Exploring the impact of metabolic-based insecticide resistance on the clonal diversity of Plasmodium falciparum in Anopheles funestus

Exploring the impact of metabolic-based insecticide resistance on the clonal diversity of Plasmodium falciparum in Anopheles funestus | Research Square 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 Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Exploring the impact of metabolic-based insecticide resistance on the clonal diversity of Plasmodium falciparum in Anopheles funestus Pierre Fongho Suh, Darus Tagne1, Auriane Babiane Nzeumi3, Carine Nkodo Ndjebakal, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8989122/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The rapid spread of insecticide resistance in malaria vectors raises concerns about its potential impact on disease transmission. This study investigated the influence of two key metabolic resistance markers (GSTe2 and CYP9K1) on the clonal diversity of Plasmodium falciparum in Anopheles funestus population from southern Cameroon. Methods A total of 138 P. falciparum isolates infecting a natural population of An. funestus sampled in Emana-Benyada, Nkol-Feb and Ezenglassi, three localities of southern Cameroon, were genotyped at block 2 of msp1 and block 3 of msp2 to assess parasite polymorphism. The genetic diversity was then compared according to host insecticide resistance genotypes. Results The overall clonal richness was highest in GSTe2 homozygote susceptible mosquitoes with 65 clones followed by RS (N = 53) and RR (N = 52) genotypes. In contrast, for CYP9K1 maker, RR showed the highest number of clones (N = 70), compared with 66 in SS and 52 in RS. Clones common to all genotypes were significantly more frequent (p < 0.0001), representing 71.78% to 90.36% of the entire population of P. falciparum . The heterozygosity was high across markers, ranging from 0.896 to 0.947 in RR, 0.824 to 0.944 in RS and 0.91 to 0.970 in SS. Conclusion Our study indicates that metabolic-based markers influence P. falciparum clonal richness in An. funestus , but do not substantially affect the overall likelihood of multiclonal infection. This study provides novel insight into how vector resistance mechanisms may shape parasite population structure. Molecular Epidemiology Anopheles funestus Plasmodium falciparum GSTe2 CYP9K1 clonal diversity Cameroon Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Plasmodium falciparum is the most prevalent and virulent malaria parasite infecting humans. It is highly endemic in Sub-Africa where it is responsible of the major part of malaria-related morbidity and mortality. In 2023 alone, the region recorded an estimated 246 million cases and 569 000 deaths ( 1 ). One of the major challenges for malaria control is the extensive genetic diversity of the parasite. Multiplicity of infection (MOI), defined as the number of genetically distinct parasite clones co-infecting a single host a single host, is used as a proxy to assess transmission intensity in endemic regions ( 2 – 4 ). MOI may arise either from multiple infective mosquito bites or from a single bite inoculating different clones simultaneously ( 5 – 7 ). High parasite polymorphism could contribute to immune evasion, undermines anti-malaria vaccines development and threatens the efficacy of artemisinin-based combination therapies (ACTs) ( 8 – 10 ). Routine monitoring of parasite genetic diversity is therefore an important component of malaria surveillance and control strategies ( 11 ). The merozoite surface protein genes 1 and 2 ( msp1 and msp2 ) are among the most commonly used markers for assessing P. falciparum genetic diversity ( 12 ). Both proteins participate in erythrocyte invasion ( 13 ) and are major targets of host immune response ( 14 , 15 ). The msp1 gene, located on chromosome 9, consists of 17 blocks sequences blocks, with block 2 being the most polymorphic and classified into three allelic families: K1, MAD20 and R033 ( 16 , 17 ). The msp2 gene is located on chromosome 2 and contains five sequence blocks of which block 3 is the most polymorphic ( 18 ), with alleles clustering into two families named FC27 and 3D7/IC1 ( 12 ). Between 2000 and 2015, large-scale distribution of insecticide-treated nets (ITNs) and expanded access to effective antimalarials contributed to nearly 