Host diet and phylogeny interact to shape the bacterial and fungal microbiome in the regurgitant of four Spodoptera species

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C. Cuny, Guillaume Cailleau, Pilar Junier, Betty Benrey This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6114576/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Jul, 2025 Read the published version in Microbial Ecology → Version 1 posted 13 You are reading this latest preprint version Abstract The gut microbiome of Lepidopteran insects is highly dynamic, influenced by both host diet and phylogeny. While microbial communities are thought to facilitate host adaptation to diverse diets and environments, the existence of a core microbiome shared among closely related herbivores remains largely untested. In this study, we examined the microbial communities in the regurgitant of four S podoptera species ( S. exigua , S. frugiperda , S. latifascia , and S. littoralis ) across different diets (artificial diet, cotton, maize, and squash). Using a high-throughput sequencing, we characterized bacterial and fungal community composition and diversity. Bacterial communities were shaped by both diet and host species, indicating species-specific bacterial selection. In contrast, fungal communities were exclusively structured by diet, with lower diversity and dominance of a few key taxa. Notably, no operational taxonomic units were consistently shared across all species or diets, challenging the concept of a conserved core microbiome in these generalist herbivores. Understanding how microbial communities shape generalist herbivores’ ability to feed on diverse plants may offer potential strategies for microbiome-based pest management. Microbial community 16S rRNA sequencing ITS rRNA sequencing gut bacteria gut fungi microbiota foregut microbiome Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Herbivorous insects often associate with gut microorganisms, including bacteria and fungi, which influence various aspects of their biology [ 1 , 2 ]. Among the factors influencing these interactions, diet plays a central role [ 3 ]. This is particularly evident in lepidopteran larvae, whose gut microbiome is characterized as simple and highly variable [ 4 ]. Such variability is attributed to continuous moulting [ 5 ], the absence of specialized gut compartments, and the alkaline conditions of their digestive system [ 6 ]. As a result, Lepidopteran gut microbiomes are largely determined by environmental factors and diet, showing variation both among and within species [ 7 ]. Despite this variability, gut bacteria contribute to key functions, including digestion [ 8 ], use of suboptimal diets [ 9 ], pathogen resistance [ 10 ], insecticide resistance [ 11 ] and manipulation of plant defences [ 12 ]. While some studies suggest that caterpillars lack a resident gut microbiome [ 13 ], others have shown that some bacteria can colonize and persist in their gut [ 5 , 9 , 14 ]. It has also been proposed that a subset of functionally important bacteria may form a core microbiome shared across lepidopteran species [ 15 , 16 ]. For such core microbiome to exist, either vertical transmission or constant horizontal acquisition of microbes would be required [ 7 ]. However, only a limited number of systems have tested this hypothesis, yielding inconsistent results [ 4 , 7 , 17 , 18 ]. Thus, whether closely related herbivorous insects harbour a core gut microbiome remains unresolved. The superfamily Noctuoidea is the largest and more diverse group within Lepidoptera, including many agricultural pest species [ 19 , 20 ]. Within this group, the genus Spodoptera (Lepidoptera: Noctuidae) comprises several economically important species, including S. frugiperda , S. littoralis , S. exigua and S. latifascia [ 19 , 21 ]. S podoptera frugiperda and S. exigua have invaded multiple continents, and all four species are highly polyphagous [ 19 , 22 ]. Understanding the factors enabling Spodoptera species to exploit diverse host plants is crucial for pest management strategies that target the insect microbiome through biochemicals or foreign microbes [ 23 ]. Gut microbes are thought to play a key role in this adaptability by facilitating host shifts and supporting polyphagous diets [ 7 ]. Over the past decade, several studies have characterized the gut bacterial composition of S. frugiperda [ 24 – 29 ], S. littoralis [ 4 , 5 , 30 , 31 ] and S. exigua [ 32 – 36 ]. However, the gut microbiome of S. latifascia remains unexplored, and no comparative studies have examined gut microbiota across multiple Spodoptera species. This limits our understanding on how microbial communities contribute to the genus’ dietary adaptability. In contrast to bacteria, the fungal microbiota of Spodoptera caterpillars has received less attention [ 37 – 39 ], despite evidence from other lepidopteran insects showing that fungi are common in their guts [ 40 – 43 ]. Like bacteria, gut fungi may contribute to food detoxification and nutrient supply [ 44 , 45 ], but it is unclear whether fungal microbiota is as simple and variable as bacterial communities in lepidopteran species. In this study, we hypothesized that Spodoptera species may share a core microbiome that facilitates their adaptation to diverse host plants. To test this, we examined 1) whether microbial community compositions in the regurgitant of four Spodoptera species ( S. exigua , S. frugiperda , S. latifascia , and S. littoralis ) exhibit similarities, and 2) how these communities are influenced by different diets (artificial diet, cotton, maize and squash). These insights can inform pest management strategies and contribute to broader ecological and evolutionary perspectives on host-microbe interactions. Materials and Methods Artificial diet and plants The artificial diet used in this experiment (“beet armyworm diet”) was obtained from BioServ (U.S.A). Cotton, maize and squash plants were grown from seeds. Cotton seeds were collected on feral plants near Puerto Escondido (Oaxaca, Mexico). Maize and squash seeds were obtained from Delley Semences et Plantes SA (Switzerland) and Zollinger (biolgische Samengartenerei (Switzerland), respectively. These diets were selected to reflect the natural feeding habits of Spodoptera species, with artificial diet serving as a standardized control and the plant species representing common agricultural crops frequently consumed by these pests. Insects Spodoptera littoralis is native to Africa and has spread to Southern Europe and the Middle East, where it feeds on crops such as wheat, maize, cotton and rice [ 46 ]. S. exigua , originally from Asia, has become a globally distributed pest that targets major crops such as cotton, soybean, potato and sugar beet [ 47 ]. S. frugiperda is an invasive species native to the Americas, primarily attacking maize and other cereals [ 22 ]. S. latifascia is a polyphagous insect native to Mexico and Central America that commonly feeds on maize, bean, cotton and potato [ 48 ]. Eggs of S. littoralis and S. exigua were obtained from Syngenta (Stein, Switzerland) and Entomos AG (Grossdietwil,Switzerland), respectively. S. frugiperda and S. latifascia were initially collected near Puerto Escondido (Oaxaca, Mexico; 15°55’33.3”N,97°09’03.0”W) and reared for several generations on a chickpea flour-based artificial diet [ 49 ] under controlled conditions (26°C, 60% r.h., and L12:D12 photoperiod) at the University of Neuchâtel. Experimental design A total of 140 seeds from each plant species were individually sown in plastic pots and placed in a greenhouse. After one month, three egg batches from each Spodoptera species were placed in separate Petri dishes, in a climate-controlled room (24 ± 2°C, 40 ± 5% r.h.), under light benches (16:8 h L:D, approx.150 lmol m − 2 sec − 1 ), until the end of the experiment. All eggs hatched on the same day, except for S. latifascia , which hatched one day later. On the day of hatching, first instar caterpillars were randomly selected from the three egg batches and transferred to 16 rearing trays (one tray with 32 cells per diet treatment, Frontier Agricultural Sciences, USA). Two caterpillars were placed in each cell to compensate for the high mortality of first instars. After one week, if both caterpillars survived, one was removed. Caterpillars were fed one of the four diet treatments: small cubes (1cm 3 ) of artificial diet or cut pieces of cotton, maize or squash leaves (2cm 2 ). Food was replaced as needed, at least twice per week. Once caterpillars reached the fourth instar, regurgitant was collected daily over three consecutive days. To collect regurgitant, larvae were gently poked by hand and placed in a clean Petri dish. Regurgitant was then extracted using a 20-200ul pipet and transferred into 1.5mL Eppendorf tubes stored at -80°C until further analyses. Each Eppendorf tube contained pooled regurgitant from 10–11 individuals. In total, we obtained three regurgitant solutions per combination of Spodoptera species and diet (number of replicates per treatment combination: n = 3). To prevent cross-contamination, gloves, Petri dishes and pipet tips were changed between treatments, and the work surface was disinfected with alcohol after each procedure. DNA extraction and sequencing DNA was extracted using the FastDNA Spin Kit for soil (MP Biomedicals), following the standard protocol provided with the kit. DNA quantification was performed using the Qubit® dsDNA HS Assay Kit on a Qubit® 2.0 Fluorometer (Invitrogen, Carlsbad, CA, USA). Purified DNA extracts were sent to Fasteris (Geneva, Switzerland) for 16S rDNA and ITS amplicon sequencing using an Illumina MiSeq platform (Illumina, San Diego, USA), generating 250 bp paired-end reads. For the 16S rDNA, the V3–V4 region was amplified using the universal primers Bakt_341F (5’-CCT ACG GGN GGC WGC AG-3’) and