Akorli et al. Parasites & Vectors           (2019) 12:27 
https://doi.org/10.1186/s13071-019-3287-0
RESEARCH Open Access
Generational conservation of composition
and diversity of field-acquired midgut
microbiota in Anopheles gambiae (sensu
lato) during colonization in the laboratory
Jewelna Akorli1,2*, Philomena Asor Namaali2, Godwin Williams Ametsi1, Richardson Kwesi Egyirifa2 and
Nana Adjoa Praba Pels2
Abstract
Background: The gut microbiota is known to play a role in a mosquito vector’s life history, a subject of increasing
research. Laboratory experiments are essential for such studies and require laboratory colonies. In this study, the
conservation of field-obtained midgut microbiota was evaluated in laboratory-reared Anopheles gambiae (s.l.)
mosquitoes continuously hatched in water from field breeding habitats.
Methods: Pupae and late instars were obtained from the field and reared, and the emerged adults were blood-fed.
The eggs obtained from them were hatched in either water from the field or in dechlorinated tap water. The
mosquito colonies were maintained for 10 generations. Midguts of female adults from unfed F0 (emerging from
field-caught pupae and larvae), F5 and F10 were dissected out and genomic DNA was extracted for 16S
metagenomic sequencing. The sequences were compared to investigate the diversity and bacterial compositional
differences using ANCOM and correlation clustering methods.
Results: Less than 10% of the bacterial families identified had differential relative abundances between generational
groups and accounted for 46% of the variation observed. Although diversity reduced in F10 mosquitoes during
laboratory colonization (Shannon-Weaver; P-value < 0.05), 50% of bacterial genera were conserved in those bred
continuously in field-water compared to 38% in those bred in dechlorinated tap water.
Conclusions: To our knowledge, this study is the first report on the assessment of gut bacterial community of
mosquitoes during laboratory colonization and recommends the use of water from the natural breeding habitats if
they are intended for microbiota research.
Keywords: Midgut microbiota, Anopheles gambiae (sensu lato), Laboratory colonization, Field water, Breeding
habitat
Background challenges posed by resistance to insecticides, there have
Mosquito vector-borne diseases are major health concerns, been increased efforts to find innovative methods of control
causing significant morbidity and mortality worldwide [1]. through gaining a better understanding of factors that also
Although chemotherapy and the search for vaccines for influence both vector competence and capacity. One prom-
these diseases have improved over the years, vector control ising strategy involves exploring the use of midgut micro-
remains a very important strategy. Faced with the biota for transmission-blocking [2].
Bacteria inhabiting the midgut of mosquitoes contribute
significantly to reducing the developmental capabilities of
* Correspondence: jakorli@noguchi.ug.edu.gh; jewelna.akorli@gmail.com
1West African Centre for Cell Biology of Infectious Pathogens, University of parasites that are ingested during a blood meal [3–5]. To
Ghana, P. O. Box LG 54, Legon, Accra, Ghana date, a few bacterial species isolated from the midgut of
2Department of Parasitology, Noguchi Memorial Institute for Medical wild-caught mosquitoes have been characterised for their
Research, University of Ghana, P. O. Box LG 581, Legon, Accra, Ghana
© The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Akorli et al. Parasites & Vectors           (2019) 12:27 Page 2 of 9
roles in the life history of the mosquito (reviewed in [6]). representing the experimental treatment. Replicate compari-
Although such bacteria are isolated from natural popula- sons were therefore impossible for such groups, i.e. Lab_F5
tions, studies to investigate their functions make use of la- and Lab_F10. Shannon-Weaver and Faith’s phylogenetic di-
boratory populations. Laboratory colonies of mosquitoes versity (PD) indices demonstrated similarity between repli-
are usually well-adapted to laboratory conditions and are cates of the same experimental treatment (P-values > 0.05)
useful in performing experiments involving large numbers (Table 1), although variations in means were observed (Fig.
of mosquitoes. However, continuous laboratory mainten- 1a, b). The evenness index gave no indication of a dominant
ance of field-derived populations over several generations species (index ≠ 0) (Fig. 1c), although an index of 0.36 (Fig.
results in the loss of the native microbiota [7, 8], most 1c; Field_F10_2) may suggest a slight shift in taxon evenness
likely due to changes in larval breeding water and other la- in this replicate.
boratory procedures. Therefore, results obtained by stud-
ies on microbiota might not be an accurate representation Number of observed features differed across generations
of what occurs in the wild [9]. The diversity indices also gave a first-hand indication of
In this study, we investigated the potential for breeding differences between some experimental groups (Table 2).
