Prevalence, Genetic Diversity of Microsporidia MB, and Correlation to Insecticide Resistance in Anopheles gambiae s.l. and Anopheles funestus Mosquitoes in Busia, Kenya

Prevalence, Genetic Diversity of Microsporidia MB, and Correlation to Insecticide Resistance in Anopheles gambiae s.l. and Anopheles funestus Mosquitoes in Busia, Kenya | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Prevalence, Genetic Diversity of Microsporidia MB, and Correlation to Insecticide Resistance in Anopheles gambiae s.l. and Anopheles funestus Mosquitoes in Busia, Kenya Herzel Tiffany Wandera, Godfrey Nattoh, Daniel Kiboi, Manase Onyango Aloo, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6352339/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Oct, 2025 Read the published version in BMC Microbiology → Version 1 posted 10 You are reading this latest preprint version Abstract Background: The discovery of Microsporidia MB , a malaria-impairing symbiont in Anopheles arabiensis , suggests its potential for malaria control. This study investigated its prevalence and diversity in An. gambiae s.l. and An. funestus in Busia, Kenya. The study also explored its association with insecticide resistance and environmental factors. Methods: Mosquito larvae and adults were collected from three sub-counties in Busia, Kenya. Species were identified morphologically, and DNA was extracted. PCR was used to determine species distribution and Microsporidia MB prevalence. Insecticide resistance markers were identified using TaqMan genotyping. Microsporidia MB -positive samples underwent whole-genome sequencing and phylogenetic analysis. Statistical tests, including chi-square, ANOVA, and regression, were used to assess relationships between Microsporidia MB , insecticide resistance, and ecological variables. Results: There was notable variation in the distribution of species in Busia, where An. gambiae s.l. emerged as the most prevalent species in Teso South and Butula sub-counties, whereas An. funestus was the most prevalent in Budalangi sub-county. Microsporidia MB was observed at a low to moderate occurrence of 0 to 6.4%, with the highest prevalence noted in An. gambiae s.s. Despite the significant fluctuations in temperature, pH, and dissolved oxygen levels across different ecological habitats, only the variation in pH was associated with the prevalence of Microsporidia MB . There was no correlation between Microsporidia MB infection and molecular markers of insecticide resistance. Phylogenetic analysis revealed significant genetic diversity in Microsporidia MB , with geographic location influencing lineage divergence. Conclusion: The present study highlights the occurrence of Microsporidia MB in multiple Anopheles vectors associated with malaria and highlights the potential role of ecological factors in sustaining the prevalence of Microsporidia MB . Future studies will tease out whether malaria-protective phenotypes are conserved traits among the distinct evolutionary lineages to enhance our understanding of critical considerations that are necessary for the successful implementation of this novel malaria control strategy in areas with varying strains and ecological conditions. Geographic location significantly shapes the genetic diversity of Microsporidia MB in mosquitoes, revealing distinct evolutionary lineages and dispersal patterns. Anopheles Microsporidia MB Insecticide Resistance Ecology Phylogenetics Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Anopheles mosquitoes are the primary vectors of malaria in sub-Saharan Africa, causing millions of deaths annually( 1 ). Busia County is a malaria-endemic region characterized by a warm and humid climate, providing favourable conditions for mosquito breeding and transmission( 2 ). With two rainfall patterns annually, the mosquito vector population in this region is usually high, and malaria transmission is intense due to suitable climate conditions( 3 ). The current primary method for controlling malaria is vector management, which involves the use of long-lasting insecticidal nets (LLINs), insecticide residual sprays (IRSs), and mosquito breeding sites( 4 ). While interventions such as IRS have been effective, they have also led to insecticide resistance challenges( 5 , 6 ). New vector control strategies are emerging, and several studies have shown that microbes associated with mosquitoes can significantly impact their ability to transmit pathogens. For example, Aedes aegypti , a primary vector for dengue, chikungunya, and Zika viruses, is substantially diminished in the presence of the intracellular endosymbiont Wolbachia( 7 ). Microsporidia MB has recently gained significant attention as a promising candidate for malaria control( 8 ). This microorganism can spread within Anopheles populations, block Plasmodium transmission, and is harmless to the host. Although it has been naturally found in Anopheles arabiensis , other studies have reported its presence in various Anopheles species( 9 – 11 ). Wolbachia -based strategies have been successfully implemented to control dengue fever transmission( 12 – 14 ). Wolbachia has also been identified in Anopheles species, but its low intensity impedes its inherent ability to efficiently spread within these hosts( 15 ). Therefore, Microsporidia MB , which is a native Anopheles endosymbiont without any apparent negative fitness effects on its host, could be a gamechanger in malaria control( 16 ). Unlike most of the known parasitic microsporidians of mosquitoes, this symbiont is in a different group, clade IV, which is distinct from other microsporidia clades. This suggests that it might have unique biological properties and interactions with its host. M icrosporidia MB is both vertically transmitted (mother to offspring) and horizontally transmitted (sexually)( 17 , 18 ). Therefore, this fungus is capable of spreading efficiently through mosquito populations. However, further research is needed to determine whether there are multiple lineages of Microsporidia MB in wild Anopheles especially in malaria-endemic areas and assess their prevalence and any potential association with insecticide resistance. This will help undertake further investigations to unravel whether malaria protective phenotypes hold true across all the wild lineages of Microsporidia MB and set the stage to identify any potential conflicts or synergies between different strains and compare with the current control methods. Larval habitat diversity can also be used as a proxy to identify potential hotspots for Microsporidia MB for the development and successful implementation of targeted vector control measures. This study examined the natural infection rates of An. gambiae s.l. and An. funestus with Microsporidia MB in larvae reared to adults and adult mosquitoes collected from three sub-counties within malaria-endemic Busia County, Kenya. Additionally, this study assessed the potential association between Microsporidia MB infection and insecticide resistance. Understanding the genetic diversity of Microsporidia MB is crucial for elucidating its evolutionary dynamics and potential for conservation of malaria-transmission blocking traits. This diversity can be shaped by various factors, including geographic location and host species. However, limited information exists on the genetic diversity of Microsporidia MB in Anopheles mosquitoes from Busia and the relative influence of these factors. Therefore, this study aimed to investigate the genetic diversity of Microsporidia MB in Anopheles gambiae s.l. and An. funestus mosquitoes from Busia compared with the original reference strain from Ahero, Kenya( 19 ), to assess the impact of geographic location and host species on the observed genetic variation. Methods Study sites With a cross-sectional study design, field sampling of Anopheles indoor-resting adult and larval mosquitoes was carried out in at least three villages of three geographically dispersed sub-counties in Busia, which is a malaria endemic zone in Kenya. The sub-counties were Teso South approximately (0° 39' 0.0" N, 34° 21' 0.0" E), Butula (0° 20' 30" N, 34° 20' 7" E) and Budalangi (0° 27' 43" N, 34° 9' 30" E). Busia County generally has a warm and humid climate. The temperatures are relatively high throughout the year, with average daily temperatures ranging from − 20°C to 30°C. Busia County experiences a bimodal rainfall pattern, with long rains occurring from March-June and short rains occurring from August-November. The annual rainfall in Busia County varies, but it is generally high, ranging from 760 mm to 1,940 mm on average. The northern areas receive more than 1750 mm of precipitation, and the southern areas closer to Lake Victoria receive between 760 and 1,250 mm of precipitation( 20 ). Mosquito sampling, handling and rearing For better representation of the mosquito population and distribution, mosquito collection in Busia was carried out in June 2023 during the wet season, since the wet season has increased the population density of mosquitoes because of increased breeding and larval development. Indoor-resting gravid and engorged female An. gambiae s.l. and An. funestus were collected between 0700 and 1000 hours via both manual aspirators and Prokopack aspiration to maximize sample collection. Similarly, larvae were collected via standard scoopers from larval sites such as swamps, sand harvests, borrow pits and rain pools that were adjacent to the homesteads where adults were collected. The larval sites sampled included accessible still water bodies with relative sunlight or shade and some organic matter that can support larval development. Larval habitat characteristics, including habitat type, substrate type, light intensity, vegetation cover, water temperature, pH and dissolved oxygen, were recorded. The larval samples were transported to the KEMRI/CBRD Molecular Entomology Laboratory, reared into adult insects and morphologically identified. The field-collected adult mosquitoes were transported in cooler boxes and stored at -80°C until DNA extraction, molecular species identification and Microsporidia MB detection. Molecular species and Microsporidia MB identification DNA was extracted from individual mosquitoes via the protein precipitation method (Puregene; Qiagen, the Netherlands). Members of the An. gambiae species complex and An. funestus groups were distinguished via molecular assay methods as described by Scott et al.( 21 ) and Koekemoer and others( 22 ) respectively. The detection and quantification of Microsporidia MBs were performed with specific primers (MB18SF: CGCCGGCCGTGAAAAATTTA and MB18SR: CCTTGGACGTGGGAGCTATC) previously designed to detect the 560 bp Microsporidia MBs in An . arabiensis ( 23 ). The PCRs were performed in a final volume of 10.1 µl containing 2 µl of HOTFirepol® Blend Master mix Ready-To-Load (Solis Biodyne, Estonia; mix composition: 7.5 mM magnesium chloride, 2 mM each dNTP, HOT FIREPol® DNA polymerase), 0.5 µl of 10 µM forward and reverse primers, 2 µl of template and 5.1 µl of nuclease-free PCR water. The PCR cycling conditions were as follows: initial denaturation at 95°C for 15 min, followed by 35 cycles of denaturation at 95°C for 1 min, annealing at 62°C for 90 s and extension at 72°C for 60 s. The final elongation was performed at 72°C for 5 min. The Microsporidia MB DNA positive control was obtained from Jeremy Herren’s laboratory (icipe) and included in each analysis. Analysis of molecular markers associated with insecticide resistance The TaqMan assay described by Riveron et al.