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Genetic markers associated with the widespread insecticide resistance in malaria vector Anopheles funestus populations across Tanzania

Parasites & Vectors volume 17, Article number: 230 (2024) Cite this article

Abstract

Background

Anopheles funestus is a leading vector of malaria in most parts of East and Southern Africa, yet its ecology and responses to vector control remain poorly understood compared with other vectors such as Anopheles gambiae and Anopheles arabiensis. This study presents the first large-scale survey of the genetic and phenotypic expression of insecticide resistance in An. funestus populations in Tanzania.

Methods

We performed insecticide susceptibility bioassays on An. funestus mosquitoes in nine regions with moderate-to-high malaria prevalence in Tanzania, followed by genotyping for resistance-associated mutations (CYP6P9a, CYP6P9b, L119F-GSTe2) and structural variants (SV4.3 kb, SV6.5 kb). Generalized linear models were used to assess relationships between genetic markers and phenotypic resistance. An interactive R Shiny tool was created to visualize the data and support evidence-based interventions.

Results

Pyrethroid resistance was universal but reversible by piperonyl-butoxide (PBO). However, carbamate resistance was observed in only five of the nine districts, and dichloro-diphenyl-trichloroethane (DDT) resistance was found only in the Kilombero valley, south-eastern Tanzania. Conversely, there was universal susceptibility to the organophosphate pirimiphos-methyl in all sites. Genetic markers of resistance had distinct geographical patterns, with CYP6P9a-R and CYP6P9b-R alleles, and the SV6.5 kb structural variant absent or undetectable in the north-west but prevalent in all other sites, while SV4.3 kb was prevalent in the north-western and western regions but absent elsewhere. Emergent L119F-GSTe2, associated with deltamethrin resistance, was detected in heterozygous form in districts bordering Mozambique, Malawi and the Democratic Republic of Congo. The resistance landscape was most complex in western Tanzania, in Tanganyika district, where all five genetic markers were detected. There was a notable south-to-north spread of resistance genes, especially CYP6P9a-R, though this appears to be interrupted, possibly by the Rift Valley.

Conclusions

This study underscores the need to expand resistance monitoring to include An. funestus alongside other vector species, and to screen for both the genetic and phenotypic signatures of resistance. The findings can be visualized online via an interactive user interface and could inform data-driven decision-making for resistance management and vector control. Since this was the first large-scale survey of resistance in Tanzania’s An. funestus, we recommend regular updates with greater geographical and temporal coverage.

Graphical Abstract

Background

Vector control constitutes one of the key pillars of malaria control in Africa [1], where it primarily constitutes the deployment of insecticide-treated nets (ITNs) and indoor residual spraying (IRS). Despite great successes, in particular since 2000, malaria remains a significant challenge [2], partly due to biological threats, notably the evolution and spread of insecticide resistance [3, 4]. To accelerate progress, affected countries must adopt proactive strategies to manage widespread resistance and optimize the allocation of insecticidal interventions.

In the east and southern African region, malaria transmission is mediated primarily by Anopheles funestus [5], which, like Anopheles gambiae, is highly adapted to human habitations [6]. In Tanzania, studies in the high prevalence settings in the north-east and south-eastern regions have shown that this vector species can mediate as high as 90% of the local malaria infections or more [7, 8]. Unfortunately, compared with An. gambiae and Anopheles arabiensis, for which there have been several insecticide resistance surveys in Tanzania [9], only a handful of studies have profiled the resistance patterns in An. funestus [7, 8, 10, 11]. This lack of data, especially the phenotypic data, can be attributed to difficulties associated with rearing An. funestus and identifying its aquatic habitats [12, 13], which complicates the collection of age-synchronized larvae required for age-standardized resistance bioassays [14]. The resulting information gap can derail the effective planning, implementation and monitoring of vector control programs in settings where An. funestus dominates.

Comprehensive information on molecular mechanisms driving resistance in An. funestus in Tanzania is similarly scarce [11]. Unlike phenotypic bioassays, the genetic signals of insecticide resistance can enable early detection and mitigation before the problem is widely established in vector populations [15]. By establishing the exact mechanisms underlying the observed phenotypes, molecular monitoring also enables the elucidation of causes and risk factors of resistance in specific locations. However, establishing direct correlations between the phenotypes and molecular resistance can be challenging due to the vast number of mechanisms and gene families, as well as the stochastic nature of the genetic interactions leading to resistance [16, 17]. In An. funestus, the cytochrome P450 (CYP450) monooxygenases gene family constitutes the dominant family of pyrethroid resistance genes and has been associated with reduced efficacy of pyrethroid-based ITNs [18]. Notably, CYP6P9a and CYP6P9b are highly expressed in pyrethroid-resistant An. funestus populations in east and southern Africa [19], leading to operational failures in vector control in the region [20]. Another cytochrome P450 gene, CYP9K1, is significantly overexpressed in pyrethroid-resistant An. funestus populations from Uganda [21, 22], while CYP6P4a and CYP6P4b have been found in Ghana [23]. While many of these genes are associated with resistance to a narrow range of insecticides, certain mutations can confer resistance to multiple insecticide classes. For instance, the L119F mutation in Glutathione S-Transferases epsilon 2 (GSTe2) facilitates broad detoxification of pyrethroids, organochlorides and organophosphates in An. funestus [24]. Until now, genetic analysis of L119F-Gste2 in field-collected mosquitoes is geographically restricted to west and central Africa [24,25,26,27], with its spread resistance role in east and southern Africa remaining unclarified.

Novel IR mechanisms such as copy number variants have also recently been demonstrated to increase the expression of insecticide-detoxifying genes, leading to resistance in An. funestus [28, 29]. Monitoring the evolution and spread of these resistance genes in the face of sustained pressure from insecticide-based vector control is vital to managing resistance. Fortunately, field-adaptable molecular assays are now available that can track some molecular resistance genes in An. funestus [18, 19, 26, 28, 29], and can be leveraged to monitor resistance and inform appropriate deployment of the limited vector control tools.

In this study, we comprehensively analysed the insecticide resistance phenotypes and genotypes An. funestus mosquitoes in nine regions representing different eco-epidemiological settings in Tanzania. We aimed to understand the potential responsiveness of these mosquitoes to public health insecticides, generate insights for more effective strategies for malaria control, and establish a baseline chart of insecticide resistance in Tanzania’s An. funestus. We screened for five major genes and structural variants known to confer metabolic resistance and conducted standard susceptibility tests against pyrethroids, carbamates, organophosphates and organochlorides. This study represents the first large-scale survey of the insecticide resistance patterns in An. funestus, which despite being a dominant vector in the country, remains far less investigated compared with other malaria vectors.

Methods

Study area and mosquito collection

Mosquitoes were collected from November 2021 to December 2022 in selected villages across nine administrative districts of Tanzania (Fig. 1). The locations represent a comprehensive geographical and epidemiological cross-section of the country and were selected for their high prevalence of An. funestus [30]. Due to difficulties in finding An. funestus immature stages and unsuccessful attempts to get sufficient offspring from wild blood-fed females, we adopted a previously tested approach relying on unfed and non-gravid females of unknown ages for the resistance bioassays [8, 10]. Following consent of household heads, adult mosquitoes were sampled inside houses using a combination of centres for disease control and prevention (CDC)-miniature light traps (CDC-LT) [31] and a miniaturized double-net trap (DN-Mini) [32]. The collected mosquitoes were first sorted by taxa using taxonomic keys [33,34,35], and the An. funestus mosquitoes were further sorted on the basis of physiological features [33,34,35] and abdominal status (unfed). The unfed and non-gravid females were maintained ad libitum on 10% sucrose-soaked cotton wool for a day to acclimatize and select weak individuals not suitable for the resistance tests.