70% reduction in malaria mortality and prevented over 663 million clinical cases worldwide ( 19 ). However, a stagnation in malaria decline was observed recently and is attributed to the rapid spread of insecticide resistance in major vector species including An. gambiae , An. coluzzii , An. arabiensis and An. funestus ( 20 ). This resistance arises through metabolic, target site, cuticular and behavioural mechanisms, with the first two being the most operationally significant. Metabolic resistance, unlike target site resistance, often imposes substantial energetic demands because it relies on the enhanced production of detoxification enzymes that quickly degrade the insecticides before they exert lethal effects ( 21 ). As insecticide-susceptible populations are being replaced by resistant ones, human-vector contact rates may increase, potentially altering malaria transmission dynamics ( 20 ). There is a growing interest and scientific debate whether insecticide-resistant mosquitoes exhibit altered vector competence ( 22 ). Several studies suggest that metabolically resistant mosquitoes may become more permissive to Plasmodium infections, possibly due to physiological changes linked to increased detoxification activity. In An. funestus , insecticide is predominantly driven by the overexpression of cytochrome P450s (CYPs) including CYP6P9a and CYP6P9b and CYP9K1 ( 23 , 24 ). Resistance is also associated with mutations in Glutathione S-transferases (GSTs) such as the GSTe2 -L119F substitution, which modify binding affinity further enhancing detoxification efficiency ( 25 , 26 ). Because physiological changes due to metabolic resistance may influence parasite development, An. funestus represents an ideal system for examining the relationship between parasite clonal diversity and insecticide resistance mechanism. In this study, we used msp1 and msp2 markers to characterize the genetic diversity of P. falciparum developing in naturally infected An. funestus populations from southern Cameroon and, assessed whether key metabolic resistance markers (GSTe2 and CYP9K1) influence or not the clonal diversity of this parasite. Methods Origin of biological samples Specimens of An. funestus infected by P. falciparum were obtained from a previous study carried out in three localities of the Centre region of Cameroon: Nkol-Feb (3˚59̛'9''N et 11˚23̛'5'' E) a rural area located in Okola sub-division, Emana-Benyada (4°15'35.8''N, 11°36'41.9''E) another rural area located in Obala subdivision and Ezenglassi (3°41'48''N, 11°34'32''E) a peri-uban area located in Mfou subdivision. These localities belong to the equatorial forest zone and have a Guinean-type climate characterized by two dry seasons (mid-June to mid-August; mid-November to mid-March) and two rainy seasons (mid-March to mid-June; mid-August to mid-November). In Ezenglassi, the vegetation is considerably degraded by farming activities and infrastructure development than in the two other localities. The transmission of malaria parasite in the Centre region is perennial with high intensity ( 27 ). The mosquitoes were caught either through Human Landing Catches (Emana-Benyada and Nkol-Feb) or through indoor aspiration (Ezenglassi). The sampling was part of a research project aimed primarily at determining the insecticide resistance profile of major malaria vectors and/or evaluate the level of malaria transmission (Human Biting Rate and Entomological Inoculation Rate) in rural and peri-urban areas in the Centre region. The collection periods were as follows: May to July 2021 in Emana-Benyada, February 2022 in Ezenglassi and June 2024 in Nkol-Feb (Tagne, manuscript in preparation). The identity of all specimens was determined morphologically and confirmed molecularly using a cocktail polymerase chain reaction assay ( 28 ). The infection status was established in the head-thorax and abdomen through the Tapman method ( 29 ). In Emana-Benyada, 1473 An. funestus specimens were collected, and a subset of 300 were selected for molecular analysis. In Ezenglassi, 276 specimens were collected, of which 120 were analyzed molecularly. In Nkol-Feb; 135 specimens were collected and all were included in the molecular analysis. The presence of nucleic acid of Plasmodium falciparum was confirmed in 138 DNA extracts: 35 samples (24 abdomens, 11 