Bakt_805R (5’-GAC TAC HVG GGT ATC TAA TCC-3’) [ 50 ]. The primers ITS3_KYO2 (5’-GAT GAA GAA CGY AGY RAA-3’) and ITS4 (5’-TCC TCC GCT TAT TGA TAT GC-3’) [ 51 ] were used for the amplification of the ITS2 region. Bioinformatic analysis Demultiplexed and trimmed sequence reads provided by Fasteris were processed using QIIME2 [ 52 ] with DADA2 [ 53 ] for the denoising step. For 16S reads, sequences were truncated to optimized lengths based on quality scores, with forward and reverse reads set to 264 and 216 bases, respectively. This resulted in a total composite length of 480 bases for unjoined sequences. The truncated reads allowed for the joining of denoised paired-end sequences with at least 12 identical bases, yielding full-length denoised sequences of 468 bases. The resulting ASVs were taxonomically classified using QIIME2’s VSEARCH-based consensus taxonomy classifier [ 54 ] with the SILVA database, release 138 [ 55 ]. A refined version of the SILVA database, curated using the RESCRIPt QIIME2 plugin [ 56 ] and provided by the QIIME2 team, was employed for this purpose. For ITS reads, sequences were truncated to optimized lengths based on quality scores, with forward and reverse reads set to 264 and 216 bases, respectively, resulting in a composite length of 480 bases for unjoined sequences. These truncated reads allowed the joining of denoised paired-end sequences with at least 12 identical overlapping bases, producing full-length denoised sequences of 468 bases. The sequences were grouped into Amplicon Sequence Variants (ASVs), which were subsequently taxonomically classified using QIIME2’s VSEARCH-based consensus taxonomy classifier with the UNITE database (version 8.2, release date February 4, 2020 [ 57 ]). The data set was decontaminated using the microDecon R package [ 58 , 59 ] based on a PCR control sample [ 60 ]. We applied a 0.1% rarefaction threshold [ 40 , 61 , 62 ] and rarefied the sequences to 2000 per sample to correct for uneven sequence numbers. Samples below this threshold were discarded. Statistical analyses All statistical analyses were performed using R version 4.4.1 [ 63 ]. Rarefaction curves, illustrating the relationship between sequencing depth and the number of observed Operational Taxonomic Units (OTUs), were generated using the ‘Phyloseq’ package in R. To visualize bacterial and fungal community composition, we used the ‘Vegan’ package [ 64 ] which generates non-metric multidimensional scaling (NMDS) using the Bray–Curtis coefficient as the distance measure. The Bray-Curtis coefficient was calculated using Hellinger-transformed relative abundance data of observed bacteria and fungi in each sample. Differences in microbial communities across diet treatments and Spodoptera species were tested using permutational multivariate analysis of variance (PERMANOVA), via the adonis function in ‘Vegan’ [ 65 ]. The model included diet, species and their interactions as fixed factors. Significance was determined through 1,000 permutations, followed by posthoc pairwise comparisons ( pairwise.adonis ) when significant effects were detected. Shannon diversity indices were calculated using the ‘Phyloseq’ package in R, and differences in diversity were analyzed using ‘lme4’ [ 66 ], ‘nlme’ [ 67 ] and ‘emmeans’ [ 68 ] packages in R. Residuals were visually inspected for normality and homogeneity. Separate linear models were used to analyse Shannon diversity for bacteria and fungi, while a linear mixed model was applied to assess bacterial and fungal diversity jointly. In the mixed model, diet and Spodoptera species were treated as random factors, while in the linear models, they were fixed factors. When significant differences (< 0.05) were detected, posthoc Tukey’s honestly significant difference (HSD) tests were conducted for pairwise comparisons. Results After quality filtering and rarefying, we retained a total of 4648 bacterial OTUs and 843 fungal OTUs for further analyses. Rarefaction curves approached saturation, indicating that our sequencing depth adequately captured microbial diversity (Fig. S1 ). Due to high larval mortality, the following treatment combinations were excluded from the analyses: Spodoptera littoralis fed on cotton, Spodoptera exigua fed on maize and Spodoptera latifascia fed on squash. The remaining treatments included in the analyses were: S. exigua fed on artificial diet, cotton, and squash; S. frugiperda fed on artificial diet, cotton, maize and squash; S. latifascia fed on artificial diet, cotton and maize; S. littoralis fed on artificial diet, maize and squash. NMDS Diet had a significant effect on both bacterial ( F = 9.925, df = 3, p < 0.001) and fungal ( F = 5.037, df = 3, p < 0.001) community compositions in caterpillar regurgitant (Fig. 1 a,b). Posthoc pairwise analyses revealed that each diet treatment influenced bacterial and fungal community compositions differently (Fig. 1 a,b). The Spodoptera species also had a smaller but significant effect on bacterial community composition ( F = 2.704, df = 3, p < 0.001), with the exception of S. frugiperda and S. latifascia which showed no significant differences (posthoc pairwise analysis: F = 1.186, df = 1, p = 0.260) (Fig. 1 c). In contrast, fungal community composition was consistent across the four Spodoptera species ( F = 1.328, df = 3, p = 0.120) (Fig. 1 d). A significant interaction between diet and species was observed for bacterial community composition ( F = 2.245, df = 6, p < 0.001) (Fig. S2a), while no significant interaction was found for fungi ( F = 1.342, df = 6, p = 0.080) (Fig S2b). Community composition The bacterial communities in Spodoptera regurgitant were primarily dominated by members of the phyla Firmicutes and Proteobacteria. Notably, Enterococcus species were highly abundant in caterpillars fed on maize and squash, but were absent in those fed on artificial diet or cotton (Fig. 2 ). Overall, bacterial community profiles were largely consistent among Spodoptera species within the same diet, except for S. frugiperda on cotton, which showed a distinct bacterial composition. Fungi identified in the regurgitant belonged primarily to the phyla Ascomycota and Basidiomycota. Cladosporium species were the most abundant fungi in caterpillars fed on maize or squash, whereas Malassezia species dominated in those fed on artificial diet and cotton (Fig. 3 ). Similar to bacteria, fungal community composition was primarily shaped by diet rather than Spodoptera species, although this pattern was less clear in caterpillars fed on cotton. Diversity Shannon diversity indices for bacterial OTUs varied significantly according to caterpillar diet ( F = 28.559, df = 3, p < 0.001) (Fig. 4 a). Posthoc analyses indicated no significant differences between caterpillars fed on artificial diet and cotton ( t = 0.269, df = 34, p = 0.836) or between those fed on maize and squash ( t = 1.366, df = 34, p = 0.529). Spodoptera species also significantly influenced Shannon diversity indices ( F = 8.729, df = 3, p < 0.001), with S. frugiperda exhibiting higher diversity than S. littoralis (posthoc: t = 2.818, df = 34, p = 0.038) (Fig. 4 c). A significant interaction between diet and species was observed for bacterial OTUs ( F = 5.176, df = 6, p = 0.001) (Fig. S3). In contrast, neither diet ( F = 2.527, df = 3, p = 0.081) nor species ( F = 0.490, df = 3, p = 0.692) significantly affected Shannon diversity indices for fungal OTUs (Fig. 4 b, d). Overall, bacterial diversity in Spodoptera regurgitant was significantly higher than fungal diversity ( F = 113.600, df = 1, p < 0.001) (Fig. 5 ). Discussion We hypothesized that Spodoptera species might share a core microbiome that facilitates their adaptation to diverse host plants. To test this, we examined microbial community compositions in the regurgitant of four Spodoptera species ( S. exigua , S. frugiperda , S. latifascia , and S. littoralis ), and assessed the influence of diet. Our results revealed that diet strongly shaped both bacterial and fungal communities in Spodoptera regurgitant. While bacterial communities differed significantly among species, fungal communities remained largely consistent. Moreover, diet effects on microbial composition varied by Spodoptera species, suggesting species-specific microbial selection mechanisms. However, no bacterial or fungal OTU was consistently present across all treatments, indicating the absence of core microbiome shared by Spodoptera species. Overall, bacterial and fungal communities were shaped more by diet than by species, and fungal communities were less diverse than bacterial communities. Diet as a Driver of Microbial Composition The strong effects of diet on microbial communities aligns with previous research on lepidopteran larvae, where gut bacterial community composition is often diet-dependent [ 16 ]. For example, host plant species or genotype significantly influenced gut bacterial microbiota in S. frugiperda [ 24 , 26 – 29 , 39 ], S. exigua [ 34 – 36 ] and S. littoralis [ 4 ]. Similarly, Yuning et al. [ 39 ] found that fungal communities in S. frugiperda midgut varied with host plant species ( Brassica campestris vs B. oleracea ). To our knowledge, no studies have examined microbial community compositions in S. latifiacia or fungal communities in S. exigua and S. latifascia . Diet-driven shifts in microbial communities have important implications for plant-insect interactions. Microbial communities can mediate digestion [ 9 ], pesticide resistance [ 11 ], plant defense manipulation [ 12 ], and even attraction of hyperparasitoids [ 58 ]. Understanding these dynamics may help identify symbionts essential for host adaptability or potential microbial targets for biological control strategies [ 23 ]. Absence of a Core Microbiome Previous studies suggested that Spodoptera species share a core microbiome [ 4 , 7 , 16 ], potentially including