Microbiota Anatomical Niche Partitioning of Simulium vanluni (Diptera: Simuliidae)

Abstract

Background: Microbial communities of insects have distinct roles for their respective hosts. For the black fly (Diptera: Simuliidae), an important vector and ecological indicator, the representative microbiota from the different body regions are not known. Here, we investigated the microbial composition and diversity of the head, thorax, and abdomen of wild-caught Simulium vanluni. Methods: Adult Simulium vanluni were surface-sterilized and dissected into head, thorax, and abdomen. For each body region, 20 individuals were pooled into one sample with six replicates per region. DNA was extracted and sequenced using the 16S rRNA amplification method to assess for possible microbial diversity. Data were analyzed using MicrobiomeAnalyst, where we calculated alpha diversity, beta diversity, and tested compositional differences using PERMANOVA. Results: Across 17 pooled samples, three core genera, Wolbachia (78.33%), Rickettsia (9.74%), and Acinetobacter (9.20%), accounted for more than 97% of the 16S rRNA sequencing reads. Head communities were compositionally distinct compared to the thorax and abdomen (PERMANOVA, p < 0.05). Heads were nearly monodominated by Wolbachia (95–97%), exhibiting significantly lower diversity and evenness compared to other body regions. In contrast, the thoracic and abdominal communities were more even, where thoraces were enriched with Acinetobacter (19.16%) relative to Rickettsia (10.85%), while abdomens harbored higher Rickettsia (10.96%) than Acinetobacter (5.68%). Collectively, the near-monodominance of Wolbachia in heads and inverse abundances of Acinetobacter and Rickettsia in thoraces and abdomens suggest possible anatomical niche partitioning or competition exclusion of microbiota across body regions. Conclusions: Our findings reveal fine-scale anatomical niche partitioning in S. vanluni microbiota, with the heads being almost exclusively colonized by Wolbachia, while the thoracic and abdominal niche regions exhibit distinct enrichment patterns for Acinetobacter and Rickettsia. These spatially distinct microbial distributions suggest potential functional specialization across anatomical regions of S. vanluni.

1. Introduction

Insects are among the most diverse groups of living organisms on Earth, with species richness estimated at more than 20 million [1]. Although insects are widespread and ecologically important, we still know very little about their relationship with microbes, even though microbes can make up to 10% of an insect’s biomass [2]. Microbes are ubiquitous and can interact with insects as hosts in commensal, mutualistic, parasitic, or incidental relationships. These symbionts can provide direct or indirect nutritional support, boost immunity, influence development, and even manipulate host reproduction [3,4,5,6]. Moreover, symbiont interactions can be shaped not only by host-driven microevolution but also by co-evolution, where both parties evolve in response to each other’s adaptation, resulting in a unique dynamic relationship. In recent years, studies have shown that insect microbiomes vary not only across developmental stages but also among organs and different body parts, specifically in agriculturally and medically important species [7,8,9]. Much of this work, however, has focused on gut microbiota, given the gut’s central role in digestion and physiology [6,10,11], and salivary glands, which mediate pathogen transmission in vectors such as mosquitoes and ticks [12,13]. Over time, there has been increasing recognition that different body parts harbor distinct microbial communities that could contribute to the overall microbial profile of the insect. Studying these region-specific microbiomes is therefore key to understanding host–microbe interaction, potentially revealing new mechanisms of vector adaptation, immunity, or ecological fitness.

Black flies (Simulium spp.) are particularly important due to their hematophagous nature and role as vectors of pathogens infecting farm animals [14,15] and humans [15,16,17,18]. They are known as vectors for the transmission of Onchocerca volvulus, the causative agent of onchocerciasis, or river blindness, mainly reported in Africa [18,19], with additional reports in Latin America [20,21] and regions of the Middle East [22]. Black flies are known to breed exclusively in clean running water; hence, they serve as an indicator of water quality. Despite more than 2400 black fly species being documented [15], the microbial communities associated with only a few studies explore the microbiome in laboratory-reared adults emerging from the wild-caught pupae [23], identifying culturable bacteria communities in one of the most common species in Malaysia [24] and wild-caught adults in Cameroon [25,26].

