Breeding water parameters are important determinants of Microsporidia MB prevalence in the aquatic stages of Anopheles mosquitoes

doi:10.21203/rs.3.rs-5024342/v1

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Breeding water parameters are important determinants of Microsporidia MB prevalence in the aquatic stages of Anopheles mosquitoes

https://doi.org/10.21203/rs.3.rs-5024342/v1

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The fungus, Microsporidia MB disrupts Plasmodium development in Anopheles mosquitoes. It has been associated with An. gambiae and An. coluzzii (Ghana), and An. arabiensis (Kenya) from rice fields. As a tool for vector/disease control, its ecological distribution and factors that determine their occurrence must be defined. We investigated the prevalence of Microsporidia MB in Anopheles mosquitoes across selected sites in Ghana, and the breeding water physicochemical parameters that are associated with the occurrence of the fungus by fitting regression models. DNA was extracted from the abdomens of 4255 adult Anopheles mosquitoes that emerged from larvae and pupae collected from selected sites in northern and southern Ghana between August and October of 2021 and 2022. Detection of Microsporidia MB was achieved using qPCR, while species were molecularly identified using SINE, RFLP methods, and the ANOSPP algorithm. Overall Microsporidia MB prevalence was 2.2%. Male mosquitoes exhibited higher prevalence of infections and had increased predicted probability of infection 85% higher than females. Sawla, in Ghana's Savannah zone had the highest MB prevalence (56.52%) despite lacking rice fields. Biochemical oxygen demand in mosquito breeding water was estimated to be positively associated with and, significantly predicts MB prevalence in mosquitoes with an accuracy of 94%. According to our data, all Anopheles species were at equal probability of MB infection. These results provide preliminary information on micro-ecological factors that could support the sustainability of MB infection in mosquitoes during their aquatic life stages. It will be important, therefore, to explore the impact of strategies for larval source management on these factors to ensure that the symbiont's persistence during the host's aquatic stages may not be adversely affected should it be used as an integrated approach for mosquito/disease control.

Microsporidia MB

Anopheles mosquitoes

mosquito breeding site

physicochemical parameters

Novel malaria control measures using mosquito symbionts that target the transmission of the Plasmodium parasite in the mosquito vector are being explored. Candidate anti-parasitic micro-organisms for disease control are required to be, among other characteristics, generationally transferrable, present little or no fitness costs to the vector host and environmentally restricted to the target hosts [1–3]. Microsporidia MB, a fungus, has been identified to naturally infect major malaria Anopheline vectors in sub-Saharan Africa [4–6], and is associated with reduced Plasmodium oocysts in infected An. arabiensis [5]. Although not yet tested for their effects on the parasite in An. gambiae, MB is also avirulent in this Anopheles species and has transmission characteristics [7] similar to what was observed in An. arabiensis [4]. This makes MB a plausible candidate for malaria transmission-blocking in Anopheline mosquitoes. Fundamentally, it appears to satisfy most, if not all the preferred characteristics of an ideal micro-organism for biocontrol of disease.

Larval source management, monitoring and understanding the ecological determinants of conducive environments for mosquito sustainment remains an integral part of mosquito and disease control. Ultimately, removing larval breeding sites, particularly those created by human activities such as urban farming would reduce the incidence of mosquitoes and their associated diseases [8–10]. Due to rising temperatures, increasing incidence of rains resulting from climate change [11, 12] and changes in landscapes due to urbanization [13–16], larval sites continue to emerge in new environments and mosquitoes are adapting to new niches [17, 18]. That notwithstanding, larval source mapping and larviciding have demonstrated significant contributions to disease control [19–22]. Larval breeding sites have also been proposed as a potential means to introduce paratransgenic micro-organisms to mosquitoes [23]. This is primarily because, mosquito larvae would obtain a substantial part of their gut microbiota from the water in which they breed and can be trans-stadially transferred from one developmental stage to another [24]. The physicochemical properties of the larval breeding water impact the microbial diversity at breeding sites, mosquito larval abundance and body size [25–29].

