New Results

North American fireflies host low bacterial diversity

Emily A. Green, Scott R. Smedley, Jonathan L. Klassen
doi: https://doi.org/10.1101/2020.10.06.328070

ABSTRACT

Although there are numerous studies of fireflies’ mating flashes, lantern bioluminescence, and anti-predation lucibufagin metabolites, almost nothing is known about their microbiome. We therefore used 16S rRNA community amplicon sequencing to characterize the gut and body microbiomes of four North American firefly species: Ellychnia corrusca, Photuris sp., Pyractomena borealis, and Pyropyga sp. These firefly microbiomes all have very low species diversity, often dominated by a single species, and each firefly species has a characteristic microbiome. Although the microbiomes of male and female fireflies did not differ, Photuris sp. gut and body microbiomes did, with their gut microbiomes being enriched in Pseudomonas and Acinetobacter. E. corrusca egg and adult microbiomes differed except for a single egg microbiome that shared a community type with E. corrusca adults, which could suggest microbial transmission from mother to offspring. Mollicutes that had been previously isolated from fireflies were common in our firefly microbiomes. These results set the stage for further research concerning focus on the function and transmission of these bacterial symbionts.

INTRODUCTION

Beetles (Order: Coleoptera) are one of the most diverse insect groups, containing nearly 400,000 species (1). The beetles in the family Lampyridae are commonly known by many names: fireflies, lightning bugs, glow worms, lamp-lighters, and night-travelers, all describing the bioluminescent lantern that they use to signal to their mates (2). Fireflies are found on every continent except Antarctica, living in woods, plains, and marshes. North America is home to over 125 firefly species, including Ellychnia corrusca, Photuris sp., Pyractomena borealis and Pyropyga species (2). These 4 species occur sympatrically, ranging from the eastern coast of Canada, south to Florida, and west to the Great Plains (3, 4). E. corrusca, P. borealis and Pyropyga sp. are all closely related to each other, and both E. corrusca and Pyropyga sp. are diurnal with no lantern (2, 5). E. corrusca, P. borealis, and many other North American fireflies use lucibufagins as a chemical defense. Lucibufagins are cardioactive C-24 steroidal pyrenes and a subclass of the bufadienolides (6–11) that create a disagreeable taste to predators and thereby provide fireflies with protection from bats, birds and spiders (12, 13). However, the biosynthetic origin of lucibufagins is not known.

In this study, we focus especially on two of these firefly species with unique lifestyles: E. corrusca and Photuris. E. corrusca is nicknamed the winter firefly because it has a winter-spring activity cycle, in contrast to the late spring-summer activity cycles of most other fireflies (1, 3). E. corrusca larvae in New England (North America) extend their larval stage across two years instead of one to ensure they ingest ample calories, emerging in the late fall of the second year as adults and sexually maturing during the winter to start mating, which lasts until late spring (14). Normally, adult fireflies do not feed. However, northern (Massachusetts, USA) E. corrusca firefly adults ingest interstitial fluid and sap from maple trees, which is thought to help them survive during the cold winters (4, 14). The Photuris adult life cycle occurs during the summer months (June-August), but unlike other fireflies, Photuris sp. females mimic the mating light display of female Photinus fireflies to attract Photinus males, earning them the nickname “femme fatale” (15). Once a male Photinus firefly is near, the Photuris female will attack and kill the male, gaining nutrients and lucibufagins that she uses to protect herself, her eggs, and her pupae, which do not themselves produce lucibufagins (6). Although E. corrusca are not active during the same season as Photuris sp., Photuris sp. will attack and eat E. corrusca to gain protective lucibufagins in the lab (2, 10). This predation in a lab setting could give insight into the selective pressures that caused E. corrusca to mate in the winter instead of in the summer.

Beetles feed on many different substrates, following herbivorous (16) omnivorous (17), xylophagous (18, 19), detritivorous (20), and predatory diets (6). Beetles belonging to similar taxonomic families but that have different diets have distinct microbial communities (21, 22). Diets lacking in nutrients often cause insects to rely on nutritional symbionts to provide vital nutrients (23), as in dung (24), carabid (17), and carrion beetles (25). Although there is a vast amount of research studying beetle microbiomes, only a handful of studies consider firefly-associated microbes. In those studies, several Mollicute bacteria were isolated from the fireflies E. corrusca and Photuris spp., but this does not give insight into the composition of their microbiome beyond these strains (26–30). To fill this gap, we used 16S rRNA community amplicon sequencing to survey the microbiomes of Photuris sp., E. corrusca, Pyractomena borealis, and Pyropyga sp. fireflies. Our results show that fireflies have simple, species-specific microbiomes, and generate hypotheses about how diet and seasonality may drive firefly microbiome structure and function.

METHODS

Sample collection

Live E. corrusca and Photuris sp. fireflies were collected by S. Smedley during the winter of 2016-17 and spring/summer of 2017 from residential and camping/forest areas within Vernon, Bolton and Andover, Connecticut, U.S.A. Pyractomena borealis larvae and adults were raised in the laboratory by S. Smedley. After collection, E. corrusca, Photuris sp., and P. borealis fireflies were stored at −80°C in air-filled vials. Pyropyga sp. fireflies were collected by Lynn Faust in summer 2016 from Ohio and Tennessee and stored in 95% ethanol. Collection dates and sample locations are listed in Supplemental File 1.

Sample preparation & DNA extraction

All fireflies were surface-sterilized using 3 rounds of a 10 second submersion in 70% ethanol, followed by a 10 second submersion in phosphate-buffered saline (PBS) (31). Surface-sterilized fireflies were dissected into two separate tissues: a gut sample and the remaining carcass “body” sample. DNA from the tissue dissections were extracted using a bead beating and chloroform-isopropanol protocol as described in (32), except using 0.7 g of 1 mm and 0.3 g of 0.1 mm silica/zirconium beads and 5 cycles of bead-beating & chilling on ice. Negative controls containing only the DNA extraction reagents were processed alongside each batch of tissue samples. The DNA concentration of each extract and negative control was determined using the Qubit dsDNA high-sensitivity assay protocol and a Qubit 3.0 fluorimeter (Invitrogen, Carlsbad California).