More than 90 species of black flies have been documented in Malaysia [15,27], but none are known as vectors for human diseases. Although in general, most black fly species are hematophagous, only one species, Simulium asakoae, has been confirmed to bite humans in Malaysia [28]. Despite the lack of reported disease transmission to date, understanding microbial diversity in local black fly species is crucial for elucidating their ecological roles and potential influence on vector fitness and capacities for pathogen transmission. Among the documented species, Simulium vanluni, a unique cryptic black fly species found exclusively in Peninsular Malaysia, diverged from Simulium nobile [29], was selected due to its high prevalence in terrestrial ecosystems and its potential ecological significance [29,30,31]. S. vanluni inhabits lowland forests, with immature stages developing in medium to fast, clean, flowing streams [29,32,33], while the adults forage at temperatures ranging from 28 °C to 32 °C and under normal humidity conditions [30]. Since microbial diversity in insects is not homogenous across different body parts, understanding the key differences could provide invaluable insight into the specific interactions between microbial communities with their host at the different regions of the body and the surrounding environment. Therefore, in the current study, we aim to reveal at finer anatomical scales the microbiota of S. vanluni and establish the foundation for understanding symbiont roles in black fly ecology.

2. Methods

2.1. Sampling and Species Identification

Wild black fly adults were trapped at Sungkai Wildlife Conservation Centre, Sungkai, Perak (Figure 1), an animal sanctuary managed by the Department of Wildlife and National Parks (PERHILITAN), Peninsular Malaysia. The trappings were carried out using four malaise traps (width, 110 cm; length, 180 cm; height, 150 cm) that were deployed for 24 h at different animal enclosures. All collected samples were sorted and preserved in 80% ethanol and transported to the Tropical Infectious Diseases Research and Education Centre (TIDREC), Universiti Malaya. All flies were identified morphologically using identification keys applicable to the species in Malaysia [27,34] and confirmed using DNA barcoding based on the COI gene as previously described [35,36]. At the study site, two black fly species were captured: Simulium roslihashimi and Simulium vanluni. Only S. vanluni, which was the more abundant, was chosen for microbial analysis to ensure adequate replication.

2.2. Sample Preparation and DNA Extraction

Prior to the dissection, all adult black flies underwent surface sterilization (1% bleach, 30 s; 95% ethanol, 30 s; 70% ethanol, 30 s; nuclease-free water, 3 min) as previously described [37]. Subsequently, dissections were performed to separate each fly into three anatomical regions—head, thorax, and abdomen. For each anatomical region, tissues from 20 adults were combined into one pooled sample. The pooling process was repeated six times for each body part, yielding 18 pooled samples in total. DNA extraction was performed using a QIAamp DNA micro kit (Qiagen, Hilden, Germany) following the manufacturer’s protocol. DNA concentration was measured using an Invitrogen Qubit 4 Fluorometer (Thermo Fisher Scientific, Waltham, MA, USA). The extracted DNA samples were stored at −80 °C for further analysis.

2.3. 16S rRNA Amplification and Sequencing

Two primer sets targeting V2-4-8 and V3-6, 7-9 were used to amplify seven hypervariable regions of the 16S rRNA gene in separate reactions with the Ion 16S™ Metagenomics Kit (Thermo Fisher Scientific, Carlsbad, CA, USA) following the manufacturer’s protocol. A negative extraction control (buffer only) was included to monitor for potential contamination. Amplicons from both reactions were pooled in equal volumes and then purified using Agencourt AMPure XP beads (Beckman Coulter Inc., Brea, CA, USA). The concentration of amplicons was quantified and then converted into sequencing libraries using the Ion Plus Fragment Library Kit (Thermo Fisher Scientific, Carlsbad, CA, USA). Library preparation steps, including end repair, adaptor and barcode ligation, nick repair, and library amplification, were performed according to the manufacturer’s protocol. The final libraries were purified and templated using the Ion 520™ and Ion 530™ Kit-Chef (Thermo Fisher Scientific, Carlsbad, CA, USA) on the Ion Chef instrument (Thermo Fisher Scientific, Carlsbad, CA, USA), followed by loading onto the Ion 530 Chip (Thermo Fisher Scientific, Carlsbad, CA, USA). Sequencing was performed using the Ion GeneStudio S5 platform (Thermo Fisher Scientific, Carlsbad, CA, USA).

