Seasonal variation of microbiota composition in Anopheles gambiae and Anopheles coluzzii in two different eco‐geographical localities in Cameroon

Abstract Understanding the environmental factors affecting the microbiota in malaria vectors may help in the development of novel vector control interventions, similar to paratransgenesis. This study evaluated seasonal and geographical variations in the microbial community of the two major malaria vectors. Adult Anopheles mosquitoes were collected across two different eco‐geographical settings in Cameroon, during the dry and wet seasons. DNA was extracted from the whole individual mosquitoes from each group and processed for microbial analysis using Illumina Miseq sequencing of the V3‐V4 region of the 16S rRNA gene. Data analysis was performed using QIIME2 and R software programs. A total of 1985 mosquitoes were collected and among them, 120 were selected randomly corresponding to 30 mosquitoes per season and locality. Overall, 97 bacterial taxa were detected across all mosquito samples, with 86 of these shared between dry and wet seasons in both localities and species. There were significant differences in bacterial composition between both seasons, with a clear separation observed between the dry and wet seasons (PERMANOVA comparisons of beta diversity, Pseudo‐F = 10.45; q‐value = 0.01). This study highlights the influence of seasonal variation on microbial communities and this variation's impact on mosquito biology and vectorial capacity should be further investigated.


BACKGROUND
Mosquitoes are among the most investigated group of insects because of their remarkable role in the transmission of various parasites and pathogens to humans, domestic animals and wildlife (Muturi et al., 2018).
They serve as vectors of several pathogens that cause diseases such as malaria, dengue, yellow fever, Zika, West Nile fever and chikungunya.
Among these, malaria alone accounts for around 229 million cases and about 400,000 deaths every year, globally (Muturi et al., 2018). Malaria is caused by Plasmodium parasites and is transmitted to humans by female Anopheles mosquitoes including An. coluzzii, An. gambiae ss and An.
funestus as the main malaria vector in Cameroon (Antonio-Nkondjio et al., 2019). Considering the lack of an effective vaccine and increasing resistance to chemical insecticides, efforts to find innovative methods for controlling malaria through a better understanding of factors that influence both vector competence and vectorial capacity are urgently needed (Karimian et al., 2019;Naseri-Karimi et al., 2015;Shaw & Catteruccia, 2019;Wang et al., 2017).
Research on mosquito-microbiome interactions may lead to new tools for mosquito and mosquito-borne disease control. Mosquitoes have been shown to harbour microbial gut communities that enhance their fitness by contributing to digestion, nutrition, reproduction or providing protection from pathogens (Douglas, 2014). The mosquito gut microbiota is mainly acquired from the environment (Strand, 2018;Wang et al., 2017) and its composition is largely influenced by its aquatic breeding environment (Coon et al., 2014;Gimonneau et al., 2014). In addition, the microbiota composition is highly dynamic, varying greatly with species and even among individuals of the same species (Hegde et al., 2018;Muturi et al., 2016;Osei-Poku et al., 2012;Scolari et al., 2019), stage of mosquito development, and geographical areas (Duguma et al., 2019;Krajacich et al., 2017;Minard et al., 2017;Tchioffo et al., 2015). These variations of microbiota composition between field individual mosquitoes may partly explain the variability in mosquito infection levels in the field (Rosenberg, 2008). Some studies have shown that there is minor seasonal variation in An. gambiae s.s. mosquito microbiota from the forest-savannah regions with perennial larval sites during the dry season in Ghana, but that limited differences were found within An. coluzzii between seasons and locations (Akorli et al., 2016). Others revealed that microbiota composition was also different between the seasons and localities in Mali (Krajacich et al., 2017) and that the composition of microbiota can be shaped by host sampling location (Muturi et al., 2018). Also, spatial and temporal environmental variations altering microbiota have been shown in fruit flies (Corby-Harris et al., 2007). Although the mosquito microbiota composition is largely influenced by their aquatic stage breeding site, all the exact factors defining the structure of adult mosquito microbiota are currently unknown (Gendrin & Christophides, 2013) thus the extension of the microbiome study to other localities and Anopheles species could be used for malaria control.
Identifying the effect of environmental variables on the microbiome and widely characterizing the native microbiota composition of mosquito vectors is important for developing and deploying symbiotic control strategies. However, there has been no work to date on the spatio-temporal variability in the microbiota of Cameroonian mosquitoes. Therefore, this study aimed to evaluate the composition and structure of the microbiota in An. coluzzii and An. gambiae, two main malaria vectors in Cameroon, were collected from different locations during dry and wet seasons. In addition, this study aims at identifying potential natural bacteria symbionts that can be used to develop novel approaches for mosquito control.

