Comparative 16S rRNA metabarcoding analysis of gut bacterial diversity across seven termite species from Senegal: ecological roles and community structure

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The analysis identified 1,674 ASVs, spanning 26 phyla and 132 genera. Dominant phyla in both type of termites were Bacteroidota, Bacillota, Spirochaetota, and Fibrobacterota. Fungus-growers were enriched in Candidatus Patescibacteria, Pseudomonadota, and Planctomycetota, while non-fungus-growers harbored more Acidobacteriota, Spirochaetota, and Fibrobacterota. Differential abundance confirmed Acidobacteriota, Spirochaetota, and Fibrobacterota as enriched in non-fungus-growers (FDR < 0.05), and Patescibacteria, Thermoplasmatota, and Elusimicrobiota in fungus-growers. Among minor phyla, Spirochaetota comprised 12 ASVs, Actinomycetota 15 ASVs, more frequent in fungus-growers, and Myxococcota 3 ASVs, sporadically detected. Phylogenetic analysis revealed termite-specific clustering of Treponema ASVs, consistent with long-term host-symbiont co-diversification. In Trinervitermes spp., glyphosate-treated and untreated colonies showed no significant differences in alpha or beta diversity, though untreated samples tended towards higher richness. Overall, it seems that feeding ecology shaped termite gut microbiota more strongly than possible glyphosate exposure. The detection of the Actinomycetota phylum (which includes Streptomyces spp.) suggests antibiotic potential, while possible resistance genes and microbial filtering highlight the need for meta-omics approaches to link community structure with biosynthetic capacity and antimicrobial resistance. Biological sciences/Ecology Earth and environmental sciences/Ecology Biological sciences/Microbiology Termites gut microbiota 16S rRNA metabarcoding Senegal fungus-growing termites glyphosate Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Termites (Isoptera, Termitidae) comprise more than 3,000 described species and can account for up to 10% of animal biomass in some ecosystems [ 1 , 2 ]. Ecologically, they are social insects living in caste-organized colonies and playing major roles in soil structure, nutrient cycling, and organic matter decomposition. Some termite species, such as Macrotermes bellicosus , are not only consumed by humans as a natural food source due to their significant protein and vitamin content, but are also used in traditional medicine as analgesics and anti-inflammatory agents [ 3 – 7 ]. Classically, termites are divided into “lower” and “higher” groups based on the presence of protozoa in the hindgut: the former harbor flagellated protists, whereas Termitidae (“higher” termites) rely primarily on a bacterial microbiota to degrade lignocellulose, supported by a longer, compartmentalized gut [ 8 – 10 ]. Consequently, this microbiota contributes substantially to global carbon and nitrogen cycles [ 11 , 12 ]. Among higher termites, marked differences distinguish soil-feeding non-fungus-growing lineages (e.g., Trinervitermes ) from fungus-growing Macrotermitinae, including Macrotermes, Ancistrotermes , and Microtermes , which cultivate Termitomyces fungi. Accordingly, bacterial community composition is highly species-specific and shaped by diet, caste, and environment [ 13 – 17 ]. African termite microbiomes are understudied [ 18 ]. Beyond their ecological functions, termite gut microbiota may also harbor taxa acting as reservoirs of antimicrobial resistance genes, reflecting a broader environmental dimension of the resistome [ 19 , 20 ]. Understanding the balance between protective microbial filters and potential carriers of multidrug resistance is essential for assessing both ecosystem health and possible spillover risks [ 21 , 22 ]. Alongside natural drivers, multiple anthropogenic pressures can disrupt these microbiomes, most notably the use of glyphosate-based herbicides (GBHs). Although the primary target of glyphosate (EPSPS in the shikimate pathway) is absent in animals, but present in plants and in many bacteria, indirect effects on insect gut microbiota have been reported, and its metabolite AMPA (aminomethylphosphonic acid) also raises ecotoxicological concerns [ 23 – 25 ]. Among termite-associated bacteria, spirochetes of the genus Treponema are of particular interest. These lineages are virtually absent from urban populations but rather associated with non-industrialized ones [ 26 , 27 ]. Their termite-specific clades are thought to play key roles in lignocellulose degradation, hydrogen metabolism, and nitrogen cycling [ 28 – 30 ], highlighting their long-term coevolution with termites. Focusing on these symbionts thus provides insights into the functional specialization of the termite gut microbiota. In addition to dominant bacterial groups, low-abundance phyla such as Actinomycetota are also noteworthy. Members of this lineage, notably the genus Streptomyces , are prolific producers of secondary metabolites, including antibiotics such as β-lactams and aminoglycosides [ 31 , 32 ]. Their detection in termite guts may thus provide valuable clues about microbial interactions, colonization resistance, and potential responses to environmental stressors [ 33 ]. In this study, we compare using 16S rRNA metabarcoding (QIIME 2) the gut bacterial communities of seven termite species from Senegalese agroecosystems ( Macrotermes bellicosus , Macrotermes subhyalinus , Microtermes sp., Ancistrotermes cavithorax , Trinervitermes geminatus , Trinervitermes trinervius and Trinervitermes occidentalis ). We quantify alpha diversity (Shannon, Simpson) and beta diversity (Bray-Curtis; PERMANOVA) [ 34 ], describe taxonomic profiles with a focus on treponemes (phylum Spirochaetota), and test the association between environmental GBH exposure and microbiome structure. Finally, we consider the dual ecological role of the termite gut microbiota: on the one hand, it represents an obligatory symbiosis, since termites cannot survive without their gut bacteria, many of which are host-restricted; on the other hand, it acts as a biological filter, processing ingested environmental microbes and potentially selecting for resistant strains. In this context, termite gut communities may also constitute a possible reservoir of antimicrobial resistance genes, as has been observed in other animal-associated microbiomes, such as the gut of migratory birds carrying numerous ARGs [ 35 ]. Results 1. Collection and molecular identification of termites Termites were collected from multiple sites across Senegal, yielding a total of 42 termitaries representing seven species: Macrotermes subhyalinus (n = 13), Macrotermes bellicosus (n = 9), Microtermes sp. (n = 5), Ancistrotermes cavithorax (n = 4), Trinervitermes geminatus (n = 5), Trinervitermes occidentalis (n = 5), and Trinervitermes trinervius (n = 1). Molecular identification based on the COI (Cytochrome c oxidase subunit I) gene produced high-quality sequences (650–700 bp) with 92–100% similarity to reference sequences in GenBank. The glyphosate exposure analysis was restricted to Trinervitermes spp., which represented the only genus consistently found from both treated and untreated agricultural fields. Species-level identification confirmed five T. geminatus , five T. occidentalis , and one T. trinervius . Because T. trinervius was absent from glyphosate-treated fields, it was excluded from the glyphosate exposure comparison, which was therefore restricted to the 10 samples of T. geminatus and T. occidentalis (five per condition). All COI sequences generated in this study were deposited in GenBank under the accession numbers provided in Supplementary data Table S1 . 2. Metagenomic analysis 2.1. Data Visualization and Comparative Statistical Analysis of Termite Gut Microbiota The analysis identified a total of 1,674 ASVs and 37,509 reads, spanning across 26 phyla and 132 genera (Table 1 ), and revealed that gut microbiota composition varies markedly across termite species, probably reflecting specific digestive adaptations and functional specialization of major bacterial phyla such as Spirochaetota, Bacillota, Bacteroidota, and Desulfobacterota (Fig. 1 , Supplementary data Figure S1 ). The circular phylogenetic tree constructed from 16S rRNA gene sequences further illustrates this broad taxonomic diversity, with additional bacterial lineages including Fibrobacterota, Verrucomicrobiota, and Acidobacteriota ( Supplementary data Figure S2 ). Interestingly, archaeal taxa were also recovered (mainly Euryarchaeota, along with Crenarchaeota, Halobacterota, and Thermoplasmatota). While only members of methanogens (Euryarchaeota) are established in termite guts, the detection of other archaeal lineages highlights additional archaeal diversity within these communities. The tree additionally highlights Candidate Phyla Radiation (CPR) groups such as Patescibacteria and Margulisbacteria, as well as predatory bacterial lineages like Bdellovibrionales from Myxococcota. (Fig. 1 ). Among the three phyla investigated, Spirochaetota clearly dominated the termite gut communities, with 12 ASVs representing a total of 4,698 reads distributed across nearly all species ( Supplementary data Figure S3 ). This group accounted for the majority of reads in both fungus-growing and non-fungus-growing termites, consistent with the well-documented specialization of termite-associated Treponema lineages. Actinomycetota were less abundant, represented by 15 ASVs (617 reads), but showed a higher contribution in fungus-growing termites ( Macrotermes spp.), suggesting a potential association with fungal substrate degradation or secondary metabolite (probably, antifungal and antibacterial) production. In contrast, Myxococcota were detected at very low levels (3 ASVs, 17 reads) and occurred sporadically among samples. Despite their rarity, their detection is noteworthy given the predatory lifestyle of many myxobacteria and their potential role in shaping microbial community dynamics. Overall, the barplot highlights a phylum-level structure dominated by Spirochaetota, complemented by low-abundance but ecologically relevant lineages such as Actinomycetota and Myxococcota. (Fig. 2 , Supplementary data Figure S4 ). Table 1 Comparison of sequencing and diversity metrics (GBH-treated vs. untreated). Metric Mean (Treated) SD (Treated) Mean (Untreated) SD (Untreated) Mann-Whitney U p-value Total reads 637.80 98.92 943.60 227.41 3.00 0.056 ASV richness 33.00 11.98 44.20 8.93 6.00 0.222 Shannon diversity 3.18 0.31 3.44 0.22 6.00 0.222 Table reports mean ± SD for total reads, ASV richness, and Shannon diversity, with Mann–Whitney U tests. No significant differences are detected (p ≥ 0.056), though treated samples tend to have lower values across metrics. 2.2.1. Global Taxonomic Composition and Phylogenetic Structure The circular phylogenetic tree and abundance heatmap revealed that termite gut bacterial communities are dominated by four major phyla Bacteroidota, Bacillota, Spirochaetota, and Fibrobacterota with an uneven distribution across host species ( Supplementary data Figures S1 -S2-S3 ). The resulting tree showed that termite-derived ASVs clustered predominantly within termite-specific Treponema lineages (e.g., the Termite Treponema cluster), clearly separated from human pathogenic clades such as the T. pallidum complex. Additional ASVs were grouped with uncultured Treponema lineages closely related to T. caldarium and other fermentative taxa commonly associated with polysaccharide degradation and hydrogen metabolism. No termite-derived sequences clustered with human or animal pathogens, supporting the host-specific adaptation and ecological specialization of Treponema in the termite gut (Fig. 3 ). Trees were visualized and annotated with iTOL v6 (12), allowing the integration of metadata such as termite species and relative abundance (Fig. 3 , Supplementary data Figure S3 ). To further refine this phylogenetic assessment, analysis of 16S rRNA sequences within the phylum Spirochaetota identified eight termite-derived ASVs, five (62.5%) clustering within termite-specific lineages such as the Termite Treponema cluster and the M2PT2-76 group, highlighting their strong ecological specialization to the termite gut. Two ASVs (25%) grouped with uncultured Treponema lineages were related to T. caldarium and other anaerobic fermentative taxa involved in polysaccharide degradation, while the remaining ASV (12.5%) clustered with Spirochaeta , a non-pathogenic genus implicated in lignocellulose breakdown ( Supplementary data Figure S5 ). Phylogenetic analysis based on 16S rRNA gene sequences revealed that the Treponema strains isolated from African termites were distributed into three well-supported clusters in relation to reference sequences from GenBank. The first cluster (red) included sequences forming a homogeneous group closely related to termite-associated Treponema . The second cluster (blue) consisted of sequences that also grouped with termite-specific Treponema but showed a distinct phylogenetic affinity with T. paraluiscuniculi and T. pallidum (JX120547.1, PV524036.1, M88726.1, DQ648782.1). The third cluster (green) comprised a divergent lineage including several Treponema sequences associated with Macrotermes bellicosus and Macrotermes subhyalinus , originating from different geographic sampling sites, and also showing close relationships with T. caldarium (MW652580.1, MW652583.1, MW652591.1). Reference sequences from GenBank used for phylogenetic anchoring are shown in black. The overall tree topology and bootstrap support values (> 70%) confirm the existence of three distinct Treponema lineages associated with termite hosts, reflecting both intra- and interspecific diversity within the termite gut microbiota (Fig. 4 ). Overall, these findings confirm the dominance of termite-adapted Treponema lineages and their key ecological roles in lignocellulose degradation and nitrogen cycling, while demonstrating a clear evolutionary divergence from pathogenic counterparts. 