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Identification of the mutual gliding locus as a factor for gut colonization in non-native bee hosts using the ARTP mutagenesis

Abstract

Background

The gut microbiota and their hosts profoundly affect each other’s physiology and evolution. Identifying host-selected traits is crucial to understanding the processes that govern the evolving interactions between animals and symbiotic microbes. Current experimental approaches mainly focus on the model bacteria, like hypermutating Escherichia coli or the evolutionary changes of wild stains by host transmissions. A method called atmospheric and room temperature plasma (ARTP) may overcome the bottleneck of low spontaneous mutation rates while maintaining mild conditions for the gut bacteria.

Results

We established an experimental symbiotic system with gnotobiotic bee models to unravel the molecular mechanisms promoting host colonization. By in vivo serial passage, we tracked the genetic changes of ARTP-treated Snodgrassella strains from Bombus terrestris in the non-native honeybee host. We observed that passaged isolates showing genetic changes in the mutual gliding locus have a competitive advantage in the non-native host. Specifically, alleles in the orphan mglB, the GTPase activating protein, promoted colonization potentially by altering the type IV pili-dependent motility of the cells. Finally, competition assays confirmed that the mutations out-competed the ancestral strain in the non-native honeybee gut but not in the native host.

Conclusions

Using the ARTP mutagenesis to generate a mutation library of gut symbionts, we explored the potential genetic mechanisms for improved gut colonization in non-native hosts. Our findings demonstrate the implication of the cell mutual-gliding motility in host association and provide an experimental system for future study on host-microbe interactions.

Video Abstract

Background

Animals are intimately associated with highly complex and dynamic communities of microbes that inhabit virtually every surface of their bodies, especially in their digestive tracts [1]. Previous research has revealed that both the gut microbiota and the hosts they colonize profoundly influence the physiology and evolution of one another [2]. The host gut environment usually exerts relatively strong selective pressure on the gut members, driving the gut bacteria to surmount many complex challenges for successful living and replicating [3]. Therefore, the animal digestive tract houses bacterial communities whose composition is distinct from those in surrounding environments and other relative individuals in most cases [4, 5]. This suggests that host-associated bacteria maintain certain traits that enable them to colonize their hosts, which is the key evolutionary process in symbiotic relationships. Thus, identifying traits under host selection is crucial to understanding the processes governing nascent symbiotic interactions between animals and gut microbes [6]. The genetic basis of host colonization has focused on pathogenic bacteria [7], but only a few studies are available for intestinal symbionts.

Recently, experimental approaches involving the serial passage of free-living bacteria through digestive tracts have been used to explore the traits that mediate gut microbial association with hosts [8,9,10]. Combined with genomic sequencing of transmitted lineages, this strategy enables the identification of evolutionary changes in genotypes [11]. By experimentally evolving hypermutating Escherichia colimutS in the stinkbug Plautis stali, single mutations disrupting the carbon catabolite repression can make non-symbiotic strains evolve into insect mutualists [12]. Through transgenerational maintenance of ecologically distinct Vibrio fischeri from pineapple fish and plankton within the light organs of the squid Euprymna scolopes, the non-native lines exhibited mutations in the binK sensor kinase gene that conferred selective advantages by immune evasion [6]. Thus, single mutations of large effect can evoke broad consequences to evolve towards mutualism in bacteria.

Genome-scale random mutations are a major driving force of natural evolution; however, the low rate of spontaneous mutations is insufficient for effective screening, and large-scale or long-term host association experiments have always been required [13]. By deleting the mutS encoding the DNA mismatch repair enzyme, the hypermutating strains with elevated mutation rates are often used for experimental evolution studies [6, 8, 12]. Although the genetic toolsets are available for the model species, most gut bacteria cannot be genetically manipulated [14]. Atmospheric and room temperature plasma (ARTP) is a newly developed whole-cell mutagenesis tool based on glow discharge plasma [15]. The construction of mutant libraries using ARTP is used for industrial breeding, which obtains strains with higher production efficiency and tolerance to extreme environments [16, 17]. Since ARTP can cause mutagenesis while maintaining mild conditions, it provides a promising approach for exploring molecular evolutionary traits of gut microbes [14].

As members of the family Apidae, the honeybee and bumblebee species harbor similar sets of gut bacteria specific to the hosts [18]. Although they share core gut microbiota mainly composed of five phylotypes: Gilliamella, Snodgrassella, Bifidobacterium, Lactobacillus Firm4, and Firm5, phylogenetic analysis shows that strains from different bee species separate into divergent lineages [19, 20]. Previous genomic analyses show that some strains from Gilliamella, Lactobacillus Firm5, and Bifidobacterium exhibit different metabolic potentials for carbohydrates, which may be relevant to the adaption of bee hosts [21,22,23]. Interestingly, Snodgrassella strain wkB12 from Bombus bimaculatus could not colonize the A. mellifera gut, while cross-host microbe transfer is observed across Apis species [18]. Our recent study showed that Snodgrassella strains from different hosts exhibited a markedly different enrichment of single nucleotide polymorphisms in the type IV pili (T4P) genes, possibly associated with microbiota-host interactions [24]. Recently, protocols have been established for generating microbiota-depleted individuals of both honey and bumble bees and gnotobiotic models with defined microbial constituents [25, 26]. Thus, they provide experimentally tractable models to reveal mechanisms by which beneficial organisms colonize specific hosts.

Here, we constructed a mutant library of Snodgrassella strains from Bombus terrestris using ARTP mutagenesis. By serial passaging of replicate lines of the mutants, we identified genetic traits promoting colonization in the non-native host Apis mellifera. The evolved populations showed mutations in the mglB gene encoding the mutual gliding motility protein B, which emerged first and rapidly became fixed. Further, in vivo competition assays indicated that the point mutation may confer a competitive advantage in the non-native host, suggesting a potential role of the T4P-dependent cell motility establish of microbiota-host association.

Materials and methods

Isolation and whole-genome sequencing of Snodgrassella strain B10998

Snodgrassella communis strain B10998 was isolated from the gut homogenate of bumblebee, B. terrestris, from Jinan, China. The factory-farmed bumblebees were reared year-round in a climate-controlled room at 28 °C, 65% relative humidity, and continuous darkness. The dry canola pollen was sterilized by gamma irradiation (30 kGy), and the sterilizations were verified by plating suspensions on BHI plates and incubating them at 37 °C for 12 h. The sugar syrup (50% weight/volume, w/v) containing double-distilled water was filtered through 0.22-µm filters (Jet Bio-Filtration, Guangzhou, China). Bees were fed on the dry sterilized pollen and sugar syrup (50% w/v) ad libitum [27]. The pure culture of S. communis strain B10998 was isolated from bumblebee guts as previously described [26]. Glycerol stocks of gut homogenates were plated on brain heart infusion agar (BHIA) (Oxoid, Basingstoke, UK) supplemented with 5% (v/v) defibrinated sheep’s blood (Solarbio, Beijing, China). After 2 days of incubation at 35 °C under a CO2-enriched atmosphere (5%), visible colonies were identified by sequencing of the 16S rRNA gene with primers 27F (5′-AGAGTTTGATCMTGGCTCAG-3′) and 1492R (5′-GGTTACCTTGTTACGACTT-3′). Sanger sequencing was performed at Sangon Biotech (Shanghai, China). The identified isolates were then stocked in 25% (v/v) glycerol solution at − 80 °C. To obtain a complete and high-quality reference genome for accurate sequence alignment and mutation identification, the whole-genome sequencing was performed by the combination of short reading sequencing (Illumina platform) and long reading sequencing (Nanopore platform) at Novogene (Beijing, China) and GrandOmics (Wuhan, China). The hybrid assembly was then performed with Unicycler 0.5.0 [28], and the complete genome is deposited in NCBI database (GCF_031445945.1). To understand the relationship between strain B10998 and other Snodgrassella strains isolated from honeybees and bumblebees [19, 24], the phylogenetic tree was conducted with the PhyloPhlAn 3.0 under the “diversity low” parameter [29], and the iTOL web-based software was used for the visualization of the phylogenetic tree [30]. The whole-genome average nucleotide identity (ANI) was calculated using the fastANI software [31].

