Overabundance of Asaia and Serratia bacteria is associated with deltamethrin insecticide susceptibility in Anopheles coluzzii from Agboville, Côte d’Ivoire

doi:10.21203/rs.3.rs-335885/v1

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Overabundance of Asaia and Serratia bacteria is associated with deltamethrin insecticide susceptibility in Anopheles coluzzii from Agboville, Côte d’Ivoire

https://doi.org/10.21203/rs.3.rs-335885/v1

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published 31 Oct, 2021

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Background Insecticide resistance among mosquito species is now a pervasive phenomenon, which threatens to jeopardise global malaria vector control efforts. Evidence of links between the mosquito microbiota and insecticide resistance is emerging, with significant enrichment of insecticide degrading bacteria and enzymes in resistant populations. Using 16S rRNA amplicon sequencing, we characterised and compared the microbiota of Anopheles (An.) coluzzii in relation to their deltamethrin resistance and exposure profiles.

Results Comparisons between 2-3 day old deltamethrin resistant and susceptible mosquitoes, demonstrated significant differences in microbiota diversity (PERMANOVA, pseudo-F = 19.44, p=0.0015). Ochrobactrum, Lysinibacillus and Stenotrophomonas genera, each of which comprised insecticide degrading species, were significantly enriched in resistant mosquitoes. Susceptible mosquitoes had a significant reduction in alpha diversity compared to resistant individuals (Shannon index: H=13.91, q=0.0003, Faith’s phylogenetic diversity: H=6.68, q=0.01), with Asaia and Serratia dominating microbial profiles. There was no significant difference in deltamethrin exposed and unexposed 5-6 day old individuals, suggesting that insecticide exposure had minimal impact on microbial composition. Serratia and Asaia were also dominant in 5-6 day old mosquitoes, regardless of exposure or phenotype, and had reduced microbial diversity compared with 2-3 day old mosquitoes.

Conclusions Our findings revealed significant alterations of An. coluzzii microbiota associated with deltamethrin resistance, highlighting the potential for identification of novel microbial markers for insecticide resistance surveillance. qPCR detection of Serratia and Asaia was consistent with 16S rRNA sequencing, suggesting that population level field screening of the bacterial microbiota may be feasibly integrated into wider resistance monitoring if reliable and reproducible markers associated with phenotype can be identified.

Bacteriology

Anopheles coluzzii

insecticide resistance

microbiota

deltamethrin

malaria

Côte d’Ivoire

Asaia

Serratia

Malaria remains a considerable public health problem with an estimated 229 million cases worldwide, including 409,000 deaths in 2019 alone[1] Malaria mortality has fallen since 2010, largely due to the scale-up of treatment, diagnostics and insecticide-based vector control interventions, principally long-lasting insecticidal nets (LLINs) and indoor residual spraying (IRS). However, global gains in malaria control have begun to stall[2]. Insecticide resistance among major malaria vector species is now a pervasive phenomenon, affecting more than 90% of countries with ongoing transmission[2]. Of particular concern is the continued spread of resistance to pyrethroids, which were until recently, the only class of insecticide recommended for use in LLINs. Pyrethroids are still a crucial component of next-generation LLINs[3], and resistance may severely threaten the long-term effectiveness of contemporary vector control programmes.

Control of insecticide-resistant vector populations is predicated on a clear understanding of the complex interplay between molecular mechanisms and fitness costs which contribute to mosquito behaviour, phenotype and vectorial capacity, the genetic and local environmental factors driving ongoing resistance selection and the implications of resistance for intervention operational efficacy. Substantial progress has been made elucidating key target site mutations[4]–[7], over-expression of detoxification enzymes[8]–[12] and alternate gene families and pathways[13]–[17], all of which play important roles in resistance modulation. Furthermore, the recent publication of genome data for more than 1000 Anopheles (An.) gambiae sensu lato (s.l.) has illustrated the considerable genetic diversity among natural vector populations, raising concerns for the rapid evolution and spread of novel resistance mechanisms[18], [19].

In addition to host-mediated resistance mechanisms, evidence is emerging that changes in mosquito microbiota may confer resistance to certain insecticides. The mosquito microbiota is a heterogenous and variable network of microorganisms, comprising the bacterial, archaeal, viral, fungal, and other eukaryotic microbial communities which inhabit the mosquito cuticle and internal structures such as the midgut, salivary glands, and ovaries. Constituents of the microbiota can be either inherited from mother to offspring[20] or acquired from the environment, predominantly the larval habitat[21]. Characterisation of the microbiota in mosquitoes has shown varied phenotypic impacts on the host species including on fitness[22], blood feeding[23], fecundity[24], immunity[25], [26], pathogen infection[27]–[33] and transmission[34]. There is increasing interest in investigating symbionts of mosquito vectors because they may offer unique transmission-blocking opportunities. Similarly, studies on the role played by mosquito symbionts in insecticide resistance may offer a better understanding of the underlying mechanisms, and the potential for designing innovative control techniques[35] and developing new insecticide resistance monitoring tools.

To date, information on field populations of the An. gambiae complex, the main malaria vectors in sub-Saharan Africa, is limited to recent reports of significant enrichment of known pyrethroid degrading taxa (Sphingobacterium, Lysinibacillus and Streptococcus) in permethrin-resistant An. gambiae sensu stricto (s.s.) from Kenya[43]. To address this deficit, we comparatively characterised the bacterial microbiota of An. coluzzii, collected from an area of high pyrethroid resistance in Côte d’Ivoire. We specifically focused on determining the effects of deltamethrin resistance intensity on host microbiota and identifying any microbial taxa associated with resistance phenotypes.

Mosquito collections and mass rearing

This study was conducted in Agboville (GPS: 5°55’21” N 4°13’13” W), Agnéby-Tiassa region, south-east Côte d'Ivoire. The location was chosen because of its high mosquito densities, malaria prevalence (26% in children <5 years in recent estimates[44]) and intense deltamethrin resistance[45]. The main industry is agriculture, with livestock such as cows, goats and chickens living close to households and cultivation of crops including bananas, cocoa and rice[46].

Sampling was conducted between 5th July and 26th July 2019, coinciding with the long rainy season (May-November) and peak malaria transmission. Adult mosquitoes were collected using HLCs, inside and outside households from 18:00h to 06:00h. Fieldworkers used individual haemolysis tubes to collect host-seeking mosquitoes, which were transported each morning to the Centre Suisse de Recherche Scientifique en Côte d'Ivoire (CSRS) in Abidjan. Blood-fed mosquitoes, morphologically identified as female An. gambiae s.l.[47], were transferred to cages with 10% sugar solution and left for 2-3 days to become fully gravid.

Five hundred and eighty fully gravid females were used for forced oviposition. Oviposition was achieved by placing a single gravid mosquito into an 1.5 ml Eppendorf tube, half filled with damp cotton wool, with small holes in the tube cap for ventilation[48]. Mosquitoes were held under standard insectary conditions (25°C, 70% humidity and a 12-hour light-dark cycle) until eggs were laid or adult death. Eggs were removed daily and placed into sterile paper cups containing distilled water and NISHIKOI (Nishikoi, United Kingdom)[49] staple fish food pellets. Emergent larvae were reared in 50 cm washing up bowls, in distilled water under the same insectary conditions. Pupae were removed daily and separated by sex with the aid of a stereomicroscope. Female pupae were put in a clean plastic cup with distilled water and placed in a cage for eclosion, while male pupae were discarded. Adults were housed in cages in an incubator (26.6°C, 70% humidity) with a 12-hour light-dark cycle and given unlimited access to 10% glucose solution. The cages were checked to ensure that only virgin females were used in bioassays, as mating can potentially introduce changes to the microbiome[20]. Care was also taken to ensure that no mosquito obtained a blood meal during handling, as this can significantly decrease bacterial diversity in the gut[50].

Determining deltamethrin resistance status of adult F1 progeny of field-caught An. gambiae s.l.