Fig. 1
figure 1

Map of Tanzania showing districts (red dots) selected for insecticide resistance surveillance. A Sites were selected on the basis of sentinel data from Tanzania’s national malaria control program that confirmed the presence of An. funestus group mosquitoes (all sites) and B national malaria prevalence data encompassing moderate (10–19.9%) (Missenyi, Misungwi, Kilombero, Tunduru and Muheza) and high (> 20%) (Tanganyika, Mtwara rural, Bagamoyo and Mtama) malaria burden. Malaria prevalence data were adopted from the Tanzanian Ministry of Health report on the School Malaria and Nutrition Survey (SMNS) [36]

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Tests for susceptibility of Anopheles funestus to different insecticides

The acclimatized mosquitoes were sugar-starved for at least 2 hours before being utilized in the bioassays, following the guidelines for World Health Organization (WHO) tube tests for mosquito susceptibility [14]. Room temperature and relative humidity were recorded at the time of each test. The candidate insecticides included those commonly used for vector control either on ITNs (permethrin, piperonyl butoxide and deltamethrin) or for IRS (bendiocarb, pirimiphos-methyl,) [30]. In addition, we included dichloro-diphenyl-trichloroethane (DDT), the persistent organic pollutant, which is no longer used for vector control in Tanzania [37].

The susceptibility of adult An. funestus mosquitoes were determined by exposing the acclimatized females for 60 min to WHO-approved insecticide-impregnated papers containing the diagnostic doses of the candidate insecticides: bendiocarb (0.1%), pirimiphos-methyl (0.25%), DDT (4%), permethrin (0.75%) and deltamethrin (0.05%). For each round of the bioassays and each insecticide, we included four test tubes lined with the impregnated papers and two control tubes lined with untreated control papers containing respective solvent oils. A total of 20 mosquitoes were exposed in each tube. The tests were thus conducted in four test replicates with two control tubes. After the 1-hour exposure and knock-down monitoring, the mosquitoes were maintained in insecticide-free tubes for 24 h to monitor mortality.

Tests for the intensity of pyrethroid resistance in Anopheles funestus

Intensity assays are generally used to determine the operational significance of the resistance phenotypes once resistance is confirmed using the discriminating insecticide doses. In line with WHO guidelines, the intensity of resistance against the two pyrethroids was determined by exposing the mosquitoes to increasing concentrations of the insecticides. We used either 5× and 10× the doses of permethrin (i.e. 3.75% and 7.5%, respectively) and 5× and 10× the doses of deltamethrin (i.e. 0.25% and 0.5%, respectively). Knockdown was recorded after 1 h following insecticide exposure and mortality was recorded after a further 24 h of monitoring. The tests were also conducted in four test replicates with two control tubes in which mosquitoes were exposed to papers containing silicone oil but no insecticides.

Tests of the synergistic effect of piperonyl butoxide (PBO) on pyrethroid-resistant Anopheles funestus

Piperonyl butoxide (PBO) is a synergist commonly used on ITNs to enhance their insecticidal activity on resistant mosquitoes by inhibiting the function of cytochrome P450 monooxygenases. In this study, the role of PBO in reversing pyrethroid resistance phenotype was determined by first exposing the female An. funestus mosquitoes to 4% PBO for 1 h, followed by a 1-h exposure to the diagnostic dose of deltamethrin (0.05%). The tests were conducted in three replicate tubes with two control tubes, in which mosquitoes were exposed to control papers containing only the solvent oil. Knockdown was recorded after 1 h following insecticide exposure and mortality was recorded after a further 24 h of monitoring [14].

Tests to confirm the genetic identity of the Anopheles funestus mosquitoes.

To enhance the probability of the collected mosquitoes being An. funestus s.s., rather than the other species within the An. funestus group, which comprises at least 11 known members [34, 35], the field collections had been strategically conducted in villages with high densities of An. funestus s.s. The collections were specifically done indoors, aligning with the species’ endophilic behaviour. Nonetheless, to confirm their genetic identity, polymerase chain reaction (PCR) assays were performed on the mosquitoes once the resistance assays had been completed.

A subset of the mosquitoes (~ 20%) that were either alive or dead after 24 h following insecticide exposure were randomly selected from the replicates and preserved in 80% ethanol. Genomic DNA was extracted from individual mosquitoes using a DNeasy Blood & Tissue Kit (Qiagen, UK). Species identification followed a PCR-based protocol utilizing species-specific primers targeting the non-coding internally transcribed spacer region (ITS2) between the 5.8S and 28S ribosomal DNA sequence to distinguish between at least seven members of An. funestus group [38, 39]. Briefly, 0.33 µl of each 10 µm primer was added to 6.25 µl of 2X GoTaq® G2 Green Master Mix (Promega, USA) in a 20 µl reaction volume. The cycling conditions were as follows: 5 min at 95 °C followed by 30 cycles of 94 °C denaturation, 45 °C annealing and 72 °C extension, and a final extension at 72 °C for 5 min. The PCR products were separated on a 2% agarose gel stained with SYBR™ Safe DNA Gel Stain (Invitrogen, UK).

In addition to testing the subset of mosquitoes used for the actual resistance bioassays, we also tested a larger sub-sample of mosquitoes originating from a broader field survey that had been completed in these same areas to provide more representative data on the diversity of the An. funestus group.

Genotyping of selected metabolic resistance genes and structural variants (SV)

A total of 20 randomly selected mosquitoes that were either alive [10] or dead [10] 24 h post-exposure to discriminating insecticide doses of deltamethrin were identified by PCR to species level and genotyped to screen for the insecticide resistance genes L119F-GSTe2, CYP6P9a and CYP6P9b, as well as structural variants SV4.3 kb and SV6.5 kb, known to be associated with the overexpression of the resistance genes [28, 29]. Increased expression of these genes, or transposons in the case of SV, has been associated with resistance phenotypes in An. funestus [18, 19, 29].

The L119F-GSTe2 mutation was genotyped following an allele-specific PCR protocol using two inner and outer primer pairs resulting in two fragments (850 bp and 312 bp) for the susceptible mosquitoes and three bands (850 bp, 12 bp and 523 bp) for the resistant haplotype [26]. The CYP6P9a gene was genotyped following a PCR assay targeting a restriction fragment length polymorphism (RFLP), where a restriction enzyme TaqI-V2 cuts a 450 bp fragment into 350 and 100 bp in mosquitoes carrying the resistance alleles but not in susceptible where the haplotypes remain uncut [18]. A similar assay was used for detecting CYP6P9b, but with restriction enzyme Tsp45I, which digests a 550 bp fragment into 400 bp and 150 bp in susceptible mosquitoes whereas resistant haplotypes remain uncut [18]. Finally, the detection of SV 6.5 kb and 4.3 kb was performed following methods described by Mugenzi et al. [28, 29]. Mosquitoes carrying the SV 6.5 kb variant had an amplicon fragment size of 569 bp for positive samples, 266 bp for negative samples, and both fragment sizes in heterozygotes. Similarly, the presence of the SV 4.3 kb variant was indicated by a 780 bp amplicon in positive individuals and a 280 bp amplicon in negatives, and both amplicons were observed in heterozygous individuals.

Data analysis

In the initial susceptibility tests, we assessed the observed mortality in both treatment and control tests and used Abbott’s mortality adjustments in cases where control mortality exceeded ≥ 5% [14]. Tests were discarded if control mortality was 20% or more. The data were summarised, and mosquito populations were considered susceptible to an insecticide if mean mortality was ≥ 98% and resistant if mean mortality was ≤ 90% mortality [14]. In the resistance intensity assays, resistance was recorded as low if 24-h mortality at 5× was between ≥ 98%, moderate if mortality was below ≤ 98% at 5× but above ≥ 98% at 10× the dose, and high if 10× mortality was below 98%.