Study sites and mosquito collection
This study was conducted in two localities in Cameroon, namely Gounougou (Northern region 9 03 0 00 00 N, 13 43 0 59 00 E) and Bankeng (Centre region, 4 40 0 26.4 00 N, 12 22 0 30 00 E.) ( Figure S1). These are two (2) different eco-geographical areas where the occurrence of Anopheles mosquitoes has already been demonstrated (Elanga-Ndille et al., 2020;Fadel et al., 2019). Gounougou is Sudano-Sahelian, while Bankeng is a tropical forested area. Gounougou is a village in the commune of Lagdo located in the department of Bénoué, Northern Region. It lies along the River Bénoué, in an area with cotton farming and rice cultivation. The climate of Gounougou is characterized by a short rainy season from May to September (mean annual rainfall of 900-1000 mm) and a long dry season from October to April. Bankeng is a small village in the department of Haute Sanaga, an area of tropical forest. This village is also a rice-growing area with watercourses (along the Sanaga River). The climate is characterized by a short dry season from December to March and a long rainy season from April to November (mean annual rainfall of 1000-2000 mm).
Mosquito collections were conducted in both study sites during the dry season from December 2018 to January 2019, and during the wet season in August 2019. Indoor resting female mosquitoes were collected between 06:00 AM and 09:00 AM following verbal consent from the village chief and each household representative. Mosquitoes were collected using Prokopack electrical aspirators (John W. Hook, Gainesville, FL). The collected mosquitoes were kept in individual tubes and subsequently transported to the insectary at the Centre for Research in Infectious Diseases (CRID), Yaoundé for morphological identification using keys for Afro-tropical anopheline mosquitoes (Gillies & De Meillon, 1968). In the end, these samples collected were used to characterize Anopheles species composition in both localities during both seasons, and the downstream microbiota characterization was focused on the predominant mosquito species or taxa in each location.
Sample processing, DNA extraction, and molecular identification of Anopheles s.l species A total of 120 individual mosquito samples from the two localities during the two seasons of collection corresponding to 30 randomly selected individuals per location and season were processed for microbial analysis. Prior to genomic DNA extraction, individual adult female mosquitoes were surface sterilized by washing them in 70% ethanol for 5 min and then rinsing twice with sterile distilled water in order to remove superficial bacteria to avoid external contamination.
Genomic DNA was extracted from whole individual mosquitoes using a Genejet extraction kit following the manufacturer's recommendations. Isolated DNA was reconstituted in 100 μl of Elution buffer and two aliquots of 50 μl each were prepared and stored at À20 C until further processing. One 50 μl aliquot of the resulting DNA isolate was utilized to build a microbiota library for Illumina sequencing. The other 50 μl aliquot of the DNA isolate was used for molecular identification of the members of the An. gambiae s.l. complex using the Short Interspersed Elements (SINE) (Santolamazza et al., 2008). Three negative controls, in which the extraction procedure was performed without adding any mosquito template were included to check for microbial contamination.
16S rRNA gene amplification, library preparation, and 16S rRNA sequencing All the 120 selected DNA samples were sent to Polo d'Innovazione di Genomica, Genetica e Biologia (Infravec) for sequencing using the For the Illumina library preparation, a two-step PCR amplification process was used. The first involved amplification of the bacterial 16S rRNA gene V3-V4 for each sample. The 25 μl reaction contained 2.5 μl DNA (5 ng/μl), 5 μl each primer (1 μM), and 12.5 μl 2x KAPA HiFi HotStart Ready mix. The cycling conditions consisted of an initial denaturation at 94 C for 3 min, followed by 25 cycles of denaturation at 94 C for 30 s, annealing at 55 C for the 30 s, elongation at 72 C for 30s with a final elongation at 72 C for 5 min. The expected sizes of the PCR products were verified by running 1 μl of the PCR product on a Bioanalyzer DNA 1000 (expected size~550 bp). PCR products were cleaned up using AMPure XP beads at 1x sample volume to purify the 16S V3 and V4 amplicon away from free primers and primer-dimer species. A second PCR was carried out to incorporate dual Illumina adapter sequences using the Nextera XT Index Kit. PCR reactions were performed in a total volume of 25 