16S V4 rRNA PCR screen & community amplicon sequencing

DNA samples were PCR amplified using primers 515F and 806R (targeting the bacterial 16S rRNA gene V4 region) to determine the presence of bacterial DNA (33). Ten nanograms of template DNA was added to 5 μl Green GoTaq Reaction Mix Buffer (Promega, Madison, Wisconsin, USA), 1.25 units of GoTaq DNA Polymerase (Promega, Madison, Wisconsin, USA), 10 μmol of each primer, and 300 ng/μl BSA (New England BioLabs Inc. Ipswitch Massachusetts), to which nuclease free H2O was added to a volume of 25 μl. Thermocycling conditions (BioRad, Hercules, California) were: 3 min at 95°C, 30 cycles of 30 sec at 95°C, 30 sec at 50°C, and 60 sec at 72°C, followed by a 5 min cycle at 72°C and then an indefinite hold at 4°C. Gel electrophoresis was used to confirm the expected band size of 300—350 bp.

All samples that had a gel band of the expected size were prepared for community amplicon sequencing of the 16S rRNA gene V4 region using an Illumina MiSeq at the University of Connecticut Microbial Analysis, Resources and Services (MARS) facility. Approximately 30 ng of DNA from each sample was added to a 96-well plate containing 10 μmol each of the forward and reverse Illumina-barcoded versions of primers 515F and 806R, 5 μl AccuPrime buffer (Invitrogen, Carlsbad, California), 50 mM MgSO4 (Invitrogen, Carlsbad, California), 300 ng/μl BSA (New England BioLabs Inc. Ipswitch, Massachusetts), a 1μmol spike-in of both non-barcoded primers 515F and 806R, and 1 unit AccuPrime polymerase (Invitrogen, Carlsbad, California), to which nuclease-free H2O was added to a volume of 50 μl. Reaction mixes were separated in triplicate reactions (each with a volume of 16.7 μl) into a 384 well plate using an epMotion 5075 liquid handling robot (Eppendorf, Hamburg, Germany). The resulting 384 well plate was then transferred to a thermocycler (Eppendorf, Hamburg, Germany), which used the following conditions: 2 min at 95°C, 30 cycles of 15 sec at 95°C, 60 sec at 55°C, and 60 sec at 68°C, followed by a final extension for 5 min at 68°C and then an indefinite hold at 4°C. After PCR, triplicate reactions were re-pooled using the epMotion, and DNA concentrations were quantified using a QIAxcel Advanced capillary electrophoresis system (QIAgen, Hilden, Germany). Samples that had concentrations >0.5 ng/μl were pooled using equal weights of DNA to create the final sequencing libraries. Libraries were then bead-cleaned using Mag-Bind RXNPure plus beads (OMEGA, Norcross, Georgia) in a 1:0.8 ratio of sequencing library to bead volume. Cleaned library pools were adjusted to a concentration of 1.1 ng/μl ± 0.1 ng/μl, which was confirmed using the Qubit dsDNA high-sensitivity assay on a Qubit 3.0 fluorimeter (Invitrogen, Carlsbad, California). Microbial community sequencing on an Illumina MiSeq (Illumina, San Diego, California) was completed in 2 batches, the first composed of 52 samples and the second composed of 195 samples.

Post-sequencing & bioinformatic analyses

Our DNA sequencing produced 245 16S rRNA community amplicon sequencing datasets, including 22 negative controls. Reads were analyzed using R v3.5.3 (34) and the dada2 v1.11.1 (35) pipeline for amplicon sequence variants (ASVs) (https://benjjneb.github.io/dada2/tutorial.html, accessed: November 11, 2017). Read counts ranged from 2 to 888,868 (Suppl. File S1). Metadata files for the samples were imported into phyloseq v1.26.1 (31, 32), creating a phyloseq R object that was used for subsequent analyses. Reads that were not classified as belonging to the kingdom Bacteria using the SILVA database v128 were removed (38, 39). ASVs that matched to mitochondria were then removed separately, because SILVA included them in the kingdom Bacteria. Samples were screened for contamination using the decontam v1.2.1 (40) prevalence protocol with a default threshold value of 0.1. No reads were flagged as contamination, and 17 samples with 0 reads were removed, resulting in 1,675 unique ASVs (Supplemental File S1). Negative control samples were not considered further. All samples in the dataset were then rarefied to 10,000 reads and read counts were converted to relative abundances. The final phyloseq object contained 133 samples from 20 Photuris adults, 4 Pyropyga adults, 7 Pyractomena borealis larvae, 1 Pyractomena borealis adult, 95 Ellychnia corrusca adults, and 6 E. corrusca eggs. Despite amplification during the initial PCR screen, of the initial 223 insect samples, 74 samples were removed due to low read counts (53 E. corrusca adult samples, 3 E. corrusca egg samples, 9 Photuris sp. samples, 2 Pyropyga sp. samples, 4 P. borealis adult samples & 3 P. borealis larvae samples). These low read counts may have been due to: 1) high amounts of host DNA acting as a PCR inhibitor; 2) there being a minimal firefly microbiome, leading to limited template concentrations; or 3) other technical issues such as inefficient PCR amplification using primers that contained the Illumina barcodes compared to our initial PCR screen using non-barcoded primers.

Alpha diversity was measured using the phyloseq ‘plot_richness’ command, and Beta diversity was measured using weighted and unweighted unifrac distance metrics. Weighted and unweighted unifrac distances were calculated, ordinated, and viewed using the ‘distance’, ‘ordinate’, and ‘plot_ordinate’ phyloseq commands, respectively. PERMANOVA statistical tests were calculated using vegan v 2.5-4 (41). Although weighted unifrac (WUF) and unweighted unifrac (UUF) distances were used for each test, to keep the text concise only one test is listed in the text and the complimentary values are presented in Supplementary Tables S1-S4.

Differences in the relative abundances of taxa in firefly microbiomes were compared using DESeq2 (42), using the Parametric fitType, Wald tests, and an adjusted alpha value of 0.01. A heatmap was constructed for the 26 most abundant genera in the dataset using the gplots v3.03 (https://www.rdocumentation.org/packages/gplots) command ‘heatmap.2’ and using the Euclidean and ward.D distance metrics. To avoid redundancy in the heatmap, ASVs were grouped by genus name using the phyloseq command ‘tax_glom’. Covariance between the top 26 taxa were calculated using SpiecEasi v1.0.7 (43), and the SpiecEasi output file was exported into Cytoscape v3.7.2 (44) using igraph v1.2.4.2 (45) for visualization.