vertically transmitted microbes providing essential functions. For instance, Enterococcus mundtii was found in the second generation of S. littoralis after ingestion [ 5 ]. Similarly, geographically distant S. frugiperda populations harboured similar gut bacterial communities [ 18 ]. In contrast, our findings revealed distinct bacterial compositions in Spodoptera regurgitant, except for similarities between S. frugiperda and S. latifascia , possibly due to their shared evolutionary history in southern Mexico’s, milpa agroecosystem [ 69 , 70 ]. Previous research also showed that gut bacterial communities of S. frugiperda [ 29 ] and S. exigua [ 36 ] differ when collected from the same plant species but different locations. Unlike bacteria, fungal communities were similar across species. However, as no fungal OTU was consistently shared among species when fed different diets, Spodoptera species likely do not share a core mycobiome. Research on tree caterpillars similarly suggests that host species play a minor role in shaping fungal communities [ 42 ]. Diet-Species Interactions Shape Bacterial Communities The interaction between diet and species influenced both bacterial composition and diversity. For example, S. frugiperda showed higher bacterial diversity than S. latifascia and S. littoralis when fed maize, likely reflecting its specialization on this crop [ 71 ]. Lower bacterial diversity in maize- and squash-fed caterpillars, may be attributed to the dominance of Enterococcus species, which were absent in caterpillars fed artificial diet or cotton. Enterococcus species are particularly relevant as they may contribute to pesticide tolerance [ 72 ]. These findings suggest that caterpillars selectively retain specific bacteria from their diet. For example, E. mundtii in S. littoralis produces bactericidal compounds that selectively eliminate foreign bacteria [ 1 , 73 ]. Additionally, microbial selection may occur due to the highly alkaline conditions of the digestive system [ 6 ]. The Importance of Studying Fungal Communities Consistent with previous studies [ 39 , 40 ], fungal diversity was lower than bacterial diversity, with fungal communities dominated by a few highly prevalent taxa, such as Cladosporium and Malassezia . Cladosporium species, commonly found in insect guts, play roles in nutrient absorption and pesticide degradation [ 74 ]. Malassezia species have been reported in lepidopteran insects [ 40 ], but their function remains unknown. Despite their ecological significance, fungi have often been overlooked in studies on gut microbes. However, they may contribute to nutrient supply, food breakdown and detoxification [ 44 , 45 ]. Further research on fungal communities in herbivorous insects could reveal important interactions affecting host health and adaptability. Conclusion In conclusion, this study provides a novel comparison of gut bacterial and fungal communities across different Spodoptera species, including S. latifascia , whose gut microbiome had not been previously characterized. Given its broad host range and potential for range expansion due to climate change, S. latifascia could emerge as a significant agricultural pest in the Americas. Proactively studying its microbiome may help mitigate future risks. Our findings confirm that diet plays a key role in shaping bacterial communities in Spodoptera regurgitant, while significant interspecies variation suggests species-specific bacterial selection. In contrast, fungal communities were shaped exclusively by diet, supporting the idea that fungi in the foregut are more transient and less diverse than bacteria. Notably, we found no evidence of a core gut microbiome shared among Spodoptera species. However, since the caterpillars in this study were reared in the laboratory for multiple generations, potential effects on vertically transmitted microbiomes cannot be ruled out. Understanding the gut microbiome of Spodoptera species offers valuable insights into their adaptability to diverse host plants. This knowledge could inform microbiome-based strategies for more effective and environmentally sustainable pest management [ 23 ]. Declarations Authors’ contributions Betty Benrey, Pilar Junier and Maximilien A.C. Cuny conceived the study. Maximilien A.C. Cuny reared the caterpillars and collected the regurgitant. Guillaume Cailleau performed bioinformatic analyses and wrote the corresponding sections. Maximilien A.C. Cuny performed statistical analyses and wrote the first version of the manuscript. All authors reviewed and edited the manuscript. Funding This research was financially supported by a grant from the Swiss National Science Foundation (Project No. 310030-149544) awarded to B.B. Competing Interests The authors have no relevant financial or non-financial interests to disclose. Data availability The bacterial and fungal OTU count data per sample are available in the supplementary material (Tables S1 and S2). Ethics approval Although this study did not require ethics approval, we took measures to minimize insect suffering. The insects were handled gently, kept in clean cages with sufficient food, and killed by freezing. References Dillon RJ, Dillon VM (2004) The gut bacteria of insects: Nonpathogenic interactions. Annu Rev Entomol 49:71–92. https://doi.org/10.1146/annurev.ento.49.061802.123416 Engel P, Moran NA (2013) The gut microbiota of insects – diversity in structure and function. 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Sci Rep 6:29505. https://doi.org/10.1038/srep29505 Shao Y, Arias-Cordero E, Guo H et al (2014) In vivo pyro-SIP assessing active gut microbiota of the cotton leafworm, Spodoptera littoralis . PLoS ONE 9:e85948. https://doi.org/10.1371/journal.pone.0085948 Eski A, Demir İ, Güllü M, Demirbağ Z (2018) Biodiversity and pathogenicity of bacteria associated with the gut microbiota of beet armyworm, Spodoptera exigua Hübner (Lepidoptera: Noctuidae). Microb Pathog 121:350–358. https://doi.org/10.1016/j.micpath.2018.05.012 Gao X, Li W, Luo J et al (2019) Biodiversity of the microbiota in Spodoptera exigua (Lepidoptera: Noctuidae). J Appl Microbiol 126:1199–1208. https://doi.org/10.1111/jam.14190 Martínez-Solís M, Collado MC, Herrero S (2020) Influence of diet, sex, and viral infections on the gut microbiota composition of Spodoptera exigua caterpillars. Front Microbiol 11:753. https://doi.org/10.3389/fmicb.2020.00753 Mugo-Kamiri L, Querejeta M, Raymond B, Herniou EA (2024) The effect of diet composition on the diversity of active gut bacteria and on the growth of Spodoptera exigua (Lepidoptera: Noctuidae). J Insect Sci 24:13. https://doi.org/10.1093/jisesa/ieae031 Zhou L, Chen C, Wang X (2022) Gut bacterial diversity and community structure of Spodoptera exigua (Lepidoptera: Noctuidae) in the welsh onion-producing areas of north China. J Econ Entomol 115:1102–1114. https://doi.org/10.1093/jee/toac103 Ismail MA, Abdel-Sater MA (1993) Fungi associated with the Egyptian cotton leafworm Spodoptera littoralis Boisdoval. Mycopathologia 124:79–86. https://doi.org/10.1007/BF01103106 Watson M, May G, Bushley KE (2023) Sources of fungal symbionts in the microbiome of a mobile insect host, Spodoptera frugiperda . 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Sci Rep 12:15552. https://doi.org/10.1038/s41598-022-19855-5 Višňovská D, Pyszko P, Šigut M et al (2020) Caterpillar gut and host plant phylloplane mycobiomes differ: a new perspective on fungal involvement in insect guts. FEMS Microbiol Ecol 96:fiaa116. https://doi.org/10.1093/femsec/fiaa116 Biedermann PHW, Vega FE (2020) Ecology and evolution of insect–fungus mutualisms. Annu Rev Entomol 65:431–455. https://doi.org/10.1146/annurev-ento-011019-024910 Guo Q, Yao Z, Cai Z et al (2022) Gut fungal community and its probiotic effect on Bactrocera dorsalis . Insect Sci 29:1145–1158. https://doi.org/10.1111/1744-7917.12986 ElShahed SM, Mostafa ZK, Radwan MH, Hosni EM (2023) Modeling the potential global distribution of the Egyptian cotton leafworm, Spodoptera littoralis under climate change. Sci Rep 13:17314. https://doi.org/10.1038/s41598-023-44441-8 Fu X, Feng H, Liu Z, Wu K (2017) Trans-regional migration of the beet armyworm, Spodoptera exigua (Lepidoptera: Noctuidae), in North-East Asia. PLoS ONE 12:e0183582. https://doi.org/10.1371/journal.pone.0183582 Habib MEM, Paleari LM, Amaral MEC (1982) Effet of three larval diets on the development of the armyworm, Spodoptera latifascia Walker, 1856 (Noctuidae, Lepidoptera). Rev Bras Zool 1:. https://doi.org/10.1590/S0101-81751982000300007 Maag D, Dalvit C, Thevenet D et al (2014) 3-β-d-Glucopyranosyl-6-methoxy-2-benzoxazolinone (MBOA- N -Glc) is an insect detoxification product of maize 1,4-benzoxazin-3-ones. Phytochemistry 102:97–105. https://doi.org/10.1016/j.phytochem.2014.03.018 Herlemann DPR, Labrenz M, Jürgens K et al (2011) Transitions in bacterial communities along the 2000 km salinity gradient of the Baltic Sea. ISME J 5:1571–1579. https://doi.org/10.1038/ismej.2011.41 Toju H, Tanabe AS, Yamamoto S, Sato H (2012) High-Coverage ITS primers for the DNA-based identification of Ascomycetes and Basidiomycetes in environmental samples. PLoS ONE 7:e40863. https://doi.org/10.1371/journal.pone.0040863 Bolyen E, Rideout JR, Dillon MR et al (2019) Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol 37:852–857. https://doi.org/10.1038/s41587-019-0209-9 Callahan BJ, McMurdie PJ, Rosen MJ et al (2016) DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods 13:581–583. https://doi.org/10.1038/nmeth.3869 Rognes T, Flouri T, Nichols B et al (2016) VSEARCH: a versatile open source tool for metagenomics. PeerJ 4:e2584. https://doi.org/10.7717/peerj.2584 Quast C, Pruesse E, Yilmaz P et al (2013) The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res 41:D590–D596. https://doi.org/10.1093/nar/gks1219 Robeson MS, O’Rourke DR, Kaehler BD et al (2021) RESCRIPt: Reproducible sequence taxonomy reference database management. PLoS Comput Biol 17:e1009581. https://doi.org/10.1371/journal.pcbi.1009581 Nilsson RH, Larsson K-H, Taylor AFS et al (2019) The UNITE database for molecular identification of fungi: handling dark taxa and parallel taxonomic classifications. Nucleic Acids Res 47:D259–D264. https://doi.org/10.1093/nar/gky1022 Bourne ME, Gloder G, Weldegergis BT et al (2023) Parasitism causes changes in caterpillar odours and associated bacterial communities with consequences for host-location by a hyperparasitoid. PLoS Pathog 19:e1011262. https://doi.org/10.1371/journal.ppat.1011262 McKnight DT, Huerlimann R, Bower DS et al (2019) microDecon: A highly accurate read-subtraction tool for the post‐sequencing removal of contamination in metabarcoding studies. Environ DNA 1:14–25. https://doi.org/10.1002/edn3.11 Davis NM, Proctor DM, Holmes SP et al (2018) Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome 6:226. https://doi.org/10.1186/s40168-018-0605-2 Gorrens E, De Smet J, Vandeweyer D et al (2022) The bacterial communities of black soldier fly larvae ( Hermetia illucens ) during consecutive, industrial rearing cycles. JIFF 8:1061–1076. https://doi.org/10.3920/JIFF2021.0150 Gloder G, Bourne ME, Cuny MAC et al (2024) Caterpillar–parasitoid interactions: species-specific influences on host microbiome composition. FEMS Microbiol Ecol 100:fiae115. https://doi.org/10.1093/femsec/fiae115 R Core Team (2024) R: A Language and. Environment for Statistical Computing Oksanen J, Simpson G, Blanchet F et al (2015) Vegan: Community ecology package Anderson MJ (2001) A new method for non-parametric multivariate analysis of variance. Austral Ecol 26:32–46. https://doi.org/10.1111/j.1442-9993.2001.01070.pp.x Bates D, Mächler M, Bolker B, Walker S (2015) Fitting linear mixed-effects models using lme4. J Stat Softw 67:1–48. https://doi.org/10.18637/jss.v067.i01 Pinheiro J, Bates D, DebRoy S, Sarkar D (2009) nlme: Linear and nonlinear mixed effects models. R package version 3:1–166 Lenth R, Singmann H, Love J et al (2018) Estimated marginal means, aka least-squares means Grof-Tisza P, Muller MH, Gónzalez-Salas R et al (2024) The Mesoamerican milpa agroecosystem fosters greater arthropod diversity compared to monocultures. Agric Ecosyst Environ 372:109074. https://doi.org/10.1016/j.agee.2024.109074 Benrey B, Bustos-Segura C, Grof-Tisza P (2024) The mesoamerican milpa system: Traditional practices, sustainability, biodiversity, and pest control. Biol Control 198:105637. https://doi.org/10.1016/j.biocontrol.2024.105637 Köhler A, Maag D, Veyrat N et al (2015) Within-plant distribution of 1,4-benzoxazin-3-ones contributes to herbivore niche differentiation in maize. Plant Cell Environ 38:1081–1093. https://doi.org/10.1111/pce.12464 Gomes AFF, De Almeida LG, Cônsoli FL (2023) Comparative genomics of pesticide-degrading Enterococcus symbionts of Spodoptera frugiperda (Lepidoptera: Noctuidae) leads to the identification of two new species and the reappraisal of insect-associated Enterococcus species. Microb Ecol 86:2583–2605. https://doi.org/10.1007/s00248-023-02264-0 Shao Y, Chen B, Sun C et al (2017) Symbiont-derived antimicrobials contribute to the control of the lepidopteran gut microbiota. Cell Chem Biology 24:66–75. https://doi.org/10.1016/j.chembiol.2016.11.015 Nicoletti R, Russo E, Becchimanzi A (2024) Cladosporium—Insect Relationships JoF 10:78. https://doi.org/10.3390/jof10010078 Additional Declarations No competing interests reported. Supplementary Files SupplementarytablesS1andS2.xlsx Cite Share Download PDF Status: Published Journal Publication published 22 Jul, 2025 Read the published version in Microbial Ecology → Version 1 posted Editorial decision: Revision requested 09 Apr, 2025 Reviews received at journal 01 Apr, 2025 Reviews received at journal 01 Apr, 2025 Reviews received at journal 24 Mar, 2025 Reviewers agreed at journal 11 Mar, 2025 Reviewers agreed at journal 06 Mar, 2025 Reviewers agreed at journal 05 Mar, 2025 Reviewers agreed at journal 04 Mar, 2025 Reviewers agreed at journal 04 Mar, 2025 Reviewers invited by journal 04 Mar, 2025 Editor assigned by journal 04 Mar, 2025 Submission checks completed at journal 04 Mar, 2025 First submitted to journal 26 Feb, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6114576","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":424776446,"identity":"e30ec688-8728-412f-aa71-3d789539d84a","order_by":0,"name":"Maximilien A. C. Cuny","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYJCCAwwGFjxgFpCUY2AGM+QIaZGAazGGajEmZJEEA0xLYgMDAS267WcMD90okJDhl25g/PCmZlt6fzvvww9vGAzycWkxO5OWcDgH6DDJOQeYJeccu5074zC7seQcBgPLBlxaDiQfAGsxuJHAIM3Ddjt3AzMbkMHwxwCnLecfNoC12N9IYP7N8+92ugEzG5DBYIBbyw2YLRIJbNK8bbcTgFrYpPFreQbxi8SNxDbLuX23DWccZmOznGOAR8v5HOPPOX9s7PlnJB++8ebbbXn+/mPMN95U4NaCBBgbkDjEaBgFo2AUjIJRgBMAAOSjTyiVJPK8AAAAAElFTkSuQmCC","orcid":"","institution":"Laboratory of Evolutionary Entomology, Institute of Biology, University of Neuchâtel","correspondingAuthor":true,"prefix":"","firstName":"Maximilien","middleName":"A. C.","lastName":"Cuny","suffix":""},{"id":424776448,"identity":"c1fff0c0-40a6-4cb9-833b-3522929adc81","order_by":1,"name":"Guillaume Cailleau","email":"","orcid":"","institution":"Laboratory of Microbiology, Institute of Biology, University of Neuchâtel","correspondingAuthor":false,"prefix":"","firstName":"Guillaume","middleName":"","lastName":"Cailleau","suffix":""},{"id":424776449,"identity":"74ae14d0-9159-4e8b-96b2-a6145d54db78","order_by":2,"name":"Pilar Junier","email":"","orcid":"","institution":"Laboratory of Microbiology, Institute of Biology, University of Neuchâtel","correspondingAuthor":false,"prefix":"","firstName":"Pilar","middleName":"","lastName":"Junier","suffix":""},{"id":424776450,"identity":"a8610ac9-f2e4-4fe9-b58d-714356d1059d","order_by":3,"name":"Betty Benrey","email":"","orcid":"","institution":"Laboratory of Evolutionary Entomology, Institute of Biology, University of Neuchâtel","correspondingAuthor":false,"prefix":"","firstName":"Betty","middleName":"","lastName":"Benrey","suffix":""}],"badges":[],"createdAt":"2025-02-26 15:38:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6114576/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6114576/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00248-025-02582-5","type":"published","date":"2025-07-22T15:57:12+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":77900738,"identity":"f166e81d-430a-4301-9ab1-ebe716f25387","added_by":"auto","created_at":"2025-03-06 15:32:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":313927,"visible":true,"origin":"","legend":"\u003cp\u003eNon-metric multidimensional scaling (NMDS) ordination plots based on Bray-Curtis distances of Hellinger-transformed relative abundances of \u003cem\u003eSpodoptera\u003c/em\u003e regurgitant microbial communities. (a) Bacterial and (b) fungal communities in \u003cem\u003eSpodoptera\u003c/em\u003eregurgitant grouped by diet. (c) Bacterial and (d) fungal communities in \u003cem\u003eSpodoptera\u003c/em\u003eregurgitant grouped by species. Each point represents one regurgitant sample. Ellipses represent 95% confidence intervals around the group mean (diet or species). Different letters indicate significant differences among treatments (pairwise posthoc analysis). NMDS stress values (goodness of fit): bacteria = 0.118; fungi = 0.121.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-6114576/v1/5816ba09b970811586c0f7c8.png"},{"id":77900737,"identity":"8fa8cbec-e6bd-475d-bc58-644176bdd593","added_by":"auto","created_at":"2025-03-06 15:32:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":100474,"visible":true,"origin":"","legend":"\u003cp\u003eBacterial community profiles of regurgitant collected from caterpillars of four species of \u003cem\u003eSpodoptera\u003c/em\u003e (\u003cem\u003eS. exigua\u003c/em\u003e, \u003cem\u003eS. frugiperda\u003c/em\u003e, \u003cem\u003eS. latifascia\u003c/em\u003e, and \u003cem\u003eS. littoralis\u003c/em\u003e) fed on four different diets (artificial diet, cotton, maize, or squash plants). The bacterial taxa shown represent the 35 most prevalent OTUs identified at the genus level (full list is shown in Table S1). Colours represent the average prevalence (%) of bacterial taxa within each group, with white indicating absence.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-6114576/v1/1b54e6d8222788b9ea0a2a01.png"},{"id":77901556,"identity":"5fcafcdd-1a5a-4353-b18c-195019854b70","added_by":"auto","created_at":"2025-03-06 15:40:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":94839,"visible":true,"origin":"","legend":"\u003cp\u003eFungal community profiles of regurgitant collected from caterpillars of four species of \u003cem\u003eSpodoptera\u003c/em\u003e (\u003cem\u003eS. exigua\u003c/em\u003e, \u003cem\u003eS. frugiperda\u003c/em\u003e, \u003cem\u003eS. latifascia\u003c/em\u003e, and \u003cem\u003eS. littoralis\u003c/em\u003e) fed on four different diets (artificial diet, cotton, maize, or squash plants). The fungal taxa shown represent the 30 most prevalent OTUs identified at the genus level (full list is shown in Table S2). Colours represent the average prevalence (%) of fungal taxa within each group, with white indicating absence.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-6114576/v1/8082c8ee70d87b1f0f93ad3a.png"},{"id":77900740,"identity":"71f12d16-ba74-4053-8283-a09cc300f942","added_by":"auto","created_at":"2025-03-06 15:32:37","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":139782,"visible":true,"origin":"","legend":"\u003cp\u003eShannon diversity indices of bacteria (a, c) and fungi (b, d) in \u003cem\u003eSpodoptera\u003c/em\u003eregurgitant, grouped by diet (a, b) and species (c, d). Grey dots represent individual raw data points (jittered for clarity), while black dots represent outliers included in the analyses. Different letters indicate significant differences among treatments (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, Tukey’s HSD).