μl containing 5 μl DNA, 5 μl each primer (Nextera XT Index Primer i7 and i5), and 25 μl 2x KAPA HiFi HotStart Ready-mix and 10 μl PCR Grade water. The amplification program consisted of an initial denaturation step at 95 C for 3 min, followed by 8 cycles each consisting of denaturation at 94 C for 30 s, annealing at 55 C for 30 s, elongation at 72 C for Sequence data processing and generation of amplicon sequence variants (ASV S ) table Raw sequence data derived from the sequencing process were demultiplexed and transferred into FASTA files for each sample, along with sequencing quality files. The resulting data were processed and analysed using the Quantitative Insights Into Microbial Ecology (QIIME2 v. 2020.2) pipeline as follows. Primers and adapter sequences were removed using the QIIME2 v.2020.2 cutadapt plugins v.2020.2 (Martin, 2011). The divisive amplicon denoise algorithm DADA2 (Callahan et al., 2016) plugin in QIIME2 was used to denoise sequence reads; this step filters out noise and corrects errors in marginal sequences, removes chimeric sequences and singletons, merges paired-end reads, and finally dereplicates the resulting sequences, resulting in high-resolution amplicon sequences variants (ASVs) for downstream analysis. Using the denoisepaired command, the DADA2 options passed were trunc_len_f: 245, trunc_len_r: 245. The ASVs were further filtered to remove ASVs associated with the negative control and any reads assigned to PCR control were also filtered using the filtered taxa plugin of QIIME2. In addition, ASVs with a minimum frequency of 200 were removed. Table S1 shows sequencing reads and ASVs summary statistics.

Diversity indices
Analysis of microbial diversity was described within (alpha diversity) and between (beta diversity) samples. The Shannon diversity index and Observed features alpha diversity indices were calculated to estimate the inter-individual variation of bacterial diversity in different localities during dry and wet seasons. The Observed ASVs metric was used to estimate the number of unique ASVs (or richness) present within each mosquito, while the Shannon diversity index was used to estimate both ASVs richness and evenness. To establish whether alpha diversity differs across mosquito species per season all samples were rarified to a depth of 1000 ASVs per sample, which was sufficient to capture the typical low microbiota diversity in individual mosquitoes. The resulting average Shannon and Observed indices were compared between species collected from two different localities for both seasons' using pairwise the Kruskal-Wallis tests with Benjamini-Hochberg false discovery rate (FDR) corrections for multiple comparisons.
To compare the differences in microbiota composition and structure between mosquito groups based on measures of distance or dissimilarity, the Bray-Curtis dissimilarity indices were computed with or without rarefaction, and the resulting indices were compared between samples using pairwise PERMANOVA tests (999 permutations) with FDR corrections. There were no discernable differences between the results of rarefied and non-rarefied data. Thus, results of Bray-Curtis dissimilarity indices using rarefied data were visualized by Principal Co-ordinates Analysis (PCoA) plots in R (Ligges, 2009) and the nonmetric multidimensional scaling (NMDS) using the phyloseq R package (Mcmurdie & Holmes, 2013). Significance for both pairwise analyses was set to q < 0.05 (i.e., post FDR p-value corrections).

Taxonomic analysis and differentially abundant microbial taxa
Taxonomic analysis of ASVs was performed using QIIME2's pre-trained Naïve Bayes classifier (Zhang & Su, 2004) and the q2-feature-classifier plugin (Bokulich et al., 2018). Before analysis, the classifier was trained on the QIIME-compatible 16S SILVA reference (99% identity) database v.128 (Quast et al., 2012), and using the extract-reads command of the q2-feature-classifier plugin, the reference sequences were trimmed to the V3-V4 region (450 bp) of the 16S rRNA gene. The resulting relative abundance tables of annotated ASVs were exported into R and ggplot2 v.4 (Wickham, 2011) and used to generate stacked bar plots to visualize the relative abundance of bacterial taxa across individual samples in each group of mosquitoes (An. gambiae-Dry, An. gambiae-Wet, An. coluzzii-Dry and An. coluzzii-Wet). The relative abundance of the main phyla, main family, and the main genera (median relative abundance > 0.1%) were calculated. The detection of any inter-individual variation was estimated using the relative abundance of main bacteria genera with the median relative abundance.