The ecological distribution of Microsporidia MB is still not clearly understood. In Kenya where it was first described in An. arabiensis, prevalence was observed to peak some weeks after rains and was suggested that MB transmission between mosquitoes was influenced by environmental factors [5]. Other microsporidian species have been particularly observed to be more prevalent at sites where there are irrigated rice fields [30], and the report of MB was also near irrigation sites [5]. Whether this is mainly because of the permanent availability of breeding ponds, or there are ecological factors that influence this, remains undefined. One potential route for using MB is by spore dispersal into the environment where mosquitoes would be infected by ingestion [3], and this necessitates clear understanding of the environmental requirements to explore such opportunities for control. Thus, in this study, we investigated the prevalence and distribution of Microsporidia MB in Anopheles mosquitoes from Ghana, and the association of these with selected physicochemical properties in the Anopheles larval breeding ponds. We used regression models to find factors that significantly influence the predictability of MB infection in mosquitoes.

Study areas

This study was carried out in 10 areas spread across the 6 agro-ecological zones of Ghana (Fig. 1_Map); Zebilla (Sudan Savannah), Sawla (Guinea Savannah), Nkoranza (Forest-Savannah Transition zone), Tarkwa and Prestea (Rain Forest), Kade and Aveyime (Semi-deciduous), Ada, Afife, and Dodowa (Coastal Savannah). These sites were selected based on convenience as there were on-going malaria surveillance activities, and from previous observed prevalence of MB-infected mosquitoes from archived DNA samples that were obtained from some of these areas [6]. Two rice irrigation sites were purposely included to evaluate whether MB prevalence was correlated with these fields as suggestive of results from elsewhere [5].

Mosquito sampling

Anopheles larvae and pupae were collected from breeding ponds within the study areas. Sampling was conducted in two consecutive years: August to September 2021 and September to October 2022 which were within, periods after the rainfall season in Ghana (Additional file: Fig S1). The duration taken within each collection period to obtain appreciable numbers of Anopheles-positive breeding ponds varied across the study sites. We spent between 2–12 days to obtain 5–13 breeding ponds per area per collection year. The GPS co-ordinates of each breeding pond was recorded using the KoBoCollect app. Anopheles larvae and pupae were collected by the standard dipping method using a 350mL standard dipper or ladle, and transferred into separate labelled transparent plastic containers with lids for each breeding pond. In field-improvised insectaries or in the Vector Labs at the Noguchi Memorial Institute for Medical Research (NMIMR), the contents of the plastic containers were transferred into labelled breeder cups and reared to adults. The emerged adult mosquitoes were initially identified on basic morphology of leg and wing band patterns using established keys [31], euthanized through freezing at -20^oC and stored in 1.5mL tubes with 70% ethanol. Female and male mosquitoes were separated into different tubes for each breeding pond.

Measuring water physicochemical parameters

At each breeding pond, selected physicochemical parameters of the breeding water were determined prior to collecting immature mosquitoes. The water temperature, pH, and dissolved oxygen (DO) were measured in situ using a thermometer (Lollipop Traceable thermometer) pH meter (Ohaus pH meter starter pen) and dissolved oxygen metre (SETSCZY), respectively and recorded. After collecting mosquitoes from a pond, 2L of the breeding water was collected into plastic bottles and stored on ice while on the field. These were refrigerated upon return to the laboratory at NMIMR. The water samples were submitted to the Water Research Institute of the Council for Scientific and Industrial Research, Ghana to measure other water parameters such as biochemical oxygen demand (BOD), turbidity, salinity, nitrate nitrogen (NO3-N), phosphate-phosphorus (PO4-P), sulphate (SO4), ammonium-nitrogen (NH4-N), manganese (Mn), copper (Cu), calcium (Ca), and zinc (Zn) ions. These parameters were selected based on the reported germination requirements of some microsporidians [32–34] and the availability of funds.