\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-6114576/v1/8cd4b035bbfdcf3fd4dbc298.png"},{"id":77900739,"identity":"63e6dcab-57a6-42de-afab-11733b34bdb2","added_by":"auto","created_at":"2025-03-06 15:32:37","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":38172,"visible":true,"origin":"","legend":"\u003cp\u003eShannon diversity indices of bacteria and fungi in the regurgitant of four \u003cem\u003eSpodoptera\u003c/em\u003especies (\u003cem\u003eS. exigua\u003c/em\u003e, \u003cem\u003eS. frugiperda\u003c/em\u003e, \u003cem\u003eS. latifascia\u003c/em\u003e and \u003cem\u003eS. littoralis\u003c/em\u003e) fed on four different diets (artificial diet, cotton, maize and squash). Grey dots represent individual data points (jittered for clarity), and the black dot indicates an outlier included in the analysis. Asteriks indicate significant differences between bacterial and fungal diversity (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, Tukey’s HSD).\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-6114576/v1/dde45a2eae667825b137c0da.png"},{"id":87756666,"identity":"0c14bc61-d2ee-4b48-b872-a51a3f50bbb6","added_by":"auto","created_at":"2025-07-28 16:06:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1287748,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6114576/v1/69c64409-a567-4c2b-a94d-e11b91d22c5e.pdf"},{"id":77901557,"identity":"844e008d-d1b6-405a-bab5-2819c5d39e5c","added_by":"auto","created_at":"2025-03-06 15:40:37","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1067873,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementarytablesS1andS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6114576/v1/044e95ef01efb3c17718073f.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Host diet and phylogeny interact to shape the bacterial and fungal microbiome in the regurgitant of four Spodoptera species","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHerbivorous insects often associate with gut microorganisms, including bacteria and fungi, which influence various aspects of their biology [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Among the factors influencing these interactions, diet plays a central role [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This is particularly evident in lepidopteran larvae, whose gut microbiome is characterized as simple and highly variable [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Such variability is attributed to continuous moulting [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], the absence of specialized gut compartments, and the alkaline conditions of their digestive system [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. As a result, Lepidopteran gut microbiomes are largely determined by environmental factors and diet, showing variation both among and within species [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Despite this variability, gut bacteria contribute to key functions, including digestion [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], use of suboptimal diets [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], pathogen resistance [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], insecticide resistance [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] and manipulation of plant defences [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. While some studies suggest that caterpillars lack a resident gut microbiome [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], others have shown that some bacteria can colonize and persist in their gut [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. It has also been proposed that a subset of functionally important bacteria may form a core microbiome shared across lepidopteran species [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. For such core microbiome to exist, either vertical transmission or constant horizontal acquisition of microbes would be required [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, only a limited number of systems have tested this hypothesis, yielding inconsistent results [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Thus, whether closely related herbivorous insects harbour a core gut microbiome remains unresolved.\u003c/p\u003e \u003cp\u003eThe superfamily Noctuoidea is the largest and more diverse group within Lepidoptera, including many agricultural pest species [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Within this group, the genus \u003cem\u003eSpodoptera\u003c/em\u003e (Lepidoptera: Noctuidae) comprises several economically important species, including \u003cem\u003eS. frugiperda\u003c/em\u003e, \u003cem\u003eS. littoralis\u003c/em\u003e, \u003cem\u003eS. exigua\u003c/em\u003e and \u003cem\u003eS. latifascia\u003c/em\u003e [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. S\u003cem\u003epodoptera frugiperda\u003c/em\u003e and \u003cem\u003eS. exigua\u003c/em\u003e have invaded multiple continents, and all four species are highly polyphagous [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Understanding the factors enabling \u003cem\u003eSpodoptera\u003c/em\u003e species to exploit diverse host plants is crucial for pest management strategies that target the insect microbiome through biochemicals or foreign microbes [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Gut microbes are thought to play a key role in this adaptability by facilitating host shifts and supporting polyphagous diets [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOver the past decade, several studies have characterized the gut bacterial composition of \u003cem\u003eS. frugiperda\u003c/em\u003e [\u003cspan additionalcitationids=\"CR25 CR26 CR27 CR28\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], \u003cem\u003eS. littoralis\u003c/em\u003e [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] and \u003cem\u003eS. exigua\u003c/em\u003e [\u003cspan additionalcitationids=\"CR33 CR34 CR35\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. However, the gut microbiome of \u003cem\u003eS. latifascia\u003c/em\u003e remains unexplored, and no comparative studies have examined gut microbiota across multiple \u003cem\u003eSpodoptera\u003c/em\u003e species. This limits our understanding on how microbial communities contribute to the genus\u0026rsquo; dietary adaptability. In contrast to bacteria, the fungal microbiota of \u003cem\u003eSpodoptera\u003c/em\u003e caterpillars has received less attention [\u003cspan additionalcitationids=\"CR38\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], despite evidence from other lepidopteran insects showing that fungi are common in their guts [\u003cspan additionalcitationids=\"CR41 CR42\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Like bacteria, gut fungi may contribute to food detoxification and nutrient supply [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], but it is unclear whether fungal microbiota is as simple and variable as bacterial communities in lepidopteran species.\u003c/p\u003e \u003cp\u003eIn this study, we hypothesized that \u003cem\u003eSpodoptera\u003c/em\u003e species may share a core microbiome that facilitates their adaptation to diverse host plants. To test this, we examined 1) whether microbial community compositions in the regurgitant of four \u003cem\u003eSpodoptera\u003c/em\u003e species (\u003cem\u003eS. exigua\u003c/em\u003e, \u003cem\u003eS. frugiperda\u003c/em\u003e, \u003cem\u003eS. latifascia\u003c/em\u003e, and \u003cem\u003eS. littoralis\u003c/em\u003e) exhibit similarities, and 2) how these communities are influenced by different diets (artificial diet, cotton, maize and squash). These insights can inform pest management strategies and contribute to broader ecological and evolutionary perspectives on host-microbe interactions.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eArtificial diet and plants\u003c/h2\u003e \u003cp\u003eThe artificial diet used in this experiment (\u0026ldquo;beet armyworm diet\u0026rdquo;) was obtained from BioServ (U.S.A). Cotton, maize and squash plants were grown from seeds. Cotton seeds were collected on feral plants near Puerto Escondido (Oaxaca, Mexico). Maize and squash seeds were obtained from Delley Semences et Plantes SA (Switzerland) and Zollinger (biolgische Samengartenerei (Switzerland), respectively. These diets were selected to reflect the natural feeding habits of \u003cem\u003eSpodoptera\u003c/em\u003e species, with artificial diet serving as a standardized control and the plant species representing common agricultural crops frequently consumed by these pests.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eInsects\u003c/h3\u003e\n\u003cp\u003e \u003cem\u003eSpodoptera littoralis\u003c/em\u003e is native to Africa and has spread to Southern Europe and the Middle East, where it feeds on crops such as wheat, maize, cotton and rice [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. \u003cem\u003eS. exigua\u003c/em\u003e, originally from Asia, has become a globally distributed pest that targets major crops such as cotton, soybean, potato and sugar beet [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. \u003cem\u003eS. frugiperda\u003c/em\u003e is an invasive species native to the Americas, primarily attacking maize and other cereals [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. \u003cem\u003eS. latifascia\u003c/em\u003e is a polyphagous insect native to Mexico and Central America that commonly feeds on maize, bean, cotton and potato [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Eggs of \u003cem\u003eS. littoralis\u003c/em\u003e and \u003cem\u003eS. exigua\u003c/em\u003e were obtained from Syngenta (Stein, Switzerland) and Entomos AG (Grossdietwil,Switzerland), respectively. \u003cem\u003eS. frugiperda\u003c/em\u003e and \u003cem\u003eS. latifascia\u003c/em\u003e were initially collected near Puerto Escondido (Oaxaca, Mexico; 15\u0026deg;55\u0026rsquo;33.3\u0026rdquo;N,97\u0026deg;09\u0026rsquo;03.0\u0026rdquo;W) and reared for several generations on a chickpea flour-based artificial diet [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] under controlled conditions (26\u0026deg;C, 60% r.h., and L12:D12 photoperiod) at the University of Neuch\u0026acirc;tel.