The list of bacterial genera, as derived from ASVs, in each sample and group of samples was compared using Venn diagrams. These Venn diagrams were created using an online tool (http://bioinformatics.psb. ugent.be/webtools/Venn/). Differentially abundant microbial taxa across mosquito species collected from different localities in both seasons were identified using QIIME2's analysis of the composition of microbiota (ANCOM) (Mandal et al., 2015) plugin. The cut-off for F I G U R E 1 Shannon and observed diversity indices showed a significant difference in diversity of bacterial taxa between dry and wet seasons in An. coluzzii from Gounougou and in An. gambiae from Bankeng, there was no significant difference between dry and wet seasons. The comparison was performed using the Kruskal-Wallis test. Significance was determined at p < 0.05 differential abundance was set to an effect size of log F ≥ 20 and W ≥ 20, that is, a taxon was differentially abundant across species per season or localities if the ratio of its abundance to those of at least 20 other taxa (25% of all included taxa) differed significantly across species per season or localities. Prior to each analysis, ASVs frequency data were normalized by log10 transformation following the addition of pseudocounts of 1. All other parameters were set to default. The resulting statistic, W, and its default cut-off were used to identify differentially abundant features between the seasons and between the localities. Statistical analyses were conducted using non-parametric Kruskal-Wallis and Mann-Whitney tests. Finally, this analysis allowed us to explore the flux of bacterial symbionts acquired between two different species collected from both localities and seasons.

Anopheles species composition and abundance
A total of 1985 mosquitoes were collected and identified as Anopheles gambiae s.l. in the two localities during both seasons using morphological identification. The distribution of the mosquito species between the two localities was similar (Bankeng = 975; Gounougou = 1010; p = 0.6), but in the rainy season, there were more mosquitoes compared to the dry season across both locations (dry season total = 425; rainy season total = 1560; p = 0.004) (Table S1).
To confirm the mosquito's species, molecular identification was conducted on all the 425 mosquitoes collected during the dry season, and 672 randomly selected mosquitoes from the rainy season collection. In Bankeng, during both seasons, the collection contained predominantly An. gambiae ss (n = 555, 99%), with only three An. coluzzii individuals. In Gounougou, during both seasons, the collection was comprised of predominantly An. coluzzii (n = 503, 93%) and the proportion An. arabiensis were only 7% (n = 36). In addition, this result showed the absence of An. gambiae ss in Gounougou. The microbiota diversity of two main Anopheles species (An. gambiae from Bankeng and An. coluzzii from Gounougou) was further characterized.
Sample characteristics, 16S rRNA sequencing reads, and quality-control statistics Among the 120 samples sequenced, 118 have been selected because of a large number of high-quality sequences obtained after quality control and were used for data analysis. A total of 4,833,081 paired-end reads were generated. A total number of reads per F I G U R E 2 A principal coordinate analysis (PCoA) plot of bray-Curtis distances between the microbiota of Anopheles collected from two different localities during the dry and wet season. Each point on the plot represents the microbial composition of a single mosquito. The bray-Curtis comparison using permutational multivariate analysis of variance (999 permutations) showed a significant difference in microbial composition between samples collected between dry and wet seasons (Pseudo-F = 10.45, p = 0.01). This is represented by the larger ovals or spheres where red represents both Anopheles species collected from Bankeng and Gounougou during the dry season and blue the mosquito's species collected from Bankeng and Gounougou during the wet season sample (size per localities and season) is reported in Table S2. After quality filtering, 1,589,743 reads were assigned to ASVs at 99% identity and on average, there were 13,473 reads per mosquito sample (Table S2). To establish whether alpha diversity differs across mosquito species per season all samples were rarified to a depth of 1000 ASVs per sample, which was sufficient to capture the typical low microbiota diversity in individual mosquitoes ( Figure S2).
Spatio-temporal variation in microbial taxa associated with Anopheles gambiae s.l.
In order to determine whether there was any spatio-temporal variation in Anopheles microbiota, we compared microbial taxa and relative abundance between mosquitoes sampled in dry or wet seasons, from two different geographic localities. Since An. gambiae ss was only found in Bankeng and An. coluzzii in Gounougou, the data has been presented by species and localities stratified by seasons. For An. Pairwise PERMANOVA comparisons of Bray-Curtis distances also showed significant differences in microbial community structure of mosquitoes (q < 0.01) between every pair of seasons and every pair of species collected from different localities (Table S3). These results were corroborated by NMDS which showed that mosquito microbiota, clustered distinctly by seasons. We also investigated the F I G U R E 3 Bar plots showing the relative abundance of taxonomically annotated amplicon sequence variants (ASVs) from adults An. gambiae ss and An. coluzzii. ASVs showing an overall abundance equal to or greater than 0.1% were taxonomically annotated to the genus level. The bar plots show the relative abundance of annotated ASVs of individual mosquitoes across the two localities during both seasons. N represents the number of samples per group (An. gambiae collected in Bankeng during the dry and the wet seasons; An. coluzzii collected in Gounougou during the dry and the wet season). Microbiota was dominated by Asaia in Bankeng during the dry season, while the ASVs assigned to the genera Serratia, and pseudomonas were most predominant in the wet season. In Gounougou during the dry season, the ASVs assigned to the genera Enterobacter and Pantoea were most predominant, while the ASVs assigned to the genera pseudomonas and Klebsiella were most predominant in the wet season. ASVs that were not identified at the genus level are presented as unassigned taxa and rare taxa. Sa: sample. similarity between the microbiota in both mosquito species collected from different localities during both seasons. For each species per locality tested, visualizations of Bray-Curtis diversity distance matrices showed the microbiota of mosquitoes collected in the dry season clustering distinctly away from those collected in the wet season ( Figure 2 and Table S4).