Generalised linear mixed models within the lme4 package [35] were used to analyse the phenotypic resistance levels against different insecticides in R statistical software (version 4.1.1). Since these tests were done in different study sites, it was not possible to maintain a standard microclimatic condition in all cases, and as such, statistical analysis was used to discern the potential influence of these factors on the mortality outcomes. Mortality to each candidate insecticide was determined at the group level (i.e. averages of replicates for a given insecticide) in a binomial model with the response as the number of dead out of the number exposed and explanatory variables insecticide type (including controls), location of the test (district), temperature and relative humidity (RH) recorded at the time of test and interaction between insecticide and district; replicates were fitted as a random effect. The binomial model for the association between phenotype and the IR genotypes had mortality (alive/dead) response variable and explanatory variables as either resistant or susceptible genotypes of the five genes (CYP6P9a, CYP6P9b, L119F-GSTe2, SV 6.5 kb and SV 4.3 kb). Drop1 command was used to examine the models and determine the variable to remove in a stepwise format starting with the most complex model. Additionally, the likelihood ratio test (LRT) was used to compare two nested models. The R command ggemmeans was used to run the model, and gglot2 was used to plot the predictions. The variation in frequency of the five resistance genes by district was determined by analysis of variance (ANOVA) test on two logistic regressions, one with genes as the response and district as the predictor, and the other with just the intercept term.

Results

Confirmation of the molecular identity of the mosquitoes tested

Overall, PCR analysis showed that the proportions of An. funestus s.s in the sub-sample of mosquitoes collected in the nine districts (N = 6724 An. funestus group females) were as follows: Bagamoyo 89.8%, Kilombero 98.7%, Missenyi 99.4%, Misungwi 91.3%, Mtama 95.5%, Mtwara Rural 89.7%, Muheza 91%, Tanganyika 80.5% and Tunduru 97.7%. The residual proportions from these represented other sibling species within the An. funestus group, which included An. leesoni, An. rivulorum, An. parensis and An. funestus/An leesoni hybrid.

However, the mosquitoes used in the insecticide resistance bioassays were identified by PCR to be 100% An. funestus s.s. in all the study locations except in Tanganyika District, Katavi region in western Tanzania, where 15% were An. parensis. In Misungwi district, Mwanza region in north-western Tanzania, more than 90% of the mosquitoes of the samples were found to be An. parensis in the initially selected village (Ngaya), leading to the exclusion of this dataset and relocation to an alternative village with a higher occurrence of An. funestus (Nyang’omango). These notable instances of unusually high prevalences of An. parensis are discussed separately in an upcoming publication by Mapua et al. (unpublished).

All subsequent genetic screening for insecticide resistance markers for all study sites was performed only on An. funestus s.s. from the resistance tests and did not include any other sibling species in the An. funestus group.

Resistance of An. funestus to pyrethroids

An. funestus mosquitoes showed resistance to pyrethroid insecticides in all study sites, and 1-h knock-down (KD1h) to permethrin was lowest in Morogoro (35%) and highest in Muheza district (84%), while KD1h deltamethrin was lowest in Mtama district 9% and highest in Tanganyika (48%). In tests against permethrin, the lowest 24-h mortality was 29% (confidence interval, CI 19.8–39.6) in Tunduru district, Ruvuma region in southern Tanzania, while the highest mortality was 76% (CI 67.1–85.4) in Missenyi district, in Kagera region, north-western Tanzania. Furthermore, in tests against deltamethrin, the lowest mortality was 7% (3.4–15.7) in Mtama district, Lindi region in south-eastern Tanzania, while the highest was 55% (CI 43.9–65.5) in Tanganyika district, Katavi region in western Tanzania (Fig. 2). In the analysis of factors influencing the observed mortality, we observed that the percentage mortality observed against the candidate insecticides varied between sites (insecticide × site interaction: χ2 = 187.4, P < 0.00005) but was not influenced by temperature (χ2 = 0.6168, P = 0.4345) or relative humidity (χ2 = 0.2326, P = 0.6296).

Fig. 2
figure 2

Insecticide resistance profile of An. funestus: Figure shows resistance to the discriminating insecticide doses, the intensity of resistance as observed in tests at 5× and 10× the diagnostic doses, and the synergistic effect of PBO in reversing the observed resistance. The colours represent the different districts where the bioassays were conducted, the error bars represent the 95% confidence intervals of the predictions and the dotted line on the y-axis indicates a 98% mortality threshold

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Intensity of resistance to pyrethroids

The intensity of pyrethroid resistance was determined as low if 24-h mortality at the 5× dose was between ≥ 98%, moderate if the mortality was below 98% at 5× but above ≥ 98% at 10× the dose, and high if 10× mortality is < 98%. KD1h was greater than 80% in all locations for 5× and 10× permethrin, lowest in Misungwi district (34%) and highest in Tanga (91%) for 5× deltamethrin and ranged 35% in Mtwara Rural and 84% in Tanganyika for 10× deltamethrin. In tests using permethrin, high-intensity resistance was observed in the coastal region, in Bagamoyo district [5× mortality: 86% (76.1–92.4); 10× mortality: 95% (87.4–98.1)], and Tanganyika district in the western part of the country [5 × mortality: 83% CI (72.5–89.3); 10× mortality: 95% (87.4–98.1)]. The rest of the study sites had only low-intensity resistance to this insecticide. In contrast, the resistance to deltamethrin was of high intensity in five out of the nine study sites districts. These included Bagamoyo, Mtama and Mtwara rural districts in eastern Tanzania; Tanganyika district in western Tanzania; and Misungwi district in the north of the country, where 24-h mortality was below 98% even at 10× the deltamethrin dose (Fig. 2).

Synergistic effect of PBO and the reversal of pyrethroid resistance

Pre-exposure of the mosquitoes to PBO (synergist) for 1 h, followed by exposure to the candidate insecticide, resulted in the reversal of the initially observed resistance to the discriminating doses of deltamethrin (Fig. 2), and 1-h knock-down (KD1h) was greater than 98% in all locations. The resulting 24-h mortality in the PBO pre-exposed mosquitoes was greater than 98% in all sites except in Tanganyika, where it was only 80% CI (68–88.3). Due to limited supplies and the difficulty of obtaining sufficient mosquitoes, these PBO tests were conducted only for deltamethrin.

Resistance of An. funestus to bendiocarb, DDT and pirimiphos-methyl

The 1-h knock-down (KD1h) to bendiocarb was greater than 80% in all locations, but DDT KD1h less in Morogoro (55%) with complete knock-down in Mtama, Lindi region. Less KD1h to pirimiphos-methyl was in Missenyi district (74%) but greater than 90% in all other districts. Phenotypic resistance to carbamates (bendiocarb) was observed in five of the nine study sites across the country, namely the southern sites of Mtama, Mtwara Rural and Tunduru, as well as the western site of Tanganyika and the north-western site of Misenyi, all showing less than 90% mortality. Conversely, high mortality rates, indicative of susceptibility to bendiocarb were observed in the eastern sites of Bagamoyo, Kilombero and Muheza, as well as in the northern site of Misungwi (Fig. 3). Resistance to the organochlorine, DDT was observed exclusively in the Kilombero district, in south-eastern Tanzania (mortality: 68% (57.7–77.9). This study also showed the universal susceptibility of An. funestus populations in all the study sites to the organophosphate, pirimiphos-methyl, as evidenced by the 24-h mortalities exceeding 98% (Fig. 3).