\u003c/p\u003e\n\u003ch3\u003eExperimental design\u003c/h3\u003e\n\u003cp\u003eA total of 140 seeds from each plant species were individually sown in plastic pots and placed in a greenhouse. After one month, three egg batches from each \u003cem\u003eSpodoptera\u003c/em\u003e species were placed in separate Petri dishes, in a climate-controlled room (24\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u0026deg;C, 40\u0026thinsp;\u0026plusmn;\u0026thinsp;5% r.h.), under light benches (16:8 h L:D, approx.150 lmol m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e sec\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), until the end of the experiment. All eggs hatched on the same day, except for \u003cem\u003eS. latifascia\u003c/em\u003e, which hatched one day later. On the day of hatching, first instar caterpillars were randomly selected from the three egg batches and transferred to 16 rearing trays (one tray with 32 cells per diet treatment, Frontier Agricultural Sciences, USA). Two caterpillars were placed in each cell to compensate for the high mortality of first instars. After one week, if both caterpillars survived, one was removed. Caterpillars were fed one of the four diet treatments: small cubes (1cm\u003csup\u003e3\u003c/sup\u003e) of artificial diet or cut pieces of cotton, maize or squash leaves (2cm\u003csup\u003e2\u003c/sup\u003e). Food was replaced as needed, at least twice per week. Once caterpillars reached the fourth instar, regurgitant was collected daily over three consecutive days. To collect regurgitant, larvae were gently poked by hand and placed in a clean Petri dish. Regurgitant was then extracted using a 20-200ul pipet and transferred into 1.5mL Eppendorf tubes stored at -80\u0026deg;C until further analyses. Each Eppendorf tube contained pooled regurgitant from 10\u0026ndash;11 individuals. In total, we obtained three regurgitant solutions per combination of \u003cem\u003eSpodoptera\u003c/em\u003e species and diet (number of replicates per treatment combination: n\u0026thinsp;=\u0026thinsp;3). To prevent cross-contamination, gloves, Petri dishes and pipet tips were changed between treatments, and the work surface was disinfected with alcohol after each procedure.\u003c/p\u003e\n\u003ch3\u003eDNA extraction and sequencing\u003c/h3\u003e\n\u003cp\u003eDNA was extracted using the FastDNA Spin Kit for soil (MP Biomedicals), following the standard protocol provided with the kit. DNA quantification was performed using the Qubit\u0026reg; dsDNA HS Assay Kit on a Qubit\u0026reg; 2.0 Fluorometer (Invitrogen, Carlsbad, CA, USA). Purified DNA extracts were sent to Fasteris (Geneva, Switzerland) for 16S rDNA and ITS amplicon sequencing using an Illumina MiSeq platform (Illumina, San Diego, USA), generating 250 bp paired-end reads. For the 16S rDNA, the V3\u0026ndash;V4 region was amplified using the universal primers Bakt_341F (5\u0026rsquo;-CCT ACG GGN GGC WGC AG-3\u0026rsquo;) and Bakt_805R (5\u0026rsquo;-GAC TAC HVG GGT ATC TAA TCC-3\u0026rsquo;) [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. The primers ITS3_KYO2 (5\u0026rsquo;-GAT GAA GAA CGY AGY RAA-3\u0026rsquo;) and ITS4 (5\u0026rsquo;-TCC TCC GCT TAT TGA TAT GC-3\u0026rsquo;) [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] were used for the amplification of the ITS2 region.\u003c/p\u003e\n\u003ch3\u003eBioinformatic analysis\u003c/h3\u003e\n\u003cp\u003eDemultiplexed and trimmed sequence reads provided by Fasteris were processed using QIIME2 [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e] with DADA2 [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e] for the denoising step. For 16S reads, sequences were truncated to optimized lengths based on quality scores, with forward and reverse reads set to 264 and 216 bases, respectively. This resulted in a total composite length of 480 bases for unjoined sequences. The truncated reads allowed for the joining of denoised paired-end sequences with at least 12 identical bases, yielding full-length denoised sequences of 468 bases. The resulting ASVs were taxonomically classified using QIIME2\u0026rsquo;s VSEARCH-based consensus taxonomy classifier [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e] with the SILVA database, release 138 [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. A refined version of the SILVA database, curated using the RESCRIPt QIIME2 plugin [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e] and provided by the QIIME2 team, was employed for this purpose. For ITS reads, sequences were truncated to optimized lengths based on quality scores, with forward and reverse reads set to 264 and 216 bases, respectively, resulting in a composite length of 480 bases for unjoined sequences. These truncated reads allowed the joining of denoised paired-end sequences with at least 12 identical overlapping bases, producing full-length denoised sequences of 468 bases. The sequences were grouped into Amplicon Sequence Variants (ASVs), which were subsequently taxonomically classified using QIIME2\u0026rsquo;s VSEARCH-based consensus taxonomy classifier with the UNITE database (version 8.2, release date February 4, 2020 [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]). The data set was decontaminated using the microDecon R package [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e] based on a PCR control sample [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. We applied a 0.1% rarefaction threshold [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e] and rarefied the sequences to 2000 per sample to correct for uneven sequence numbers. Samples below this threshold were discarded.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using R version 4.4.1 [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Rarefaction curves, illustrating the relationship between sequencing depth and the number of observed Operational Taxonomic Units (OTUs), were generated using the \u0026lsquo;Phyloseq\u0026rsquo; package in R. To visualize bacterial and fungal community composition, we used the \u0026lsquo;Vegan\u0026rsquo; package [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e] which generates non-metric multidimensional scaling (NMDS) using the Bray\u0026ndash;Curtis coefficient as the distance measure. The Bray-Curtis coefficient was calculated using Hellinger-transformed relative abundance data of observed bacteria and fungi in each sample. Differences in microbial communities across diet treatments and \u003cem\u003eSpodoptera\u003c/em\u003e species were tested using permutational multivariate analysis of variance (PERMANOVA), via the \u003cem\u003eadonis\u003c/em\u003e function in \u0026lsquo;Vegan\u0026rsquo; [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. The model included diet, species and their interactions as fixed factors. Significance was determined through 1,000 permutations, followed by posthoc pairwise comparisons (\u003cem\u003epairwise.adonis\u003c/em\u003e) when significant effects were detected.\u003c/p\u003e \u003cp\u003eShannon diversity indices were calculated using the \u0026lsquo;Phyloseq\u0026rsquo; package in R, and differences in diversity were analyzed using \u0026lsquo;lme4\u0026rsquo; [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e], \u0026lsquo;nlme\u0026rsquo; [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e] and \u0026lsquo;emmeans\u0026rsquo; [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e] packages in R. Residuals were visually inspected for normality and homogeneity. Separate linear models were used to analyse Shannon diversity for bacteria and fungi, while a linear mixed model was applied to assess bacterial and fungal diversity jointly. In the mixed model, diet and \u003cem\u003eSpodoptera\u003c/em\u003e species were treated as random factors, while in the linear models, they were fixed factors. When significant differences (\u0026lt;\u0026thinsp;0.05) were detected, posthoc Tukey\u0026rsquo;s honestly significant difference (HSD) tests were conducted for pairwise comparisons.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eAfter quality filtering and rarefying, we retained a total of 4648 bacterial OTUs and 843 fungal OTUs for further analyses. Rarefaction curves approached saturation, indicating that our sequencing depth adequately captured microbial diversity (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Due to high larval mortality, the following treatment combinations were excluded from the analyses: \u003cem\u003eSpodoptera littoralis\u003c/em\u003e fed on cotton, \u003cem\u003eSpodoptera exigua\u003c/em\u003e fed on maize and \u003cem\u003eSpodoptera latifascia\u003c/em\u003e fed on squash. The remaining treatments included in the analyses were: \u003cem\u003eS. exigua\u003c/em\u003e fed on artificial diet, cotton, and squash; \u003cem\u003eS. frugiperda\u003c/em\u003e fed on artificial diet, cotton, maize and squash; \u003cem\u003eS. latifascia\u003c/em\u003e fed on artificial diet, cotton and maize; \u003cem\u003eS. littoralis\u003c/em\u003e fed on artificial diet, maize and squash.