2.3. Gut microbiota comparison between fungus-growing and non-fungus-growing termites At the phylum level, the gut microbiota of fungus-growing termites ( Macrotermes bellicosus, Macrotermes subhyalinus, Ancistrotermes cavithorax , and Microtermes sp.) was dominated by Bacteroidota (36.9% of reads, detected in 30/31 samples) and Bacillota (28.6%, 29/31), whereas non-fungus-growing termites ( Trinervitermes geminatus, T. trinervius, T. occidentalis ) displayed higher proportions of Spirochaetota (35.1%, 11/11) and Fibrobacterota (18.4%, 11/11) (Fig. 5 . a , Table 1 ). Alpha diversity indices did not differ significantly between the two groups; however, non-fungus-growers showed slightly higher microbial complexity, with mean ASV richness of 39.1 ± 11.1 and Shannon diversity of 3.33 ± 0.28 compared to 39.8 ± 21.3 and 3.19 ± 0.89 in fungus-growers (Fig. 5 . b ). Differential abundance testing confirmed these ecological shifts (Fig. 5 . c ), identifying Acidobacteriota (log₂FC = -4.18, FDR = 8.95 × 10⁻⁵), Spirochaetota (log₂FC = -2.52, FDR = 0.0035), and Fibrobacterota (log₂FC = -2.36, FDR = 0.0017) as significantly enriched in non-fungus-growing termites, while Patescibacteria (log₂FC = 3.16, FDR = 0.0031), Thermoplasmatota (log₂FC = 3.58, FDR = 0.0103), and Elusimicrobiota (log₂FC = 2.60, FDR = 0.0221) were significantly enriched in fungus-growers ( Supplementary data Figure S6 ). Taken together, these results indicate that although overall diversity metrics are comparable, feeding ecology drives marked phylum-level compositional differences between the two termite groups. 2.4. Gut Microbiota Comparison in Glyphosate-Exposed vs Unexposed Trinervitermes The gut microbiota of Trinervitermes spp. collected from glyphosate-treated (DT30, DT31, DT15, DT24, DT25) and untreated fields (DT10, TC07, TC09, TC12, TC14) showed overall comparable diversity patterns ( Supplementary data Table S1 ), with the dominant phyla in both groups being Bacillota, Bacteroidota, Spirochaetota, and Fibrobacterota ( Fig. 6 a). Although the mean sequencing depth was slightly higher in untreated samples (943.60 ± 227.41 reads) than in treated ones (637.80 ± 98.92 reads), the difference was not statistically significant (U = 3.00, p = 0.06). Similarly, alpha diversity metrics, including ASV richness (44.20 ± 8.93 in untreated vs. 33.00 ± 11.98 in treated, U = 6.00, p = 0.22) and Shannon diversity (3.44 ± 0.22 vs. 3.18 ± 0.31, U = 6.00, p = 0.22), revealed slightly higher values in untreated termites (Fig. 6 . b ) although no statistical significance was observed (Table 1 ). Consistently, beta diversity analysis based on Bray-Curtis dissimilarities did not reveal a clear separation between groups, and PERMANOVA (Table 2 ) confirmed the absence of significant compositional shifts (F_pseudo = 0.26, R² = 0.01, p = 0.82). Furthermore, differential abundance testing at both the phylum and genus levels, corrected for multiple comparisons, identified no significantly different taxa (q > 0.05) (Fig. 6 . c ), although some phyla such as Fibrobacterota and Acidobacteriota tended to be relatively more abundant in untreated fields (Fig. 7. c ). Taken together, these results indicate that while untreated termites displayed a tendency toward greater microbial richness and diversity, glyphosate exposure under the conditions tested did not induce significant shifts in either alpha or beta diversity, and only marginal trends were observed at the taxonomic level ( Supplementary data Figure S7 ). Table 2 Beta diversity by GBH treatment (Bray-Curtis PERMANOVA). Metric F pseudo R² p-value Beta diversity (Bray–Curtis, PERMANOVA) 0.26 0.006 0.824 PERMANOVA shows no treatment effect on community composition (pseudo-F = 0.26, R² = 0.006, p = 0.824). GBH treatment explains ~ 0.6% of variance-statistically non-significant. Statistical comparison of gut microbial community composition (Bray-Curtis dissimilarities) between glyphosate-treated and untreated Trinervitermes spp. using PERMANOVA (999 permutations). Data are expressed as mean ± standard deviation (SD) from rarefied ASV tables (depth = 1900 reads per sample). Error estimates were based on permutation residuals, with 95% confidence intervals ranging from 0.18–0.34 for F pseudo and 0.009–0.015 for R². Variability among replicates was evaluated using SD, and no significant differences were detected ( p = 0.82). Alpha diversity indices (Observed ASVs and Shannon) are presented as mean ± SD for descriptive comparison. Discussion Comparative 16S rRNA metabarcoding analysis of seven termite species from Senegal provides new insights into the composition and structuring of termite gut microbiota, highlighting both ecological differentiation between fungus-growing and non-fungus-growing lineages and the potential effects of glyphosate exposure. Across all species, gut bacterial communities were dominated by Bacteroidota, Bacillota, Spirochaetota, and Fibrobacterota, in agreement with previous studies on higher termites [ 11 , 13 , 36 ], and these phyla are recognized for their functional roles in lignocellulose degradation and nitrogen cycling [ 11 , 12 , 37 ], thereby underscoring their ecological importance in termite-mediated decomposition. Although all termites were sampled on the same territory in close proximity to each other, when ecological groups were compared, clear differences emerged: fungus-growing termites ( Macrotermes spp.) were enriched in Bacteroidota and Bacillota, taxa commonly associated with the digestion of fungal biomass and plant polysaccharide fermentation [ 16 ], whereas non-fungus-growing termites ( Trinervitermes , Microtermes , Ancistrotermes ) harbored higher relative abundances of Spirochaetota and Fibrobacterota, reflecting adaptations to soil-feeding and fibrous plant material digestion, as also noted in earlier studies [ 14 , 38 , 39 ]. Differential abundance analysis confirmed these ecological signatures, showing that specific phyla were consistently associated with either fungus-growing or non-fungus-growing termites, thereby supporting the hypothesis that feeding ecology is a primary driver of gut microbial structuring in termites, consistent with findings in other xylophagous insects [ 13 , 16 ]. In terms of diversity, alpha diversity indices did not differ significantly between fungus-growing and non-fungus-growing termites, although non-fungus-growers tended to display slightly higher microbial complexity, while beta diversity analysis revealed a clear separation of microbial communities between ecological groups, reinforcing the conclusion that feeding ecology drives strong shifts in microbiota composition without altering overall richness [ 16 , 18 ]. Beyond the dominant bacterial phyla, our phylogenetic and taxonomic analyses highlighted additional lineages of interest. Spirochaetota were particularly abundant, represented by 12 ASVs and totaling 4,698 reads, and were detected across nearly all termite species. Actinomycetota were also recovered, with 15 ASVs corresponding to 617 reads, and showed a higher contribution in fungus-growing termites ( Macrotermes spp.) compared to non-fungus-growing species. In contrast, Myxococcota were rare, represented by only 3 ASVs and 17 reads, and were detected sporadically among samples. Methanogenic archaea (Euryarchaeota) were consistently present, supporting their role in hydrogen turnover and methane production in termite guts [ 28 , 37 ]. Candidate phyla radiation (CPR) taxa, including Patescibacteria and Margulisbacteria, were also identified, together with low-abundance predatory bacterial groups, reflecting the broad phylogenetic diversity of termite gut communities. CPR taxa were also present, pointing to symbiotic or parasitic bacterial groups with reduced genomes that may depend on cross-feeding interactions, as reported in other insect-associated microbiomes [ 36 , 40 , 41 ]. In addition, predatory taxa such as Bdellovibrionales (Myxococcota) were recovered, suggesting that microbial predation contributes to community regulation and may help stabilize dense microbial consortia in termite hindguts [ 42 – 44 ]. Within the Spirochaetota phylum, termite-derived Treponema ASVs clustered exclusively with termite-adapted lineages, clustered exclusively with termite-adapted lineages, in contrast to most vertebrate-associated microbiomes where Treponema is rarely detected [ 11 , 13 , 28 ]. This finding underscores the long-term coevolution of termites and their treponemes, which appear to specialize in lignocellulose degradation, hydrogen turnover, and nitrogen metabolism, thus reinforcing their pivotal ecological role [ 37 , 45 ]. None of the treponemal ASVs could be attributed to one of few known termite-associated Treponema species such as Leadbettera azotonutricia or Treponema primitia , such suggesting probable species-specific association among termites and treponemes. Actinomycetota were also detected at low abundance. Although relatively minor, members of this phylum are known for their capacity to produce secondary metabolites and for their tolerance to environmental pollutants, suggesting a potential contribution to microbial interactions and resilience in termite guts. Such effects may be, at least partially, responsible for antimicrobial activity of fungus combs constructed by fungus-growing higher termites from the faeces of young workers [ 46 ]. Moreover, mediated by Actinomycetota permanent presence of antibacterials in the gut of termites and, subsequently, in fungus combs, may play role in natural selection of antibiotic-resistance in bacteria, such, for example, Klebsiella pneumoniae [ 34 , 47 ]. Actinomycetota may be also relevant in the context of glyphosate exposure, although no significant differences were detected between treated and untreated termites in our study [ 33 ]. Actinomycetota have been implicated in tolerance to environmental pollutants, raising the possibility that their persistence in termites is linked to detoxification processes. This is particularly relevant in the context of glyphosate exposure. Although our comparison of Trinervitermes spp. from treated and untreated fields revealed no significant differences in alpha or beta diversity, glyphosate and its metabolite AMPA are known to impact microbial communities in soils and insects [ 19 , 21 , 23 ]. The presence of Actinomycetota in our samples may thus indicate adaptive responses to herbicide stress or a protective role within termite guts, consistent with the known ability of some actinomycetes (Actinomycetota)within this phylum to produce secondary metabolites and influence microbial interactions. However, the absence of detectable compositional changes between treated and untreated groups could reflect the timing of sampling relative to herbicide application, microbial resilience mechanisms, or functional redundancy within the community. Previous studies in bees and beetles demonstrated that glyphosate can alter gut microbiota under certain exposure regimes [ 22 – 24 ], suggesting that stronger effects might be revealed in termites under different temporal or environmental contexts. Taken together, our results demonstrate that feeding ecology exerts a stronger influence than agrochemical exposure in shaping termite gut microbiota. Fungus-growing and non-fungus-growing termites harbor distinct phylum-level signatures reflecting their dietary strategies, while glyphosate-treated and untreated termites displayed broadly comparable communities. Importantly, the apparent stability of termite gut microbiota following glyphosate exposure should not be equated with functional stability, since metabolic shifts may occur in the absence of taxonomic changes. Future studies employing shotgun metagenomics, metatranscriptomics or culturomics will therefore be essential to assess functional resilience, detect sub-lethal effects of herbicide exposure, and clarify the ecological roles of archaeal, CPR, predatory, and actinomycete lineages in termite gut ecosystems. Conclusion In conclusion, our analysis reveals that termite feeding ecology exerts a stronger influence than glyphosate exposure on gut microbiota composition. Fungus-growing termites were enriched in Bacteroidota and Bacillota, whereas non-fungus-growing termites harbored more Spirochaetota and Fibrobacterota, reflecting dietary adaptations. Although glyphosate treatment did not significantly alter community structure, the presence of archaeal methanogens, CPR taxa, and Actinomycetota points to overlooked microbial groups that may contribute to functional resilience. Together, these findings underscore the central role of diet in shaping termite gut microbiota, while also raising new questions about microbial interactions and responses to environmental stressors. Materials and Methods 1. Study area and termite collection Termites were collected from eight sites across Senegal: 50 Km-Thiés (Thiès region), Oussouye and Ziguinchor (Lower Casamance region), and the villages of Dandé, Dindéfelo, Kondoji, Nandoumary, and Ségou in the Kédougou region ( Supplementary data Table S2) . Sampling was conducted between October 30th and December 20th, 2020. At each site, colonies were selected from fields either regularly treated with glyphosate-based herbicides or from untreated fields, based on information provided by local farmers ( Supplementary data Table S2) . Written permission for collection was obtained from the owners of the fields. Adult termites, including both soldiers and workers, were manually collected from termite mounds using forceps and shovels. For each mound, the surrounding soil substrates and, when present, fungus combs were also sampled and transported alongside collected termites in ventilated plastic boxes kept at room temperature. A part of the termites was preserved aseptically in 70% ethanol, while the remaining samples were frozen at − 20°C until further processing. 