Fig. 3
figure 3

Insecticide resistance of An. funestus to bendiocarb, DDT and pirimiphos-methyl. The colours represent the different districts where the bioassays were conducted, the error bars represent the 95% confidence intervals of the predictions and the dotted line on the y-axis indicates a 98% mortality threshold

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Genetic markers of insecticide resistance in An. funestus in Tanzania

The resistant allele CYP6P9a-R was prevalent in the eastern part of the country and was fixed (100% prevalence) in both phenotypically resistant (alive), and susceptible (dead) mosquitoes collected from Mtama, Mtwara rural and Kilombero districts, but was also observed in Tunduru, Bagamoyo and Muheza at very high frequencies (Fig. 4). In addition to the CYP6P9a-RR genotype, mosquitoes in Tunduru, Bagamoyo and Muheza districts also carried the heterozygous resistant genotype CYP6P9a-RS. This heterozygous form was also prevalent in the western district of Tanganyika. The CYP6P9a-RR genotype was completely absent in the north-western sites and was observed only at very low levels in Tanganyika in western Tanzania. Instead, An. funestus from Missenyi and Misungwi districts were all homozygous susceptible CYP6P9a-SS, with Tanganyika having all three genotypes; RR, RS and SS (Fig. 4). Similarly, CYP6P9b-R alleles were fixed in all districts except the north-western districts of Misungwi and Missenyi, where the gene did not amplify. In Tanganyika, both CYP6P9b-RR and CYP6P9b-RS genotypes were identified alongside a small number of non-amplifications (na) (Fig. 4). Contrastingly, the L119F-GSTe2-RR resistance genotype was absent in all study sites. Instead, An. funestus in six districts were homozygous susceptible for L119F-GSTe2-SS, while those in Tunduru, Mtwara rural and Tanganyika districts had the heterozygous haplotype of the gene L119F-GSTe2-RS in some surviving mosquitoes (Fig. 4).

Fig. 4
figure 4

The distribution of metabolic resistance genotypes, CYP6P9a, CYP6P9b and L119F-GSTe2, SV 4.3 kb and SV 6.5 kb in phenotypically resistant (alive), and susceptible (dead) An. funestus across nine districts in Tanzania

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In tests for the structural variants, it was observed that the homozygous resistant genotypes of SV 4.3 kb (RR) were near fixation and restricted to the north-western districts of Missenyi and Misungwi and were also found at low frequencies in the western district of Tanganyika (Fig. 4). Conversely, the homozygous resistant genotypes of SV 6.5 kb (RR) were widespread and fixed across most districts except the north-western districts of Missenyi and Misungwi, where the SV did not amplify in the PCR (i.e. na) (Fig. 4).

Further analysis revealed statistically significant geographic variations in the frequencies of CYP6P9a (χ2 = 172.62, P < 0.0005), CYP6P9b (χ2 = 16.04, P = 0.0135) and SV 4.3 kb (χ2 = 169.35, P < 0.0005). In contrast, no significant differences were observed for L119F-GSTe2 (χ2 = 0.2324, P = 0.9116) or SV 6.5 kb (χ2 = 9.39, P = 0.1518) by district.

Associations between genotypic and phenotypic expression of insecticide resistance

Using equal samples of live and dead An. funestus following exposure to the candidate insecticides, and the subsequent genotyping as described above, we used generalised linear mixed models to test for associations between phenotypic expression of resistance (i.e. percentage surviving exposure) and the presence of specific genotypes (i.e. CYP6P9a, CYP6P9b, L119F-GSTe2, SV 4.3 kb and SV 6.5 kb). This analysis was done only for deltamethrin. The final model had only the L119F-GSTe2 gene; indicating only variants of this gene were predictive of surviving lethal deltamethrin insecticide doses (χ2 = 9.0482, OR 2.5875, P = 0.0026) (Fig. 5). However, there was no significant association between either CYP6P9a (χ2 = 1.465, P = 0.4808), CYP6P9b (χ2 = 1.206, P = 0.5471), SV 4.3 kb (χ2 = 1.065, P = 0.587) or SV 6.5 kb (χ2 = 1.795, P = 0.4076) with survival to lethal deltamethrin doses (Fig. 5).

Fig. 5
figure 5

The association between metabolic resistance genes CYP6P9a, CYP6P9b, L119F-GSTe2 and structural variants SV 6.5 kb and SV 4.3 kb with deltamethrin resistance phenotype; the y-axis indicates the predicted mortalities from each single genotype, with the point indicating the mean and line 95% confidence interval and the genotypes on the x-axis indicate SS, homozygous susceptible; RS, heterozygous resistant; and RR, homozygous resistant

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Interactive map for visualisation of insecticide resistance

Since this was the first large-scale survey of insecticide resistance in Tanzania’s An. funestus, the findings could provide a critical foundation for future updates, preferably with a more granular coverage both geographically and temporally. More importantly, it could contribute to data-driven decision-making for resistance management when deploying vector control interventions. Therefore, to enhance practical application of the findings, we developed an interactive tool using R Shiny [36], enabling users to visualize the predicted insecticide resistance in An. funestus across Tanzania, and which could, in the future, be extended to other vector species. This user-friendly tool allows for the selection of various tested insecticides, displaying their predicted mortality rates on an interactive map. It highlights regional resistance patterns, aiding in the interpretation and application of our research data. The tool is accessible online at https://boydorr.gla.ac.uk/lucanelli/odero_ins_res_map/, and will be a valuable resource for researchers and public health practitioners working on malaria control.

Discussion

Anopheles funestus is a major malaria vector in Tanzania and is particularly dominant in the north-western region and south-eastern regions [7, 8, 40]. Yet, its resistance to key public health insecticides has been patchy at best in comparison with the other malaria vectors such as An. arabiensis and An. gambiae, for which the resistance data are more commonly collected. This study provides the first large-scale contemporaneous dataset on both the genotypic and phenotypic expression of resistance in An. funestus to a range of insecticides, including two pyrethroids, deltamethrin and permethrin; an organochlorine, DDT; a carbamate, bendiocarb; and an organophosphate, pirimiphos-methyl.

The findings show widespread multiple resistance in wild An. funestus mosquito populations in mainland Tanzania and provides a basis for decision-making for national malaria control programs on the management of insecticide resistance and selection of appropriate interventions. The findings show pyrethroid resistance to be particularly common in An. funestus with high resistance intensities to deltamethrin in most locations. Similar pyrethroid resistance profiles in this vector have been shown in earlier but separate studies in the south and north of Tanzania [7, 8]. Continentally, phenotypic expression of pyrethroid resistance in An. funestus has been reported in other countries including Uganda [41], Mozambique [42], Malawi [43, 44], Ghana [23, 45] and Cameroon [46], with most countries still lacking bioassay data on this vector species. The synergistic effect of PBO, leading to the restoration of susceptibility in pyrethroid-resistant An. funestus mosquitoes were evident in all tested populations, pointing to the predominant involvement of metabolic-mediated resistance mechanisms. Malaria vectors of the An. gambiae complex has also shown similar restoration of pyrethroid-susceptibility with PBO [9]. In epidemiological studies evaluating ITNs treated with both PBO and pyrethroids in Tanzania [47] and Uganda [48], increased community protection against malaria was observed, compared to protection accrued with pyrethroid-only nets. Our results therefore add to the body of evidence on the efficacy of PBO-ITNs against pyrethroid-resistant malaria vector populations [49, 50].