head-thorax) from Emana-Benyada, 66 samples (53 abdomens and 13 head-thorax) from Nkol-Feb and 37 samples (25 abdomens and 12 head-thorax) from Ezenglassi. The DNA extracts were stored at -20°C until genotyping mosquito resistance markers and characterization P. falciparum msp1 and msp2 allelic variants were undertaken, in April 2025. Genotyping of L119F-GSTe2 and G454A CYP9K1 mutations DNA extracts were subjected to two allele specific PCR (AS-PCR) assays designed for the genotyping of L119F-GSTe2 ( 30 ) and G454A CYP9K1 ( 23 ) mutations, respectively. The PCR reactions were performed in a final volume of 15µl containing 10X Kapa Taq buffer A, 0.2 mM dNTPs, 1.5 mM MgCl 2, 10 mM of each primer (2 pairs for each marker, Table 1 S), 1U Kapa Taq (Kapa biosystems) and 1ul of genomic DNA as template. The amplification conditions for GSTe2 genotyping were as follows: an initial denaturation at 95°C for 2 min, 30 cycles of 94°C for 30 s, 58°C for 30 s and 72°C for 1 min, followed by a final extension step at 72°C for 10 min. The conditions for G454A CYP9K1 genotyping were identical except the annealing temperature which was set at 59°C. The PCR products were separated on 1.5% agarose gel stained with Midori Green advance DNA Stain (Nippon Genetics Europe GmbH) and visualized under a UV transilluminator. Genotyping of P. falciparum msp1 and msp2 genes A Nested PCR using specific primers was performed out to genotype msp1 and msp2 genes from P. falciparum isolates ( 31 ). The primary PCR reaction mixture contained 10X Kapa Taq buffer A, 0.2 mM dNTPs, 1.5 mM MgCl 2, 10 mM of each primer, 1U Kapa Taq (Kapa biosystems) and 1ul of genomic DNA as. For the second PCR round (nested), 1µl of the primary PCR product was used as template. The cycling conditions for the primary PCR were as follows: an initial denaturation at 95°C for 5min,35 cycles of 94°C for 30 s, 62°C for 30 s and 72°C for 1 min and followed by a final extension step at 72°C for 10 min. The nested PCR was run for 40 cycles using the same amplification conditions as the primary PCR except the annealing temperature which was primer-specific. Primer sequence and their respective annealing temperature are presented in Table 1 S. PCR products were separated on a 2% agarose gel, stained with Midori Green advance DNA Stain and visualized using a UV transilluminator. The 100 bp DNA ladder (Introgen, Thermo Fisher Scientific) was used to estimate fragment sizes. For allele calling, msp1 fragments within a range of 10bp were considered the same allele/clone, whereas for msp2 a range of 20bp was applied ( 32 , 33 ). Data analysis Data were entered in Microsoft Excel 2013 and analyzed in Graph Pad Prism (version 8.0.2). Throughout the text, the terms allele and clone were used interchangeably. Distinct clones were defined as PCR amplicons falling within overlapping fragment size. Clones identified exclusively in a single locality; mosquito resistance genotype, or mosquito body part (Head-thorax or abdomen) were categorized as unique clones. In contrast, clones detected across two or more localities, genotypes; or body compartments were classified as shared clones or common clones. The frequency of each allelic family was expressed as the proportion of samples that amplified for that family out of the total number of isolates successfully amplified for the msp1 or msp2 genes. The relative frequency of a group of clones was calculated by dividing the number of clones within this group by the total number clones detected. Polyclonal infections were determined as the proportion of samples presenting more than one amplified clone among all samples that were positive for msp1 or msp2 . The mean multiplicity of infection (MOI) was obtained by dividing the total number of clones identified by the number of positive samples for the corresponding marker ( 34 ). Allelic diversity or Heterozygosity index ( He ) for each marker was calculated from allele frequencies using the following formula: where n is the sample size and pi is the allele frequency at a given locus. Its value varies between 0 (no diversity and 1 (all sampled alleles are different) ( 35 ). The Chi square test or Fisher’s exact test was used to compare proportions. The Kruskal-Wallis test was used to compare MOI. Statistical significance was set at p values < 0.05. Results