bottle assays protocol) [36] to a range of insecticides (Table S1). This included type I (permethrin) and II pyrethroids (deltamethrin and alpha-cypermethrin), the carbamate bendiocarb, fenitrothion and the newly approved WHO neonicotinoid clothianidin and pyrrole chlorfenapyr. Insecticide resistance was examined not only against standard diagnostic concentrations, but also intensity assays for pyrethroid insecticides only. Two - five replicates of around 15\u0026ndash;25 mosquitoes per tube were exposed to insecticide impregnated filter papers for 1h and then transferred to a clean holding tube supplied with 10% sugar. Mortalities were determined 24h after exposure. Additionally, cytochrome P450 genes involvement in metabolic resistance was assessed using PBO (piperonyl butoxide), an inhibitor of P450 activity.\u0026nbsp;Mosquitoes exposed to non-impregnated papers were included as controls. These bioassays were conducted at 26\u0026plusmn;2˚C and 70\u0026plusmn;10% relative humidity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePolymorphism analysis of \u003cem\u003eGSTe\u003c/em\u003e2 gene in \u003cem\u003eAn. funestus\u003c/em\u003e across Kenya\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenetic polymorphisms were determined through manual examination of \u003cem\u003eGSTe2\u003c/em\u003e coding sequences using BioEdit version 7.2.3.0 [37] and sequence differences in multiple alignments using ClustalW sequence analyser. Construction of a phylogenetic maximum likelihood tree was done using MEGA v7 [38]. A best-fit substitution model was tested based on Bayesian information criteria using Tamura-2 parameter which best described the sequence dataset. The model was then used with 1000 bootstrap replicates and a maximum likelihood tree generated. Haplotype network analysis was plotted using the Templeton Crandall Singleton (TCS) and TCS beautifier to beautify the generated haplotype network [39].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData was entered into an Excel sheet to plot counts, proportions and frequencies. The distribution of mutations for each marker was assessed by determining allelic frequencies. GraphPad Prism (version 10.6.1) and/or R v 4.1.0 software were used for data analysis at 95% confidence limit. \u003cem\u003ePlasmodium\u003c/em\u003e infection rates among the genotypes for each marker were compared using the Fisher\u0026rsquo;s Exact Test/Pearson\u0026rsquo;s Chi-square tests.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eVariation in densities of host-seeking\u003cem\u003e\u0026nbsp;An. funestus\u0026nbsp;\u003c/em\u003es.l.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGeographic regions along the Kenyan Rift Valley were sampled for adult anopheline mosquitoes both indoors and outdoors using CDC light traps. Sampling sites included the Kerio Valley (Kapluk and Kapnarok), western Kenya (Busia, Ahero, and Bungoma), and coastal areas (Kilifi\u0026mdash;Sihu and Jaribuni\u0026mdash;and Kwale\u0026mdash;Marigiza and Taveta) (Figure 1). Only the outdoor traps were baited with dry ice.\u003c/p\u003e\n\u003cp\u003eA total of 1,967 \u003cem\u003eAn. funestus\u003c/em\u003e s.l. specimens were processed by PCR, with \u003cem\u003eAn. funestus\u003c/em\u003e emerging as the predominant sibling species, accounting for 66.6% (1310/1967) of the total. In contrast, captures of \u003cem\u003eAn. rivulorum\u003c/em\u003e, \u003cem\u003eAn. leesoni\u003c/em\u003e, \u003cem\u003eAn. parensis\u003c/em\u003e, and \u003cem\u003eAn. longipalpis\u0026nbsp;\u003c/em\u003eC were comparatively low. Notably, \u003cem\u003eAn. longipalpis C\u003c/em\u003e was detected exclusively at Kapluk. A considerable proportion (23.7%) of mosquitoes captured in Taveta (coastal site) failed to amplify during molecular analysis. Interestingly, \u003cem\u003eAn. funestus\u0026nbsp;\u003c/em\u003ewas the dominant species captured indoors across western sites (Ahero, Busia, and Bungoma) and Kerio Valley locations (Kapluk and Kapnarok) and Coast (Kilifi, Kwale) except Taveta. The species was predominant outdoors across all the sites except in Kapluk in the RV and Taveta at the coast. (Figure 2). Interestingly, \u003cem\u003eAn. funestus\u0026nbsp;\u003c/em\u003ewas the dominant species captured indoors across western sites (Ahero, Busia, and Bungoma) and Kerio Valley locations (Kapluk and Kapnarok) and Coast (Kilifi, Kwale) except Taveta. The species was predominant outdoors across all the sites except in Kapluk in the RV and Taveta at the coast (Figure 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMalaria parasite infection and association with allele frequency of resistance markers\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA subset of \u003cem\u003eAn. funestus\u0026nbsp;\u003c/em\u003e(n=463) was analysed for infection with \u003cem\u003ePlasmodium\u003c/em\u003e sporozoite and genotyped for metabolic resistance gene markers; \u003cem\u003eL119F-Gste2\u003c/em\u003e and \u003cem\u003eG454A-Cyp9k1\u003c/em\u003e and one structural variant \u003cem\u003e4.3Kb-SV\u003c/em\u003e. Of these, 8.2% (range: 3.3 \u0026ndash; 44.4%) tested positive for \u003cem\u003ePlasmodium\u003c/em\u003e sporozoite infection (95% \u003cem\u003eP. falciparum;\u0026nbsp;\u003c/em\u003e5%\u003cem\u003e\u0026nbsp;P. ovale\u003c/em\u003e) with variation among sites (\u003cstrong\u003eTable 1\u003c/strong\u003e). Cumulative \u003cem\u003ePlasmodium\u003c/em\u003e infection rates were highest among locations in western (12.7%; 21/165; range: 5.7 \u0026ndash; 44.4%), followed by KV (7.1%; 7/99; range 5.5 \u0026ndash; 9.1) and then coast (5.1%; 10/196; range 3.3 -9.5%). