Persistence of the ground beetle (Coleoptera: Carabidae) microbiome to diet manipulation

Anita Silver; Sean Perez; Melanie Gee; Bethany Xu; Shreeya Garg; Kipling Will; Aman Gill

doi:10.1371/journal.pone.0241529

. 2021 Mar 19;16(3):e0241529. doi: 10.1371/journal.pone.0241529

Persistence of the ground beetle (Coleoptera: Carabidae) microbiome to diet manipulation

Anita Silver ¹, Sean Perez ¹, Melanie Gee ¹, Bethany Xu ¹, Shreeya Garg ¹, Kipling Will ^1,^2,^*, Aman Gill ¹

Editor: Omri Finkel³

PMCID: PMC7978345 PMID: 33739998

Abstract

Host-associated microbiomes can play important roles in the ecology and evolution of their insect hosts, but bacterial diversity in many insect groups remains poorly understood. Here we examine the relationship between host environment, host traits, and microbial diversity in three species in the ground beetle family (Coleoptera: Carabidae), a group of roughly 40,000 species that synthesize a wide diversity of defensive compounds. This study used 16S amplicon sequencing to profile three species that are phylogenetically distantly related, trophically distinct, and whose defensive chemical secretions differ: Anisodactylus similis LeConte, 1851, Pterostichus serripes (LeConte, 1875), and Brachinus elongatulus Chaudoir, 1876. Wild-caught beetles were compared to individuals maintained in the lab for two weeks on carnivorous, herbivorous, or starvation diets (n = 3 beetles for each species-diet combination). Metagenomic samples from two highly active tissue types—guts, and pygidial gland secretory cells (which produce defensive compounds)—were processed and sequenced separately from those of the remaining body. Bacterial composition and diversity of these ground beetles were largely resilient to controlled changes to host diet. Different tissues within the same beetle harbor unique microbial communities, and secretory cells in particular were remarkably similar across species. We also found that these three carabid species have patterns of microbial diversity similar to those previously found in carabid beetles. These results provide a baseline for future studies of the role of microbes in the diversification of carabids.

Introduction

Insects are by far the most diverse group of animals [1, 2], and it is becoming clear that the success of several major insect groups is due in part to their resident microbiomes [2, 3]. However, microbiomes remain understudied in many major groups of insects, including Carabidae ground beetles. Carabidae consists of around 40,000 described species, making it one of the most species-rich animal families on earth [4]. Moreover, the variety of defensive chemicals produced in the carabid pygidial gland system is an impressive example of evolutionary diversification [5]. Secretory cells of the pygidial gland system produce such diverse classes of molecules as carboxylic acids, formic acid, quinones, hydrocarbons, and aromatics; chemical diversity exists even within some genera [5]. Whether microbes play a functional role in carabid chemical diversity has not yet been studied.

Interactions between insects and their associated microbiomes can contribute to insect diversification [3]. Microbiomes can benefit host insects in many ways, such as producing vitamin B [6], regulating host metabolism in response to stress [7], and contributing to host development [8]. Notable examples of microbial symbionts supporting nutrient acquisition in insects include Buchnera bacteria producing essential amino acids allowing aphids to live on a nutrient-poor diet [9] and highly diverse termite gut microbes digesting cellulose for their wood-feeding hosts [10, 11]. Unlike aphids and termites, carabids tend to be dietary generalists, but microbial species are also known to contribute to other host phenotypes, including nutrient acquisition and detoxification. In ants [12], Harpalus pensylvanicus (Degeer, 1774) (Carabidae) [13] and Cephaloleia (Coleoptera: Chrysomelidae) [14], microbial symbionts assist their hosts in metabolizing different food sources. It is known that bacterial symbionts enable several beetle species to thrive in chemically hostile environments. For example, the mountain pine beetle Dendroctonus ponderosae (Hopkins, 1902) (Coleoptera: Curculionidae) can inhabit pine trees because its microbes break down defensive terpenes produced by the trees [15]. The microbiomes of Nicrophorus vespilloides Herbst, 1783 (Coleoptera: Silphidae) and other carrion beetles protect their hosts from toxins and speed up host digestion, making it easier for these beetles to feed on decaying carcasses [16, 17]. Insects are well known to benefit from defensive and protective symbioses. Lagria villosa (F.) (Coleoptera: Tenebrionidae) beetles live in symbiosis with Burkholderia gladioli that protect their host’s eggs from pathogens by producing the antifungal compound lagriamide [18]. Paederus (Coleoptera: Staphylinidae) beetles are well known for producing toxic hemolymph that causes severe dermatitis; the toxin, pederin, is produced by a Pseudomonas-like symbiont [19]. The Asian citrus psyllid Diaphorina citri (Kuwafyama, 1908) (Hemiptera: Liviidae), an invasive pest in the U.S. that causes citrus disease, harbors endosymbiotic Candidatus Profftella armatura (Betaproteobacteria) that produce diaphorin, a toxin similar to pederin [20].

In carabid beetles, pygidial gland secretory cells perform an important metabolic function by synthesizing defensive chemicals for secretion. To our knowledge, no previous studies have compared the microbial communities of secretory cells from different carabid species. Given the multitude of established insect-bacterial associations as well as the diversity of ground beetle defensive chemistry, it is worth investigating the microbial diversity of secretory cell tissues as a first step in exploring potential functional links between secretory cell microbes and carabid defensive chemical biosynthesis.

If bacteria have contributed to the diversification of carabid beetle phenotypes, this connection may be reflected in patterns of carabid microbiome composition and diversity. Insect microbiome composition can be explained by several factors, such as host phylogeny [2, 21–23], dietary guild [23] or sampling locality [24]. Although many insects have persistent host-associated communities, some do not, highlighting the potential for fluctuations in microbial diversity; for example, some lepidoptera caterpillar microbiomes consist entirely of microbes ingested with leaves, with constant turnover based on short-term diet [25]. If the carabid microbiome is characterized by rapid, near-complete compositional turnover similar to that of the caterpillar microbiome, then it would be sensitive to dietary shifts [12, 26] or other changes to the local environment [11], and not obviously correlate with factors such as host phylogeny, chemistry, or tissue type. The composition of persistent host-associated microbiomes can also be influenced by changes to host diet, as has previously been found in some Coleoptera and Lepidoptera species [14, 27], but would not exhibit near-complete compositional turnover as a result of these short-term perturbations.

As insects have an open circulatory system that allows hemolymph to flow throughout the body, microbial communities are found in many insect tissues [28]; but as in other animals, insects often have distinct microbial communities in different tissues [10, 12, 28]. Some of this diversity may relate to the variety of conditions found within insect anatomy, including aerobic and anaerobic regions and extreme pH gradients [11, 28]. Tissue-specific diversity could also be explained by a co-evolutionary relationship between hosts and symbiotic microbiota, in that hosts can harbor functionally useful bacteria in specialized tissues. For example, termites regulate unique microbiomes in each of several gut pouches [11], and many insect species maintain useful symbionts in specialized cells called bacteriocytes [28]. The present study focuses on the pygidial gland secretory cells (hereafter simply "secretory cells") and the gut. We focus on the microbial communities of these tissues because they are responsible for defensive chemical synthesis and digestion of food respectively—metabolic processes known to involve bacterial symbionts in other insect taxa.

In this study, we used 16S metagenomic amplicon sequencing to quantify the bacterial diversity hosted by three carabid species under several dietary treatments. Each host species produces distinct primary defensive compounds: Anisodactylus similis produces formic acid, Pterostichus serripes carboxylic acids, and Brachinus elongatulus quinones [29] [Will & Attygalle, unpublished data]. Anisodactylus similus has a distinct natural feeding preference from the other two species, so together these three species represent two different trophic types. Brachinus elongatulus and P. serripes are naturally generalist predator-scavengers, preferring animal matter but observed in nature and in the lab to eat a wide variety of sugar and protein rich plant and animal material; in contrast, A. similis is typically observed feeding on fallen fruits, seeds, and pollen [30] [Will unpbl.]. In addition to sequencing wild-caught beetles preserved at the time of collection, we also subjected live beetles of each species to three controlled dietary treatments.

This preliminary study was intended to reveal how short-term dietary shifts in beetle hosts affect bacterial diversity and composition, providing an initial step in broader efforts to characterize carabid microbial diversity in relation to host diversification. If carabids lack established microbiomes altogether, as seen in caterpillars [25], then diet treatment alone might explain a significant amount of the variation across microbial communities. In that case, relative to conditions observed in the field, same-host communities under different diet treatments might diverge, and different-host communities under the same diet treatment might converge. On the other hand, if carabids have resilient microbial communities—structured by environmental exposure at earlier life stages, vertical transmission, and/or symbiosis—we would expect those communities to remain largely intact during controlled changes to host diet. Further, in that case we expect that community diversity and composition would correlate with host characteristics such as species, tissue type, or defensive chemistry. To better distinguish these possible associations, our study quantifies the effect of dietary shifts on several host species and tissues separately. Notably, to our knowledge this study is the first to compare microbial communities of carabid pygidial gland secretory cells from multiple species. This work provides a baseline for future studies to investigate the connection between host-associated microbes and carabid beetle phylogenetic and chemical diversity.