a population of ‘field’ mosquitoes in the laboratory with These were explored further by comparing features, op-
the aim of maintaining the midgut microbiota compos- erational taxonomic units (OTUs) and taxa classifica-
ition and diversity over generations. This could help es- tions between treatments. The average number of OTUs
tablish large numbers of mosquitoes under laboratory ranged from as low as 6 in Lab_F10 to 35 in Field_F5
conditions while conserving the natural midgut micro- (Fig. 2). Interestingly, the average number of OTUs did
biota profile for further studies. not differ between baseline (F0) samples and any of the
other experimental groups, likely due to the wide range
Results of recorded points in some of the experimental groups
Alpha diversity indices indicate similarity between and the reduced number of replicates. Field_F5 samples
treatment replicates differed from all other groups except for the baseline
Taxon similarity and alpha diversity were compared between (Fig. 2).
replicates for all treatment groups. This evaluation was done To be able to understand the source of the differences
to assess possible differences between replicates, as sampling detected, the sequences were analysed based on bacterial
of mosquitoes and water samples from the field were per- families. In total, 99 families were taxonomically identi-
formed over several days during the experimental period. fied ranging from 1 to 31 per sample. The average num-
Following rarefaction of sequences, some replicates were lost ber of bacterial families in mosquitoes reared in
due to low sequence counts and resulted in only one dechlorinated water was significantly lower than in
Table 1 Pairwise comparison (Kruskal-Wallis test) of diversity indices between sample replicates
Group 1 Group 2 Shannon-Weaver Faith’s PD Pielou’s evenness
H Adjusted P-value H Adjusted P-value H Adjusted P-value
Baseline_1 (n = 4) Baseline_2 (n = 3) 3.12 0.22 0.13 0.77 4.5 0.22
Baseline_3 (n = 1) 2.00 0.31 2.00 0.30 2.0 0.44
Baseline_4 (n = 2) 3.43 0.20 3.43 0.20 1.9 0.44
Baseline_5 (n = 1) 2.00 0.31 0.50 0.56 2.0 0.44
Baseline_2 (n = 3) Baseline_3 (n = 1) 0.20 0.74 1.80 0.30 0.2 0.75
Baseline_4 (n = 2) 0.33 0.67 1.33 0.35 0.3 0.72
Baseline_5 (n = 1) 1.80 0.33 1.80 0.30 1.8 0.44
Baseline_3 (n = 1) Baseline_4 (n = 2) 1.50 0.37 0.00 1.00 0.0 1.00
Baseline_5 (n = 1) 1.00 0.46 1.00 0.42 1.0 0.62
Baseline_4 (n = 2) Baseline_5 (n = 1) 1.50 0.37 1.50 0.35 0.0 1.00
Field F10_1 (n = 3) Field_F10_2 (n = 3) 0.05 0.86 1.19 0.38 0.4 0.72
Field_F10_3 (n = 4) 0.13 0.77 1.13 0.39 1.2 0.58
Field_F10_2 (n = 3) Field_F10_3 (n = 4) 0.13 0.77 0.50 0.56 0.4 0.72
Field_F5_1 (n = 4) Field_F5_2 (n = 4) 1.33 0.39 1.33 0.35 1.3 0.55
Field_F5_3 (n = 4) 4.08 0.20 0.33 0.63 2.1 0.44
Field_F5_2 (n = 4) Field_F5_3 (n = 4) 1.33 0.39 1.33 0.35 3.0 0.32
Akorli et al. Parasites & Vectors           (2019) 12:27 Page 3 of 9
Fig. 1 Shannon-Weaver (a), Faith’s phylogenetic piversity (PD) (b) and Pielou’s evenness indices (c) for samples. Black dots indicate index measure
for each sample and error bars show standard error of the mean for each treatment replicate. Red dotted lines are the total average for all
replicates for an experimental group
field-water-reared (5 vs 12; Mann-Whitney U-test: U(30) differences between groups of midguts (Fig. 5; Additional file
= 45.5, P = 0.026) and baseline mosquitoes (5 vs 11; 1: Table S1) and to 46% of the variance (Additional file 2:
Mann-Whitney U-test: U(26) = 27 P = 0.0052) (Fig. 3a). Table S3). Five (out of 8) of these significant taxa,Thorsellia,
Mesorhizobium, Microbacterium, Shingomonas and some
Differential compositional analyses unspecified Proteobacteria, were more likely to cluster or
We looked closer at the field water-bred mosquitoes to de- co-occur (Fig. 6). The balance formed by these genera
termine whether the observation made for Field_F5 during (y0_numerator) was more pronounced in samples bred in
the OTU analyses persisted when analysed at the bacterial dechlorinated water for 5 generations (Lab_F5). The
family level. Both groups of field-water-bred generations (F5 remaining 3 differential bacterial groups separately joined
and F10) differed in family number from the baseline, the F5 balances with other low relative abundance bacteria (Fig. 6).