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e: \u003cem\u003ePlasmodium\u003c/em\u003e infection rates in \u003cem\u003eAn. funestus\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.0871%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.7456%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSite\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46.1672%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ePlasmodium\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003einfection rates (proportion)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.0871%;\"\u003e\n \u003cp\u003eWestern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.7456%;\"\u003e\n \u003cp\u003eBusia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46.1672%;\"\u003e\n \u003cp\u003e10.6 (10/94)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.0871%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.7456%;\"\u003e\n \u003cp\u003eBungoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46.1672%;\"\u003e\n \u003cp\u003e44.4 (8/18)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.0871%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.7456%;\"\u003e\n \u003cp\u003eAhero\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46.1672%;\"\u003e\n \u003cp\u003e5.7 (3/53)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.0871%;\"\u003e\n \u003cp\u003eKerio Valley\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.7456%;\"\u003e\n \u003cp\u003eKapnarok\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46.1672%;\"\u003e\n \u003cp\u003e5.5 (3/55)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.0871%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.7456%;\"\u003e\n \u003cp\u003eKapluk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46.1672%;\"\u003e\n \u003cp\u003e9.1 (4/44)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.0871%;\"\u003e\n \u003cp\u003eCoast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.7456%;\"\u003e\n \u003cp\u003eMarigiza-Kwale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46.1672%;\"\u003e\n \u003cp\u003e7.1 (4/56)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.0871%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.7456%;\"\u003e\n \u003cp\u003eTaveta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46.1672%;\"\u003e\n \u003cp\u003e9.5 (2/21)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.0871%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.7456%;\"\u003e\n \u003cp\u003eJaribuni-Kilifi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46.1672%;\"\u003e\n \u003cp\u003e3.3 (4/122)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.0871%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.7456%;\"\u003e\n \u003cp\u003eTotal\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46.1672%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e8.2 (38/463)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eGenotype frequencies for three key metabolic resistance markers; \u003cem\u003eL119F-GSTe2\u003c/em\u003e, \u003cem\u003eG45A-CYP9K1\u003c/em\u003e, and \u003cem\u003e4.3-Kb SV\u003c/em\u003e transposon insertion, were assessed in \u003cem\u003eAn. funestus\u003c/em\u003e populations from western, Rift Valley and coastal regions. The results revealed striking regional contrasts (Figure 3), indicating heterogeneous selection pressures across ecological zones, likely reflecting differences in insecticide exposure and local transmission dynamics. The \u003cem\u003eL119F-GSTe2\u003c/em\u003e, successfully assessed in 392 specimens, yielded an overall allele frequency of 0.33 (range: 0.16\u0026ndash;0.74). The resistant \u003cem\u003e119F-GSTe2\u003c/em\u003e allele was most prevalent along the coast (55/138; 39.9%), at lower frequency in KV (7/95; 7.4%), and least frequent in western Kenya (7/159; 4.4%) (Figure 3a). By contrast, genotyping of the \u003cem\u003eG454A-CYP9K1\u003c/em\u003e (n = 445) and \u003cem\u003e4.3-Kb SV\u003c/em\u003e (n = 336) revealed much higher frequencies of the RR genotype, approaching fixation in western Kenya (~98.8%), followed by the Rift Valley (~91%), and the coastal sites of Kwale and Kilifi (~82%) (Figure 3b\u0026ndash;c). For the\u003cem\u003e\u0026nbsp;CYP6P9a\u003c/em\u003e marker (n = 139), the SS genotype was detected only sporadically in KV and western Kenya and was entirely absent from the coast.