\u003c/p\u003e \n\u003ch3\u003eNMDS\u003c/h3\u003e\n\u003cp\u003eDiet had a significant effect on both bacterial (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9.925, \u003cem\u003edf\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and fungal (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.037, \u003cem\u003edf\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) community compositions in caterpillar regurgitant (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea,b). Posthoc pairwise analyses revealed that each diet treatment influenced bacterial and fungal community compositions differently (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea,b). The \u003cem\u003eSpodoptera\u003c/em\u003e species also had a smaller but significant effect on bacterial community composition (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.704, \u003cem\u003edf\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with the exception of \u003cem\u003eS. frugiperda\u003c/em\u003e and \u003cem\u003eS. latifascia\u003c/em\u003e which showed no significant differences (posthoc pairwise analysis: \u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.186, \u003cem\u003edf\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.260) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). In contrast, fungal community composition was consistent across the four \u003cem\u003eSpodoptera\u003c/em\u003e species (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.328, \u003cem\u003edf\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.120) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). A significant interaction between diet and species was observed for bacterial community composition (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.245, \u003cem\u003edf\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig. S2a), while no significant interaction was found for fungi (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.342, \u003cem\u003edf\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.080) (Fig S2b).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCommunity composition\u003c/h2\u003e \u003cp\u003eThe bacterial communities in \u003cem\u003eSpodoptera\u003c/em\u003e regurgitant were primarily dominated by members of the phyla Firmicutes and Proteobacteria. Notably, \u003cem\u003eEnterococcus\u003c/em\u003e species were highly abundant in caterpillars fed on maize and squash, but were absent in those fed on artificial diet or cotton (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Overall, bacterial community profiles were largely consistent among \u003cem\u003eSpodoptera\u003c/em\u003e species within the same diet, except for \u003cem\u003eS. frugiperda\u003c/em\u003e on cotton, which showed a distinct bacterial composition.\u003c/p\u003e \u003cp\u003eFungi identified in the regurgitant belonged primarily to the phyla Ascomycota and Basidiomycota. \u003cem\u003eCladosporium\u003c/em\u003e species were the most abundant fungi in caterpillars fed on maize or squash, whereas \u003cem\u003eMalassezia\u003c/em\u003e species dominated in those fed on artificial diet and cotton (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Similar to bacteria, fungal community composition was primarily shaped by diet rather than \u003cem\u003eSpodoptera\u003c/em\u003e species, although this pattern was less clear in caterpillars fed on cotton.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDiversity\u003c/h2\u003e \u003cp\u003eShannon diversity indices for bacterial OTUs varied significantly according to caterpillar diet (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;28.559, \u003cem\u003edf\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Posthoc analyses indicated no significant differences between caterpillars fed on artificial diet and cotton (\u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.269, \u003cem\u003edf\u003c/em\u003e\u0026thinsp;=\u0026thinsp;34, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.836) or between those fed on maize and squash (\u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.366, \u003cem\u003edf\u003c/em\u003e\u0026thinsp;=\u0026thinsp;34, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.529). \u003cem\u003eSpodoptera\u003c/em\u003e species also significantly influenced Shannon diversity indices (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8.729, \u003cem\u003edf\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with \u003cem\u003eS. frugiperda\u003c/em\u003e exhibiting higher diversity than \u003cem\u003eS. littoralis\u003c/em\u003e (posthoc: \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.818, \u003cem\u003edf\u003c/em\u003e\u0026thinsp;=\u0026thinsp;34, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.038) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). A significant interaction between diet and species was observed for bacterial OTUs (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.176, \u003cem\u003edf\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) (Fig. S3). In contrast, neither diet (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.527, \u003cem\u003edf\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.081) nor species (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.490, \u003cem\u003edf\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.692) significantly affected Shannon diversity indices for fungal OTUs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb, d). Overall, bacterial diversity in \u003cem\u003eSpodoptera\u003c/em\u003e regurgitant was significantly higher than fungal diversity (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;113.600, \u003cem\u003edf\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe hypothesized that \u003cem\u003eSpodoptera\u003c/em\u003e species might share a core microbiome that facilitates their adaptation to diverse host plants. To test this, we examined microbial community compositions in the regurgitant of four \u003cem\u003eSpodoptera\u003c/em\u003e species (\u003cem\u003eS. exigua\u003c/em\u003e, \u003cem\u003eS. frugiperda\u003c/em\u003e, \u003cem\u003eS. latifascia\u003c/em\u003e, and \u003cem\u003eS. littoralis\u003c/em\u003e), and assessed the influence of diet. Our results revealed that diet strongly shaped both bacterial and fungal communities in \u003cem\u003eSpodoptera\u003c/em\u003e regurgitant. While bacterial communities differed significantly among species, fungal communities remained largely consistent. Moreover, diet effects on microbial composition varied by \u003cem\u003eSpodoptera\u003c/em\u003e species, suggesting species-specific microbial selection mechanisms. However, no bacterial or fungal OTU was consistently present across all treatments, indicating the absence of core microbiome shared by \u003cem\u003eSpodoptera\u003c/em\u003e species. Overall, bacterial and fungal communities were shaped more by diet than by species, and fungal communities were less diverse than bacterial communities.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eDiet as a Driver of Microbial Composition\u003c/h2\u003e \u003cp\u003eThe strong effects of diet on microbial communities aligns with previous research on lepidopteran larvae, where gut bacterial community composition is often diet-dependent [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor example, host plant species or genotype significantly influenced gut bacterial microbiota in \u003cem\u003eS. frugiperda\u003c/em\u003e [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan additionalcitationids=\"CR27 CR28\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], \u003cem\u003eS. exigua\u003c/em\u003e [\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] and \u003cem\u003eS. littoralis\u003c/em\u003e [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Similarly, Yuning et al. [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] found that fungal communities in \u003cem\u003eS. frugiperda\u003c/em\u003e midgut varied with host plant species (\u003cem\u003eBrassica campestris\u003c/em\u003e vs \u003cem\u003eB. oleracea\u003c/em\u003e). To our knowledge, no studies have examined microbial community compositions in \u003cem\u003eS. latifiacia\u003c/em\u003e or fungal communities in \u003cem\u003eS. exigua\u003c/em\u003e and \u003cem\u003eS. latifascia\u003c/em\u003e. Diet-driven shifts in microbial communities have important implications for plant-insect interactions. Microbial communities can mediate digestion [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], pesticide resistance [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], plant defense manipulation [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], and even attraction of hyperparasitoids [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Understanding these dynamics may help identify symbionts essential for host adaptability or potential microbial targets for biological control strategies [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eAbsence of a Core Microbiome\u003c/h2\u003e \u003cp\u003ePrevious studies suggested that \u003cem\u003eSpodoptera\u003c/em\u003e species share a core microbiome [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], potentially including vertically transmitted microbes providing essential functions. For instance, \u003cem\u003eEnterococcus mundtii\u003c/em\u003e was found in the second generation of \u003cem\u003eS. littoralis\u003c/em\u003e after ingestion [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Similarly, geographically distant \u003cem\u003eS. frugiperda\u003c/em\u003e populations harboured similar gut bacterial communities [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In contrast, our findings revealed distinct bacterial compositions in \u003cem\u003eSpodoptera\u003c/em\u003e regurgitant, except for similarities between \u003cem\u003eS. frugiperda\u003c/em\u003e and \u003cem\u003eS. latifascia\u003c/em\u003e, possibly due to their shared evolutionary history in southern Mexico\u0026rsquo;s, milpa