Deltamethrin resistance was characterised using Centre for Disease Control (CDC) bottle bioassays[51], with some modifications. Two to three-day old (d) virgin F₁ females were exposed to 1, 5 or 10 times the diagnostic dose of deltamethrin (12.5μg/bottle) for 30 minutes. Stock solutions of deltamethrin were prepared using 100% ethanol as the solvent. Per bioassay, multiple 250mL Wheaton bottles, and their lids, were coated with 1mL stock solution and left to dry in a dark storage area to avoid exposure to UV light. A control bottle, treated with 1mL ethanol, was assayed in parallel. Prior to bioassay testing, approximately 20-25 mosquitoes were aspirated into holding cups. After 1-2 hours of acclimatisation, they were introduced into each test or control bottle.

Knock-down was scored at 0, 15 and 30 minutes. A subset of mosquitoes which were alive at 60 minutes were held for 72 hours, with mortality recorded every 24 hours. These were housed in paper cups in the insectary, with unlimited access to sterile 10% glucose made with distilled water. Mosquitoes were counted as dead if they were unable to stand as per WHO criteria[51].

At the end of the bioassay and subsequent holding time, mosquitoes were classified as: susceptible if they were knocked-down following exposure to 1x deltamethrin, resistant if they survived 60 minutes or 72 hours post-exposure to 1x, 5x or 10x deltamethrin, or controls if they were in the ethanol coated bottle. Specimens were separated into their respective phenotype and concentration/time group and stored at -70°C.

DNA extraction

DNA was extracted from 380 mosquitoes which had been categorised as resistant, susceptible or unexposed to deltamethrin. Individuals were homogenised in a QIAGEN® TissueLyser II with sterilised 5mm stainless steel beads for 5 minutes at 30hz/sec and incubated overnight at 56°C. DNA was extracted using a QIAGEN DNeasy® 96 Blood and Tissue Kit (Qiagen®, UK) as per the manufacturers protocol[52] with DNA eluted in 45μL of buffer AE. Extracted DNA was stored at -70°C.

Four blank extraction controls were processed alongside mosquitoes: three blanks containing RNase-free water as the extraction template and one blank containing the 70% ethanol used for reagent dilution and sterilisation of instruments. All steps were performed under sterile conditions, with tweezers and other instruments being rinsed with 70% ethanol in between handling each mosquito, to avoid microbial or DNA contamination.

PCR for mosquito species identification

Individual mosquitoes were identified to species level according to Santolamazza et al[53]. PCR reactions contained 2μL of 10μM forward primer (5’-TCGCCTTAGACCTTGCGTTA-3’), 2 μL of 10μM reverse primer (5’-CGCTTCAAGAATTCGAGATAC-3’), 1μL extracted DNA and 10μL HotStart Taq Master Mix (New England Biolabs, UK), for a final reaction volume of 20μL. Prepared reactions were run on a BioRad T100™ thermal cycler with the following conditions: 10 minutes denaturation time at 94°C, followed by 35 amplification cycles of 94°C for 30 seconds, 54°C for 30 seconds and 72°C for 60 seconds, followed by a final extension at 72°C for 10 minutes. PCR products were visualised on 2% E-gel agarose gels in an Invitrogen E-gel iBase Real-Time Transilluminator. A Quick-Load^® 100bp DNA ladder (New England Biolabs, UK) was used to determine band size. Amplified PCR products of 479 bp or 249 bp were indicative of An. coluzzii or An. gambiae s.s., respectively. As the dominant species, only An. coluzzii individuals of the same age and resistance phenotype were selected and pooled for 16S rRNA sequencing.

16S rRNA gene amplicon sequencing

DNA concentration from each mosquito was measured using an Invitrogen Qubit™ 4 Fluorometer (Thermo Fisher Scientific, USA). Pools were prepared by combining equal concentrations of DNA from 3 mosquitoes of the same phenotype/deltamethrin concentration/time group to give 100ng in a final volume of 20μL (Table S1). Two negative controls, one comprised of a pool of the three RNase-free water blanks mentioned above, and the 70% ethanol blank, were processed in parallel.

The microbial composition of the microbiome was determined by amplification of the V3-V4 region of the 16S rRNA gene, using the following primers: 5’-CCTACGGGNGGCWGCAG-3’ and 5’-GGACTACHVGGGTATCTAATCC-3’. PCR reactions were prepared in a 25μL reaction volume, comprising 12.5μL of KAPA^© HiFi Hot Start ReadyMixPCR Kit[54] (Roche, Switzerland), 0.5μL of forward and reverse primers (10μM) and 12.5ng DNA. The following PCR cycling was used: 95°C for 3 minutes, 35 cycles of 95°C for 30 seconds, 55°C for 30 seconds and 72°C for 30 seconds, followed by a final extension at 72°C for 5 minutes. The resulting amplified PCR products were purified with AMPure XP beads (Beckman Coulter, UK) at 1x sample volume.

Next an index PCR was performed using 5μL purified PCR products, 5μL of Nextera XT Index 1 Primers (N7XX), 5μL of Nextera XT Index 2 Primers (S5XX) (both from the Nextera XT Index kit, Illumina, USA), 10μL PCR grade water and 25 μL of KAPA^© HiFi Hot Start ReadyMix (Roche, Switzerland). The following PCR cycling was used: 95°C for 3 minutes, 12 cycles of 95°C for 30 seconds, 55°C for 30 seconds and 72°C for 30 seconds, followed by a final extension at 72°C for 5 minutes. The final library was purified with AMPure XP beads, at 1.12x sample volume, before quantification.

Sequencing was performed on an Illumina^© MiSeq^© platform. Libraries were sequenced as 250 bp paired-end reads. Sequences were demultiplexed and filtered for read quality using Bcl2Fastq conversion software (Illumina, Inc.). In total, 1,156,076 sequences were generated in the FASTQPhred33 format44.

Data cleaning and filtering

Sequencing data were imported into the ‘Quantitative Insights Into Microbial Ecology’ pipeline, version 2020.8 (Qiime2)[55], and primary analysis was performed on the reverse reads, as the quality of the forward reads were not sufficient for merging (Figure S1). Sequencing primers and adapters were removed using the ‘cutadapt’ plugin[56] with an error rate of 10%. The divisive amplicon denoising algorithm (DADA2) plugin[57] was used to ‘denoise’ sequencing reads, removing phiX reads and chimeric sequences, to produce high resolution, ASVs[58]. DADA2 was run using the denoise-single command, with samples truncated at 206 nucleotides (trunc-len 206), to remove bases with a low-quality score. All other parameters were set to default. The resulting feature table[59] and sequences were filtered to remove ASVs present in the two blank samples and those with a frequency of below 100 to reduce biases in comparison of diversity indices across groups, and especially in differential abundance tests.

Taxonomic annotation

Taxonomic annotation of ASVs was performed using the -feature-classifier plugin[60], with a Naïve-Bayes classifier[61] pretrained on the 16S SILVA reference (99% identity) database version 132. The -extract-reads command was used to trim the reference sequences to span the V3-V4 region (425bp) of the 16S rRNA gene. Any features not classified to phylum level were also removed, these included hosts’ mitochondrial 16S rRNA genes. The resulting ASV table was exported into R (version 3.6.3) for analysis with the phyloseq package.

Bacterial diversity analysis

A rooted and unrooted phylogenetic tree was generated using the qiime phylogeny plugin[62]^,[63]^,[64] and were used to compute alpha and beta diversity metrics using the qiime2-diversity[65] plugin. For alpha diversity metrics, samples were rarefied[66] at a depth of 2359; where alpha rarefaction curves plateaued, indicating that there was adequate sampling of the microbiota during sequencing. Beta diversity metrics were computed for both rarefied and non-rarefied data, with no significant differences between methods (Table S2); non-rarefied data are presented herein. 2-3 day old and 5-6 day old mosquitoes were analysed separately, as age was shown to significantly impact the bacterial composition of the microbiota.