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe next compared the distribution of genotypes between \u003cem\u003ePlasmodium\u003c/em\u003e-infected and non-infected specimens across the detected resistance markers. For both \u003cem\u003eG454A-CYP9K1\u003c/em\u003e and \u003cem\u003e4.3kb-SV\u003c/em\u003e, infection was more frequently observed among individuals carrying the RR genotype (Fig. 4a; Figure S1) but this association was not significant between the genotypes for \u003cem\u003e4.3kb-SV\u003c/em\u003e and \u003cem\u003eG454A-\u003c/em\u003e\u003cem\u003eCYP9K1\u003c/em\u003e (Fig. 4b). Further analysis at the allelic level revealed that by combining all samples from different regions hence increasing sample size for phenotype-genotype association, only the R individuals with the 4.3Kb-SV significantly carried higher infection than S (OR = 5.7, p = 0.049) (Figure S1). In contrast, for the \u003cem\u003eL119F-Gste2\u003c/em\u003e marker, revealed an opposite trend, with a higher proportion of infections occurring in individuals carrying the SS genotype (Figure 4a). There was a 5-fold significant likelihood of parasite infection in mosquitoes carrying the RS than RR genotype for \u003cem\u003eL119F-GSTe2\u003c/em\u003e (Figure S2). Nonetheless, there was no difference in infection prevalence between the genotypes and alleles for this marker (Figure 4b, Figure S1 and S2). Although not statistically significant, this suggests a potential negative association between the resistant \u003cem\u003e119F-GSte2\u003c/em\u003e allele and parasite infection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSequence analysis of the \u003cem\u003eGSTe\u003c/em\u003e-2 gene\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe negative relationship between resistant allele of \u003cem\u003eGSTe\u003c/em\u003e-2 gene and parasite infectivity contrasts with previous literature [35,40], prompted further investigations. A segment of the GSTe-2 gene (666 bp) was sequenced among selected Kenyan genotypes to infer the evolutionary relationship with reference sequences generated across Africa. A pronounced genetic differentiation was evident between the Coastal Kenyan populations and rest of other African regions (Fig. 5). The analysis identified two major clades, a primary clade that included sequences from several African countries, including Benin, Uganda, Malawi, Mozambique, Ghana, Cameroon, and sequences from Rift Valley of Kenya (Figure 5a). A second clade contained sequences exclusively from Coastal site of Kenya. Further, genetic differentiation was demonstrated by the haplotype network analysis which revealed two distinct networks, one containing two dominant haplotypes (H\u003csub\u003e1\u003c/sub\u003e and H\u003csub\u003e2\u003c/sub\u003e) comprising sequences from various geographical regions in Africa, including West Africa (Ghana and Benin), southern Africa (Malawi and Mozambique), Central Africa (Cameroon), and eastern Africa (Uganda and Rift Valley Kenya) (Fig 5b). Conversely, the second haplotype network included sequences exclusively from Coastal Kenya, featuring two major haplotypes (H\u003csub\u003e3\u003c/sub\u003e and H\u003csub\u003e4\u003c/sub\u003e) specific to this Kenyan region. Overall, the analyses revealed high genetic divergence in GSTe2 sequences between most \u003cem\u003eAn. funestus\u003c/em\u003e populations from Coastal Kenya and those from the rest of Kenya.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNucleotide polymorphism analysis within the \u003cem\u003eGSTe2\u003c/em\u003e gene \u003cem\u003ein An. funestus\u0026nbsp;\u003c/em\u003eacross Kenya\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNucleotide polymorphism analysis was performed to investigate the nucleotide changes encompassing the different haplotypes of the \u003cem\u003eGSTe2\u003c/em\u003e gene across Africa. The analysis shows that primary dominant haplotype (H\u003csub\u003e1\u003c/sub\u003e) common to all African samples (Figure 6a) is characterized by the absence of single nucleotide polymorphism (SNP) across the \u003cem\u003eGSTe2\u0026nbsp;\u003c/em\u003eopen reading frame (ORF). Twenty-three (23) out of 98 sequences examined harboured this haplotype. The secondary dominant haplotype (H\u003csub\u003e2\u003c/sub\u003e) found at high frequency in Benin (15 sequences), moderate frequencies in Cameroon (4 sequences) and Ghana (5 sequences) is characterized by a single nucleotide change: a cytosine (C) to thymine nucleotide transition at position 355, resulting in an amino acid change from leucine (L) to phenylalanine (F) on codon 119. The third and fourth haplotype (H\u003csub\u003e3\u0026nbsp;\u003c/sub\u003eand H\u003csub\u003e4\u003c/sub\u003e), which are exclusively found