The most abundant Mollicute ASVs from our dataset included four Mesoplasma ASVs, two Spiroplasma ASVs, and one Entomoplasma ASV. A phylogenetic tree was constructed to show the relationships between the 16S rRNA sequences of Mollicutes from this study and those that had been previously isolated from fireflies. Reference Mollicute sequences from taxa belonging to the same genera as our firefly ASVs were selected from the SILVA database, especially those Mollicutes that had been isolated from fireflies and other beetles. Additional Mollicute reference sequences for fireflies that were not represented in the SILVA database were downloaded from NCBI. Sequences were aligned using MUSCLE v3.8.31 (46) and trimmed to the same length. The phylogenetic tree, rooted by the 16S rRNA genes from Bacillus subtilis and Mycoplasma haemominutum, was calculated using a GTRGAMMAI substitution model and 500 bootstrap replicates in RAxML v8.2.11 (47). The topology of the tree created using these selected sequences agreed with that constructed using all Mollicute sequences in SILVA, ensuring that taxon selection did not bias our phylogenetic analysis.

The commands used for all analyses is attached as Suppl. File S2. All data are available on NCBI under BioProject PRJNA563849. Raw sequencing reads are deposited in SRA under BioSample numbers SAMN14678004 – SAMN14678257.

RESULTS

Firefly microbiomes are typically dominated by single taxa

We characterized the bacterial communities in 133 firefly gut and body dissections (Suppl. File S1) using community amplicon sequencing of the 16S rRNA gene. All firefly microbiomes had low α-diversity (Suppl. Fig. S1). Shannon diversity scores for the firefly microbiomes ranged from 0.70 to 2.52, and alpha diversities of Photuris sp. and E. corrusca microbiomes differed from each other, as did those from Pyropyga sp. and E. corrusca (Dunn test, p = 0.001 and p = 0.003 respectively). A heatmap of the top 26 bacteria genera found in the microbiomes reflects these low levels of α-diversity, with most firefly microbiomes dominated by a single taxon but with minute amounts of other taxa also present (Fig. 1). Unsupervised clustering of these data grouped E. corrusca samples tightly together at the left side of the heat map, while the samples from other species clustered together on the right. Samples from different tissue dissections and sexes did not cluster together for any species of firefly.

Figure 1
Figure 1

Heat map of the 26 most abundant bacterial genera found in the studied fireflies. To the left, a dendrogram represents the Euclidian distances between the relative abundances of reads assigned to each bacterial genus, which are labeled on the right. The top dendrogram clusters the relative abundances of these genera in each firefly sample using ward.D distances. On the bottom, 3 different rows indicate the host taxon, tissue type, and sex type for each sample, indicated by the different colors in the key to the left of the figure. The topmost color key represents the relative abundance of reads in each sample that were assigned to each bacterial genus, with white and dark blue representing 0 and 100% relative abundance, respectively. n = 133

Using these top 26 bacteria genera, we identified taxa whose relative abundances were correlated with each other to infer potential interactions between them (Suppl. Fig. S2). Of the 26 bacteria genera, the relative abundances of 14 genera did not correlate with those of another genus in our dataset, and the relative abundances of 11 genera were positively correlated with those of another genus. The relative abundances of Ralstonia and Cupriavidus, found in the Pyropyga sp. samples, were strongly and positively correlated with each other, as were the relative abundances of Tanticharoenia and Gluconobacter, found in E. corrusca eggs, and the relative abundances of Gordonia and Tsukamurella, found in Photuris sp.. Only the relative abundances of Mesoplasma and Acinetobacter were negatively correlated with each other, and these taxa were not found together in any sample.

In the heatmap, many samples clustered together that were dominated by single taxa. Based on these clusters, we defined community types based on the genus or genera of bacteria that were present in these samples with relative abundances ≥ 30% (Fig. 2). E. corrusca adults were assigned to 6 distinct community types: T-1 (Mesoplasma), T-2 (Salmonella), T-3 (Mesoplasma & Salmonella), T-4 (Serratia), T-5 (Rickettsia) and T-6 (Pseudomonas). E. corrusca egg microbiomes were all assigned to T-13 (Gluconobacter), except for a single egg microbiome that belonged to T-2 (Salmonella), the same as some E. corrusca adult microbiomes. Photuris sp. microbiomes were assigned to 3 community types: T-8 (Acinetobacter), T-9 (Yersinia) and T-6 (Pseudomonas). Pyractomena borealis and Pyropyga sp. were assigned to T-10 (Empedobacter) and T-12 (Cupriavidus), respectively.

Figure 2
Figure 2

Firefly microbial community types. The X axis indicates the number of samples that were assigned to each community type. Colors indicate firefly species, as indicated by the key. Note that some gut and body samples originate from the same individual firefly and so are counted twice.

Firefly species harbor unique microbial communities

The four sampled firefly species had distinct bacterial communities (Weighted Unifrac (WUF) PERMANOVA: R2=0.215, p=0.001; Fig. 3A, Suppl. Fig. S3, Suppl. Table S1). In a PCoA ordination of WUF beta-diversity distances, E. corrusca samples clustered together, spanning from left to right (Fig. 3). Pyropyga sp. samples clustered together with some E. corrusca samples and those from Photuris sp. on the left side of Figure 3A, and Photuris sp. samples grouped together down the left side and at the bottom, along with the Pyractomena borealis samples. Species-specific clustering can be seen in the UUF plot, with E. corrusca having the most variance (Suppl. Fig. S3B). The separation between the E. corrusca and Photuris sp. microbiomes was maintained in a better-balanced comparison when the few Pyropyga sp. and P. borealis samples were excluded (WUF PERMANOVA: R2=0.164, p=0.001; Suppl. Fig S4, Suppl. Table S2).

Figure 3
Figure 3

A: PCoA of Weighted Unifrac distances between microbial communities for all four firefly species. Species are differentiated using colors. n = 133. B: Over- and underrepresented ASV sequences in E. corrusca and Photuris sp. adults, including both gut and body samples. The X-axis indicates the genus of bacteria whose relative abundances differed between firefly species, and the Y-axis indicates the log2 fold change in relative abundance between samples, where the higher numbers indicate overrepresentation in Photuris and negative numbers indicate overrepresentation in E. corrusca. Colors indicate phyla. n = 114

We identified the bacterial taxa whose relative abundance differed between E. corrusca and Photuris sp. fireflies using DESeq. Although Pseudomonas ASVs were found in both fireflies, one Pseudomonas ASV was more abundant in Photuris sp. and two other Pseudomonas ASVs were more abundant in E. corrusca (Fig. 3B). Two Mesoplasma ASVs, Salmonella, Serratia, Rickettsia and Rickettsiella were all more abundant in E. corrusca than in Photuris sp., and Tsukamurella, Gordonia, Curvibacter and Sphingobacterium were all more abundant in Photuris sp. than in E. corrusca (Fig. 3B).