Two methods of alpha diversity were selected: Shannon diversity Index, which considers the abundance and evenness of ASVs present, and Faith’s Phylogenetic Diversity, a measure of community richness which incorporates phylogenetic relationships between species. Pairwise Kruskal-Wallis comparisons of these alpha diversity indices between groups of insecticide resistance phenotypes were performed, with Benjamini-Hochberg false discovery rate (FDR) correction for multiple comparisons[67]. Significance was set to FDR adjusted p value i.e. q value < 0.05.

Bray-Curtis Dissimilarity Index[68][69], which measures differences in relative species composition between samples, was chosen as the beta diversity metric. Comparisons of this index between insecticide resistance phenotype groups were conducted using pairwise PERMANOVA tests with 999 permutations[70]. Results were visualised using PCoA generated using the phyloseq[71] package. Significance was set to p value < 0.05.

Determination of association between microbiota composition and insecticide resistance phenotype, and identification of differentially abundant microbial taxa

Comparison of alpha and beta diversity indices indicated that both insecticide resistance phenotype and mosquito age affected the bacterial composition of An. coluzzii in this study. Following taxonomic annotation of ASVs, multinomial regression and differential abundance analysis was performed using Songbird[72] to determine the microbial taxa which were associated with and differentially abundant across insecticide resistance phenotype for mosquitoes separated by age group. Songbird is a compositionally aware differential abundance method which ranks features based on their log fold change with respect to covariates of interest[72] The following Songbird parameters were used: epochs 10000, number of random test examples 15, differential prior 0.5. The fit of the model was tested against the null hypothesis (-p-formula “1”). Differential log ratios of features were computed in Qurro[73]. We present the highest and lowest 10% ranked features associated with resistance phenotype. Analysis of Composition of Microbiome method (ANCOM) was used to complement Songbird analysis, and this was computed using the composition plugin[74] with all parameters set to default. Significance was determined using the automatic cut off for the test statistic, W[75].

Quantitative PCR (qPCR) validation of sequencing data

The abundance of Serratia spp. and Asaia spp. was assessed using qPCR, relative to the nuclear single-copy An. gambiae s.l. ribosomal protein S7 housekeeping gene (RPS7). Serratia reactions contained 1μL of 10μM forward primer (5’-CCGCGAAGGCAAAGTGCACGAACA-3’), 1μL of 10μM reverse primer (5’-CTTGGCCAGAAGCGCACCATAG-3’)[76], 2μL of pooled DNA and 5μL LightCycler® 480 SYBR Green Master Mix (Roche, UK), for a final reaction volume of 10μL. Prepared reactions were run on an Agilent Technologies Stratagene Mx3005P qPCR system which performed 40 cycles of 95°C for 15 seconds and 60°C for 1 minute, followed by a dissociation curve. Asaia reactions contained 1μL of 10μM forward primer (5’-GCGCGTAGGCGGTTTACAC-3’), 1μL of 10μM reverse primer (5’-AGCGTCAGTAATGAGCCAGGTT-3’)[77], 2μL of pooled DNA and 5μL LightCycler® 480 SYBR Green Master Mix (Roche, UK), for a final reaction volume of 10μL. Prepared reactions were run on an Agilent Technologies Stratagene Mx3005P qPCR system with the following conditions: 95^oC for 15 minutes, 40 cycles of 95°C for 10 seconds, 60^oC for 10 seconds and 72°C for 10 seconds, followed by a dissociation curve. RSP7 reactions contained 1μL of 10μM forward primer (5’-TCCTGGAGCTGGAGATGA AC-3’), 1μL of 10μM reverse primer (5’-GACGGGTCTGTACCTTCT GG-3’), [78]2μL of pooled DNA and 5μL LightCycler® 480 SYBR Green Master Mix (Roche, UK), for a final reaction volume of 10μL. Prepared reactions were run on an Agilent Technologies Stratagene Mx3005P qPCR system with the following conditions: 40 cycles of: 95°C for 10 seconds, 65°C for 60 seconds and 97°C for 1 second, followed by a dissociation curve. All samples were run in technical triplicate. Relative bacterial abundance was normalised relative to the endogenous control gene (RPS7). qPCR results were analysed using the MxPro software (Agilent Technologies).

Species identification and deltamethrin resistance profiles

In total, 580 An. gambiae s.l. were collected from Agboville using human-landing catches (HLCs), during the rainy season in July 2019. Of these, 245 (42%) laid eggs via forced oviposition. Following larval development, 1015 F₁ An. gambiae s.l. pupae were identified as female and tested in deltamethrin resistance intensity assays as 2-3 day old adults. Individuals were classified as susceptible if they were knocked-down following exposure to 1x deltamethrin, resistant if they survived 60 minutes (2-3 day old) or 72 hours (5-6 day old) post-exposure to 1x, 5x or 10x deltamethrin, or controls if they were unexposed to insecticide (comprising a mix of age-matched individuals of unknown phenotype). A total of 380 mosquitoes were randomly selected for DNA extraction, across all exposure and time groups, with 338 individuals identified as An. coluzzii (78.3%). From the remaining individuals, 31 were An. gambiae s.s. (8.1%), 10 failed to amplify (2.6%), and one individual was an An. gambiae s.s.–An. coluzzii hybrid (0.26%). Table S1 summarises the number of mosquitoes selected for DNA extraction, pooling and sequencing.

Sequencing metrics

A total of 1,156,076 reverse reads were obtained from sequencing. Quality control and denoising resulted in 2,999 unique amplicon sequence variants (ASVs), 878,155 in total. Filtering of ASVs associated with water and ethanol blanks, low frequency ASVs and ASVs not classified to phylum level resulted in 210 unique ASVs, totalling 556,254 across 94 pools of mosquitoes. Table S3 summarises the number of sequences processed per sample and the number of reads remaining after denoising and filtering.

Susceptible An. coluzzii had microbiota which were significantly different to, and less diverse than, resistant mosquitoes

Comparison of the Bray-Curtis dissimilarity index using pair-wise PERMANOVA with 999 permutations showed significant differences in bacterial composition between microbiota of 2-3 day old deltamethrin resistant and susceptible An. coluzzii (pseudo-F = 19.44, p=0.0015). Principal Coordinate Analysis (PCoA) visualisations showed the microbiota of susceptible mosquitoes clustered away from resistant and control mosquitoes (Figure 1), indicating that the microbiota of susceptible mosquitoes were more similar to each other than to resistant and control mosquitoes.

Susceptible mosquitoes had significantly lower Shannon and Faith Phylogenetic Diversity indices than resistant (Shannon: H=13.91, q=0.0003, Faith: H=6.68, q=0.01) and control mosquitoes (Shannon: H=22.6 q=0.000006, Faith: H = 16.6, q = 0.0001) of the same age, indicating that the susceptible group had reduced microbial diversity. There was no significant difference in alpha or beta diversity in deltamethrin exposed and unexposed 5-6 day old mosquitoes (Shannon: H=5.12, q=0.02, Faith: H=0.27, q=0.6, Bray-Curtis: pseudo-F=1.61, q=0.17), suggesting that insecticide exposure during the CDC bottle bioassays had minimal impact on microbial composition.

Serratia and Asaia dominated in older, and younger susceptible An. coluzzii

Following taxonomic annotation of ASVs to the genus or lowest possible taxonomic level, 114 and 57 bacterial taxa were detected in 2-3 day old and 5-6 day old mosquitoes, respectively. The less diverse 5-6 day old microbiota was predominantly comprised of ASVs assigned to the genera Serratia (75.5%) and Asaia (13.6%) (Figure S2). In 2-3 day old mosquitoes, microbial composition varied by resistance phenotype. Control mosquitoes had the highest number of taxa present (n=97), followed by resistant (n=90) and susceptible (n=66). 20 taxa were unique to control mosquitoes, and 15 to resistant mosquitoes. No taxa were unique to the susceptible group of mosquitoes, and 60 taxa were common to all groups (Figure 2).