( 24 ) was utilized to determine the presence of selected molecular markers associated with insecticide resistance in all the An. gambiae s.l. and An. funestus found to be positive for Microsporidia MB . Knockdown resistance (KDR) mutations in the voltage-gated sodium channel (Vgsc-1014F) 15 , which are associated with resistance to pyrethroids and DDT, and Ace1-G119S28, which is associated with resistance to carbamates and organophosphates, were assayed in An. gambiae s.l. The L114T-GSTe2 mutation in the glutathione S-transferase epsilon 2, associated with resistance to DDT and permethrin resistance ( 24 ), was assayed in both An. funestus and An. gambiae s.l. samples. A matching number of Microsporidia MB -negative samples based on the species, sex, abdominal status during collection, village and development stage of the Microsporidia MB -positive mosquito were included in the analysis of the same molecular markers as controls. Analysis of Microsporidia MB infection and ecological factors across larval habitats To determine the environmental predictors of Microsporidia MB infection, the associations among larval habitat type, characteristics and Microsporidia MB distribution were evaluated. Diverse larval habitats (sand harvest, swamps, borrow pit, rain pool and hoofprints) were sampled in each sub-county. Larval water parameters (temperature, pH and dissolved oxygen) were recorded using a Hach HQ series pH/DO/Conductivity portable meter at one time-point during the day between 12:00pm and 3:00pm. Habitat characteristics, such as vegetation and sunlight, were also recorded during larvae collection. Microsporidia MB infection rates were quantified from various larval habitats, and the prevalence was calculated. Genetic diversit After DNA quality was assessed via Qubit Fluorometric Quantitation (ThermoFisher Scientific, Waltham, USA), Microsporidia MB- positive samples were selected for sequencing, and real-time PCR screening of 18S and, Microsporidia MB density was performed. Highly infected samples with threshold cycle (cT) values of 17 to 22 were sequenced by preparing paired short-insert libraries via the KAPA HiFi HotStart Library Amp Kit and sequenced via DNBSeq technology (2 × 150 bp reads) at the Beijing Genomics Institute (BGI) (Shenzen, China). FastQC was used to check the quality of the generated reads. The host reads were removed by mapping to the reference genomes of An. gambiae s.s (GenBank: GCA_000005575.1 and An. arabiensis (GenBank: GCA_000349185.1) from the NCBI RefSeq database (release 219) via the Burrows–Wheeler Aligner (BWA) v0.7.17. SAMtools v1.3.1 was used to filter out host-mapped reads. Kraken2 (v2.0.8) was applied to remove bacterial contaminants via the minikraken_8 GB_20200312 database. The clean reads were de novo assembled with SPAdes v4.0, and a megablast search was conducted against microsporidia proteins to remove nontarget contigs. The raw reads were reassembled into the cleaned assembly via BWA-MEM v0.7.17, generating a consensus assembly. Multiple alignment via fast Fourier transform (MAAFT v 7.487) was used to align protein sequences from the assembly to the Ahero reference sequence (GenBank: GCA_032191455.1)( 19 , 25 ). Geneious prime ® (v.2025.0.3) was used to generate a Neighbor-Joining phylogenetic tree, with linear topology from the protein alignement. Jukes-cantor was used as a genetic distance model with bootstrap method of 100 replicates at > 50% support threshold. Statistical analysis All the statistical analyses were performed with the R program (v.4.4.1) and GraphPad Prism Software (v.10.3.1). The chi-square test was used to test significant differences between Microsporidia MB positivity rates and Anopheles species and the associations between Microsporidia MB infection status and genotypic molecular markers of insecticide resistance. Repeated measures one-way ANOVA was used to determine the associations between larval habitat characteristics and Microsporidia MB infection. Bonferroni correction or Tukey’s HSD method post hoc tests were used to identify specific pairs of categories that were significantly different. GraphPad Prism was also used to check for associations between insecticide-resistant alleles of Microsporidia MB -infected and non-infected mosquitoes via chi-square or Fisher’s exact tests. Results Distribution of Anopheles species In total, 1,229 Anopheles mosquitoes were collected from the three sub-counties and identified on the basis of their morphological characteristics. Among these, 1030 were molecularly identified via PCR techniques, with Budalangi consisting of 203 Anopheles mosquitoes, Butula 239 Anopheles mosquitoes and Teso South having 588 Anopheles mosquitoes. Larvae that were collected and reared to adults made up 37.3% (384/1030) of the molecularly identified mosquitoes and consisted of 48.4% (186/384) females and 51.6% (198/384) males. All larvae collected from the Budalangi sub-county failed to develop into adult mosquitoes. The Anopheles species distribution did not differ significantly between male and female mosquitoes for the larvae reared in adult collections (Chi-square, χ² = [1.6552, 1.8226] df = 2, P = 0.437, 0.177). However, significant variations in species distributions were observed among the larvae collected and reared to adults from Butula and Teso South (Chi-square, χ² = [110.2, 170.8], df = 2, P < 0.001). The percentage of indoor-resting adults was 62.7% (646/1030), with 59.3% (383/646) being females and 40.7% (263/646) being males. The species distribution did not differ significantly between male and female mosquitoes for the adult collections (Chi-square, χ² = 0.4333, df = 2, P = 0.805, 1). However, significant variations in species distribution were observed in the adult collections across the three sub-counties (Chi-square, χ² = [256.03, 73.4, 385.6], df = 2, P < 0.001). Additionally, the distribution of species exhibited notable differences between the adult collections and adults that emerged from the larval collections in both Butula and Teso South, where such a comparison was noted (Chi-square, χ² = [54.4, 117.7], df = 2, P < 0.001). The distributions and prevalence rates of the species by development stage are shown in Table 1 . The analysis revealed significant variation in species distribution, with An. gambiae s.s. dominated the mosquito population at 57.3% (590/1030). An. funestus followed with a proportion of 25.7% (265/1030), whereas that of An. arabiensis constituted 17% (175/1030) (Chi-square, χ² = 22.08, df = 2, P < 0.001). In Budalangi sub-county An. funestus was the most abundant species among the adult mosquitoes collected indoors, representing 85.7% (174/203) of the total. No adults emerged from the collected larvae. In contrast, An. gambiae s.s. was the dominant species in the Butula sub-county, comprising 53.8% (86/160) of the adult mosquitoes collected indoors and 88.6% (70/79) of the larvae that were successfully reared to adults. Similarly, in Teso South sub-county, An. gambiae s.s. dominated as well, comprising 88.3% (250/283) of the adult mosquitoes collected indoors and 59.5% (194/305) of the larvae that matured into adults. (Fig. 2 ). The detection of Microsporidia MB across An. gambiae s.l. and An. funestus Overall, Microsporidia MB was detected at a prevalence of 6.4% [(66/1030), (95% CI: 0.0491–0.0763)] in Busia County. The Microsporidia MB prevalence rates did not significantly differ between male mosquitoes, which had a prevalence of 5.4% (36/66), and female mosquitoes, which had a prevalence of 3.4% (30/66) (Chi-square, χ² = 2.3, df = 1, P = 0.127). Additionally, pairwise comparisons between the sexes did not indicate any significant differences ( P = 0.983). There were no significant differences in the prevalence of Microsporidia MB between indoor-collected adults and larvae reared into adults in Butula and Teso South (Chi-square, χ2 = 1.05, df = 1, P = 0.306). Furthermore, all larvae sampled from Budalangi sub-county failed to develop into adults. The prevalence of Microsporidia MB varied significantly across the three species, with the prevalence of infections in An. gambiae s.s. being 9.8% (58/66), An. funestus s.s. 1.9% (5/66) and An. arabiensis 1.7% (3/66) [(Chi-square test, χ² = 26.985, df = 2, P < 0.001)]. Pairwise comparison of p values between molecular species with Bonferroni’s adjustment revealed that the prevalence of Microsporidia MB varied significantly between An. gambiae s.s. and An. arabiensis ( P = 0.003) as well as, An. gambiae s.s. and An. funestus ( P < 0.001). However, there was no significant difference in the prevalence of Microsporidia MB between An. funestus and An. arabiensis ( P = 1). At the sub-county level, Budalangi had a prevalence of 2.5% (5/203), with a lack of Microsporidia MB infections recorded in larvae since there was no emergence among the samples collected. However, the prevalence of Microsporidia MB was 1.2% (2/160) in adults and 1.3% (1/79) in larvae, whereas the prevalence of Microsporidia MB was higher in Teso South adults (14.5%, 41/283) and larvae reared to adults (5.6%, 17/305) (Fig. 3 ). The prevalence of Microsporidia MB varied significantly, with the highest prevalence recorded in Teso South at 9.9% (n = 58 [Chi-square test, χ² = 35.97, df = 2, P < 0.001]). However, in Teso South, the prevalence of Microsporidia MB in An. gambiae s.s. and An . arabiensis was not significantly different (Chi-square test, χ² = 0, df = 2, P = 1). Relationships between Microsporidia MB infection and ecological factors across larval habitats The mean water temperature across the five habitats sampled, namely, the sand harvest, borrow pit, rainpool, hoof print and swamp habitats, was 31°C (± 2.76), while the mean water pH was 7.3, and the mean dissolved oxygen content was 6.6 mg/l ± 0.79. (Table 2 ). Water temperature, dissolved oxygen and water pH varied significantly across habitats (ANOVA F (4, 401) = 110.5, p < 0.001), (ANOVA F(4, 401) = 108.5, P < 0.001) and (ANOVA F(4, 401) = 528.4, P < 0.001, respectively). Bonferroni correction revealed that the rain pool habitat type had a significantly greater mean water temperature than the borrow pit habitat type did (mean difference = 5.6°C, 95% CI [4.50, 6.43], P < 0.001) and that the dissolved oxygen levels in rain pools and borrow pits also varied significantly (mean difference = 1.22 mg/L, 95% CI [0.72, 1.72], P < 0.001). A linear regression model fitted to assess the associations between Microsporidia MB infection and the water quality parameters of temperature, pH, and dissolved oxygen showed an overall significant effect on Microsporidia MB infection [adjusted R-squared = 0.01824, F (3, 402) = 3.508, P = 0.01543). However, the individual contributions of water temperature and dissolved oxygen to Microsporidia MB infection were not significant ( P = 0.125, P = 0.453), but the water pH contribution was significant (K-squared = Inf, df = 4, P = 0.01). A t test also revealed a significant difference in the mean pH between mosquitoes with Microsporidia MB infection at 7.76 (ranging from 7.25–7.8) and those without Microsporidia MB infection at 7.31 (ranging from 5.63–7.8) (t = -9.52, df = 70.26, P < 0.001). A significant difference in Microsporidia MB prevalence among the different habitat types was observed (ANOVA, F (4, 401) = 2.88, P = 0.0226), with the highest prevalence being associated with the sand harvest at 7.7% (16/208). Bartlett's test revealed unequal variances across groups, but Tukey's HSD post hoc test did not reveal any significant variation in Microsporidia MB prevalence between all the habitat pairwise comparisons, likely due to low statistical power related to small sample sizes in the pairwise comparisons (Table 5 ). Table 1 Prevalence of Microsporidia MB by development stage, species and sub-county Sub-county Species Sample Size (N) Field collected adults (%) Larvae reared to adults (%) Budalangi An. arabiensis 28 3.57 - An. funestus 174 2.3 - An. gambiae s.s. 1 - - Total 203 5.9 Butula An. arabiensis 12 - - An. funestus 71 - - An. gambiae s.s. 156 2.33 - Total 239 2.3 Teso South An. arabiensis 145 15.38 0.76 An. funestus 20 5 - An. gambiae s.s. 444 15.2 8.76 Total 609 35.6 9.5 Table 2 Statistics of water parameters (temperature, pH and dissolved oxygen) Stats Water Temperature Water pH Dissolved Oxygen N 406 406 406 Mean 30.92143 7.333818 6.579951 Min 25.2 5.63 2.19 Max 35.6 7.8 9.77 Range 10.4 2.17 7.58 SD 2.821189 0.6534799 1.323772 Table 3 Statistics of water parameters (temperature, pH and dissolved oxygen) by habitat Habitat Comparison Temperature Range (°C) pH Range Dissolved Oxygen Range Rainpool-Borrowpit (5.47, p < 0.001) (0.5, p < 0.001) (0.51, p < 0.001) Sand Harvest-Borrowpit (-1.26, p < 0.001) (0.98, p < 0.001) (1.29, p < 0.001) Hoofprint-Borrowpit (3.69, p = 0.008) (0.76, p < 0.001) (0.81, p < 0.001) Rainpool-Hoofprint (6.73, p < 0.001) (0.26, p = 0.11) (0.3, p = 0.591) Sand Harvest-Hoofprint (4.95, p < 0.001) (0.22, p = 0.098) (0.48, p = 0.042) Swamp-Hoofprint (3.93, p < 0.001) (-1.5, p < 0.001) (-2.38, p < 0.001) Sand Harvest-Rainpool (-1.78, p < 0.001) (0.48, p < 0.001) (0.78, p < 0.001) Swamp-Rainpool (-2.79, p < 0.001) (-1.23, p < 0.001) (-2.08, p < 0.001) Swamp-Sand Harvest (-1.02, p = 0.007) (-1.72, p < 0.001) (-2.86, p < 0.001) Swamp-Borrowpit (2.67, p < 0.001) (-40.73, p < 0.001) (-1.57, p < 0.001) Table 4 Prevalence of Microsporidia MB by Habitat Habitat Type Total Samples Positive Samples Prevalence (%) Borrowpit 65 0 0 Hoofprint 42 1 2.4 Rainpool 42 1 2.4 Sand Harvest 208 16 7.7 Swamp 49 0 0 Table 5 Pairwise comparison of Microsporidia MB prevalence by habitat Habitat Comparison Microsporidia MB Prevalence Range Rainpool-Borrow pit [-0.09, 0.13] [p = 0.977] Sand Harvest-Borrow pit [-0.00, 0.16] [p = 0.063] Hoofprint-Borrow pit [-0.09, 0.13] [p = 0.977] Rainpool-Hoof print [-0.12, 0.12] [p = 1.000] Sand Harvest-Hoof print [-0.04, 0.15] [p = 0.539] Swamp-Hoof print [-0.14, 0.09] [p = 0.981] Sand Harvest-Rain pool [-0.04, 0.15] [p = 0.539] Swamp-Rain pool [-0.14, 0.09] [p = 0.981] Swamp-Sand Harvest [-0.17, 0.01] [p = 0.125] Swamp-Borrow pit [-0.11, 0.11] [p = 1.000] Correlation between Microsporidia MB infection and molecular markers of insecticide resistance A total of 134 samples comprising Microsporidia MB -positive (n = 67) and Microsporidia MB -negative samples (n = 67) were genotyped for the presence of the KDR-e, KDR-w, Ace-1, and GSTe-2 molecular markers of insecticide resistance. Only the KDR-e and KDR-w mutations were detected, whereas the Ace-1 and GSTe2 mutations were absent. The frequency of KDR-e in Microsporidia MB -infected mosquitoes was 94.7% (n = 55) and that of the susceptible allele was 5.3% (n = 3), and that of the resistant allele was 87.7% (n = 50) and 12.3% (n = 7) in uninfected mosquitoes. The frequency of KDR-w in infected mosquitoes was 60% (n = 6) for the resistant allele and 40% (n = 4) for the susceptible allele, and it was 40% (n = 4) and 60% (n = 6) for the resistant allele in uninfected mosquitoes. There were no significant associations between Microsporidia MB infection status and either mutation [Kdr-e, (Chi-square, χ² = 5.78, df = 3, p = 0.123; Kdr-w χ² = 6.23, df = 3, P = 0.101)]. Table 6 KDR allele frequencies in Microsporidia MB -infected and uninfected Anopheles Mosquitoes Microsporidia MB Positive Genotypes Molecular Marker N RR RS SS Allele Freq R % & [95% CI] Allele Freq S % & [95% CI] KDR-e 58 53 2 3 94.7 [0.8787–0.9699] 5.3 [0.3006 − 0.1212] KDR-w 10 2 4 4 60 [0.2161–0.6399] 40 [0.3601-7839] Ace-1 