Gut and body microbiomes are distinct, but these differences are firefly-specific

The above analysis indicated that the four sampled firefly species all hosted distinct microbiomes (Fig. 3). However, further analyses using these same data also indicated that different firefly tissues might also host distinct microbiomes, in a species-specific manner (WUF PERMANOVA for the interactions between species and tissue: R2=0.048, p=0.003; Suppl. Fig. S3, Suppl. Table S1). In contrast, a parallel analysis using only samples for which sex was determined indicated that microbiomes did not differ between the sexes, regardless of species or tissue type (all PERMANOVAs testing for differences between sexes: p > 0.05; Suppl. Fig. S5, Suppl. Table S3). In the analysis described above with Pyractomena borealis and Pyropyga sp. samples removed to avoid possible artifacts due to unbalanced sample sizes, firefly tissue microbiomes again differed in a species-specific manner (WUF PERMAONVA for interactions between E. corrusca and Photuris sp. gut and body microbiomes: R2=0.053, p=0.001; Suppl. Fig. S4, Suppl. Table S2).

Because these PERMANOVAs indicated that tissue microbiomes differed in a species-specific manner, we repeated our analysis for each species separately. E. corrusca eggs, gut and body microbiomes differed from each other (WUF PERMANOVA: R2=0.122, p=0.001), with most gut and body samples clustered together in the PCoA and most egg samples clustered separately (Fig. 4A, Suppl. Table S4, Suppl. Fig. S6). When the egg samples were removed from the analysis, E. corrusca gut and body microbiomes differed from each other only when using the UUF distance metric (UUF PERMANOVA: R2= 0.072, p=0.001; WUF: Suppl. Table S4). Wolbachia had low relative abundance in all E. corrusca samples, but was the only bacterial genus that more abundant in E. corrusca eggs than in adults. Mesoplasma, Pseudomonas, Acinetobacter and several other genera were all more abundant in E. corrusca adults than in eggs (Fig. 4B). E. corrusca eggs and adults also had distinct community types, with E. corrusca egg microbiomes mainly assigned to T-13 (Gluconobacter), and only a single egg microbiome assigned to T-3 (Salmonella) like those of E. corrusca adults (Fig. 2). In contrast, Photuris sp. gut and body microbiomes more strongly differed from each other (WUF PERMANOVA: R2= 0.340, p=0.001; Fig. 4C, Suppl. Fig. S7, Suppl. Table S4), with Pseudomonas, Acinetobacter, Leucobacter, Elizabethkingia, Curvibacter and Achromobacter all being more abundant in Photuris sp. guts than in bodies (Fig. 4D). Pyropyga sp. gut and body microbiomes did not differ from each other (WUF PERMANOVA: R2=0.312, p=0.667; Suppl. Table S4), and P. borealis gut and body microbiomes differed only when using the UUF distance metric (UUF PEMANOVA: R2= 0.616, p=0.008; Suppl. Table S4). These conclusions should be considered preliminary because of the small sample sizes available for both Pyropyga sp. and P. borealis.

Figure 4
Figure 4

A) PCoA of Weighted Unifrac distances between microbiomes of E. corrusca eggs and adults. Egg, gut and body samples are differentiated using colors. n = 100. B) Over- and underrepresented ASV sequences in E. corrusca egg and adult samples. The X-axis indicates the genera of bacteria whose relative abundance differed between E. corrusca eggs and adults, and the Y-axis indicates the log2 fold change in these relative abundances between tissues, where the higher numbers indicate overrepresentation in eggs and negative numbers indicate overrepresentation in adults. Colors indicate phyla. C) PCoA of Weighted Unifrac distances between microbiomes of Photuris sp. gut and body samples. Gut and body samples are differentiated using colors. D) Over- and underrepresented ASV sequences of Photuris sp. gut and body samples. The X-axis indicates the genus of bacteria whose relative abundance differed between Photuris sp. gut and body samples, and the Y-axis indicates the log2 fold change in these relative abundances between tissues, where the higher numbers indicate an overrepresentation in the body samples. Colors indicate phyla. n = 20

Fireflies host an abundance of potentially symbiotic Mollicutes

Mollicutes were the most abundant bacteria in our 133 firefly samples, consistent with the handful of Mollicutes that had previously been isolated from different firefly species (27, 29, 30, 48–50). We therefore created a phylogenetic tree to discover how our most abundant Mollicute ASVs were related to the sequences of these known firefly Mollicute isolates (Fig. 5). The most prevalent Spiroplasma ASV (ASV Spiroplasma1), found in many of the E. corrusca samples, was similar to the 16S rRNA sequence of S. corruscae, a species that was first isolated from E. corrusca (28). ASV Spiroplasma2 was found in one Photuris sp. sample, and was similar to the 16S rRNA sequence of S. ixodetis, a male-killing agent in butterflies (51). S. ixodetis has not been found in fireflies to date. The four Mesoplasma ASVs, mainly found in E. corrusca, are all similar to the 16S rRNA sequences for M. corruscae and Entomoplasma ellychniae, which had both been isolated previously from E. corrusca (29, 48, 49). Although originally separated using cell morphology and culture media requirements, the genera Mesoplasma and Entomoplasma are paraphyletic (52). Our phylogenetic tree showing that both M. corruscae and E. ellychniae cluster tightly together suggests that E. ellychniae should be reclassified as a member of the genus Mesoplasma. The single Entomoplasma ASV, found in Pyractomena borealis adults, closely resembled the 16S rRNA sequence for E. somnilux, which had been previously isolated from Pyractomena angulate (27). This phylogenetic tree shows that the Mollicutes sequences detected in study are highly similar to strains that had been previously isolated from fireflies, except for that related to S. ixodetis.