In control mosquitoes, an unclassified species within the Enterobacteriaceae family (15.24%), Acinetobacter (8.83%) and Staphylococcus (8.29%) were most abundant, whilst Enterobacteriaceae (15.12%), Acinetobacter (14.26%) and Serratia (11.8%) were the most abundant in resistant mosquitoes. In susceptible mosquitoes, Serratia (56.4%) and Asaia (30.92%) were the dominant genera, with Acinetobacter (1.96%), Enterobacteriaceae (1.57%) and Staphylococcus (1.4%) present at low abundance (Figure 3). The remaining 61 taxa were present at an abundance of less than 1% of total ASVs present (Figure S2, Table S4).

Differential rankings confirmed Asaia and Serratia were significantly associated with susceptibility and Stenotrophomonas, Ochrobactrum, Lysinibacillus and Alphaproteobacteria were significantly associated with phenotypic resistance

Songbird was used to identify taxa which were differentially abundant in 2-3 day old resistant, susceptible or control mosquitoes. Evaluation of our Songbird model with resistance phenotype as the variable, against a baseline model with no variable resulted in a pseudo Q-squared value of 0.42, indicating that the model had not been overfit and that roughly 42% of variation in the model was predicted by resistance phenotype (Figure S3). There were significant differences in the log ratios of highest to lowest ranked taxa between resistant and susceptible microbiota (Figure 4, Table S5), suggesting that the highest ranked taxa were significantly overabundant in resistant microbiota, and the lowest ranked taxa were significantly overabundant in susceptible microbiota.

Stenotrophomonas, Ochrobactrum, Lysinibacillus and Alphaprotebacteria (highest ranked) were most strongly associated with insecticide resistance whilst Serratia, Aerococcus, E. shigella and Asaia (lowest ranked) were most strongly associated with insecticide susceptibility (Figure 5). Comparing log ratios of control and susceptible pools indicated that Rhodococcus, Sphingomonas, Haemophilus and E. shigella were most strongly associated with controls, whilst an uncultured Chroocooccidiopsaceae, Serratia, an unclassified member of Enterobacteriacea and Asaia were most strongly associated with susceptible mosquitoes (Table S5).

These results were confirmed by the ANCOM method. Serratia (W=208) and Asaia (W=208) were significantly overabundant in susceptible mosquitoes relative to resistant and controls, whilst Ochrobactrum (W=199), Lysinibacillus (W=188) and Enterobacteriaceae (W=201) were overabundant in resistant and control mosquitoes (Figure S4).

Increased abundance of Serratia and Asaia species in susceptible individuals confirmed by qPCR

Quantitative PCR assays confirmed that Serratia was significantly overabundant in 2-3 day old susceptible mosquitoes compared to deltamethrin resistant (5x p=0.028, 10x p=0.002) and control (p=0.02) (average CT value for susceptible: -8.4 [95% CI: -9.0 – -7.74]; 5x: -7.43 [-7.9 – -6.9]; 10x -6.3 [-7.2 – -5.4]; control: -7.2 [-7.9 – -6.6]). Asaia was also significantly overabundant in 2-3 day old susceptible mosquitoes compared to resistant (5x p= <0.001, 10x p<0.001) and control (p<0.001) (average CT values for susceptible: -7.6 [95% CI: -8.5 – -6.8]; 5x: 0.8 [-1.6 – 3.2]; 10x: 5.1 [3.0 – 7.1]; control: 4.5 [3.3 – 5.7]). Five to six day old mosquitoes also had increased abundance of both bacterial species (average CT value for 5-6 day old 5x: Serratia -8.2 [95% CI: -8.9 – -7.4] Asaia -1.3 [-5.6 – 2.9]; 5-6 day old 10x: Serratia -7.5 [-7.9 – -7.1] Asaia 5.4 [4.6 – 6.3]; 5-6 day old control Serratia -8.7 [-9.6 – -7.8] and Asaia -0.68 [-3.0 –1.7].

There is increasing evidence for an association between insecticide resistance phenotype[38], [43], [79] and Anopheles spp. microbiota. This study revealed distinct differences between the microbiota of deltamethrin resistant and susceptible An. coluzzii, with significant enrichment of insecticide degrading taxa in resistant individuals and an overabundance of Serratia and Asaia taxa in susceptible individuals. This population of field-caught An. gambiae s.l. from Agboville, South-East Côte d’Ivoire has previously been characterised as intensely resistant to pyrethroids, with average vector mortality of 14.56% [95% CI: 8.92-22.88%], 61.62% [95% CI: 51.58-70.75%] and 73.79% [95% CI: 64.35-81.45%] to one, five and ten times the diagnostic dose of deltamethrin, respectively, and pyrethroid resistance associated with over-expression of CYP450 enzymes (CYP6P4, CYP6Z1 and CYP6P3)[45].

Our study demonstrated significant differences in alpha and beta diversity between deltamethrin-resistant and susceptible An. coluzzii. Resistant mosquitoes harboured a wider variety of microbial taxa and had more microbial diversity both within and between themselves. Susceptible mosquitoes had fewer bacterial taxa and were far more homogenous, with Serratia and Asaia dominating in all samples. Previous studies have demonstrated differences between the microbiota of insecticide resistant and susceptible An. stephensi[38], An. arabiensis[42], and An. gambiae s.s[43]. This study is the first to detect an increase in alpha diversity in resistant mosquitoes with other studies reporting no difference[39], [43] or a decrease[80]. There are multiple potential reasons for the decreased microbial diversity previously identified, including processing of individuals rather than pooled mosquitoes, host species-specific differences, and variability among geographical collection sites[80]. Furthermore, field-caught mosquitoes may be of unknown age and physiological status; prior environmental insecticide exposure might also be responsible for reducing overall diversity, as bacteria with the ability to metabolise insecticides have greater access to compounds for growth and reproduction and can outcompete other species[38]. In our study, larvae were raised in distilled water, adults were age-standardised, unable to mate or blood-feed and had no insecticide exposure prior to resistance assays. We therefore consider this inherited microbiota to be linked to the resistance status of the host, with mosquitoes characterised by a wider range of bacteria having an increased chance of harbouring an insecticide degrading strain.

The role of bacteria in the degradation of pesticides has been widely studied[36], [37], [81], mainly due to the interest in bioremediation – use of bacteria to restore pesticide contaminated soils and water. However, emerging evidence that these bacteria could also contribute to pesticide resistance is concerning. Our study identified significant enrichment of several insecticide degrading taxa in resistant compared with susceptible mosquitoes: Ochrobactrum, Lysinibacillus and Stenotrophomonas. Ochrobactrum spp. have been isolated from contaminated soil, and shown to degrade a variety of insecticides including pyrethroids[82], [83] and organophosphates[84], [85]. Similarly, Lysinibacillus spp. derived from soil and sewage can metabolise deltamethrin[86] and cyfluthrin[87] whilst Stenotrophomonas in the microflora of cockroaches living in pesticide treated environments can degrade endosulfan in vitro[88]. Elevated expression of xenobiotic degrading genes[86] and enzymes[38] may be contributing to the insecticide degrading properties of these bacteria as well as direct degradation of pesticides.

While certain species of bacteria can confer insecticide resistance to the host, others may influence susceptibility. Indigenous gut bacteria have been implicated in the susceptibility of the gypsy moth, L. dispar, to the insecticidal toxin Bacillus (B.) thuringiensis. Treatment of larvae with antibiotics eliminated gut microbiota, and subsequently reduced mortality to B. thuringiensis; susceptibility was restored upon oral administration of Enterobacter sp., a gram negative bacterium widely present in the L. dispar gut[89]. In our study, Serratia and Asaia were found to be significantly overabundant in susceptible mosquitoes. Whilst there are no prior reports of an association of these species with mosquito insecticide resistance phenotype, when the relative abundance of Serratia sp. in the gut of the diamondback moth was increased, susceptibility to chlorpyrifos significantly increased[81]. Serratia marcescens plays a role in the susceptibility of field-caught Aedes to dengue virus infection by secreting SmEnhancin, an enzyme which digests gut epithelia mucins, enabling the virus to penetrate the gut[35]. The Bel protein, similar to SmEnhancin, and produced by the bacteria B. thuringiensis, has been shown to significantly increase the toxicity of Cry1Ac toxin in the cotton bollworm larvae, Helicoverpa armigera; by perforating the midgut peritrophic matrix and degrading the insect intestinal mucin, enabling the toxin to reach the target epithelial membrane[90]. A similar mechanism may occur in these mosquitoes, whereby proteins produced by Serratia spp. increase the permeability of the internal organs to deltamethrin, enabling it to reach its target in the mosquito nervous system.