Methods

Beetle husbandry and dissection

Twelve individuals each of Anisodactylus similis LeConte, 1851, Pterostichus serripes (LeConte, 1875), and Brachinus elongatulus Chaudoir, 1876 were collected (total 36 specimens). Pterostichus serripes and A. similus were collected from U.C. Berkeley’s Whitaker’s Forest, Tulare County, CA (36.7022°, -118.933°). Brachinus elongatulus were collected from national forest land in Madera Canyon, Santa Cruz County, AZ (31.72°, -110.88°). The number of replicate beetles used for this study was constrained by the practical difficulties of finding and catching sufficient numbers of live specimens from multiple species. For each species, three wild-caught specimens were preserved in 95% ethanol immediately upon collection, and the remaining beetles were transported live to laboratory facilities on the U.C. Berkeley campus. For each species, in addition to wild-caught specimens, three diet treatments (banana, mealworm, and starvation) were tested in triplicate. Diet-treated beetles were kept in sterile containers with sterilized soil and water for 17 days in July, 2018. This time-frame, long enough for beetles to feed 5–6 times, was chosen to examine short-term dietary impacts. Banana-fed (Trader Joe’s, Dole Banana Ecuador) and mealworm-fed (Timberline, Vita-bugs Mini Mealworms 500 count) beetles were fed on the first day, and subsequently fed and watered every three days using heat-sterilized forceps and autoclaved water. All feeding portions consisted of 0.04g (+/- 0.01g) non-sterile food. Banana and mealworm bacterial communities were sequenced as controls and were removed from the analysis after confirming samples were not contaminated. Starved beetles received water, but no food. On the last day, beetles were quickly anesthetized by placing them for one minute at -80°C in their plastic containers. All specimens, including wild-caught beetles, were dissected as described by McManus et al. [31]. Each beetle was dissected into three groups of tissues: secretory cells, gut (including foregut, midgut, and hindgut), and the rest of the body minus the secretory cells and gut (subsequently referred to as ‘partial body’). Parasitic worms (Nematomorpha) found to be infecting one starved beetle and one mealworm-fed beetle were removed from those specimens and the worm tissues not included in downstream analysis.

DNA extraction, PCR, and next generation sequencing

Tissues were incubated overnight in a 9:1 ratio of buffer ATL and proteinase K (Qiagen DNeasy Blood & Tissue Kit) at 55°C on a rocking tray. Lysate from overnight incubation was transferred to sterile 1.5ml O-ring tubes containing 0.25g (+/- 0.02g) of 0.1mm diameter zirconium beads and bead beat at 2000rpm for 3 minutes in a PowerLyzer to lyse bacterial cells. DNA was extracted from the lysed homogenate using Solid Phase Reversible Immobilization (SPRI) magnetic beads made following the method of Rohland [32]: 100μL lysate was mixed with 180μL of well-mixed, room temperature SPRI beads, incubated for approximately 5 minutes on the bench, then transferred to a magnetic rack. After the SPRI beads pelleted, 200μL 80% ethanol was added. After 30 seconds the supernatant was removed, the ethanol wash was repeated a second time and the supernatant was removed again. Then, the tubes containing SPRI bead tubes were removed from the magnetic rack and allowed to air dry completely. DNA was eluted by adding 50μL TB solution (10mM Tris) directly onto the beads and incubating for 5 minutes, then returning samples to the magnetic rack to pellet the SPRI beads and retrieve the DNA-containing supernatant.

The V4 region of the 16S rRNA gene was PCR amplified in duplicate in 25μL reactions using GoTaq Green Master Mix (Promega), and the resulting PCR products were subsequently pooled. During the first round, previously described primers [33] 515FB_in (5’-ACA CTC TTT CCC TAC ACG ACG CTC TTC CGA TCT GTG YCA GCM GCC GCG GTA A-3’) and 806RB_in (5’-GTG ACT GGA GTT CAG ACG TGT GCT CTT CCG ATC TGG ACT ACH VGG GTW TCT AAT-3’), which were adapted to be complementary to the second round primers [34], were added to the ends of all 16S genes with the following conditions (BioRad thermocycler): initial denaturation at 94°C for 3 min, followed by 30 cycles of 94°C for 45 sec, 50°C for 1 min, 72°C for 1:30 min, and a final extension step of 72°C for 10 min. A second round of PCR was performed using unique combinations of barcoded forward (5’-AAT GAT ACG GCG ACC ACC GAG ATC TAC ACX XXX XXX XAC ACT CTT TCC CTA CAC GA-3’) and reverse (5’-CAA GCA GAA GAC GGC ATA CGA GAT XXX XXX XXG TGA CTG GAG TTC AGA CGT G-3’) primers [34] to create a dual-index amplicon library for Illumina sequencing (position of barcodes indicated by ’X’ characters). The conditions for the second PCR reaction were: initial denaturation at 94°C for 3 min, followed by 10 cycles of 94°C for 45 sec, 50°C for 1 min, 72°C for 1:30 min, and a final extension step of 72°C for 10 min. All pooled duplicate PCR products were run on a 1% agarose gel for 30 min at 100V, and imaged under UV light to verify successful PCR. DNA concentration was quantified using a Qubit fluorometer, and equimolar amounts were pooled. The pooled library was purified (Qiagen Qiaquick PCR Purification Kit) and sent for Illumina MiSeq sequencing at the U.C. Berkeley Genomics Sequencing Laboratory.

Analysis

Amplicon reads for the V4 region of 16S were de-multiplexed with deML [35] and processed using DADA2 [36], including quality filtering with maxEE = 2. Reads were de-replicated into unique 16S amplicon sequence variants (ASVs, also referred to as phylotypes) using a read error model parameterized from the data. Paired-end reads were merged and mapped to ASVs to construct a sequence table. Chimeric sequences were removed. Taxonomic assignments for exact matches of ASVs and reference strains were made using the Ribosomal Database Project database [37]. Sequence tables and taxonomic assignments were imported into R version 3.5 [38] for downstream analysis and combined into a single phyloseq [39] object for convenience. To account for variation in sequencing effort across samples, samples were scaled according to variance stabilized ASV abundances using DESeq2 [40, 41]. ASV alignments made using DECIPHER [42, 43] were used to construct a neighbor-joining tree, and this tree was then used as the starting point for deriving a maximum likelihood tree from a generalized time-reversible model with gamma rate variation, implemented with the phangorn package in R [44]. The tree was rooted using QsRutils [45]. For comparative analysis between beetles, ASV data from all three tissues of each specimen were combined into an aggregate bacterial community. Alpha diversity measures were calculated using the packages phyloseq [39] and picante [46]. Non-metric multidimensional scaling (NMDS) plots of beta diversity were created using phyloseq [39], and analysis of similarities (ANOSIM) tests were run using the package vegan [47]. Bray-Curtis distances were calculated both for aggregate community data and for the original dataset. Venn diagrams of phylotypes present by diet were rendered by VennDiagram [48]. To control for possible sequencing errors, only phylotypes occurring at least twice in the entire dataset were included in venn diagram analysis. Hierarchical clustering of communities was performed with the package ape [49]. Secretory cells were tested for differential abundance of microbe phylotypes using an equivalent method to RNA-seq differential expression analysis, implemented using DESeq2 [39, 40].

Ethics statement

No permits were required for the described study, which complied with all relevant regulations.

Results

Sequencing results

After quality filtering, the mean number of reads per sample was 19,868, and the median number of reads per sample was 17,532.

Alpha diversity

There was a median of 95 and a mean of 98.6 ASVs present per sample. Phylogenetic diversity (PD), evenness, phylotype richness, and Shannon index were calculated for each sample.

Diet. PD of aggregate communities was not associated significantly with diet treatment. When tissues were considered individually, only PD of the partial body varied significantly across diet treatments (Fig 1A). Richness results were similar to PD. Neither evenness nor Shannon diversity showed any significant effect of diet in either individual tissues or aggregate microbiomes. Compared to wild-caught beetles, captivity (including both treated and control specimens) had a minor effect on PD but not on other alpha diversity measures of the microbiome. Host diet did not correlate with community alpha diversity.

Fig 1 — (A) Plots grouped by diet treatment. PD of partial bodies varied significantly by diet treatment (H = 8.96, p = 0.030), but PD of all other tissues and of aggregate communities did not. (B) Plots grouped by host species. PD of partial bodies varied significantly by host species (Kruskal Wallis H = 8.11, p = 0.017), but PD of all other tissues and of aggregate communities did not.

Tissue

Tissue explained a large portion of the variance in PD of aggregate communities (Kruskal Wallis H = 55.5, p < 0.0001), so results were plotted separately for each tissue (Fig 1). Secretory cell microbiomes had higher PD than gut microbiomes (Fig 1). Richness, Shannon diversity, and evenness all varied significantly by tissue (p < 0.0001) as well. Guts had the lowest community evenness on average (average evenness 0.60, versus 0.75 in secretory cells and 0.67 in partial bodies).

Species

Evidence of an effect of host species on microbial community diversity was relatively weak, and varied by tissue. Overall PD and evenness did not vary significantly by species. Richness (p = 0.043) and Shannon diversity (p = 0.041) varied only slightly significantly by species. Secretory cells had a relatively consistent alpha diversity level across species, only varying significantly by the measure of evenness (p = 0.030). Gut alpha diversity varied significantly across species by richness (p = 0.027), Shannon diversity (p < 1e-05), and evenness (p < 1e-05) but not PD. Differences in gut alpha diversity appear to be driven by the exceptionally low evenness in A. similis guts. The partial body microbiome had significantly different PD (Fig 1B) and richness (p = 0.0033), but no change in evenness, across host species. PD of partial bodies was highest in P. serripes.

Community diversity distance analysis

Bray-Curtis distances for aggregate community data (Fig 2A) and for the original dataset (Fig 2B–2D) reveal that community similarity is associated with several of the factors tested. Results of ANOSIM performed with Unifrac distances were consistent with results using Bray-Curtis distances reported below.