60 0 0 0 0 [0.00-0.3102] 100 [0.9690-1.0] GSTE-2 60 0 0 0 0 [0.00-0.3102] 100 [0.9690-1.0] Microsporidia MB Negative Genotypes Molecular Marker N RR RS SS Allele Freq R % & [95% CI] Allele Freq S % & [95% CI] KDR-e 57 50 0 7 87.7 [0.8044–0.9254] 12.3 [0.7458 − 0.1956] KDR-w 10 3 1 6 40 [0.1812–0.5672] 60 [0.4329–0.8188] Ace-1 57 0 0 57 0 [0.00-0.3260] 100 [0.9674-1.0] GSTE-2 57 0 0 57 0 [0.00-0.3260] 100 [0.9674-1.0] Genetic diversity A total of 66 samples positive for Microsporidia MB were subjected to a quality check by undertaking qubit analysis to estimate nucleic acid abundance and qPCR to establish the melting profile and ct while gel loading to establish sample integrity and base size before being sent for sequencing at the Beijing Genomics Institute (BGI). Of these, 33.3% (22/66) had a relative 18 s cT range of 18–24 and were shipped for sequencing. After library preparation and quality checking, only 3 Microsporidia MB- positive samples reached the optimum nucleic acid concentration (> 8.0 ng/ul) and were successfully sequenced. Teso South samples (Teso08_consensus and Teso98_consensus) yielded 94,127,442 and 295,188,214 reads respectively, whereas the Budalangi sample (Busia_consensus) yielded 360,470,585 clean reads. The genomic content (GC%) content of the Teso08 and Teso98 samples were 44.01% and 46.06%, respectively. The Busia_consensus sample had a GC content of 44.77%. Both samples from Teso South mapped to An. gambiae s.s. host, after mapping, host and other bacterial reads were filtered to remain with Microsporidia MB. it was noted that the Budalangi sample mapped to An. arabiensis host. Given that the Budalangi sample had degraded Microsporidia MB , which was observed during assembly and this deemed it unsuitable to include in phylogenomic analysis. Anopheles arabiensis sample from Busia that was sequenced earlier was included in the analysis to make the phylogenetic tree to tease out diversity. This sample yielded 360,389,934 reads with a GC of 44.52%. Phylogenetic analysis of the microsporidian isolates from Busia revealed a distinct cluster within Clade IV, indicating a close evolutionary relationship among them (Fig. 4 ). Neighbor-Joining phylogenetic tree of protein consensus sequences from microsporidia species, generated using Geneious Prime (v.2025.0.3) with a linear topology based on protein sequence alignment. The Jukes-Cantor model was used to calculate genetic distances. Bootstrap support values, based on 100 replicates, are shown at the nodes; only values above 50% are displayed. The tree is rooted with Neoconidiobolus osmodes as the outgroup. Clade I represent host specificity to Aedes mosquitoes, Clade III represents represent host specificity to Aedes , Culex and Anopheles mosquitoes, and Clade IV represents broad host range specificity from insects, mammals to fish. Study samples (Teso94, Teso8, Busia) are highlighted. The scale bar represents 2.0 substitutions per site. This tree illustrates the phylogenetic relationships among microsporidia species based on protein sequence divergence and supports the close clustering of study samples to reference sequence from Ahero. Discussion Understanding the factors that influence the infection of mosquitoes with Microsporidia MB in nature and how infection might influence factors that affect malaria transmission is key in designing effective malaria vector control tools employing this strategy. The present study revealed a low to moderate prevalence of Microsporidia MB in naturally occurring An. gambiae s.s., An. arabiensis and An. funestus mosquito populations from Busia, Kenya, which were sampled during the rainy season. Microsporidia MB were detected both in indoor-resting adult mosquitoes and in mosquitoes collected as larvae and then reared to adults. For the larval samples, temperature, dissolved oxygen and water pH were found to vary significantly across habitats, but only pH had a significant influence on the likelihood of infection with Microsporidia MB . A lack of association between Microsporidia MB infection status and the presence of molecular markers of insecticide resistance was noted, but this could also be because the markers were not the exhaustive list of markers leading to resistance in this setting and could be a limitation of the study. The results of the present study revealed varying patterns of Anopheles species distributions across the three sub-counties of Busia. Anopheles gambiae s.s. emerged as the dominant species in Teso South and Butula, which is consistent with previous studies assessing the distribution of Anopheles species in Busia during the rainy season( 26 , 27 ). The high prevalence of An. gambiae s.s. and An. arabiensis , an efficient vector of malaria in sub-Saharan Africa, and increasing insecticide resistance are concerning, as this may compromise the success of conventional vector control. In contrast, An. funestus was the most abundant species in Budalangi. This could be attributed to Budalangi being in close proximity to Lake Victoria compared with the other two sub-counties sampled, as studies have shown that a reduction in Lake Victoria water levels has created more breeding habitats for An. funestus ( 28 ). The failure to successfully rear adults from the larvae collected from Budalangi in the insectary could be attributed to the fact that An. funestus was the prevalent species in this area (over 85%), and the general difficulty in rearing this species is due to poor larval survival under laboratory conditions ( 29 ). Since An. funestus may require a natural setup, which may be difficult to mimic in the laboratory. The detection of Microsporidia MB in both An. gambiae s.l . and An. funestus highlights the natural occurrence of this potential biological control agent in Busia, which could be an advantage because both species are key malaria vectors and Microsporidia MB can also spread naturally in infected hosts. The prevalence of Microsporidia MB also varied significantly across the three species and was most prevalent in An. gambiae s.s. is in accordance with similar studies that have shown that Microsporidia MB can naturally occur in other Anopheles species, including An. gambiae s.s.( 10 , 11 , 16 , 30 ) , other than the predominant An. arabiensis , where it was originally found in nature( 23 ). Although the larval habitat characteristics assayed varied significantly across habitats, variations in water temperature and dissolved oxygen did not seem to influence the prevalence of Microsporidia MB , suggesting that the observed parameters are within the range that supports the sustainability of Microsporidia MB in nature. However, the mean water temperature in the sand-harvesting larval habitat, which was the most abundant Microsporidia MB -infected larvae reared to adults, was 32°C. This finding is in accordance with the findings of Herren et al., who reported that 32°C was the best temperature for rearing Microsporidia MB -infected larvae because of the relatively short development time and high infection rate( 31 ). Studies have shown that high temperatures can lead to a reduction in Wolbachia loads, but the degree to which temperature affects Wolbachia intensity can vary depending on the specific Wolbachia strain. Some strains are more tolerant of heat stress than others are( 32 , 33 ). This study revealed that water pH has a significant effect on Microsporidia MB infection. The narrow pH range (7.25–7.8) associated with Microsporidia MB infection suggests that slightly alkaline environments may favour the proliferation of Microsporidia MB. Other studies have also shown how pH affects the enzymatic activity and metabolic processes of both the insect and its symbionts, with the pH of the infected cuticle rising from approximately 6.3 to 7.7 during fungal penetration( 34 ). Long-lasting insecticidal nets (LLINs) have been used in Busia for malaria control for over two decades, with widespread distribution beginning in the late 1990s and continuing through the 2000s and 2010s( 35 ), which could explain the high frequencies of the KDR-e and KDR-w mutations observed( 6 ). Laboratory studies revealed that microsporidia-infected mosquitoes were more susceptible to insecticides than non-infected mosquitoes were, probably due to the reduced fitness of infected mosquitoes( 36 ), noting that this was an unrelated pathogenic microsporidian. However, the present study did not find a significant association between Microsporidia MB infection and the evaluated molecular markers of insecticide resistance. These findings suggest that the presence of KDR mutations may not impose significant fitness costs on the mosquito species studied, even those harbouring Microsporidia MB. Studies have shown that fitness costs associated with the presence of these mutations are greater in the absence of insecticide exposure( 37 , 38 ). Given that there is widespread use of LLINs in the study area, it is expected that the fitness cost associated with KDR mutations would be diminished( 39 , 40 ). Therefore, the absence of a significant association between the presence of KDR mutations and Microsporidia MB infection would be in tandem with the expected reduced fitness cost associated with the presence of KDR mutations in the presence of insecticide exposure. Furthermore, since studies have demonstrated that the presence of KDR mutations is not always closely linked to phenotypic resistance to some pyrethroids, further studies are necessary to shed light on the interactions between insecticide resistance and Microsporidia MB infection ( 41 , 42 ). Ace-1 and GSTe2 mutations are associated with resistance to organophosphates and carbamate-based insecticides, with GSTe2 having cross-resistance mechanisms to DDT and pyrethroids. These mutations were absent in the study area, likely because organophosphates and carbamates have not been used in the study area for vector management or pest control. Phylogenetic analysis revealed a close relationship between Busia Microsporidia MB isolates and Microsporidia sp. MB reference from Ahero (Clade IV). This is significant given the high malaria prevalence in Busia and Microsporidia MB ability to impair Plasmodium transmission( 43 ). The close relationship, supported by the clustering of Busia isolates, suggests potential for utilizing Microsporidia MB control. This highlights the potential for exploiting shared traits for malaria control strategies in the region. Limitations This study focused on the wet season, yet there could be seasonal variation in Microsporidia MB across the wet and dry seasons. The low prevalence of Microsporidia MB did not allow analysis of the relationship between infection and molecular markers of insecticide resistance by mosquito species and study site. Conclusion The study revealed varying distributions of species with An. funestus being most prevalent in Budalangi, which may be linked to its closer proximity to the lake. The finding of Microsporidia MBs in An. gambiae s.s. and An. funestus rather than predominantly An. arabiensis , as previously reported, has potential as a vector control tool for these species. Ecological factors are important for sustaining the development of Microsporidia MB . Further research is needed to fully understand the dynamics among Anopheles mosquitoes, Microsporidia MB , and insecticide resistance to develop effective strategies for malaria prevention and control. These phylogenetic diversity findings contribute to our understanding of the evolutionary dynamics and potential dispersal patterns of Microsporidia MB endosymbionts in mosquitoes. Abbreviations PCR Polymerase chain reaction qPCR Quantitative polymerase chain reaction ANOVA Analysis of variance LLIN Long-lasting insecticidal nets IRS Insecticide residual sprays KEMRI Kenya Medical Research Institute CBRD Centre for Biotechnology Research and Development DNA Deoxyribonucleic Acid DDT Dichloro-diphenyl-trichloroethane s.l. Sensu Lato s.s. Sensu Stricto NCBI National Center for Biotechnology Institute Declarations Ethics approval and consent to participate Not applicable Consent for publication Not applicable. Availability of data and materials All figures and tables supporting the conclusions of this article are included within the manuscript. The raw reads and genomic isolates for the study samples have been deposited at NCBI under the BioProject accession PRJNA1242000. Raw reads have been deposited to the Sequence Read Archive (SRA) under accession number SAMN47576098, SAMN47576099 and SAMN47626518. The Whole Genome Shotgun (WGS) project for the Microsporidia MB genomic isolates has been deposited at DDBJ/ENA/GenBank under the accession JBMNCT000000000 and JBMNCU000000000. The versions described in this paper are version JBMNCT010000000 and JBMNCU010000000. Competing interests The authors declare that they have no competing interests. Funding The study was supported by the University Court of the University of Glasgow ANTI-Vec African Anopheles Symbiont Survey Grant No. AV/AASS/006 to Luna Kamau. GN was funded by the International Foundation of Science award number I-1-F-5852-1. Authors' contributions LK, DMM, EO, JKH conceptualized and designed the study. HTW, SK, SN, MA, EO, DMM, JKH, LK conducted sample collection, processing, and data collection. HTW and LK conducted the data analysis and interpretation and drafted the manuscript GN, DN, MOA, JKH conducted the data analysis and interpretation and assisted in drafting the manuscript. All the authors read, reviewed and approved this manuscript. Acknowledgements The authors express their sincere gratitude to the homeowners in Teso South, Butula, and Budalangi, Busia County, for granting access to their homes during mosquito collection, which was essential for this study. This work is published with the permission of the Director General of KEMRI. 