Frequencies of allelic families A total of 128 samples were successfully genotyped for L119F-GSTe2 mutation and 132 for G454A CYP9K1. The frequency of the resistant (R) allele was 47.65% (122/256) for GSTe2 and 53.79% (142/264) for CYP9K1 . The differences between the frequency of the R and S alleles were not statistically significant (p = 0.245 for GSTe2 ; p = 0.121 for CYP9K1 ). For P. falciparum genotyping, 124 samples were successfully amplified for msp1 and 121 for msp2 . Among the msp1 allelic families, RO33 was the most prevalent (87.90%; 109/124), followed by MAD20 (67.74%; 84/124) and K1 (49.93%; 52/124). For msp2 , the 3D7 family predominated overwhelmingly (99.17%; 120/121) whereas FC27 was detected in 22.31% (27/121) of samples. Alleles size varied from 110-580bp for K1, from100-590bp for MAD20, from 120-610bp for RO33, from 100-800bp for 3D7 and from 200-900bp for FC27 (Figure S1). Comparison of Plasmodium falciparum clonal diversity and MOI among localities The proportion of P. falciparum isolates successfully genotyped was 91.43% with rates of 91.43% (32/35) in Emana-Benyada, 95.45% (63/66) in Nkol-feb and 78.38% (29/37) in Ezenglassi. The frequency of clonal families varied significantly within each locality for both the msp1 and msp2 genes, and their distribution differed significantly among the three localities. In Emana and Ezenglassi, RO33 family was the most prevalent msp1 allele type, whereas in Nkol-feb the MAD20 family predominated. Form msp2 , the 3D7 family was by far the most frequent than the FC27 family (Table 1 ). Table 1 Frequency of Plasmodium falciparum msp1 and msp2 allelic families according to locality Gene Emana-Benyada (n1 = 32; n2 = 32) Nkol-feb (n1 = 63; n2 = 60) Ezenglassi (n1 = 29; n2 = 29) p value msp1 K1 17 (53.12%) 28 (44.44%) 7 (24.14%) 0.0146 MAD20 20 (62.50%) 44 (69.84%) 22 (75.86%) RO33 27 (84.37%) 22 (34.92% 24 (82.76%) p value 0.0246 0.0003 < 0.0001 msp2 3D7 32 (100%) 60 (100%) 28 (96.55% 0.0156 FC27 14 (43.75%) 6 (10%) 7 (24.14%) p value < 0.0001 < 0.0001 < 0.0001 The highest number of distinct clones was observed in Nkol-Feb ( msp1 = 43 clones, msp2 = 24 clones), followed by Emana-Benyada ( msp1 = 33 clones, msp2 = 23 clones) and Ezenglassi ( msp1 = 6 clones, msp2 = 10 clones). The peri-urban locality shared almost all its clones with at least one of the rural localities (Fig. 1 ). Heterozygosity was highest in Emana (HE msp1 = 0.972; HE msp2 = 0.911) followed by Nkol-Feb (HE msp1 =0.953; HE msp2 =0.882) and Ezenglassi (HE msp1 =0.755; HE msp2 =0.687). Polyclonal infection (Pi) for msp1 was 79.36% (50/63) in Nkol-Feb, 68.75% (22/32) in Emana-Benyada and 17.24% (5/29) in Ezenglassi (p < 0.0001). For msp2 , Pi was 84.37% (247/32) in Emana-Benyada, 61.67% (37/60) in Nkol-Feb and 58.62% (17/29) in Ezenglassi (p = 0.048). Multiplicity of Infection (MOI) was higher in the two rural areas than in the peri-urban locality, although the difference was not statistically significant. In Emana-Benyada, Nkol-Feb and Ezenglassi, the MOI for msp1 were 1.47, 1.48 and 1.10, respectively (p = 0.296). For msp2 , the MOI values were 2.04, 1.67 and 1.55 in Emana-Benyada, Nkol-Feb and Ezenglassi, respectively (p = 0.333). Comparison of Plasmodium falciparum clonal diversity between thorax and abdomen of the mosquito Out of 102 P. falciparum isolates obtained from the abdomen, 100 (93 for msp1 and 94 for msp2 ) were successfully amplified. For the head-thorax region, 33 (33 for msp1 and 28 for msp2 ) out of 36 were amplified. In the abdomen the frequencies of clonal families varied significantly for both msp1 and msp2 . In the head-thorax, significant variation in clonal family frequencies was observed only for msp2 . The distribution of clonal families differed significantly between the two body parts at msp1 gene: RO33 was the most frequent family in the abdomen, whereas MAD20 predominated in the head-thorax (Table 2 ). Overall, a higher number of distinct clones was detected in the abdomen than the in head-thorax for both genes. However, approximately half of all distinct clones identified were shared between the two both body parts (35 out of 63 distinct clones for msp1 and 25 out of 48 for msp2 ). The shared clones were also the dominant group in both compartments, representing nearly 80% of all clones detected in the abdomen, and more than 