2. Molecular analysis 2.1. Molecular analysis A longitudinal section was made using a sterile surgical blade to obtain two equal parts of each termite used. One part was used for molecular analyses. The second part was stored in a sterile polypropylene tube and kept at -20°C for further analysis. Genomic DNA was extracted individually using half of each termite. DNA was extracted using an EZ1 DNA Tissue Kit (Qiagen), according to the manufacturer's recommendations. Prior to DNA extraction, a physical bead-based disruption of the sample using the Tissue-Lyser apparatus (Qiagen, Hilden, Germany) and 24 h of enzymatic digestion at 56 ° C using G2 buffer supplemented with 20% proteinase K were performed. DNA was eluted in 100 µL and stored at -20°C until further use. 2.2. Molecular identification of termites Standard Polymerase Chain Reaction (PCR) targeting the Cytochrome c oxidase subunit I (COI) gene was employed to achieve species-level identification of termites [ 34 ]. Each PCR reaction was carried out in a total volume of 50 µL, containing 25 µL AmpliTaq Gold master mix, 18 µL DNase-/RNase-free ultrapure water, 1 µL of each primer at 20 µM, and 5 µL of DNA template. Thermal cycling conditions consisted of an initial incubation at 95°C for 15 min, followed by 40 cycles of denaturation at 95°C for 1 min, annealing at the specific melting temperature of each primer pair for 30 s, and extension at 72°C for 30 s to 1 min depending on fragment size, with a final elongation step of 5 min at 72°C. Amplifications were performed using an Applied Biosystems 2720 Thermal Cycler (Applied Biosystems, Thermo Fisher Scientific, Waltham, MA, USA), and PCR products were resolved on 1.5% agarose gels. Amplicons were subsequently purified using NucleoFast 96 PCR plates (Macherey-Nagel EURL, Hoerdt, France) according to the manufacturer’s instructions and sequenced with the BigDye Terminator v3.1 Cycle Sequencing Kit (Perkin Elmer Applied Biosystems, Foster City, CA, USA) on an ABI automated sequencer (Applied Biosystems). Resulting electropherograms were assembled and manually edited using ChromasPro v1.7 (Technelysium Pty Ltd., Tewantin, Australia). The edited sequences were then compared against the GenBank database using NCBI BLAST ( https://blast.ncbi.nlm.nih.gov/Blast.cgi ), and further cross-validated among themselves as well as with related reference sequences. Phylogenetic relationships were inferred using the neighbor-joining method, and trees were reconstructed with MEGA v7 ( https://www.megasoftware.net/ ). Branch robustness was assessed by bootstrap analysis with 1,000 replicates. 3. Metagenomic sequencing Metagenomic DNA was amplified for the 16S rRNA “V3-V4” regions using PCR with 45 cycles, employing the Kapa HiFi HotStart ReadyMix 2× (Kapa Biosystems Inc., Wilmington, MA, USA) and the conserved region primers V3-V4 with Illumina overhang adapters (Supplementary data Table S1 ). Amplicons were purified using AMPure XP magnetic beads (Beckman Coulter Inc., Fullerton, CA, USA), and DNA concentration was measured with a Qubit fluorometer using the High Sensitivity assay (Life Technologies, Carlsbad, CA, USA). Samples were then normalized to 3.5 ng/µL prior to library preparation. At this stage, libraries generated with protocol 1 were pooled volume-to-volume with those from protocol 5, after which Illumina sequencing adapters and dual-index barcodes were ligated to the amplicons. A second round of purification was performed with AMPure XP beads, and the resulting libraries were pooled into two sequencing sets: one comprising 95 multiplexed samples and the other 41 samples. Global library concentration was quantified with a Qubit assay (High Sensitivity kit), and the final pool was diluted to 8 pM before sequencing. Cluster generation and paired-end sequencing (2 × 250 Base pairs (bp)) with dual-index reads were carried out on an Illumina MiSeq platform (Illumina Inc., San Diego, CA, USA) in a single 39-hour run. Paired-end reads were quality-filtered, and raw data were demultiplexed into FASTQ files (84 files total for R1 and R2 reads), which were deposited in the NCBI BioProject database under accession number PRJNA1304467 (Supplementary data Table S2). 4. QIIME 2-Based Microbiome Analysis 4.1. Microbial Diversity and Phylogeny via QIIME 2 Raw 16S rRNA gene sequencing data were processed using QIIME 2 (version 2024.2) [ 48 , 49 ], following a standardized pipeline for microbial community analysis. Paired-end FASTQ reads were imported using the PairedEndFastqManifestPhred33V2 format and subjected to quality filtering and denoising with DADA2 [ 50 , 51 ], which performs error correction, chimera removal, and generation of high-resolution Amplicon Sequence Variants (ASVs). Representative sequences were aligned using MAFFT [ 52 ] and a phylogenetic tree was constructed using FastTree2 [ 53 ], yielding both unrooted and rooted trees for downstream phylogenetic analyses. Taxonomic classification was performed using a Naive Bayes classifier trained on the SILVA 138 reference database at 99% similarity [ 54 , 55 ], allowing taxonomic resolution from phylum to genus. Alpha and beta diversity metrics (Bray-Curtis, unweighted and weighted UniFrac distances) were calculated using the core-metrics-phylogenetic workflow, based on rarefied feature tables with a sampling depth of 1900 sequences. Group differences in community composition were assessed using PERMANOVA [ 56 ]. Taxonomic profiles were visualized via interactive “« barplot »” (QIIME taxa « barplot »), and ordination results were explored using Principal Coordinates Analysis (PCoA) plots rendered with Emperor [ 57 ]. Phylogenetic trees and relative abundance data (heatmap) were exported and visualized using the Interactive Tree of Life (iTOL v6) platform [ 58 ] for annotated tree representations. All statistical analyses and visualizations were conducted using Python v3.10 and the scientific libraries pandas for data wrangling, NumPy for numerical computation, and seaborn and matplotlib for data visualization. Taxonomic abundance tables generated from QIIME 2 outputs were processed to calculate absolute and relative abundances at the phylum, genus, and species levels. Data were grouped according to termite species and ecological traits, aggregated with pandas groupby operations, and visualized using stacked bar plots constructed with « barplot » and « catplot » functions to illustrate microbial composition across taxa. Principal Coordinates Analysis plots based on Bray-Curtis dissimilarities were exported from QIIME 2 and visualized either with the Emperor tool or redrawn in Python using extracted coordinates. To examine group-wise distributions of microbial abundance, histograms and kernel density estimates were generated with seaborn « histplot » and « kdeplot » functions. Boxplots were used to evaluate differences in relative abundance between treatment groups, while dot plots were applied to illustrate individual sample variability. Data preprocessing steps included filtering, logarithmic transformations, and normalization. All Figures were produced in high-resolution format (PNG, 300 dpi) suitable for publication. 4.2. Phylogenetic Tree of Spirochaetota Derived from 16S rRNA Metagenomic Analysis Sequences assigned to the phylum Spirochaetota were extracted from the taxonomic classification results of the 16S rRNA metabarcoding pipeline. Representative sequences were selected based on amplicon sequence variants (ASVs) obtained after quality filtering and chimera removal with DADA2. Multiple sequence alignment of these ASVs was performed with MAFFT v7 [ 52 ] using default parameters, and the aligned sequences were trimmed to the V3-V4 region to ensure positional homology. As most termite-derived Spirochaetota ASVs belonged to the genus Treponema , but many could not be resolved beyond this rank, 28 reference Treponema sequences from NCBI were incorporated into the alignment. This step provided a broader phylogenetic framework and enabled the placement of termite ASVs relative to known clades, including those containing potentially pathogenic species. Phylogenetic trees were inferred with IQ-TREE v2 [ 59 ] using the maximum-likelihood method under the GTR + F + I + G4 ( GTR = General Time Reversible model, F = Empirical base frequencies, I = Invariant sites and G4 = Gamma-distributed rate heterogeneity with 4 categories) model selected by ModelFinder and 1,000 ultrafast bootstrap replicates to assess branch support. 4.3. Gut microbiota comparison between fungus-growing and non-fungus-growing termites Gut microbiota profiles of fungus-growing termites ( Macrotermes bellicosus, Macrotermes subhyalinus, Ancistrotermes cavithorax, Microtermes sp.) and non-fungus-growing termites ( Trinervitermes geminatus, Trinervitermes trinervius, Trinervitermes occidentalis ) were compared at the phylum level. Amplicon sequence variant (ASV) tables generated with QIIME 2 were aggregated by ecological groups to calculate relative abundances. Differences in phylum-level abundances between the two groups were assessed using the non-parametric Mann-Whitney U test, and p-values were corrected for multiple testing with the Benjamini-Hochberg false discovery rate (FDR) method, with FDR < 0.05 considered significant. Alpha diversity indices (ASV richness and Shannon diversity) were computed per sample, and group differences were tested with the same non-parametric framework. Visualization included bar charts for relative abundances, boxplots for diversity comparisons, and volcano plots displaying log₂ fold change versus -log₁₀ (p-value). All analyses and visualizations were performed in Python ( pandas, matplotlib, seaborn ). 4.4. Comparison Between termites collected on Glyphosate Treated and Untreated Fields To evaluate the potential impact of glyphosate exposure on the gut microbiota of termites ( Trinervitermes spp.), we compared five samples from glyphosate-treated fields (DT30, DT31, DT15, DT24 and DT25) with five samples from untreated fields (DT10, TC07, TC09, TC12 and TC14), as Trinervitermes was the most represented genus across both environments. Sequencing data were processed through the QIIME 2 pipeline (v2023.5;[ 48 ]) using DADA2 for denoising [ 50 ], and taxonomic assignments were performed against the SILVA 138 database [ 55 , 60 ] with a confidence threshold of 0.7. Alpha diversity metrics, including observed ASV richness, Shannon index, and total read counts, were calculated using the QIIME 2 diversity plugin, while beta diversity was assessed with Bray Curtis dissimilarities [ 61 ] and visualized through Principal Coordinates Analysis (PCoA) with the vegan package in R [ 62 ]. To test statistical differences, group-wise alpha diversity comparisons were conducted using the Mann-Whitney U test, whereas variations in community composition were evaluated with PERMANOVA [ 56 ] based on 999 permutations. Furthermore, differential abundance analyses at both the phylum and genus levels were performed using Mann-Whitney tests on relative abundances, followed by Benjamini-Hochberg correction for multiple testing [ 63 ]. Finally, data wrangling, statistical analyses, and visualizations, including barplots , diversity tables, and ordination Figures, were carried out using a combination of R [ 62 , 64 ] and Python [ 65 , 66 ], ensuring robust and reproducible analytical outputs. Declarations Acknowledgement s We are grateful to the Direction des Parcs Nationaux and the Direction des Eaux et Forests Chasses et de la Conservation des Sols for permission to work in Senegal. We sincerely thank the team of the Jane Goodall Institute Senegal for their kind assistance, cooperation, and continuous support during this research. We would like to thank the Infectiopole Sud Foundation for the thesis grant awarded to Cheikh Tidiane HOUMENOU. Funding This work was supported by a grant from the French Government managed by the National Research Agency under the “Investissements d’avenir (Investments for the Future)” programme with the reference ANR-10-IAHU-03 (Méditerranée Infection), by the Contrat Plan Etat-Région and the European funding FEDER IHUPERF. Institutional Review Board Statement The study was conducted in accordance with the Declaration of Helsinki and approved by the National Health Research Ethics Committee of the Ministry of Health and Social Action of Senegal (NAGOYA No:001042; 2021-09-08 ). The samples were collected during patient care with their informed consent, specifically for the purposes of scientific and non-profit research. Author Contributions C.T.H. and O.M. conceived and designed the study. C.S. obtained the official authorization for field sampling in Senegal (Nagoya Protocol). M.G. and O.M. conducted the field collections and specimen identification. M.G. performed molecular analyses and prepared the 16S rRNA sequencing. C.T.H. processed the sequencing data and carried out bioinformatic analyses under the supervision of O.M. C.T.H. and O.M. interpreted the results and wrote the manuscript. F.F. and C.S. contributed to data interpretation and critical revision of the manuscript. O.M. supervised the overall project, secured funding, and approved the final version of the manuscript. All authors read and approved the final version of the manuscript. Data Availability All raw 16S rRNA metabarcoding sequence data generated and analyzed in this study have been deposited in the NCBI Sequence Read Archive (SRA) under the BioProject accession number PRJNA1304467 (Supplementary data Table S2). All scripts and bioinformatic workflows used for data processing (QIIME 2 pipeline, diversity analyses, and taxonomic assignments) are available upon reasonable request from the corresponding author. Additional processed data supporting the findings of this study are included within the article and its supplementary information files. Compliance with Ethical Standards Conflict of interest The authors declare that they have no conflict of interest (“ The authors declare no competing interests ”). References Legendre, F. et al. The phylogeny of termites (Dictyoptera: Isoptera) based on mitochondrial and nuclear markers: Implications for the evolution of the worker and pseudergate castes, and foraging behaviors. Mol. Phylogenet. Evol. 48 , 615–627 (2008). Ahmad, I. et al. Growth and Yield of Solanum melongena L. as influenced by foliar application of yeast extract with different time intervals. 