Asaia and Serratia sp. have also both previously been implicated in modulation of Anopheles vector competence. Asaia sp. have been shown to activate antimicrobial peptide expression in An. stephensi[91] while some strains of S. marcescens isolated from An. sinensis can inhibit Plasmodium development by altering the immunity-related effector genes TEP1 and FBN9[27] Serratia spp. may also directly inhibit malaria parasite development by secretion of serralysin proteins and prodigiosin, which can have a pathogen-killing effect in vitro[92]; the latter can also act as a larvicidal agent against Ae. aegypti and An. stephensi[93]. By comparison, the presence of a dominant commensal Enterobacteriaceae has been positively correlated with Plasmodium infection[33]. Elucidating the impact of mosquito host microbiota composition and molecular and metabolic resistance mechanisms on parasite infection dynamics is crucial for the design of novel transmission-blocking strategies.

No significant difference in the microbiota of deltamethrin exposed and unexposed mosquitoes was observed at 60 minutes or 72 hours post-exposure. One hour is likely insufficient time for the microbial composition to significantly shift in response to insecticide, given the relatively slow rate of bacterial growth, and the fact that any bacteria killed by insecticide exposure would still have been present during microbiota extraction. At 72 hours, differences in the microbiota of exposed and unexposed mosquitoes, if present, should have been apparent. The lack of difference observed may in part be due to the low sample size of the 5 day old 10x resistant group and may also reflect the short insecticide contact time. Bioassays are a single exposure at a lethal dose in a sterile environment used to determine resistance phenotype[51]. In the wild, multiple insecticide exposures are likely to happen at sub-lethal doses at both the larval stage, as habitats are contaminated with agricultural pesticides, and adult stage, as there is frequent interaction with treated surfaces or materials indoors[94]. Bioassays may therefore not induce the same shifts in microbiota as insecticide exposure in the wild. Furthermore, as mosquitoes acquire most of their microbiota from the aquatic environment at the larval stage[21], and may obtain insecticide metabolising bacteria at this stage, studying the effects of deltamethrin exposure on larvae, or adults which were exposed as larvae, may be more informative.

Our results demonstrated a significant relative reduction in alpha diversity in resistant and control 5-6 day old mosquitoes compared with the 2-3 day old group, as expected based on prior reports that microbial diversity declines with age[50]. Older mosquitoes also had lower relative abundances of Ochrobactrum, Lysinibacillus and Stenotrophomonas, the insecticide degrading species shown to be significantly enriched in resistant 2-3 day old individuals. Serratia and Asaia, the species associated with susceptibility, were present in increased abundance in the older age group. It has been widely reported that insecticide resistance declines with age[95]–[100]. The shift in microbiota may also be a contributing factor, and further research is warranted to determine the association between the microbiota, resistance phenotype and age.

In our study, qPCR detection of Serratia and Asaia was consistent with the 16S rRNA sequencing data, with susceptible mosquitoes having significantly lower CT values than resistant or control individuals. Population-level field screening using qPCR, as a cheaper, faster and more feasible option that amplicon sequencing, should be considered for integration in wider insecticide resistance monitoring, if reliable and reproducible bacterial markers associated with phenotype can be identified.

Insecticide susceptibility is influenced by a range of diverse factors including host genetics, detoxification systems and behaviour as well as the mosquito microbiota. We report significant differences in the microbiota of deltamethrin-resistant An. coluzzii and have identified several bacterial species which were associated with either resistance or susceptibility to the host and therefore may represent important markers of resistance phenotype. The role of bacteria in determining resistance phenotype is highly complex and specific to the host and bacterial species, and insecticide and likely involves multiple, parallel mechanisms, including direct degradation of insecticide, altered host immune system, and changes to the midgut. In addition, these interactions may have important implications for host species fitness, vector competence and pathogen development and transmission. Further investigation into the mechanisms of microbiota mediated susceptibility is necessary as this may provide opportunities for preventing or reducing insecticide resistance, which is crucial to maintain gains in malaria vector control.

Ethics approval and consent to participate

The study protocol was reviewed and approved by the Comite National d’Ethique des Sciences de la Vie et de la Sante (#069-19/MSHP/CNESVS-kp) and the institutional review board (IRB) of the London School of Hygiene and Tropical Medicine (#16860); all study procedures were performed in accordance with relevant guidelines and regulations. Prior to study initiation, community consent was sought from village leaders and written, informed consent was obtained from the heads of all households selected for participation and from all fieldworkers who performed HLCs. Fieldworkers participating in HLCs were provided with doxycycline malaria prophylaxis for the duration of the study.

Consent for publication

Not applicable

Availability of data and material

Sequence data generated by this study is available at Sequence Read Archive (SRA) BioProject PRJNA702915 (accession numbers: SRR13743435 – SRR13743530). All other relevant data are available from the corresponding author upon reasonable request.

Codes can be accessed at the public repository Zenodo (http://zenodo.org) under https://doi.org/10.5281/zenodo.4548776

Competing interests

The authors declare that they have no competing interests.

Funding

This study was supported by the Sir Halley Stewart Trust (LAM) and the Wellcome Trust/Royal Society (http://www.wellcome.ac.uk and https://royalsociety.org; 101285/Z/13/Z to TW). CE was also supported by the Wellcome Trust (110430/Z/15/Z). BP was supported by the LSHTM-Nagasaki University Joint PhD Programme for Global Health.

Author Contributions

ND, LAM, BP, and TW designed the study. BP, EC, AM and CE conducted fieldwork and BP, MK and LAM undertook mosquito rearing, phenotyping and preparation of samples for sequencing. Laboratory supervision was provided by LAM, CLJ and TW. BP, ND, and LAM performed the data analysis; and BP and LAM drafted the manuscript. All authors read and approved the final manuscript.

Acknowledgements

The authors express their sincere thanks to Fidele Assamoa, Claver Adjobi and Laurent Didier Dobri, laboratory technicians at Centre Suisse de Recherches Scientifiques en Côte d’Ivoire (CSRS), for their support in mosquito collection and rearing; the chief and population of the village of Aboudé (Agboville), and the entomology fieldworkers of CSRS.

[1] World Health Organization, “World Malaria Report 2020: 20 years of global progress and challenges.,” 2020. [Online]. Available: https://www.who.int/teams/global-malaria-programme/reports/world-malaria-report-2020. [Accessed: 15-Dec-2020].

[2] World Health Organization, “World malaria report 2019.” [Online]. Available: https://www.who.int/publications/i/item/world-malaria-report-2019. [Accessed: 26-Oct-2020].

[3] World Health Organization, “Prequalified Vector Control Products.” [Online]. Available: https://www.who.int/pq-vector-control/prequalified-lists/VCP_PQ-List_26August2020.pdf?ua=1. [Accessed: 26-Oct-2020].

[4] C. Clarkson, A. Miles, N. Harding, D. Weetman, D. Kwiatkowski, and M. Donnelly, “The genetic architecture of target-site resistance to pyrethroid insecticides in the African malaria vectors Anopheles gambiae and Anopheles coluzzii,” bioRxiv, p. 323980, Aug. 2018.

[5] L. M. J. Mugenzi et al., “Cis-regulatory CYP6P9b P450 variants associated with loss of insecticide-treated bed net efficacy against Anopheles funestus,” Nat. Commun., vol. 10, no. 1, pp. 1–11, Dec. 2019.

