The relationships between microbiome diversity and epidemiology in domestic species of malaria-mediated mosquitoes of Korea

Background

Malaria, which continues to cause major disease burden of human, is transmitted by the Anopheles mosquito[1]. Generally effective vaccines against malaria are not currently offered to the public. Subsequently, most intervention efforts focus on controlling mosquito populations, typically using chemical insecticides. However, widespread use of several pesticides has resulted in pesticide resistance in mosquito populations[2] which extends to employ alternative mosquito control strategies including mosquito-microbiome.

The microbiome is an ecosystem of commensal, symbiotic, and pathogenic bacteria that interact with a host[3]. Mosquitoes are act as natural hosts to a diverse range of microorganisms usually including bacteria, fungi, and viruses[4]. Among these, bacteria are continuously exposed to mosquitoes[5] and can influence nutrition, development, immune and behaviors of host mosquitoes[6, 7, 8]. For instance, infection with the bacterial endosymbiont Wolbachia pipientis prevents numerous arbovirus infections[9, 10]. Indeed, the introduction of wMel strain of Wolbachia pipientis into the Aedes aegypti population was effective in reducing the incidence of symptomatic dengue as well as the case of hospitalizations by dengue fever[11]. Also, Chromobacterium sp. exposure causes high mortality in larval and adult mosquitoes and reduces mosquitoes’ susceptibility to malaria and dengue infection[12]. These novel approach suggests that specific microbiome in mosquitoes can alter susceptibility to infection[13].

There is evidence that mosquitos have a core microbiome which is shared throughout populations of the same species[14, 15, 16]. For instance, Anopheles mosquitoes possess common bacterial genera such as Acinetobacter, Bacillus, Enterobacter, Staphylococcus, Pseudomonas, Chryseobacterium and Serratia[17, 18, 19, 20] and Aedes and Anopheles vectors share taxa including Pseudomonas, Asaia, Serratia and Enterobacter[21, 22]. Furthermore, microbiome investigations of Anopheles mosquitoes collected in the field reveal greater levels of inter-mosquito heterogeneity in community composition[23]. Previous studies have demonstrated the bacterial composition of mosquitoes collected from natural habitats is highly variable depending on the geographical origin and ecology[21, 24, 25, 26].

In this study, we focus on the regional difference of microbiome profiles in Anopheles sinensis mosquitoes, which is a major vector of Plasmodium vivax malaria in Republic of Korea. Republic of Korea was officially certified as a malaria-free country by the World Health Organization (WHO) in 1979[27], but Plasmodium vivax malaria re-emerged in 1993 in a soldier[28]. Indeed, malaria mainly occurs in border regions with North Korea where civilian access is restricted, such as Incheon, northern Gyeonggi, and Gangwon-do, where malaria patients were reported 300 to 500 cases every year. The sporadic malaria cases in this area may results from the most well-preserved natural habitats for wildlife and may be caused by long range migration from malaria hyperendemic area from the northern region. Therefore, it's worthwhile to investigate patterns of microbiome profiles in host mosquitoes, as this might be a key to figuring out where malaria vector insects come from.

In light of these observations, we identify the microbial diversity of Anopheles sinensis female from endemic malaria transmission region and non-endemic areas. By using metagenomics analysis, this study delves into determining whether this Anopheline mosquito in malaria endemic areas possesses distinct microbial profiles, which could be reliably linked to their habitats. Therefore, Investigating the microbiome profiles of female Anopheles sinensis might be a potential way to monitor malaria cases and develop effective vector control strategies[5, 29, 30].

Methods

Sample collection and Identification

Female adult Anopheles sinensis female mosquitoes were collected in 2020 (June 22 to 26) from 12 rural area in Republic of Korea. The collection sites were categorized as hyperendemic if the number of cases exceeds one patient per 100,000 persons. Otherwise, it was categorized as hypoendemic areas (Fig. 1). Mosquitoes were sorted to genus using the morphological keys [31], and species identifications were confirmed using a diagnostic PCR assay based on DNA barcode analysis [32]. Mosquito body parts such as legs and wings were transferred to 1.5 ml tube (Axygen), after which DNA extraction was carried from mosquito parts using G-spin™ Total DNA Extraction kit (iNtRON’s, Korea) according to the manufacturer’s instructions. PCR cycle parameters involved an initial denaturation at 95 °C for three min, 35 cycles of 30 sec at 95 °C, 30 sec at 63 °C, and two min at 72 °C. A final extension at 72 °C for 10 min was completed. PCR products were subjected to electrophoresis on a 1.5% agarose gel and visualized under ultraviolet light.