in Coastal Kenya, have multiple SNPs that produce a unique protein sequence. This protein sequence is marked by 10 amino acid changes in linkage disequilibrium: asparagine (N) to thymine (T) at codon 33, a glycine (G) to alanine (A) at codon 80, lysine (K) \u0026nbsp;changed to valine (V) at codon 146, aspartate (D) to asparagine (N) at codon 147, serine (S) to alanine (A) at codon 153, glutamate (E) to aspartate (D) at codon 176, histidine (H) to tyrosine (Y) at codon 180, arginine (R) to glutamine (Q) at codon 182, glutamate (E) to glycine (G) at codon 185 and aspartate (D) \u0026nbsp;to asparagine (N) at codon 188 (T\u003csup\u003e33\u0026nbsp;\u003c/sup\u003eA\u003csup\u003e80\u0026nbsp;\u003c/sup\u003eV\u003csup\u003e146\u0026nbsp;\u003c/sup\u003eN\u003csup\u003e147\u0026nbsp;\u003c/sup\u003eA\u003csup\u003e153\u0026nbsp;\u003c/sup\u003eD\u003csup\u003e176\u0026nbsp;\u003c/sup\u003eY\u003csup\u003e180\u0026nbsp;\u003c/sup\u003eQ\u003csup\u003e182\u0026nbsp;\u003c/sup\u003eG\u003csup\u003e185\u0026nbsp;\u003c/sup\u003eN\u003csup\u003e188\u003c/sup\u003e). This Coastal Kenyan haplotype is present at a moderate frequency, with 10 out of 30 sequences harbouring this specific haplotype.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInsecticide resistance profile\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInsecticide resistance and intensity were higher for type 1 (permethrin) than type II (deltamethrin, alpha-cypermethrin) pyrethroids and was more pronounced for \u003cem\u003eAn. funestus\u003c/em\u003e in RV than western Kenya, a traditional malaria hotspot (Figure 7 a,b). Increasing dosage only had a modest effect on mortality with permethrin in KV (average mortality: 1\u0026times; = 3%, 5\u0026times; = 7.1% and 10\u0026times; = 11.2%) and Busia (average mortality: 1\u0026times; = 0%, 5\u0026times; = 5.4% and 10\u0026times; = 24%). Mortality increased with an increasing dosage of \u0026alpha;-cypermethrin in Busia: (average mortality; 1\u0026times; = 20.4%, 5\u0026times; = 87.4.1% and 10\u0026times; = 100%) but not so much in KV (average mortality; KV: 1\u0026times; = 3.2%, 5\u0026times; = 5.2% and 10\u0026times; = 23.2%). With increasing concentration, the mortality greatly increased with deltamethrin in Busia (average mortality: 1\u0026times; = 68.3%, 5\u0026times; = 84.6% and 10\u0026times; = 86.5%) and KV: average mortality; 1\u0026times; = 2.7%, 5\u0026times; = 64.5% and 10\u0026times; = 71.5%) (Figure 7a-c).\u003c/p\u003e\n\u003cp\u003ePre-exposure to PBO notably increased the mortality with alpha-cypermethrin (mean mortality=39.5% and 65.3% in KV and Busia, respectively) and deltamethrin (mean mortality= 64.2% and 89.6% in KV and Busia, respectively). However, only a modest increase in mortality with PBO was observed with permethrin in KV (mean mortality=5%), lower than in Busia (mean mortality=10.8%). Coastal population exhibited intense resistance to permethrin only (1\u0026times; = 6.7%, 5\u0026times; = 7.5% and 10\u0026times; = 9.1%) and similar low mortality in PBO synergist assay with this insecticide (mean mortality= 30.8%). Both Busia and KV populations were resistant to DDT (mean mortality; KV: 36.8%; Busia=68.3%) except coast that was susceptible (mean mortality= 98.4%). By contrast, all three populations were fully susceptible (mortality =100%) to bendiocarb, pirimiphos-methyl, clothianidin and chlorfenapyr.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOviposited \u003cem\u003eF\u003c/em\u003e\u003cem\u003e0\u003c/em\u003e \u003cem\u003eAn. funestus\u003c/em\u003e s.l. mosquitoes (from which F1 progeny used for resistance exposure was derived) identified by cocktail PCR revealed 99% (129/130) and 99% (125/126) were \u003cem\u003eAn. funestus\u003c/em\u003e from KV and Busia, respectively. In contrast, only a minor proportion of \u003cem\u003eAn. funestus\u003c/em\u003e s.l. from the coast was \u003cem\u003eAn. funestus\u003c/em\u003e (5%), with the majority being \u003cem\u003eAn. rivulorum\u003c/em\u003e (80%, 72/90).\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eInvestigating the spread of resistance markers is critical for insecticide resistance monitoring in malaria vectors, gauging insight into associated mechanisms and informing control strategies. In this study, vector bionomics, distribution of key metabolic resistance markers and resistance profiles to several insecticides were investigated among \u003cem\u003eAn. funestus\u003c/em\u003e populations along the Rift Valley divide spanning major malaria risk zones of Kenya. Spatial distribution analysis revealed two distinct patterns; that in both KV and western sites, \u003cem\u003eAn. funestus\u003c/em\u003e is the predominant sibling species in the \u003cem\u003eFunestus\u003c/em\u003e group and mostly encountered indoors. The coast, also dominated by \u003cem\u003eAn. funestus\u003c/em\u003e in some sites which were mostly encountered outdoors, consistent