[6] B. S. Assogba et al., “An ace-1 gene duplication resorbs the fitness cost associated with resistance in Anopheles gambiae, the main malaria mosquito,” Sci. Rep., vol. 5, no. 1, p. 14529, Oct. 2015.

[7] X. Grau-Bové et al., “Resistance to pirimiphos-methyl in West African Anopheles is spreading via duplication and introgression of the Ace1 locus,” bioRxiv, p. 2020.05.18.102343, May 2020.

[8] P. Müller et al., “Field-Caught Permethrin-Resistant Anopheles gambiae Overexpress CYP6P3, a P450 That Metabolises Pyrethroids,” PLoS Genet., vol. 4, no. 11, p. e1000286, Nov. 2008.

[9] B. J. Stevenson et al., “Cytochrome P450 6M2 from the malaria vector Anopheles gambiae metabolizes pyrethroids: Sequential metabolism of deltamethrin revealed,” Insect Biochem. Mol. Biol., vol. 41, no. 7, pp. 492–502, Jul. 2011.

[10] T. L. Chiu, Z. Wen, S. G. Rupasinghe, and M. A. Schuler, “Comparative molecular modeling of Anopheles gambiae CYP6Z1, a mosquito P450 capable of metabolizing DDT,” Proc. Natl. Acad. Sci. U. S. A., vol. 105, no. 26, pp. 8855–8860, Jul. 2008.

[11] S. S. Ibrahim, J. M. Riveron, R. Stott, H. Irving, and C. S. Wondji, “The cytochrome P450 CYP6P4 is responsible for the high pyrethroid resistance in knockdown resistance-free Anopheles arabiensis,” Insect Biochem. Mol. Biol., vol. 68, pp. 23–32, Jan. 2016.

[12] J. M. Riveron et al., “A single mutation in the GSTe2 gene allows tracking of metabolically based insecticide resistance in a major malaria vector,” Genome Biol., vol. 15, no. 2, p. R27, 2014.

[13] V. A. Ingham, P. Pignatelli, J. D. Moore, S. Wagstaff, and H. Ranson, “The transcription factor Maf-S regulates metabolic resistance to insecticides in the malaria vector Anopheles gambiae,” BMC Genomics, vol. 18, no. 1, Aug. 2017.

[14] V. A. Ingham, S. Wagstaff, and H. Ranson, “Transcriptomic meta-signatures identified in Anopheles gambiae populations reveal previously undetected insecticide resistance mechanisms,” Nat. Commun., vol. 9, no. 1, Dec. 2018.

[15] V. A. Ingham et al., “A sensory appendage protein protects malaria vectors from pyrethroids,” Nature, vol. 577, no. 7790, pp. 376–380, 2020.

[16] A. T. Isaacs, H. D. Mawejje, S. Tomlinson, D. J. Rigden, and M. J. Donnelly, “Genome-wide transcriptional analyses in Anopheles mosquitoes reveal an unexpected association between salivary gland gene expression and insecticide resistance,” BMC Genomics, vol. 19, no. 1, p. 225, 2018.

[17] V. Balabanidou et al., “Mosquitoes cloak their legs to resist insecticides,” Proc. R. Soc. B Biol. Sci., vol. 286, no. 1907, Jul. 2019.

[18] C. S. Clarkson et al., “Genome variation and population structure among 1142 mosquitoes of the African malaria vector species Anopheles gambiae and Anopheles coluzzii,” Genome Res., vol. 30, no. 10, pp. 1533–1546, Oct. 2020.

[19] A. Miles et al., “Genetic diversity of the African malaria vector anopheles gambiae,” Nature, vol. 552, no. 7683, pp. 96–100, Dec. 2017.

[20] G. Favia et al., “Bacteria of the genus Asaia stably associate with Anopheles stephensi, an Asian malarial mosquito vector.,” Proc. Natl. Acad. Sci. U. S. A., vol. 104, no. 21, pp. 9047–51, May 2007.

[21] G. Gimonneau et al., “Composition of Anopheles coluzzii and Anopheles gambiae microbiota from larval to adult stages,” Infect. Genet. Evol., vol. 28, pp. 715–724, Dec. 2014.

[22] L. C. Ezemuoka, E. A. Akorli, F. Aboagye-Antwi, and J. Akorli, “Mosquito midgut Enterobacter cloacae and Serratia marcescens affect the fitness of adult female Anopheles gambiae s.l.,” PLoS One, vol. 15, no. 9, p. e0238931, Sep. 2020.

[23] E. V Kozlova et al., “Microbial interactions in the mosquito gut determine Serratia colonization and blood-feeding propensity,” ISME J., 2020.

[24] A. D. O. Gaio, D. S. Gusmão, A. V. Santos, M. A. Berbert-Molina, P. F. P. Pimenta, and F. J. A. Lemos, “Contribution of midgut bacteria to blood digestion and egg production in aedes aegypti (diptera: Culicidae) (L.),” Parasites and Vectors, vol. 4, no. 1, 2011.

[25] A. Cappelli et al., “Asaia activates immune genes in mosquito eliciting an anti-plasmodium response: Implications in malaria control,” Front. Genet., vol. 10, no. SEP, Sep. 2019.

[26] A. Muhammad, P. Habineza, T. Ji, Y. Hou, and Z. Shi, “Intestinal Microbiota Confer Protection by Priming the Immune System of Red Palm Weevil Rhynchophorus ferrugineus Olivier (Coleoptera: Dryophthoridae),” Front. Physiol., vol. 10, Oct. 2019.

[27] L. Bai, L. Wang, J. Vega-Rodríguez, G. Wang, and S. Wang, “A Gut Symbiotic Bacterium Serratia marcescens Renders Mosquito Resistance to Plasmodium Infection Through Activation of Mosquito Immune Responses,” Front. Microbiol., vol. 10, no. JULY, p. 1580, Jul. 2019.

[28] N. Jupatanakul, S. Sim, and G. Dimopoulos, “The insect microbiome modulates vector competence for arboviruses,” Viruses, vol. 6, no. 11. MDPI AG, pp. 4294–4313, 11-Nov-2014.

[29] C. M. Cirimotich, Y. Dong, L. S. Garver, S. Sim, and G. Dimopoulos, “Mosquito immune defenses against Plasmodium infection,” Developmental and Comparative Immunology, vol. 34, no. 4. Dev Comp Immunol, pp. 387–395, Apr-2010.

[30] N. J. Dennison, R. G. Saraiva, C. M. Cirimotich, G. Mlambo, E. F. Mongodin, and G. Dimopoulos, “Functional genomic analyses of Enterobacter, Anopheles and Plasmodium reciprocal interactions that impact vector competence,” Malar. J., vol. 15, no. 1, Aug. 2016.

[31] L. Gonzalez-Ceron, F. Santillan, M. H. Rodriguez, D. Mendez, and J. E. Hernandez-Avila, “Bacteria in Midguts of Field-Collected Anopheles albimanus Block Plasmodium vivax Sporogonic Development,” J. Med. Entomol., vol. 40, no. 3, pp. 371–374, May 2003.

[32] Y. Dong, F. Manfredini, and G. Dimopoulos, “Implication of the Mosquito Midgut Microbiota in the Defense against Malaria Parasites,” PLoS Pathog., vol. 5, no. 5, p. e1000423, May 2009.

[33] A. Boissière et al., “Midgut Microbiota of the Malaria Mosquito Vector Anopheles gambiae and Interactions with Plasmodium falciparum Infection,” PLoS Pathog., vol. 8, no. 5, p. e1002742, May 2012.

[34] M. Gendrin et al., “Antibiotics in ingested human blood affect the mosquito microbiota and capacity to transmit malaria,” Nat. Commun., vol. 6, Jan. 2015.

[35] P. Wu et al., “A Gut Commensal Bacterium Promotes Mosquito Permissiveness to Arboviruses,” Cell Host Microbe, vol. 25, no. 1, pp. 101-112.e5, Jan. 2019.