The study also shows An. funestus across Tanzania to be susceptible to the IRS insecticide pirimiphos-methyl, unlike in An. gambiae, where resistance has been confirmed in parts of the country [51]. However, the observed resistance to the carbamate (bendiocarb) in parts of the country is concerning and should be factored in by malaria control programs if IRS campaigns are planned in the future. Though Tanzania has recently discontinued the use of IRS [52], this remains an important tool and could be especially useful in high transmission settings or emergencies to curb outbreaks. Phenotypic resistance to DDT was isolated to the Kilombero valley in south-eastern Tanzania and has been linked to the recent discovery of knockdown resistance (kdr) in An. funestus in the same location [53]. The kdr evolution in Kilombero is thought to be due to agricultural DDT contamination and a legacy of past extensive DDT stockpiles [53]. Without proactive management of resistance, the observed resistance situation in the country is likely to worsen, as similar insecticide classes are heavily used for agricultural pest control [54].

As vector control evolves in sub-Saharan Africa and as new or improved insecticidal interventions are introduced, it is crucial to monitor the genetic basis of insecticide resistance and track its evolution within and beyond Tanzania. The genotype frequencies of CYP6P9a/b and SV 6.5 kb IR genes have previously been estimated as low–moderate in Tanzania but fixed in other southern African countries [18, 19, 29]. Here, we found the resistant alleles of these three genes to be either fixed or near fixation across most localities in the eastern region of Tanzania, indicating how these mutants have recently selectively swept through An. funestus populations in Tanzania. The high frequencies of homozygous susceptible genotypes of CYP6P9a-SS in the northern Tanzania districts and the evidence of mixed genotypes in west of Tanzania (near Lake Tanganyika) could suggest a gene flow barrier in the country (possibly including landscape features such as the Great Rift Valley); or an ongoing south–north directional selection of the alternative homozygous resistant genotype CYP6P9a-RR arising from the southern Africa region [55]. This potential gene flow restriction could further explain the contrasting distribution of structural variants, with SV 4.3 kb fixed and restricted in the northern Tanzania districts (Missenyi and Misungwi) and SV 6.5 kb in the rest of the country. The cause of high non-amplifications of SV 6.5 kb and CYP6P9b in northern Tanzania is unclarified and warrants investigation. Nonetheless, it has been postulated to be due to the SV 4.3 kb [28]. Both structural variants have been demonstrated to exacerbate resistance in An. funestus by increasing the overexpression of CYP6P9a and CYP6P9b [28, 29]. Recent analysis had similarly linked such copy number variants with increased expression of the genes encoding metabolic resistance in An. gambiae and An. coluzzii [56, 57]. The lack of statistical association of CYP6P9a/b, SV 4.3 kb and SV 6.5 kb genes with pyrethroid resistance in this study is likely due to their already strong selection and fixation in the An. funestus populations in Tanzania. This could also be a stronger effect of alternate metabolic genes not analysed here but previously shown to be associated with An. funestus pyrethroid resistance in East Africa such as CYP6M4, CYP9K1, CYP6M1 and CYP6Z1 [11, 21]. Additionally, this observation could also have resulted from matching the number of samples genotyped, which did not consider the natural frequencies of these genes in the populations, hence lacking sufficient statistical power to detect a true effect.

L119F-Gste2, which confers cross-resistance to DDT and pyrethroids in An. funestus have previously been identified in West and Central Africa [24, 25, 27] and Uganda [41] is the only previous detection in Eastern Africa. However, findings of this study now establish that the mutation has now spread to Eastern Africa, with low frequencies of the L119F-Gste2 resistance alleles detected in the Tanzanian districts bordering the Democratic Republic of the Congo (Tanganyika), Mozambique (Mtwara rural) and Malawi (Tunduru). The strong association observed between this genotype and deltamethrin resistance phenotypes is concerning and warrants close monitoring of its spread and evolution. Though it is not possible to conclusively describe the evolutionary path of this resistance allele in the region, its strong association with pyrethroid resistance could allude to a recent selection.

Overall, this study has highlighted the importance of broadening the resistance monitoring efforts to include all principal malaria vectors, including not just the members of the An. gambiae complex, but also An. funestus. This is particularly important in areas where this vector species dominates malaria transmission. One complementary outcome of this study was the extension to develop a user-friendly online platform for stakeholders, particularly scientists and public health officials to access the data. This platform lays the groundwork for informed decision-making regarding both the management of insecticide resistance and the actual deployment of effective interventions. The study also has the advantage of providing contemporaneous data from multiple sites with both genetic and phenotypic observations. However, it will be essential to regularly update this information, preferably with greater granularity in terms of both the geographical coverage at regional and sub-regional levels, and in terms of the temporal frequency of data collection.

One limitation of this study was the utilization of adult wild female mosquitoes of unknown ages in the bioassays. It is standard practice to utilize age-synchronized, 2–3-day-old mosquitoes in resistance bioassays to avoid ageing-related weaknesses [58] and environmental stresses. However, despite efforts to characterize the breeding habitats of An. funestus [13], this species is notoriously difficult to locate in most ecological settings, making it challenging to apply the standard approach. This also explains why resistance data on An. funestus have been largely missing in previous Tanzania’s sentinel surveys [9]. In this study, given the difficulties of finding the larvae, and the logistical challenges due to the geographical expanse covered, a decision was made to use adult samples. Therefore, as insecticide susceptibility increases with age in mosquitoes [59] we cannot completely rule out the potential confounding effect of ageing in our mortality data, which could have underestimated the resistant levels of our samples. However, this issue was mitigated by several steps including (a) using only unfed non-gravid adults, (b) holding the mosquitoes for at least 24 h to eliminate any mortality associated with mosquito handling, (c) normalizing excess mortalities using a set of controls, where natural mortality due to senescence was considered and (d) screening for both the genotypic and phenotypic expression of resistance in the mosquitoes.

Another significant constraint of this study was the potentially low statistical power to discern the associations between specific gene variants and phenotypic resistance. Given the sample sizes used, there was a predisposition towards detecting only variants with large effect sizes, while more subtle or moderate effects likely remained unidentified. Lastly, the study design and sample sizes did not permit an exploration of the cumulative or epistatic effects exerted by multiple gene mutations within the same individual mosquito. It is conceivable that a combination of mutations across the five genes investigated, rather than the impact of each mutation in isolation, could more accurately account for the variations observed in the phenotypic resistance. This complexity underscores the need for future studies to consider larger sample sizes and more sophisticated statistical and genetic analyses that can capture these nuances.

One area of improvement is that future tests should incorporate some of the newly approved insecticides for vector control, including the pyrrole chlorfenapyr, which is used in the dual-active Interceptor G2, nets and neonicotinoids, such as clothianidin, which is also approved by WHO for IRS [60]. Such expansion will provide essential data for the national malaria control program to guide their selection of interventions in the context of the evolving resistance patterns.

Conclusions

This comprehensive study presents a cross-country landscape of insecticide resistance in An. funestus, a principal malaria vector in Tanzania. It marks an important step towards the understanding both the distribution and evolution of insecticide resistance mechanisms in Tanzania and will contribute significantly to evidence-based decision-making for improved vector control in the country. The study revealed widespread resistance to pyrethroids, albeit with notable restoration upon pre-exposure to the synergist piperonyl butoxide (PBO), underscoring the potential of PBO-impregnated nets in combating pyrethroid resistant populations of An. funestus. Additionally, the presence of resistance to carbamates, and in some sites, and the emergent resistance to DDT in the south-eastern part of the country, highlight the complexity of resistance in the vector and the significant threat to the existing arsenal of vector control insecticides. Fortunately, An. funestus populations across all study sites appear to remain susceptible to the organophosphate pyrimiphos-methyl, suggesting that this chemical, which is already widely used in agriculture, may be one of the few remaining options suitable for vector control, should the country reinstate the use of indoor residual spraying. The detection of genetic markers, including CYP6P9a/b, associated structural variants, and the mutation L119F-GSTe2 across different districts, provide an explanation for genetic foundation underlying the observed phenotypic resistance patterns. Additionally, these genetic markers reveal some underlying gene flow patterns in the An. funestus populations and are suggestive of a barrier (potentially including the Great Rift Valley) as well as an ongoing south–north directional sweep of some genotypes, notably the CYP6P9a-RR. The findings of this study have been made readily accessible through an interactive online interface to make it directly applicable for insecticide resistance management and to emphasize the need for continuous surveillance and updates on this dataset. Given the significance of these insights for the development and deployment of effective vector control tools, further research is required on all major vector species, with a focus on increased granularity in the spatial and temporal analysis, to enable adaptive resistance management and improved malaria control in Tanzania and the region.