Diet

Aggregate communities did not cluster by diet (Fig 2A). They did cluster by captive (treatments as well as starved controls) versus wild-caught beetles (ANOSIM R statistic = 0.3336, p < 0.001). The only tissue that clustered significantly by diet was secretory cells, but these clustered with a lower R statistic by diet (R = 0.23) than by species (R = 0.28). Clustering by diet was explained by significant differences between captive and wild-caught beetles. Phylotypes present in aggregate communities were compared across diet treatments (Fig 3). A total of 1003 phylotypes were present across all diet conditions, 613 of which were present in wild-caught beetles. Of the phylotypes present in wild-caught beetles, 78% were present in at least one other diet condition. Just over a quarter of phylotypes were shared across all four diet conditions.

Tissue

Microbial communities clustered clearly and significantly by host tissue.

Species

Aggregate communities clustered by host species, with the B. elongatulus microbiome being the most distinct (Fig 2A). Individual tissues also clustered by species. The secretory cells had a much lower clustering statistic than the other tissues, indicating that microbial diversity in secretory cells is less differentiated than other tissues.

Community composition

The most abundant phyla across all samples were Proteobacteria (mean abundance 48.7%), Bacteroidetes (mean abundance 17.8%), Tenericutes, and Firmicutes. Together, these four phyla comprised a mean of 94.6% of the bacteria in each sample. Communities in all beetle species and tissues had similar phylum-level compositions. Differences by host species arose more clearly at the level of bacterial genera, so community composition of each beetle species was plotted at this level (Fig 4). Bacterial genera with median relative abundance across all samples of 1.5% or above were, in descending order of median relative abundance: Acinetobacter, Spiroplasma, Yersinia, Flavobacterium, Pseudomonas, Enterobacter, and Enterococcus.

Fig 4 — (A) Mean abundance in the secretory cells of the ten bacterial genera that were most abundant on average in all samples (n = 36, 12 per species). (B) Mean abundance of these bacterial genera in the guts (n = 36, 12 per species). (C) Mean abundance of these bacterial genera in the partial bodies (n = 36, 12 per species).

Diet

Community composition was not significantly different across diet treatments (Fig 5).

Tissue

Differential abundance analysis of secretory cells versus all other tissues revealed that four phylotypes associated with two families were differentially abundant (p < 0.002). Two Flavobacterium phylotypes were more abundant in secretory cells than other tissues by factors of 10.22 and 16.23. Two Comamonadaceae phylotypes of unknown species were 9.38 and 9.92 fold more abundant in secretory cells. Secretory cell community composition is relatively conserved at the level of bacterial genera (Fig 4A). Compared to other tissues, gut microbiomes were more dominated by the ten most abundant bacterial genera; these ten genera composed over 50% of microbial abundance in all host species’ guts, and over 60% of abundance in B. elongatulus guts (Fig 4B).

Species

Hierarchical clustering of community similarity showed that community differences corresponded with host species for all tissues. Brachinus elongatulus guts have more Firmicutes, and less Tenericutes and Actinobacteria, than the other two host species. Breaking down community composition to the genus level confirmed the status of Brachinus as the most distinct host species (Fig 4).

Discussion

The present study assessed the extent to which the microbiomes of three carabid beetle species are influenced by short-term community turnover driven by diet. Sequencing gut and secretory cell communities separately provided an opportunity to examine how bacterial community composition and diversity are associated with host tissue type. We found that shifts to controlled carnivorous, herbivorous, or starvation diets had at most minor effects on bacterial species diversity or composition, regardless of host species or tissue type. In contrast, host species and tissue type explained a significant amount of the variation in microbial communities across samples. The findings of this small-scale study provide preliminary evidence that carabids harbor diverse, relatively resilient microbiomes that are persistent to short-term changes in host diet. These results contribute to broader efforts to understand host-associated microbial diversity in ground beetles.

Microbiome resilience to host dietary change

By subjecting carabid beetles to different dietary treatments in a controlled, sterile environment, our study quantified how changes to carabid host diet influence microbiome composition and diversity. The similarity of microbial community composition regardless of host dietary treatment indicates recently ingested food is not a primary driver of microbial community structure in these beetles. This interpretation is also supported by the finding that transient changes in host diet do not significantly alter community diversity (Fig 1). We did notice a reduction in microbial richness and diversity between wildtype beetles and all other treatment groups which were husbanded in the lab (Fig 1A), as has previously been described in carrion beetles [17] and lepidopteran species [27]. The resilience of carabid microbial communities to short-term perturbations in diet suggests that unlike caterpillars [25], carabid beetles appear to possess persistent microbial communities.

The finding that carabid microbiomes are resilient to short-term dietary shifts does not help pinpoint the factors that are most important in microbial community assembly in ground beetles. It does not address, for example, how juvenile carabids acquire microbes during their egg, larval, or pupal stages. During these life stages, microbiome acquisition and assembly may occur by selective uptake from the environment [11], vertical transmission from a parent [8], or via other routes. In addition, the nature of our study is such that it cannot distinguish whether microbiome variability is affected by long-term changes to host diet or environment. Long-term environment may affect microbial communities in carabids, as it does in Drosophila [11] and houseflies [24]. Future studies could investigate these and other factors that may affect carabid microbiomes throughout the carabid life cycle.

Microbiome associations with host characteristics

Microbial communities were similar within individuals of the same host species, and distinct across the three host species. This apparent connection to host species could be a result of host defensive chemistry, host geography, vertical or horizontal transmission between individual members of the same species, environmental niche differences, or other factors. The diversity in microbiome composition we observed across host species agrees with a previous study of microbiomes in the Carabidae family which showed that the gut microbiomes of two species of carabids, Harpalus pensylvanicus (Degeer, 1774) and Anisodactylus sanctaecrucis (Fabricius, 1798), have different composition and species richness from each other [50]. The prevalence of the genus Spiroplasma in our results agrees with the findings of previous studies of carabid microbiomes [31, 50]. Dysgonomonas, which has previously been found to be prevalent in B. elongatulus [31], was one of the most abundant genera, and also had higher relative abundance in B. elongatulus than in other beetle species, especially in the guts (Fig 3). Enterococcus, which was previously found in the digestive tract of B. elongatulus [31], was again found in that species and also in P. serripes, both at a greater abundance in the guts than in other tissues (Fig 3). Although microbiome community structure is often not correlated with insect host phylogeny at higher taxonomic level such as order [11, 21], we note that some of our results align with previous findings in the order Coleoptera. Specifically, several highly abundant bacterial genera in our samples were previously found in Cephaloleia (Chrysomelidae) beetles [14]. The phylogenetic diversity of these carabid microbiomes is also within the compass of previous studies in Coleoptera [23].

We believe our study to be the first to comparatively describe microbial communities found specifically in the pygidial gland secretory cells, a functionally important tissue with homologous structures found in carabids and other Adephaga. Our preliminary study found that the pygidial glands possess a differentiated microbiome from other carabid tissues, warranting further investigation especially given the unique metabolic capabilities of these cells. In our samples, the phylogenetic diversity of microbial communities in the secretory cells of these glands is particularly high (Fig 1). The composition of secretory cell communities is more similar across host species than that of other tissues (Figs 2B and 4). Secretory cells were also the only tissue to see a significant change in their microbial community in response to diet treatment. This finding is notable since guts would presumably be the tissue most closely associated with diet. It is tempting to speculate that changes to metabolic inputs that come with dietary shifts deprive secretory cell bacteria of required substrates, but a larger, more targeted experimental study would be required to understand how secretory cell bacteria and metabolic activity respond to host diet. As a baseline for future studies of carabid secretory cell microbes to build upon, we determined that several bacterial genera in particular (Flavobacterium and an unknown Comamonadaceae genus) are differentially abundant in the secretory cells over other tissues. In addition to more thorough investigations of secretory cell microbiome composition across Carabidae, we propose that future studies should directly test the possibility that a symbiotic relationship with microbes plays a role in host chemical biosynthesis. This could be done in several ways, such as by using antibiotics to flush beetles of their microbes, or by experimentally confirming the metabolic activity of bacterial isolates from secretory cells.

The present study found a strong association between host tissue type and microbiome characteristics, not only in secretory cells, but also in guts. Out of all the factors controlled for in this study, tissue type (secretory cells, guts, or partial bodies) has the closest association with microbiome composition and diversity. Tissue identity explains much of the variation between communities, in composition and distance ordination, and most of the variation in PD (Fig 1). Previous research has found that factors such as environmental filtering [3] and routes of microbe dispersal [22] can shape microbiome composition, and the especially strong association with tissue type could be related to these factors. Future efforts to assess functional microbe-host interactions, such as possible connections between gut microbes and carabid host nutrition, might consider directly quantifying functional genes and metabolic pathways present in the microbial communities.

Acknowledgments

We thank Wendy Moore and her lab group at the University of Arizona, Tucson, for providing the Brachinus elongatulus for the study.

Data Availability

All data files are available via the NCBI Sequence Read Archive (BioProject PRJNA703093).