DNA extraction

DNA extraction protocols were used for midgut and salivary glands from adult female mosquitoes. Prior to dissection, mosquitoes were surface sterilized using 70% ethanol for one min followed by dissection in PBS. Throughout the dissection, the dissecting stereomicroscope working area was also kept sterilized by using 70% ethanol. Pooled midguts and salivary glands (24 tubes of five each) were collected, and stored at -80 °C. Under aseptic conditions, whole genome DNA was extracted using the Power soil kit (Qiagen), according to manufacturer’s instructions. Following DNA quality and quantity examination, samples were used for 16S metagenomics analysis. Two amplicon-based 16S rRNA tagged libraries were generated for each organ.

Sample preparation, 16S Sequencing and Taxonomic Analysis

For all samples, National Instrumentation Center for Environmental Management (NICEM), Republic of Korea, performed commercial PCR amplification, sample processing, 16s rRNA gene sequencing, and taxonomic analysis (www.nicem.snu.ac.kr). The samples were amplified using the 2x KAPA HiFi HotStart ReadyMix and primers for the V3-V4 region of the 16S rRNA gene (Additional file 1: Table S1). The following were the PCR conditions: three minutes at 96°C, then 30 cycles of 30 seconds at 96°C, 30 seconds at 55°C, 30 seconds at 72°C, and finally five minutes at 72°C. All of the samples' PCR results were then performed on 1.2% agarose gels to determine band size and intensity. Ampure XP beads were used to purify amplified DNA from each sample. According to the content of DNA and molecular weight, samples were pooled in identical quantities and utilized to create Illumina DNA libraries. The libraries were then sequenced in three separate Illumina MiSeq runs to obtain 2\(\times\)300bp paired end reads. The chunlab analytical pipeline PKSSU 4.0 DB was used to process all of the data collected by the sequencing. Raw sequences were assembled after barcode sequences were deleted. Short assemblies, as well as sequences with ambiguous base calls, were excluded from the collection. By grouping sequences at a 3% divergence (or 97% similarity) level, sequences were denoised and allocated to an operational taxonomic unit (OTU). Chimeric sequences that could not be definitively linked to an OTU were eliminated. OTUs that could not be taxonomically categorized were labeled "unclassified" and were excluded from further analysis.

Bacterial profiles

At the phylum, genus, and species levels, read count and abundance data for bacterial OTUs were evaluated. Low abundance taxa with a value of less than 0.05% were eliminated from the dataset after the highest percentage abundance for each bacterial OTU was computed across all samples. The abundance values were then recalculated using the corrected read count data, but without the species that were missing. All remaining taxa that appeared in lower abundance were grouped into a "<1%" category.

Statistical Analysis

The statistical analyses described above were performed using R version 4.1.0 in RStudio Version 1.4.1106 [33] (session info in supplementary details), EZbiocloud [34] and Microsoft Excel [35].

Results

Microbiome profiles of Anopheles sinensis female in Korea

We collected 60 emerging adult females of Anopheles sinensis from 12 different areas across Republic of Korea. A total of 1936902 sequences were generated from 24 samples. The average number of raw sequence reads was 80704 (between 29,566 and 97,799 reads). We then assessed metrics of alpha diversity (Additional file 1: Table S2). The microbiome profiles of mosquitoes were prepared at the phylum and genus level for each region and organ (Fig. 2). A total of 31 bacterial phyla was found among all samples. The most dominant one was Proteobacteria (74.55%), followed by Firmicutes (14.68%), Actinobacteria (5.65%) and Bacteroidetes (2.57%). These four phyla accounted for 96–99% of the total OTU in the majority of the samples. However, the hyperendemic 8 midgut and hyperendemic 1 midgut and salivary glands showed different patterns. Spirochaetes (31.12%) were found in midgut of hyperendemic 8 area, and Firmicutes (88.80%, 42.67%) were found in midgut and salivary glands in the hyperendemic area 1.