showing a higher number than F10 in comparison to the Four balances (y1, y2, y5 and y9) significantly demonstrated
baseline (Fig. 3b). However, about 91% (90 out of 99) of the which bacterial taxa contributed to the differences in
families identified each represented < 1% of the total num- Field_F5 and other samples bred in field water (Field_F0 and
ber of analysed sequences. Re-analysis based on the 9 fam- F10) (Additional file 2: Table S4). Notable among these are
ilies with relative abundance ≥ 1% revealed that Field_F5 Micrococcaceae (genus Arthrobacter), Xanthomonadaceae
remained higher than the baseline (Mann-Whitney U-test: (genus Stenotrophomonas), Enterobacteriaceae (genus
U(20) = 14, P < 0.0001), but Field_F10 and the dechlori- Thorsellia) and Phyllobacteriaceae (genus Mesorhizobium)
nated water-reared mosquitoes were both similar to the which significantly increased, while Acetobacteraceae
baseline (P > 0.05) (Fig. 4). (genus Acetobacter) and Pseudomonadaceae (genus
The ANCOM results, which were based on bacterial gen- Pseudomonas) decreased in Field_F5 compared to Field_F0
era, confirmed the contribution of a few taxa to the observed (baseline) (Fig. 6). Following 10 generations of laboratory
Table 2 Pairwise comparison of diversity indices between experimental groups
Group 1 Group 2 Shannon-Weaver Faith’s PD Pielou’s Evenness
Baseline (n = 11) Field F5 (n = 12) 0.14 0.28 0.12
Field F10 (n = 10) 0.02 0.005 0.07
Lab F5 (n = 4) 0.12 0.01 0.16
Lab F10 (n = 2) 0.04 0.04 0.25
Field F5 (n = 12) Field F10 (n = 10) <0.001 <0.001 0.001
Lab F5 (n = 4) 0.008 0.0008 0.01
Lab F10 (n = 2) 0.004 0.007 0.05
Field F10 (n = 10) Lab F5 (n = 4) 0.81 0.67 1.0
Lab F10 (n = 2) 0.50 0.64 0.92
Lab F5 (n = 4) Lab F10 (n = 2) 0.45 0.90 0.94
Numbers are P-values following a Kruskal-Wallis test corrected for multiple comparisons by controlling false discovery rate
Akorli et al. Parasites & Vectors           (2019) 12:27 Page 4 of 9
Fig. 2 Comparison of observed OTUs between experimental
treatments. Points are OTUs from samples (pool of 5 midguts) of
Fig. 4 Comparison of 9 bacterial families (relative abundance ≥ 1%
each treatment. Error bars represent standard error of the mean
of sequences) between experimental groups. Points represent
samples. Error bars represent standard error of the mean
maintenance, 50 and 38% of bacterial genera were con-
served (maintained as present or absent) in field-water and that conservation of midgut microbiota can be achieved
dechlorinated tap water-bred mosquitoes, respectively using water from breeding sites in the laboratory mainten-
(Additional file 1: Table S2). ance of mosquitoes. Relative bacterial abundances varied
between generations of colonies continuously bred in
Discussion field-water, while still maintaining significant field-derived
Laboratory colonization of mosquitoes is useful for produ- taxa. However, dechlorinated water, as used in standard in-
cing large numbers of samples for experiments on various sectary procedures for egg hatching, resulted in a significant
aspects of mosquito life history, and selection of specific decrease in the relative abundance of many bacterial taxa
traits. However, for microbiota studies, the bacterial com- while selectively keeping a few at high abundance.