18 , (2021). Jouquet, P., Traoré, S., Choosai, C., Hartmann, C. & Bignell, D. 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10:36:35","extension":"jpeg","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1728507,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7962666/v1/3ae2d175a6d8cb17cd6ce25a.jpeg"},{"id":95818396,"identity":"c807b0e5-611d-495c-87fc-6723364a1eb6","added_by":"auto","created_at":"2025-11-13 10:14:32","extension":"jpeg","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":368399,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7962666/v1/1a651ee734702cd66aa5998c.jpeg"},{"id":95818376,"identity":"e5a7657b-eb17-47c1-b162-3f40aee30e47","added_by":"auto","created_at":"2025-11-13 10:14:32","extension":"jpeg","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1074,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7962666/v1/33109178cc667f8d5ef5a47d.jpeg"},{"id":95819044,"identity":"73430ac7-92c1-46d5-a3ed-008d1b908a3c","added_by":"auto","created_at":"2025-11-13 10:37:43","extension":"jpeg","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":921957,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7962666/v1/2196c253ea3e1289c98817eb.jpeg"},{"id":95818384,"identity":"294a4b2d-343a-4bd1-8ea0-00b4093fb3c5","added_by":"auto","created_at":"2025-11-13 10:14:32","extension":"jpeg","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":10239,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7962666/v1/3e5dc38ccbe901f9f0ce0b06.jpeg"},{"id":95819148,"identity":"b451f9e2-24e8-442a-b705-b47db94a21a6","added_by":"auto","created_at":"2025-11-13 10:38:07","extension":"jpeg","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1074,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7962666/v1/d25d297b50600aad6cd57940.jpeg"},{"id":95819133,"identity":"7e2a78f9-3196-433d-aa9b-9e90747735b7","added_by":"auto","created_at":"2025-11-13 10:38:00","extension":"png","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":77809,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7962666/v1/209d8a0e81baa56cc98bc760.png"},{"id":95818397,"identity":"66241449-123d-4534-b1a2-86b035e9e1fd","added_by":"auto","created_at":"2025-11-13 10:14:32","extension":"png","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":192417,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-7962666/v1/7569c88cdeb8bf063fe174ac.png"},{"id":95818383,"identity":"03017c58-973e-4b9b-bc13-e9e2d413f44e","added_by":"auto","created_at":"2025-11-13 10:14:32","extension":"png","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1020,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-7962666/v1/de319d06648e9515698c832e.png"},{"id":95818959,"identity":"9d72433e-faaf-497d-ae6d-99ecbc203243","added_by":"auto","created_at":"2025-11-13 10:36:12","extension":"png","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":71653,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7962666/v1/4bc4ae7283e2cb710bc3cd18.png"},{"id":95818400,"identity":"dac16c10-23c8-4aad-8166-e4198de8c052","added_by":"auto","created_at":"2025-11-13 10:14:32","extension":"png","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":186170,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7962666/v1/d0fb544c381b8f002b01604d.png"},{"id":95818905,"identity":"2a95863f-8226-40aa-bd6b-0c63e9f8b31f","added_by":"auto","created_at":"2025-11-13 10:35:10","extension":"png","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":368502,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7962666/v1/b4cf793e4689d0e110d19da1.png"},{"id":95818387,"identity":"d2d3cdc6-c758-4d14-be71-c246b5485dba","added_by":"auto","created_at":"2025-11-13 10:14:32","extension":"png","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":59910,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7962666/v1/a940a42e728b22af407e42f6.png"},{"id":95818386,"identity":"b5b9fdb1-b94b-4c59-9e10-65c1ec50c3e4","added_by":"auto","created_at":"2025-11-13 10:14:32","extension":"png","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":935,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7962666/v1/d31b6552037433f6b0b3c9af.png"},{"id":95818399,"identity":"eaa6b483-30b4-4f23-8586-fbf267fe5339","added_by":"auto","created_at":"2025-11-13 10:14:32","extension":"png","order_by":23,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":128064,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7962666/v1/1dfbe33bc92577407ee7379d.png"},{"id":95818394,"identity":"778c1ff9-1c55-4035-b534-a2bf388639e9","added_by":"auto","created_at":"2025-11-13 10:14:32","extension":"png","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":3938,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-7962666/v1/0430d966de6fab7bef3caa51.png"},{"id":95818838,"identity":"2272fa0f-c5a0-48f2-81dc-d32fdd3fc173","added_by":"auto","created_at":"2025-11-13 10:34:09","extension":"png","order_by":25,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":935,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7962666/v1/126d218ea7d60d8611c0a826.png"},{"id":95819167,"identity":"145afa0b-faee-4566-9b05-1171f75ca1f9","added_by":"auto","created_at":"2025-11-13 10:38:13","extension":"xml","order_by":26,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":154288,"visible":true,"origin":"","legend":"","description":"","filename":"017fb1c9aafc4a9e8fc2a51384b971ca1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7962666/v1/4dc4a66fc21208ccf84e49ed.xml"},{"id":95818404,"identity":"7ff655f0-0d93-47bd-8c96-1cd5c6a4c6fb","added_by":"auto","created_at":"2025-11-13 10:14:33","extension":"html","order_by":27,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":177837,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7962666/v1/3d4438bb22e34e95b9b6ab85.html"},{"id":95818369,"identity":"834c2b15-8d8d-4346-b4a9-4033c32f2fdc","added_by":"auto","created_at":"2025-11-13 10:14:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":432040,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCircular phylogenetic tree of termite gut microbial communities.\u003c/strong\u003e\u003cbr\u003e\n16S rRNA gene-based phylogenetic reconstruction of bacterial and archaeal ASVs detected in the gut of seven termite species. Branches are colored by phylum, highlighting the dominance of Bacteroidota, Spirochaetota, Firmicutes, and Pseudomonadota, as well as termite-specific clades (e.g., Rs-K70). Phylogenetic inference was performed with IQ-TREE2 using the maximum-likelihood method with 1,000 bootstrap replicates, and the tree was visualized and annotated in iTOL v6.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7962666/v1/68e559f9efbe888e97354e04.png"},{"id":95818368,"identity":"cfd0dd86-d5d8-4bac-ae26-cff55b626523","added_by":"auto","created_at":"2025-11-13 10:14:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":65207,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelative distribution of Spirochaetota, Actinobacteriota, and Myxococcota across termite species from Senegal.\u003c/strong\u003e\u003cbr\u003e\nStacked bar plot showing the total read counts per phylum for each termite species. Spirochaetota dominated across all species, while Actinobacteriota occurred at low abundance and were more pronounced in fungus-growing termites. Myxococcota were rare, detected only in a few samples. This figure was generated by filtering decontaminated ASV tables, aggregating read counts per phylum and termite species using pandas, and visualizing the data as a stacked bar plot with matplotlib.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7962666/v1/fa419479186c360cb5e74b3d.png"},{"id":95819177,"identity":"fe63636c-6c5a-450d-a3af-ee90d9917369","added_by":"auto","created_at":"2025-11-13 10:38:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":336852,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCircular phylogenetic tree of Spirochaetota ASVs with a normalized abundance heatmap.\u003c/strong\u003e\u003cbr\u003e\nSubset of the global phylogenetic tree restricted to Spirochaetota ASVs, with an external track showing normalized relative abundance values (0 = red, 1 = green). The abundance ring highlights termite-associated Treponema ASVs, with most showing high abundance in termite guts.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7962666/v1/630d54a475345fe234aba1cf.png"},{"id":95818372,"identity":"a7da44ec-5607-4331-8a61-08f58063e71b","added_by":"auto","created_at":"2025-11-13 10:14:32","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":643014,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePhylogenetic tree (placement of termite-associated Treponema ASVs) of 16S rRNA sequences of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eTreponema\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e spp. isolated from African termites.\u003c/strong\u003e\u003cbr\u003e\nThe phylogenetic tree was reconstructed using the Maximum Likelihood method based on aligned 16S rRNA gene sequences obtained in this study and reference sequences from GenBank. Bootstrap values greater than 70% are indicated at the nodes. Maximum-likelihood tree of 16S rRNA sequences showing termite gut-derived Treponema ASVs (in black) in relation to reference Treponema species and uncultured spirochetes (GenBank). Termite ASVs clustered exclusively with termite-specific Treponema lineages, and remained distinct from pathogenic Treponema (e.g., \u003cem\u003eT. pallidum\u003c/em\u003e complex, in red). Bootstrap support values (1,000 replicates) are indicated at nodes.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7962666/v1/b5d330046ee0d596acf870ae.png"},{"id":95818373,"identity":"cad86a3a-8617-4bff-b897-354af27dff74","added_by":"auto","created_at":"2025-11-13 10:14:32","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":191907,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGut microbiota differences between fungus-growing and non-fungus-growing termites.\u003c/strong\u003e\u003cbr\u003e\n(a) Bar plot of total phylum-level abundances showing dominance of Bacteroidota and Firmicutes in fungus-growing termites, whereas Spirochaetota and Fibrobacterota were more abundant in non-fungus-growing species.\u003cbr\u003e\n(b) Alpha diversity metrics (total reads, ASV richness, Shannon diversity) showed slightly higher diversity in fungus-growing termites.\u003cbr\u003e\n(c) Volcano plot of differential abundance testing highlights Acidobacteriota, Spirochaetota, and Fibrobacterota enriched in non-fungus-growers, while Patescibacteria, Thermoplasmatota, and Elusimicrobiota were enriched in fungus-growers (FDR \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7962666/v1/c8f309fdc7a03544db01a10b.png"},{"id":95818378,"identity":"67f704ba-b256-4b6c-a619-09c36d893187","added_by":"auto","created_at":"2025-11-13 10:14:32","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":333580,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImpact of glyphosate exposure on gut microbiota composition in Trinervitermes spp.\u003c/strong\u003e\u003cbr\u003e\n(a) Relative phylum-level abundances of \u003cem\u003eT. geminatus\u003c/em\u003e and \u003cem\u003eT. occidentalis\u003c/em\u003e from treated and untreated fields show dominance of Bacteroidota, Firmicutes, Spirochaetota, and Fibrobacterota, with no major compositional shifts. (b) Alpha diversity metrics (total reads, ASV richness, Shannon index) were slightly lower in glyphosate-treated termites, though not statistically significant. (c-d) Mann-Whitney U tests (relative and absolute abundances) confirmed the absence of significant differences between treated and untreated groups, suggesting limited microbiota disruption by glyphosate under the conditions studied.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7962666/v1/24bf19caacd70bafb3e8c306.png"},{"id":101191628,"identity":"4cd41454-8d1b-419b-a485-3fd2a9bc90e0","added_by":"auto","created_at":"2026-01-27 07:12:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3688097,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7962666/v1/ad347534-a50c-4c7c-8a53-f97733643c80.pdf"},{"id":95818371,"identity":"d0460f74-5f06-4112-947b-39a6194ab48f","added_by":"auto","created_at":"2025-11-13 10:14:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1205444,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementarydataSR.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7962666/v1/987bcee1d121afd6c9f5f6fa.