[36] X. Xia et al., “DNA Sequencing Reveals the Midgut Microbiota of Diamondback Moth, Plutella xylostella (L.) and a Possible Relationship with Insecticide Resistance,” PLoS One, vol. 8, no. 7, p. e68852, Jul. 2013.

[37] Y. Kikuchi, M. Hayatsu, T. Hosokawa, A. Nagayama, K. Tago, and T. Fukatsu, “Symbiont-mediated insecticide resistance,” Proc. Natl. Acad. Sci., vol. 109, no. 22, pp. 8618–8622, May 2012.

[38] N. Dada, M. Sheth, K. Liebman, J. Pinto, and A. Lenhart, “Whole metagenome sequencing reveals links between mosquito microbiota and insecticide resistance in malaria vectors.,” Sci. Rep., vol. 8, no. 1, p. 2084, 2018.

[39] N. Dada et al., “Pyrethroid exposure alters internal and cuticle surface bacterial communities in Anopheles albimanus,” ISME J., 2019.

[40] A. Arévalo-Cortés, A. M. Mejia-Jaramillo, Y. Granada, H. Coatsworth, C. Lowenberger, and O. Triana-Chavez, “The Midgut Microbiota of Colombian Aedes aegypti Populations with Different Levels of Resistance to the Insecticide Lambda-cyhalothrin,” Insects, vol. 11, no. 9, p. 584, Sep. 2020.

[41] A. Soltani, H. Vatandoost, M. A. Oshaghi, A. A. Enayati, and A. R. Chavshin, “The role of midgut symbiotic bacteria in resistance of Anopheles stephensi (Diptera: Culicidae) to organophosphate insecticides,” Pathog. Glob. Health, vol. 111, no. 6, pp. 289–296, Aug. 2017.

[42] K. Barnard, A. C. S. N. Jeanrenaud, B. D. Brooke, and S. V Oliver, “The contribution of gut bacteria to insecticide resistance and the life histories of the major malaria vector Anopheles arabiensis (Diptera: Culicidae),” Sci. Rep., vol. 9, no. 1, p. 9117, 2019.

[43] D. Omoke et al., “Western Kenyan Anopheles gambiae showing intense permethrin resistance harbour distinct microbiota,” Malar. J., vol. 20, no. 1, p. 77, 2021.

[44] M. du P. et du D. (MPD) Institut National de la Statistique (INS), M. de la S. et de l’Hygiène P. (MSHP) Programme National de Lutte contre le Paludisme (PNLP), I. The DHS Program, and U. Rockville, Maryland, “Côte d’Ivoire Enquête de prévalence parasitaire du paludisme et de l’anémie 2016.”

[45] A. Meiwald et al., “Reduced long-lasting insecticidal net efficacy and pyrethroid insecticide resistance are associated with over-expression of CYP6P4, CYP6P3 and CYP6Z1 in populations of Anopheles coluzzii from South-East Côte d’Ivoire,” J. Infect. Dis., Nov. 2020.

[46] B. K. Fodjo et al., “Insecticides Resistance Status of An. gambiae in Areas of Varying Agrochemical Use in Côte D’Ivoire,” Biomed Res. Int., vol. 2018, pp. 1–9, Oct. 2018.

[47] M. T. Gillies and M. Coetzee, “A supplement to the Anophelinae of Africa south of the Sahara (Afrotropical Region).,” A Suppl. to Anophelinae Africa south Sahara (Afrotropical Reg., 1987.

[48] J. C. Morgan, H. Irving, L. M. Okedi, A. Steven, and C. S. Wondji, “Pyrethroid Resistance in an Anopheles funestus Population from Uganda,” PLoS One, vol. 5, no. 7, p. e11872, Jul. 2010.

[49] Nishikoi Aquaculture Ltd, “Nishikoi Aquaculture Ltd.” [Online]. Available: https://www.nishikoi.com/. [Accessed: 23-Nov-2020].

[50] Y. Wang, T. M. Gilbreath, P. Kukutla, G. Yan, and J. Xu, “Dynamic Gut Microbiome across Life History of the Malaria Mosquito Anopheles gambiae in Kenya,” PLoS One, vol. 6, no. 9, p. 24767, 2011.

[51] “Guideline for Evaluating Insecticide Resistance in Vectors Using the CDC Bottle Bioassay.”

[52] “(EN) - DNeasy Blood & Tissue Handbook - QIAGEN.” [Online]. Available: https://www.qiagen.com/gb/resources/resourcedetail?id=6b09dfb8-6319-464d-996c-79e8c7045a50&lang=en. [Accessed: 14-Feb-2020].

[53] F. Santolamazza, E. Mancini, F. Simard, Y. Qi, Z. Tu, and A. della Torre, “Insertion polymorphisms of SINE200 retrotransposons within speciation islands of Anopheles gambiae molecular forms,” Malar. J., vol. 7, no. 1, p. 163, Aug. 2008.

[54] “KAPA HiFi HotStart ReadyMix | Roche Sequencing Store.” [Online]. Available: https://rochesequencingstore.com/catalog/kapa-hifi-hotstart-readymix/. [Accessed: 14-Feb-2020].

[55] E. Bolyen et al., “Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2,” Nat. Biotechnol., vol. 37, no. 8, pp. 852–857, Aug. 2019.

[56] M. Martin, “Cutadapt removes adapter sequences from high-throughput sequencing reads,” EMBnet. J., vol. 17, no. 1, p. pp--10, 2011.

[57] B. J. Callahan, P. J. McMurdie, M. J. Rosen, A. W. Han, A. J. A. Johnson, and S. P. Holmes, “DADA2: High-resolution sample inference from Illumina amplicon data,” Nat. Methods, vol. 13, no. 7, pp. 581–583, Jul. 2016.

[58] B. J. Callahan, P. J. McMurdie, and S. P. Holmes, “Exact sequence variants should replace operational taxonomic units in marker-gene data analysis,” ISME J., vol. 11, no. 12, pp. 2639–2643, Dec. 2017.

[59] D. McDonald et al., “The Biological Observation Matrix (BIOM) format or: how I learned to stop worrying and love the ome-ome,” Gigascience, vol. 1, no. 1, p. 7, 2012.

[60] N. A. Bokulich et al., “Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin,” Microbiome, vol. 6, no. 1, p. 90, Dec. 2018.

[61] F. Pedregosa et al., “Scikit-learn: Machine learning in Python,” J. Mach. Learn. Res., vol. 12, no. Oct, pp. 2825–2830, 2011.

[62] M. N. Price, P. S. Dehal, and A. P. Arkin, “FastTree 2--approximately maximum-likelihood trees for large alignments,” PLoS One, vol. 5, no. 3, p. e9490, 2010.

[63] K. Katoh and D. M. Standley, “MAFFT multiple sequence alignment software version 7: improvements in performance and usability,” Mol. Biol. Evol., vol. 30, no. 4, pp. 772–780, 2013.

[64] D. J. Lane, “16S/23S rRNA sequencing,” in Nucleic Acid Techniques in Bacterial Systematics, E. Stackebrandt and M. Goodfellow, Eds. New York: John Wiley and Sons, 1991, pp. 115–175.

[65] D. P. Faith, “Conservation evaluation and phylogenetic diversity,” Biol. Conserv., vol. 61, no. 1, pp. 1–10, 1992.

[66] S. Weiss et al., “Normalization and microbial differential abundance strategies depend upon data characteristics,” Microbiome, vol. 5, no. 1, p. 27, Mar. 2017.

[67] W. H. Kruskal and W. A. Wallis, “Use of ranks in one-criterion variance analysis,” J. Am. Stat. Assoc., vol. 47, no. 260, pp. 583–621, 1952.

[68] T. Sorenson, “A method of establishing groups of equal amplitude in plant sociology based on similarity of species content.,” K. Danske Vide- nskabernes Selsk., 1948.

[69] P. Legendre and L. Legendre, “Numerical Ecology,” Third., Elsevier, 2012, p. 499.