munities found in wild mosquitoes could be lost after a few Bacteria in the larval environment form a major part
generations, thus creating a discordance which limits the of the mosquito midgut through its aquatic developmen-
usefulness of laboratory colonies in efforts to understand tal stages to emerged adults [10–12]. Breeding water is,
the roles of microbiota [9]. The present study demonstrates however, dynamic with its bacterial community differing
Fig. 3 Comparison of 99 taxonomically identified bacterial families between experimental groups. Each point represents a sample (pool of 5
midguts). a Comparison between the baseline and all field and lab water bred samples. b Comparison of baseline and generational groups of
field-water-bred mosquitoes. Error bars represent standard error of the mean
Akorli et al. Parasites & Vectors           (2019) 12:27 Page 5 of 9
at different surface layers [13], and with abiotic and bi-
otic effects such as contamination [14] and seasonal var-
iations [15]. The laboratory environment is more
controlled, presenting with less effects from natural exter-
nal sources. Nevertheless, various factors in the laboratory
may also cause changes in larval breeding water during
mosquito maintenance. Such environmental variations
could result in same mosquito species maintained in differ-
ent insectaries having different distinct microbial communi-
ties. Further investigation on the influence of variations in
laboratory environment on changes in microbiota could be
important in understanding both the effect of laboratory
conditions in shaping the midgut bacteria of colonized
mosquitoes, and how this contributes to potential differ-
ences in experimental results between laboratories. With 10
generations of breeding field-caught mosquitoes in
field-water under laboratory conditions, approximately 50%
of the ‘natural’ microbiota was conserved compared to 38%
in mosquitoes reared using dechlorinated tap water. The
study did not show a significant difference in these percent-
ages, likely due to small sample size.
Fig. 5 Volcano plot for the analysis of composition of microbiomes Continuous breeding in field water may help replace some
(ANCOM) test. Only significant bacterial taxa are labelled. Taxa on bacteria and introduce new ones that have not already been
the top-left corner are distinct species but with small proportions, i.e. observed in the founding population, as observed in the un-
low f-score. Truly different taxa are depicted as one moves towards
the far right (high W-statistic) expected increase in the number of bacterial families in the
fifth generation of field-bred mosquitoes. Other bacteria may
also be consistently lost once under laboratory conditions
(Additional file 1: Table S2). The field-water returned to the
laboratory at each collection point was not tested for bacteria
in this study, but the dynamics of this environment to both
Fig. 6 Dendogram of bacterial families resulting from unsupervised correlation clustering. Balances (y0-y9) are shown by pink (numerator) and red
(denominator) vertical bars on the left side of the map, and are not related to the scale
Akorli et al. Parasites & Vectors           (2019) 12:27 Page 6 of 9
natural biotic and abiotic factors could explain the variations populations of mosquitoes for controlled experiments
observed from one field group to another. could help provide answers to the contribution of micro-
The use of dechlorinated tap water, which is a standard biota to vector competencies to disease transmission.
practice in mosquito insectaries, poses an initial bottle-
neck for mosquito colonization as was observed in our Methods
study. Chlorine is an effective bacteria-inactivating and Mosquito samples and experimental set-up
killing agent [16, 17]. This effect resulted in a small num- Late (3rd and 4th) instars and pupae of An. gambiae
ber of replicates in our tap-water reared as compared to (s.l.) were sampled together with water from a breeding
field-water samples. That notwithstanding, these samples site in peri-urban Accra and transported to the labora-
were able to demonstrate the reduction of bacterial fam- tory in plastic containers. Three to four batches of mos-
ilies and relative abundance in samples brought into the quito samples were collected from the field within 3–4
laboratory after ten generations, consistent with reports of days of each other. The pupae were separated into cups
loss of bacterial populations in laboratory colonized mos- and placed in cages with no source of sugar meal for the
quitoes [7, 8]. emerging adults. The larvae were transferred into larval
The use of balances on our dataset enabled the identifi- trays and maintained without adding larval food for a
cation of major taxa whose relative abundances were most maximum of 3 days under standard insectary conditions.
significant in explaining compositional variations. Most Remaining larvae were discarded. Emerging pupae were
notable among these are the four taxonomically identified collected from the larval trays each day and transferred
genera that formed part of the largest balance (y0): Thor- into cages wiped with 70% ethanol. For each batch of field
sellia, Mesorhizobium, Microbacterium and Sphingomo- collection, 30 one-day-old non-sugar-fed females were
nas. These co-occurring bacteria became most stored at -20 °C until needed for midgut dissection. The
pronounced in tap-water reared samples despite rarely oc- remaining adult mosquitoes in the cage were offered 10%
curring at baseline. This is indicative of these bacterial glucose through cotton balls for 4–5 days and blood-fed.