Funding Statement

KW. NSF DEB #1556957. National Science Foundation Division of Environmental Biology. https://www.nsf.gov/bio/deb/about.jsp The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

1.Stork NE. How Many Species of Insects and Other Terrestrial Arthropods Are There on Earth? Annu Rev Entomol. 2018;63(1):31–45. 10.1146/annurev-ento-020117-043348 [DOI] [PubMed] [Google Scholar]
2.Yun J-H, Roh SW, Whon TW, Jung M-J, Kim M-S, Park D-S, et al. Insect Gut Bacterial Diversity Determined by Environmental Habitat, Diet, Developmental Stage, and Phylogeny of Host. Drake HL, editor. Appl Environ Microbiol. 2014. September 1;80(17):5254–64. 10.1128/AEM.01226-14 [DOI] [PMC free article] [PubMed] [Google Scholar]
3.Vavre F, Kremer N. Microbial impacts on insect evolutionary diversification: from patterns to mechanisms. Curr Opin Insect Sci. 2014. October;4:29–34. 10.1016/j.cois.2014.08.003 [DOI] [PubMed] [Google Scholar]
4.Lorenz W, 1950-. Systematic list of extant ground beetles of the world [Internet]. W. Lorenz; 2005 [cited 2019 Sep 27]. Available from: http://agris.fao.org/agris-search/search.do?recordID=US201300114567
5.Will KW, Attygalle Athula B, Herath K. New defensive chemical data for ground beetles (Coleoptera: Carabidae): interpretations in a phylogenetic framework. Biol J Linn Soc. 2000. November;71(3):459–81. [Google Scholar]
6.Douglas AE. The microbial dimension in insect nutritional ecology. Funct Ecol. 2009. February;23(1):38–47. [Google Scholar]
7.Wong AC-N, Dobson AJ, Douglas AE. Gut microbiota dictates the metabolic response of Drosophila to diet. J Exp Biol. 2014. June 1;217(11):1894–901. [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Schwab DB, Riggs HE, Newton ILG, Moczek AP. Developmental and Ecological Benefits of the Maternally Transmitted Microbiota in a Dung Beetle. Am Nat. 2016. December;188(6):679–92. 10.1086/688926 [DOI] [PubMed] [Google Scholar]
9.Douglas AE. Nutritional Interactions in Insect-Microbial Symbioses: Aphids and Their Symbiotic Bacteria Buchnera. Annu Rev Entomol. 1998. January;43(1):17–37. 10.1146/annurev.ento.43.1.17 [DOI] [PubMed] [Google Scholar]
10.Breznak JA. Intestinal Microbiota of Termites and other Xylophagous Insects. Annu Rev Microbiol. 1982. October;36(1):323–323. 10.1146/annurev.mi.36.100182.001543 [DOI] [PubMed] [Google Scholar]
11.Engel P, Moran NA. The gut microbiota of insects–diversity in structure and function. FEMS Microbiol Rev. 2013. September;37(5):699–735. 10.1111/1574-6976.12025 [DOI] [PubMed] [Google Scholar]
12.Russell JA, Moreau CS, Goldman-Huertas B, Fujiwara M, Lohman DJ, Pierce NE. Bacterial gut symbionts are tightly linked with the evolution of herbivory in ants. Proc Natl Acad Sci. 2009. December 15;106(50):21236–41. 10.1073/pnas.0907926106 [DOI] [PMC free article] [PubMed] [Google Scholar]
13.Lundgren JG, Lehman RM. Bacterial Gut Symbionts Contribute to Seed Digestion in an Omnivorous Beetle. Van der Heijden M, editor. PLoS ONE. 2010. May 26;5(5):e10831. 10.1371/journal.pone.0010831 [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Blankenchip CL, Michels DE, Braker HE, Goffredi SK. Diet breadth and exploitation of exotic plants shift the core microbiome of Cephaloleia, a group of tropical herbivorous beetles. PeerJ. 2018. May 17;6:e4793. 10.7717/peerj.4793 [DOI] [PMC free article] [PubMed] [Google Scholar]
15.Adams AS, Aylward FO, Adams SM, Erbilgin N, Aukema BH, Currie CR, et al. Mountain Pine Beetles Colonizing Historical and Naïve Host Trees Are Associated with a Bacterial Community Highly Enriched in Genes Contributing to Terpene Metabolism. Appl Environ Microbiol. 2013. June 1;79(11):3468–75. 10.1128/AEM.00068-13 [DOI] [PMC free article] [PubMed] [Google Scholar]
16.Vogel H, Shukla SP, Engl T, Weiss B, Fischer R, Steiger S, et al. The digestive and defensive basis of carcass utilization by the burying beetle and its microbiota. Nat Commun. 2017. August;8(1):15186. 10.1038/ncomms15186 [DOI] [PMC free article] [PubMed] [Google Scholar]
17.Kaltenpoth M, Steiger S. Unearthing carrion beetles’ microbiome: characterization of bacterial and fungal hindgut communities across the Silphidae. Mol Ecol. 2014. March;23(6):1251–67. 10.1111/mec.12469 [DOI] [PubMed] [Google Scholar]
18.Flórez LV, Scherlach K, Miller IJ, Rodrigues A, Kwan JC, Hertweck C, et al. An antifungal polyketide associated with horizontally acquired genes supports symbiont-mediated defense in Lagria villosa beetles. Nat Commun. 2018. June 26;9(1):1–10. 10.1038/s41467-017-02088-w [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Kellner RLL. Molecular identification of an endosymbiotic bacterium associated with pederin biosynthesis in Paederus sabaeus (Coleoptera: Staphylinidae). Insect Biochem Mol Biol. 2002. April;32(4):389–95. 10.1016/s0965-1748(01)00115-1 [DOI] [PubMed] [Google Scholar]
20.Nakabachi A, Ueoka R, Oshima K, Teta R, Mangoni A, Gurgui M, et al. Defensive Bacteriome Symbiont with a Drastically Reduced Genome. Curr Biol. 2013. August 5;23(15):1478–84. 10.1016/j.cub.2013.06.027 [DOI] [PubMed] [Google Scholar]
21.Jones RT, Sanchez LG, Fierer N. A Cross-Taxon Analysis of Insect-Associated Bacterial Diversity. Gilbert JA, editor. PLoS ONE. 2013. April 16;8(4):e61218. 10.1371/journal.pone.0061218 [DOI] [PMC free article] [PubMed] [Google Scholar]
22.Brooks AW, Kohl KD, Brucker RM, van Opstal EJ, Bordenstein SR. Phylosymbiosis: Relationships and Functional Effects of Microbial Communities across Host Evolutionary History. Relman D, editor. PLOS Biol. 2016. November 18;14(11):e2000225. 10.1371/journal.pbio.2000225 [DOI] [PMC free article] [PubMed] [Google Scholar]
23.Colman DR, Toolson EC, Takacs-Vesbach CD. Do diet and taxonomy influence insect gut bacterial communities? Mol Ecol. 2012. October;21(20):5124–37. 10.1111/j.1365-294X.2012.05752.x [DOI] [PubMed] [Google Scholar]
24.Bahrndorff S, de Jonge N, Skovgård H, Nielsen JL. Bacterial Communities Associated with Houseflies (Musca domestica L.) Sampled within and between Farms. Wilson BA, editor. PLOS ONE. 2017. January 12;12(1):e0169753. 10.1371/journal.pone.0169753 [DOI] [PMC free article] [PubMed] [Google Scholar]
25.Hammer TJ, Janzen DH, Hallwachs W, Jaffe SP, Fierer N. Caterpillars lack a resident gut microbiome. Proc Natl Acad Sci. 2017. September 5;114(36):9641–6. 10.1073/pnas.1707186114 [DOI] [PMC free article] [PubMed] [Google Scholar]
26.Scully ED, Geib SM, Carlson JE, Tien M, McKenna D, Hoover K. Functional genomics and microbiome profiling of the Asian longhorned beetle (Anoplophora glabripennis) reveal insights into the digestive physiology and nutritional ecology of wood feeding beetles. BMC Genomics. 2014;15(1):1096. 10.1186/1471-2164-15-1096 [DOI] [PMC free article] [PubMed] [Google Scholar]
27.Tang X, Freitak D, Vogel H, Ping L, Shao Y, Cordero EA, et al. Complexity and Variability of Gut Commensal Microbiota in Polyphagous Lepidopteran Larvae. Dale C, editor. PLoS ONE. 2012. July 17;7(7):e36978. 10.1371/journal.pone.0036978 [DOI] [PMC free article] [PubMed] [Google Scholar]
28.Douglas AE. Lessons from Studying Insect Symbioses. Cell Host Microbe. 2011. October;10(4):359–67. 10.1016/j.chom.2011.09.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
29.Attygalle AB, Xu S, Moore W, McManus R, Gill A, Will K. Biosynthetic origin of benzoquinones in the explosive discharge of the bombardier beetle Brachinus elongatulus. Sci Nat. 2020. August 1;107(4):1–11. 10.1007/s00114-020-01683-0 [DOI] [PubMed] [Google Scholar]
30.Larochelle A. The food of carabid beetles (Coleoptera: Carabidae, including Cicindelinae). Fabreries. 1990;Supplément 5:1–132. [Google Scholar]
31.McManus R, Ravenscraft A, Moore W. Bacterial Associates of a Gregarious Riparian Beetle With Explosive Defensive Chemistry. Front Microbiol. 2018. October 5;9:2361. 10.3389/fmicb.2018.02361 [DOI] [PMC free article] [PubMed] [Google Scholar]
32.Rohland N, Reich D. Cost-effective, high-throughput DNA sequencing libraries for multiplexed target capture. Genome Res. 2012. May 1;22(5):939–46. 10.1101/gr.128124.111 [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Huntley J, Fierer N, et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 2012. August;6(8):1621–4. 10.1038/ismej.2012.8 [DOI] [PMC free article] [PubMed] [Google Scholar]
34.Lange V, Böhme I, Hofmann J, Lang K, Sauter J, Schöne B, et al. Cost-efficient high-throughput HLA typing by MiSeq amplicon sequencing. BMC Genomics. 2014. January 24;15:63. 10.1186/1471-2164-15-63 [DOI] [PMC free article] [PubMed] [Google Scholar]
35.Renaud G. grenaud/deML [Internet]. 2019 [cited 2019 Sep 25]. Available from: https://github.com/grenaud/deML
36.Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016. July;13(7):581–3. 10.1038/nmeth.3869 [DOI] [PMC free article] [PubMed] [Google Scholar]
37.Cole JR, Wang Q, Fish JA, Chai B, McGarrell DM, Sun Y, et al. Ribosomal Database Project: data and tools for high throughput rRNA analysis. Nucleic Acids Res. 2014. January 1;42(Database issue):D633–42. 10.1093/nar/gkt1244 [DOI] [PMC free article] [PubMed] [Google Scholar]
38.R Core Team. R: A language and environment for statistical computing [Internet]. Vienna, Austria: R Foundation for Statistical Computing; 2018. [cited 2019 Sep 25]. Available from: https://www.R-project.org/ [Google Scholar]
39.McMurdie PJ, Holmes S. phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data. PLOS ONE. 2013. April 22;8(4):e61217. 10.1371/journal.pone.0061217 [DOI] [PMC free article] [PubMed] [Google Scholar]
40.Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550. 10.1186/s13059-014-0550-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
41.McMurdie PJ, Holmes S. Waste Not, Want Not: Why Rarefying Microbiome Data Is Inadmissible. PLOS Comput Biol. 2014. April 3;10(4):e1003531. 10.1371/journal.pcbi.1003531 [DOI] [PMC free article] [PubMed] [Google Scholar]
42.Wright ES. DECIPHER: harnessing local sequence context to improve protein multiple sequence alignment. BMC Bioinformatics. 2015. October 6;16(1):322. 10.1186/s12859-015-0749-z [DOI] [PMC free article] [PubMed] [Google Scholar]
43.Wright E S. Using DECIPHER v2.0 to Analyze Big Biological Sequence Data in R. R J. 2016;8(1):352. [Google Scholar]
44.Schliep KP. phangorn: phylogenetic analysis in R. Bioinformatics. 2011. February 15;27(4):592–3. 10.1093/bioinformatics/btq706 [DOI] [PMC free article] [PubMed] [Google Scholar]
45.Quensen J. QsRutils: R Functions Useful for Community Ecology [Internet]. 2019 [cited 2019 Sep 25]. Available from: https://github.com/jfq3/QsRutils
46.Kembel SW, Cowan PD, Helmus MR, Cornwell WK, Morlon H, Ackerly DD, et al. Picante: R tools for integrating phylogenies and ecology. Bioinformatics. 2010. June 1;26(11):1463–4. 10.1093/bioinformatics/btq166 [DOI] [PubMed] [Google Scholar]
47.Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, et al. vegan: Community Ecology Package [Internet]. 2019 [cited 2019 Sep 25]. Available from: https://CRAN.R-project.org/package=vegan
48.Chen H. VennDiagram: Generate High-Resolution Venn and Euler Plots [Internet]. 2018 [cited 2019 Sep 25]. Available from: https://CRAN.R-project.org/package=VennDiagram
49.Paradis E, Schliep K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics. 2019. February 1;35(3):526–8. 10.1093/bioinformatics/bty633 [DOI] [PubMed] [Google Scholar]
50.Lundgren JG, Lehman RM, Chee-sanford J. Bacterial Communities within Digestive Tracts of Ground Beetles (Coleoptera: Carabidae). Ann Entomol Soc Am. 2007. March 1;100(2):275–82. [Google Scholar]