DNA extraction from mosquitoes

Adult mosquitoes were retrieved from storage, and surface-sterilized in 5% bleach, 70% ethanol and 1× sterile PBS. Each mosquito was sectioned into two: head and thorax, and abdomen and placed separately into 1.5mL tubes. With the abdomen containing the reproductive tissues and midgut where most MB are localised in the mosquito [4, 5, 7], this strategy was used to reduce mosquito DNA contamination which would result from using the whole mosquito body. Total DNA was extracted using an adapted protocol from remaining DNeasy Blood and Tissue DNA reagents (Qiagen, Hilden Germany). Mosquito abdomens were rinsed briefly in sterile 1× PBS to get rid of excess ethanol. The PBS was discarded and, 1.0mm diameter glass beads and 180µL ATL lysis buffer was added to the tubes. A blank (tube without mosquito samples) was included with each set of samples extracted to check for cross contamination. Sample homogenization was achieved with the Fisherbrand™ Bead Mill 24 Homogenizer and centrifuged at 12,000 ×g for 20 minutes. The supernatant of homogenised samples was carefully transferred into new tubes, 20µL of proteinase K (10mg/mL) added and incubated at 56^oC overnight. 200µL of AL was added to each sample, mixed by vortexing and incubated at 70^oC for 10 minutes. Samples were centrifuged at 12,000 ×g for 15 minutes and supernatants were carefully transferred into new tubes. 300µL of ice-cold isopropanol was added to each tube, mixed, incubated at -40^oC for 1 hour and then centrifuged at 12,000g for 30 minutes at 4^oC. Supernatants were subsequently discarded, and the DNA pellet was washed with washing buffers AW1 and AW2 for 5 minutes each. The DNA pellet was dried at 50^oC for 2–4 minutes, reconstituted in 50µL AE buffer and stored at -80^oC for future use.

Species identification of mosquitoes

Molecular identification of mosquito species was performed with extracted DNA using PCR-RFLP [35] followed by SINE PCR [36] to further distinguish between An. gambiae and An. coluzzii. PCR products were run on 2% agarose gels stained with SYBR Safe and visualised on an ultraviolet (UV) transilluminator. All samples that showed negative for species ID were submitted to the ANOSPP [37, 38] platform at the Wellcome Sanger Institute to identify them.

Detection of Microsporidia MB

Mosquito DNA was pooled (5–10 mosquitoes) in equal volumes according to breeding pond for detection of Microsporidia MB. When a pool tested positive for the presence of the fungus, all samples from that pool were tested individually to allow accurate estimation of prevalence of infection. Using established Microsporidia MB specific primers [5], Microsporidia MB DNA was amplified in a 20µL reaction consisting of 10µL Luna universal qPCR master mix (NEB, M3003), 0.5µL each of 10µM forward and reverse primers, 1µL of DNA template and 8µL of nuclease-free water. The real-time PCR conditions were an initial denaturation at 95^oC for 5 minutes, and 35 cycles of denaturation at 95^oC for 1 minute, annealing at 62^oC for 90 seconds and extension at 72^oC for 60 seconds. Final elongation was done at 72^oC for 5 minutes and melting curves were generated at temperature ranges from 65^oC to 95^oC. Since the real-time was used qualitatively for detection, we used the amplification of the Anopheles ribosomal S7 gene with S7F: 5’-TCCTGGAGCTGGAGATGAAC-3’ and S7R: 5’-GACGGGTCTGTACCTTCTGG-3’ as a DNA quality control. Positive controls used in the reaction were Microsporidia MB DNA obtained from Jeremy Herren’s laboratory in KEMRI. Samples were considered positive for Microsporidia MB if the melt curve was similar to that of the positive control.