taxa having a proliferation advantage when many others F1 eggs collected from each batch of field collection
have been lost due to some selective pressure [18]. The were divided into two groups and placed in larval trays
great extent of bacteria loss during laboratory rearing re- for the experiments. One group was placed in a tray
sults in distinct profiles dominated by few species com- containing field water (collected the previous day and
pared to field-caught mosquitoes [19], as demonstrated in sieved through a cloth to ensure mosquito eggs and
our study. Again, the small number of tap-water reared early stage larvae were removed before use) and the
samples limited the extent to which this could be ob- other in dechlorinated tap water (chlorinated tap water
served. Despite this Microbacterium and Sphingomonas left standing for at least 24 h). To standardize the set-up
showed distinctly in our tap-water reared mosquitoes and and prevent overcrowding, the egg to water ratio of 1 egg
demonstrated significant difference in abundance. Both to 20 ml was maintained. Yeast and larval food were
bacterial taxa have been identified in field-caught and added to all trays daily. The water level was replenished
lab-reared Anopheles species [20, 21]. with one-third of water (as appropriate for each tray)
The persistence and increase in the incidence of Thor- every other day to prevent drying. The larvae were ob-
sellia in laboratory colonized mosquitoes cannot be ig- served daily, and the dead were removed. Emerging adult
nored. This bacterial genus has been isolated from the mosquitoes were maintained through generations as de-
midguts of some Anopheles malaria vectors [8, 22, 23] scribed above on sugar and blood. At 5 and 10 genera-
and Culex mosquitoes [24]. They are known to increase tions, emerged 1-day-old unfed female mosquitoes were
growth in blood medium [23], hence could potentially sub-sampled (~30 female adults) for midgut dissections.
be involved in blood digestion in mosquitoes. Besides
the midgut, Thorsellia spp. are also found to inhabit the Midgut preparation and sequencing
reproductive tracts of both sexes of Anopheles gambiae Midguts were dissected from stored female adult mosqui-
and An. coluzzii [25], necessitating further investigations toes sampled from F0 (baseline), F5 and F10 generations. A
on the functions of these bacteria in malaria mosquitoes. sample contained 5 dissected mosquito midguts, and for
each treatment replicate a maximum of 4 samples (i.e. 20
Conclusions midguts) was obtained. The total number of samples ob-
We have demonstrated the conservation of field-derived tained for each experimental group (F0, Field_F5,
bacterial community in mosquitoes maintained under la- Field_F10, Lab_F5, Lab_F10) ranged between 4 and 20
boratory conditions for ten generations by field-water re- (Additional file 3: Table S5). Three sham samples were
placement. This study also confirms the loss of microbial prepared during dissections as previously described [15].
profile when mosquitoes are bred in tap water, which is a Genomic DNA was extracted from each replicate and the
standard laboratory practice. The ability to breed large 3 shams using QiaAmp Micro DNA kit (Qiagen, Hilden,
Akorli et al. Parasites & Vectors           (2019) 12:27 Page 7 of 9
Germany) according to manufacturer’s recommendations. described above. The cut-off criterion for pruning the for-
The bacterial 16S V1-3 region was sequenced for 55 DNA ward and reverse sequences changed to positions 291 and
samples (Additional file 3: Table S5) on an Illumina MiSeq 247, respectively, and resulted in 52,312 sequences. Se-
platform (Pretoria, South Africa) using the primers 27F quences designated as ‘Unidentified bacteria’ and ‘Un-
(5'-AGA GTT TGA TCM TGG CTC AG-3') and 534R assigned’ were excluded from dataset used for the diversity
(5'-ATT ACC GCG GCT GCT GG-3'). and differential composition analyses.
Quality filtering, which included trimming the se- Rarefaction for diversity analyses was performed at a
quences to retain bases with > 20 Phred score and demul- sampling depth of 250 sequences per sample, which re-
tiplexing, was performed, and a total of 527,005 sequences sulted in the loss of 8 samples with low sequence counts.
were retrieved. The length of the retained sequences The remaining 44 samples were analysed for taxon rich-
ranged between 35–295 bp. We performed further se- ness, evenness and diversity using the Faith’s phylogenetic
quence filtering to remove very low sequence lengths, diversity (PD), Pielou’s and Shannon-Weaver indices, and
which could be problematic in downstream data analyses, explored for observed operational taxonomic units
such as taxonomic assignments [26]. A total of 513,720 se- (OTUs). The significance of alpha diversity was deter-
quences with lengths between 150–295 bp were extracted. mined using Kruskal-Wallis test, accepting only adjusted
The minimum and maximum sequence count per sample P-values < 0.05 as significant.