PLoS One. doi: 10.1371/journal.pone.0241529.r001

Decision Letter 0

Omri Finkel

30 Dec 2020

PONE-D-20-32322

Persistence of the ground beetle (Coleoptera: Carabidae) microbiome to diet manipulation

PLOS ONE

Dear Dr. Kipling

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Both reviewers found the manuscript interesting, but have recomended substantial modifications. Please pay special attention to the comment by reviewr #2 regarding the sample sizes.

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Reviewer #1: Partly

Reviewer #2: Partly

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: I Don't Know

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Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

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Reviewer #1: Summary

The manuscript investigates the bacterial community of three species of carabid beetles, evaluating the differences according to insect host, tissue, diet and environment. The dataset is valuable, as there is otherwise scarce research on the microbial communities associated to this beetle family. The consideration of different diets and tissues is additionally relevant. However, from my view there is an important methodological issue that should be addressed, and which might confound the interpretation of the data. Also, the way in which some of the questions are posed, as well as some of the arguments used in the discussion can be misleading. I point out specific issues and suggest some improvements to better align the conclusions with the approach and observations.

General comments

1. In lines 117-118, the authors put forward an aim consistent with the experiments that were carried out, i.e. “This study examined how the transient factor of diet treatment, and more permanent factors including host species and tissue, contribute to the observed variation in carabid associated microbiomes”. However, later in Lines 124-131 three predictions are put forward in relation to whether the communities are non-transient and to potential functions. This, in my opinion, generates confusion as to what can actually be deduced from the data, specially given the experimental approach. To test for non-transient communities, a broader sampling from different life stages and/or across individuals, or at least placing focus on individual variation would be appropriate. I suggest that the authors remain by the narrative in lines 117-118 and are more cautious in the interpretations, as expanded on in several of the next comments.

2. There is an important confounding factor in the methodology: given that the individuals were not cleared of their original microbiota, it is rather expected that the existing communities are not easily replaced. Another very important aspect is the original establishment of the microbial community, which is likely in earlier life stages. Although the authors do bring this point into discussion, I believe there should be much more emphasis on the relevance of the originally established community and the factors that determine that. These are likely confounding factors, which could be tied to host species and tissue, and thus explain the observed results.

3. Also related to the previous point: on what basis was 17 days chosen as an evaluation period? It might indeed be hard to know which duration is most appropriate to see potential changes, but additional information is useful: How long do these beetles spend in their adult stage? How often do they feed?

4. Associating the (partially) characteristic bacterial composition according to host tissue to potential function is rather weakly founded. Authors somehow acknowledge it, but the overall approach of trying to tie function to this kind of analysis is loose /weak (see specific comment for lines 129-131 and line 347). Also, the fact that the sample size from natural conditions are low make it hard to conclude on consistency, which is likely important for function.

5. I suggest reconsidering the conclusion that: “Symbiosis is a possibility, especially in secretory cells” (see specific comment on lines 454).

6. Presentation of the data in the figures and the references to it in the text can be considerably improved. It was hard to relate to each figure (they are not in order and panel letters not always given). The figures can also become more informative, please see specific comments.

7. The methods section, as well as the background information provided in the introduction are clearly written and structured.

Specific comments:

- Lines 25-26: both here and in the discussion (line 418), the “typical” microbial diversity found in “other insect hosts” is rather ambiguous, since there is a relatively broad spectrum of community types, richness and diversity levels across insect orders, and even within orders. Please instead refer to closer groups for comparison.

- Lines 29-31: as mentioned in the general comments, this conclusion is not supported by the data. There is a significant difference in the bacterial community composition in secretory cells according to species (Fig 2b + legend). Also, if the defensive chemistry is different between species (Line 21), it would also not be coherent to expect highly similar communities if they are involved in producing the defensive compounds.

- Lines 77-79: This is not necessarily true. The set of metabolic pathways of two communities with distinct microbial compositions can in some cases match to a good extent. Different bacteria can play similar roles, the same holds for groups of bacteria.

- Line 124: the (1) for numbering the predictions should be placed in line 125, at “…, then 1) diet..” But see next comment.

- Lines 124 – 125: how this is written suggests that the question addressed is whether carabids harbor non-transient microbes. The experimental approach does not address this though (see general comment 1).

- Lines 129-131: True to some extent, but the link to the question of function is quite loose. Especially considering the contrary situation (microbial communities are not random in respect to tissue), since this is still far from indicating function, specially in rather complex microbial communities like those in carabids.

- Lines 147-149: Does this mean that the diets were completely sterile? Please indicate explicitly.

- Line 226: Here and throughout the manuscript, please indicate the specific panel of the figure referred to (e.g. Fig 1B). Also, please reorder to follow the sequential order of the figure numbers.

- Lines 229-230: I suggest mentioning here or in the methods the list of alpha diversity measures that were evaluated.

- Lines 241: I consider it useful to provide these results here or as a supplement.

- Line 307: the four phylotypes hare present in different abundance, not differentially expressed. Please correct.

- Lines 311-314: this is probably easier to express and understand in the context of evenness. This observation and its importance are hard to grasp as currently stated.

- Lines 330-333: as mentioned in the general comments, the experimental setup involved a relatively short period of time and used beetles with an already established microbiota. Thus, I suggest toning down this statement “these findings demonstrate that carabid microbiomes are highly persistent…”

- Line 347: The hypothesis is supported, but the composition in specific tissues is by no means evidence of function. Because the result can be explained by the different physicochemical conditions in each tissue, or the exposure of certain tissues to different sources of microbes (as also mentioned by the authors), this should not be set as a strong proxy for function, but rather mentioned as a side observation which can be addressed differently.

- Lines 354-355: I disagree with this conclusion based on general comment 2. Please reconsider.

- Lines 358-360: this is different to the 2nd hypothesis mentioned in the introduction, or what are the authors referring to? Also, is there a reason to believe that they transmit them between conspecifics? Gregarious or social behavior would be more in line with such hypothesis, not the fact of seeing similarities across individuals. This could also be the case for vertically transmitted symbionts.