Data analyses

Data was analysed using custom scripts in R statistical software [39]. Chi-square tests were used to assess the independence mosquito proportions to MB status, study sites and mosquito sex. Breeding ponds from each study area were regarded as replicates. The median proportions of mosquito species identified, and MB prevalence were compared with Kruskal-Wallis test. All the categorical factors i.e. site, species and mosquito sex, were fitted into a generalized linear model (GLM) in a binary outcome formula to identity those that significantly correlate with the probability of detecting MB. A binomial distribution was fitted using all the explanatory variables singly and then as interaction terms.

Principal Component Analysis (PCA) biplots was used to show the correlation of measured water physiochemical parameters with MB infection. Multivariate binary logistic regression models were fitted to determine the odds of MB positivity given the parameters tested. Four models were fitted and evaluated as follows: model 1 = all water parameters, model 2 = reduced from model 1; using only parameters that showed significance or marginally significant contribution model one following ANOVA test, models 3 and 4 were expanded forms of model 1 and 2, respectively. The variable ‘site’ was included to allow downstream analyses of predicted probability of MB at each study site. All models were compared with the tab_model function in the sjPlot R package [40], and the best model was selected based on the Akaike Information Criteria (AIC) instead of the pseudo-R² values because of the different number of parameters in each model, and to control for overfitting due to complexity.

Receiver Operating Characteristic (ROC) curves were generated to show accuracy of selected models. Predicted probabilities of MB infection were estimated for each study site as a function of significant predictors in each selected model.

Anopheles species distribution

A total of 4255 adult mosquitoes were obtained from rearing of the immature stages that were collected from breeding ponds (Table S1). Mosquito counts did not differ between the sampling years (Chi-square: 𝜒² = 20, df = 16, P = 0.22) but their proportions per site relative to the total number of mosquitoes collected were different between study sites (Kruskal test: 𝜒² = 66.44, df = 9, P < 0.0001). Nine confirmed and distinct Anopheles species were identified (Fig. 2). These included minor species; An. coustani, An. pharoensis, An. pretoriensis, An. rufipes and An. squamosus which together represented 2.9% of mosquitoes analysed. The predominant An. gambiae s.s (38.6%) and An. coluzzii (37.4%), and An. arabiensis (5.4%) were mostly identified through SINE PCR and RFLP while the other species were distinguished with the ANOSPP algorithm. About 3.7% of the samples were determined as Anopheles gambiae s.l, and 1.6% were identified broadly as belonging to the genus Anopheles but could not be further distinguished. About 10% of mosquito samples remained unidentified. Zebilla had the highest species richness and Dodowa, the least (Fig. 2). Mosquito species distribution showed marginal significance by ecological zones (Kruskal test: 𝜒² = 10.81, df = 5, P = 0.05).

MB prevalence and distribution

Overall prevalence of Microsporidia MB infection was 2.2% (92/4255), detected in 6 groups of mosquitoes including the unspecified Anopheles species and mosquito samples that were unidentified. Approximately 53% of the infected were An. gambiae s.s and 32% were An. coluzzii (Fig. 3A). Only one An. arabiensis (1/92) was positive for MB, representing 1.1% of the total number of mosquitoes infected with the fungus. Despite these, MB positivity did not show significant association with mosquito species (Chi-square: 𝜒² = 12.27, df = 11, P = 0.34).

MB positivity was associated with the study site (Chi-square: 𝜒² = 43.83, df = 9, P < 0.0005) and ecological zones (Chi-square: 𝜒² = 39.66, df = 5, P < 0.0005) suggesting that infections were more likely to be found in mosquitoes from some sites than others. However, the data showed no differences in the observed MB prevalences among sites and ecological zones (Kruskal test: P > 0.05), implying that higher numbers of mosquitoes are probably required to observe significant MB prevalences between sites. A higher number of males were MB positive (59% males vs 40% females; 𝜒² = 8.432, P = 0.004) (Fig. 3B).