was 1276 and 36,171, respectively, with a mean of 9340. Analysis of composition of microbiomes (ANCOM) was
used to identify differential relative abundance of bacterial
Screening sequences for potential ‘contamination’ genera [33], and balance trees to evaluate changes (growth
Analyses of microbial data were performed using QIIME or decline) in microbial sub-communities between experi-
2 (https://qiime2.org) following the “Moving Pictures” mental groups [34]. As both methods of analysis are sensi-
tutorials [27]. The paired end reads for all samples were tive to less informative features (sequence grouping), the
imported into qiime2 and the sequence summaries were taxa frequency table was filtered to remove bacterial clas-
visualized by using the qiime demux summarize plugin. sifications with less than 10 reads and those observed in
The resulting Interactive Quality Plot was examined to less than 3 samples in the study. To tolerate the zero fre-
truncate the sequences in both read directions at base quencies of bacterial counts, a pseudo-count composition
positions where the read quality fell below the threshold table was created by adding a count of 1 to every value.
of 20. Using this cut-off criterion resulted in pruning the For ANCOM, the composition table was log-transformed
forward and reverse sequences at 295 and 246 base posi- and significance determined from f-scores and ‘W’ statis-
tions, respectively, and produced a set of output repre- tics. The f-score measures the strength of the difference of
sentative sequences (32,504 in total). These were aligned a feature between groups. A high score indicates the more
and masked to remove highly variable positions and likelihood that the null hypothesis (the average of the fea-
used to build a mid-point rooted tree for phylogenetic ture in all groups are the same) can be rejected. The ‘W’
diversity analyses. The Naive Bayes classifier was trained statistics indicate the number of times a feature is detected
on SILVA 128 [28, 29] 97% OTUs, with taxonomic refer- to be significantly different across groups. Principal bal-
ence set to extract and include the target sequence be- ances were built with unsupervised hierarchical clustering
tween the forward and reverse primer. Taxonomic and isometric log ratio (ILR) transformation [35] to group
classification was performed for representative se- features based on how frequently they co-occur. An ordin-
quences with classify-sklearn [30] in the qiime2 ary least square regression model was fitted to the bal-
feature-classifier plugin [31]. ances using the different generation of samples as the only
The resulting dataset was screened at the bacterial fam- predictor variable. Coefficient P-values were accepted at a
ily level for possible ‘contamination’. To do this, the aver- stringent significance level of 0.01.
age relative abundance of bacterial families was calculated
for the shams and those > 0.01 (Additional file 4: Tables
S6, S7) were analysed for correlation with initial DNA Additional files
concentration as previously described [15, 32]. No bacter-
ial taxon was identified as a contaminant in our dataset. Additional file 1: Table S1. ANCOM results. Table S2 Calculation of
bacterial genera conservation in laboratory-colonized samples. (XLSX 17 kb)
Additional file 2: Table S3. Simplicial linear regression summary.
Analyses of experimental samples Table S4. Regression coefficients and P-values. (XLSX 15 kb)
The inclusion of the sham sequences could potentially in- Additional file 3: Table S5. Details of 55 samples submitted for
fluence downstream analyses, therefore a second dataset of metagenomic sequencing. (XLSX 9 kb)
470,772 for the 52 test samples was created, which ex- Additional file 4: Table S6. Relative abundance of bacterial families in
cluded the shams from the initial 513,720 sequences. These shams. Table S7. Relative abundance of bacterial families in experimental
samples. (XLSX 36 kb)
were taken through the processing and analyses pipeline as
Akorli et al. Parasites & Vectors           (2019) 12:27 Page 8 of 9
Abbreviations 8. Rani A, Sharma A, Rajagopal R, Adak T, Bhatnagar RK. Bacterial diversity
ANCOM: Analysis of Composition of Microbiomes; OTU: Operational analysis of larvae and adult midgut microflora using culture-dependent and
Taxonomic Unit culture-independent methods in lab-reared and field-collected Anopheles
stephensi - an Asian malarial vector. BMC Microbiol. 2009;9:96.
Acknowledgements 9. Romoli O, Gendrin M. The tripartite interactions between the mosquito, its
We thank the farmers who gave permission for the collection of mosquitoes microbiota and Plasmodium. Parasit Vectors. 2018;11:200.
and water from breeding sites in the field. We are also grateful to staff of 10. Lindh JM, Borg-Karlson A-K, Faye I. Transstadial and horizontal transfer of
Vestergaard NMIMR Vector Labs for their support in mosquito housing and bacteria within a colony of Anopheles gambiae (Diptera: Culicidae) and
breeding. oviposition response to bacteria-containing water. Acta Trop. 2008;107:242–50.