84% of all clones detected in the head-thorax. This pattern was consistent across all allelic families for both genes (Fig. 2 ). Table 2 Frequency of Plasmodium falciparum msp1 and msp2 allelic families according to body part of the mosquito Gene Abdomen (n1 = 93; n2 = 94) Head-thorax (n1 = 33; n2 = 28) p value msp1 K1 36 (38.71%) 16 (48.48%) < 0.0001 MAD20 62 (66.67%) 24 (72.73%) RO33 85 (91.40%) 23 (69.70%) p value < 0.0001 0.0831 msp2 3D7 92 (97.87%) 28 (100%) 0.901 FC27 21 (22.34%) 6 (21.42%) p value < 0.0001 < 0.0001 Comparison of Plasmodium falciparum msp1 and msp2 clonal diversity and MOI among L119F GSTe2 genotypes A total of 36 P. falciparum isolates were obtained from RR mosquitoes, among these 33 (91.67%) were successfully amplified for msp1 and 34 (94.44%) for msp2 . Of the 50 isolates from RS mosquitoes, 49 (98%) were positive for msp1 and 48 (96%) for msp2 . For the SS genotypes, 42 (100%) isolates amplified for msp1 and 39 (92.86%) for msp2. Although the frequencies of clonal families varied significantly within each genotype for both genes, their distribution did not differ statistically among genotypes (Table 3 ). Table 3 Frequency of Plasmodium falciparum msp1 and msp2 allelic families according to GSTe2 genotype of the vector Gene RR (n1 = 33; n2 = 34) RS (n1 = 49; n2 = 48) SS (n1 = 42; n2 = 39) p value msp1 K1 10 (30.30%) 23 (46.94%) 19 (45.23%) 0.9609 MAD20 19 (57.58%) 38 (77.55%) 29 (69.05%) RO33 26 (78.79%) 44 (91.67%) 38 (90.48%) p value 0.0004 < 0.0001 0.0005 msp2 3D7 34 (100%) 47 (97.92%) 39 (100%) 0.5377 FC27 7 (20.59%) 13 (27.08%) 6 (15.38%) p value < 0.0001 < 0.0001 < 0.0001 Overall and for both markers, 52 distinct clones were identified in RR mosquitoes, 53 in RS and 65 in SS. The homozygotes susceptible group presented the highest number of clones within the K1 (13 clones) and 3D7 (20 clones) families, whereas the RS genotype harboured the largest number of clones in RO33 (16 clones) and FC27 (10 clones) families. All three genotypes exhibited the same number of clones in MAD20 family. The RR genotype harboured fewer exclusive clones than the susceptible (SS) mosquitoes, except in the RO33 family (Fig. 3 ). Across all isolates, 31.82% (35/110) of the distinct clones detected were shared by three genotypes, whereas 68.18% (75/110) were unique to one genotype, the difference in proportions being statistically significant (P < 0.0001). The clonal family with the highest proportion of shared clones was RO33 (11/24; 45.83%), followed by FC27 (5/15; 33.33%), 3D7 (9/29; 31.03%), MAD20 (5/20; 25%) and K1 (5/21; 23.81%). However, these values were not statistically significant (p = 0.520). Shared clones represented 71.78% (263/365) of all clones detected for msp1 (p < 0.0001). For msp2 , the shared clones represented 72.76% (203/279) of all the clones identified (p < 0.0001). When examined within each allelic family, shared clones generally predominated over unique clones, except in FC27 family (Fig. 3 ). The heterozygosity values were consistently high across all genotypes, ranging from 0.824 to 0.947 for both markers (Table 4 ). The mean MOI for combined msp1 and msp2 data was 1.53 for RR, 1.49 for RS and 1.76 for SS, with no statistically significant difference (p = 0.684). At the level of individual marker, MOI values were similar across genotypes: the RR genotype showed slightly higher MOI than SS for msp1 , whereas the opposite tendency was observed for msp2 (Table 4 ). Table 4 Multiplicity of Infection and heterozygosity in P. falciparum according to GSTe2 genotypes Genes Genotypes p value RR RS SS msp1 Polyclonal infection 53.12% 66.67% 57.14% 0.432 MOI 1.44 1.39 1.39 0.985 HE 0.946 0.925 0.945 -- Polyclonal infection 66.65% 68.75% 61.54% 0.760 msp2 MOI 1.68 1.64 2.32 0.333 HE 0.917 0.824 0.929 -- Comparison of Plasmodium falciparum msp1 and msp2 clonal diversity and MOI among CYP9K1 genotypes Fifty P. falciparum isolates from the homozygote resistant mosquitoes were successfully amplified for both markers (49 for msp1 = and 45 for msp2 ). Among heterozygotes mosquitoes, 42 isolates were positive (39 for msp1 and 42 for msp2 ), while 40 isolates from homozygote susceptible mosquitoes amplified successfully (38 for msp1 and 40 for msp2 ). The frequencies of clonal families varied significantly within each mosquito genotypes for both