- Line 361: referring to co-evolution is a long stretch in this system. Co-evolution would be expected if there is consistent vertical transmission, but there is no evidence for that here, and it is also not the appropriate data set to address this question.

- Lines 404-407: it was not clear to me what the authors mean and how this connects to the previous lines. Please revise.

- Lines 415-416: I believe this is not a fair claim. While the study is valuable for learning about these 3 insect species, these are only 3 of the most diverse group of animals.

- Line 444: does this match the fact that they were found in all species or a specific species? i.e. are quinones produced in all hosts?

- Line 454: I don’t think that this an appropriate interpretation of the data, at least not based on the results shown. If the definition of symbiosis is used: “the consistent association of individuals from different species for all or most of their lifetime”. If the figures provide support that the communities within each species are consistent across individuals from different populations or collection sites, then this conclusion is better supported. The results from the secretory glands just show some degree of convergence in composition, but this is not per se evidence for symbiosis.

- Figure 4. This figure could become more informative and easier to grasp by

o Showing the composition of each replicate, to give a better impression of the amount of variation per individual.

o Separating or arranging the panels to address a single factor (species or tissue) and therefore message per figure

o In panel 4D, it is not so clear why the species are merged if Fig. 2 already shows that there is clear clustering of composition per species within each body part. I suggest separating this per species, and (in line with the point above), make it a separate figure.

- Figures 4 and 5: I suggest labelling the y-axis as “Relative abundance” for clarity.

Reviewer #2: This paper reports carabid associated microbiomes. There are 3 species, 4 diet groups (3 lab, 1 field), and 3 tissues examined (includes partial body). The hypotheses involve development of microbiomes that are specific to beetle species, their chemical defense capabilities, and possible role in evolution of these capabilities.

It is obvious that the authors have conducted a lot of research in this area and are familiar with related literature. The writing is generally clear, well-organized, and understandable. The abstract should be restructured – findings (lines 19-20) are ahead of methods. No mention in the Abstract of gut microbiota across the 3 species, just pygidial gland. Intro, Line 78 – “they” = carabids (right?). Perhaps restructure sentence to make clear.

The biggest problem I have is the n=3 beetles for each of the treatment groups (12) wherein a treatment group is defined by the carabid species (3) and diet treatment (4 including wild). This is OK for a preliminary study, but not a full-fledged study that would support robust conclusions. If this paper is to be published with such a low n, then the writing needs to clearly convey that these are preliminary findings, reduce the level of confidence in concluding statements, and shorten the Discussion considerably.

The second issue is that “soil” or “environment” are not tested in this study, and mention of such needs to be eliminated from the manuscript (see lines 16, 24, 119, 125, 321, 323, 327, 339, 344, etc). Three wild caught beetles do not compare soil or environment with three groups of beetles lab-reared on 3 different diets.

The third issue is the statistics. Is Chi-Square the right analysis for this study? Can you give more details of what data was used for the chi-sq? Was is it a binary presence absence of bacterial taxa or just a single diversity metric? How many taxa were considered? What were the p values for the non-significant tests? When I look at figure 1, I see tissue types have different diversity. And Wild capture partial body maybe different than lab diets. That all makes sense. Beyond that, I don’t know. Figure 2 suggest differences between carabid species for each tissue microbiome. with secretory cells having the least range across species. And diet having little effect. But remember in this type of analysis, its all relative. If you just isolated diet for a single species, you might see distinct clustering there. I’m unconvinced that chi-square can answer to hypotheses proposed in lines 124-131. Did you test randomness by other tests (lines 119-121)? What did you used to explain variation in microbiomes?

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Reviewer #1: Yes: Laura V. Florez

Reviewer #2: No

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PLoS One. 2021 Mar 19;16(3):e0241529. doi: 10.1371/journal.pone.0241529.r002

Author response to Decision Letter 0

20 Feb 2021

We would like to thank the reviewers for their thoughtful and thorough comments regarding our manuscript, entitled, “Persistence of the ground beetle (Coleoptera: Carabidae) microbiome to diet manipulation.” We have addressed each comment individually, below. We believe these changes have significantly strengthened our paper, and we hope this revised version satisfies the publication standards of PLOS ONE.

1. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Partly

Reviewer #2: Partly

Thank you for your feedback.

________________________________________

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: I Don't Know

Thank you for your feedback.

________________________________________

3. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

Reviewer #2: Yes

Thank you.

________________________________________

4. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

Thank you.

________________________________________

5. Review Comments to the Author

Reviewer #1:

Summary

Thank you for your summary of our study and its importance. We appreciate your suggestions of how to better align our discussion with the data which we collected.

General comments

Thank you for this helpful suggestion. We have completely rewritten the last paragraph of our introduction to better fit the type of conclusions that can be drawn from this experimental approach. We have also emphasized throughout the paper that we are only interested in distinguishing whether communities experience significant turn-over of species as a result of short-term perturbations.

This is an excellent point, and we agree that our study is not able to disentangle confounding factors that might be relevant in the establishment of a host-associated microbiome. Instead, our aim was to establish whether pre-existing communities were in place and determine the degree of persistence of these microbial communities to short term diet changes. We believe the extensive revisions we have made to the discussion section have helped to emphasize this.

Based on this helpful comment, we have added a sentence to the methods section providing reasoning for the choice of a 17-day period.

This is an important point. As part of our revisions, we have edited passages which previously implied that our data demonstrates microbial community involvement with host functions.

5. I suggest reconsidering the conclusion that: “Symbiosis is a possibility, especially in secretory cells” (see specific comment on lines 454).

Thank you for the suggestion. As stated above, we decided based on your feedback to cut that conclusion from our revised manuscript.

This is helpful to know, and we have made several changes to this end.

7. The methods section, as well as the background information provided in the introduction are clearly written and structured.

Thank you.

Specific comments:

Based on your feedback, we have removed comparisons across Insecta where they occur in the Abstract and also in the Discussion.

We have cut this sentence from the paper, and softened all conclusions based upon secretory cell similarity.

We have rewritten this sentence to take a more tentative stance about this possibility.

- Line 124: the (1) for numbering the predictions should be placed in line 125, at “…, then 1) diet..” But see next comment.

Thank you. We have made this change.

We have revised the paper significantly to address this concern, as described under general comment 1, and throughout the highlighted sections of the revised manuscript. We have also rewritten this last paragraph of the introduction to better align with the aims and scope of our experiments.

We have made this sentence more preliminary and less definitive.

- Lines 147-149: Does this mean that the diets were completely sterile? Please indicate explicitly.

We have added, “All feeding portions consisted of 0.04g (+/- 0.01g) non-sterile food.”

- Line 226: Here and throughout the manuscript, please indicate the specific panel of the figure referred to (e.g. Fig 1B). Also, please reorder to follow the sequential order of the figure numbers.

We have specified panels in most figure references.

- Lines 229-230: I suggest mentioning here or in the methods the list of alpha diversity measures that were evaluated.

The list of alpha diversity measures has been added.

- Lines 241: I consider it useful to provide these results here or as a supplement.

We appreciate this suggestion, but we felt that the limited scope of this study did not warrant creating a supplementary file.

- Line 307: the four phylotypes hare present in different abundance, not differentially expressed. Please correct.

Thank you for catching this error. It has been corrected.

- Lines 311-314: this is probably easier to express and understand in the context of evenness. This observation and its importance are hard to grasp as currently stated.

We have added a sentence earlier in the results section to note that the evenness of guts is lower than that of other tissues, across samples. We have also kept this observation in the paper to provide additional detail.

We have toned down this passage by rewriting it as follows:

The findings of this small-scale study provide preliminary evidence that carabids harbor diverse, relatively resilient microbiomes that are persistent to short-term changes in host diet. These results contribute to broader efforts to understand host-associated microbial diversity in ground beetles.

We have modified the manuscript so that it is clear that associations may be due to functional relationships or one of various other reasons. The emphasis on function has been reduced to some brief passages noting associations, as suggested.

- Lines 354-355: I disagree with this conclusion based on general comment 2. Please reconsider.

As part of significant re-organization and editing of the discussion, we have removed this sentence.

As part of significant re-organization and editing of the discussion, we have removed this sentence. In its place, we briefly mention several possible reasons why microbial communities may be similar in individuals of the same species.

This conjecture has been removed from the manuscript, thank you for the feedback.

- Lines 404-407: it was not clear to me what the authors mean and how this connects to the previous lines. Please revise.

This section of the discussion has been rearranged, and the problematic passage has been clarified so that it is easier to follow and understand.

- Lines 415-416: I believe this is not a fair claim. While the study is valuable for learning about these 3 insect species, these are only 3 of the most diverse group of animals.

In our efforts to make the discussion section shorter and more concise, we have removed the paragraph that compares our results to others across Insecta.

- Line 444: does this match the fact that they were found in all species or a specific species? i.e. are quinones produced in all hosts?

We have removed this sentence, and so the comment no longer applies.

We have removed this conclusion based on your general comments, so this comment no longer applies.

- Figure 4. This figure could become more informative and easier to grasp by

o Showing the composition of each replicate, to give a better impression of the amount of variation per individual.o Separating or arranging the panels to address a single factor (species or tissue) and therefore message per figure

This would be a helpful view but we felt that the ordination plots show that compositional differences among replicates within groups are not large compared to variation between groups, so for simplicity we retained the averaged plots.

We have removed figure 4D, since upon reviewing the results and discussion we felt that it did not make an important contribution that was worthy inclusion as a separate figure.

- Figures 4 and 5: I suggest labelling the y-axis as “Relative abundance” for clarity.