11. Coon KL, Vogel KJ, Brown MR, Strand MR. Mosquitoes rely on their gut
Funding microbiota for development. Mol Ecol. 2014;23:2727–39.
This work was supported by WACCBIP Postdoctoral Fellowship funds to JA 12. Wang Y, Gilbreath TM, Kukutla P, Yan G, Xu J. Dynamic gut microbiome
from a DELTAS Africa grant (DEL-15-007: Awandare). The DELTAS Africa across life history of the malaria mosquito Anopheles gambiae in Kenya.
Initiative is an independent funding scheme of the African Academy of PLoS One. 2011;6:e24767.
Sciences (AAS) Alliance for Accelerating Excellence in Science in Africa 13. Hörtnagl P, Pérez MT, Zeder M, Sommaruga R. The bacterial community
(AESA) and supported by the New Partnership for Africa’s Development composition of the surface microlayer in a high mountain lake. FEMS
Planning and Coordinating Agency (NEPAD Agency) with funding from the Microbiol Ecol. 2010;73:458–67.
Wellcome Trust (107755/Z/15/Z: Awandare) and the UK government. The 14. Pennington MJ, Prager SM, Walton WE, Trumble JT. Culex quinquefasciatus
views expressed in this publication are those of the author(s) and not larval microbiomes vary with instar and exposure to common wastewater
necessarily those of AAS, NEPAD Agency, Wellcome Trust or the UK contaminants. 2016;6:21969.
government. 15. Akorli J, Gendrin M, Pels NAP, Yeboah-Manu D, Christophides GK, Wilson MD.
Seasonality and locality affect the diversity of Anopheles gambiae and Anopheles
Availability of data and materials coluzziimidgut microbiota from Ghana. PLoS One. 2016;11:e0157529.
The datasets supporting the conclusions of this article are included within 16. Huang J, Wang L, Ren N, Ma F. Juli. Disinfection effect of chlorine dioxide
the article and its additional files. Raw sequence reads used in this study are on bacteria in water. Water Res. 1997;31:607–13.
available at Dryad Digital Repository under the accession doi:10.5061/ 17. Virto R, Mañas P, Alvarez I, Condon S, Raso J. Membrane damage and
dryad.98jj7gk microbial inactivation by chlorine in the absence and presence of a
chlorine-demanding substrate. Appl Environ Microbiol. 2005;71:5022–8.
Authors’ contributions 18. Stubbendieck RM, Vargas-Bautista C, Straight PD. Bacterial communities:
JA designed, conducted and coordinated experiments. PAN, GWA, RKE and interactions to scale. Front Microbiol. 2016;7:1234.
NAPP conducted experiments. JA analysed data and wrote the manuscript. 19. Hegde S, Khanipov K, Albayrak L, Golovko G, Pimenova M, Saldaña MA, et
All authors read and approved the final manuscript. al. Microbiome interaction networks and community structure from
laboratory-reared and field-collected Aedes aegypti, Aedes albopictus, and
Ethics approval and consent to participate Culex quinquefasciatus mosquito vectors. Front Microbiol. 2018;9:2160.
Not applicable. 20. Dong Y, Manfredini F, Dimopoulos G. Implication of the mosquito midgut
microbiota in the defense against malaria parasites. PLoS Pathog. 2009;5:e1000423.
Consent for publication 21. Ngo CT, Aujoulat F, Veas F, Jumas-Bilak E, Manguin S. Bacterial diversity
Not applicable. associated with wild caught Anopheles mosquitoes from Dak Nong Province,
Vietnam using culture and DNA fingerprint. PLoS One. 2015;10:e0118634.
Competing interests 22. Lindh JM, Terenius O, Faye I. 16S rRNA gene-based identification of midgut
The authors declare that they have no competing interests. bacteria from field-caught Anopheles gambiae sensu lato and A. funestus
mosquitoes reveals new species related to known insect symbionts. Appl
Publisher’s Note Environ Microbiol. 2005;71:7217–23.
Springer Nature remains neutral with regard to jurisdictional claims in 23. Briones AM, Shililu J, Githure J, Novak R, Raskin L. Thorsellia anophelis is the
published maps and institutional affiliations. dominant bacterium in a Kenyan population of adult Anopheles gambiae
mosquitoes. ISME J. 2008;2:74–82.