The distribution of bacterial taxa among the 118 samples at the phylum, family and genus levels, and their relative abundance for individual samples in each sample group (two seasons and two localities) are reported. A total of 11 bacterial phyla, 66 families and 97 genera were identified across all samples (Table S5). Following taxonomic analysis of ASVs to the phylum level, 11 phyla were recovered, but only 5 of the 11 ASVs had an overall abundance equal to or greater than 0.1%. Most of the sequences were dominated by Proteobacteria  and Pantoea (3.20%) across all the samples (Figure 3 and Table S3). while during the wet season, Acinetobacter, Klebsiella, Serratia, Enterobacter, Asaia, and Pseudomonas were predominant respectively F I G U R E 5 Volcano plots of differentially abundant bacterial taxa in An. gambiae s.l during the dry and wet seasons. The plots show the results of the analysis of the composition of microbiomes (ANCOM) tests for differentially abundant microbial taxa between dry and wet seasons, with an effect size set to log F ≥ 20, and a cut-off of differential abundance set to W ≥ 20 (i.e., a taxon was differentially abundant across seasons if the ratio of its abundance to those of at least 20 other taxa (25% of all included taxa) differed significantly across seasons). Truly different taxa are depicted as one moves towards the far right (high W-statistic) as indicated by the arrow on the figure. Differentially abundant taxa are highlighted (blue shaded area) and the taxa names and seasons in which they were most abundant are presented in

Common or differentially abundant bacteria in mosquitoes and presence of main bacterial symbionts according to the seasons and mosquito species per localities
Some bacterial genera were specific to one group or shared between groups (An. gambiae-Dry, An. gambiae-Wet, An. coluzzii-Dry and An. coluzzii-Wet). In Bankeng (An. gambiae), 81 genera were shared between the dry and wet seasons, whereas in Gounougou (An. coluzzii) 70 genera were shared between the dry and wet seasons. Only 18 and 6 bacteria genera were unique to the dry season respectively to Gounougou and Bankeng (Figure 4a,b and Table S7). During the wet season, 7 and 4 bacterial genera were unique respectively to Gounougou and Bankeng. Across both seasons overall, 86 genera were shared between the dry and wet seasons and only 10 bacteria genera were unique to the dry season and only 2 bacteria genera were unique to the wet season ( Figure S6A). Across both localities, 88 genera of bacteria were shared between the two Anopheles species and localities, and only 3 bacteria genera were observed in An.
gambiae ss from Bankeng and only 6 bacteria genera were observed in An. coluzzii from Gounougou. ( Figure S6B).
Overall, two ASVs were more abundant in the dry season (Listeria and Meiothermus), while seven were more abundant in the wet season (Serratia, Dermacoccus, Pantoea, Klebsiella, Comamonas, Eubacterium, Acinetobacter) ( Figure 5). In both Anopheles species and localities, five ASVs were found to be differentially more abundant in Bankeng (An. gambiae) compared to Gounougou (An. coluzzii), with one more abundant (Listeria) in Bankeng, and four Rhizobium, Dermacoccus, an uncultured soil bacterium and Corynebacterium1 more abundant in Gounougou. Among significant differential bacteria observed, Listeria was only detected in the dry season and only in An. gambiae ss from Bankeng, while an uncultured soil bacterium was only detected in An.

DISCUSSION
The mosquito microbial composition is complex and depends on several factors such as the acquisition of environmental microbes (localities) and seasonality. Here, we characterized the bacterial composition in An. coluzzii and An. gambiae sampled from two different eco-geographical localities during the dry and the wet seasons. Overall, our data suggest that the bacterial communities of the two Anopheles species vary significantly between the seasons. This corroborates previous studies showing that seasonality impacts the adult mosquito microbiota composition (Akorli et al., 2016).