agroecosystem [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. Previous research also showed that gut bacterial communities of \u003cem\u003eS. frugiperda\u003c/em\u003e [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] and \u003cem\u003eS. exigua\u003c/em\u003e [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] differ when collected from the same plant species but different locations. Unlike bacteria, fungal communities were similar across species. However, as no fungal OTU was consistently shared among species when fed different diets, \u003cem\u003eSpodoptera\u003c/em\u003e species likely do not share a core mycobiome. Research on tree caterpillars similarly suggests that host species play a minor role in shaping fungal communities [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eDiet-Species Interactions Shape Bacterial Communities\u003c/h2\u003e \u003cp\u003eThe interaction between diet and species influenced both bacterial composition and diversity. For example, \u003cem\u003eS. frugiperda\u003c/em\u003e showed higher bacterial diversity than \u003cem\u003eS. latifascia\u003c/em\u003e and \u003cem\u003eS. littoralis\u003c/em\u003e when fed maize, likely reflecting its specialization on this crop [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. Lower bacterial diversity in maize- and squash-fed caterpillars, may be attributed to the dominance of \u003cem\u003eEnterococcus\u003c/em\u003e species, which were absent in caterpillars fed artificial diet or cotton. \u003cem\u003eEnterococcus\u003c/em\u003e species are particularly relevant as they may contribute to pesticide tolerance [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. These findings suggest that caterpillars selectively retain specific bacteria from their diet. For example, \u003cem\u003eE. mundtii\u003c/em\u003e in \u003cem\u003eS. littoralis\u003c/em\u003e produces bactericidal compounds that selectively eliminate foreign bacteria [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. Additionally, microbial selection may occur due to the highly alkaline conditions of the digestive system [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eThe Importance of Studying Fungal Communities\u003c/h2\u003e \u003cp\u003eConsistent with previous studies [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], fungal diversity was lower than bacterial diversity, with fungal communities dominated by a few highly prevalent taxa, such as \u003cem\u003eCladosporium\u003c/em\u003e and \u003cem\u003eMalassezia\u003c/em\u003e. \u003cem\u003eCladosporium\u003c/em\u003e species, commonly found in insect guts, play roles in nutrient absorption and pesticide degradation [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. \u003cem\u003eMalassezia\u003c/em\u003e species have been reported in lepidopteran insects [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], but their function remains unknown. Despite their ecological significance, fungi have often been overlooked in studies on gut microbes. However, they may contribute to nutrient supply, food breakdown and detoxification [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Further research on fungal communities in herbivorous insects could reveal important interactions affecting host health and adaptability.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this study provides a novel comparison of gut bacterial and fungal communities across different \u003cem\u003eSpodoptera\u003c/em\u003e species, including \u003cem\u003eS. latifascia\u003c/em\u003e, whose gut microbiome had not been previously characterized. Given its broad host range and potential for range expansion due to climate change, \u003cem\u003eS. latifascia\u003c/em\u003e could emerge as a significant agricultural pest in the Americas. Proactively studying its microbiome may help mitigate future risks. Our findings confirm that diet plays a key role in shaping bacterial communities in \u003cem\u003eSpodoptera\u003c/em\u003e regurgitant, while significant interspecies variation suggests species-specific bacterial selection. In contrast, fungal communities were shaped exclusively by diet, supporting the idea that fungi in the foregut are more transient and less diverse than bacteria. Notably, we found no evidence of a core gut microbiome shared among \u003cem\u003eSpodoptera\u003c/em\u003e species. However, since the caterpillars in this study were reared in the laboratory for multiple generations, potential effects on vertically transmitted microbiomes cannot be ruled out. Understanding the gut microbiome of \u003cem\u003eSpodoptera\u003c/em\u003e species offers valuable insights into their adaptability to diverse host plants. This knowledge could inform microbiome-based strategies for more effective and environmentally sustainable pest management [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBetty Benrey, Pilar Junier and Maximilien A.C. Cuny conceived the study. Maximilien A.C. Cuny reared the caterpillars and collected the regurgitant. Guillaume Cailleau performed bioinformatic analyses and wrote the corresponding sections. Maximilien A.C. Cuny performed statistical analyses and wrote the first version of the manuscript. All authors reviewed and edited the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was financially supported by a grant from the Swiss National Science Foundation (Project No. 310030-149544) awarded to B.B.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe bacterial and fungal OTU count data per sample are available in the supplementary material (Tables S1 and S2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlthough this study did not require ethics approval, we took measures to minimize insect suffering. \u0026nbsp;The insects were handled gently, kept in clean cages with sufficient food, and killed by freezing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDillon RJ, Dillon VM (2004) The gut bacteria of insects: Nonpathogenic interactions. 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While microbial communities are thought to facilitate host adaptation to diverse diets and environments, the existence of a core microbiome shared among closely related herbivores remains largely untested. In this study, we examined the microbial communities in the regurgitant of four S\u003cem\u003epodoptera\u003c/em\u003e species (\u003cem\u003eS. exigua\u003c/em\u003e, \u003cem\u003eS. frugiperda\u003c/em\u003e, \u003cem\u003eS. latifascia\u003c/em\u003e, and \u003cem\u003eS. littoralis\u003c/em\u003e) across different diets (artificial diet, cotton, maize, and squash). Using a high-throughput sequencing, we characterized bacterial and fungal community composition and diversity. Bacterial communities were shaped by both diet and host species, indicating species-specific bacterial selection. In contrast, fungal communities were exclusively structured by diet, with lower diversity and dominance of a few key taxa. Notably, no operational taxonomic units were consistently shared across all species or diets, challenging the concept of a conserved core microbiome in these generalist herbivores. Understanding how microbial communities shape generalist herbivores\u0026rsquo; ability to feed on diverse plants may offer potential strategies for microbiome-based pest management.\u003c/p\u003e","manuscriptTitle":"Host diet and phylogeny interact to shape the bacterial and fungal microbiome in the regurgitant of four Spodoptera species","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-06 15:32:32","doi":"10.21203/rs.3.rs-6114576/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-09T19:55:18+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-02T02:47:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-01T19:07:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-24T06:21:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"239631533012606583302783271558294569243","date":"2025-03-11T20:26:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"139395927733503544980410175172974136739","date":"2025-03-06T18:47:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"280528742777522076283111485624105281391","date":"2025-03-06T00:20:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"216257189473185413155983404140497057517","date":"2025-03-04T21:09:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"50974068615288185830688541587604905558","date":"2025-03-04T16:12:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-04T15:31:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-04T08:57:12+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-04T08:54:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"Microbial Ecology","date":"2025-02-26T15:32:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"microbial-ecology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meco","sideBox":"Learn more about [Microbial Ecology](https://www.springer.com/journal/248)","snPcode":"248","submissionUrl":"https://submission.nature.com/new-submission/248/3","title":"Microbial Ecology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f181d8f5-7a5a-4bc5-8ec8-b01de353e28c","owner":[],"postedDate":"March 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-07-28T15:59:51+00:00","versionOfRecord":{"articleIdentity":"rs-6114576","link":"https://doi.org/10.1007/s00248-025-02582-5","journal":{"identity":"microbial-ecology","isVorOnly":false,"title":"Microbial Ecology"},"publishedOn":"2025-07-22 15:57:12","publishedOnDateReadable":"July 22nd, 2025"},"versionCreatedAt":"2025-03-06 15:32:32","video":"","vorDoi":"10.1007/s00248-025-02582-5","vorDoiUrl":"https://doi.org/10.1007/s00248-025-02582-5","workflowStages":[]},"version":"v1","identity":"rs-6114576","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6114576","identity":"rs-6114576","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}