Figure 5
Figure 5

Phylogenetic tree of the Mollicute 16S rRNA gene ASVs in our dataset (in bold) and reference 16S rRNA gene sequences, particularly those isolated from fireflies and beetles (indicated by a ^). The tree was constructed using RAxML with 500 bootstraps and rooted using Bacillus subtilis and Mycoplasma haemominutum. Bootstrap values of 60–79 and 80–100 are indicated by * and **, respectively. Numbers in parentheses indicate the number of samples in which that ASV was found, and colors group sequences from the same genera. NCBI accession numbers are shown to the right of each reference sequence. Me = Mesoplasma, My = Mycoplasma.

DISCUSSION

Our results show that all of our sampled species of firefly have low-complexity microbiomes. The alpha diversity of our firefly microbiomes was very low, and all samples had only a few taxa present in high abundance (Fig 1). The low number of correlations between the relative abundances of firefly taxa (Suppl Fig. S2) is consistent with this predominance of simple microbiomes, because (by definition) few interactions can occur when only a single bacterium is abundant in a microbiome, and correlations will inevitably be weak when there is high variability between microbial communities. This variance and low diversity could be explained by microbiomes being acquired via neutral community assembly. Fireflies may be exposed to environmental bacteria by chance, and these bacteria may then multiply and form a stable microbiome. This would explain why there is high variance between the microbiomes from individual insects of the same firefly species.

Samples could be assigned to one of thirteen community types, defined by the taxa in these samples with >30% abundance. Different firefly species rarely shared the same community type, and most community types were defined by a single taxon, with only one community type having two taxa that were each >30% abundant (Fig. 2). The presence of multiple community types in each firefly species creates questions about whether the bacteria are either: 1) resident members replicating within the firefly, or 2) transient members that replicate more slowly than the rate of expulsion from the firefly (53). Although such transient microbiomes are not likely to be conserved between fireflies, it is possible that they do have some effect on their hosts (positive or negative). Community types vary within the same firefly species, suggesting that these microbiomes are transient, some bacteria were found in extremely high relative abundances (Fig. 1) that could suggest a stable resident microbiome of unknown function. It is possible that each community type provides a different function for the firefly. It is also possible that the multiple, highly abundant bacteria in different community types, such as Mesoplasma, Salmonella, and Pseudomonas, could provide similar functions for their hosts, although this may be unlikely for Mollicute bacteria due to their reduced genomes.

Fireflies from different species also had distinct microbiomes (Fig. 3A). E. corrusca and Photuris sp. had distinct bacterial communities, and samples from the same species clustered together in PCoA plots of beta-diversity (Suppl. Fig. 4). Differences between the microbiomes of these two species might be due to their different lifestyles. North American E. corrusca are active in winter and may feed on tree sap (4, 14), and so E. corrusca microbial communities may therefore be acquired via the ingestion of these fluids. Unlike E. corrusca, Photuris sp. are predatory and active in the summer. Insect gut microbiomes often vary based on differences in their host’s habitat and diet (e.g., omnivory vs. carnivory) (22), and such differences might underlie the differences that we observed between E. corrusca and Photuris sp. microbiomes. Both E. corrusca and Photuris sp. hosted Pseudomonas ASVs that were similar to those previously found on trees and in soils, which could be explained by E. corrusca and Photuris sp. living in soil, on tree bark, and on plant leaves (2), where Pseudomonas is common (54). All of the E. corrusca and Photuris sp. samples used in this study were collected from Connecticut, USA, and whether these microbiomes vary in other geographic locations needs future research.

For some firefly species, eggs, guts, and bodies had distinct microbiomes. E. corrusca adult and egg microbiomes differed from each other, with samples clustering separately in PCoAs (Fig. 4A, Suppl. Fig. S6), but an E. corrusca egg also had the same community type (T-2; Salmonella) as that of some adults. Although it is unknown which firefly laid this egg, these results suggest that it might be possible for Salmonella to be transmitted vertically between E. corrusca adults and eggs. The mechanisms that E. corrusca may use to acquire these microbes from the environment are unknown, but there are other insects that rely on horizontal transmission for their symbionts, such as the bean bug Riptortus pedestris, which acquires its Burkholderia symbiont from soil each generation using a mucus-filled organ that the Burkholderia specifically penetrates and colonizes (55–57). Although E. corrusca is not known to have an organ that selects for a specific bacterial symbiont, Mesoplasma was found in many samples of E. corrusca. It is possible that E. corrusca may select this bacterium from the environment, or that Mesoplasma selects E. corrusca as a preferred host. E. corrusca is the most sampled firefly species in our study, and hosts microbiomes belonging to the most community types. This increased sampling may therefore have increased the number of community types detected in this organism relative to the other species in our study.

Photuris sp. gut and body microbiomes also differed from each other, with gut microbiomes having higher amounts of Pseudomonas and Acinetobacter than body microbiomes (Figs. 4C and 4D). These ASVs may be acquired from the firefly’s environment, because both ASVs were similar to other strains of bacteria that are common in soil and on plants. The bacteria present in the Photuris sp. gut may be derived from eating other fireflies, and thus the Pseudomonas ASVs that are common in Photuris sp. guts may originate from their last meal. The Photuris microbiome may therefore be transient and composed of the prey’s microbiome. Alternatively, it is possible that the Photuris sp. gut microbiome is not transient, and the gut provides a niche that is favorable for the growth of Pseudomonas and Acinetobacter microbes that might originate from soil, plants, or the prey’s microbiome and subsequently colonize the Photuris sp. gut. The origin of the body microbiome remains unclear. Body dissections include all other parts of the firefly except for the gut, meaning that other non-gut tissues and hemolymph from inside of the firefly could have been colonized with bacteria. Alternatively, adult firefly abdomens are surrounded with sclerotized segments, called tergites, on their ventral and dorsal sides (2). These tergites have small gaps between them that allow for movement. Although our firefly samples were ethanol-washed before dissection, this sterilization may not be absolutely perfect, and the body microbiome may therefore include bacteria that are trapped within these segments (58). The bacteria trapped between these tergites may be acquired during constant movement on tree bark or in the leaf litter, where many bacteria are found.