We have made this revision.

Reviewer #2:

This paper reports carabid associated microbiomes. There are 3 species, 4 diet groups (3 lab, 1 field), and 3 tissues examined (includes partial body). The hypotheses involve development of microbiomes that are specific to beetle species, their chemical defense capabilities, and possible role in evolution of these capabilities.

Thank you for your clear and concise summary of our work.

It is obvious that the authors have conducted a lot of research in this area and are familiar with related literature. The writing is generally clear, well-organized, and understandable.

Thank you.

The abstract should be restructured – findings (lines 19-20) are ahead of methods. No mention in the Abstract of gut microbiota across the 3 species, just pygidial gland. Intro, Line 78 – “they” = carabids (right?). Perhaps restructure sentence to make clear.

Thank you for pointing this out. We have revised the abstract. All results are described after methods, and our findings are described more clearly.

We appreciate this feedback, and have taken several steps to improve our manuscript based on it. We have completely re-written the scope of hypotheses we are intending to address to better fit with our dataset, modified the language of the manuscript to be more tentative throughout, and shortened the Discussion section significantly. We have also added a brief nod to the low sample size in the Methods section.

We realize that this choice of wording was somewhat misleading, and we have revised the manuscript to make our actual results more clear.

The third issue is the statistics. Is Chi-Square the right analysis for this study? Can you give more details of what data was used for the chi-sq?

The specific test used was Kruskal-Wallis, and we have corrected references of “chi-squared” to “H”, as this is the correct term for the Kruskal-Wallis statistic. We used this statistic because it is a common way for performing a non-parametric comparison between multiple groups.

Was is it a binary presence absence of bacterial taxa or just a single diversity metric?

Each diversity metric we used (richness, evenness, Shannon index, phylogenetic diversity) takes different factors (taxa presence/absence, relative abundance, etc.) into account in a slightly different way. Phylogenetic diversity considers not only which bacterial taxa are present, but also their degree of phylogenetic relatedness to each other.

How many taxa were considered?

All bacterial taxa were included in the metric.

What were the p values for the non-significant tests?

The p-values for non-significant results referenced in figure captions are as follows:

Figure panel in which the test is referenced Value Group (independent variable) p-value

Figure 1A PD of guts Host species 0.70

Figure 1A PD of secretory cells Host species 0.16

Figure 1A PD of aggregate communities Host species 0.27

Figure 1B PD of guts Diet treatment 0.09

Figure 1B PD of secretory cells Diet treatment 0.49

Figure 1B PD of aggregate communities Diet treatment 0.99

Figure 2A Bray-Curtis clustering of aggregate communities Diet treatment 0.06

Figure 2C Bray-Curtis clustering of guts Diet treatment 0.67

Figure 2D Bray-Curtis clustering of partial bodies Diet treatment 0.09

When I look at figure 1, I see tissue types have different diversity. And Wild capture partial body maybe different than lab diets. That all makes sense.

Thank you.

Beyond that, I don’t know. Figure 2 suggest differences between carabid species for each tissue microbiome. with secretory cells having the least range across species. And diet having little effect. But remember in this type of analysis, its all relative.

Thank you for pointing this out.

If you just isolated diet for a single species, you might see distinct clustering there. I’m unconvinced that chi-square can answer to hypotheses proposed in lines 124-131.

This is a fair point. We believe it has been addressed by the significant changes we have made in our manuscript, and in particular by the substantial changes we have made to the end of the Introduction, which clarify what our study is able to accomplish.

Did you test randomness by other tests (lines 119-121)?

Based on your feedback, we have changed the way we write about our hypotheses and findings to be clearer and more accurate (instead of generically referring to ‘variation’). We believe that the edited manuscript has addressed this comment.

What did you used to explain variation in microbiomes?

We defined variation between communities as significant differences in their community diversity and composition. Our study tested several factors that might potentially explain such variation.

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PLoS One. doi: 10.1371/journal.pone.0241529.r003

Decision Letter 1

Omri Finkel

4 Mar 2021

Persistence of the ground beetle (Coleoptera: Carabidae) microbiome to diet manipulation

PONE-D-20-32322R1

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PLoS One. doi: 10.1371/journal.pone.0241529.r004

Acceptance letter

Omri Finkel

12 Mar 2021

PONE-D-20-32322R1

Persistence of the ground beetle (Coleoptera: Carabidae) microbiome to diet manipulation

Dear Dr. Will:

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[pone.0241529.ref001] 1.Stork NE. How Many Species of Insects and Other Terrestrial Arthropods Are There on Earth? Annu Rev Entomol. 2018;63(1):31–45. 10.1146/annurev-ento-020117-043348 [DOI] [PubMed] [Google Scholar]

[pone.0241529.ref002] 2.Yun J-H, Roh SW, Whon TW, Jung M-J, Kim M-S, Park D-S, et al. Insect Gut Bacterial Diversity Determined by Environmental Habitat, Diet, Developmental Stage, and Phylogeny of Host. Drake HL, editor. Appl Environ Microbiol. 2014. September 1;80(17):5254–64. 10.1128/AEM.01226-14 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0241529.ref003] 3.Vavre F, Kremer N. Microbial impacts on insect evolutionary diversification: from patterns to mechanisms. Curr Opin Insect Sci. 2014. October;4:29–34. 10.1016/j.cois.2014.08.003 [DOI] [PubMed] [Google Scholar]

[pone.0241529.ref004] 4.Lorenz W, 1950-. Systematic list of extant ground beetles of the world [Internet]. W. Lorenz; 2005 [cited 2019 Sep 27]. Available from: http://agris.fao.org/agris-search/search.do?recordID=US201300114567

[pone.0241529.ref005] 5.Will KW, Attygalle Athula B, Herath K. New defensive chemical data for ground beetles (Coleoptera: Carabidae): interpretations in a phylogenetic framework. Biol J Linn Soc. 2000. November;71(3):459–81. [Google Scholar]

[pone.0241529.ref006] 6.Douglas AE. The microbial dimension in insect nutritional ecology. Funct Ecol. 2009. February;23(1):38–47. [Google Scholar]

[pone.0241529.ref007] 7.Wong AC-N, Dobson AJ, Douglas AE. Gut microbiota dictates the metabolic response of Drosophila to diet. J Exp Biol. 2014. June 1;217(11):1894–901. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0241529.ref008] 8.Schwab DB, Riggs HE, Newton ILG, Moczek AP. Developmental and Ecological Benefits of the Maternally Transmitted Microbiota in a Dung Beetle. Am Nat. 2016. December;188(6):679–92. 10.1086/688926 [DOI] [PubMed] [Google Scholar]

[pone.0241529.ref009] 9.Douglas AE. Nutritional Interactions in Insect-Microbial Symbioses: Aphids and Their Symbiotic Bacteria Buchnera. Annu Rev Entomol. 1998. January;43(1):17–37. 10.1146/annurev.ento.43.1.17 [DOI] [PubMed] [Google Scholar]

[pone.0241529.ref010] 10.Breznak JA. Intestinal Microbiota of Termites and other Xylophagous Insects. Annu Rev Microbiol. 1982. October;36(1):323–323. 10.1146/annurev.mi.36.100182.001543 [DOI] [PubMed] [Google Scholar]

[pone.0241529.ref011] 11.Engel P, Moran NA. The gut microbiota of insects–diversity in structure and function. FEMS Microbiol Rev. 2013. September;37(5):699–735. 10.1111/1574-6976.12025 [DOI] [PubMed] [Google Scholar]

[pone.0241529.ref012] 12.Russell JA, Moreau CS, Goldman-Huertas B, Fujiwara M, Lohman DJ, Pierce NE. Bacterial gut symbionts are tightly linked with the evolution of herbivory in ants. Proc Natl Acad Sci. 2009. December 15;106(50):21236–41. 10.1073/pnas.0907926106 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0241529.ref013] 13.Lundgren JG, Lehman RM. Bacterial Gut Symbionts Contribute to Seed Digestion in an Omnivorous Beetle. Van der Heijden M, editor. PLoS ONE. 2010. May 26;5(5):e10831. 10.1371/journal.pone.0010831 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0241529.ref014] 14.Blankenchip CL, Michels DE, Braker HE, Goffredi SK. Diet breadth and exploitation of exotic plants shift the core microbiome of Cephaloleia, a group of tropical herbivorous beetles. PeerJ. 2018. May 17;6:e4793. 10.7717/peerj.4793 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0241529.ref015] 15.Adams AS, Aylward FO, Adams SM, Erbilgin N, Aukema BH, Currie CR, et al. Mountain Pine Beetles Colonizing Historical and Naïve Host Trees Are Associated with a Bacterial Community Highly Enriched in Genes Contributing to Terpene Metabolism. Appl Environ Microbiol. 2013. June 1;79(11):3468–75. 10.1128/AEM.00068-13 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0241529.ref016] 16.Vogel H, Shukla SP, Engl T, Weiss B, Fischer R, Steiger S, et al. The digestive and defensive basis of carcass utilization by the burying beetle and its microbiota. Nat Commun. 2017. August;8(1):15186. 10.1038/ncomms15186 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0241529.ref017] 17.Kaltenpoth M, Steiger S. Unearthing carrion beetles’ microbiome: characterization of bacterial and fungal hindgut communities across the Silphidae. Mol Ecol. 2014. March;23(6):1251–67. 10.1111/mec.12469 [DOI] [PubMed] [Google Scholar]