We also assessed genus level data to identify bacterial OTUs present in all mosquito samples. The data showed 51 bacterial genera (Fig. 2), of which the five most abundant bacterial genera (average abundance) were Pseudomonas (29.36%), Staphylococcus (10.62%), Erwinia (9.42%), Serratia (7.02%) and Acinetobacter (6.86%). At the species level, Pseudomonas synxantha (24.68%) was dominant followed by Serratia ficaria (6.58%), Acinetobacter soli (6.16%), Arcobacter butzleri (4.89%), Staphylococcus aureus (4.59%), Pantoea agglomerans (3.72%) and Erwinia persicina (3.4%).

Table 2 depicts a taxonomic biomarker, which is a taxon that can be discovered exclusively in one region. The hyperendemic areas 1,2,3,5,6,7 showed significant values. The region-specific microbiome was dominating in most samples, such as Serratia ficaria, Staphylococcus sciuri, Erwinia persicina, Staphylococcus aureus, Arcobacter butzleri, and Erwinia iniecta.

Organ specific differences in mosquito microbiome composition

The salivary gland microbiome profile consisted of Pseudomonas (29.32%), Acinetobacter (12.48), Staphylococcus (12.11%), Arcobacter (9.56%) and Serratia (5.37%). Similarly, midgut microbiome profile consisted of Pseudomonas (29.35%), Erwinia (14.85%), Staphylococcus (9.03%), Serratia (8.65%), and Pantoea (6.02%). We evaluated the microbiome profiles of the midgut and salivary glands based on whether or not microbiomes were present (Fig. 3). In both organs, 21 genera which commonly found, were found to be prevalent, with the midgut microbiome having more organ-specific taxa (20 genera) than the salivary glands (10 genera, Table 1). Although organ-specific microbiomes such as Arcobacter, Brevibacterium, Chryseobacterium, Asaia, and Enterobacteriaceae were identified, only Acinetobacter had a statistically significant difference (Wilcoxon rank-sum test, P = 0.043). Salivary glands and midgut microbiota profile had no significant difference in beta diversity analysis (PERMANOVA, p = 0.877, No. of permutations = 999).

Table 1

Taxonomic biomarker
Sample	Taxon name	p-value	p-value (FDR)	Taxonomic relative abundance
Hyperendemic area 1	Serratia ficaria	0.03359	0.42652	68.4779
Hyperendemic area 1	Gibbsiella quercinecans	0.02578	0.33312	11.9534
Hyperendemic area 2	Staphylococcus sciuri	0.04194	0.79094	19.5395
Hyperendemic area 3	Erwinia persicina group	0.02578	0.76076	40.8605
Hyperendemic area 3	Enterobacteriaceae	0.03359	0.79616	18.9266
Hyperendemic area 5	Staphylococcus aureus	0.0342	0.50815	50.0184
	Brevibacterium avium	0.01262	0.3396	23.2605
	Staphylococcus equorum	0.03682	0.53963	14.7103
Hyperendemic area 6	Arcobacter butzleri	0.03074	0.44759	49.2067
Hyperendemic area 6	AF166259_s	0.01727	0.33253	16.3773
Hyperendemic area 7	Erwinia iniecta	0.02578	0.58038	33.8485
Hyperendemic area 7	Erwinia_uc	0.02119	0.49879	15.3244

Epidemiological differences in mosquito microbiome composition

We also compared the data based on patient incidence which divided hyperendemic and hypoendemic areas. Both areas shared two microbiomes namely Pseudomonas and Acinetobacter. Figure 4 shows that hyperendemic areas mosquitoes has more unique taxa (12) which dominated by Staphylococcus (15.59%), Erwinia (14.10%), Pseudomonas (10.65%), Serratia (10.28%), Acinetobacter (9.61%). In hypoendemic areas, however, Pseudomonas (66.71%) dominates the microbiome profile, followed by Anaerobacillus and Cutibacterium. These microbiome profile have significant differences on Pseudomonas and Pantoea (Fig. 5). Pantoea was only observed in hyperendemic areas, with a larger percentage in the midgut. Pseudomonas, on the other hand, was found at a low proportion in the hyperendemic area but was dominating in the hypoendemic area as mentioned above.