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Institute","correspondingAuthor":false,"prefix":"","firstName":"Eric","middleName":"","lastName":"Ochomo","suffix":""},{"id":436831658,"identity":"91e08002-96b4-4e16-8d22-e038298e6081","order_by":8,"name":"Damaris Matoke Muhia","email":"","orcid":"","institution":"Kenya Medical Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Damaris","middleName":"Matoke","lastName":"Muhia","suffix":""},{"id":436831659,"identity":"86f8af77-59e8-4a9c-9866-9c6e49b38178","order_by":9,"name":"Jeremy Keith Herren","email":"","orcid":"","institution":"International Centre of Insect Physiology and Ecology","correspondingAuthor":false,"prefix":"","firstName":"Jeremy","middleName":"Keith","lastName":"Herren","suffix":""},{"id":436831661,"identity":"59150b59-f6b9-42c8-b305-42e2f5c0d2f0","order_by":10,"name":"Luna Kamau","email":"","orcid":"","institution":"Kenya Medical Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Luna","middleName":"","lastName":"Kamau","suffix":""}],"badges":[],"createdAt":"2025-04-01 10:53:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6352339/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6352339/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12866-025-04324-6","type":"published","date":"2025-10-02T15:57:12+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79743031,"identity":"43196d7c-bab8-4319-8cd3-32309605e6c5","added_by":"auto","created_at":"2025-04-02 08:18:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":465695,"visible":true,"origin":"","legend":"\u003cp\u003eMap showing the Anopheles sampling sites in Busia, western Kenya.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6352339/v1/ece4c3eee2948c50636ef8fc.png"},{"id":79742408,"identity":"bc92356b-451d-4086-b55f-659bc6f1662e","added_by":"auto","created_at":"2025-04-02 08:10:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":68133,"visible":true,"origin":"","legend":"\u003cp\u003eThe proportions of Anopheles species across different developmental stages from three sub-counties of Busia, Kenya.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6352339/v1/466e99c5ad8ea252abe07adf.png"},{"id":79742405,"identity":"4f92f402-024c-4c45-bc19-b3d661e46234","added_by":"auto","created_at":"2025-04-02 08:10:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":42767,"visible":true,"origin":"","legend":"\u003cp\u003ePrevalence of Microsporidia MB by sub-county and development stage.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6352339/v1/887e9a725611901092fb654b.png"},{"id":79742422,"identity":"5d1b96ce-283e-491e-878e-5c746be074b0","added_by":"auto","created_at":"2025-04-02 08:10:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":311893,"visible":true,"origin":"","legend":"\u003cp\u003ePhylogenetic tree showing relationship between samples from Busia, other microsporidians and Microsporidia MB reference sequence.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6352339/v1/de7a0f1006abcfee43a329f4.png"},{"id":92883701,"identity":"1c5b20b1-23ab-4ca1-9b61-7423aaae5d37","added_by":"auto","created_at":"2025-10-06 16:08:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1854575,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6352339/v1/c661b88a-b46b-4263-9fdf-1fa53e3b3bc5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prevalence, Genetic Diversity of Microsporidia MB, and Correlation to Insecticide Resistance in Anopheles gambiae s.l. and Anopheles funestus Mosquitoes in Busia, Kenya","fulltext":[{"header":"Background","content":"\u003cp\u003e \u003cem\u003eAnopheles\u003c/em\u003e mosquitoes are the primary vectors of malaria in sub-Saharan Africa, causing millions of deaths annually(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Busia County is a malaria-endemic region characterized by a warm and humid climate, providing favourable conditions for mosquito breeding and transmission(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). With two rainfall patterns annually, the mosquito vector population in this region is usually high, and malaria transmission is intense due to suitable climate conditions(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe current primary method for controlling malaria is vector management, which involves the use of long-lasting insecticidal nets (LLINs), insecticide residual sprays (IRSs), and mosquito breeding sites(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). While interventions such as IRS have been effective, they have also led to insecticide resistance challenges(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). New vector control strategies are emerging, and several studies have shown that microbes associated with mosquitoes can significantly impact their ability to transmit pathogens. For example, \u003cem\u003eAedes aegypti\u003c/em\u003e, a primary vector for dengue, chikungunya, and Zika viruses, is substantially diminished in the presence of the intracellular endosymbiont Wolbachia(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). \u003cem\u003eMicrosporidia MB\u003c/em\u003e has recently gained significant attention as a promising candidate for malaria control(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). This microorganism can spread within \u003cem\u003eAnopheles\u003c/em\u003e populations, block \u003cem\u003ePlasmodium\u003c/em\u003e transmission, and is harmless to the host. Although it has been naturally found in \u003cem\u003eAnopheles arabiensis\u003c/em\u003e, other studies have reported its presence in various \u003cem\u003eAnopheles\u003c/em\u003e species(\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eWolbachia\u003c/em\u003e-based strategies have been successfully implemented to control dengue fever transmission(\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). \u003cem\u003eWolbachia\u003c/em\u003e has also been identified in \u003cem\u003eAnopheles\u003c/em\u003e species, but its low intensity impedes its inherent ability to efficiently spread within these hosts(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Therefore, \u003cem\u003eMicrosporidia MB\u003c/em\u003e, which is a native \u003cem\u003eAnopheles\u003c/em\u003e endosymbiont without any apparent negative fitness effects on its host, could be a gamechanger in malaria control(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Unlike most of the known parasitic microsporidians of mosquitoes, this symbiont is in a different group, clade IV, which is distinct from other microsporidia clades. This suggests that it might have unique biological properties and interactions with its host. M\u003cem\u003eicrosporidia MB\u003c/em\u003e is both vertically transmitted (mother to offspring) and horizontally transmitted (sexually)(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Therefore, this fungus is capable of spreading efficiently through mosquito populations. However, further research is needed to determine whether there are multiple lineages of \u003cem\u003eMicrosporidia MB\u003c/em\u003e in wild \u003cem\u003eAnopheles\u003c/em\u003e especially in malaria-endemic areas and assess their prevalence and any potential association with insecticide resistance. This will help undertake further investigations to unravel whether malaria protective phenotypes hold true across all the wild lineages of \u003cem\u003eMicrosporidia MB\u003c/em\u003e and set the stage to identify any potential conflicts or synergies between different strains and compare with the current control methods. Larval habitat diversity can also be used as a proxy to identify potential hotspots for \u003cem\u003eMicrosporidia MB\u003c/em\u003e for the development and successful implementation of targeted vector control measures. This study examined the natural infection rates of \u003cem\u003eAn. gambiae\u003c/em\u003e s.l. and \u003cem\u003eAn. funestus\u003c/em\u003e with \u003cem\u003eMicrosporidia MB\u003c/em\u003e in larvae reared to adults and adult mosquitoes collected from three sub-counties within malaria-endemic Busia County, Kenya. Additionally, this study assessed the potential association between \u003cem\u003eMicrosporidia MB\u003c/em\u003e infection and insecticide resistance.\u003c/p\u003e \u003cp\u003eUnderstanding the genetic diversity of \u003cem\u003eMicrosporidia MB\u003c/em\u003e is crucial for elucidating its evolutionary dynamics and potential for conservation of malaria-transmission blocking traits. This diversity can be shaped by various factors, including geographic location and host species. However, limited information exists on the genetic diversity of \u003cem\u003eMicrosporidia MB\u003c/em\u003e in \u003cem\u003eAnopheles\u003c/em\u003e mosquitoes from Busia and the relative influence of these factors. Therefore, this study aimed to investigate the genetic diversity of \u003cem\u003eMicrosporidia MB\u003c/em\u003e in \u003cem\u003eAnopheles gambiae\u003c/em\u003e s.l. and \u003cem\u003eAn. funestus\u003c/em\u003e mosquitoes from Busia compared with the original reference strain from Ahero, Kenya(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), to assess the impact of geographic location and host species on the observed genetic variation.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy sites\u003c/h2\u003e \u003cp\u003eWith a cross-sectional study design, field sampling of \u003cem\u003eAnopheles\u003c/em\u003e indoor-resting adult and larval mosquitoes was carried out in at least three villages of three geographically dispersed sub-counties in Busia, which is a malaria endemic zone in Kenya. The sub-counties were Teso South approximately (0\u0026deg; 39' 0.0\" N, 34\u0026deg; 21' 0.0\" E), Butula (0\u0026deg; 20' 30\" N, 34\u0026deg; 20' 7\" E) and Budalangi (0\u0026deg; 27' 43\" N, 34\u0026deg; 9' 30\" E). Busia County generally has a warm and humid climate. The temperatures are relatively high throughout the year, with average daily temperatures ranging from \u0026minus;\u0026thinsp;20\u0026deg;C to 30\u0026deg;C. Busia County experiences a bimodal rainfall pattern, with long rains occurring from March-June and short rains occurring from August-November. The annual rainfall in Busia County varies, but it is generally high, ranging from 760 mm to 1,940 mm on average. The northern areas receive more than 1750 mm of precipitation, and the southern areas closer to Lake Victoria receive between 760 and 1,250 mm of precipitation(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMosquito sampling, handling and rearing\u003c/h3\u003e\n\u003cp\u003eFor better representation of the mosquito population and distribution, mosquito collection in Busia was carried out in June 2023 during the wet season, since the wet season has increased the population density of mosquitoes because of increased breeding and larval development. Indoor-resting gravid and engorged female \u003cem\u003eAn. gambiae\u003c/em\u003e s.l. and \u003cem\u003eAn. funestus\u003c/em\u003e were collected between 0700 and 1000 hours via both manual aspirators and Prokopack aspiration to maximize sample collection. Similarly, larvae were collected via standard scoopers from larval sites such as swamps, sand harvests, borrow pits and rain pools that were adjacent to the homesteads where adults were collected. The larval sites sampled included accessible still water bodies with relative sunlight or shade and some organic matter that can support larval development. Larval habitat characteristics, including habitat type, substrate type, light intensity, vegetation cover, water temperature, pH and dissolved oxygen, were recorded. The larval samples were transported to the KEMRI/CBRD Molecular Entomology Laboratory, reared into adult insects and morphologically identified. The field-collected adult mosquitoes were transported in cooler boxes and stored at -80\u0026deg;C until DNA extraction, molecular species identification and \u003cem\u003eMicrosporidia MB\u003c/em\u003e detection.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMolecular species and\u003c/b\u003e \u003cb\u003eMicrosporidia MB\u003c/b\u003e \u003cb\u003eidentification\u003c/b\u003e\u003c/p\u003e \u003cp\u003eDNA was extracted from individual mosquitoes via the protein precipitation method (Puregene; Qiagen, the Netherlands). Members of the \u003cem\u003eAn. gambiae\u003c/em\u003e species complex and \u003cem\u003eAn. funestus\u003c/em\u003e groups were distinguished via molecular assay methods as described by Scott et al.(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) and Koekemoer and others(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) respectively. The detection and quantification of \u003cem\u003eMicrosporidia MBs\u003c/em\u003e were performed with specific primers (MB18SF: CGCCGGCCGTGAAAAATTTA and MB18SR: CCTTGGACGTGGGAGCTATC) previously designed to detect the 560 bp \u003cem\u003eMicrosporidia MBs\u003c/em\u003e in \u003cem\u003eAn\u003c/em\u003e. \u003cem\u003earabiensis\u003c/em\u003e(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). The PCRs were performed in a final volume of 10.1 \u0026micro;l containing 2 \u0026micro;l of HOTFirepol\u0026reg; Blend Master mix Ready-To-Load (Solis Biodyne, Estonia; mix composition: 7.5 mM magnesium chloride, 2 mM each dNTP, HOT FIREPol\u0026reg; DNA polymerase), 0.5 \u0026micro;l of 10 \u0026micro;M forward and reverse primers, 2 \u0026micro;l of template and 5.1 \u0026micro;l of nuclease-free PCR water. The PCR cycling conditions were as follows: initial denaturation at 95\u0026deg;C for 15 min, followed by 35 cycles of denaturation at 95\u0026deg;C for 1 min, annealing at 62\u0026deg;C for 90 s and extension at 72\u0026deg;C for 60 s. The final elongation was performed at 72\u0026deg;C for \u003cem\u003e5 min.