[pone.0241529.ref018] 18.Flórez LV, Scherlach K, Miller IJ, Rodrigues A, Kwan JC, Hertweck C, et al. An antifungal polyketide associated with horizontally acquired genes supports symbiont-mediated defense in Lagria villosa beetles. Nat Commun. 2018. June 26;9(1):1–10. 10.1038/s41467-017-02088-w [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0241529.ref019] 19.Kellner RLL. Molecular identification of an endosymbiotic bacterium associated with pederin biosynthesis in Paederus sabaeus (Coleoptera: Staphylinidae). Insect Biochem Mol Biol. 2002. April;32(4):389–95. 10.1016/s0965-1748(01)00115-1 [DOI] [PubMed] [Google Scholar]

[pone.0241529.ref020] 20.Nakabachi A, Ueoka R, Oshima K, Teta R, Mangoni A, Gurgui M, et al. Defensive Bacteriome Symbiont with a Drastically Reduced Genome. Curr Biol. 2013. August 5;23(15):1478–84. 10.1016/j.cub.2013.06.027 [DOI] [PubMed] [Google Scholar]

[pone.0241529.ref021] 21.Jones RT, Sanchez LG, Fierer N. A Cross-Taxon Analysis of Insect-Associated Bacterial Diversity. Gilbert JA, editor. PLoS ONE. 2013. April 16;8(4):e61218. 10.1371/journal.pone.0061218 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0241529.ref022] 22.Brooks AW, Kohl KD, Brucker RM, van Opstal EJ, Bordenstein SR. Phylosymbiosis: Relationships and Functional Effects of Microbial Communities across Host Evolutionary History. Relman D, editor. PLOS Biol. 2016. November 18;14(11):e2000225. 10.1371/journal.pbio.2000225 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0241529.ref023] 23.Colman DR, Toolson EC, Takacs-Vesbach CD. Do diet and taxonomy influence insect gut bacterial communities? Mol Ecol. 2012. October;21(20):5124–37. 10.1111/j.1365-294X.2012.05752.x [DOI] [PubMed] [Google Scholar]

[pone.0241529.ref024] 24.Bahrndorff S, de Jonge N, Skovgård H, Nielsen JL. Bacterial Communities Associated with Houseflies (Musca domestica L.) Sampled within and between Farms. Wilson BA, editor. PLOS ONE. 2017. January 12;12(1):e0169753. 10.1371/journal.pone.0169753 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0241529.ref025] 25.Hammer TJ, Janzen DH, Hallwachs W, Jaffe SP, Fierer N. Caterpillars lack a resident gut microbiome. Proc Natl Acad Sci. 2017. September 5;114(36):9641–6. 10.1073/pnas.1707186114 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0241529.ref026] 26.Scully ED, Geib SM, Carlson JE, Tien M, McKenna D, Hoover K. Functional genomics and microbiome profiling of the Asian longhorned beetle (Anoplophora glabripennis) reveal insights into the digestive physiology and nutritional ecology of wood feeding beetles. BMC Genomics. 2014;15(1):1096. 10.1186/1471-2164-15-1096 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0241529.ref027] 27.Tang X, Freitak D, Vogel H, Ping L, Shao Y, Cordero EA, et al. Complexity and Variability of Gut Commensal Microbiota in Polyphagous Lepidopteran Larvae. Dale C, editor. PLoS ONE. 2012. July 17;7(7):e36978. 10.1371/journal.pone.0036978 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0241529.ref028] 28.Douglas AE. Lessons from Studying Insect Symbioses. Cell Host Microbe. 2011. October;10(4):359–67. 10.1016/j.chom.2011.09.001 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0241529.ref029] 29.Attygalle AB, Xu S, Moore W, McManus R, Gill A, Will K. Biosynthetic origin of benzoquinones in the explosive discharge of the bombardier beetle Brachinus elongatulus. Sci Nat. 2020. August 1;107(4):1–11. 10.1007/s00114-020-01683-0 [DOI] [PubMed] [Google Scholar]

[pone.0241529.ref030] 30.Larochelle A. The food of carabid beetles (Coleoptera: Carabidae, including Cicindelinae). Fabreries. 1990;Supplément 5:1–132. [Google Scholar]

[pone.0241529.ref031] 31.McManus R, Ravenscraft A, Moore W. Bacterial Associates of a Gregarious Riparian Beetle With Explosive Defensive Chemistry. Front Microbiol. 2018. October 5;9:2361. 10.3389/fmicb.2018.02361 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0241529.ref032] 32.Rohland N, Reich D. Cost-effective, high-throughput DNA sequencing libraries for multiplexed target capture. Genome Res. 2012. May 1;22(5):939–46. 10.1101/gr.128124.111 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0241529.ref033] 33.Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Huntley J, Fierer N, et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 2012. August;6(8):1621–4. 10.1038/ismej.2012.8 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0241529.ref034] 34.Lange V, Böhme I, Hofmann J, Lang K, Sauter J, Schöne B, et al. Cost-efficient high-throughput HLA typing by MiSeq amplicon sequencing. BMC Genomics. 2014. January 24;15:63. 10.1186/1471-2164-15-63 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0241529.ref035] 35.Renaud G. grenaud/deML [Internet]. 2019 [cited 2019 Sep 25]. Available from: https://github.com/grenaud/deML

[pone.0241529.ref036] 36.Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016. July;13(7):581–3. 10.1038/nmeth.3869 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0241529.ref037] 37.Cole JR, Wang Q, Fish JA, Chai B, McGarrell DM, Sun Y, et al. Ribosomal Database Project: data and tools for high throughput rRNA analysis. Nucleic Acids Res. 2014. January 1;42(Database issue):D633–42. 10.1093/nar/gkt1244 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0241529.ref038] 38.R Core Team. R: A language and environment for statistical computing [Internet]. Vienna, Austria: R Foundation for Statistical Computing; 2018. [cited 2019 Sep 25]. Available from: https://www.R-project.org/ [Google Scholar]

[pone.0241529.ref039] 39.McMurdie PJ, Holmes S. phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data. PLOS ONE. 2013. April 22;8(4):e61217. 10.1371/journal.pone.0061217 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0241529.ref040] 40.Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550. 10.1186/s13059-014-0550-8 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0241529.ref041] 41.McMurdie PJ, Holmes S. Waste Not, Want Not: Why Rarefying Microbiome Data Is Inadmissible. PLOS Comput Biol. 2014. April 3;10(4):e1003531. 10.1371/journal.pcbi.1003531 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0241529.ref042] 42.Wright ES. DECIPHER: harnessing local sequence context to improve protein multiple sequence alignment. BMC Bioinformatics. 2015. October 6;16(1):322. 10.1186/s12859-015-0749-z [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0241529.ref043] 43.Wright E S. Using DECIPHER v2.0 to Analyze Big Biological Sequence Data in R. R J. 2016;8(1):352. [Google Scholar]

[pone.0241529.ref044] 44.Schliep KP. phangorn: phylogenetic analysis in R. Bioinformatics. 2011. February 15;27(4):592–3. 10.1093/bioinformatics/btq706 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0241529.ref045] 45.Quensen J. QsRutils: R Functions Useful for Community Ecology [Internet]. 2019 [cited 2019 Sep 25]. Available from: https://github.com/jfq3/QsRutils

[pone.0241529.ref046] 46.Kembel SW, Cowan PD, Helmus MR, Cornwell WK, Morlon H, Ackerly DD, et al. Picante: R tools for integrating phylogenies and ecology. Bioinformatics. 2010. June 1;26(11):1463–4. 10.1093/bioinformatics/btq166 [DOI] [PubMed] [Google Scholar]

[pone.0241529.ref047] 47.Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, et al. vegan: Community Ecology Package [Internet]. 2019 [cited 2019 Sep 25]. Available from: https://CRAN.R-project.org/package=vegan

[pone.0241529.ref048] 48.Chen H. VennDiagram: Generate High-Resolution Venn and Euler Plots [Internet]. 2018 [cited 2019 Sep 25]. Available from: https://CRAN.R-project.org/package=VennDiagram

[pone.0241529.ref049] 49.Paradis E, Schliep K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics. 2019. February 1;35(3):526–8. 10.1093/bioinformatics/bty633 [DOI] [PubMed] [Google Scholar]

[pone.0241529.ref050] 50.Lundgren JG, Lehman RM, Chee-sanford J. Bacterial Communities within Digestive Tracts of Ground Beetles (Coleoptera: Carabidae). Ann Entomol Soc Am. 2007. March 1;100(2):275–82. [Google Scholar]

PERMALINK

Persistence of the ground beetle (Coleoptera: Carabidae) microbiome to diet manipulation

Anita Silver

Sean Perez

Melanie Gee

Bethany Xu

Shreeya Garg

Kipling Will

Aman Gill

Roles

Abstract

Introduction

Methods

Beetle husbandry and dissection

DNA extraction, PCR, and next generation sequencing

Analysis

Ethics statement

Results

Sequencing results

Alpha diversity

Fig 1. Boxplots of Phylogenetic Diversity (PD), with outliers depicted as points.

Tissue

Species

Community diversity distance analysis

Fig 2. Bray-Curtis ordination of microbiome beta diversity using non-metric dimensional scaling.

Diet

Fig 3. Venn diagram of phylotypes present in aggregate communities by diet treatment.

Tissue

Species

Community composition

Fig 4. Relative abundances of prevalent bacterial taxa by host tissue and species.

Diet

Fig 5. Relative abundances of prevalent bacterial taxa by host diet treatment.

Tissue

Species

Discussion

Microbiome resilience to host dietary change

Microbiome associations with host characteristics

Acknowledgments

Data Availability

Funding Statement

References

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