2.4. Bioinformatics and Data Analysis

Base-calling and demultiplexing were performed by Torrent Suite version 5.18.1 (Thermo Fisher Scientific, Carlsbad, CA, USA). Subsequently, the obtained sequence reads were analyzed using the 16S rRNA profiling workflow as implemented in the Ion Reporter^TM software ver. 5.20.2.0. During the analysis, primer sequences were trimmed from low-quality reads, and reads shorter than 150 bp were removed. Only unique sequences with ≥10 copies were mapped to the curated MicroSEQ^® 16S Library v.2013.1 and Greengenes v13.5 references in the Ion Torrent Reporter^TM software for taxonomic assignment following the Clinical and Laboratory Standards Institute (CLSI) guidelines, where genus-level identification was accepted at >97%. The resulting ASV table and metadata file were imported into MicrobiomeAnalyst 2.0 [38,39,40]. Data filtering was performed using the default setting, which included a low-count filter (prevalence <10% across samples) and a low-variance filter based on the inter-quantile range. Data were then normalized using the trimmed mean of M-values (TMM). The alpha diversity indices (Chao1, Shannon, and Simpson) were calculated, and relative taxonomic abundance was generated. The processed dataset was subsequently used for beta diversity, which was estimated using Bray–Curtis distance and visualized by Principal Coordinates Analysis (PCoA), followed by Permutational Multivariate Analysis of Dispersion (PERMDISP) to assess dispersion and permutational multivariate analysis of variance (PERMANOVA) for compositional differences. Shared and unique genera across different anatomical regions were visualized using InteractiVenn [41]. The differentially abundant genera were identified by Linear Discriminant Analysis (LDA) Effect Size (LEfSe) with LDA threshold > 2 [42].

3. Results

3.1. 16S rRNA Gene Sequencing Results

A total of 18 samples, representing six replicates each for the head, thorax, and abdomen of S. vanluni, alongside negative extraction controls, were sequenced. After data processing, the negative control yielded only 155 reads, exhibited a distinct, low diversity profile, and was excluded from downstream analysis. After quality filtering, 4,754,021 clean reads remained across 17 body parts (252,563 and 317,751 reads per sample;

Table 1. Summary of sequencing reads for 17 S. vanluni anatomical region libraries.

Samples	Body Parts	Total Number of Reads	Number of Clean Reads
P1H1	Head	369,191	276,441
P1T1	Thorax	418,936	308,485
P1A1	Abdomen	435,034	317,751
P2T2	Thorax	382,248	271,357
P2A2	Abdomen	343,296	245,202
P3H3	Head	365,979	268,458
P3T3	Thorax	412,073	289,589
P3A3	Abdomen	443,328	303,872
P4H4	Head	408,299	295,727
P4T4	Thorax	398,968	279,199
P4A4	Abdomen	394,098	283,428
P5H5	Head	383,360	280,551
P5T5	Thorax	363,642	252,563
P5A5	Abdomen	372,017	264,475
P6H6	Head	367,587	263,108
P6T6	Thorax	398,850	266,679
P6A6	Abdomen	399,922	287,136

), as one head replicate (P2H2) yielded a low number of reads and was excluded from further analysis.

3.2. Taxonomic Composition by Anatomical Regions

The bacterial communities across all samples were predominantly composed of Proteobacteria, which accounted for 99.57% of the reads (Figure 2A). Three genera, Wolbachia (78.33%), Rickettsia (9.74%), and Acinetobacter (9.20%), dominated across all samples (Figure 2B).