with previous results [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The result might indicate divergence in the biting and resting behavior of this mosquito at the coast from western/KV areas. High outdoor biting behavior of malaria vectors could be associated with low exposure to ITNs consequently affecting expression of insecticide resistance differently among the vector populations. The divergence could be ascribed to a combination of factors, including vector adaptation to human behavior, climate, and even genetics [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Previous studies found genetic divergence of coastal \u003cem\u003eAn. funestus\u003c/em\u003e populations from other areas in Kenya[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] but genetic heterogeneities that underpin indoor versus outdoor habits and perhaps biting times should be investigated further.\u003c/p\u003e\u003cp\u003eThe study also revealed a high \u003cem\u003ePlasmodium\u003c/em\u003e sporozoite infection rate in \u003cem\u003eAn. funestus\u003c/em\u003e, comparable or even higher than values observed for this species in Central [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] and East Africa [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Notably, study sites in western Kenya displayed the overall highest \u003cem\u003ePlasmodium\u003c/em\u003e infection rates, followed by KV and then coast. The results suggest underlying difference in competence of vector populations or levels of usage or protection provided by ITNs and/or IRS. Evidence of sustained high Plasmodial transmission and infection resurgence in western Kenya despite intensified malaria control interventions since 2006 [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], indicate the need to investigate this trend further. The vector infection results confirm the active role this mosquito species plays in contemporary and persistent malaria transmission [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The other sibling species or unknown species were most prevalent at the coast. Because these other sibling species, including cryptic species [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]are equally important in transmission, characterization of their resistance profiles remains primordial. Further longitudinal surveys will address seasonal dynamics of these species and role in malaria transmission.\u003c/p\u003e\u003cp\u003eOur study reveals distinct spatial gradients in metabolic resistance marker allele frequencies among Kenyan \u003cem\u003eAn. funestus\u003c/em\u003e populations, with \u003cem\u003eG454A-CYP9K1\u003c/em\u003e and the \u003cem\u003e4.3-kb SV\u003c/em\u003e increasing from the western regions through the Rift Valley to the coast, while \u003cem\u003eL199F-GSTe2\u003c/em\u003e declines, suggesting region-specific selective pressures. The \u003cem\u003eCYP6P9a\u003c/em\u003e mutation was rare, restricted to homozygous susceptible genotypes in western and Rift Valley sites, consistent with the limited geographic spread of the mutant variants of the southern Africa \u003cem\u003eCYP6P9\u003c/em\u003e markers into eastern Africa[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] except central west and south to northeastern Tanzania [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Although the \u003cem\u003eGSTe2\u003c/em\u003e RR genotype predominated at the coast, east of the Rift Valley, mosquitoes carrying the susceptible \u003cem\u003eGSTe2\u003c/em\u003e SS genotype were more frequently infected with \u003cem\u003ePlasmodium\u003c/em\u003e, contrasting with Central African studies reporting higher parasite prevalence among resistant genotypes [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The discovery of a novel \u003cem\u003eGSTe2\u003c/em\u003e haplotype unique to coastal populations perhaps further explains this contrast and further supports differential local selection pressures that could impact vector-parasite interaction. Interestingly, we observed a strong positive association between the \u003cem\u003e4.3-kb SV\u003c/em\u003e mutant alleles and parasite infection, opposite to prior reports where infection was higher in mosquitoes lacking this variant [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. This discrepancy may reflect regional differences of the impact of this marker on gene expression, vector competence, environmental adaptation, or insecticide-driven selection that could modulate parasite susceptibility. The G454A mutation in CYP9K1, a major driver of type II pyrethroid resistance in East and Central Africa [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], approached fixation alongside the \u003cem\u003e4.3-kb SV\u003c/em\u003e, consistent with observations in eastern Uganda [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Presence of multi-locus combinations of \u003cem\u003eL119F-GSTe2\u003c/em\u003e, \u003cem\u003eG454A-CYP9K1\u003c/em\u003e, and \u003cem\u003e4.3-kb SV\u003c/em\u003e alleles did not correlate with infection status. Known antagonistic interactions between \u003cem\u003eCYP6P9a/b\u003c/em\u003e and the \u003cem\u003e4.3-kb SV\u003c/em\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] and the cumulative impact of multiple resistance factors on extreme pyrethroid resistance (\u0026gt;\u0026thinsp;1000-fold) highlight the complex adaptive landscape. Collectively, these findings reveal heterogeneous selection acting on key resistance genes across Kenyan populations, shaped by ecological variation and local insecticide exposure, and underscore the need for genomic studies to clarify adaptive signatures, potential fitness costs, and their implications for malaria transmission.\u003c/p\u003e\u003cp\u003eThis study provides the first evidence of pyrethroid resistance escalation in Kenyan \u003cem\u003eAnopheles funestus\u003c/em\u003e, using 5\u0026times; and 10\u0026times; discriminating concentrations. Resistance intensity varied markedly across regions, with coastal populations exhibiting lower mortality than those from the Kerio Valley (Rift Valley) and Busia (western Kenya). Species composition likely contributes to this pattern: coastal samples were dominated by \u003cem\u003eAn. rivulorum\u003c/em\u003e (\u0026lt;\u0026thinsp;5% \u003cem\u003eAn. funestus\u003c/em\u003e), whereas KV and Busia populations were \u0026gt;\u0026thinsp;99% \u003cem\u003eAn. funestus\u003c/em\u003e. Whether these sibling species differ in resistance mechanisms remains unclear, but this may partly explain the lower resistance observed at the coast. Nonetheless, the low mortality to permethrin at 10\u0026times; concentration is highly concerning and aligns with recent reports of pyrethroid resistance in coastal \u003cem\u003eAn. funestus\u003c/em\u003e [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Notably, resistance intensity in KV was higher than in Busia, possibly reflecting intensified insecticide selection from agricultural practices such as cotton cultivation, which relies heavily on pyrethroids, compared with public health interventions like ITNs and IRS that dominate the coast and western Kenya [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSynergist assays revealed low mortality following pre-exposure to PBO, particularly with permethrin in KV, suggesting that cytochrome P450\u0026ndash;mediated detoxification is an important but not exclusive mechanism driving resistance. The persistence of high resistance despite PBO exposure indicates that other metabolic pathways, cuticular modifications, or additional genetic factors may also contribute. Moreover, the presence of multilocus resistance genotypes, including combinations of CYP9K1, 4.3-kb SV, and \u003cem\u003eGSTe2\u003c/em\u003e, suggests complex interactions that may influence both insecticide tolerance and vectorial capacity. These findings highlight the need for molecular and genomic studies to unravel the adaptive signatures and functional mechanisms underlying resistance escalation in these populations [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eEncouragingly, full susceptibility was observed to bendiocarb in all populations, in contrast to previous and recent reports in Uganda [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e], indicating that carbamates remain viable alternatives for IRS in these localities. Full susceptibility to organophosphates and newer chemistries, including pyrimiphos-methyl, clothianidin, and chlorfenapyr, further expands the portfolio of tools for pyrethroid resistance management [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. The escalation of pyrethroid resistance documented here has clear implications for vector control efficacy, particularly for ITN and IRS strategies [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] and similar trends have been observed in the neighboring country, Uganda [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The observed heterogeneity in resistance across species, regions, and selection pressures emphasizes the necessity for locally tailored interventions and sustained resistance monitoring to anticipate shifts in adaptive dynamics and inform effective malaria control programs.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, the study confirms high malaria parasite infection in \u003cem\u003eAn. funestus\u003c/em\u003e populations exhibiting pyrethroid resistance escalation and different evolutionary histories in resistance genes (at least in \u003cem\u003eGSTe-2\u003c/em\u003e) along a west-RV-coast gradient in Kenya. The role of the Rift Valley should be considered in insecticide resistance spread and effective resistance management in Kenya. Further work is needed to elucidate the underlying molecular basis of resistance escalation and the significance of the observed resistance mutations on malaria transmission.