\u003c/em\u003e The \u003cem\u003eMicrosporidia MB\u003c/em\u003e DNA positive control was obtained from Jeremy Herren\u0026rsquo;s laboratory (icipe) and included in each analysis.\u003c/p\u003e\n\u003ch3\u003eAnalysis of molecular markers associated with insecticide resistance\u003c/h3\u003e\n\u003cp\u003eThe TaqMan assay described by Riveron et al.(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e) was utilized to determine the presence of selected molecular markers associated with insecticide resistance in all the \u003cem\u003eAn. gambiae\u003c/em\u003e s.l. and \u003cem\u003eAn. funestus\u003c/em\u003e found to be positive for \u003cem\u003eMicrosporidia MB\u003c/em\u003e. Knockdown resistance (KDR) mutations in the voltage-gated sodium channel (Vgsc-1014F)\u003csup\u003e15\u003c/sup\u003e, which are associated with resistance to pyrethroids and DDT, and Ace1-G119S28, which is associated with resistance to carbamates and organophosphates, were assayed in \u003cem\u003eAn. gambiae\u003c/em\u003e s.l. The L114T-GSTe2 mutation in the glutathione S-transferase epsilon 2, associated with resistance to DDT and permethrin resistance (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), was assayed in both \u003cem\u003eAn. funestus\u003c/em\u003e and \u003cem\u003eAn. gambiae\u003c/em\u003e s.l. samples.\u003c/p\u003e \u003cp\u003eA matching number of \u003cem\u003eMicrosporidia MB\u003c/em\u003e-negative samples based on the species, sex, abdominal status during collection, village and development stage of the \u003cem\u003eMicrosporidia MB\u003c/em\u003e-positive mosquito were included in the analysis of the same molecular markers as controls.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAnalysis of\u003c/b\u003e \u003cb\u003eMicrosporidia MB\u003c/b\u003e \u003cb\u003einfection and ecological factors across larval habitats\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo determine the environmental predictors of \u003cem\u003eMicrosporidia MB\u003c/em\u003e infection, the associations among larval habitat type, characteristics and \u003cem\u003eMicrosporidia MB\u003c/em\u003e distribution were evaluated. Diverse larval habitats (sand harvest, swamps, borrow pit, rain pool and hoofprints) were sampled in each sub-county. Larval water parameters (temperature, pH and dissolved oxygen) were recorded using a Hach HQ series pH/DO/Conductivity portable meter at one time-point during the day between 12:00pm and 3:00pm. Habitat characteristics, such as vegetation and sunlight, were also recorded during larvae collection. \u003cem\u003eMicrosporidia MB\u003c/em\u003e infection rates were quantified from various larval habitats, and the prevalence was calculated.\u003c/p\u003e\n\u003ch3\u003eGenetic diversit\u003c/h3\u003e\n\u003cp\u003eAfter DNA quality was assessed via Qubit Fluorometric Quantitation (ThermoFisher Scientific, Waltham, USA), \u003cem\u003eMicrosporidia MB-\u003c/em\u003epositive samples were selected for sequencing, and real-time PCR screening of 18S and, \u003cem\u003eMicrosporidia MB\u003c/em\u003e density was performed. Highly infected samples with threshold cycle (cT) values of 17 to 22 were sequenced by preparing paired short-insert libraries via the KAPA HiFi HotStart Library Amp Kit and sequenced via DNBSeq technology (2 \u0026times; 150 bp reads) at the Beijing Genomics Institute (BGI) (Shenzen, China). FastQC was used to check the quality of the generated reads. The host reads were removed by mapping to the reference genomes of \u003cem\u003eAn. gambiae s.s\u003c/em\u003e (GenBank: GCA_000005575.1 and \u003cem\u003eAn. arabiensis\u003c/em\u003e (GenBank: GCA_000349185.1) from the NCBI RefSeq database (release 219) via the Burrows\u0026ndash;Wheeler Aligner (BWA) v0.7.17. SAMtools v1.3.1 was used to filter out host-mapped reads. Kraken2 (v2.0.8) was applied to remove bacterial contaminants via the minikraken_8 GB_20200312 database. The clean reads were de novo assembled with SPAdes v4.0, and a megablast search was conducted against microsporidia proteins to remove nontarget contigs. The raw reads were reassembled into the cleaned assembly via BWA-MEM v0.7.17, generating a consensus assembly. Multiple alignment via fast Fourier transform (MAAFT v 7.487) was used to align protein sequences from the assembly to the Ahero reference sequence (GenBank: GCA_032191455.1)(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Geneious prime \u0026reg; (v.2025.0.3) was used to generate a Neighbor-Joining phylogenetic tree, with linear topology from the protein alignement. Jukes-cantor was used as a genetic distance model with bootstrap method of 100 replicates at \u0026gt;\u0026thinsp;50% support threshold.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll the statistical analyses were performed with the R program (v.4.4.1) and GraphPad Prism Software (v.10.3.1). The chi-square test was used to test significant differences between \u003cem\u003eMicrosporidia MB\u003c/em\u003e positivity rates and \u003cem\u003eAnopheles\u003c/em\u003e species and the associations between \u003cem\u003eMicrosporidia MB\u003c/em\u003e infection status and genotypic molecular markers of insecticide resistance. Repeated measures one-way ANOVA was used to determine the associations between larval habitat characteristics and \u003cem\u003eMicrosporidia MB\u003c/em\u003e infection. Bonferroni correction or Tukey\u0026rsquo;s HSD method post hoc tests were used to identify specific pairs of categories that were significantly different. GraphPad Prism was also used to check for associations between insecticide-resistant alleles of \u003cem\u003eMicrosporidia MB\u003c/em\u003e-infected and non-infected mosquitoes via chi-square or Fisher\u0026rsquo;s exact tests.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eDistribution of\u003c/b\u003e \u003cb\u003eAnopheles species\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIn total, 1,229 \u003cem\u003eAnopheles\u003c/em\u003e mosquitoes were collected from the three sub-counties and identified on the basis of their morphological characteristics. Among these, 1030 were molecularly identified via PCR techniques, with Budalangi consisting of 203 \u003cem\u003eAnopheles\u003c/em\u003e mosquitoes, Butula 239 \u003cem\u003eAnopheles\u003c/em\u003e mosquitoes and Teso South having 588 \u003cem\u003eAnopheles\u003c/em\u003e mosquitoes.\u003c/p\u003e \u003cp\u003eLarvae that were collected and reared to adults made up 37.3% (384/1030) of the molecularly identified mosquitoes and consisted of 48.4% (186/384) females and 51.6% (198/384) males. All larvae collected from the Budalangi sub-county failed to develop into adult mosquitoes. The \u003cem\u003eAnopheles\u003c/em\u003e species distribution did not differ significantly between male and female mosquitoes for the larvae reared in adult collections (Chi-square, χ\u0026sup2; = [1.6552, 1.8226] df\u0026thinsp;=\u0026thinsp;2, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.437, 0.177). However, significant variations in species distributions were observed among the larvae collected and reared to adults from Butula and Teso South (Chi-square, χ\u0026sup2; = [110.2, 170.8], df\u0026thinsp;=\u0026thinsp;2, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eThe percentage of indoor-resting adults was 62.7% (646/1030), with 59.3% (383/646) being females and 40.7% (263/646) being males. The species distribution did not differ significantly between male and female mosquitoes for the adult collections (Chi-square, χ\u0026sup2; = 0.4333, df\u0026thinsp;=\u0026thinsp;2, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.805, 1). However, significant variations in species distribution were observed in the adult collections across the three sub-counties (Chi-square, χ\u0026sup2; = [256.03, 73.4, 385.6], df\u0026thinsp;=\u0026thinsp;2, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eAdditionally, the distribution of species exhibited notable differences between the adult collections and adults that emerged from the larval collections in both Butula and Teso South, where such a comparison was noted (Chi-square, χ\u0026sup2; = [54.4, 117.7], df\u0026thinsp;=\u0026thinsp;2, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The distributions and prevalence rates of the species by development stage are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe analysis revealed significant variation in species distribution, with \u003cem\u003eAn. gambiae\u003c/em\u003e s.s. dominated the mosquito population at 57.3% (590/1030). \u003cem\u003eAn. funestus\u003c/em\u003e followed with a proportion of 25.7% (265/1030), whereas that of \u003cem\u003eAn. arabiensis\u003c/em\u003e constituted 17% (175/1030) (Chi-square, χ\u0026sup2; = 22.08, df\u0026thinsp;=\u0026thinsp;2, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eIn Budalangi sub-county \u003cem\u003eAn. funestus\u003c/em\u003e was the most abundant species among the adult mosquitoes collected indoors, representing 85.7% (174/203) of the total. No adults emerged from the collected larvae. In contrast, \u003cem\u003eAn. gambiae\u003c/em\u003e s.s. was the dominant species in the Butula sub-county, comprising 53.8% (86/160) of the adult mosquitoes collected indoors and 88.6% (70/79) of the larvae that were successfully reared to adults. Similarly, in Teso South sub-county, \u003cem\u003eAn. gambiae\u003c/em\u003e s.s. dominated as well, comprising 88.3% (250/283) of the adult mosquitoes collected indoors and 59.5% (194/305) of the larvae that matured into adults. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eThe detection of\u003c/b\u003e \u003cb\u003eMicrosporidia MB\u003c/b\u003e \u003cb\u003eacross\u003c/b\u003e \u003cb\u003eAn. gambiae\u003c/b\u003e \u003cb\u003es.l.\u003c/b\u003e \u003cb\u003eand An. funestus\u003c/b\u003e\u003c/p\u003e \u003cp\u003eOverall, \u003cem\u003eMicrosporidia MB\u003c/em\u003e was detected at a prevalence of 6.4% [(66/1030), (95% CI: 0.0491\u0026ndash;0.0763)] in Busia County. The \u003cem\u003eMicrosporidia MB\u003c/em\u003e prevalence rates did not significantly differ between male mosquitoes, which had a prevalence of 5.4% (36/66), and female mosquitoes, which had a prevalence of 3.4% (30/66) (Chi-square, χ\u0026sup2; = 2.3, df\u0026thinsp;=\u0026thinsp;1, \u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.127). Additionally, pairwise comparisons between the sexes did not indicate any significant differences (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.983). There were no significant differences in the prevalence of \u003cem\u003eMicrosporidia MB\u003c/em\u003e between indoor-collected adults and larvae reared into adults in Butula and Teso South (Chi-square, χ2\u0026thinsp;=\u0026thinsp;1.05, df\u0026thinsp;=\u0026thinsp;1, \u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.306).\u003c/p\u003e \u003cp\u003eFurthermore, all larvae sampled from Budalangi sub-county failed to develop into adults. The prevalence of \u003cem\u003eMicrosporidia MB\u003c/em\u003e varied significantly across the three species, with the prevalence of infections in \u003cem\u003eAn. gambiae\u003c/em\u003e s.s. being 9.8% (58/66), \u003cem\u003eAn. funestus\u003c/em\u003e s.s. 1.9% (5/66) and \u003cem\u003eAn. arabiensis\u003c/em\u003e 1.7% (3/66) [(Chi-square test, χ\u0026sup2; = 26.985, df\u0026thinsp;=\u0026thinsp;2, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001)]. Pairwise comparison of p values between molecular species with Bonferroni\u0026rsquo;s adjustment revealed \u003cem\u003ethat the\u003c/em\u003e prevalence of \u003cem\u003eMicrosporidia MB\u003c/em\u003e varied significantly between \u003cem\u003eAn. gambiae\u003c/em\u003e s.s. and \u003cem\u003eAn. arabiensis\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003) as well as, \u003cem\u003eAn. gambiae\u003c/em\u003e s.s. and \u003cem\u003eAn. funestus\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, there was no significant difference in the prevalence of \u003cem\u003eMicrosporidia MB\u003c/em\u003e between \u003cem\u003eAn. funestus\u003c/em\u003e and \u003cem\u003eAn. arabiensis\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1).