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparative 16S rRNA metabarcoding analysis of gut bacterial diversity across seven termite species from Senegal: ecological roles and community structure","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTermites (Isoptera, Termitidae) comprise more than 3,000 described species and can account for up to 10% of animal biomass in some ecosystems [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Ecologically, they are social insects living in caste-organized colonies and playing major roles in soil structure, nutrient cycling, and organic matter decomposition. Some termite species, such as \u003cem\u003eMacrotermes bellicosus\u003c/em\u003e, are not only consumed by humans as a natural food source due to their significant protein and vitamin content, but are also used in traditional medicine as analgesics and anti-inflammatory agents [\u003cspan additionalcitationids=\"CR4 CR5 CR6\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Classically, termites are divided into \u0026ldquo;lower\u0026rdquo; and \u0026ldquo;higher\u0026rdquo; groups based on the presence of protozoa in the hindgut: the former harbor flagellated protists, whereas \u003cem\u003eTermitidae\u003c/em\u003e (\u0026ldquo;higher\u0026rdquo; termites) rely primarily on a bacterial microbiota to degrade lignocellulose, supported by a longer, compartmentalized gut [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Consequently, this microbiota contributes substantially to global carbon and nitrogen cycles [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Among higher termites, marked differences distinguish soil-feeding non-fungus-growing lineages (e.g., \u003cem\u003eTrinervitermes\u003c/em\u003e) from fungus-growing Macrotermitinae, including \u003cem\u003eMacrotermes, Ancistrotermes\u003c/em\u003e, and \u003cem\u003eMicrotermes\u003c/em\u003e, which cultivate \u003cem\u003eTermitomyces\u003c/em\u003e fungi. Accordingly, bacterial community composition is highly species-specific and shaped by diet, caste, and environment [\u003cspan additionalcitationids=\"CR14 CR15 CR16\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAfrican termite microbiomes are understudied [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Beyond their ecological functions, termite gut microbiota may also harbor taxa acting as reservoirs of antimicrobial resistance genes, reflecting a broader environmental dimension of the resistome [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Understanding the balance between protective microbial filters and potential carriers of multidrug resistance is essential for assessing both ecosystem health and possible spillover risks [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Alongside natural drivers, multiple anthropogenic pressures can disrupt these microbiomes, most notably the use of glyphosate-based herbicides (GBHs). Although the primary target of glyphosate (EPSPS in the shikimate pathway) is absent in animals, but present in plants and in many bacteria, indirect effects on insect gut microbiota have been reported, and its metabolite AMPA (aminomethylphosphonic acid) also raises ecotoxicological concerns [\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Among termite-associated bacteria, spirochetes of the genus \u003cem\u003eTreponema\u003c/em\u003e are of particular interest. These lineages are virtually absent from urban populations but rather associated with non-industrialized ones [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Their termite-specific clades are thought to play key roles in lignocellulose degradation, hydrogen metabolism, and nitrogen cycling [\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], highlighting their long-term coevolution with termites. Focusing on these symbionts thus provides insights into the functional specialization of the termite gut microbiota. In addition to dominant bacterial groups, low-abundance phyla such as Actinomycetota are also noteworthy. Members of this lineage, notably the genus \u003cem\u003eStreptomyces\u003c/em\u003e, are prolific producers of secondary metabolites, including antibiotics such as β-lactams and aminoglycosides [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Their detection in termite guts may thus provide valuable clues about microbial interactions, colonization resistance, and potential responses to environmental stressors [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn this study, we compare using 16S rRNA metabarcoding (QIIME 2) the gut bacterial communities of seven termite species from Senegalese agroecosystems (\u003cem\u003eMacrotermes bellicosus\u003c/em\u003e, \u003cem\u003eMacrotermes subhyalinus\u003c/em\u003e, \u003cem\u003eMicrotermes\u003c/em\u003e sp., \u003cem\u003eAncistrotermes cavithorax\u003c/em\u003e, \u003cem\u003eTrinervitermes geminatus\u003c/em\u003e, \u003cem\u003eTrinervitermes trinervius\u003c/em\u003e and \u003cem\u003eTrinervitermes occidentalis\u003c/em\u003e). We quantify alpha diversity (Shannon, Simpson) and beta diversity (Bray-Curtis; PERMANOVA) [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], describe taxonomic profiles with a focus on treponemes (phylum Spirochaetota), and test the association between environmental GBH exposure and microbiome structure. Finally, we consider the dual ecological role of the termite gut microbiota: on the one hand, it represents an obligatory symbiosis, since termites cannot survive without their gut bacteria, many of which are host-restricted; on the other hand, it acts as a biological filter, processing ingested environmental microbes and potentially selecting for resistant strains. In this context, termite gut communities may also constitute a possible reservoir of antimicrobial resistance genes, as has been observed in other animal-associated microbiomes, such as the gut of migratory birds carrying numerous ARGs [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e"},{"header":"Results","content":"\u003ch3\u003e1. Collection and molecular identification of termites\u003c/h3\u003e\n\u003cp\u003eTermites were collected from multiple sites across Senegal, yielding a total of 42 termitaries representing seven species: \u003cem\u003eMacrotermes subhyalinus\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;13), \u003cem\u003eMacrotermes bellicosus\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;9), \u003cem\u003eMicrotermes\u003c/em\u003e sp. (n\u0026thinsp;=\u0026thinsp;5), \u003cem\u003eAncistrotermes cavithorax\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;4), \u003cem\u003eTrinervitermes geminatus\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;5), \u003cem\u003eTrinervitermes occidentalis\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;5), and \u003cem\u003eTrinervitermes trinervius\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;1). Molecular identification based on the COI (Cytochrome c oxidase subunit I) gene produced high-quality sequences (650\u0026ndash;700 bp) with 92\u0026ndash;100% similarity to reference sequences in GenBank. The glyphosate exposure analysis was restricted to \u003cem\u003eTrinervitermes\u003c/em\u003e spp., which represented the only genus consistently found from both treated and untreated agricultural fields. Species-level identification confirmed five \u003cem\u003eT. geminatus\u003c/em\u003e, five \u003cem\u003eT. occidentalis\u003c/em\u003e, and one \u003cem\u003eT. trinervius\u003c/em\u003e. Because \u003cem\u003eT. trinervius\u003c/em\u003e was absent from glyphosate-treated fields, it was excluded from the glyphosate exposure comparison, which was therefore restricted to the 10 samples of \u003cem\u003eT. geminatus\u003c/em\u003e and \u003cem\u003eT. occidentalis\u003c/em\u003e (five per condition). All COI sequences generated in this study were deposited in GenBank under the accession numbers provided in \u003cstrong\u003eSupplementary data Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/strong\u003e.\u003c/p\u003e\n\u003cdiv class=\"Heading\"\u003e\u003cstrong\u003e2. Metagenomic analysis\u003c/strong\u003e\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n\u003ch2\u003e2.1. Data Visualization and Comparative Statistical Analysis of Termite Gut Microbiota\u003c/h2\u003e\n\u003cp\u003eThe analysis identified a total of 1,674 ASVs and 37,509 reads, spanning across 26 phyla and 132 genera (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e), and revealed that gut microbiota composition varies markedly across termite species, probably reflecting specific digestive adaptations and functional specialization of major bacterial phyla such as Spirochaetota, Bacillota, Bacteroidota, and Desulfobacterota (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cstrong\u003eSupplementary data Figure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/strong\u003e). The circular phylogenetic tree constructed from 16S rRNA gene sequences further illustrates this broad taxonomic diversity, with additional bacterial lineages including Fibrobacterota, Verrucomicrobiota, and Acidobacteriota (\u003cstrong\u003eSupplementary data Figure S2\u003c/strong\u003e). Interestingly, archaeal taxa were also recovered (mainly Euryarchaeota, along with Crenarchaeota, Halobacterota, and Thermoplasmatota). While only members of methanogens (Euryarchaeota) are established in termite guts, the detection of other archaeal lineages highlights additional archaeal diversity within these communities. The tree additionally highlights Candidate Phyla Radiation (CPR) groups such as Patescibacteria and Margulisbacteria, as well as predatory bacterial lineages like Bdellovibrionales from Myxococcota. (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Among the three phyla investigated, Spirochaetota clearly dominated the termite gut communities, with 12 ASVs representing a total of 4,698 reads distributed across nearly all species (\u003cstrong\u003eSupplementary data Figure S3\u003c/strong\u003e). This group accounted for the majority of reads in both fungus-growing and non-fungus-growing termites, consistent with the well-documented specialization of termite-associated \u003cem\u003eTreponema\u003c/em\u003e lineages. Actinomycetota were less abundant, represented by 15 ASVs (617 reads), but showed a higher contribution in fungus-growing termites (\u003cem\u003eMacrotermes\u003c/em\u003e spp.), suggesting a potential association with fungal substrate degradation or secondary metabolite (probably, antifungal and antibacterial) production. In contrast, Myxococcota were detected at very low levels (3 ASVs, 17 reads) and occurred sporadically among samples. Despite their rarity, their detection is noteworthy given the predatory lifestyle of many myxobacteria and their potential role in shaping microbial community dynamics. Overall, the barplot highlights a phylum-level structure dominated by Spirochaetota, complemented by low-abundance but ecologically relevant lineages such as Actinomycetota and Myxococcota. (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cstrong\u003eSupplementary data Figure S4\u003c/strong\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eComparison of sequencing and diversity metrics (GBH-treated vs. untreated).\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMetric\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMean (Treated)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSD (Treated)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMean (Untreated)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSD (Untreated)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMann-Whitney U\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ep-value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal reads\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e637.80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e98.92\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e943.60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e227.41\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.056\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eASV richness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e33.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e11.98\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e44.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8.93\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.222\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eShannon diversity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.222\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable reports mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD for total reads, ASV richness, and Shannon diversity, with Mann\u0026ndash;Whitney U tests. No significant differences are detected (p\u0026thinsp;\u0026ge;\u0026thinsp;0.056), though treated samples tend to have lower values across metrics.