genes, with the RO33 being the most frequent msp1 family and 3D7 the most frequent for msp2 . No significant differences were observed in the distribution of clonal families among genotypes (Table 5 ). Table 5 Frequency of Plasmodium falciparum msp1 and msp2 allelic families according to CYP9K1 genotypes Gene RR (n1 = 49; n2 = 45) RS (n1 = 39; n2 = 42) SS (n1 = 38; n2 = 40) p value msp1 K1 18 (19.35%) 18 (22.78%) 16 (21.62%) 0.859 MAD20 34 (36.56%) 24 (30.76%) 28 (37.83%) RO33 41 (44.08%) 37 (46.83%) 30 (40.54%) P value 0.0012 0.0046 0.0306 msp2 3D7 45 (83.33%) 39 (86.67%) 36 (75%) 0.161 FC27 09 (16.67%) 06 (13.33%) 12 (25%) p value < 0.0001 < 0.0001 < 0.0001 Across both markers, a total of 70 distinct clones were detected in RR, 52 in the RS and 66 in the SS. Regarding clonal richness per family, the homozygote resistant (RR) genotypes harboured the highest number of clones in RO33 (16 clones), 3D7 (24 clones) and FC27 (13 clones) families. The heterozygotes (RS) showed the highest number of clones in K1 (13 clones), whereas the susceptible genotype (SS) had the highest number within the R033 (16 clones) family. The homozygous resistant mosquitoes possessed more unique clones than the susceptible ones in all clonal families except K1 (Fig. 4 ). Overall, 41.49% (39/94) of the distinct clones were shared among the three genotypes, while 58.51% (55/94) were unique to a single genotype but the proportions were not statistically different (p = 0.0607). The greatest number of shared clones occurred in RO33 family (14/19; 73.68%), followed by FC27 (10/17; 58.82%), MAD20 (7/19; 36.84%), K1 (9/29; 31.03%) and 3D7 (9/30; 30%); and these were statistically different across families (p = 0.0108). Shared clones constituted a significantly higher proportion of all detected clones for both markers: 90.36% (300/332) for msp1 (p < 0.0001) and78.52% (223/284) for msp2 (p < 0.0001). At the family level, the summed relative frequency of unique clones did not exceed 30%, and in some cases such as the RO33 family, it was as low as 3% (Fig. 4 ). Heterozygosity values were consistently high across all genotypes, ranging from 0.871 to 0.918. The mean MOI values were comparable among genotypes (p = 0.953): 1.62 for RR, 1.57 for RS and 1.64 for SS. Similarly, no significant difference was observed when MOIs were analyzed separately for msp1 and msp2 (Table 6 ). Table 6 Multiplicity of Infection and heterozygosity in P. falciparum according to CYP9K1 genotypes Gene Genotypes p-value RR RS SS msp1 Polyclonal infection 51.02% 71.79% 71.05% 0.067 MOI 1.28 1.48 1.53 0.611 HE 0.947 0.944 0.970 -- msp2 Polyclonal infection 62.22% 62.50% 70.27% 0.708 MOI 2.13 1.70 1.79 0.533 HE 0.896 0.871 0.918 -- Discussion This study examined the genetic diversity of P. falciparum infecting An. funestus , collected across contrasting ecological settings in southern Cameroon, using two highly polymorphic markers notably the merozoite surface protein-1 ( msp-1 ) and the merozoite surface protein-2 ( msp-2 ). In the current context of a rapid replacement of insecticide-susceptible mosquito populations by metabolically resistant ones in Cameroon, we also explored whether the resistance markers GSTe2 (L119F) and CYP9K1 (G454A) influence parasite clonal diversity, and whether resistant vectors migth contribute disproportionately to malaria transmission. Our finding revealed markedly higher genetic diversity in rural localities compared with the peri-urban site. This pattern suggests that vector populations in rural settings are more likely to harbour multiple and genetically distinct parasite clones, reflecting more intense and stable transmission, in agreement with observations from other regions of Africa ( 33 ). Difference between markers were also locality-dependent. In rural localities, msp1 exhibited greater diversity than the msp2 , consistent with observations from Madibou District, a semi-urban area of the Republic of Congo ( 33 ). Conversely, msp2 showed greater diversity than at msp1 in the peri-urban site, a pattern previously reported in a rural area in the south-west region of Cameroon ( 36 ) and in urban Ethiopia ( 37 ). The predominant msp2 family (3D7) was consistent across sites, while msp1 family dominance differed between rural areas, suggesting localized micro-epidemiological differences in parasite population