Mollicutes have been isolated from several different firefly species, but their prevalence and function is unclear (26, 27, 50, 59, 60). We detected 4 Mesoplasma ASVs in North American fireflies, mainly in E. corrusca, that all closely resembled M. corruscae and Entomoplasma ellychniae, which were both isolated previously from E. corrusca. As mentioned in the results, M. corruscae and E. ellychniae very likely both belong to genus Mesoplasma (52). Mesoplasma frequently occur on plants without causing disease, and therefore it could be horizontally acquired by E. corrusca when they feed on nutrient-poor tree sap to aid in survival during cold winter months (4, 14).. Future work will be required to determine the function, if any, of these Mollicutes in their firefly hosts.

Our results provide the first description of the microbiomes found in E. corrusca, Photuris sp., Pyropyga sp. and Pyractomena borealis. These microbiomes have low alpha diversities and are species-specific. Some firefly species also have distinct egg, gut, and body microbiomes. Future research will determine the function of these microbiomes and how they are acquired from their environment or transmitted between hosts.

FUNDING

This work was supported by a University of Connecticut Scholarship Facilitation Fund grant to J.L.K.

ACKNOWLEDGEMENTS

We would like to thank Erin L. Mostoller and Dr. Craig W Schneider both from Trinity College, Hartford, CT, and Dr. Steven Deyrup from Siena College, Loudonville, NY for their assistance with obtaining the firefly samples. We would also like to thank the members of the Klassen Lab for their thoughtful feedback on this manuscript before submission, and the UConn Microbial Analysis, Resources, and Services facility for microbiome sequencing.