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\n\u003ch2\u003e2.2.1. Global Taxonomic Composition and Phylogenetic Structure\u003c/h2\u003e\n\u003cp\u003eThe circular phylogenetic tree and abundance heatmap revealed that termite gut bacterial communities are dominated by four major phyla Bacteroidota, Bacillota, Spirochaetota, and Fibrobacterota with an uneven distribution across host species (\u003cstrong\u003eSupplementary data Figures \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e-S2-S3\u003c/strong\u003e). The resulting tree showed that termite-derived ASVs clustered predominantly within termite-specific \u003cem\u003eTreponema\u003c/em\u003e lineages (e.g., the Termite \u003cem\u003eTreponema\u003c/em\u003e cluster), clearly separated from human pathogenic clades such as the \u003cem\u003eT. pallidum\u003c/em\u003e complex. Additional ASVs were grouped with uncultured \u003cem\u003eTreponema\u003c/em\u003e lineages closely related to \u003cem\u003eT. caldarium\u003c/em\u003e and other fermentative taxa commonly associated with polysaccharide degradation and hydrogen metabolism. No termite-derived sequences clustered with human or animal pathogens, supporting the host-specific adaptation and ecological specialization of \u003cem\u003eTreponema\u003c/em\u003e in the termite gut (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Trees were visualized and annotated with iTOL v6 (12), allowing the integration of metadata such as termite species and relative abundance (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cstrong\u003eSupplementary data Figure S3\u003c/strong\u003e). To further refine this phylogenetic assessment, analysis of 16S rRNA sequences within the phylum \u003cem\u003eSpirochaetota\u003c/em\u003e identified eight termite-derived ASVs, five (62.5%) clustering within termite-specific lineages such as the \u003cem\u003eTermite Treponema\u003c/em\u003e cluster and the M2PT2-76 group, highlighting their strong ecological specialization to the termite gut. Two ASVs (25%) grouped with uncultured \u003cem\u003eTreponema\u003c/em\u003e lineages were related to \u003cem\u003eT. caldarium\u003c/em\u003e and other anaerobic fermentative taxa involved in polysaccharide degradation, while the remaining ASV (12.5%) clustered with \u003cem\u003eSpirochaeta\u003c/em\u003e, a non-pathogenic genus implicated in lignocellulose breakdown (\u003cstrong\u003eSupplementary data Figure S5\u003c/strong\u003e). Phylogenetic analysis based on 16S rRNA gene sequences revealed that the \u003cem\u003eTreponema\u003c/em\u003e strains isolated from African termites were distributed into three well-supported clusters in relation to reference sequences from GenBank. The first cluster (red) included sequences forming a homogeneous group closely related to termite-associated \u003cem\u003eTreponema\u003c/em\u003e. The second cluster (blue) consisted of sequences that also grouped with termite-specific \u003cem\u003eTreponema\u003c/em\u003e but showed a distinct phylogenetic affinity with \u003cem\u003eT. paraluiscuniculi\u003c/em\u003e and \u003cem\u003eT. pallidum\u003c/em\u003e (JX120547.1, PV524036.1, M88726.1, DQ648782.1). The third cluster (green) comprised a divergent lineage including several \u003cem\u003eTreponema\u003c/em\u003e sequences associated with \u003cem\u003eMacrotermes bellicosus\u003c/em\u003e and \u003cem\u003eMacrotermes subhyalinus\u003c/em\u003e, originating from different geographic sampling sites, and also showing close relationships with \u003cem\u003eT. caldarium\u003c/em\u003e (MW652580.1, MW652583.1, MW652591.1). Reference sequences from GenBank used for phylogenetic anchoring are shown in black. The overall tree topology and bootstrap support values (\u0026gt;\u0026thinsp;70%) confirm the existence of three distinct \u003cem\u003eTreponema\u003c/em\u003e lineages associated with termite hosts, reflecting both intra- and interspecific diversity within the termite gut microbiota (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eOverall, these findings confirm the dominance of termite-adapted \u003cem\u003eTreponema\u003c/em\u003e lineages and their key ecological roles in lignocellulose degradation and nitrogen cycling, while demonstrating a clear evolutionary divergence from pathogenic counterparts.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n\u003ch2\u003e2.3. Gut microbiota comparison between fungus-growing and non-fungus-growing termites\u003c/h2\u003e\n\u003cp\u003eAt the phylum level, the gut microbiota of fungus-growing termites (\u003cem\u003eMacrotermes bellicosus, Macrotermes subhyalinus, Ancistrotermes cavithorax\u003c/em\u003e, and \u003cem\u003eMicrotermes\u003c/em\u003e sp.) was dominated by Bacteroidota (36.9% of reads, detected in 30/31 samples) and Bacillota (28.6%, 29/31), whereas non-fungus-growing termites (\u003cem\u003eTrinervitermes geminatus, T. trinervius, T. occidentalis\u003c/em\u003e) displayed higher proportions of Spirochaetota (35.1%, 11/11) and Fibrobacterota (18.4%, 11/11) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003cstrong\u003ea\u003c/strong\u003e, Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Alpha diversity indices did not differ significantly between the two groups; however, non-fungus-growers showed slightly higher microbial complexity, with mean ASV richness of 39.1\u0026thinsp;\u0026plusmn;\u0026thinsp;11.1 and Shannon diversity of 3.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28 compared to 39.8\u0026thinsp;\u0026plusmn;\u0026thinsp;21.3 and 3.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89 in fungus-growers (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003cstrong\u003eb\u003c/strong\u003e). Differential abundance testing confirmed these ecological shifts (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003cstrong\u003ec\u003c/strong\u003e), identifying Acidobacteriota (log₂FC = -4.18, FDR\u0026thinsp;=\u0026thinsp;8.95 \u0026times; 10⁻⁵), Spirochaetota (log₂FC = -2.52, FDR\u0026thinsp;=\u0026thinsp;0.0035), and Fibrobacterota (log₂FC = -2.36, FDR\u0026thinsp;=\u0026thinsp;0.0017) as significantly enriched in non-fungus-growing termites, while Patescibacteria (log₂FC\u0026thinsp;=\u0026thinsp;3.16, FDR\u0026thinsp;=\u0026thinsp;0.0031), Thermoplasmatota (log₂FC\u0026thinsp;=\u0026thinsp;3.58, FDR\u0026thinsp;=\u0026thinsp;0.0103), and Elusimicrobiota (log₂FC\u0026thinsp;=\u0026thinsp;2.60, FDR\u0026thinsp;=\u0026thinsp;0.0221) were significantly enriched in fungus-growers (\u003cstrong\u003eSupplementary data Figure S6\u003c/strong\u003e). Taken together, these results indicate that although overall diversity metrics are comparable, feeding ecology drives marked phylum-level compositional differences between the two termite groups.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003e2.4. Gut Microbiota Comparison in Glyphosate-Exposed vs Unexposed \u003cem\u003eTrinervitermes\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eThe gut microbiota of \u003cem\u003eTrinervitermes\u003c/em\u003e spp. collected from glyphosate-treated (DT30, DT31, DT15, DT24, DT25) and untreated fields (DT10, TC07, TC09, TC12, TC14) showed overall comparable diversity patterns (\u003cstrong\u003eSupplementary data Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/strong\u003e), with the dominant phyla in both groups being Bacillota, Bacteroidota, Spirochaetota, and Fibrobacterota \u003cstrong\u003e(\u003c/strong\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ea). Although the mean sequencing depth was slightly higher in untreated samples (943.60\u0026thinsp;\u0026plusmn;\u0026thinsp;227.41 reads) than in treated ones (637.80\u0026thinsp;\u0026plusmn;\u0026thinsp;98.92 reads), the difference was not statistically significant (U\u0026thinsp;=\u0026thinsp;3.00, p\u0026thinsp;=\u0026thinsp;0.06). Similarly, alpha diversity metrics, including ASV richness (44.20\u0026thinsp;\u0026plusmn;\u0026thinsp;8.93 in untreated vs. 33.00\u0026thinsp;\u0026plusmn;\u0026thinsp;11.98 in treated, U\u0026thinsp;=\u0026thinsp;6.00, p\u0026thinsp;=\u0026thinsp;0.22) and Shannon diversity (3.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22 vs. 3.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31, U\u0026thinsp;=\u0026thinsp;6.00, p\u0026thinsp;=\u0026thinsp;0.22), revealed slightly higher values in untreated termites (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003cstrong\u003eb\u003c/strong\u003e) although no statistical significance was observed (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Consistently, beta diversity analysis based on Bray-Curtis dissimilarities did not reveal a clear separation between groups, and PERMANOVA (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) confirmed the absence of significant compositional shifts (F_pseudo\u0026thinsp;=\u0026thinsp;0.26, R\u0026sup2; = 0.01, p\u0026thinsp;=\u0026thinsp;0.82). Furthermore, differential abundance testing at both the phylum and genus levels, corrected for multiple comparisons, identified no significantly different taxa (q\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003cstrong\u003ec\u003c/strong\u003e), although some phyla such as Fibrobacterota and Acidobacteriota tended to be relatively more abundant in untreated fields (Fig.\u0026nbsp;7.\u003cstrong\u003ec\u003c/strong\u003e). Taken together, these results indicate that while untreated termites displayed a tendency toward greater microbial richness and diversity, glyphosate exposure under the conditions tested did not induce significant shifts in either alpha or beta diversity, and only marginal trends were observed at the taxonomic level (\u003cstrong\u003eSupplementary data Figure S7\u003c/strong\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eBeta diversity by GBH treatment (Bray-Curtis PERMANOVA).\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMetric\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eF pseudo\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eR\u0026sup2;\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ep-value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBeta diversity (Bray\u0026ndash;Curtis, PERMANOVA)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.006\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.824\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003ePERMANOVA shows no treatment effect on community composition (pseudo-F\u0026thinsp;=\u0026thinsp;0.26, R\u0026sup2; = 0.006, p\u0026thinsp;=\u0026thinsp;0.824). GBH treatment explains\u0026thinsp;~\u0026thinsp;0.6% of variance-statistically non-significant. Statistical comparison of gut microbial community composition (Bray-Curtis dissimilarities) between glyphosate-treated and untreated \u003cem\u003eTrinervitermes\u003c/em\u003e spp. using PERMANOVA (999 permutations). Data are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) from rarefied ASV tables (depth\u0026thinsp;=\u0026thinsp;1900 reads per sample). Error estimates were based on permutation residuals, with 95% confidence intervals ranging from 0.18\u0026ndash;0.34 for \u003cem\u003eF\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;sub\u0026thinsp;\u0026gt;\u0026thinsp;pseudo\u0026lt;/sub\u0026thinsp;\u0026gt;\u0026thinsp;and 0.009\u0026ndash;0.015 for R\u0026sup2;. Variability among replicates was evaluated using SD, and no significant differences were detected (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.82). Alpha diversity indices (Observed ASVs and Shannon) are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD for descriptive comparison.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eComparative 16S rRNA metabarcoding analysis of seven termite species from Senegal provides new insights into the composition and structuring of termite gut microbiota, highlighting both ecological differentiation between fungus-growing and non-fungus-growing lineages and the potential effects of glyphosate exposure. Across all species, gut bacterial communities were dominated by Bacteroidota, Bacillota, Spirochaetota, and Fibrobacterota, in agreement with previous studies on higher termites [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], and these phyla are recognized for their functional roles in lignocellulose degradation and nitrogen cycling [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], thereby underscoring their ecological importance in termite-mediated decomposition. Although all termites were sampled on the same territory in close proximity to each other, when ecological groups were compared, clear differences emerged: fungus-growing termites (\u003cem\u003eMacrotermes\u003c/em\u003e spp.) were enriched in Bacteroidota and Bacillota, taxa commonly associated with the digestion of fungal biomass and plant polysaccharide fermentation [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], whereas non-fungus-growing termites (\u003cem\u003eTrinervitermes\u003c/em\u003e, \u003cem\u003eMicrotermes\u003c/em\u003e, \u003cem\u003eAncistrotermes\u003c/em\u003e) harbored higher relative abundances of Spirochaetota and Fibrobacterota, reflecting adaptations to soil-feeding and fibrous plant material digestion, as also noted in earlier studies [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Differential abundance analysis confirmed these ecological signatures, showing that specific phyla were consistently associated with either fungus-growing or non-fungus-growing termites, thereby supporting the hypothesis that feeding ecology is a primary driver of gut microbial structuring in termites, consistent with findings in other xylophagous insects [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In terms of diversity, alpha diversity indices did not differ significantly between fungus-growing and non-fungus-growing termites, although non-fungus-growers tended to display slightly higher microbial complexity, while beta diversity analysis revealed a clear separation of microbial communities between ecological groups, reinforcing the conclusion that feeding ecology drives strong shifts in microbiota composition without altering overall richness [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBeyond the dominant bacterial phyla, our phylogenetic and taxonomic analyses highlighted additional lineages of interest. Spirochaetota were particularly abundant, represented by 12 ASVs and totaling 4,698 reads, and were detected across nearly all termite species. Actinomycetota were also recovered, with 15 ASVs corresponding to 617 reads, and showed a higher contribution in fungus-growing termites (\u003cem\u003eMacrotermes\u003c/em\u003e spp.) compared to non-fungus-growing species. In contrast, Myxococcota were rare, represented by only 3 ASVs and 17 reads, and were detected sporadically among samples. Methanogenic archaea (Euryarchaeota) were consistently present, supporting their role in hydrogen turnover and methane production in termite guts [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Candidate phyla radiation (CPR) taxa, including Patescibacteria and Margulisbacteria, were also identified, together with low-abundance predatory bacterial groups, reflecting the broad phylogenetic diversity of termite gut communities. CPR taxa were also present, pointing to symbiotic or parasitic bacterial groups with reduced genomes that may depend on cross-feeding interactions, as reported in other insect-associated microbiomes [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. In addition, predatory taxa such as \u003cem\u003eBdellovibrionales\u003c/em\u003e (Myxococcota) were recovered, suggesting that microbial predation contributes to community regulation and may help stabilize dense microbial consortia in termite hindguts [\u003cspan additionalcitationids=\"CR43\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Within the Spirochaetota phylum, termite-derived \u003cem\u003eTreponema\u003c/em\u003e ASVs clustered exclusively with termite-adapted lineages, clustered exclusively with termite-adapted lineages, in contrast to most vertebrate-associated microbiomes where \u003cem\u003eTreponema\u003c/em\u003e is rarely detected [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. This finding underscores the long-term coevolution of termites and their treponemes, which appear to specialize in lignocellulose degradation, hydrogen turnover, and nitrogen metabolism, thus reinforcing their pivotal ecological role [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. None of the treponemal ASVs could be attributed to one of few known termite-associated \u003cem\u003eTreponema\u003c/em\u003e species such as \u003cem\u003eLeadbettera azotonutricia\u003c/em\u003e or \u003cem\u003eTreponema primitia\u003c/em\u003e, such suggesting probable species-specific association among termites and treponemes.