Construction of the mutant library using ARTP

Mutagenesis was performed using the ARTP mutation breeding system, a newly developed whole-cell mutagenesis tool based on radio-frequency atmospheric-pressure glow discharge plasma (Additional file 3: Fig. S1). The atmospheric-pressure helium plasma jet ejected from the plasma generator mainly consists of chemical reactive species, including helium lines, oxygen atom lines, N2 lines, and hydroxyl (OH) molecular band, irradiated on sample plates for microbial mutation [15]. The ARTP mutation breeding system has been reported to feature higher mutation rates while maintaining low treatment temperatures and employed for the mutation breeding of microorganisms [32]. We applied an ARTP setup (Luoyang Tmaxtree Biotechnology, China) to generate the input mutant library, including a plasma generator, a helium gas supply and control sub-system, and a sample plate made of stainless steel, which can be moved up and down.

To determine an appropriate exposure time, 10 µL of the bacterial suspension (~ 106 CFU/mL) was spread onto the 8-mm disinfected stainless steel plates, followed by 0–20-s exposure time to the ARTP jet every 15 s. The flow rate of working helium gas was set at 10 standard L/min, and the power of radio frequency was set at 100 W. After ARTP treatment, the steel plates were shaken and eluted with sterile BHI broth, and the cells in the eluate were plated on BHIA for 48-h incubation. The numbers of control (a) and survival (b) clones were determined respectively to calculate the mortality rate by (a − b)/a × 100%. We set the treatment time to 45 s for a high mutation rate of the input mutant library. Finally, ~ 20,000 colonies were collected and mixed homogeneously as the input mutant library for the subsequent experiments.

In vivo colonization assays in A. mellifera

The A. mellifera bees were from colonies maintained in the experimental apiary of the China Agricultural University. The microbiota-depleted bees were obtained as described by Zheng et al. [25] with modifications. Late-stage pupae were collected manually from brood frames and placed in sterile plastic bins. The pupae of A. mellifera emerged in an incubator at 35 °C, with a humidity of 50%. Newly emerged microbiota-depleted bees were kept in axenic cup cages, fed with sterilized sucrose syrup (50%, w/v) for 24 h, and divided into groups for subsequent colonization and in vivo competition assays. Before all subsequent experiments, we examined the gnotobiotic status of the bees. We picked two to three bee individuals from each cup cage and dissected the whole guts into 200 μL 1 × PBS buffer. We plated 50 μL of the gut homogenates of each bee onto the BHIA plates. We checked the plates 2–3 days after incubation, and all groups of bees showed no colony formation.

The concentrations of bacterial suspensions of the WT and the input mutant library were adjusted according to the OD600 nm, and colony-forming units were determined to confirm the number of cells. Subsequently, 1 mL of bacterial suspension aliquots at different concentrations (106, 107, 109 CFU/mL) was mixed with 1 mL of sterilized sucrose solution (50%, w/v) and 0.3 g sterilized pollen. To assess the colonization of the strains, 15 newly emerged microbiota-depleted bees were placed into 1 cup cage, and 3 cages were set up for 2 experimental groups, WT and mutant library. For each group, bees were starved for 2–3 h and fed on bacterial suspensions for another 24 h. The intake volume of bacterial solution per bee in each cup was recorded before and after the colonization process, and the average number of ingested bacteria cells of individual bees was calculated. After colonization, the bees were given sterilized pollen and sugar water (50%, w/v) ad libitum for 10 days. On day 5 and day 10, 6 bees were randomly selected from each cup cage, and the entire guts were aseptically dissected by gently pulling the stingers without touching the abdomen surface using sterilized forceps [23]. Subsequently, the dissected guts were crushed in 100 μL 25% (v/v) glycerol using an electric tissue grinder (OSE-Y30; Tiangen Biotech, Beijing, China). A dilution of bacterial samples was plated on BHIA plates supplemented with 5% (v/v) defibrinated sheep’s blood, and the colonization levels of bees were determined by counting colony-forming units.

Since an increased colonization level was observed, we further confirmed the colonization levels when bees were fed a higher number of mutant cells (average 109 per bee) in 5 replicating cup cages. Around 30 newly emerged microbiota-depleted individuals were placed into 1 cup cage as a lineage, and each bee was fed on 109 bacteria cells. On day 5 and day 10 after colonization, 12 bees from each lineage were randomly selected. Their whole intestines were combined into a 2-mL tube containing 1.2 mL 1 × PBS, then homogenized using the electric tissue grinder to recover the bacterial populations. We obtained 10 samples from all 5 replicating cup cages on the 2 time points (D5, D10).

Serial in vivo passage of the mutant library

To further identify traits that promote host association of the mutants, we performed serial passaging experiments in A. mellifera in 5 lineages (Fig. 1F). For each generation, we serially passaged the mutation library through microbiota-depleted bee hosts, each time specifically using gut-colonized populations as inoculum for the subsequent passage. For every cup cage, 15 newly emerged microbiota-depleted bees were placed into 1 cup cage, and the individuals were firstly inoculated by bacterial suspensions from the input mutant library mixed with 1 mL sterilized sucrose solution and 0.3 g sterilized pollen for 24 h. The intake volume of bacterial solution per bee in each cup was recorded before and after the colonization process. After colonization, the bees were given sterilized pollen and sugar water (50%, w/v) ad libitum for 5 days, and then the intestines of bees were removed by dissection. While Snodgrassella is mainly localized to the ileum region, it can also be found in the midgut and the rectum [33]. Therefore, the whole intestines from all bees in the same cage were dissected and mixed into a 2-mL tube containing 1.2 mL 1 × PBS; then, they were homogenized using the electric tissue grinder as P1 samples. The number of S. communis populations in different samples was determined by plating a small aliquot (20 μL) of the mixed gut sample on BHI plates. Then, the colonies were counted and collected for further metagenomic sequencing. Half of each homogenate (600 μL) was mixed with 600 μL of sterile 50% glycerol and was stored at − 80 °C. The remaining homogenates were centrifuged (5000 × g, 5 min), resuspended, and added to the food of the next cages of microbiota-depleted bees as the inoculum. Similarly, after 1-day colonization and 5-days feeding with sterilized pollen and sugar water (50% w/v) ad libitum, the whole guts of bees in the same cage were dissected, pooled together, and homogenized as P2-group samples for subsequent plate counting and bacterial colonies collection. Half of each homogenate was stored at − 80 °C, and the remaining was used as the inoculum for the next cup of bees. We also repeated the colonization process to obtain P3-group samples.