Prediction of MB positivity in study sites, mosquito sex and species

Given the study site, mosquito sex and species, the model that best explained the occurrence of MB was with study site and mosquito sex as multivariate non-interaction terms (Additional file: Table S2). In this model, study site and mosquito sex, but not mosquito species, were significant factors that correlated with MB positivity (Additional file: Table S3). Male mosquitoes demonstrated the highest odds ratio (OR) for MB infection [OR (95% CI) = 1.85 (1.22, 2.85); P = 0.004] with probability of MB infection ~ 85% higher than in females (Table 1). With reference to Zebilla which recorded 12% of the total MB prevalence, Sawla was the only site with significant odds for finding mosquitoes with MB infection [OR (95% CI) = 3.78 (2.04, 7.68); P < 0.001] with 275% higher probability. The model was 71% accurate (Fig. 4A), and predicted that overall, a male mosquito has 3.23% chance of MB infection while a female has 1.61%. Each study site, however, showed varied probabilities with highest in Sawla and almost non-existing in Nkoranza, Dodowa, and Aveyime (Fig. 4B, Additional file: Fig S2).

Table 1

Summary characteristics of regression model results for MB positivity in study sites and sex of mosquito.
Characteristic	OR¹	95% CI¹	P-value
site
Zebilla	—	—
Sawla	3.78	2.04, 7.68	< 0.001
Nkoranza	0	0.00, 5.9E1 + 18	> 0.9
Kade	1.4	0.39, 4.14	0.6
Prestea	1.43	0.55, 3.56	0.4
Tarkwa	1.14	0.26, 3.68	0.8
Dodowa	0	0.00,2.26E + 22	> 0.9
Aveyime	0	0.00, 263	> 0.9
Ada	1.6	0.71, 3.67	0.3
Afife	0.32	0.02, 1.65	0.3
sex
female	—	—
male	1.85	1.22, 2.85	0.004
¹ OR = Odds Ratio, CI = Confidence Interval

Breeding water physicochemical parameters among study sites

A few measured water parameters were below the detectable limits, and to allow plotting and further analyses, such values were replaced with the next smallest positive number (Table S4). All the parameters showed significant differences among the study sites except, biochemical oxygen demand (BOD), ammonium-nitrogen (NH4-N) and manganese (Mn) (Fig. 5). Nkoranza differed most with Ada and Afife, being only similar in the PO4-P, DO concentrations and temperature (Table S5). Salinity was the most variable parameter, followed by sulphate (SO4) generally higher as one moves from the north to the south (Fig. 5).

Correlation of MB positivity with breeding water physicochemical parameters

We visualised the correlation between the water parameters and the proportion of MB-infected mosquitoes using PCA biplots. The first two axes explained ~ 65% of the total variance observed in the data, with calcium (Ca) ions, sulphate (SO4) and salinity contributing most to the first principal component (Fig. 6A). Biochemical oxygen demand (BOD), phosphate-phosphorus (PO4-P), ammonium-nitrogen (NH4-N), dissolved oxygen (DO), manganese ions (Mn) and temperature positively correlated with MB. The remaining parameters tested, except pH, SO4, Ca, and salinity, had a negative relationship with the prevalence of the fungus (Fig. 6A).

Based on the best model (Additional file: Table S6), which incorporates turbidity, PO4-P, SO4, NH4-N, BOD, Mn, Cu, Zn, DO and site as explanatory variables for MB status, the odds for detecting MB increases by a factor of 1.04 with every unit increase in BOD [OR (95% CI) = 1.04 (1.02, 1.10); P = 0.02] and decreases marginally with increasing units of NH4-N and Mn (Table 2). With 94% accuracy (Fig. 6B), the model predicts varied probabilities for MB infection at the sampled sites which appears dependent on a site-related interaction between the BOD, Mn and NH4-N (Fig. 6C, TableS5).