\u003c/p\u003e\u003cp\u003eActinomycetota were also detected at low abundance. Although relatively minor, members of this phylum are known for their capacity to produce secondary metabolites and for their tolerance to environmental pollutants, suggesting a potential contribution to microbial interactions and resilience in termite guts. Such effects may be, at least partially, responsible for antimicrobial activity of fungus combs constructed by fungus-growing higher termites from the faeces of young workers [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Moreover, mediated by Actinomycetota permanent presence of antibacterials in the gut of termites and, subsequently, in fungus combs, may play role in natural selection of antibiotic-resistance in bacteria, such, for example, \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eActinomycetota may be also relevant in the context of glyphosate exposure, although no significant differences were detected between treated and untreated termites in our study [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Actinomycetota have been implicated in tolerance to environmental pollutants, raising the possibility that their persistence in termites is linked to detoxification processes. This is particularly relevant in the context of glyphosate exposure. Although our comparison of \u003cem\u003eTrinervitermes\u003c/em\u003e spp. from treated and untreated fields revealed no significant differences in alpha or beta diversity, glyphosate and its metabolite AMPA are known to impact microbial communities in soils and insects [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The presence of Actinomycetota in our samples may thus indicate adaptive responses to herbicide stress or a protective role within termite guts, consistent with the known ability of some actinomycetes (Actinomycetota)within this phylum to produce secondary metabolites and influence microbial interactions. However, the absence of detectable compositional changes between treated and untreated groups could reflect the timing of sampling relative to herbicide application, microbial resilience mechanisms, or functional redundancy within the community. Previous studies in bees and beetles demonstrated that glyphosate can alter gut microbiota under certain exposure regimes [\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], suggesting that stronger effects might be revealed in termites under different temporal or environmental contexts.\u003c/p\u003e\u003cp\u003eTaken together, our results demonstrate that feeding ecology exerts a stronger influence than agrochemical exposure in shaping termite gut microbiota. Fungus-growing and non-fungus-growing termites harbor distinct phylum-level signatures reflecting their dietary strategies, while glyphosate-treated and untreated termites displayed broadly comparable communities. Importantly, the apparent stability of termite gut microbiota following glyphosate exposure should not be equated with functional stability, since metabolic shifts may occur in the absence of taxonomic changes. Future studies employing shotgun metagenomics, metatranscriptomics or culturomics will therefore be essential to assess functional resilience, detect sub-lethal effects of herbicide exposure, and clarify the ecological roles of archaeal, CPR, predatory, and actinomycete lineages in termite gut ecosystems.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, our analysis reveals that termite feeding ecology exerts a stronger influence than glyphosate exposure on gut microbiota composition. Fungus-growing termites were enriched in Bacteroidota and Bacillota, whereas non-fungus-growing termites harbored more Spirochaetota and Fibrobacterota, reflecting dietary adaptations. Although glyphosate treatment did not significantly alter community structure, the presence of archaeal methanogens, CPR taxa, and Actinomycetota points to overlooked microbial groups that may contribute to functional resilience. Together, these findings underscore the central role of diet in shaping termite gut microbiota, while also raising new questions about microbial interactions and responses to environmental stressors.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\n\u003ch3\u003e1. Study area and termite collection\u003c/h3\u003e\n\u003cp\u003eTermites were collected from eight sites across Senegal: 50 Km-Thi\u0026eacute;s (Thi\u0026egrave;s region), Oussouye and Ziguinchor (Lower Casamance region), and the villages of Dand\u0026eacute;, Dind\u0026eacute;felo, Kondoji, Nandoumary, and S\u0026eacute;gou in the K\u0026eacute;dougou region (\u003cb\u003eSupplementary data Table S2)\u003c/b\u003e. Sampling was conducted between October 30th and December 20th, 2020. At each site, colonies were selected from fields either regularly treated with glyphosate-based herbicides or from untreated fields, based on information provided by local farmers (\u003cb\u003eSupplementary data Table S2)\u003c/b\u003e. Written permission for collection was obtained from the owners of the fields. Adult termites, including both soldiers and workers, were manually collected from termite mounds using forceps and shovels. For each mound, the surrounding soil substrates and, when present, fungus combs were also sampled and transported alongside collected termites in ventilated plastic boxes kept at room temperature. A part of the termites was preserved aseptically in 70% ethanol, while the remaining samples were frozen at \u0026minus;\u0026thinsp;20\u0026deg;C until further processing.\u003c/p\u003e\n\u003ch3\u003e2. Molecular analysis\u003c/h3\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Molecular analysis\u003c/h2\u003e\u003cp\u003eA longitudinal section was made using a sterile surgical blade to obtain two equal parts of each termite used. One part was used for molecular analyses. The second part was stored in a sterile polypropylene tube and kept at -20\u0026deg;C for further analysis. Genomic DNA was extracted individually using half of each termite. DNA was extracted using an EZ1 DNA Tissue Kit (Qiagen), according to the manufacturer's recommendations. Prior to DNA extraction, a physical bead-based disruption of the sample using the Tissue-Lyser apparatus (Qiagen, Hilden, Germany) and 24 h of enzymatic digestion at 56 \u0026deg; C using G2 buffer supplemented with 20% proteinase K were performed. DNA was eluted in 100 \u0026micro;L and stored at -20\u0026deg;C until further use.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Molecular identification of termites\u003c/h2\u003e\u003cp\u003eStandard Polymerase Chain Reaction (PCR) targeting the Cytochrome c oxidase subunit I (COI) gene was employed to achieve species-level identification of termites [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Each PCR reaction was carried out in a total volume of 50 \u0026micro;L, containing 25 \u0026micro;L AmpliTaq Gold master mix, 18 \u0026micro;L DNase-/RNase-free ultrapure water, 1 \u0026micro;L of each primer at 20 \u0026micro;M, and 5 \u0026micro;L of DNA template. Thermal cycling conditions consisted of an initial incubation at 95\u0026deg;C for 15 min, followed by 40 cycles of denaturation at 95\u0026deg;C for 1 min, annealing at the specific melting temperature of each primer pair for 30 s, and extension at 72\u0026deg;C for 30 s to 1 min depending on fragment size, with a final elongation step of 5 min at 72\u0026deg;C. Amplifications were performed using an Applied Biosystems 2720 Thermal Cycler (Applied Biosystems, Thermo Fisher Scientific, Waltham, MA, USA), and PCR products were resolved on 1.5% agarose gels. Amplicons were subsequently purified using NucleoFast 96 PCR plates (Macherey-Nagel EURL, Hoerdt, France) according to the manufacturer\u0026rsquo;s instructions and sequenced with the BigDye Terminator v3.1 Cycle Sequencing Kit (Perkin Elmer Applied Biosystems, Foster City, CA, USA) on an ABI automated sequencer (Applied Biosystems). Resulting electropherograms were assembled and manually edited using ChromasPro v1.7 (Technelysium Pty Ltd., Tewantin, Australia). The edited sequences were then compared against the GenBank database using NCBI BLAST (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://blast.ncbi.nlm.nih.gov/Blast.cgi\u003c/span\u003e\u003cspan address=\"https://blast.ncbi.nlm.nih.gov/Blast.cgi\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and further cross-validated among themselves as well as with related reference sequences. Phylogenetic relationships were inferred using the neighbor-joining method, and trees were reconstructed with MEGA v7 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.megasoftware.net/\u003c/span\u003e\u003cspan address=\"https://www.megasoftware.net/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Branch robustness was assessed by bootstrap analysis with 1,000 replicates.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003e3. Metagenomic sequencing\u003c/h3\u003e\n\u003cp\u003eMetagenomic DNA was amplified for the 16S rRNA \u0026ldquo;V3-V4\u0026rdquo; regions using PCR with 45 cycles, employing the Kapa HiFi HotStart ReadyMix 2\u0026times; (Kapa Biosystems Inc., Wilmington, MA, USA) and the conserved region primers V3-V4 with Illumina overhang adapters (Supplementary data Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Amplicons were purified using AMPure XP magnetic beads (Beckman Coulter Inc., Fullerton, CA, USA), and DNA concentration was measured with a Qubit fluorometer using the High Sensitivity assay (Life Technologies, Carlsbad, CA, USA). Samples were then normalized to 3.5 ng/\u0026micro;L prior to library preparation. At this stage, libraries generated with protocol 1 were pooled volume-to-volume with those from protocol 5, after which Illumina sequencing adapters and dual-index barcodes were ligated to the amplicons. A second round of purification was performed with AMPure XP beads, and the resulting libraries were pooled into two sequencing sets: one comprising 95 multiplexed samples and the other 41 samples. Global library concentration was quantified with a Qubit assay (High Sensitivity kit), and the final pool was diluted to 8 pM before sequencing. Cluster generation and paired-end sequencing (2 \u0026times; 250 Base pairs (bp)) with dual-index reads were carried out on an Illumina MiSeq platform (Illumina Inc., San Diego, CA, USA) in a single 39-hour run. Paired-end reads were quality-filtered, and raw data were demultiplexed into FASTQ files (84 files total for R1 and R2 reads), which were deposited in the NCBI BioProject database under accession number PRJNA1304467 (Supplementary data Table S2).\u003c/p\u003e\n\u003ch3\u003e4. QIIME 2-Based Microbiome Analysis\u003c/h3\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e\u003cb\u003e4.1. Microbial Diversity and Phylogeny via QIIME 2\u003c/b\u003e\u003c/h2\u003e\u003cp\u003eRaw 16S rRNA gene sequencing data were processed using QIIME 2 (version 2024.2) [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], following a standardized pipeline for microbial community analysis. Paired-end FASTQ reads were imported using the PairedEndFastqManifestPhred33V2 format and subjected to quality filtering and denoising with DADA2 [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], which performs error correction, chimera removal, and generation of high-resolution Amplicon Sequence Variants (ASVs). Representative sequences were aligned using MAFFT [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e] and a phylogenetic tree was constructed using FastTree2 [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], yielding both unrooted and rooted trees for downstream phylogenetic analyses. Taxonomic classification was performed using a Naive Bayes classifier trained on the SILVA 138 reference database at 99% similarity [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e], allowing taxonomic resolution from phylum to genus. Alpha and beta diversity metrics (Bray-Curtis, unweighted and weighted UniFrac distances) were calculated using the core-metrics-phylogenetic workflow, based on rarefied feature tables with a sampling depth of 1900 sequences. Group differences in community composition were assessed using PERMANOVA [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Taxonomic profiles were visualized via interactive \u0026ldquo;\u0026laquo;\u003cem\u003ebarplot\u003c/em\u003e\u0026raquo;\u0026rdquo; (QIIME taxa \u0026laquo;\u003cem\u003ebarplot\u003c/em\u003e\u0026raquo;), and ordination results were explored using Principal Coordinates Analysis (PCoA) plots rendered with Emperor [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Phylogenetic trees and relative abundance data (heatmap) were exported and visualized using the Interactive Tree of Life (iTOL v6) platform [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e] for annotated tree representations.