Fig. 1
figure 1

Colonization of Snodgrassella communis mutants obtained from ARTP treatment in non-native host species. A Schematic diagram of the input mutant library generation using the ARTP biological breeding system. B Effect of ARTP treatment on the mortality rate of S. communis. C Schematic of in vivo colonization assays. Age-controlled bees were inoculated with different amounts of WT bacteria and input mutant library (106, 107, 109 CFU). D Changes in the bacterial loads of bee individuals (n = 6) with different inoculation levels. Statistical analysis was performed using Student’s t-test. E Evaluation of the bacterial colonization for bees fed with 109 CFU bacterial mutants. Each dot represents a bee lineage. Statistical analysis was performed using two-way ANOVA. F Schematic of the serial passage experiment. The bees were first colonized with the input mutant library, and the gut homogenates were used for subsequent serial transfer. G Evaluation of the bacterial colonization for bees during the passage experiment. Each dot represents a bee lineage. Statistical analysis was performed using two-way ANOVA

Metagenomic sequencing and analysis

We obtained 15 samples from 5 lineages of the serial passage experiment and 5 samples (P1, P2, P3) from the gut samples after 10-day colonization (D10 group). The bacterial populations were recovered by plate cultivation, and ~ 300 single colonies from each plate were collected by scrapping and resuspending in 1 × PBS for DNA extraction. The Ezup Column Bacteria Genomic DNA Purification Kit (Sangon Biotech, Shanghai, China) was used for DNA extraction for pooled colonies and the input mutant library. NEBNext UltraTM II DNA Library Prep Kit for Illumina (New England Biolabs, MA, USA) was used for the generation of sequencing libraries, and Qubit 3.0 Fluorometer (Life Technologies, Grand Island, NY, USA) and Agilent 4200 (Agilent, Santa Clara, CA, USA) system were used for library quality assessment. The libraries were then sequenced on the Illumina HiSeq platform with 150-bp paired-end reads. We obtained about 1 Gbases sequencing data for each sample, with an average sequencing depth of 500 × for S. communis B10998. Fastp [34] was used for adaptor trimming and quality control of the raw sequencing data.

To confirm whether the recovered Snodgrassella strains derive from our inoculum, we profiled the species-level community of the populations using the Metagenomic Intra-Species Diversity Analysis System (MIDAS) pipeline [35] with the custom database that included genomes of pure isolates from the guts of both bumblebees and honeybees [24]. The relative abundance of bacteria species was estimated by mapping quality-filtered reads to the database of phylogenetic marker genes using HS-BLASTN, and the results were combined using the “merge_midas.py species” module. Mutations in all sequenced populations were identified using the breseq 0.37.0 pipeline [36], and our de novo assembled high-quality genome of strain B10998 was used as the reference. The pipeline uses a reference sequence-based mapping strategy, which includes evaluating new sequence junctions supported by split-read alignments and tracking multiple mapped reads to predict point mutations and structural mutations from short-read DNA resequencing data. Mutations were called only for regions covered by a minimum of 20 reads. The polymorphism prediction may be prone to false positives because of many biases in NGS data. Thus, low-frequency base substitutions (< 5%) based on read alignment evidence are treated as false positives [36].

To initially compare the mutational diversity between the groups, we calculated the number of polymorphic mutations and the frequency-weighted average, F = ∑fi, with fi for all the detected mutations of each sample. As described by Turner et al. [37], we also used the Bray–Curtis dissimilarity index, considering the frequency of the mutations in each population to measure the genetic similarities between populations. For genes that mutated more than once in the same population, we used the sum of the different mutations for each gene in each population. To further assess the mutation pattern in different populations, the SNPEff software [38] was used for functional annotation of coding single nucleotide polymorphisms (SNPs). We first used the gdtools utility GF2VCF to convert the breseq variant call files from gd format into vcf files (the required input for SNPEff). In addition, the distribution of variant types in different samples was visualized by using the R-tidyverse package.

Allele identification by sanger sequencing

The mutations in the mglB gene predicted by breseq were confirmed by Sanger sequencing of the amplicon sequencing of a 250-bp fragment that contains the mutation sites. The L2-group and WT bacterial populations were recovered by plate cultivation, and eight bacteria colonies from each plate were identified by colony PCR. A pair of primers targeting the 235th nucleotide position within the functional domain, B01065-F (5′-CAGCTCTTTCAGGGCTCGTT-3′) and B01065-R (5′-TGCATTACAAAGCGGCATGG-3′), were designed. We performed the sequence alignment using ClustalW2 [39] and recorded the genotypes of different colonies.

Prediction of mutation affection on the protein stability

The SMART (Simple Modular Architecture Research Tool) web server was used to annotate the protein domains of MglB [40]. According to the result of BLASTp, the protein MXAN_5770 (PDB 7CY1, resolution 2.19Å) was used as a template to homology model the structure of the mglB in S. communis B10998 with 99% confidence using Phyre2 (version 2.0) in intensive mode [41]. Based on the predicted PDB file, the impact of the variant on mglB structure was analyzed using Missense3D [11], which predicts structural changes leading to the disruption of alpha helices or beta sheets, alterations in inter-residue interactions in the structures, and unfavorable energy changes that affect the protein’s function. The variant site locations were submitted to the Protein Variation Effect Analyzer (PROVEAN) [42], which introduces a versatile alignment-based score to predict the damaging effects of single amino acid substitutions. We also carried out a free energy (ΔΔG) calculation for point mutations in the available protein structure using MAESTRO [43] to evaluate the change in protein stability upon the mutation.

Phylogenetic analysis of MglB

The representative arrangements of the mgl operons from bacteria were identified based on the dataset from Wuichet et al. [44]. We performed Blastp using MglB from S. communis B10998 as a query against genomes of all Snodgrassella strains from our custom bee gut bacteria database [24]. We also obtained homologous sequences from the UniRef 90, including hits with e values < 0.0001. The multiple-sequence alignments were built using the L-INS-i algorithm of the MAFFT (version 7.487) [45]. Phylogenetic trees of MglB were built with FastTree (version 2.1.3) [46] using the slow option and were visualized using the R package ggtree [47].

Colony expansion and competition assays in vivo

To test whether the missense mutations identified in the mglB of S. communis alter the bacterial phenotype, we recovered bacterial populations from the samples. The freezer stocks were streaked onto plates and incubated for 2 days, and 24 isolated colonies were picked for purification. We exacted the DNA of 9 pure isolations and submitted them to the Illumina platform for sequencing. After adaptor trimming and quality control, mutations in all sequenced isolations were identified using the breseq 0.37.0 pipeline [36] with the B10998 genome as the reference. We successfully screened 2 strains, including mutant SA01065, which harbored the designated variant in mglB (G78R) and a background mutant SA01065’ harboring all variants except for the mutation on mglB (Additional file 3: Fig. S2).

Motility assays were performed as described by Keilberg et al. [48] with modification. Bacterial cells of S. communis (SA01065, SA01065’, WT) were harvested and resuspended in BHI broth to a final density of ~ 109 cells/ml. Subsequently, 5-µL aliquots of cells were placed on 0.5% and 1.5% BHIA and incubated at 35 °C. After 72 h, the colony edges were observed using a microscope (SMZ745T, Nikon).

To confirm that the mutation in mglB influenced the gut colonization characteristics of S. communis, we assayed by competing the mutant SA01065 against the background strain SA01065’ and the WT in both honey (A. mellifera) and bumble (B. terrestris) bee hosts. The strains (SA01065, SA01065’, WT) were grown on BHIA plates supplemented with 5% (v/v) defibrinated sheep’s blood. The colonies were scraped and resuspended in 1 × PBS. The optical density (OD600 nm) was adjusted according to the subsequent conditions.

For competition in A. mellifera, the SA01065 mutant was adjusted to an OD600 nm of 0.005 as the reference. The WT and the background SA01065’ were adjusted to an OD600 nm of either 0.005, 0.05, or 0.5 as the competitors. Competing strains were mixed at 1:1 (competitor: reference). Then, 1 mL of the mixture was added to 1 mL of sterile sugar water (50%, w/v) and 0.3 g of sterile pollen and fed to newly emerged A. mellifera for 24 h. Conversely, for competition in B. terrestris, the WT and the background strain SA01065’ were adjusted to an OD600 nm of 0.005 as the reference. The SA01065 mutant was adjusted to an OD600 nm of either 0.005, 0.05, or 0.5 as the competitor. To obtain microbiota-depleted B. terrestris, we picked pupae from dark cocoons in the bumble bee colonies, as previously reported [49]. Each cocoon was opened with sterilized forceps, and mature pupae were aseptically removed from their cocoons with sterilized forceps. The pupa was then placed into sterile plastic bins and maintained at 28 °C, 65% relative humidity, to obtain microbiota-depleted individuals. Single bumblebees were kept separately in every cup to ensure the generation of microbiota-depleted individuals.