In the region of the S. vanluni heads, Wolbachia accounted for 91.19% of the sequencing reads; Rickettsia and Acinetobacter comprised 7.05% and 1.35%, respectively (Figure 3). In the thoraces, Wolbachia accounted for 68.37%, followed by Acinetobacter (19.16%), then Rickettsia (10.85%). In abdomens, Wolbachia comprised 77.42%, followed by Rickettsia (10.96%), Acinetobacter (5.68%), Enterobacter (2.51%), and Citrobacter (1.13%). One abdomen replicate (A3), however, diverged markedly with Wolbachia composition and fell below 50% of the total reads, followed by Enterobacter (21.55%), Citrobacter (9.68%), Rickettsia (7.61%), and Trabulsiella (6.65%), indicating occasional enrichment of non-core taxa (Figure 2).

3.3. Alpha Diversity

The Chao 1 (richness) did not show significant differences in microbial diversity between heads, thoraces, and abdomens (Figure 4A), suggesting similar taxonomic richness across different anatomical regions. In contrast, the Shannon index (diversity) showed significant differences (Kruskal–Wallis, p = 0.0121, Figure 4B) across different anatomical regions. Pairwise comparisons revealed that the Shannon index was significantly lower in the head compared to the thorax and abdomen, while no significant difference was observed between the thorax and abdomen. The Simpson index (evenness) varied among different anatomical regions, where the heads showed reduced evenness relative to thoraces and abdomens, and this trend approached statistical significance (Kruskal–Wallis, p = 0.0537, Figure 4C). Collectively, the results suggested that the head of S. vanluni harbors a much less microbial diversity, whereas the thorax and abdomen show more diverse and evenly distributed microbial populations.

3.4. Beta Diversity

In the Bray–Curtis distance-based PCoA visualization, PCoA1 (Axis 1) accounted for 50.5% of the total variance, and PCoA2 (Axis 2) accounted for 30.8%, together explaining 81.3% of the total variation in the bacterial community structure in S. vanluni (Figure 5). The PCoA plot revealed a clear separation of head samples from thorax and abdomen samples. PERMANOVA demonstrated significant differences in microbial community composition across anatomical regions (R² = 0.2644; p < 0.05). Pairwise PERMANOVA comparison revealed head–thorax (R² = 0.3066; p < 0.05) and head–abdomen (R² = 0.2097; p < 0.05) differences, while there were no significant differences between thorax and abdomen (R² = 0.1344; p > 0.05). PERMDISP showed no significant differences in dispersion among groups (p = 0.3050). Collectively, these results suggest that the observed differences were due to actual variation in microbial community composition rather than differences in within-group dispersion.

3.5. Core and Unique Microbiome Across Different Body Parts

Analysis of the microbial community composition revealed that 19 genera were common across all anatomical regions (head, thorax, and abdomen), suggesting they established stable associations throughout the adult black flies (Figure 6). These stable core genera include Wolbachia, Enterobacter, Enterococcus, Bacillus, Staphylococcus, Pseudomonas, and Rickettsia. However, 7 genera, including Corynebacterium and Streptococcus, were found exclusively in both the head and thorax but were absent from the abdomen. A total of 17 genera, including Proteus, were found in both the thorax and abdomen but not in the head. Despite the head and abdomen being anatomically distant, 2 shared genera were found (Micrococcus and Phaseolibacter). In addition to that, each region harbored unique genera, where the head contained 31 unique genera, including Bartonella and Actinomyces. The thorax harbored 8 unique genera, while the abdomen had 11 unique genera.

3.6. Differential Abundance Analysis

LEfSe was used to identify genera whose relative abundances differed significantly across the anatomical regions (LDA > 2). At the more stringent threshold (p = 0.05), Acinetobacter was significantly enriched in thoraces. No other genus reached LDA > 2 at p < 0.05 in heads or abdomens. At a more relaxed threshold (p < 0.10), two genera, Acinetobacter in the thorax and Wolbachia in the abdomen, were obtained.