We report the diversity of the microbiota composition of two Anopheles species collected during the dry and wet seasons. These were An. gambiae and An. coluzzii collected from two localities in longicornis (Berasategui et al., 2016;Durand et al., 2015;Zhang, Huang, et al., 2019;. In contrast, there was no separation between the microbiota of Anopheles mosquitoes collected in both localities during the dry season. This could be explained by the same permanent breeding site (rice cultivation) during this season.
We also identified some bacteria symbionts with a high number of reads in our microbiota data. One promising mosquito-borne disease control approach would be the use of symbiotic bacteria naturally associated with mosquitoes, to impede the development of Plasmodium during the sporogonic cycle inside the mosquito (Mancini et al., 2016). This included four main bacterial symbionts (Asaia, Serratia, Klebsiella, and Pantoea) with variable relative abundance by Anopheles species and seasons ( Figure 6). These variations could be explained by the fact that breeding sites were very variable between both localities and seasons, resulting in variable bacterial symbionts.
The capacity to colonize and remain among the bacterial communities independent of Anopheles species and geographical localities will guarantee their success in a paratransgenesis approach. Some members of these bacterial taxa are known to be effective against Plasmodium development (Bando et al., 2013;Cirimotich et al., 2011), while others have been reported to be positively associated with P. falciparum infections in mosquitoes (Boissière et al., 2012). Asaia, Serratia, Pantoea, Klebsiella have been proposed as promising symbiotic control agents of malaria vectors such as An. gambiae by paratrangenesis.
Bacteria from the genus Asaia have been evaluated as paratransgenic agents to control malaria transmission (Damiani et al., 2010;Favia et al., 2007;Maffo et al., 2021). Bacteria from this genus are considered viable candidates due to their stable association with anopheline mosquitoes throughout the body and different life stages; they can also be easily cultivated and transformed. A previous study on genetically modified Serratia shows promise for controlling malaria by killing Plasmodium ookinete/oocysts in the mosquito midgut, the 'location' in which the cycle within the mosquito undergoes a severe bottleneck (Wang et al., 2011). The natural mosquito symbiont Pantoea can cross-colonize several mosquito species and is readily transformed and cultured; thus, Pantoea has been proposed for paratransgenic applications (Bisi & Lampe, 2011;Djadid et al., 2011). Finally, in An.
stephensi the presence of Klebsiella reduced the sporogonic development of P. berghei (Jadin, 1967). Future studies should be directed to characterize these bacteria symbionts Asaia, Serratia, Klebsiella, Pantoea, to evaluate their biological functions in the Anopheles gambiae mosquitoes and potential antiparasitic activity in the Cameroonian context.

CONCLUSION
This study has shown for the first-time significant differences in microbiota composition between dry and wet seasons in two Anopheles species collected from two different localities in Cameroon. We provide evidence that the seasons were significantly affecting both the bacterial composition and relative abundance of the bacterial genera with more microbial diversity in the dry season in both species collected from two different localities (Gounougou and Bankeng). In addition, this study provides evidence of the presence of potential bacteria symbionts that can be used to develop novel approaches for mosquito control. Further studies will evaluate the suitability of these bacteria symbionts as candidates for paratransgenesis.

SUPPORTING INFORMATION
Additional supporting information may be found in the online version of the article at the publisher's website.      Table S8. Table S1. Number of Anopheles sl mosquitoes collected according to the localities and seasons Table S2. Sequencing outputs and proportion of reads used for downstream analysis following quality control and dereplication Table S3. Beta-diversity comparisons show differential bacterial composition between the localities and between seasons: Pair-wise comparisons of beta diversity (Bray Curtis) of mosquitoes' microbiota between seasons (dry vs. wet), and between localities (Bankeng vs. Gounougou) showed significant differences in bacterial composition. Comparisons were conducted using PERMANOVA (999 permutations) tests with Benjamini-Hochberg FDR correction (q-value).
Significance is set to q-value (adjusted p-value). Table S4. Beta-diversity comparisons show differential bacterial composition between seasons in each locality. Pair-wise comparisons of beta diversity (Bray Curtis) of mosquito's microbiota between seasons in each locality (Bankeng vs. Gounougou) showed significant differences in bacterial composition. Comparisons were conducted using PERMANOVA (999 permutations) tests with Benjamini-Hochberg FDR correction (q-value). Significance is set to q-value (adjusted p-value).