After colonization, all bee individuals were given sterilized pollen and sugar water ad libitum. Six bees per experiment were dissected on day 5 after colonization. The relative abundance of the two competing strains was analyzed by Sanger sequencing, which can help identify specific mutation loci of mglB in genomes. Eight individual colonies from each sample were determined by colony PCR reactions with the designed primers mglB-F/R (Additional file 3: Fig. S3). We assigned a classification to each bacterial colony based on the type of base at the mutant position we identified for estimating the proportion of different strains in the population.

Furthermore, the amplicon sequencing targeted to the mutation site was used to detect the community composition. The gut homogenates for six honeybees or bumblebees per group were mixed, and the DNA was extracted using the FastPure Blood/Cell/Tissue/Bacteria DNA Isolation Mini Kit (Vazyme Biotech., Nanjing, China). The DNA amplification was performed with designed primer amp-F (5′-TTGGCCTCATGAGTAGTGGT-3′), amp-R (5′-ACGGTTACCCAAAGACAGCA-3′), and 2 × Phanta Flash Master Mix (Dye Plus) (Vazyme Biotech., Nanjing, China). After purification with the FastPure Gel DNA Extraction Mini Kit (Vazyme Biotech., Nanjing, China), the amplification products were used for the library preparation with MGIEasy Universal DNA Library Prep Set (MGI Tech., Shenzhen, China). The libraries were then sequenced on the DNBSEQ-E25 platform (2 × 150 bp) (MGI Tech., Shenzhen, China). After quality control with fastp [34], the obtained reads were then analyzed with the custom script “04-Amplicon.sh”, which was available on GitHub (https://github.com/mengyujiee/ARTP).

Results

Specific ARTP-induced mutations potentially contributing to the colonization of Snodgrassella in non-native hosts

The strain Snodgrassella B10998 was isolated from the gut homogenate of B. terrestris, and the results of phylogenetic analysis and ANI comparison showed that this strain belongs to Snodgrassella communis, which is formally described and named recently [19] (Additional file 3: Fig. S4). We first constructed an input mutant library of wild-type (WT) Snodgrassella communis B10998 isolated from B. terrestris using the ARTP mutation breeding system (Fig. 1A). Cell suspensions with ~ 104 CFUs were placed on the steel plates. They were exposed to the ARTP jet (radio frequency power = 100 W). First, to determine an appropriate exposure time for ARTP, we evaluated the mortality rate of S. communis exposed to a gradient of irradiation time (Fig. 1B). The mortality rate of the strain increased with the treatment time prolonged and reached approximately 85% when the bacterial cells were submitted for 30 s of irradiation. Almost 99% of the cells died as the exposure time exceeded 60 s. Thus, we set the treatment time to be 45 s with a cell lethality percentage of ~ 90% to provide a high mutation rate [50]. The treated cells were then incubated on BHIA for 48 h. Finally, ~ 20,000 single colonies were collected and mixed homogeneously as the input mutant library for the subsequent experiments.

To explore the genetic mechanisms contributing to colonization, we assessed the colonization potential of the input mutant library in the non-native host, A. mellifera. Different amounts of cells (106, 107, 109 CFU) of WT and the mutants were inoculated into microbiota-depleted A. mellifera individuals, and the gut microbial loads were quantified at day 5 and day 10 by plate counting (Fig. 1C). When A. mellifera were fed WT strains from bumblebee with different inoculation size, the bacterial load was maintained at 105–106 CFU/gut, which is much lower than the colonization levels of the native strains (107–108 CFU/gut) [18, 51]. There were no significant differences between day 5 and day 10 for all inoculation groups (Student’s t-test, p = 0.85, p = 0.084, p = 0.7) (Fig. 1D), indicating that the colonization level had reached a maximum after 5 days [51]. For bees fed the input mutant library, the overall colonization levels were lower or equal to the WT strain, indicating that most of the random mutations by ARTP are neutral or deleterious [52]. However, we noticed that the bacterial loads increased over time (Student’s t-test, p = 0.0027) when bees were fed a high number of mutant cells (109 CFU/bee) (Fig. 1D). This suggests a genetic factor potentially contributing to a competitive advantage in gut colonization. Thus, we specifically evaluated the bacterial colonization for bees fed a high amount of the input mutant library (109 CFU/bee) in 5 replicating lineages, and each lineage represents a cup cage of bees (10–15 individuals) fed the same input library. The bacterial densities were calculated based on plate counting for the pooled gut samples from the same cages. The same increasing trend was observed over time (two-way ANOVA, p = 0.0039) (Fig. 1E).

To further identify traits that promote gut colonization of the mutants, we serially passaged the input mutant library (109 CFU) in five replicating lineages (Fig. 1F). We serially passage the mutation libraries through microbiota-depleted bee hosts, each time specifically using gut-colonized populations as inoculum for the subsequent passage. Similarly, we observed significant increases (two-way ANOVA, p < 0.0001) in colonization density during the first passage, but it remained unchanged (two-way ANOVA, p = 0.55) for the next round of passage (Fig. 1G). Together, these results suggest the emergence of mutations potentially contributing to the competitive advantage of S. communis in the non-native host species.

Mutations underlying Snodgrassella gut colonization

To investigate the genetic determinants of gut colonization, we recovered the colonized population from all 5 replicate lineages of different groups. First, we sequenced the metagenomes of ~ 300 pooled colonies from each lineage and classified the species composition using the MIDAS pipeline (Additional file 1: Dataset S1). It showed that almost all recovered colonies belong to Snodgrassella communis, a recently described species specific to Bombus species [19]. Only small fractions of the reads are assigned to other bee gut bacteria, including Lactobacillus kunkeei (0.05–0.22%), Gilliamella apis (0.07%), Gilliamella apicola (0.02%), and Spiroplasma melliferum (0.04%). The metagenomic reads were then aligned to the genome of S. communis B10998 wild type for further mutation identification using breseq. We detected 25 mutations in the input mutant library (Additional file 2: Dataset S2), and the frequency of most loci was between 5 and 10%. These mutations are located in the coding regions of genes associated with stress response (qseC, fpr) and DNA damage repair (lrp, recC), suggesting that ARTP induces DNA damage and potentially leads to recombinant DNA repair processes [53,54,55,56]. Notably, we noticed that two mutations within rpsG and glcA occurred with a 100% frequency, which may contribute to DNA damage repair and recombination and oxidative resistance, respectively [57, 58]. These two mutations were not present in the wild-type strain but were fixed in the input mutant library and introduced into the serial passage process.

We then quantified the number and frequency of mutations in the different samples from the serial passage (Fig. 2A). No differences in the mutation numbers (one-way ANOVA, p = 0.62) and average mutation frequencies (one-way ANOVA, p = 0.457) were observed across samples collected on day 5, day 10 and from different passages (Additional file 3: Fig. S5). To further assess the mutation pattern in different populations, we annotated the types and functions of variants using SnpEff [38] (Additional file 2: Dataset S2). Although different groups did not split (PERMANOVA, p = 0.295) based on the genetic similarity calculated by the Bray–Curtis index (Fig. 2B), subtle differences in the coding effect of mutation genotypes were identified. SNPs are the most prevalent, and insertion mutation was only found in the D10 group (Fig. 2C). Although below 10% of mutations are likely to cause loss of functions (start lost, stop lost, stop gained, or frameshift mutations), most of the mutations identified are missense, which might result in changes of gene expression or protein activity (Fig. 2D). In summary, these results indicate that the frequency and pattern of mutations do not alter significantly during the in vivo passaging.