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo Juliet Onditi for designing the study map. We are grateful for the technical support of Irene Wainaina and Josephine Osalla. Special thanks to the local administration and the household heads for access to their homesteads for mosquito trapping. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by a Wellcome Trust Intermediate Fellowship to D.P.T. (award number 222005/Z/20/Z). Amine M Mustapha is supported by UNESCO-TWAS (The World Academy of Sciences) PhD Scholarship through the \u003cem\u003eicipe\u003c/em\u003e ARPPIS Program. The authors gratefully acknowledge the financial support for this research by the following organizations and agencies: the Swedish International Development Cooperation Agency (Sida); the Swiss Agency for Development and Cooperation (SDC); the Australian Centre for International Agricultural Research (ACIAR); the Government of Norway; the German Federal Ministry for Economic Cooperation and Development (BMZ); and the Government of the Republic of Kenya. The views expressed herein do not necessarily reflect the official opinion of the donors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this published article and its additional files.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDPT and WCS designed the study. DPT, AMM, GR, conducted field work. DPT, AMM, TKM, BM, performed the resistance testing in the insectary. DPT performed molecular experiments. DPT, AO, CSDT. analysed the data; DPT, LK, BT and WCS contributed to resources and funding acquisition; DPT, AO wrote the manuscript with contribution from all the authors. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Scientific and Ethical Review Unit (SERU) of the Kenya Medical Research Institute (KEMRI) (Protocol no. NON KEMRI 4592) and adhered to relevant guidelines and regulations. Prior to data collection, the purpose of the study, procedures and associated benefits or risks were provided to the local leadership at county and community levels. In addition, household heads provided written consent allowing trap placement in their premises.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col start=\"1\" type=\"1\"\u003e\n\u003cli\u003eWorld Health Organization. World malaria report 2024: addressing inequity inthe global malaria response. Geneva: World Health Organization; 2024.\u003c/li\u003e\n\u003cli\u003eKenya National Bureau of Statistics, ICF. Kenya Malaria Indicator Survey 2020. Nairobi, Kenya and Rockville, Maryland, USA: KNBS and ICF; 2021. 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TCS: a computer program to estimate gene genealogies. Mol Ecol. 2000;9(10):1657-9.\u003c/li\u003e\n\u003cli\u003eTchouakui M, Chiang MC, Ndo C, Kuicheu CK, Amvongo-Adjia N, Wondji MJ, et al. A marker of glutathione S-transferase-mediated resistance to insecticides is associated with higher \u003cem\u003ePlasmodium\u003c/em\u003e infection in the African malaria vector \u003cem\u003eAnopheles funestus\u003c/em\u003e. Sci Rep. 2019;9(1):5772.\u003c/li\u003e\n\u003cli\u003eBamou R, Mbakop LR, Kopya E, Ndo C, Awono-Ambene P, Tchuinkam T, et al. Changes in malaria vector bionomics and transmission patterns in the equatorial forest region of Cameroon between 2000 and 2017. Parasit Vectors. 2018;11(1):464.\u003c/li\u003e\n\u003cli\u003eKiuru CW, Oyieke FA, Mukabana WR, Mwangangi J, Kamau L, Muhia-Matoke D. Status of insecticide resistance in malaria vectors in Kwale County, Coastal Kenya. 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The contribution of agricultural insecticide use to increasing insecticide resistance in African malaria vectors. Malar J. 2016;15(1):107.\u003c/li\u003e\n\u003cli\u003eGadji M, Tazokong HR, Kouamo MFM, Tchouakui M, Wondji M, Mugenzi LMJ, et al. Genomic drivers of pyrethroid resistance escalation in the malaria vector \u003cem\u003eAnopheles funestus\u003c/em\u003e across Africa. Mol Biol Evol. 2025;42(10):msae198.\u003c/li\u003e\n\u003cli\u003eMorgan JC, Irving H, Okedi LM, Steven A, Wondji CS. Pyrethroid resistance in an \u003cem\u003eAnopheles funestus\u003c/em\u003e population from Uganda. PLoS One. 2010;5(7):e11872.\u003c/li\u003e\n\u003cli\u003eTchouakui M, Thiomela RF, Nchoutpouen E, Menze BD, Ndo C, Achu D, et al. High efficacy of chlorfenapyr-based net Interceptor\u0026reg; G2 against pyrethroid-resistant malaria vectors from Cameroon. Infect Dis Poverty. 2023;12(1):81.\u003c/li\u003e\n\u003cli\u003eTchouakui M, Assatse T, Tazokong HR, Oruni A, Menze BD, Nguiffo-Nguete D, et al. Detection of a reduced susceptibility to chlorfenapyr in the malaria vector \u003cem\u003eAnopheles gambiae\u003c/em\u003e contrasts with full susceptibility in \u003cem\u003eAnopheles funestus\u003c/em\u003e across Africa. Sci Rep. 2023;13(1):2363.