Availability of data and materials

No datasets were generated or analysed during the current study.

Abbreviations

WHO:

World Health Organization

IRS:

Indoor residual spraying

ITNs:

Insecticide treated nets

PBO:

Piperonyl-butoxide

PCR:

Polymerase chain reaction

GSTe2 :

Glutathione s-transferase epsilon 2

CDC:

Centres for disease control and prevention

SV:

Structural variant

CYP450 :

Cytochrome P450 gene family

References

  1. Wilson AL, Courtenay O, Kelly-Hope LA, Scott TW, Takken W, Torr SJ, et al. The importance of vector control for the control and elimination of vector-borne diseases. PLoS Negl Trop Dis. 2020;14:e0007831.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. WHO. World malaria report 2023: WHO. 2023. https://www.who.int/teams/global-malaria-programme/reports/world-malaria-report-2023.

  3. Hemingway J, Ranson H, Magill A, Kolaczinski J, Fornadel C, Gimnig J, et al. Averting a malaria disaster: will insecticide resistance derail malaria control? Lancet. 2016;387:1785–8.

    Article  PubMed  PubMed Central  Google Scholar 

  4. WHO. Malaria Threats Map 2022. https://apps.who.int/malaria/maps/threats/.

  5. Msugupakulya BJ, Urio NH, Jumanne M, Ngowo HS, Selvaraj P, Okumu FO, et al. Changes in contributions of different Anopheles vector species to malaria transmission in east and southern Africa from 2000 to 2022. Parasit Vectors. 2023;16:408.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Kahamba NF, Finda M, Ngowo HS, Msugupakulya BJ, Baldini F, Koekemoer LL, et al. Using ecological observations to improve malaria control in areas where Anopheles funestus is the dominant vector. Malar J. 2022;21:158.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Matowo NS, Martin J, Kulkarni MA, Mosha JF, Lukole E, Isaya G, et al. An increasing role of pyrethroid-resistant Anopheles funestus in malaria transmission in the Lake Zone, Tanzania. Sci Rep. 2021;11:13457.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Kaindoa EW, Matowo NS, Ngowo HS, Mkandawile G, Mmbando A, Finda M, et al. Interventions that effectively target Anopheles funestus mosquitoes could significantly improve control of persistent malaria transmission in south-eastern Tanzania. PLoS ONE. 2017;12:e0177807.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Tungu P, Kabula B, Nkya T, Machafuko P, Sambu E, Batengana B, et al. Trends of insecticide resistance monitoring in mainland Tanzania, 2004–2020. Malar J. 2023;22:100.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Pinda PG, Eichenberger C, Ngowo HS, Msaky DS, Abbasi S, Kihonda J, et al. Comparative assessment of insecticide resistance phenotypes in two major malaria vectors, Anopheles funestus and Anopheles arabiensis in south-eastern Tanzania. Malar J. 2020;19(1):408. https://0-doi-org.brum.beds.ac.uk/10.1186/s12936-020-03483-3.

  11. Matowo J, Weetman D, Pignatelli P, Wright A, Charlwood JD, Kaaya R, et al. Expression of pyrethroid metabolizing P450 enzymes characterizes highly resistant Anopheles vector species targeted by successful deployment of PBO-treated bednets in Tanzania. PLoS ONE. 2022;17:e0249440.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Ngowo HS, Hape EE, Matthiopoulos J, Ferguson HM, Okumu FO. Fitness characteristics of the malaria vector Anopheles funestus during an attempted laboratory colonization. Malar J. 2021;20:148.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Nambunga IH, Ngowo HS, Mapua SA, Hape EE, Msugupakulya BJ, Msaky DS, et al. Aquatic habitats of the malaria vector in rural south-eastern Tanzania. Malaria J. 2020;19:1.

    Article  Google Scholar 

  14. WHO. Manual for monitoring insecticide resistance in mosquito vectors and selecting appropriate interventions. 2022. https://www.who.int/publications/i/item/9789240051089.

  15. Vontas J, Mavridis K. Vector population monitoring tools for insecticide resistance management: myth or fact? Pestic Biochem Physiol. 2019;161:54–60.

    Article  CAS  PubMed  Google Scholar 

  16. Liu N. Insecticide resistance in mosquitoes: impact, mechanisms, and research directions. Annu Rev Entomol. 2015;60:537–59.

    Article  CAS  PubMed  Google Scholar 

  17. Hemingway J, Hawkes NJ, McCarroll L, Ranson H. The molecular basis of insecticide resistance in mosquitoes. Insect Biochem Mol Biol. 2004;34:653–65.

    Article  CAS  PubMed  Google Scholar 

  18. Weedall GD, Mugenzi LMJ, Menze BD, Tchouakui M, Ibrahim SS, Amvongo-Adjia N, et al. A cytochrome P450 allele confers pyrethroid resistance on a major African malaria vector, reducing insecticide-treated bednet efficacy. Sci Transl Med. 2019. https://0-doi-org.brum.beds.ac.uk/10.1126/scitranslmed.aat7386.

    Article  PubMed  Google Scholar 

  19. Mugenzi LMJ, Menze BD, Tchouakui M, Wondji MJ, Irving H, Tchoupo M, et al. Cis-regulatory CYP6P9b P450 variants associated with loss of insecticide-treated bed net efficacy against Anopheles funestus. Nat Commun. 2019;10:4652.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Wondji CS, Coleman M, Kleinschmidt I, Mzilahowa T, Irving H, Ndula M, et al. Impact of pyrethroid resistance on operational malaria control in Malawi. Proc Natl Acad Sci U S A. 2012;109:19063–70.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Sandeu MM, Mulamba C, Weedall GD, Wondji CS. A differential expression of pyrethroid resistance genes in the malaria vector Anopheles funestus across Uganda is associated with patterns of gene flow. PLoS ONE. 2020;15(11):e0240743.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Mulamba C, Irving H, Riveron JM, Mukwaya LG, Birungi J, Wondji CS. Contrasting Plasmodium infection rates and insecticide susceptibility profiles between the sympatric sibling species Anopheles parensis and Anopheles funestus s. s: a potential challenge for malaria vector control in Uganda. Parasites Vectors. 2014;7(1):71. https://0-doi-org.brum.beds.ac.uk/10.1186/1756-3305-7-71.

  23. Mugenzi LMJ, Akosah-Brempong G, Tchouakui M, Menze BD, Tekoh TA, Tchoupo M, et al. Escalating pyrethroid resistance in two major malaria vectors Anopheles funestus and Anopheles gambiae (sl) in Atatam Southern Ghana. BMC Infect Dis. 2022;22(1):799. https://0-doi-org.brum.beds.ac.uk/10.1186/s12879-022-07795-4.