Fig. 2
figure 2

Characterization of the mutations during in vivo passage. A The total number of mutations (y-axis, bars) and frequencies of mutations (z-axis, dots) for all samples; each sample is a mixture of ~ 300 bacterial colonies from the gut homogenate of 10–15 bee individuals. B The non-metric multidimensional scaling analysis (NMDS) using the Bray–Curtis dissimilarity index considering the frequency of the mutations in each sample. The differences were analyzed for statistical significance using permutational multivariate ANOVA (PERMANOVA). C, D Overview of mutation genotype (C) and the predicted effect of the mutations (D). Each bar presents situations of the mutations in every lineage

Mutation in mglB confers competitive advantages in the non-native host

To further identify the potential hotspots of variants along the genome for host colonization, we analyzed the positions and frequencies of the mutations that arose in our experimental process (Fig. 3A). The frequencies of most identified mutations ranged from 5 to 20%. These mutations are widely scattered across the whole genome of the populations from different groups, and no bias regarding genome position was identified. We noticed that the mutations within rpsG and glcA, already detected in the input mutant libraries, were retained in the following passages with 100% frequency (Fig. 3A, Additional file 2: Dataset S2). Additionally, several sites in feoB, rpsS, ttcA, and ptsH were hit more frequently and reached high frequency, but these mutations are not shared across passages (Additional file 2: Dataset S2). Notably, a base insertion in mglB encoding the mutual gliding motility protein B occurred (39.5%) in lineage 5 on day 10 (Additional file 2: Dataset S2). Moreover, the frequency of a point mutation in mglB was only 6.9% in passage 1 of lineage 2 (P1L2) but rose rapidly to 100% in passage 2 and was maintained in passage 3 (Fig. 3B). This suggests that the mglB mutation may confer a competitive advantage in gut colonization. The presence and percentage of the variants were directly verified by PCR from single isolated strains using primers targeting this polymorphism (Additional file 3: Fig. S3). By sequencing eight clones from each group, we found that the mutation frequency was highly consistent with the results predicted by sequence alignment (Fig. 3C; Additional file 3: Fig. S3).

Fig. 3
figure 3

The point mutation occurring in mglB may confer competitive advantages. A The positions and frequencies of the detected mutations. Each data point represents a mutation, and colors indicate different groups. Dashed circles indicate overlapping data points. B Mutation frequency of mglB G78R(GGA → AGA) in lineage 2 samples. C Verification of the presence and percentage of the variants by PCR from isolated strains targeting the polymorphism in mglB. D Protein architecture of MglB from S. communis B10998. Specific mutations in the evolved isolates are labeled. E The Missense3D analysis of the variant potentially causing structural alteration. The structure of MglB was predicted by modeling against the solved PDB crystal structure (PDB number 7CY1, resolution 2.19 Å)

Annotation of the mglB gene predicted it to be a 121-amino acid protein containing the functional domain—roadblock/LC7 (Robl_LC7), a widespread superfamily involved in regulating NTPases and the modulation of dynein functions [59]. The identified mutation occurred at the 235th nucleotide position within the functional Robl_LC7 domain, resulting in one non-synonymous amino acid substitution (Fig. 3D). We predicted the structure of MglB by modeling against the solved PDB crystal structure (PDB number 7CY1, resolution 2.19 Å) (Fig. 3E). The Missense3D analysis of the missense variant showed that this substitution replaces a buried uncharged residue with a charged residue, which may cause structural alteration.

Moreover, the mutation happened at a position in the transition of two anti-parallel β-sheet sandwiched and is likely deleterious with a PROVEAN score of − 7.917 [42]. According to the MAESTRO prediction, it may also strongly impact protein stability (ΔΔG = 1.738, confidence = 0.873) [43]. Therefore, the mutation identified in mglB is likely to alter the protein markedly, probably linked to the host colonization.

The mgl operon in Snodgrassella strains

MglB is a major component of the mgl operon, which is essential to regulating motility, cell polarity, predation, development, and antibiotic resistance of bacteria [44]. The operon contains co-transcribed open reading frames of the mutual gliding motility A (MglA) and its GTPase-activating protein MglB. The mgl operon of S. communis B10998 contained only one mglA and three mglB genes in the genome. Genes encoding MglA and MglB homologs are often coupled in the genomes [59]; however, one of the mglB genes from B10998 (mglB-3) was located ~ 106 bp away from the locus and was deemed orphan. Notably, the identified mutation related to the host colonization was in the coding region of the orphan mglB-3. The uncoupled MglA and MglB sequences are also observed in a subset of systems with multiple mglB genes (Fig. 4A) [44].

Fig. 4
figure 4

The mgl operon in Snodgrassella. A Distribution patterns of the mgl operon in different bacterial groups. Bacteria were grouped according to the phylogeny of the MglA sequences [44]. B Maximum-likelihood phylogeny of the MglB from Snodgrassella strains (see also Additional file 3: Fig. S4). C Genome-wide phylogenetic tree of a subset of bacterial strains containing the group 3 mgl and the gene arrangement of the loci

The MglA family members have been classified into five distinct groups [44]. The mglA from S. communis B10998 belongs to group 3, which is confined to specific taxonomic groups, including Neisseria, Stenotrophomonas, and Xanthomonas. Furthermore, the mgl operon was identified in all genomes of S. communis strains isolated from A. mellifera and Bombus spp. hosts. All bee gut S. communis contained three mglB genes, and they formed three distinct clades in the phylogenetic tree together with sequences derived from Neisseria (Fig. 4B; Additional file 3: Fig. S6). Interestingly, the orphan mglB was more closely related to the mglB-2 that is adjacent to the mglA, while the other coupled mglB-1 was distantly related. We further identified the arrangements of the mgl operons from group 3 (Fig. 4C). Almost all of them harbored three mglB genes, and the orphan mglB genes were phylogenetically closely related, suggesting an ancient duplication or horizontal transfer of an mglB gene [44]. Moreover, we performed sequence alignment for MglB-3 of Snodgrassella from honeybees and bumblebees (Additional file 3: Fig. S7), and the identified mutation in MglB-3 (G78R) is not found in the isolate genomes.

The mutation identified in mglB provides a fitness advantage

Compared to other paralogs, the orphan mglB represents an important paradigm of gene duplication and divergence [60]. Previous studies document that it plays an important role in regulating the type IV pili-dependent cellular motility, and the ΔmglB cells exhibit altered colony morphology and gliding motility [44]. Therefore, we tested whether the missense mutation we identified in the mglB of S. communis can alter the phenotype. We generated arrayed libraries by picking single colonies from different passaged populations and obtained the whole-genome sequences of different colonies. Cryo-archived passaged population samples from D5/P1, P2, P3, and D10 were plated for isolation, and the genomes of nine colony-purified isolates were re-sequenced. One mutant, SA01065, which harbored the designated variant in mglB (G78R), was selected from the L2 sample, and a background mutant, SA01065’, harboring all variants except for the mutation on mglB was used as a control (Additional file 3: Fig. S2).

We performed colony expansion assays by incubating isolates on agar plates. Although on 1.5% agar medium, the WT and the two mutants did not show different colony morphology (Additional file 3: Fig. S8), the SA01065 colony exhibited a completely different morphology from the other two strains on the 0.5% agar (Fig. 5A–F). The SA01065 mutant formed denser dendrite-like structures spreading across the surface of the media (Fig. 5E, F), implying an alteration in cell motility.