Table 2

Summary characteristics of regression model results for MB positivity explained by some breeding water parameters.
Characteristic	Odds Ratio	95% CI	P-value
TURB	1.00	1.00, 1.00	0.4
PO4-P	1.43	0.59, 9.03	0.5
SO4	0.99	0.98, 1.00	0.2
NH4-N	0.79	0.57, 0.96	0.048
BOD	1.04	1.02, 1.10	0.016
Mn	0.33	0.05, 0.81	0.083
Cu	1.77E + 04	0.00, 3.36E + 31	0.8
Zn	0.00	0.00, 1.37E + 06	0.2
DO	1.16	0.93, 1.53	0.2
Site
Ada	—	—
Afife	1.42	0.02, 95.1	0.9
Aveyime	0.00		> 0.9
Dodowa	0.00		> 0.9
Kade	4.91E + 07	0.00, NA	> 0.9
Nkoranza	0.00		> 0.9
Sawla	0.30	0.00, 23.1	0.6
Zebilla	0.04	0.00, 2.94	0.2

Prevalence of MB infection among mosquitoes is reported to be generally low [5–7] with seasonal variations that shows increased prevalence after rains [5]. As rains influence the range and dynamics of mosquito breeding sites, identifying micro-environmental factors in mosquito breeding water that influence MB prevalence would be important for providing clearer understanding feasible and cost-effective methods for using the micro-organism in mosquito-borne disease control. The current study has shown by two models that were built based on our data, that MB positivity is not a random occurrence; but is correlated with the physicochemical properties of breeding sites. Among the parameters tested, BOD has a significant positive relationship with MB and, depending on the site manganese and ammonium-nitrogen could also be marginally involved in an inverse manner. Similar to previous reports, we also found males to have a higher probability of MB infection than females at any site, and this is independent of the Anopheles species.

Microsporidians are largely obligate intracellular parasites, meaning they need the host cell to survive. The spores are the only life stage that can be found viable outside of the host cell; they are environmentally resistant and metabolically inactive until germination [41]. Microsporidia spores can be germinated under culture conditions that vary depending on the species [34]. Therefore, microsporidians require hosts to be at their optimal growth environment to ensure their sustainability, without using the resources available in the host’s surroundings. In this study, we have found BOD to significantly raise the odds of detecting MB-infected mosquitoes. Biochemical oxygen demand (BOD) is a measure of the amount of oxygen available to micro-organisms during decomposition of organic matter, making it is an important parameter for assessing organic pollution in water. While high BOD can harm aquatic life due to reduced dissolved oxygen (DO), it has also been found to be linked to abundance of Anopheles larvae [27, 42]. High organic content is essential for larval nutrition and protects larvae against UV radiation [43]. These together suggest there is an increased chance for detecting MB-infected larvae in very conducive Anopheles breeding environment, likely densely populated with mosquito larvae. Given the generally low prevalence of infection in this study country [6] this implication is not surprising; relatively more mosquitoes need to be collected to detect MB infection. It will be useful to titrate the limits of BOD for mosquito sampling and MB detection to better inform its use for surveillance and control assessment activities.

Although manganese (Mn) and ammonium-nitrogen (NH4-N) have marginal significance for decreased odds for MB-infected mosquitoes according to the regression model, they are worth discussing. Manganese could be introduced into mosquito breeding ponds as run-offs from their use in agricultural chemicals and from mining activities [44–46]. In insects, Mn is an essential cofactor for Mn superoxide dismutase which plays a role in reducing oxidative stress and helping insects fight infections [47]. However, excessive Mn also has dire consequences on the insect life functions, including increased oxidative stress, reduced locomotion and ultimately death (reviewed in [45]). Contrary to Mn, ammonium (NH4) gets into the mosquito aquatic environment through both anthropological and mosquito physiological processes. Mosquito larvae excrete ammonium into their breeding water thus, overcrowding would lead to increased NH4 ion concentrations to levels that become toxic to the larval population [48, 49]. Survival of larvae also decreases significantly with extraneous addition of increasing concentrations of NH4 to the breeding water [50], for example through agricultural fertilizers [51]. This could explain partly why some study sites, particularly those observed to be in areas where agricultural activities are high; for example, Aveyime, Afife, would have low mosquito numbers. However, these sites were sampled in only one collection period, which limited the total number of mosquitoes obtained here. Extensive studies at these rice fields could provide more insight into the relation of rice fields with mosquito abundance and MB-infectivity as previously shown in Kenya [5]. Ammonium (NH4) and sulphate (SO4) have been shown to correlate with increased abundance of An. arabiensis and Culex spp in rice fields [51] and nitrogen with Culex sp in agricultural wastewater [52]. If our assumption that the estimated water parameters were associated with MB as a direct function of mosquito abundance, then MB is expected to increase with NH4-N and SO4. However, our current study shows negative correlation of MB status with SO4 and despite having a positive correlation with NH4-N, the probability of MB decreases. According to our physicochemical parameter model, it appears that with similar BOD and NH4-N across sites, there are other ecological factors that may not have been evaluated in this study that determine the varied probabilities of finding MB-infected mosquitoes at these sites.