\u003c/p\u003e \u003cp\u003eAt the sub-county level, Budalangi had a prevalence of 2.5% (5/203), with a lack of \u003cem\u003eMicrosporidia MB\u003c/em\u003e infections recorded in larvae since there was no emergence among the samples collected. However, the prevalence of \u003cem\u003eMicrosporidia MB\u003c/em\u003e was 1.2% (2/160) in adults and 1.3% (1/79) in larvae, whereas the prevalence of \u003cem\u003eMicrosporidia MB\u003c/em\u003e was higher in Teso South adults (14.5%, 41/283) and larvae reared to adults (5.6%, 17/305) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The prevalence of \u003cem\u003eMicrosporidia MB\u003c/em\u003e varied significantly, with the highest prevalence recorded in Teso South at 9.9% (n\u0026thinsp;=\u0026thinsp;58 [Chi-square test, χ\u0026sup2; = 35.97, df\u0026thinsp;=\u0026thinsp;2, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001]). However, in Teso South, the prevalence of \u003cem\u003eMicrosporidia MB\u003c/em\u003e in \u003cem\u003eAn. gambiae s.s. and An\u003c/em\u003e. \u003cem\u003earabiensis\u003c/em\u003e was not significantly different (Chi-square test, χ\u0026sup2; = 0, df\u0026thinsp;=\u0026thinsp;2, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eRelationships between\u003c/b\u003e \u003cb\u003eMicrosporidia MB\u003c/b\u003e \u003cb\u003einfection and ecological factors across larval habitats\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe mean water temperature across the five habitats sampled, namely, the sand harvest, borrow pit, rainpool, hoof print and swamp habitats, was 31\u0026deg;C (\u0026plusmn;\u0026thinsp;2.76), while the mean water pH was 7.3, and the mean dissolved oxygen content was 6.6 mg/l\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79. (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Water temperature, dissolved oxygen and water pH varied significantly across habitats (ANOVA F (4, 401)\u0026thinsp;=\u0026thinsp;110.5, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), (ANOVA F(4, 401)\u0026thinsp;=\u0026thinsp;108.5, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and (ANOVA F(4, 401)\u0026thinsp;=\u0026thinsp;528.4, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, respectively). Bonferroni correction revealed that the rain pool habitat type had a significantly greater mean water temperature than the borrow pit habitat type did (mean difference\u0026thinsp;=\u0026thinsp;5.6\u0026deg;C, 95% CI [4.50, 6.43], \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and that the dissolved oxygen levels in rain pools and borrow pits also varied significantly (mean difference\u0026thinsp;=\u0026thinsp;1.22 mg/L, 95% CI [0.72, 1.72], \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eA linear regression model fitted to assess the associations between \u003cem\u003eMicrosporidia MB\u003c/em\u003e infection and the water quality parameters of temperature, pH, and dissolved oxygen showed an overall significant effect on \u003cem\u003eMicrosporidia MB\u003c/em\u003e infection [adjusted R-squared\u0026thinsp;=\u0026thinsp;0.01824, F (3, 402)\u0026thinsp;=\u0026thinsp;3.508, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01543). However, the individual contributions of water temperature and dissolved oxygen to \u003cem\u003eMicrosporidia MB\u003c/em\u003e infection were not significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.125, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.453), but the water pH contribution was significant (K-squared\u0026thinsp;=\u0026thinsp;Inf, df\u0026thinsp;=\u0026thinsp;4, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01). A t test also revealed a significant difference in the mean pH between mosquitoes with \u003cem\u003eMicrosporidia MB\u003c/em\u003e infection at 7.76 (ranging from 7.25\u0026ndash;7.8) and those without \u003cem\u003eMicrosporidia MB\u003c/em\u003e infection at 7.31 (ranging from 5.63\u0026ndash;7.8) (t = -9.52, df\u0026thinsp;=\u0026thinsp;70.26, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eA significant difference in \u003cem\u003eMicrosporidia MB\u003c/em\u003e prevalence among the different habitat types was observed (ANOVA, F (4, 401)\u0026thinsp;=\u0026thinsp;2.88, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0226), with the highest prevalence being associated with the sand harvest at 7.7% (16/208). Bartlett's test revealed unequal variances across groups, but Tukey's HSD post hoc test did not reveal any significant variation in \u003cem\u003eMicrosporidia MB\u003c/em\u003e prevalence between all the habitat pairwise comparisons, likely due to low statistical power related to small sample sizes in the pairwise comparisons (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrevalence of Microsporidia MB by development stage, species and sub-county\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSub-county\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSample Size (N)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eField collected adults (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLarvae reared to adults (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBudalangi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAn. arabiensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAn. funestus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAn. gambiae\u003c/em\u003e s.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eButula\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAn. arabiensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAn. funestus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAn. gambiae\u003c/em\u003e s.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTeso South\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAn. arabiensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAn. funestus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAn. gambiae\u003c/em\u003e s.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStatistics of water parameters (temperature, pH and dissolved oxygen)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStats\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater Temperature\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWater pH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDissolved Oxygen\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e406\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.92143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.333818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.579951\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.821189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6534799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.323772\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStatistics of water parameters (temperature, pH and dissolved oxygen) by habitat\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHabitat Comparison\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTemperature Range (\u0026deg;C)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003epH Range\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDissolved Oxygen Range\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRainpool-Borrowpit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(5.47, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.5, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.51, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSand Harvest-Borrowpit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-1.26, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.98, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.29, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHoofprint-Borrowpit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(3.69, p\u0026thinsp;=\u0026thinsp;0.008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.76, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.81, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRainpool-Hoofprint\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(6.73, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.26, p\u0026thinsp;=\u0026thinsp;0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.3, p\u0026thinsp;=\u0026thinsp;0.591)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSand Harvest-Hoofprint\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(4.95, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.22, p\u0026thinsp;=\u0026thinsp;0.098)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.48, p\u0026thinsp;=\u0026thinsp;0.042)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSwamp-Hoofprint\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(3.93, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-1.5, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-2.38, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSand Harvest-Rainpool\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-1.78, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.48, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.78, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSwamp-Rainpool\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-2.79, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-1.23, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-2.08, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSwamp-Sand Harvest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-1.02, p\u0026thinsp;=\u0026thinsp;0.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-1.72, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-2.86, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSwamp-Borrowpit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2.67, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-40.73, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-1.57, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrevalence of \u003cem\u003eMicrosporidia MB\u003c/em\u003e by Habitat\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHabitat Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal Samples\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePositive Samples\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePrevalence (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBorrowpit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHoofprint\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRainpool\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSand Harvest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSwamp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePairwise comparison of \u003cem\u003eMicrosporidia MB\u003c/em\u003e prevalence by habitat\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHabitat Comparison\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMicrosporidia MB\u003c/em\u003e Prevalence Range\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRainpool-Borrow pit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[-0.09, 0.13] [p\u0026thinsp;=\u0026thinsp;0.977]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSand Harvest-Borrow pit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[-0.00, 0.16] [p\u0026thinsp;=\u0026thinsp;0.063]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHoofprint-Borrow pit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[-0.09, 0.13] [p\u0026thinsp;=\u0026thinsp;0.977]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRainpool-Hoof print\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[-0.12, 0.12] [p\u0026thinsp;=\u0026thinsp;1.000]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSand Harvest-Hoof print\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[-0.04, 0.15] [p\u0026thinsp;=\u0026thinsp;0.539]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSwamp-Hoof print\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[-0.14, 0.09] [p\u0026thinsp;=\u0026thinsp;0.981]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSand Harvest-Rain pool\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[-0.04, 0.15] [p\u0026thinsp;=\u0026thinsp;0.539]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSwamp-Rain pool\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[-0.14, 0.09] [p\u0026thinsp;=\u0026thinsp;0.981]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSwamp-Sand Harvest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[-0.17, 0.01] [p\u0026thinsp;=\u0026thinsp;0.125]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSwamp-Borrow pit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[-0.11, 0.11] [p\u0026thinsp;=\u0026thinsp;1.000]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eCorrelation between\u003c/b\u003e \u003cb\u003eMicrosporidia MB\u003c/b\u003e \u003cb\u003einfection and molecular markers of insecticide resistance\u003c/b\u003e\u003c/p\u003e \u003cp\u003eA total of 134 samples comprising \u003cem\u003eMicrosporidia MB\u003c/em\u003e-positive (n\u0026thinsp;=\u0026thinsp;67) and \u003cem\u003eMicrosporidia MB\u003c/em\u003e-negative samples (n\u0026thinsp;=\u0026thinsp;67) were genotyped for the presence of the KDR-e, KDR-w, Ace-1, and GSTe-2 molecular markers of insecticide resistance. Only the KDR-e and KDR-w mutations were detected, whereas the Ace-1 and GSTe2 mutations were absent.\u003c/p\u003e \u003cp\u003eThe frequency of KDR-e in \u003cem\u003eMicrosporidia MB\u003c/em\u003e-infected mosquitoes was 94.7% (n\u0026thinsp;=\u0026thinsp;55) and that of the susceptible allele was 5.3% (n\u0026thinsp;=\u0026thinsp;3), and that of the resistant allele was 87.7% (n\u0026thinsp;=\u0026thinsp;50) and 12.3% (n\u0026thinsp;=\u0026thinsp;7) in uninfected mosquitoes. The frequency of KDR-w in infected mosquitoes was 60% (n\u0026thinsp;=\u0026thinsp;6) for the resistant allele and 40% (n\u0026thinsp;=\u0026thinsp;4) for the susceptible allele, and it was 40% (n\u0026thinsp;=\u0026thinsp;4) and 60% (n\u0026thinsp;=\u0026thinsp;6) for the resistant allele in uninfected mosquitoes. There were no significant associations between \u003cem\u003eMicrosporidia MB\u003c/em\u003e infection status and either mutation [Kdr-e, (Chi-square, χ\u0026sup2; = 5.78, df\u0026thinsp;=\u0026thinsp;3, p\u0026thinsp;=\u0026thinsp;0.123; Kdr-w χ\u0026sup2; = 6.23, df\u0026thinsp;=\u0026thinsp;3, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.101)].