4. Discussion

Overall, our study characterized and compared the microbiota in three anatomical regions (head, thorax, abdomen) of wild-caught adult S. vanluni using 16S rRNA amplicon sequencing. Our region-specific microbiota profiling of S. vanluni revealed the presence of a core set of bacterial taxa shared across all three anatomical regions, alongside distinct and region-specific microbial taxa that are unique to each anatomical region. Similar stable-core and fluctuating community patterns have been observed in the gut microbiome survey of Simulium damnosum [43], but our findings further demonstrated that these patterns manifest distinctively across different anatomical regions (head, thorax, abdomen). Generally, most of the microbes recorded in this study belonged to the phylum Proteobacteria, while the remaining microbes, which constituted less than 1%, were from other phyla. Most blood-feeding insects have restricted feeding behaviors, relying on blood-hosts and nectar as energy sources, and thus depend on specific microbes, such as a proteobacterial symbiont, to supply essential nutrients [44]. This may affect the bacterial composition in hematophagous insects compared to others, leading to selective microbial associations [6], nutritional specialization [45], and even the vertical transmission of microbes [46].

The head region exhibited low diversity, with over 90% of the reads being Wolbachia, compared to the more heterogeneous bacterial communities in the thorax and abdomen. Although high Wolbachia prevalence has been documented in the gut microbiota study of other black fly species [25,43], the present study is the first to report head-specific monodominance. Beta diversity (Bray–Curtis PCoA) and PERMANOVA concurred that the head bacterial communities are compositionally distinct from both the thorax and abdomen. Although most samples clustered closely, one thorax and two abdomen samples appeared to be distinct from their respective cluster. These outliers may reflect biological variation among wild-caught black flies, which could have acquired microbes from different environments or food sources. However, we cannot rule out that the small number of outliers may have contributed to the apparent beta-diversity differences. To further confirm these patterns, future work should include a larger sample size and biological replicates to better capture the microbial structure in different anatomical regions. Despite similar richness across all three anatomical regions, the extreme unevenness of the microbiota (near-monodominance of Wolbachia) in the S. vanluni heads implies a highly specialized niche rather than a general lack of microbial colonizers. Nevertheless, low evenness and diversity can also be a signal of ecological disturbance [47]. In our wild-caught flies, however, there is no evidence of recent pesticide or sudden diet shifts. In fact, the overwhelmingly Proteobacteria-dominated community that is anchored by vertically transmitted Wolbachia suggests a stable and host-driven microbiota. Repeated feeding cycles and insect immune-mediated clearance likely impose recurrent bottlenecks that eliminate transient taxa and favor a less diverse and endosymbiont-rich microbiome [6]. Further studies comparing flies from natural and human-impacted sites will help distinguish the true disturbance from long-term adaptive symbiosis.