Received: 18 September 2018 Accepted: 3 January 2019 24. Duguma D, Rugman-Jones P, Kaufman MG, Hall MW, Neufeld JD,
Stouthamer R, et al. Bacterial communities associated with Culex mosquito
larvae and two emergent aquatic plants of bioremediation importance.
References PLoS One. 2013;8:e72522.
1. WHO. Global vector control response 2017–2030. Geneva: World Health 25. Segata N, Baldini F, Pompon J, Garrett WS, Truong DT, Dabiré RK, et al. The
Organization; 2017. p. 52. reproductive tracts of two malaria vectors are populated by a core
2. Dennison NJ, Jupatanakul N, Dimopoulos G. The mosquito microbiota influences microbiome and by gender- and swarm-enriched microbial biomarkers. Sci
vector competence for human pathogens. Curr Opin Insect Sci. 2014;3:6–13. Rep. 2016;6:24207.
3. Cirimotich CM, Dong Y, Clayton AM, Sandiford SL, Souza-Neto JA, Mulenga 26. Bokulich NA, Subramanian S, Faith JJ, Gevers D, Gordon JI, Knight R, et al.
M, et al. Natural microbe-mediated refractoriness to Plasmodium infection in Quality-filtering vastly improves diversity estimates from Illumina amplicon
Anopheles gambiae. Science. 2011;332:855–8. sequencing. Nat Commun. 2013;10:57–60.
4. Bahia AC, Dong Y, Blumberg BJ, Mlambo G, Tripathi A, Benmarzouk-Hidalgo 27. Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello
OJ, et al. Exploring Anopheles gut bacteria for Plasmodium blocking activity. EK, et al. QIIME allows analysis of high-throughput community sequencing
Environ Microbiol. 2014;16:2980–94. data. Nat Methods. 2010;7:335–6.
5. Ramirez JL, Short SM, Bahia AC, Saraiva RG, Dong Y, Kang S, et al. 28. Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA
Chromobacterium Csp_P reduces ralaria and dengue infection in vector ribosomal RNA gene database project: improved data processing and web-
mosquitoes and has entomopathogenic and in vitro anti-pathogen based tools. Nucleic Acids Res. 2013;41:D590–6.
activities. PLoS Pathog. 2014;10:e1004398. 29. Yilmaz P, Parfey LW, Yarza P, Gerken J, Pruesse E, Quast C, et al. The SILVA
6. Guégan M, Zouache K, Démichel C, Minard G, Tran Van V, Potier P, et al. and “All-species Living Tree Project (LTP)” taxonomic frameworks. Nucleic
The mosquito holobiont: fresh insight into mosquito-microbiota Acids Res. 2014;42:D643–8.
interactions. Microbiome. 2018;6:49. 30. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al.
7. Boissière A, Tchioffo MT, Bachar D, Abate L, Marie A, Nsango SE, et al. Scikit-learn: machine learning in python. J Mach Learn Res. 2011;12:2825–30.
Midgut microbiota of the malaria mosquito vector Anopheles gambiae and 31. Bokulich NA, Kaehler BD, Rideout JR, Dillon M, Bolyen E, Knight R, et al.
interactions with Plasmodium falciparum infection. PLoS Pathog. 2012;8: Optimizing taxonomic classification of marker-gene amplicon sequences
e1002742. with qiime 2’s q2-feature-classifier plugin. Microbiome. 2018;6:90.
Akorli et al. Parasites & Vectors           (2019) 12:27 Page 9 of 9
32. Jervis-Bardy J, Leong LE, Marri S, Smith RJ, Choo JM, Smith-Vaughan HC, et
al. Deriving accurate microbiota profiles from human samples with low
bacterial content through post-sequencing processing of Illumina MiSeq
data. Microbiome. 2015;3:19.
33. Mandal S, Van TW, White RA, Eggesbø M, Knight R, Peddada SD. Analysis of
composition of microbiomes: a novel method for studying microbial
composition. Microb Ecol Health Dis. 2015;26:27663.
34. Morton JT, Sanders J, Quinn RA, McDonald D, Gonzalez A, Vázquez-Baeza Y, et al.
Balance trees reveal microbial niche differentiation. mSystems. 2017;2:e00162–16.
35. Egozcue JJ, Pawlowsky-Glahn V, Mateu-Figueras G, Barceló-Vidal C. Isometric logratio
transformations for compositional data analysis. Math Geol. 2003;35:279–300.