Microsporidia MB has so far been isolated from on Anopheles mosquito species, particularly in dominant malaria-transmitting vectors including An. funestus [4–6]. Other members of the Anopheles gambiae complex are sometimes implicated in malaria transmission. Mostly exophilic, species such as An. pharoensis, An. rufipes, An. squamosus and An. coustani have been detected with Plasmodium infections [53–56]. While their contribution to the epidemiology of malaria in a geographical area may be negligible in most malaria endemic regions due to their relatively low numbers acquired during mosquito sampling, these minor vectors are also affected by control activities that are primarily designed to target the major vectors. The wide range of mosquitoes identified with the improved ANOSPP algorithm has allowed to show, for the first time, that these minor species are equally likely to be infected with Microsporidia MB. Our results suggest that mosquito species is not a significant determining factor for MB positivity. Infact, the probability is simply higher in any Anopheles male mosquito. It will be worth investigating MB prevalence in geographical locations where these vectors are found in high numbers to confirm that these vectors can also be infected with the fungus. This will further expand the range at which MB-Anopheles research can be extended for a more inclusive MB-mediated control [3].

The implications of having mosquito populations naturally infected with Microsporidia MB are positive for future control mechanisms involving this endosymbiont. Our study has used regression models to show some biotic and abiotic factors that can be incorporated into surveillance models for mosquito larval source management and maximising the detection of MB-infected populations in a geographical location to inform the feasibility of using MB for control. Mathematical modelling can provide insights into important determinants of MB prevalence[57] but evaluating these on the field would better define these models.

Availability of data and materials

The datasets supporting the conclusions of this article are included within the article and its additional files.

Acknowledgments

We thank staff of Prof. Samuel Dadzie’s lab at NMIMR for accommodating us in their field collection activities. It would have been difficult to travel to certain sites without your support.

Funding

This study was funded by ANTI-VeC African Anopheles Symbiont Survey grant award to JA (AV/AASS/009) through Open Philanthropy Project (University of Glasgow). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Contributions

JA conceived the project, sought funding and coordinated its execution. NEA, RKE, CD, SO, conducted field sampling. NEA, EAA, RKE, SNAT, RMP, DBS, SKDO, conducted mosquito dissections. NEA, EAA, RKE, RMP, DBS, SKDO perfomed molecular detection of MB. EAA, NEA, JA analysed the results and drafted the manuscript. SKD supported fieldwork planning, and logistics All authors read and approved the final manuscript.

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Breeding water parameters are important determinants of Microsporidia MB prevalence in the aquatic stages of Anopheles mosquitoes

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Abstract

Figures

Introduction

Methods

Study areas

Mosquito sampling

Measuring water physicochemical parameters

DNA extraction from mosquitoes

Species identification of mosquitoes

Data analyses

Results

MB prevalence and distribution

Prediction of MB positivity in study sites, mosquito sex and species

Breeding water physicochemical parameters among study sites

Correlation of MB positivity with breeding water physicochemical parameters

Discussion

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1