Work Cited

  1. 1.↵
    1. Zhang, Z.-Q.
    Perry Slipinski SA, Leschen RAB, Lawrence JF. 2011. Order Coleoptera Linnaeus, 1758. In: Zhang, Z.-Q. (Ed.) Animal biodiversity: An outline of higher-level classification and survey of taxonomic richnes. Zootaxa 3148:203–208.
    OpenUrl
  2. 2.↵
    Faust LF. 2017. Fireflies, glow-worms, and lightning bugs!: Identification and Natural History of the Fireflies of the Eastern and Central United States and Canada. University of Georgia Press, Athens, Georgia.
  3. 3.↵
    Barber HS. 1951. Fireflies of the Genus Photuris. Smithson Misc Collect 117:1–66.
    OpenUrl
  4. 4.↵
    Rooney JA, Lewis SM. 2000. Notes on the life history and mating behavior of Ellychnia corrusca (Coloeptera: Lampyridae). Florida Entomol 83:324–334.
    OpenUrl
  5. 5.↵
    Martin GJ, Branham MA, Whiting MF, Bybee SM. 2017. Total evidence phylogeny and the evolution of adult bioluminescence in fireflies (Coleoptera: Lampyridae). Mol Phylogenet Evol 107:564–575.
    OpenUrl
  6. 6.↵
    Eisner T, Goetz MA, Hill DE, Smedley SR, Meinwald J. 1997. Firefly “femmes fatales” acquire defensive steroids (lucibufagins) from their firefly prey. Proc Natl Acad Sci USA 94:9723–9728.
    OpenUrlAbstract/FREE Full Text
  7. 7.
    Goetz MA, Meinwald J, Eisner T. 1981. Lucibufagins, IV. New defensive steroids and a pterin from the firefly, Photinus pyralis (Coleoptera: Lampyridae). Experientia 37:679–680.
    OpenUrl
  8. 8.
    Smedley SR, Risteen RG, Tonyai KK, Pitino JC, Hu Y, Ahmed ZB, Christofel BT, Gaber M, Howells NR, Mosey CF, Rahim FU, Deyrup ST. 2017. Bufadienolides (lucibufagins) from an ecologically aberrant firefly (Ellychnia corrusca). Chemoecology 27:141–153.
    OpenUrl
  9. 9.
    Krenn L, Kopp B. 1998. Bufadienolides from animal and plant sources. Phytochemistry 48:1–29.
    OpenUrlCrossRefPubMedWeb of Science
  10. 10.↵
    Deyrup ST, Risteen RG, Tonyai KK, Farrar MA, D’Antonio BE, Ahmed ZB, Christofel BT, Howells NR, Smedley SR, D’antonio BE, Ahmed ZB, Christofel BT, Howells NR, Smedley SR. 2017. Escape into Winter: Does a Phenological Shift by Ellychnia corrusca (Winter Firefly) Shield it from a Specialist Predator (Photuris)? Northeast Nat 24:147–166.
    OpenUrl
  11. 11.↵
    Fallon TR, Lower SE, Chang CH, Bessho-Uehara M, Martin GJ, Bewick AJ, Behringer M, Debat HJ, Wong I, Day JC, Suvorov A, Silva CJ, Stanger-Hall KF, Hall DW, Schmitz RJ, Nelson DR, Lewis SM, Shigenobu S, Bybee SM, Larracuente AM, Oba Y, Weng JK. 2018. Firefly genomes illuminate parallel origins of bioluminescence in beetles. eLife 7:e36495.
    OpenUrl
  12. 12.↵
    Eisner T, Wiemer DF, Haynest LW, Meinwaldf J, Eisner T, Meinwald J. 1978. Lucibufagins: Defensive steroids from the fireflies Photinus ignitus and P. marginellus (Coleoptera: Lampyridae). Proc Natl Acad Sci 75:905–908.
    OpenUrlAbstract/FREE Full Text
  13. 13.↵
    Lloyd JE. 1973. Firefly Parasites and Predators. Coleopt Bull 27:91–106.
    OpenUrl
  14. 14.↵
    Faust L. 2012. Fireflies in the snow: Observations on two early-season arboreal fireflies Ellychnia corrusca and Pyractomena borealis. Lampyrid 2:48–71.
    OpenUrl
  15. 15.↵
    Lloyd JE. 1965. Aggressive Mimicry in Photuris: Signal repertoires by femmes fatales. Am Assoc Adv Sci 149:653–654.
    OpenUrl
  16. 16.↵
    Arias-Cordero E, Ping L, Reichwald K, Delb H, Platzer M, Boland W. 2012. Comparative evaluation of the gut microbiota associated with the below- and above-ground life stages (larvae and beetles) of the forest cockchafer, Melolontha hippocastani. PLoS One 7:e51557.
    OpenUrlCrossRefPubMed
  17. 17.↵
    Lundgren JG, Lehman RM. 2010. Bacterial gut symbionts contribute to seed digestion in an omnivorous beetle. PLoS One 5:e10831.
    OpenUrlCrossRefPubMed
  18. 18.↵
    Grünwald S, Pilhofer M, Höll W. 2010. Microbial associations in gut systems of wood-and bark-inhabiting longhorned beetles [Coleoptera: Cerambycidae]. Syst Appl Microbiol 33:25–34.
    OpenUrlCrossRefPubMedWeb of Science
  19. 19.↵
    Schloss PD, Delalibera I, Handelsman J, Raffa KF. 2006. Bacteria associated with the guts of two wood-boring beetles: Anoplophora glabripennis and Saperda vestita (Cerambycidae). Environ Entomol 35:625–629.
    OpenUrlCrossRef
  20. 20.↵
    Kaltenpoth M, Steiger S. 2014. Unearthing carrion beetles’ microbiome: characterization of bacterial and fungal hindgut communities across the Silphidae. Mol Ecol 23:1251–1267.
    OpenUrlCrossRef
  21. 21.↵
    Kolasa M, Ścibior R, Mazur MA, Kubisz D, Dudek K, Kajtoch Ł. 2019. How hosts taxonomy, trophy, and endosymbionts shape microbiome diversity in beetles. Microb Ecol 78:995–1013.
    OpenUrl
  22. 22.↵
    Yun JH, Roh SW, Whon TW, Jung MJ, Kim MS, Park DS, Yoon C, Nam Y Do, Kim YJ, Choi JH, Kim JY, Shin NR, Kim SH, Lee WJ, Bae JW. 2014. Insect gut bacterial diversity determined by environmental habitat, diet, developmental dtage, and phylogeny of host. Appl Environ Microbiol 80:5254–5264.
    OpenUrlAbstract/FREE Full Text
  23. 23.↵
    Engel P, Moran NA. 2013. The gut microbiota of insects -diversity in structure and function. FEMS Microbiol Rev 37:699–735.
    OpenUrlCrossRefPubMedWeb of Science
  24. 24.↵
    Estes AM, Hearn DJ, Snell-Rood EC, Feindler M, Feeser K, Abebe T, Dunning Hotopp JC, Moczek AP. 2013. Brood ball-mediated transmission of microbiome members in the dung beetle, Onthophagus taurus (Coleoptera: Scarabaeidae). PLoS One 8:e79061.
    OpenUrlCrossRefPubMed
  25. 25.↵
    Shukla SP, Plata C, Reichelt M, Steiger S, Heckel DG, Kaltenpoth M, Vilcinskas A, Vogel H. 2018. Microbiome-assisted carrion preservation aids larval development in a burying beetle. Proc Natl Acad Sci U S A 115:11274–11279.
    OpenUrlAbstract/FREE Full Text
  26. 26.↵
    Hackett KJ, Whitcomb RF, Tully JG, Lloyd JE, Anderson JJ, Henegar RB, Rose DL, Clark EA, Vaughn JL, Ecology SM, Apr NM. 1992. Lampyridae (Coleoptera): A plethora of Mollicute associations. Microb Ecol 23:181–193.
    OpenUrlCrossRefPubMedWeb of Science
  27. 27.↵
    Williamson DL, Tully JG, Rose DL, Hackett KJ, Henegar R, Carle P, Bové JM, Colflesh DE, Whitcomb RF. 1990. Mycoplasma somnilux sp. nov., Mycoplasma luminosum sp. nov., and Mycoplasma lucivorax sp. nov., new sterol-requiring mollicutes from firefly beetles (Coleoptera: Lampyridae). Int J Syst Bacteriol 40:160–164.
    OpenUrlCrossRefPubMed
  28. 28.↵
    Hackett KJ, Whitcomb RF, French FE, Tully JG, Gasparich GE, Rose DL, Carle P, Bové JM, Henegar RB, Clark TB, Konai M, Clark EA, Williamson DL. 1996. Spiroplasma corruscae sp. nov., from a firefly beetle (Coleoptera: Lampyridae) and tabanid flies (Diptera: Tabanidae). Int J Syst Bacteriol 46:947–950.
    OpenUrlCrossRefPubMed
  29. 29.↵
    Tully JG, Rose DL, Hackett KJ, Whitcomb RF, Carle P, Bove JM, Colflesh DE, Williamson DL. 1989. Mycoplasma ellychniae sp. nov., a sterol-requiring Mollicute from the firefly beetle Ellychnia corrusca. Int J Syst Bacteriol 39:284–289.
    OpenUrlCrossRef
  30. 30.↵