Fig. 5
figure 5

The mutation identified in mglB affects cell motility and in vivo competitive advantage. AF Colony expansion assay of the WT (A, B), the background mutant SA01065’ (C, D), and mutant SA01065 (E, F) on 0.5% agar. G–J In vivo competition assays in the non-native Apis mellifera (G, H) and the native host Bombus terrestris (I, J). Two sets of experiments were conducted, including WT versus SA01065 and SA01065’ versus SA01065. Single-colony PCR was performed to identify the different variant types of S. communis B10998 in the population

Finally, to confirm that the mutation in mglB influenced the gut colonization characteristics of B10998, we assayed for competitive advantage by competing the mutant SA01065 against the background strain SA01065’ and the WT in both honey and bumble bee hosts. First, we co-inoculated the SA01065 with the background strain SA01065’ or the WT in the non-native host, A. mellifera. We kept the number of bacteria in the inoculum constant for SA01065 but provided SA01065’ or WT at different ratios (1:1, 10:1, 100:1) relative to SA01065 to test if SA01065 can colonize despite a numerical disadvantage (Fig. 5G). After the 5-day colonization, we quantified the colonization level by plate counting. There was no significant difference in the absolute bacterial density between the groups from the same bee species (one-way ANOVA; p = 0.164 in A. mellifera; p = 0.795 in B. terrestris) (Additional file 3: Fig. S9). The genotypes were determined by amplifying with the designed primers and Sanger sequencing. For each gut sample, we sequenced 8 randomly picked colonies, and we totally analyzed 48 colonies from 6 biological replicates for each treatment (Fig. 5G, H). Despite their low proportion in the initial intake population, almost all recovered colony-forming units from both setups were of the SA01065 mutant (Fig. 5H). This indicates that the mutant SA01065 can overcome a large numerical disadvantage and completely out-compete the background strain and the WT in A. mellifera. However, when we retested in the native bumblebee host, SA01065’ and WT became dominant in any single gut, even when SA01065 was more abundant at a ratio of 100:1 in the inoculum (Fig. 5I, J). In addition, we also confirmed the community composition with amplicon sequencing (Additional file 3: Fig. S10). We amplified a 144-bp region covering the suspected mutation in mglB. It showed that SA01065 dominated the gut of A. mellifera but was out-competed by the background strain and WT in B. terrestris (Additional file 3: Fig. S10), which agrees with the results of Sanger sequencing. Taken together, the results of the reciprocal colonization confirmed that the mutations in mglB provide a competitive advantage for the non-native bacteria strain in A. mellifera.

Discussion

In this study, we constructed a model system for studying the host association traits of gut symbionts. We found a point mutation in mglB that dominated the population during passaging in the non-native bee host. Further experiments suggest that the mutation can alter the colony morphology and provide a competitive advantage in the A. mellifera gut. We provide experimental evidence that the mutual gliding motility protein MglB may contribute to colonization in non-native hosts by altering the motility phenotype.

We subjected the ARTP-treated input mutant library of S. communis to the non-native host, A. mellifera, to track the potential molecular factors related to host association. Most corbiculate bees, including honeybees and bumblebees, retain similar core bacterial lineages. However, different bee species possess host-specific bacterial species and strains [61]. Although cross-host microbe transfer is observed across Apis species, host fidelity is rather strict in transfers between Apis and Bombus species [18, 20, 51]. We found that neither the wild type (~ 106 CFU/gut) nor the mutant library (~ 105 CFU/gut) of the non-native strain reached the colonization level of the native strain (~ 108 CFU/gut) (Fig. 1D) [18]. The lack of generalist strains capable of colonizing the distantly related honeybee and bumblebee hosts could be attributed to the significant differences in the physiologies that are too substantial to overcome [18].

Additionally, we noticed that the colonization level of the input mutant library is lower than the wild type (Fig. 1D). ARTP mutagenesis primarily operates through the damage to bacterial genetic material by highly active particles, leading to diverse random mutations via the SOS repair mechanism [32]. Here, the in vitro mutagenesis is performed without selective pressures. We suspect most mutations would be neutral or deleterious, and the advantageous mutations are rare [52]. Notably, the bacterial loads increased over time when the honeybees were fed a high number of mutant cells. Effectual alleles must confer a selective advantage in the symbiotic association and arise early during population growth to survive extinction under the host-imposed bottlenecks [6]. Clear population bottleneck must occur during our in vivo passage as only ~ 105 CFU per bee out of the input library (109) can colonize. The mutation in mglB rose from a low frequency (6.9%) to 100% within two generations. However, this mutation was not detected in the input mutant library directly after the mutagenesis. Since ARTP may create DNA damage and lead to the introduction of mutations during the consequent DNA repair [15], the mutation observed in mglB might emerge during the following in vivo passages. Alternatively, this could be attributed to the sensitivity limits of the breseq pipeline [36], or it might be that the mglB is a compensatory mutation that arises later in the mutated strains, offering them an advantage in colonizing the gut [8].

Mutual gliding motility (mgl) is a locus essential for bacterial motilities, which mainly consists of two components, mglA and mglB, encoding the small Ras-like GTPase mutual gliding motility A (MglA) and its GTPase activating protein mutual gliding motility B (MglB) [62]. The locus is present in ancient, deep-branching lineages of Bacteria and Archaea, especially widely detected in pathogens such as Neisseria, Stenotrophomonas, and Xanthomonas [44]. Mgl is a component of the polarity control system, which acts as an intracellular switch to control bacterial motility and thereby regulates cooperative movement in the bacterial community [63]. It profoundly affects biofilm formation by influencing the transition from individual to collective movement patterns [64]. Consistently, experimental evidence shows that deletion of the mglA and mglB genes individually or in tandem reduces bacterial adhesion and biofilm formation [65]. Snodgrassella normally attaches to the inner intestinal wall to form biofilm in the bee ileum [61], suggesting that the abilities of adhesion to host epithelial cells and biofilm formation are essential for their in vivo colonization. A genome-wide Tn-seq analysis showed that lack of biofilm formation was detrimental in vivo and associated with diminished host colonization of Snodgrassella in A. mellifera [66]. Thus, we hypothesize that the mgl locus is also involved in the colonization of Snodgrassella, potentially affecting the capabilities of adhesion and biofilm formation. Interestingly, we revisited the raw dataset of the TnSeq libraries and found that mglB-1 (SALWKB2_RS10975), mglB-2 (SALWKB2_RS10980), and mglB-3 (SALWKB2_RS04330) of Snodgrassella are all beneficial for gut colonization [66]. In addition, the mglB genes of Snodgrassella strains from different bee hosts were clustered separately in the phylogenetic tree, suggesting that the mgl locus may affect the unique ecological niche and host colonization of Snodgrassella.

There is usually only one coding region of mglA and multiple mglB coding regions in the bacterial genome [44]. Some mglB genes were co-transcribed with mglA, while some exist independently and are defined as “orphan” mglB. In Snodgrassella, all strains from the bee gut microbial database showed identical gene arrangements, which include two mglB genes co-transcribed with mglA and one orphan mglB. We identified that the host-associated mutation occurred in the orphan mglB. Unlike the coupled one, the orphan MglB lost its GAP activity against MglA, while it interacts with the coupled MglB homodimers to regulate cell polarity and motility. The ΔmglB mutant of M. xanthus showed reduced T4P-dependent and gliding motility in the colony expansion assays [60]. In our study, we did not observe different colony morphology of the mutants on the 1.5% agar, but the motile “flares” of cells were observed at the mutant colony edge on the soft 0.5% agar, indicating that the mutation in the orphan mglB alters the type IV pili (T4P)-dependent motility of Snodgrassella [67]. T4P are common bacterial surface appendages and encode membrane-associated proteins capable of binding bacteria to host cells, which affects the colonization of some bacteria through surface adhesion and motility regulation [66]. The genome-wide Tn-seq analysis has documented that the T4P genes of Snodgrassella are beneficial for gut colonization [66]. In addition, our previous study showed that Snodgrassella strains exhibit significantly different SNPs enrichment in the T4P genes, and the distribution of SNPs deviates markedly between genetically different hosts, suggesting that T4P is involved in microbiota-host interactions of Snodgrassella [24]. The activated MglA-GTP can stimulate the T4P formation and function, and MglB ensures T4P unipolarity by excluding MglA-GTP from the lagging pole [48]. Thus, the mgl operon may affect the Snodgrassella colonization by regulating T4P-dependent motility and biofilm formation.