structure despite similar ecological backgrounds. Multiplicity of Infection (MOI) used as a surrogate of transmission was higher in rural localities and mirrored the pattern observed for heterozygosity. This finding is consistent with previous work demonstrating higher malaria transmission in rural areas, measured either through MOI or parasite prevalence in human hosts or through entomological inoculation rates (EIR) ( 38 – 40 ). Elevated MOI in rural settings reflects frequent superinfection events and increased opportunities for parasite recombination, which sustain a large and diverse parasite reservoir capable of fueling ongoing transmission. The application of MOI to vector populations provides an additional layer of epidemiological insight; as it allows endemic settings to be ranked according to the genetic complexity of circulating Plasmodium falciparum . In this context, MOI may complement traditional entomological indicators such the entomological inoculation rate (EIR), particularly in situations where areas with comparable EIRs exhibit differences in parasite clonal diversity. Lower genetic complexity in a given setting may reflect reduced transmission stability or fewer opportunities for superinfection and recombination, even when vector density remains high. In the present study, both MOI and heterozygosity were consistently higher in the rural sites than in the peri-urban locality, in line with their known ecological and epidemiological profiles. This supports the conclusion that malaria transmission is greater in rural settings. Nevertheless, it is acknowledged that simultaneous sampling across all the sites would further minimize potential temporal confounding. Although collections were conducted during the same seasonal period, future studies employing fully synchronized sampling design would strengthen comparative inferences. As observed in most of entomological studies, oocyst infections generally exceeded sporozoite infection, indicating bottlenecks during sporogonic development. By comparing parasite diversity in mosquito abdomen and head-thorax, we showed that diversity was higher at oocyst stage, with msp1 clonal family distributions differing significantly between body parts. The dominance of RO33 at the oocyst stage and the shift toward MAD20 family in the head-thorax (sporozoite) reflect selective pressure exerted at different developmental stage. Genetic recombination occurring in the mosquito midgut ( 41 ) or the presence of multiple co-developing ookinete cohort from different infectious blood meals ( 42 , 43 ) may contribute to the greater diversity observed in the abdomen. Notably, approximately half of abdominal clones were detected in the head–thorax, and these shared clones constituted > 80% of all sporozoite-stage parasites, suggesting that certain clones are better suited to complete sporogony. Experimental studies support non-random mating of P. falciparum gametes in An. coluzzii ( 44 ) and show that oocyst development is shaped by mosquito physiological constraints, including competition with oogenesis ( 45 , 46 ). These findings reinforce the idea of a selective and dynamic host–parasite equilibrium that favors the maturation of some clones over others. Because insecticide resistance may modify vector competence, we evaluated whether metabolic resistant markers L119F-GSTe2 and G454A-CYP9K1 influenced the diversity of parasite clones in infected mosquitoes. Overall, clonal family frequencies were similar among RR, RS, and SS genotypes for both markers, with RO33 being the predominant msp1 family, consistent with previous reports identifying RO33 as the most represented family( 47 ); but in other studies, families other than RO33 were the dominant one, indicating variations attributed possibly to the difference in the degree of transmission intensity ( 47 – 49 ). However, patterns of diversity differed between the two resistance mechanisms: (i) RR mosquitoes for GSTe2 showed lower clonal richness than SS mosquitoes; (ii) RR mosquitoes for CYP9K1 showed higher clonal richness than their susceptible counterparts. These findings suggest that metabolic pathway may influence parasite permissiveness differently. Yet, the percentage of clones unique to any genotype was generally low (< 30% across most families), indicating limited