  24. Riveron JM, Yunta C, Ibrahim SS, Djouaka R, Irving H, Menze BD, et al. A single mutation in the GSTe2 gene allows tracking of metabolically based insecticide resistance in a major malaria vector. Genome Biol. 2014;15:R27.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Tchigossou GM, Atoyebi SM, Akoton R, Tossou E, Innocent D, Riveron J, et al. Investigation of DDT resistance mechanisms in Anopheles funestus populations from northern and southern Benin reveals a key role of the GSTe2 gene. Malar J. 2020;19:456.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Tchouakui 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 Plasmodium infection in the African malaria vector Anopheles funestus. Sci Rep. 2019;9:5772.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Tchigossou G, Djouaka R, Akoton R, Riveron JM, Irving H, Atoyebi S, et al. Molecular basis of permethrin and DDT resistance in an Anopheles funestus population from Benin. Parasit Vectors. 2018;11:602.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Mugenzi Leon, Theofelix Tekoh, Stevia Ntadoun, Achille Chi, Mahamat Gadji, Benjamin Menze, et al. A rapidly selected 4.3kb transposon-containing structural variation is driving a P450-based resistance to pyrethroids in the African malaria vector Anopheles funestus. Authorea. 2023. https://0-doi-org.brum.beds.ac.uk/10.22541/au.168570017.77126301/v1.

  29. Mugenzi LMJ, Menze BD, Tchouakui M, Wondji MJ, Irving H, Tchoupo M, et al. A 65-kb intergenic structural variation enhances P450-mediated resistance to pyrethroids in malaria vectors lowering bed net efficacy. Mol Ecol. 2020;29:4395–411.

    Article  CAS  PubMed  Google Scholar 

  30. Tanzania-NMCP. Tanzania National Malaria Strategic Plan 2021–2025. 2020. http://api-hidl.afya.go.tz/uploads/library-documents/1641210939-jH9mKCtz.pdf.

  31. Sudia WD, Chamberlain RW. Battery-operated light trap, an improved model. J Am Mosq Control Assoc. 1988;4:536–8.

    CAS  PubMed  Google Scholar 

  32. Limwagu AJ, Msugupakulya BJ, Kilalangongono MM, Mwalugelo YA, Okumu FO, Lyimo IN, et al. Evaluation of the DN-Mini (miniaturized double net) trap for sampling host-seeking Anopheles mosquitoes in malaria-endemic villages of southern Tanzania. PLoS ONE. 2024;19:e0294192.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Coetzee M. Key to the females of Afrotropical Anopheles mosquitoes (Diptera: Culicidae). Malar J. 2020;19:70.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Gillies MT, Coetzee M. A Supplement to the Anophelinae of Africa South of the Sahara (Afrotropical Region). Kabokweni: South African Institute for Medical Research; 1987.

    Google Scholar 

  35. Gillies MT, De Meillon B. The Anophelinae of Africa South Or the Sahara (Ethiopian Zoogeographical Region). Kabokweni: South African Institute for Medical Research; 1968.

    Google Scholar 

  36. Tanzania-MoH. The 2021 School Malaria and Nutrition Survey (SMNS) Report. 2022. https://www.nmcp.go.tz/storage/app/uploads/public/648/9a4/3b8/6489a43b8ad72010105271.pdf.

  37. Oxborough RM. Trends in US President’s Malaria Initiative-funded indoor residual spray coverage and insecticide choice in sub-Saharan Africa (2008–2015): urgent need for affordable, long-lasting insecticides. Malar J. 2016;15:146.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Cohuet A, Simard F, Toto JC, Kengne P, Coetzee M, Fontenille D. Species identification within the Anopheles funestus group of malaria vectors in Cameroon and evidence for a new species. Am J Trop Med Hyg. 2003;69:200–5.

    Article  CAS  PubMed  Google Scholar 

  39. Koekemoer LL, Kamau L, Hunt RH, Coetzee M. A cocktail polymerase chain reaction assay to identify members of the Anopheles funestus (Diptera: Culicidae) group. Am J Trop Med Hyg. 2002;66:804–11.

    Article  CAS  PubMed  Google Scholar 

  40. Mwalimu CD, Kiware S, Nshama R, Derua Y, Machafuko P, Gitanya P, et al. Dynamics of malaria vector composition and Plasmodium falciparum infection in mainland Tanzania: 2017–2021 data from the national malaria vector entomological surveillance. Malar J. 2024;23:29.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Mulamba C, Riveron JM, Ibrahim SS, Irving H, Barnes KG, Mukwaya LG, et al. Widespread pyrethroid and DDT resistance in the major malaria vector Anopheles funestus in East Africa is driven by metabolic resistance mechanisms. PLoS ONE. 2014;9:e110058.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Cuamba N, Morgan JC, Irving H, Steven A, Wondji CS. High level of pyrethroid resistance in an Anopheles funestus population of the Chokwe District in Mozambique. PLoS ONE. 2010;5:e11010.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Menze BD, Tchouakui M, Mugenzi LMJ, Tchapga W, Tchoupo M, Wondji MJ, et al. Marked aggravation of pyrethroid resistance in major malaria vectors in Malawi between 2014 and 2021 is partly linked with increased expression of P450 alleles. BMC Infect Dis. 2022;22:660.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Kumala J, Koekemoer LL, Coetzee M, Mzilahowa T. Intensity of insecticide resistance in the major malaria vector Anopheles funestus from Chikwawa, rural Southern Malawi. Parasit Vectors. 2022;15:220.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Riveron JM, Osae M, Egyir-Yawson A, Irving H, Ibrahim SS, Wondji CS. Multiple insecticide resistance in the major malaria vector Anopheles funestus in southern Ghana: implications for malaria control. Parasit Vectors. 2016;9:504.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Antonio-Nkondjio C, Sonhafouo-Chiana N, Ngadjeu CS, Doumbe-Belisse P, Talipouo A, Djamouko-Djonkam L, et al. Review of the evolution of insecticide resistance in main malaria vectors in Cameroon from 1990 to 2017. Parasit Vectors. 2017;10:472.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Protopopoff N, Mosha JF, Lukole E, Charlwood JD, Wright A, Mwalimu CD, et al. Effectiveness of a long-lasting piperonyl butoxide-treated insecticidal net and indoor residual spray interventions, separately and together, against malaria transmitted by pyrethroid-resistant mosquitoes: a cluster, randomised controlled, two-by-two factorial design trial. Lancet. 2018;391:1577–88.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Staedke SG, Gonahasa S, Dorsey G, Kamya MR, Maiteki-Sebuguzi C, Lynd A, et al. Effect of long-lasting insecticidal nets with and without piperonyl butoxide on malaria indicators in Uganda (LLINEUP): a pragmatic, cluster-randomised trial embedded in a national LLIN distribution campaign. Lancet. 2020;395:1292–303.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Maiteki-Sebuguzi C, Gonahasa S, Kamya MR, Katureebe A, Bagala I, Lynd A, et al. Effect of long-lasting insecticidal nets with and without piperonyl butoxide on malaria indicators in Uganda (LLINEUP): final results of a cluster-randomised trial embedded in a national distribution campaign. Lancet Infect Dis. 2023;23:247–58.

    Article  CAS  PubMed  Google Scholar 

  50. Gleave K, Lissenden N, Chaplin M, Choi L, Ranson H. Piperonyl butoxide (PBO) combined with pyrethroids in insecticide-treated nets to prevent malaria in Africa. Cochrane Database Syst Rev. 2021;5:CD012776.

    PubMed  Google Scholar 

  51. Kisinza WN, Nkya TE, Kabula B, Overgaard HJ, Massue DJ, Mageni Z, et al. Multiple insecticide resistance in Anopheles gambiae from Tanzania: a major concern for malaria vector control. Malar J. 2017;16:439.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Mwalimu C, Lazaro S, Kisinza W, Magesa S, Kiware S. The impact of indoor residual spraying (IRS) withdrawal in the Lake Zone Regions in Mainland Tanzania. Should Tanzania withdrawal its IRS program? Research Square. 2023. https://0-doi-org.brum.beds.ac.uk/10.21203/rs.3.rs-2888818/v1.