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eKDR allele frequencies in \u003cem\u003eMicrosporidia MB\u003c/em\u003e-infected and uninfected \u003cem\u003eAnopheles\u003c/em\u003e Mosquitoes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMicrosporidia MB\u003c/em\u003e Positive Genotypes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMolecular Marker\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAllele Freq R % \u0026amp; [95% CI]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAllele Freq S % \u0026amp; [95% CI]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKDR-e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e94.7 [0.8787\u0026ndash;0.9699]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.3 [0.3006\u0026thinsp;\u0026minus;\u0026thinsp;0.1212]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKDR-w\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e60 [0.2161\u0026ndash;0.6399]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e40 [0.3601-7839]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAce-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0 [0.00-0.3102]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e100 [0.9690-1.0]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGSTE-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0 [0.00-0.3102]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e100 [0.9690-1.0]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMicrosporidia MB\u003c/b\u003e \u003cb\u003eNegative Genotypes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMolecular Marker\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eRR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eRS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eSS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eAllele Freq R % \u0026amp; [95% CI]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eAllele Freq S % \u0026amp; [95% CI]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKDR-e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e87.7 [0.8044\u0026ndash;0.9254]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12.3 [0.7458\u0026thinsp;\u0026minus;\u0026thinsp;0.1956]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKDR-w\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e40 [0.1812\u0026ndash;0.5672]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e60 [0.4329\u0026ndash;0.8188]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAce-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0 [0.00-0.3260]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e100 [0.9674-1.0]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGSTE-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0 [0.00-0.3260]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e100 [0.9674-1.0]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eGenetic diversity\u003c/h3\u003e\n\u003cp\u003eA total of 66 samples positive for \u003cem\u003eMicrosporidia MB\u003c/em\u003e were subjected to a quality check by undertaking qubit analysis to estimate nucleic acid abundance and qPCR to establish the melting profile and ct while gel loading to establish sample integrity and base size before being sent for sequencing at the Beijing Genomics Institute (BGI). Of these, 33.3% (22/66) had a relative 18 s cT range of 18\u0026ndash;24 and were shipped for sequencing.\u003c/p\u003e \u003cp\u003eAfter library preparation and quality checking, only 3 \u003cem\u003eMicrosporidia MB-\u003c/em\u003epositive samples reached the optimum nucleic acid concentration (\u0026gt;\u0026thinsp;8.0 ng/ul) and were successfully sequenced. Teso South samples (Teso08_consensus and Teso98_consensus) yielded 94,127,442 and 295,188,214 reads respectively, whereas the Budalangi sample (Busia_consensus) yielded 360,470,585 clean reads. The genomic content (GC%) content of the Teso08 and Teso98 samples were 44.01% and 46.06%, respectively. The Busia_consensus sample had a GC content of 44.77%. Both samples from Teso South mapped to \u003cem\u003eAn. gambiae\u003c/em\u003e s.s. host, after mapping, host and other bacterial reads were filtered to remain with \u003cem\u003eMicrosporidia MB.\u003c/em\u003e it was noted that the Budalangi sample mapped to \u003cem\u003eAn. arabiensis\u003c/em\u003e host. Given that the Budalangi sample had degraded \u003cem\u003eMicrosporidia MB\u003c/em\u003e, which was observed during assembly and this deemed it unsuitable to include in phylogenomic analysis. \u003cem\u003eAnopheles arabiensis\u003c/em\u003e sample from Busia that was sequenced earlier was included in the analysis to make the phylogenetic tree to tease out diversity. This sample yielded 360,389,934 reads with a GC of 44.52%.\u003c/p\u003e \u003cp\u003ePhylogenetic analysis of the microsporidian isolates from Busia revealed a distinct cluster within Clade IV, indicating a close evolutionary relationship among them (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Neighbor-Joining phylogenetic tree of protein consensus sequences from microsporidia species, generated using Geneious Prime (v.2025.0.3) with a linear topology based on protein sequence alignment. The Jukes-Cantor model was used to calculate genetic distances. Bootstrap support values, based on 100 replicates, are shown at the nodes; only values above 50% are displayed. The tree is rooted with \u003cem\u003eNeoconidiobolus osmodes\u003c/em\u003e as the outgroup. Clade I represent host specificity to \u003cem\u003eAedes\u003c/em\u003e mosquitoes, Clade III represents represent host specificity to \u003cem\u003eAedes\u003c/em\u003e, \u003cem\u003eCulex\u003c/em\u003e and \u003cem\u003eAnopheles\u003c/em\u003e mosquitoes, and Clade IV represents broad host range specificity from insects, mammals to fish. Study samples (Teso94, Teso8, Busia) are highlighted. The scale bar represents 2.0 substitutions per site. This tree illustrates the phylogenetic relationships among microsporidia species based on protein sequence divergence and supports the close clustering of study samples to reference sequence from Ahero.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eUnderstanding the factors that influence the infection of mosquitoes with \u003cem\u003eMicrosporidia MB\u003c/em\u003e in nature and how infection might influence factors that affect malaria transmission is key in designing effective malaria vector control tools employing this strategy. The present study revealed a low to moderate prevalence of \u003cem\u003eMicrosporidia MB\u003c/em\u003e in naturally occurring \u003cem\u003eAn. gambiae\u003c/em\u003e s.s., \u003cem\u003eAn. arabiensis\u003c/em\u003e and \u003cem\u003eAn. funestus\u003c/em\u003e mosquito populations from Busia, Kenya, which were sampled during the rainy season. \u003cem\u003eMicrosporidia MB\u003c/em\u003e were detected both in indoor-resting adult mosquitoes and in mosquitoes collected as larvae and then reared to adults. For the larval samples, temperature, dissolved oxygen and water pH were found to vary significantly across habitats, but only pH had a significant influence on the likelihood of infection with \u003cem\u003eMicrosporidia MB\u003c/em\u003e. A lack of association between \u003cem\u003eMicrosporidia MB\u003c/em\u003e infection status and the presence of molecular markers of insecticide resistance was noted, but this could also be because the markers were not the exhaustive list of markers leading to resistance in this setting and could be a limitation of the study.\u003c/p\u003e \u003cp\u003eThe results of the present study revealed varying patterns of \u003cem\u003eAnopheles\u003c/em\u003e species distributions across the three sub-counties of Busia. \u003cem\u003eAnopheles gambiae\u003c/em\u003e s.s. emerged as the dominant species in Teso South and Butula, which is consistent with previous studies assessing the distribution of \u003cem\u003eAnopheles\u003c/em\u003e species in Busia during the rainy season(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). The high prevalence of \u003cem\u003eAn. gambiae\u003c/em\u003e s.s. and \u003cem\u003eAn. arabiensis\u003c/em\u003e, an efficient vector of malaria in sub-Saharan Africa, and increasing insecticide resistance are concerning, as this may compromise the success of conventional vector control.\u003c/p\u003e \u003cp\u003eIn contrast, \u003cem\u003eAn. funestus\u003c/em\u003e was the most abundant species in Budalangi. This could be attributed to Budalangi being in close proximity to Lake Victoria compared with the other two sub-counties sampled, as studies have shown that a reduction in Lake Victoria water levels has created more breeding habitats for \u003cem\u003eAn. funestus\u003c/em\u003e(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). The failure to successfully rear adults from the larvae collected from Budalangi in the insectary could be attributed to the fact that \u003cem\u003eAn. funestus\u003c/em\u003e was the prevalent species in this area (over 85%), and the general difficulty in rearing this species is due to poor larval survival under laboratory conditions (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Since \u003cem\u003eAn. funestus\u003c/em\u003e may require a natural setup, which may be difficult to mimic in the laboratory.\u003c/p\u003e \u003cp\u003eThe detection of \u003cem\u003eMicrosporidia MB\u003c/em\u003e in both \u003cem\u003eAn. gambiae s.l\u003c/em\u003e. and \u003cem\u003eAn. funestus\u003c/em\u003e highlights the natural occurrence of this potential biological control agent in Busia, which could be an advantage because both species are key malaria vectors and \u003cem\u003eMicrosporidia MB can also spread naturally in infected\u003c/em\u003e hosts. The prevalence of \u003cem\u003eMicrosporidia MB\u003c/em\u003e also varied significantly across the three species and was most prevalent in \u003cem\u003eAn. gambiae\u003c/em\u003e s.s. is in accordance with similar studies that have shown that \u003cem\u003eMicrosporidia MB\u003c/em\u003e can naturally occur in other \u003cem\u003eAnopheles\u003c/em\u003e species, including \u003cem\u003eAn. gambiae\u003c/em\u003e s.s.(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e)\u003csup\u003e,\u003c/sup\u003e other than the predominant \u003cem\u003eAn. arabiensis\u003c/em\u003e, where it was originally found in nature(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough the larval habitat characteristics assayed varied significantly across habitats, variations in water temperature and dissolved oxygen did not seem to influence the prevalence of \u003cem\u003eMicrosporidia MB\u003c/em\u003e, suggesting that the observed parameters are within the range that supports the sustainability of \u003cem\u003eMicrosporidia MB\u003c/em\u003e in nature. However, the mean water temperature in the sand-harvesting larval habitat, which was the most abundant \u003cem\u003eMicrosporidia MB\u003c/em\u003e-infected larvae reared to adults, was 32\u0026deg;C. This finding is in accordance with the findings of Herren et al., who reported that 32\u0026deg;C was the best temperature for rearing \u003cem\u003eMicrosporidia MB\u003c/em\u003e-infected larvae because of the relatively short development time and high infection rate(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Studies have shown that high temperatures can lead to a reduction in \u003cem\u003eWolbachia\u003c/em\u003e loads, but the degree to which temperature affects \u003cem\u003eWolbachia\u003c/em\u003e intensity can vary depending on the specific \u003cem\u003eWolbachia\u003c/em\u003e strain. Some strains are more tolerant of heat stress than others are(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study revealed that water pH has a significant effect on \u003cem\u003eMicrosporidia MB\u003c/em\u003e infection. The narrow pH range (7.25\u0026ndash;7.8) associated with \u003cem\u003eMicrosporidia MB\u003c/em\u003e infection suggests that slightly alkaline environments may favour the proliferation of \u003cem\u003eMicrosporidia MB.\u003c/em\u003e Other studies have also shown how pH affects the enzymatic activity and metabolic processes of both the insect and its symbionts, with the pH of the infected cuticle rising from approximately 6.3 to 7.7 during fungal penetration(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLong-lasting insecticidal nets (LLINs) have been used in Busia for malaria control for over two decades, with widespread distribution beginning in the late 1990s and continuing through the 2000s and 2010s(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e), which could explain the high frequencies of the KDR-e and KDR-w mutations observed(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Laboratory studies revealed that microsporidia-infected mosquitoes were more susceptible to insecticides than non-infected mosquitoes were, probably due to the reduced fitness of infected mosquitoes(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e), noting that this was an unrelated pathogenic microsporidian. However, the present study did not find a significant association between \u003cem\u003eMicrosporidia MB\u003c/em\u003e infection and the evaluated molecular markers of insecticide resistance. These findings suggest that the presence of KDR mutations may not impose significant fitness costs on the mosquito species studied, even those harbouring \u003cem\u003eMicrosporidia MB.