    Stevens C, Tang AY, Jenkins E, Goins RL, Tully JG, Rose DL, Konai M, Williamson DL, Carle P, Bové JM, Hackett KJ, French FE, Wedincamp J, Henegar RB, Whitcomb RF. 1997. Spiroplasma lampyridicola sp. nov., from the firefly beetle Photuris pennsylvanicus. Int J Syst Bacteriol 47:709–712.
    OpenUrlCrossRef
  31. 31.↵
    Sanders JG, Powell S, Kronauer DJC, Vasconcelos HL, Frederickson ME, Pierce NE. 2014. Stability and phylogenetic correlation in gut microbiota: Lessons from ants and apes. Mol Ecol 23:1268–1283.
    OpenUrlCrossRefPubMedWeb of Science
  32. 32.↵
    Lee KM, Adams M, Klassen JL. 2019. Evaluation of DESS as a storage medium for microbial community analysis. PeerJ 7:e6414.
    OpenUrl
  33. 33.↵
    Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Lozupone CA, Turnbaugh PJ, Fierer N, Knight R. 2011. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc Natl Acad Sci U S A 108:4516–4522.
    OpenUrlAbstract/FREE Full Text
  34. 34.↵
    R Core Team. 2018. R: A Language and Environment for Statistical Computing. Vienna.
  35. 35.↵
    Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. 2016. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods 13:581–583.
    OpenUrlCrossRefPubMed
  36. 36.
    McMurdie PJ, Holmes S. 2013. Phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 8:e61217.
    OpenUrlCrossRefPubMed
  37. 37.
    Glöckner FO, Yilmaz P, Quast C, Gerken J, Beccati A, Ciuprina A, Bruns G, Yarza P, Peplies J, Westram R, Ludwig W. 2017. 25 years of serving the community with ribosomal RNA gene reference databases and tools. J Biotechnol 261:169–176.
    OpenUrlCrossRef
  38. 38.↵
    Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, Peplies J, Glöckner FO. 2013. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res 41:590–596.
    OpenUrlCrossRef
  39. 39.↵
    Yilmaz P, Parfrey LW, Yarza P, Gerken J, Pruesse E, Quast C, Schweer T, Peplies J, Ludwig W, Glöckner FO. 2014. The SILVA and “all-species Living Tree Project (LTP)” taxonomic frameworks. Nucleic Acids Res 42:643–648.
    OpenUrlCrossRefPubMed
  40. 40.↵
    Davis NM, Proctor DIM, Holmes SP, Relman DA, Callahan BJ. 2018. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome 6:226.
    OpenUrlCrossRefPubMed
  41. 41.↵
    Jari Oksanen, F. Guillaume Blanchet, Michael Friendly, Roeland Kindt P, Legendre, Dan McGlinn, Peter R. Minchin, R. B. O’Hara, Gavin L. Simpson P, Solymos, M. Henry H. Stevens ES and HW. 2019. vegan: Community Ecology Package. R package version 2.5-4.
  42. 42.↵
    Love MI, Huber W, Anders S. 2014. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15:e550.
    OpenUrlCrossRef
  43. 43.↵
    Kurtz ZD, Müller CL, Miraldi ER, Littman DR, Blaser MJ, Bonneau RA. 2015. Sparse and compositionally robust inference of microbial ecological networks. PLOS Comput Biol 11:e1004226.
    OpenUrlCrossRefPubMed
  44. 44.↵
    Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T. 2003. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13:2498–2504.
    OpenUrlAbstract/FREE Full Text
  45. 45.↵
    Csardi G, Nepusz T. 2005. The igraph software package for complex network research. InterJournal Complex Syst 1695.
  46. 46.↵
    Edgar RC. 2004. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res 32:1792–1797.
    OpenUrlCrossRefPubMedWeb of Science
  47. 47.↵
    Stamatakis A. 2014. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30:1312–1313.
    OpenUrlCrossRefPubMedWeb of Science
  48. 48.↵
    Tully JG, Bove JM, Laigret F, Whitcomb RF. 1993. Revised taxonomy of the class Mollicutes: Proposed elevation of a monophyletic cluster of arthropod-associated Mollicutes to ordinal rank (Entomoplasmatales ord. nov.), with provision for familial rank to separate species with nonhelical morphology. Int J Syst Bacteriol 43:630–630.
    OpenUrlCrossRef
  49. 49.↵
    Tully JG, Whitcomb RF, Hackett KJ, Rose DL, Henegar RB, Bove JM, Carle P, Williamson DL, Clark TB. 1994. Taxonomic descriptions of eight new non-sterol-requiring mollicutes assigned to the genus Mesoplasma. Int J Syst Bacteriol 44:685–693.
    OpenUrlCrossRefPubMed
  50. 50.↵
    Wedincamp J, French FE, Whitcomb RF, Henegar RB. 1996. Spiroplasmas and Entomoplasmas (Procaryotae: Mollicutes) associated with tabanids (Diptera: Tabanidae) and fireflies (Coleoptera: Lampyridae). J Invertebr Pathol 68:183–186.
    OpenUrlCrossRef
  51. 51.↵
    Binetruy F, Bailly X, Chevillon C, Martin OY, Bernasconi M V., Duron O. 2019. Phylogenetics of the Spiroplasma ixodetis endosymbiont reveals past transfers between ticks and other arthropods. Ticks Tick Borne Dis 10:575–584.
    OpenUrl
  52. 52.↵
    Gasparich GE, Kuo C. 2019. Genome analysis-based union of the genus Mesoplasma with the genus Entomoplasma. Int J Syst Evol Microbiol 69:2735–2738.
    OpenUrl
  53. 53.↵
    Hammer TJ, Sanders JG, Fierer N. 2019. Not all animals need a microbiome. FEMS Microbiol Lett 366:fnz117.
    OpenUrlCrossRef
  54. 54.↵
    Novik G, Savich V, Kiseleva E. 2015. Microbiology in Agriculture and Human Health. InTech, London.
  55. 55.↵
    Kikuchi Y, Hosokawa T, Fukatsu T. 2007. Insect-microbe mutualism without vertical transmission: A stinkbug acquires a beneficial gut symbiont from the environment every generation. Appl Environ Microbiol 73:4308–4316.
    OpenUrlAbstract/FREE Full Text
  56. 56.
    Ohbayashi T, Takeshita K, Kitagawa W, Nikohc N, Koga R, Meng XY, Tago K, Hori T, Hayatsu M, Asano K, Kamagata Y, Lee BL, Fukatsu T, Kikuchi Y. 2015. Insect’s intestinal organ for symbiont sorting. Proc Natl Acad Sci U S A 112:5179–5188.
    OpenUrl
  57. 57.↵
    Itoh H, Jang S, Takeshita K, Ohbayashi T, Ohnishi N, Meng XY, Mitani Y, Kikuchi Y. 2019. Host–symbiont specificity determined by microbe–microbe competition in an insect gut. Proc Natl Acad Sci U S A 116:22673–22682.
    OpenUrlAbstract/FREE Full Text
  58. 58.↵
    Hammer TJ, Dickerson JC, Fierer N. 2015. Evidence-based recommendations on storing and handling specimens for analyses of insect microbiota. PeerJ 8:e1190.
    OpenUrl
  59. 59.↵
    Stevens C, Tang AY, Jenkins E, Goins RL, Tully JG, Rose DL, Konai M, Williamson DL, Carle P, Bové JM, Hackett KJ, French FE, Wedincamp J, Henegar RB, Whitcomb RF. 1997. Spiroplasma lampyridicola sp. nov., from the firefly beetle Photuris pennsylvanicus. Int J Syst Bacteriol 47:709–712.
    OpenUrlCrossRef
  60. 60.↵
    Tully JG, Rose DL, Hackett KJ, Whitcomb RF, Carle P, Bove JM, Colflesh DE, Williamson DL. 1989. Mycoplasma ellychniae sp. nov., a sterol-requiring Mollicute from the firefly beetle Ellychnia corrusca. Int J Syst Bacteriol 39:284–289.
    OpenUrlCrossRef
Back to top
Posted October 06, 2020.
Download PDF

Supplementary Material

Data/Code
Email
North American fireflies host low bacterial diversity
Emily A. Green, Scott R. Smedley, Jonathan L. Klassen
bioRxiv 2020.10.06.328070; doi: https://doi.org/10.1101/2020.10.06.328070
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools

Subject Area