\u003c/p\u003e\u003cp\u003eAll statistical analyses and visualizations were conducted using Python v3.10 and the scientific libraries pandas for data wrangling, \u003cem\u003eNumPy\u003c/em\u003e for numerical computation, and \u003cem\u003eseaborn\u003c/em\u003e and \u003cem\u003ematplotlib\u003c/em\u003e for data visualization. Taxonomic abundance tables generated from QIIME 2 outputs were processed to calculate absolute and relative abundances at the phylum, genus, and species levels. Data were grouped according to termite species and ecological traits, aggregated with \u003cem\u003epandas groupby\u003c/em\u003e operations, and visualized using stacked bar plots constructed with \u0026laquo;\u003cem\u003ebarplot\u003c/em\u003e\u0026raquo; and \u0026laquo;\u003cem\u003ecatplot\u003c/em\u003e\u0026raquo; functions to illustrate microbial composition across taxa. Principal Coordinates Analysis plots based on Bray-Curtis dissimilarities were exported from QIIME 2 and visualized either with the Emperor tool or redrawn in Python using extracted coordinates. To examine group-wise distributions of microbial abundance, histograms and kernel density estimates were generated with seaborn \u0026laquo;\u003cem\u003ehistplot\u003c/em\u003e\u0026raquo; and \u0026laquo;\u003cem\u003ekdeplot\u003c/em\u003e\u0026raquo; functions. \u003cem\u003eBoxplots\u003c/em\u003e were used to evaluate differences in relative abundance between treatment groups, while dot plots were applied to illustrate individual sample variability. Data preprocessing steps included filtering, logarithmic transformations, and normalization. All Figures were produced in high-resolution format (PNG, 300 dpi) suitable for publication.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e\u003cb\u003e4.2. Phylogenetic Tree of Spirochaetota Derived from 16S rRNA Metagenomic Analysis\u003c/b\u003e\u003c/h2\u003e\u003cp\u003eSequences assigned to the phylum Spirochaetota were extracted from the taxonomic classification results of the 16S rRNA metabarcoding pipeline. Representative sequences were selected based on amplicon sequence variants (ASVs) obtained after quality filtering and chimera removal with DADA2. Multiple sequence alignment of these ASVs was performed with MAFFT v7 [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e] using default parameters, and the aligned sequences were trimmed to the V3-V4 region to ensure positional homology. As most termite-derived \u003cem\u003eSpirochaetota\u003c/em\u003e ASVs belonged to the genus \u003cem\u003eTreponema\u003c/em\u003e, but many could not be resolved beyond this rank, 28 reference \u003cem\u003eTreponema\u003c/em\u003e sequences from NCBI were incorporated into the alignment. This step provided a broader phylogenetic framework and enabled the placement of termite ASVs relative to known clades, including those containing potentially pathogenic species. Phylogenetic trees were inferred with IQ-TREE v2 [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e] using the maximum-likelihood method under the GTR\u0026thinsp;+\u0026thinsp;F\u0026thinsp;+\u0026thinsp;I\u0026thinsp;+\u0026thinsp;G4 (\u003cb\u003eGTR\u003c/b\u003e\u0026thinsp;=\u0026thinsp;General Time Reversible model, \u003cb\u003eF\u003c/b\u003e\u0026thinsp;=\u0026thinsp;Empirical base frequencies, \u003cb\u003eI\u003c/b\u003e\u0026thinsp;=\u0026thinsp;Invariant sites and \u003cb\u003eG4\u003c/b\u003e\u0026thinsp;=\u0026thinsp;Gamma-distributed rate heterogeneity with 4 categories) model selected by ModelFinder and 1,000 ultrafast bootstrap replicates to assess branch support.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e4.3. Gut microbiota comparison between fungus-growing and non-fungus-growing termites\u003c/h2\u003e\u003cp\u003eGut microbiota profiles of fungus-growing termites (\u003cem\u003eMacrotermes bellicosus, Macrotermes subhyalinus, Ancistrotermes cavithorax, Microtermes\u003c/em\u003e sp.) and non-fungus-growing termites (\u003cem\u003eTrinervitermes geminatus, Trinervitermes trinervius, Trinervitermes occidentalis\u003c/em\u003e) were compared at the phylum level. Amplicon sequence variant (ASV) tables generated with QIIME 2 were aggregated by ecological groups to calculate relative abundances. Differences in phylum-level abundances between the two groups were assessed using the non-parametric Mann-Whitney U test, and p-values were corrected for multiple testing with the Benjamini-Hochberg false discovery rate (FDR) method, with FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered significant. Alpha diversity indices (ASV richness and Shannon diversity) were computed per sample, and group differences were tested with the same non-parametric framework. Visualization included bar charts for relative abundances, \u003cem\u003eboxplots\u003c/em\u003e for diversity comparisons, and volcano plots displaying log₂ fold change versus -log₁₀ (p-value). All analyses and visualizations were performed in Python (\u003cem\u003epandas, matplotlib, seaborn\u003c/em\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e4.4. \u003cb\u003eComparison Between termites collected on Glyphosate Treated and Untreated Fields\u003c/b\u003e\u003c/h2\u003e\u003cp\u003eTo evaluate the potential impact of glyphosate exposure on the gut microbiota of termites (\u003cem\u003eTrinervitermes\u003c/em\u003e spp.), we compared five samples from glyphosate-treated fields (DT30, DT31, DT15, DT24 and DT25) with five samples from untreated fields (DT10, TC07, TC09, TC12 and TC14), as \u003cem\u003eTrinervitermes\u003c/em\u003e was the most represented genus across both environments. Sequencing data were processed through the QIIME 2 pipeline (v2023.5;[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]) using DADA2 for denoising [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], and taxonomic assignments were performed against the SILVA 138 database [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e] with a confidence threshold of 0.7. Alpha diversity metrics, including observed ASV richness, Shannon index, and total read counts, were calculated using the QIIME 2 diversity plugin, while beta diversity was assessed with Bray Curtis dissimilarities [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e] and visualized through Principal Coordinates Analysis (PCoA) with the vegan package in R [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. To test statistical differences, group-wise alpha diversity comparisons were conducted using the Mann-Whitney U test, whereas variations in community composition were evaluated with PERMANOVA [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e] based on 999 permutations. Furthermore, differential abundance analyses at both the phylum and genus levels were performed using Mann-Whitney tests on relative abundances, followed by Benjamini-Hochberg correction for multiple testing [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Finally, data wrangling, statistical analyses, and visualizations, including \u003cem\u003ebarplots\u003c/em\u003e, diversity tables, and ordination Figures, were carried out using a combination of R [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e] and Python [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e], ensuring robust and reproducible analytical outputs.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003es\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;We are grateful to the Direction des Parcs Nationaux and the Direction des Eaux et Forests Chasses et de la Conservation des Sols for permission to work in Senegal. We sincerely thank the team of the Jane Goodall Institute Senegal for their kind assistance, cooperation, and continuous support during this research. We would like to thank the Infectiopole Sud Foundation for the thesis grant awarded to Cheikh Tidiane HOUMENOU.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by a grant from the French Government managed by the National Research Agency under the “Investissements d’avenir (Investments for the Future)” programme with the reference ANR-10-IAHU-03 (Méditerranée Infection), by the Contrat Plan Etat-Région and the European funding FEDER IHUPERF.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitutional Review Board Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki and approved by the National Health Research Ethics Committee of the Ministry of Health and Social Action of Senegal \u003cstrong\u003e(NAGOYA No:001042; 2021-09-08\u003c/strong\u003e). The samples were collected during patient care with their informed consent, specifically for the purposes of scientific and non-profit research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC.T.H.\u003c/strong\u003e and \u003cstrong\u003eO.M.\u003c/strong\u003e conceived and designed the study. \u003cstrong\u003eC.S.\u003c/strong\u003e obtained the official authorization for field sampling in Senegal (Nagoya Protocol). \u003cstrong\u003eM.G.\u003c/strong\u003e and \u003cstrong\u003eO.M.\u003c/strong\u003e conducted the field collections and specimen identification. \u003cstrong\u003eM.G.\u003c/strong\u003e performed molecular analyses and prepared the 16S rRNA sequencing. \u003cstrong\u003eC.T.H.\u003c/strong\u003e processed the sequencing data and carried out bioinformatic analyses under the supervision of \u003cstrong\u003eO.M.\u003c/strong\u003e \u003cstrong\u003eC.T.H.\u003c/strong\u003e and \u003cstrong\u003eO.M.\u003c/strong\u003e interpreted the results and wrote the manuscript. \u003cstrong\u003eF.F.\u003c/strong\u003e and \u003cstrong\u003eC.S.\u003c/strong\u003e contributed to data interpretation and critical revision of the manuscript. \u003cstrong\u003eO.M.\u003c/strong\u003e supervised the overall project, secured funding, and approved the final version of the manuscript. All authors read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll raw 16S rRNA metabarcoding sequence data generated and analyzed in this study have been deposited in the NCBI Sequence Read Archive (SRA) under the BioProject accession number\u0026nbsp;\u003cstrong\u003ePRJNA1304467\u0026nbsp;\u003c/strong\u003e(Supplementary data Table S2).\u003cbr\u003e\u0026nbsp;All scripts and bioinformatic workflows used for data processing (QIIME 2 pipeline, diversity analyses, and taxonomic assignments) are available upon reasonable request from the corresponding author. Additional processed data supporting the findings of this study are included within the article and its supplementary information files.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompliance with Ethical Standards Conflict of interest\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest (“\u003cem\u003eThe authors declare no competing interests\u003c/em\u003e\u003cem\u003e”).\u003c/em\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLegendre, F. et al. The phylogeny of termites (Dictyoptera: Isoptera) based on mitochondrial and nuclear markers: Implications for the evolution of the worker and pseudergate castes, and foraging behaviors. \u003cem\u003eMol. Phylogenet. Evol.\u003c/em\u003e \u003cb\u003e48\u003c/b\u003e, 615\u0026ndash;627 (2008).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAhmad, I. et al. 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The analysis identified 1,674 ASVs, spanning 26 phyla and 132 genera. Dominant phyla in both type of termites were Bacteroidota, Bacillota, Spirochaetota, and Fibrobacterota. Fungus-growers were enriched in Candidatus Patescibacteria, Pseudomonadota, and Planctomycetota, while non-fungus-growers harbored more Acidobacteriota, Spirochaetota, and Fibrobacterota. Differential abundance confirmed Acidobacteriota, Spirochaetota, and Fibrobacterota as enriched in non-fungus-growers (FDR \u0026amp;lt; 0.05), and Patescibacteria, Thermoplasmatota, and Elusimicrobiota\u003c/p\u003e\u003cp\u003ein fungus-growers. Among minor phyla, Spirochaetota comprised 12 ASVs, Actinomycetota 15 ASVs, more frequent in fungus-growers, and Myxococcota 3 ASVs, sporadically detected. Phylogenetic analysis revealed termite-specific clustering of Treponema ASVs, consistent with long-term host-symbiont co-diversification. In Trinervitermes spp., glyphosate-treated and untreated colonies showed no significant differences in alpha or beta diversity, though untreated samples tended towards higher richness. Overall, it seems that feeding ecology shaped termite gut microbiota more strongly than possible glyphosate exposure. The detection of the Actinomycetota phylum (which includes Streptomyces spp.) suggests antibiotic potential, while possible resistance genes and microbial filtering highlight the need for meta-omics approaches to link community structure with biosynthetic capacity and antimicrobial resistance.\u003c/p\u003e","manuscriptTitle":"Comparative 16S rRNA metabarcoding analysis of gut bacterial diversity across seven termite species from Senegal: ecological roles and community structure","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-13 10:14:27","doi":"10.21203/rs.3.rs-7962666/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1b942cc0-738a-4cd0-bed4-1f5c745f5053","owner":[],"postedDate":"November 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":57855562,"name":"Biological sciences/Ecology"},{"id":57855563,"name":"Earth and environmental sciences/Ecology"},{"id":57855564,"name":"Biological sciences/Microbiology"}],"tags":[],"updatedAt":"2026-01-27T07:11:24+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-13 10:14:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7962666","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7962666","identity":"rs-7962666","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}