[70] M. J. Anderson, “A new method for non-parametric multivariate analysis of variance,” Austral Ecol., vol. 26, no. 1, pp. 32–46, 2001.

[71] P. J. McMurdie and S. Holmes, “phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data,” PLoS One, vol. 8, no. 4, p. e61217, Apr. 2013.

[72] J. T. Morton et al., “Establishing microbial composition measurement standards with reference frames,” Nat. Commun., vol. 10, no. 1, p. 2719, 2019.

[73] M. W. Fedarko et al., “Visualizing ’omic feature rankings and log-ratios using Qurro,” NAR Genomics Bioinforma., vol. 2, no. 2, 2020.

[74] S. Mandal, W. Van Treuren, R. A. White, M. Eggesbø, R. Knight, and S. D. Peddada, “Analysis of composition of microbiomes: a novel method for studying microbial composition,” Microb. Ecol. Heal. Dis., vol. 26, no. 0, May 2015.

[75] S. Mandal, W. Van Treuren, R. A. White, M. Eggesbø, R. Knight, and S. D. Peddada, “Analysis of composition of microbiomes: a novel method for studying microbial composition,” Microb. Ecol. Heal. Dis., vol. 26, no. 0, May 2015.

[76] H. Zhu, S.-J. Sun, and H.-Y. Dang, “PCR detection of Serratia spp. using primers targeting pfs and luxS genes involved in AI-2-dependent quorum sensing.,” Curr. Microbiol., vol. 57, no. 4, pp. 326–330, Oct. 2008.

[77] C. L. Jeffries et al., “Novel Wolbachia strains in Anopheles malaria vectors from Sub-Saharan Africa [version 2; peer review: 3 approved],” Wellcome Open Res., vol. 3, no. 113, 2018.

[78] X. Ren and J. L. Rasgon, “Potential for the Anopheles gambiae Densonucleosis Virus To Act as an ‘Evolution-Proof’ Biopesticide,” J. Virol., vol. 84, no. 15, pp. 7726 LP – 7729, Aug. 2010.

[79] A. Soltani, H. Vatandoost, M. A. Oshaghi, A. A. Enayati, and A. R. Chavshin, “The role of midgut symbiotic bacteria in resistance of Anopheles stephensi (Diptera: Culicidae) to organophosphate insecticides,” Pathog. Glob. Health, vol. 111, no. 6, pp. 289–296, Aug. 2017.

[80] N. Dada, N. Jupatanakul, G. Minard, S. Short, J. Akorli, and L. M. Villegas, “Considerations for mosquito microbiome research from the Mosquito Microbiome Consortium,” 2020.

[81] X. Xia, B. Sun, G. M. Gurr, L. Vasseur, M. Xue, and M. You, “Gut Microbiota Mediate Insecticide Resistance in the Diamondback Moth, Plutella xylostella (L.),” Front. Microbiol., vol. 9, no. JAN, p. 25, Jan. 2018.

[82] S. Chen et al., “Biodegradation of beta-cypermethrin and 3-phenoxybenzoic acid by a novel Ochrobactrum lupini DG-S-01,” J. Hazard. Mater., vol. 187, no. 1–3, pp. 433–440, Mar. 2011.

[83] B. zhan Wang et al., “Biodegradation of synthetic pyrethroids by Ochrobactrum tritici strain pyd-1,” World J. Microbiol. Biotechnol., vol. 27, no. 10, pp. 2315–2324, Oct. 2011.

[84] M. P. Talwar, S. I. Mulla, and H. Z. Ninnekar, “Biodegradation of organophosphate pesticide quinalphos by Ochrobactrum sp. strain HZM,” J. Appl. Microbiol., vol. 117, no. 5, pp. 1283–1292, Nov. 2014.

[85] X. H. Qiu, W. Q. Bai, Q. Z. Zhong, M. Li, F. Q. He, and B. T. Li, “Isolation and characterization of a bacterial strain of the genus Ochrobactrum with methyl parathion mineralizing activity,” J. Appl. Microbiol., vol. 101, no. 5, pp. 986–994, Nov. 2006.

[86] X. Hao et al., “Screening and Genome Sequencing of Deltamethrin-Degrading Bacterium ZJ6,” Curr. Microbiol., vol. 75, no. 11, pp. 1468–1476, Nov. 2018.

[87] G. P. Hu et al., “Isolation, identification and cyfluthrin-degrading potential of a novel Lysinibacillus sphaericus strain, FLQ-11-1,” Res. Microbiol., vol. 165, no. 2, pp. 110–118, 2014.

[88] M. Ozdal, O. G. Ozdal, and O. F. Algur, “Isolation and Characterization of α-Endosulfan Degrading Bacteria from the Microflora of Cockroaches ,” Polish J. Microbiol., vol. 65, no. 1, pp. 63–68, 2016.

[89] N. A. Broderick, K. F. Raffa, and J. Handelsman, “Midgut bacteria required for Bacillus thuringiensis insecticidal activity,” 2006.

[90] S. Fang et al., “Bacillus thuringiensis Bel Protein Enhances the Toxicity of Cry1Ac Protein to Helicoverpa armigera Larvae by Degrading Insect Intestinal Mucin,” Appl. Environ. Microbiol., vol. 75, no. 16, pp. 5237–5243, 2009.

[91] A. Capone et al., “Interactions between Asaia, Plasmodium and Anopheles: New insights into mosquito symbiosis and implications in Malaria Symbiotic Control,” Parasites and Vectors, vol. 6, no. 1, p. 182, Jun. 2013.

[92] A. J. Castro, “Antimalarial activity of prodigiosin [11],” Nature, vol. 213, no. 5079. Nature Publishing Group, pp. 903–904, 01-Mar-1967.

[93] C. D. Patil, S. V. Patil, B. K. Salunke, and R. B. Salunkhe, “Prodigiosin produced by Serratia marcescens NMCC46 as a mosquito larvicidal agent against Aedes aegypti and Anopheles stephensi,” Parasitol. Res., vol. 109, no. 4, pp. 1179–1187, Oct. 2011.

[94] R. N. C. Guedes, S. S. Walse, and J. E. Throne, “Sublethal exposure, insecticide resistance, and community stress,” Current Opinion in Insect Science, vol. 21. Elsevier Inc., pp. 47–53, 01-Jun-2017.

[95] S. Rajatileka, J. Burhani, and H. Ranson, “Mosquito age and susceptibility to insecticides,” Trans. R. Soc. Trop. Med. Hyg., vol. 105, no. 5, pp. 247–253, May 2011.

[96] C. M. Jones, A. Sanou, W. M. Guelbeogo, N. Sagnon, P. C. D. Johnson, and H. Ranson, “Aging partially restores the efficacy of malaria vector control in insecticide-resistant populations of Anopheles gambiae s.l. from Burkina Faso,” Malar. J., vol. 11, no. 1, pp. 1–11, Jan. 2012.

[97] M. Rowland and J. Hemingway, “Changes in malathion resistance with age in Anopheles stephensi from Pakistan,” Pestic. Biochem. Physiol., vol. 28, no. 2, pp. 239–247, Jun. 1987.

[98] J. D. LINES and N. S. NASSOR, “DDT resistance in Anopheles gambiae declines with mosquito age,” Med. Vet. Entomol., vol. 5, no. 3, pp. 261–265, Jul. 1991.

[99] M. H. Hodjati and C. F. Curtis, “Evaluation of the effect of mosquito age and prior exposure to insecticide on pyrethroid tolerance in Anopheles mosquitoes (Diptera: Culicidae),” Bull. Entomol. Res., vol. 89, no. 4, pp. 329–337, 1999.

[100] E. Collins et al., “The relationship between insecticide resistance, mosquito age and malaria prevalence in Anopheles gambiae s.l. from Guinea,” Sci. Rep., vol. 9, no. 1, p. 8846, 2019.

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Overabundance of Asaia and Serratia bacteria is associated with deltamethrin insecticide susceptibility in Anopheles coluzzii from Agboville, Côte d’Ivoire

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