  53. Odero JO, Dennis TPW, Polo B, Nwezeobi J, Bodde M, Nagi SC, et al. Discovery of knock-down resistance in the major malaria vector Anopheles funestus reveals the legacy of persistent DDT pollution. bioRxiv. 2024. https://0-doi-org.brum.beds.ac.uk/10.1101/2024.03.13.584754.

    Article  Google Scholar 

  54. Urio NH, Pinda PG, Ngonzi AJ, Muyaga LL, Msugupakulya BJ, Finda M, et al. Effects of agricultural pesticides on the susceptibility and fitness of malaria vectors in rural south-eastern Tanzania. Parasit Vectors. 2022;15:213.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Barnes KG, Irving H, Chiumia M, Mzilahowa T, Coleman M, Hemingway J, et al. Restriction to gene flow is associated with changes in the molecular basis of pyrethroid resistance in the malaria vector Anopheles funestus. Proc Natl Acad Sci U S A. 2017;114:286–91.

    Article  CAS  PubMed  Google Scholar 

  56. Lucas ER, Nagi SC, Egyir-Yawson A, Essandoh J, Dadzie S, Chabi J, et al. Genome-wide association studies reveal novel loci associated with pyrethroid and organophosphate resistance in Anopheles gambiae and Anopheles coluzzii. Nat Commun. 2023;14:4946.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Lucas ER, Miles A, Harding NJ, Clarkson CS, Lawniczak MKN, Kwiatkowski DP, et al. Whole-genome sequencing reveals high complexity of copy number variation at insecticide resistance loci in malaria mosquitoes. Genome Res. 2019;29:1250–61.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Pinda PG, Msaky DS, Muyaga LL, Mshani IH, Njalambaha RM, Kihonda J, et al. Relationships between biological age, distance from aquatic habitats and pyrethroid resistance status of Anopheles funestus mosquitoes in south-eastern Tanzania. Malar J. 2022;21:365.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Collins E, Vaselli NM, Sylla M, Beavogui AH, Orsborne J, Lawrence G, et al. The relationship between insecticide resistance, mosquito age and malaria prevalence in Anopheles gambiae s.l. from Guinea. Sci Rep. 2019;9:8846.

    Article  PubMed  PubMed Central  Google Scholar 

  60. WHO Guidelines for Malaria [press release]. 2023. https://www.who.int/publications/i/item/guidelines-for-malaria.

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Acknowledgement

The authors are thankful to the community leaders and residents in all the study villages for permitting us to carry out our work in their localities. We also thank the volunteers from across the nine districts without whom the mosquito collections would have not been possible.

Funding

This work was supported in whole or in part by the Bill & Melinda Gates Foundation (grant no. INV-002138 to FOO). Under the grant conditions of the Foundation, a Creative Commons Attribution 4.0 Generic License has already been assigned to the Author Accepted Manuscript version that might arise from this submission. This work was also supported by Howard Hughes Medical institute (grant no: OPP1099295) awarded to F.O.O., Academy Medical Science Springboard Award (ref: SBF007\100094) to F.B. and NRF South African (SRUG2203311457) to L.L.K.

Author information

Author notes

  1. Francesco Baldini and Fredros O. Okumu equally supervised the work.

Authors and Affiliations

  1. Environmental Health and Ecological Sciences Department, Ifakara Health Institute, P.O. Box 53, Ifakara, Tanzania

    Joel O. Odero, Ismail H. Nambunga, John P. Masalu, Gustav Mkandawile, Hamis Bwanary, Emmanuel E. Hape, Rukiyah M. Njalambaha, Halfan S. Ngowo, Emmanuel W. Kaindoa, Salum A. Mapua, Najat F. Kahamba & Fredros O. Okumu

  2. School of Biodiversity, One Health, and Veterinary Medicine, University of Glasgow, Glasgow, G12 8QQ, UK

    Joel O. Odero, Najat F. Kahamba, Luca Nelli, Heather M. Ferguson, Francesco Baldini & Fredros O. Okumu

  3. School of Life Science and Biotechnology, Nelson Mandela African Institution of Science and Technology, P.O. Box 447, Arusha, Tanzania

    Emmanuel W. Kaindoa & Fredros O. Okumu

  4. Amani Medical Research Centre, National Institute for Medical Research, Muheza, Tanzania

    Patrick Tungu

  5. Wits Research Institute for Malaria, Faculty of Health Sciences, University of the Witwatersrand, 7 York Road, Parktown, Johannesburg, South Africa

    Emmanuel E. Hape & Lizette L. Koekemoer

  6. Centre for Emerging Zoonotic & Parasitic Diseases, National Institute for Communicable Diseases, A Division of the National Health Laboratory Service, Johannesburg, South Africa

    Lizette L. Koekemoer

  7. Department of Vector Biology, Liverpool School of Tropical Medicine, Liverpool, L3 5QA, UK

    Charles Wondji & David Weetman

  8. Department of Medical Entomology, Centre for Research in Infectious Diseases (CRID), Yaoundé 5, Cameroon

    Charles Wondji

Authors
  1. Joel O. Odero
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  2. Ismail H. Nambunga
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  3. John P. Masalu
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  4. Gustav Mkandawile
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  5. Hamis Bwanary
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  6. Emmanuel E. Hape
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  7. Rukiyah M. Njalambaha
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  8. Patrick Tungu
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  9. Halfan S. Ngowo
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  10. Emmanuel W. Kaindoa
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  11. Salum A. Mapua
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  12. Najat F. Kahamba
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  13. Luca Nelli
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  14. Charles Wondji
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  15. Lizette L. Koekemoer
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  16. David Weetman
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  17. Heather M. Ferguson
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  18. Francesco Baldini
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  19. Fredros O. Okumu
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Contributions

F.O.O., F.B., H.M.F. and D.W. were responsible for idea conceptualization and supervision; F.O.O., F.B. and H.M.F. for funding acquisition; J.O.O., I.H.N., J.M.P., G.M. and H.B. performed field experiments; J.O.O. conducted laboratory assays, analysed the data and wrote initial and subsequent drafts of the manuscript; C.W. developed assays for the resistance genotyping; and L.N. and J.O.O. developed the interactive map for visualizing resistance phenotypes and NFK map in Fig. 1. All authors revised and approved the final version of this manuscript.

Corresponding authors

Correspondence to Joel O. Odero or Fredros O. Okumu.

Ethics declarations

Ethics approval and consent to participate

Ethical approvals for this project were obtained from Ifakara Health Institute’s Institutional Review Board (Protocol ID: IHI/IRB/no: 26-2020) and the Medical Research Coordinating Committee (MRCC) at the National Institute for Medical Research (Protocol ID: NIMR/HQ/R.8a/Vol.IX/3495).

Consent for publication

Permission to publish this article was obtained from the National Institute for Medical Research reference no. BD.242/437/01B/46.

Competing interests

The authors declare that they have no competing interests.

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Odero, J.O., Nambunga, I.H., Masalu, J.P. et al. Genetic markers associated with the widespread insecticide resistance in malaria vector Anopheles funestus populations across Tanzania. Parasites Vectors 17, 230 (2024). https://0-doi-org.brum.beds.ac.uk/10.1186/s13071-024-06315-4

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  • DOI: https://0-doi-org.brum.beds.ac.uk/10.1186/s13071-024-06315-4

Keywords

Parasites & Vectors

ISSN: 1756-3305

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