structuring of parasite diversity by these resistance markers. This aligns with previous studies demonstrating inconsistent effects of insecticide resistance on vector competence, depending on whether oocyst infection rates, intensities, or sporozoite rates are used as indicators ( 22 ). The predominance of shared clones across genotypes suggests that long-standing mosquito–parasite interactions predating the emergence of metabolic resistance continue to play a central role in determining which parasite clones successfully complete development ( 50 ). High heterozygosity across RR, RS, and SS groups further indicates that GSTe2- and CYP9K1-associated resistance does not substantially restrict the range of P. falciparum genotypes capable of infecting An. funestus . Limitations of the study A major limitation of this study is that comparisons of Plasmodium falciparum genetic diversity among mosquito genotypes were conducted without distinguishing infection stages, as both oocyst (abdomen) and sporozoite (head-thorax) stages of the parasite were analysed together. Ideally, parasite diversity should be assessed separately at each stage to better evaluate potential relationships between insecticide resistance markers (GSTe2 and CYP9K1) and parasite polymorphism during different phases of sporogonic development. The relatively small number of sporozoite-positive samples, however, did not allow for genotype-specific analyses at the sporozoite stage. Such analysis would likely have given a more precise understanding of how insecticide resistance may influence vector competence. Nevertheless, despite this limitation, our results showed that clones shared between the head-thorax and the abdomen compartments were by far the most frequent, representing the majority of circulating parasite populations. As these shared clones likely drive most observable variation, it is reasonable to expect that separate analyses of oocyst and sporozoite isolates would yield similar overall conclusions. Finally, the polymorphism of msp1 and msp2 was determined using fragment size variation of PCR amplicons separated on a conventional gel electrophoresis. While this method remains a standard approach for assessing MOI and allelic diversity in malaria epidemiology, it lacks resolution to discriminate between haplotypes of identical or overlapping fragment sizes. As results, distinct sequence variants may be grouped under the same allele category, potentially leading to conservative estimates of parasite diversity. High resolution approaches, including targeted amplicon sequencing or genome-wide analyses, would enable more accurate discrimination of haplotypes and deeper insight into vector-parasite genetic interactions. Conclusions Monitoring parasite genetic diversity in mosquito populations can serve as an additional tool to evaluate changes in transmission patterns and to assess the impact of control interventions. The combination of high parasite diversity, elevated MOI, and successful development of multiple clones to the sporozoite stage reinforces the notion that An. funestus in southern Cameroon remains a highly competent malaria vector. The finding that resistant mosquitoes harbour similar—or in the case of CYP9K1, even greater—parasite clonal richness than susceptible mosquitoes is particularly concerning, as it suggests that the spread of metabolic resistance alleles may not impose a fitness cost with respect to vector competence. Given the increasing dominance of resistant An. funestus populations in Cameroon and across most of the African malaria endemic settings, these results underscore the urgent need for integrated vector management. Declarations Ethics approval and consent to participate The study was conducted in accordance with the Declaration of Helsinki and approved ethical clearance (N°2021/11/1404CE/CNERSH/SP and N°2023/04/1531CE/CNERSH/SP) was granted by the National Ethics Committee for Research in Human Health (NECRH). Thereafter, administrative authorizations were delivered by the Cameroon’s Ministry of Public Health. Consent for publication: Not applicable. Availability of Data and materials: The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author(s). Competing interests : The authors declare no competing interests. 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