Anatomically, the black fly’s head primarily specializes in sensory perception and feeding. The antennae serve as an essential organ for mechanosensation and thermal detection, and they also mediate chemosensation and olfaction, which are crucial in detecting the blood-host cues, such as carbon dioxide [48,49,50,51]. Meanwhile, the mouthparts play an important role in feeding, mainly in liquid form for the adult stage. During the feeding process, direct contact with various food sources and the surrounding environment may explain the presence of transient, low-abundance bacteria detected in our study. Similar observations were also observed in other insects, such as mosquitoes, lice, and ants, where the higher species richness in the head was associated with the interaction between the mouthparts and food sources [52,53,54]. Although feeding exposes the heads to transient microbes, including bacteria, antimicrobial secretions from the salivary glands may create selective microhabitats that are permissive to support the existence of selected dominant taxa [55], and any newly incoming bacteria into the head regions are either cleared by these defenses or outcompeted by Wolbachia [56,57,58]. Wolbachia is one of the most common endosymbiotic bacteria that can be found in about 70% of insects [5,59,60]. In many hosts, Wolbachia is known for its ability to manipulate host reproduction by inducing feminization, male killing, cytoplasmic incompatibility, and parthenogenesis [61,62,63,64]. Moreover, interactions between Wolbachia and some insects have been reported that are mutualistic, where Wolbachia is a nutritional symbiont providing essential nutrients to the host [62,65]. In addition, Wolbachia can also prime the insect immune system for defense against pests, insects, and pathogenic microbes [66,67,68]. Wolbachia is predominantly passed via transovarial transmission [69,70,71], with only sporadic horizontal transfers documented in other insects [72,73]. Consistent with a vertical-inheritance model, wild Simulium populations in Cameroon retain high Wolbachia prevalence, even when associated with Onchocerca volvulus [25,43], whereas laboratory-reared flies often lose detectable infection [23], indicating that vertical transmission can be one of the principal drivers of Wolbachia colonization. Together, it is plausible that the low-abundance bacteria in the head were acquired horizontally through food- or blood-feeding activities, while some may represent transient environmental taxa, or others could have functional associations with the host. The limited microbial diversity (near-monodominance) in the S. vanluni’s head reflects anatomical specialization and efficient maternal transmission.

In contrast to the head, the thorax and abdomen of S. vanluni harbor a more diverse microbial composition with greater evenness. Although Wolbachia remained the dominant taxa in both the thorax and abdomen, the secondary taxa varied by regions. The thoracic hemocoel and flight muscle microenvironment is enriched in Acinetobacter, which is known for environmental versatility and potential roles in nutrient recycling [74]. While the abdomen region hosts the gut and reproductive tissue, it may provide a more suitable habitat for intracellular Rickettsia with the capacity for vertical transmission and host reproductive manipulation [75]. Notably, samples with high Acinetobacter show lower Rickettsia and vice versa, suggesting that niche partitioning or potential microbial competition may drive this inverse relationship. The partitioning could have arisen due to differences in niche specialization [76,77], resource utilization [78], and even potential antagonistic interactions between specific bacterial groups [79,80].

The spatial segregation of the head versus body-region microbiota in S. vanluni suggests these communities could influence how black flies interact with environmental microbes. Therefore, understanding these region-specific microbial landscapes could provide crucial insight into black fly ecology and reveal finer-scale microbial–host interactions than gut-focused surveys. As the feeding status of collected black flies was undetermined, future work should therefore document adult feeding sources and evaluate how these non-hematophagous diets influence the microbial composition. In particular, examining how different diets can affect the Acinetobacter and Rickettsia balance will facilitate the understanding of the intrinsic tissue niche and diet-driven shift of microbiota. Furthermore, future work can also focus on examining tissue-specific factors, such as antennal and mouthpart anatomy or salivary gland antimicrobial secretions, in shaping these region-specific microbiomes. Longitudinal tracking of Wolbachia across developmental stages in black flies will reveal the potential role of vertical inheritance and environmental acquisition in maintaining black fly symbiont communities. Lastly, we used pools of 20 flies per region to ensure sufficient DNA yield and focus on core community patterns; however, this approach may mask the inter-individual variability in the microbiota composition. Future studies should include single-fly analysis alongside pooled samples to capture population-level trends and individual heterogeneity.

5. Conclusions

In summary, our findings revealed that adult S. vanluni harbor a shared core microbiota that is dominated by Wolbachia alongside distinct and region-specific microbial communities in the head, thorax, and abdomen. The head region exhibited near-monodominance by Wolbachia, with significantly lower diversity and evenness than the thorax and abdomen. Although Wolbachia remained the most abundant taxon in all regions, the thorax and abdomen supported more even microbiota enriched in Acinetobacter and Rickettsia, respectively, indicating fine-scale anatomical niche partitioning. These data establish a spatial framework for the black fly microbiome and provide a clear baseline for future studies of black fly microbial function and transmission. Furthermore, untargeted metagenomic and metabolomic analyses will be essential next steps to uncover the functional roles of these bacteria.