\u003c/em\u003e Studies have shown that fitness costs associated with the presence of these mutations are greater in the absence of insecticide exposure(\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGiven that there is widespread use of LLINs in the study area, it is expected that the fitness cost associated with KDR mutations would be diminished(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Therefore, the absence of a significant association between the presence of KDR mutations and \u003cem\u003eMicrosporidia MB\u003c/em\u003e infection would be in tandem with the expected reduced fitness cost associated with the presence of KDR mutations in the presence of insecticide exposure. Furthermore, since studies have demonstrated that the presence of KDR mutations is not always closely linked to phenotypic resistance to some pyrethroids, further studies are necessary to shed light on the interactions between insecticide resistance and \u003cem\u003eMicrosporidia MB\u003c/em\u003e infection (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAce-1 and GSTe2 mutations are associated with resistance to organophosphates and carbamate-based insecticides, with GSTe2 having cross-resistance mechanisms to DDT and pyrethroids. These mutations were absent in the study area, likely because organophosphates and carbamates have not been used in the study area for vector management or pest control.\u003c/p\u003e \u003cp\u003ePhylogenetic analysis revealed a close relationship between Busia \u003cem\u003eMicrosporidia MB\u003c/em\u003e isolates and Microsporidia sp. MB reference from Ahero (Clade IV). This is significant given the high malaria prevalence in Busia and \u003cem\u003eMicrosporidia MB\u003c/em\u003e ability to impair \u003cem\u003ePlasmodium\u003c/em\u003e transmission(\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). The close relationship, supported by the clustering of Busia isolates, suggests potential for utilizing \u003cem\u003eMicrosporidia MB\u003c/em\u003e control. This highlights the potential for exploiting shared traits for malaria control strategies in the region.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis study focused on the wet season, yet there could be seasonal variation in \u003cem\u003eMicrosporidia MB\u003c/em\u003e across the wet and dry seasons. The low prevalence of \u003cem\u003eMicrosporidia MB\u003c/em\u003e did not allow analysis of the relationship between infection and molecular markers of insecticide resistance by mosquito species and study site.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe study revealed varying distributions of species with \u003cem\u003eAn. funestus\u003c/em\u003e being most prevalent in Budalangi, which may be linked to its closer proximity to the lake. The finding of \u003cem\u003eMicrosporidia MBs\u003c/em\u003e in \u003cem\u003eAn. gambiae\u003c/em\u003e s.s. and \u003cem\u003eAn. funestus\u003c/em\u003e rather than predominantly \u003cem\u003eAn. arabiensis\u003c/em\u003e, as previously reported, has potential as a vector control tool for these species. Ecological factors are important for sustaining the development of \u003cem\u003eMicrosporidia MB\u003c/em\u003e. Further research is needed to fully understand the dynamics among \u003cem\u003eAnopheles\u003c/em\u003e mosquitoes, \u003cem\u003eMicrosporidia MB\u003c/em\u003e, and insecticide resistance to develop effective strategies for malaria prevention and control. These phylogenetic diversity findings contribute to our understanding of the evolutionary dynamics and potential dispersal patterns of \u003cem\u003eMicrosporidia MB\u003c/em\u003e endosymbionts in mosquitoes.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003ePCR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Polymerase chain reaction\u003c/p\u003e\n\u003cp\u003eqPCR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Quantitative polymerase chain reaction\u003c/p\u003e\n\u003cp\u003eANOVA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Analysis of variance\u003c/p\u003e\n\u003cp\u003eLLIN \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Long-lasting insecticidal nets\u003c/p\u003e\n\u003cp\u003eIRS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Insecticide residual sprays\u003c/p\u003e\n\u003cp\u003eKEMRI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Kenya Medical Research Institute\u003c/p\u003e\n\u003cp\u003eCBRD\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Centre for Biotechnology Research and Development\u003c/p\u003e\n\u003cp\u003eDNA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Deoxyribonucleic Acid\u003c/p\u003e\n\u003cp\u003eDDT\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Dichloro-diphenyl-trichloroethane\u003c/p\u003e\n\u003cp\u003es.l.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Sensu Lato\u003c/p\u003e\n\u003cp\u003es.s.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Sensu Stricto\u003c/p\u003e\n\u003cp\u003eNCBI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; National Center for Biotechnology Institute\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll figures and tables supporting the conclusions of this article are included within the manuscript. The raw reads and genomic isolates for the study samples have been deposited at NCBI under the BioProject accession PRJNA1242000. Raw reads have been deposited to the Sequence Read Archive (SRA) under accession number SAMN47576098, SAMN47576099 and SAMN47626518. The Whole Genome Shotgun (WGS) project for the \u003cem\u003eMicrosporidia MB\u003c/em\u003e genomic isolates has been deposited at DDBJ/ENA/GenBank under the accession JBMNCT000000000 and JBMNCU000000000. The versions described in this paper are version JBMNCT010000000 and JBMNCU010000000.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was supported by the University Court of the University of Glasgow ANTI-Vec African Anopheles Symbiont Survey Grant No. AV/AASS/006 to Luna Kamau.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGN was funded by the International Foundation of Science award number I-1-F-5852-1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLK, DMM, EO, JKH\u0026nbsp;conceptualized and designed the study.\u003c/p\u003e\n\u003cp\u003eHTW, SK, SN, MA, EO, DMM, JKH, LK\u0026nbsp;conducted sample collection, processing, and data collection.\u003c/p\u003e\n\u003cp\u003eHTW and LK conducted the data analysis and interpretation and drafted the manuscript\u003c/p\u003e\n\u003cp\u003eGN, DN, MOA, JKH\u0026nbsp;conducted the data analysis and interpretation and assisted in drafting the manuscript. All the authors read, reviewed and approved this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors express their sincere gratitude to the homeowners in Teso South, Butula, and Budalangi, Busia County, for granting access to their homes during mosquito collection, which was essential for this study. This work is published with the permission of the Director General of KEMRI.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld malaria report. 2024 [Internet]. [cited 2024 Dec 13]. 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Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://malariajournal.biomedcentral.com/articles/\u003c/span\u003e\u003cspan address=\"https://malariajournal.biomedcentral.com/articles/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12936-023-04818-6\u003c/span\u003e\u003cspan address=\"10.1186/s12936-023-04818-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHerren JK, Mbaisi L, Mararo E, Makhulu EE, Mobegi VA, Butungi H et al. A microsporidian impairs Plasmodium falciparum transmission in Anopheles arabiensis mosquitoes. Nature Communications 2020 11:1 [Internet]. 2020 May 4 [cited 2021 Nov 14];11(1):1\u0026ndash;10. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.nature.com/articles/s41467-020-16121-y\u003c/span\u003e\u003cspan address=\"https://www.nature.com/articles/s41467-020-16121-y\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcro","sideBox":"Learn more about [BMC Microbiology](http://bmcmicrobiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mcro","title":"BMC Microbiology","twitterHandle":"#bmcmicrobiology","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Anopheles, Microsporidia MB, Insecticide Resistance, Ecology, Phylogenetics","lastPublishedDoi":"10.21203/rs.3.rs-6352339/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6352339/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e The discovery of \u003cem\u003eMicrosporidia MB\u003c/em\u003e, a malaria-impairing symbiont in \u003cem\u003eAnopheles arabiensis\u003c/em\u003e, suggests its potential for malaria control. This study investigated its prevalence and diversity in \u003cem\u003eAn. gambiae\u003c/em\u003e s.l. and \u003cem\u003eAn. funestus\u003c/em\u003e in Busia, Kenya. The study also explored its association with insecticide resistance and environmental factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Mosquito larvae and adults were collected from three sub-counties in Busia, Kenya. Species were identified morphologically, and DNA was extracted. PCR was used to determine species distribution and \u003cem\u003eMicrosporidia MB\u003c/em\u003e prevalence. Insecticide resistance markers were identified using TaqMan genotyping. \u003cem\u003eMicrosporidia MB\u003c/em\u003e-positive samples underwent whole-genome sequencing and phylogenetic analysis. Statistical tests, including chi-square, ANOVA, and regression, were used to assess relationships between \u003cem\u003eMicrosporidia MB\u003c/em\u003e, insecticide resistance, and ecological variables.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThere was notable variation in the distribution of species in Busia, where \u003cem\u003eAn. gambiae\u003c/em\u003e s.l. emerged as the most prevalent species in Teso South and Butula sub-counties, whereas \u003cem\u003eAn. funestus\u003c/em\u003e was the most prevalent in Budalangi sub-county. \u003cem\u003eMicrosporidia MB\u003c/em\u003e was observed at a low to moderate occurrence of 0 to 6.4%, with the highest prevalence noted in \u003cem\u003eAn. gambiae \u003c/em\u003es.s.\u003cem\u003e \u003c/em\u003eDespite the significant fluctuations in temperature, pH, and dissolved oxygen levels across different ecological habitats, only the variation in pH was associated with the prevalence of \u003cem\u003eMicrosporidia MB\u003c/em\u003e. There was no correlation between \u003cem\u003eMicrosporidia MB\u003c/em\u003e infection and molecular markers of insecticide resistance. Phylogenetic analysis revealed significant genetic diversity in \u003cem\u003eMicrosporidia MB\u003c/em\u003e, with geographic location influencing lineage divergence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThe present study highlights the occurrence of \u003cem\u003eMicrosporidia MB\u003c/em\u003e in multiple \u003cem\u003eAnopheles\u003c/em\u003e vectors associated with malaria and highlights the potential role of ecological factors in sustaining the prevalence of \u003cem\u003eMicrosporidia MB\u003c/em\u003e. Future studies will tease out whether malaria-protective phenotypes are conserved traits among the distinct evolutionary lineages to enhance our understanding of critical considerations that are necessary for the successful implementation of this novel malaria control strategy in areas with varying strains and ecological conditions. Geographic location significantly shapes the genetic diversity of \u003cem\u003eMicrosporidia MB\u003c/em\u003e in mosquitoes, revealing distinct evolutionary lineages and dispersal patterns.\u003c/p\u003e","manuscriptTitle":"Prevalence, Genetic Diversity of Microsporidia MB, and Correlation to Insecticide Resistance in Anopheles gambiae s.l. and Anopheles funestus Mosquitoes in Busia, Kenya","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-02 08:09:57","doi":"10.21203/rs.3.rs-6352339/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-02T09:09:07+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-30T15:08:29+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-20T19:41:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"204963792902295327595107722008255990992","date":"2025-04-20T10:54:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"35633874044886960305234198864349635605","date":"2025-04-14T17:03:08+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-10T08:32:19+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-04-09T20:27:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-05T05:28:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-05T05:26:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Microbiology","date":"2025-04-01T10:40:26+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcro","sideBox":"Learn more about [BMC Microbiology](http://bmcmicrobiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mcro","title":"BMC Microbiology","twitterHandle":"#bmcmicrobiology","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3a63336b-9fb7-41c8-83ca-4d3495a332af","owner":[],"postedDate":"April 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-06T16:00:26+00:00","versionOfRecord":{"articleIdentity":"rs-6352339","link":"https://doi.org/10.1186/s12866-025-04324-6","journal":{"identity":"bmc-microbiology","isVorOnly":false,"title":"BMC Microbiology"},"publishedOn":"2025-10-02 15:57:12","publishedOnDateReadable":"October 2nd, 2025"},"versionCreatedAt":"2025-04-02 08:09:57","video":"","vorDoi":"10.1186/s12866-025-04324-6","vorDoiUrl":"https://doi.org/10.1186/s12866-025-04324-6","workflowStages":[]},"version":"v1","identity":"rs-6352339","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6352339","identity":"rs-6352339","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}