Table 2

Organ specific microbiome
Salivary glands unique	Mid gut unique	Common
Atopostipes	Anoxybacillus	Acinetobacter
Burkholderia	Aquihabitans	AF166259_g
Chishuiella	Bartonella	Anaerobacillus
Chryseobacterium	Cloacibacterium	Arcobacter
Clavibacter	Clostridium	Asaia
Enterococcus	Dietzia	Brevibacterium
Flavobacteriaceae_uc	EF559154_g	Corynebacterium
Novosphingobium	Enterobacter	Cutibacterium
Paralkalibacillus	Escherichia	Enterobacteriaceae_g
Rhizobium	EU517557_g	Enterobacteriaceae_uc
	Flavobacterium	Erwinia
	Ornithinicoccus	Gibbsiella
	Ornithinimicrobium	Lawsonella
	Paraburkholderia	Lonsdalea
	Paracoccus	Methylobacterium
	Phycicoccus	Orbus
	Planococcus	Pantoea
	Propioniciclava	Pseudomonas
	Rickettsia	Serratia
	Romboutsia	Sphingomonas
		Staphylococcus

At the species level, the microbiome profile of the hyperendemic area mosquito consist of Serratia ficaria (9.73%), Acinetobacter soli (9.25%), Arcobacter butzleri (7.34%), Pseudomonas synxantha (7.30%), and Staphylococcus aureus (6.59%). The microbiome profile of the hypoendemic area mosquito consist of Pseudomonas synxantha (59.43%) and Pseudomonas fulva (6.45%). From principal coordinate analysis (PCoA) using Bray-Curtis distance, we found that hyperendemic area’s points were scattered, but hypoendemic points were rather clustered (Fig. 6). In particular, the microbiome profile of hyperendemic area 1 (Cheorwon) varies from that of other samples. PERMANOVA analysis of hyperendemic and hypoendemic area found to be significant (p = 0.001, No. of permutations = 999).

Discussion

The microbiome, which is becoming increasingly important, has been discovered that closely related to the host and influences many aspects including biology, longevity, behavior, nutrition, and immunity[22, 36, 37]. The relationship of the microbiome with mosquito which transmit diseases such as dengue fever, malaria and Zika virus is continuously reported[38]. Rani et al. (2009) demonstrated that the bacterial diversity of midgut microbiota in lab-reared Anopheles stephensi was less than in field-caught ones, both for males and females[17]. Because of mosquitoes live all over the world and adapt to various environments and conditions, it is intriguing to observe microbiome dynamics in mosquitoes caught in the wild. This study hypothesized that the microbiome of mosquitoes would vary regionally and the microbiome profile of malaria hyperendemic area would be significantly different. With a focus on microbiome related to vector competence, we discribed the composition and regional variations of the microbiome profile of Anopheles sinensis.

I. Relationships between microbiome diversity and epidemiology

This study provide evidence that microbiome composition is strongly influenced by mosquito habitat. Mosquitoes in hyperendemic areas have region-specific microbiota such as Serratia ficaria, Staphylococcus sciuri, Erwinia persicina, Enterobacter kobei, Staphylococcus aureus and Arcobacter butzleri. Particularly, Among hyperendemic areas, the Cheorwon (hyperendemic area 1) which near the border has a distinct microbiome composition which consist of Staphylococcus aureus, Staphylococcus equorum, Brevibacterium avium. Mosquitoes were collected from the wild which is expected that the age and diet of the mosquitoes may contribute to the differences between our results and those of other studies. Therefore, continuous spatial-temporal microbiome studies seem to be very important to identify a regional microbiome, which will help to trace the migration path of mosquitoes which can travel great distances[39]. Our study may offer the possibility that the investigation of the mosquito microbiome can serve as a scientific basis for mosquito migration.