In A. mellifera, high levels of strain-level diversity exist, even within single-host individuals. These closely related strains with a gANI > 95% may adopt specific spatial and metabolic niches [68]. Although only one Snodgrassella species exists in A. mellifera, five Snodgrassella species have been identified from Bombus hosts [19]. Our metagenomic analysis showed that all the recovered Snodgrassella populations belong to the Bombus-specific S. communis, suggesting that they are not likely contaminated by the endogenous strains from honeybees during the serial passages. However, Snodgrassella always co-colonizes the bee ileum with Gilliamella, which supports each other’s persistence [51, 68]. In this study, we passaged the Snodgrassella without the participation of Gilliamella, so we could not illustrate how the evolutionary pressures of Gilliamella may have impacted the colonization of Snodgrassella in the bee gut. Snodgrassella form contiguous biofilms with Gilliamella along the length of the ileum [68]. Without Gilliamella, the capability to establish biofilm on its own may be a crucial factor for the successful colonization of Snodgrassella. Moreover, Snodgrassella and Gilliamella form a syntrophic network for partitioning metabolic resources. For example, Gilliamella may acquire amino acids and pyrimidines from Snodgrassella, and they contribute vitamins to each other [51]. Therefore, a divergent genetic basis conferring selective advantages and host colonization in Snodgrassella may be identified when passaging with the existence of Gilliamella strains.

Conclusions

In this study, we adopted the ARTP technique to obtain mutant libraries containing many mutant cells to explore the potential alleles associated with host colonization. By colonizing and passaging the S. communis strains within the non-native host A. mellifera, one experimental line rapidly dominated by a specific single mutation in mglB, and mutants showed a definite competitive advantage over the wild type in the competition experiments. The alleles potentially affected the bacterial T4P system recruiting in the correct pole in motility, suggesting the possible involvement of the cell motility against the host immunity system in establishing successful host colonization. Overall, our results demonstrate the adaptability of innovative ARTP technology in accelerating the host-associated studies of gut symbiosis. The ARTP, as a powerful mutagenesis method, may contribute to the progress of a comprehensive study of an organism mutation and evolution.

Availability of data and materials

Sequencing data of the metagenome and amplicons have been deposited under BioProject PRJNA1014770. All scripts and code used in this article have been uploaded to GitHub (https://github.com/mengyujiee/ARTP).

Abbreviations

ARTP:

Atmospheric and room temperature plasma

T4P:

Type IV pili

BHIA:

Brain heart infusion agar

SNP:

Single nucleotide polymorphism

MIDAS:

Metagenomic Intra-Species Diversity Analysis System

References

  1. Lee W-J, Hase K. Gut microbiota–generated metabolites in animal health and disease. Nat Chem Biol. 2014;10:416–24.

    Article  CAS  PubMed  Google Scholar 

  2. Shapira M. Gut microbiotas and host evolution: scaling up symbiosis. Trends Ecol Evol. 2016;31:539–49.

    Article  PubMed  Google Scholar 

  3. Carmona D, Fitzpatrick CR, Johnson MTJ. Fifty years of co-evolution and beyond: integrating co-evolution from molecules to species. Mol Ecol. 2015;24:5315–29.

    Article  PubMed  Google Scholar 

  4. Ley RE, Lozupone CA, Hamady M, Knight R, Gordon JI. Worlds within worlds: evolution of the vertebrate gut microbiota. Nat Rev Microbiol. 2008;6:776–88.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Thompson LR, Sanders JG, McDonald D, Amir A, Ladau J, Locey KJ, et al. A communal catalogue reveals Earth’s multiscale microbial diversity. Nature. 2017;551:457–63.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Sabrina Pankey M, Foxall RL, Ster IM, Perry LA, Schuster BM, Donner RA, et al. Host-selected mutations converging on a global regulator drive an adaptive leap towards symbiosis in bacteria. eLife. 2017;6:e24414.

    Article  PubMed  Google Scholar 

  7. Yang Y, Nguyen M, Khetrapal V, Sonnert ND, Martin AL, Chen H, et al. Within-host evolution of a gut pathobiont facilitates liver translocation. Nature. 2022;607:563–70.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Robinson CD, Klein HS, Murphy KD, Parthasarathy R, Guillemin K, Bohannan BJM. Experimental bacterial adaptation to the zebrafish gut reveals a primary role for immigration. PLOS Biol. 2018;16: e2006893.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Robinson CD, Sweeney EG, Ngo J, Ma E, Perkins A, Smith TJ, et al. Host-emitted amino acid cues regulate bacterial chemokinesis to enhance colonization. Cell Host Microbe. 2021;29:1221-1234.e8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Barrasso K, Chac D, Debela MD, Geigel C, Steenhaut A, Rivera Seda A, et al. Impact of a human gut microbe on Vibrio cholerae host colonization through biofilm enhancement. eLife. 2022;11:e73010.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Van den Bergh B, Swings T, Fauvart M, Michiels J. Experimental design, population dynamics, and diversity in microbial experimental evolution. Microbiol Mol Biol Rev. 2018;82:e00008-18.

    PubMed  PubMed Central  Google Scholar 

  12. Koga R, Moriyama M, Onodera-Tanifuji N, Ishii Y, Takai H, Mizutani M, et al. Single mutation makes Escherichia coli an insect mutualist. Nat Microbiol. 2022;7:1141–50.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Böndel KB, Samuels T, Craig RJ, Ness RW, Colegrave N, Keightley PD. The distribution of fitness effects of spontaneous mutations in Chlamydomonas reinhardtii inferred using frequency changes under experimental evolution. PLOS Genet. 2015;18: e1009840.

    Article  Google Scholar 

  14. Zheng Y, Hong K, Wang B, Liu D, Chen T, Wang Z. Genetic diversity for accelerating microbial adaptive laboratory evolution. ACS Synth Biol. 2021;10:1574–86.

    Article  CAS  PubMed  Google Scholar 

  15. Zhang X, Zhang X-F, Li H-P, Wang L-Y, Zhang C, Xing X-H, et al. Atmospheric and room temperature plasma (ARTP) as a new powerful mutagenesis tool. Appl Microbiol Biotechnol. 2014;98:5387–96.

    Article  CAS  PubMed  Google Scholar 

  16. Li X, Liu R, Li J, Chang M, Liu Y, Jin Q, et al. Enhanced arachidonic acid production from Mortierella alpina combining atmospheric and room temperature plasma (ARTP) and diethyl sulfate treatments. Bioresour Technol. 2015;177:134–40.

    Article  CAS  PubMed  Google Scholar 

  17. Fang M, Jin L, Zhang C, Tan Y, Jiang P, Ge N, et al. Rapid mutation of Spirulina platensis by a new mutagenesis system of atmospheric and room temperature plasmas (ARTP) and generation of a mutant library with diverse phenotypes. PLoS ONE. 2013;8: e77046.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Kwong WK, Medina LA, Koch H, Sing K-W, Soh EJY, Ascher JS, et al. Dynamic microbiome evolution in social bees. Sci Adv. 2017;3: e1600513.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Cornet L, Cleenwerck I, Praet J, Leonard RR, Vereecken NJ, Michez D, et al. Phylogenomic analyses of Snodgrassella isolates from honeybees and bumblebees reveal taxonomic and functional diversity. mSystems. 2022;7:e01500-21.

  20. Ellegaard KM, Brochet S, Bonilla-Rosso G, Emery O, Glover N, Hadadi N, et al. Genomic changes underlying host specialization in the bee gut symbiont Lactobacillus Firm5. Mol Ecol. 2019;28:2224–37.