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"infectious-diseases-of-poverty","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"idop","sideBox":"Learn more about [Infectious Diseases of Poverty](http://idpjournal.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/idop/default.aspx","title":"Infectious Diseases of Poverty","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Malaria transmission, pyrethroid resistance escalation, An. funestus, Rift Valley, metabolic resistance, resistance markers, geographic barriers","lastPublishedDoi":"10.21203/rs.3.rs-8224847/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8224847/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eLandscape features such as the Rift Valley (RV) can restrict gene flow in malaria vectors and influence resistance patterns. Here, we assessed resistance alleles and profiles in \u003cem\u003eAnopheles funestus\u003c/em\u003e s.s. populations across Kenyan malaria-endemic regions separated by the RV.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003e\u003cem\u003eAnopheles funestus\u003c/em\u003e s.s. populations in western, coastal and Kerio Valley (KV, within the RV) were assessed for key resistance markers and their association with \u003cem\u003ePlasmodium\u003c/em\u003e sporozoite infection. Phenotypic resistance using F1 progeny was also assessed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe 4.3Kb-SV and G454A-Cyp9k1 alleles were nearly fixed in western Kenya but declined towards the RV and coast, whereas L119F-GSTe2 increased across a west-KV-coast gradient with a novel haplotype distinct from known African variants detected at the coast. There were lower odds of \u003cem\u003ePlasmodium\u003c/em\u003e infection in mosquitoes with L119F-GSTe2-RR than RS genotype (OR\u0026thinsp;=\u0026thinsp;0.2, p\u0026thinsp;=\u0026thinsp;0.046). Likewise, mosquitoes harboring the R allele of the 4.3kb marker had higher \u003cem\u003ePlasmodium\u003c/em\u003e infection rates than the S allele (OR\u0026thinsp;=\u0026thinsp;5.7, p\u0026thinsp;=\u0026thinsp;0.049). \u003cem\u003eAn. funestus\u003c/em\u003e populations exhibited a high degree of pyrethroid resistance with intensity higher in KV compared to western Kenya, a traditional malaria hotspot. Pre-exposure to PBO increased mortality for type II (deltamethrin, alpha-cypermethrin), than I (permethrin) pyrethroids, yet recovery remained lower in KV, suggesting non-P450-mediated resistance. Coastal mosquitoes showed extreme permethrin resistance (\u0026lt;\u0026thinsp;10% mortality at 10\u0026times; dose). DDT resistance was widespread, while all populations remained fully susceptible to bendiocarb, pirimiphos-methyl, clothianidin, and chlorfenapyr.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eRegion-specific selection may drive varying resistance profiles in \u003cem\u003eAn. funestus\u003c/em\u003e across the RV, with implications for malaria transmission and insecticide resistance management.\u003c/p\u003e","manuscriptTitle":"Signature of resistance gene evolution and pyrethroid resistance escalation in the major malaria vector Anopheles funestus across the Kenyan Rift Valley","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-04 13:08:33","doi":"10.21203/rs.3.rs-8224847/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-05T06:20:49+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-31T14:38:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"49967738377235280355590209012800654571","date":"2025-12-27T06:22:34+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-13T21:05:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"39727390428395724242948454763725915186","date":"2025-12-05T11:32:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"245000087001539981657134307103726230410","date":"2025-12-05T11:29:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"138132844050936932017376243501511353979","date":"2025-12-04T00:50:16+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-02T08:04:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-01T12:57:09+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-01T12:56:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"Infectious Diseases of Poverty","date":"2025-11-27T19:56:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"infectious-diseases-of-poverty","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"idop","sideBox":"Learn more about [Infectious Diseases of Poverty](http://idpjournal.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/idop/default.aspx","title":"Infectious Diseases of Poverty","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"57b1d729-a191-433f-b8ae-8881271e0918","owner":[],"postedDate":"December 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-05T00:24:08+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-04 13:08:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8224847","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8224847","identity":"rs-8224847","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}