Moreover, Pseudomonas, which is genus of Gammaproteobacteria, is commonly found in the gut microbiome of Anopheles mosquitoes[17, 18, 40, 41], and it appears to be the dominating bacterium in our research as well. Pseudomonas synxantha was identified as the most common Pseudomonas species observed in this investigation, and it was reported to inhibit the growth of other bacteria in the medium[42]. Our results show that the microbiome composition is influenced by Pseudomonas synxantha. In the sample from the hypoendemic area, which Pseudomonas synxantha prevalent, the proportion of "<1%" was 18.28%, but the proportion of "<1%" in the hyperendemic area was 5.21%. This suggests that Pseudomonas synxantha has the potential to influence the growth of other microbiota in mosquito vectors. Furthermore, because Pseudomonas has the potential for paratransgenesis[35], it may affect the mosquito microbiome locally.

II. Microbiome can impact to mosquito host-seeking behaviors

The olfactory system of insects is directly related to survival[43]. Because mosquitoes must feed the blood of vertebrates to reproduce offspring, the role of the olfactory systems is important to find host[44, 45, 46]. The microbiome in the internal organs of mosquitoes may act as modulators of host-finding behaviors and thus affect vector competence[47].

When we compared our findings to those of other mosquito microbiomes, we discovered that our samples included numerous common mosquito-associated microbiomes. These include Acinetobacter, Bacillus, Enterobacter, Staphylococcus, Pseudomonas, Chryseobacterium and Serratia[17, 18, 19, 20]. However, just a few samples included the crucial Serratia and Asaia taxa, which are broadly dispersed in the mosquito microbiome. Serratia species have been linked to mosquito vector capability. Recent study revealed that Serratia exposure to Ae. aegypti disrupted their feeding behaviors[48] suggesting that it is possible that its absence in Anopheles sinensis could be impact on disease transmission in the field. Another key mosquito-associated microbiome including Elizabethkingia and Klebsiella[18, 49] were entirely absent. It is not currently clear why these taxa were not shown in this study. A difference between our data and other published studies indicate that microbiome profiles are highly distinct in most areas due to unique landscape patterns and isolation of some areas by spatial characteristics unique to Korean peninsula as well as the possibility of disconnection of high malaria zone from the North Korea from where major malaria vectors may migrate during the specific seasons, which may reflect distinct mosquito-microbial associations. Large longitudinal studies involving additional spatial-temporal studies may reveal whether bacterial taxa such as Klebsiella and Elizabethkingia are underrepresented in mosquitoes in Republic of Korea. Evidence from animals shows that bacterial modulation of neurotransmitters may have an influence on host physiology[47]. The different microbiome patterns in the two experimental groups most likely affected the mosquito's olfactory systems and behaviors. We found it necessary to investigate whether the proportion of Pseudomonas could have a role in alterations in olfactory ability in mosquitoes. Because mosquitoes use their sense of smell by olfactory receptors to locate their hosts using volatile compounds, the study of olfactory receptor expression levels after microbial infection will greatly contribute to understanding mosquito-microbial interactions.

III. Interactions between microbial communities

Bacterial interactions have a crucial role in regulating ecosystem features and species abundance. These interactions span the mutualism-parasitism spectrum and affect to microbiome communities. Many lines of research related to microbiome-microbiome interactions focus on the positive and negative interactions between bacteria inside mosquitoes. For instance, Serratia marcescens could inhibit the population growth of Sphingomonas and members of the family Burkholderiaceae[50]. However, bacterial interaction also seems to be synergistic interaction in a case that Asaia-Acinetobacter double infections were mostly observed as compared to the bacteria present independently[51]. Our data represent that the patterns of two microbiome-microbiome interaction. Anaerobacillus mostly co-exist with Pseudomonas, and Pseudomonas seems to be competed with Pantoea, Erwinia, Staphylococcus. As described, Pseudomonas synxantha was the most prevalent Pseudomonas species identified in our samples, and it appeared to inhibit the growth of other bacteria. In particular, Pantoea ,which is candidate species for malaria paratransgenesis[52], appears to compete strongly with Pseudomonas. These findings show that microbiota have complicated microbial interactions that may influence vector competence.

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