    Article  PubMed  Google Scholar 

  21. Zheng H, Nishida A, Kwong WK, Koch H, Engel P, Steele MI, et al. Metabolism of toxic sugars by strains of the bee gut symbiont Gilliamella apicola. mBio. 2016;7:e01326-16.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Zheng H, Perreau J, Powell JE, Han B, Zhang Z, Kwong WK, et al. Division of labor in honey bee gut microbiota for plant polysaccharide digestion. Proc Natl Acad Sci. 2019;116:25909–16.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Meng Y, Li S, Zhang C, Zheng H. Strain-level profiling with picodroplet microfluidic cultivation reveals host-specific adaption of honeybee gut symbionts. Microbiome. 2022;10:140.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Wu J, Lang H, Mu X, Zhang Z, Su Q, Hu X, et al. Honey bee genetics shape the strain-level structure of gut microbiota in social transmission. Microbiome. 2021;9:225.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Zheng H, Steele MI, Leonard SP, Motta EVS, Moran NA. Honey bees as models for gut microbiota research. Lab Anim. 2018;47:317–25.

    Article  Google Scholar 

  26. Zhang Z, Huang M, Qiu L, Song R, Zhang Z, Ding Y, et al. Diversity and functional analysis of Chinese bumblebee gut microbiota reveal the metabolic niche and antibiotic resistance variation of Gilliamella. Insect Sci. 2021;28:302–14.

    Article  CAS  PubMed  Google Scholar 

  27. Williams GR, Alaux C, Costa C, Csáki T, Doublet V, Eisenhardt D, et al. Standard methods for maintaining adult Apis mellifera in cages under in vitro laboratory conditions. J Apic Res. 2013;52:1–36.

    Article  Google Scholar 

  28. Wick RR, Judd LM, Gorrie CL, Holt KE. Unicycler: resolving bacterial genome assemblies from short and long sequencing reads. PLOS Comput Biol. 2014;13: e1005595.

    Article  Google Scholar 

  29. Asnicar F, Thomas AM, Beghini F, Mengoni C, Manara S, Manghi P, et al. Precise phylogenetic analysis of microbial isolates and genomes from metagenomes using PhyloPhlAn 3.0. Nat Commun. 2020;11:2500.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Li C, Gong Y, Wang X, Xu J, Ma B, Zheng H, et al. Interactive tree of life (iTOL) v3: an online tool for the display and annotation of phylogenetic and other trees. Nucleic Acids Res. 2016;44:W242–5.

    Article  Google Scholar 

  31. Jain C, Rodriguez-R LM, Phillippy AM, Konstantinidis KT, Aluru S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat Commun. 2018;9:5114.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Ottenheim C, Nawrath M, Wu JC. Microbial mutagenesis by atmospheric and room-temperature plasma (ARTP): the latest development. Bioresour Bioprocess. 2018;5:12.

    Article  Google Scholar 

  33. Martinson VG, Moy J, Moran NA. Establishment of characteristic gut bacteria during development of the honeybee worker. Appl Environ Microbiol. 2012;78:2830–40.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Chen S, Zhou Y, Chen Y, Gu J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics. 2018;34:i884–90.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Nayfach S, Rodriguez-Mueller B, Garud N, Pollard KS. An integrated metagenomics pipeline for strain profiling reveals novel patterns of bacterial transmission and biogeography. Genome Res. 2016;26:1612–25.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Barrick JE, Colburn G, Deatherage DE, Traverse CC, Strand MD, Borges JJ, et al. Identifying structural variation in haploid microbial genomes from short-read resequencing data using breseq. BMC Genomics. 2014;15:1039.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Turner CB, Marshall CW, Cooper VS. Parallel genetic adaptation across environments differing in mode of growth or resource availability. Evol Lett. 2018;2:355–67.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Cingolani P, Platts A, Wang LL, Coon M, Nguyen T, Wang L, et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff. Fly (Austin). 2012;6:80–92.

    Article  CAS  PubMed  Google Scholar 

  39. Larkin MA, Blackshields G, Brown NP, Chenna R, McGettigan PA, McWilliam H, et al. Clustal W and Clustal X version 2.0. Bioinformatics. 2007;23:2947–8.

    Article  CAS  PubMed  Google Scholar 

  40. Letunic I, Khedkar S, Bork P. SMART: recent updates, new developments and status in 2020. Nucleic Acids Res. 2021;49:D458–60.

    Article  CAS  PubMed  Google Scholar 

  41. Kelley LA, Mezulis S, Yates CM, Wass MN, Sternberg MJE. The Phyre2 web portal for protein modeling, prediction and analysis. Nat Protoc. 2015;10:845–58.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Choi Y, Chan AP. PROVEAN web server: a tool to predict the functional effect of amino acid substitutions and indels. Bioinformatics. 2015;31:2745–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Laimer J, Hiebl-Flach J, Lengauer D, Lackner P. MAESTROweb: a web server for structure-based protein stability prediction. Bioinformatics. 2016;32:1414–6.

    Article  CAS  PubMed  Google Scholar 

  44. Wuichet K, Søgaard-Andersen L. Evolution and diversity of the Ras superfamily of small GTPases in Prokaryotes. Genome Biol Evol. 2015;7:57–70.

    Article  Google Scholar 

  45. Rozewicki J, Li S, Amada KM, Standley DM, Katoh K. MAFFT-DASH: integrated protein sequence and structural alignment. Nucleic Acids Res. 2019;47:W5-10.

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Price MN, Dehal PS, Arkin AP. FastTree 2 – approximately maximum-likelihood trees for large alignments. PLoS ONE. 2010;5: e9490.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Yu G, Smith DK, Zhu H, Guan Y, Lam TT. ggtree: an R package for visualization and annotation of phylogenetic trees with their covariates and other associated data. Methods Ecol Evol. 2017;8:28–36.

    Article  Google Scholar 

  48. Keilberg D, Wuichet K, Drescher F, Søgaard-Andersen L. A response regulator interfaces between the Frz chemosensory system and the MglA/MglB GTPase/GAP module to regulate polarity in Myxococcus xanthus. PLoS Genet. 2012;8: e1002951.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Rothman JA, Leger L, Graystock P, Russell K, McFrederick QS. The bumble bee microbiome increases survival of bees exposed to selenate toxicity. Environ Microbiol. 2019;21:3417–29.

    Article  CAS  PubMed  Google Scholar 

  50. Ma X, Zhang H, Lu X, Han J, Zhu H, Wang H, et al. Mutant breeding of Starmerella bombicola by atmospheric and room-temperature plasma (ARTP) for improved production of specific or total sophorolipids. Bioprocess Biosyst Eng. 2020;43:1869–83.

    Article  CAS  PubMed  Google Scholar 

  51. Kwong WK, Engel P, Koch H, Moran NA. Genomics and host specialization of honey bee and bumble bee gut symbionts. Proc Natl Acad Sci. 2014;111:11509–14.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Eyre-Walker A, Keightley PD. The distribution of fitness effects of new mutations. Nat Rev Genet. 2007;8:610–8.

    Article  CAS  PubMed  Google Scholar 

  53. Deng W, Wang H, Xie J. Regulatory and pathogenesis roles of Mycobacterium Lrp/AsnC family transcriptional factors. J Cell Biochem. 2011;112:2655–62.

    Article  CAS  PubMed  Google Scholar 

  54. He L, Dai K, Wen X, Ding L, Cao S, Huang X, Wu R, Zhao Q, Huang Y, Yan Q, Ma X, Han X and Wen Y (2018) QseC mediates osmotic stress resistance and biofilm formation in Haemophilus parasuis. Front. Microbiol. 2018;9:212.

  55. Chiang SM, Schellhorn HE. Regulators of oxidative stress response genes in Escherichia coli and their functional conservation in bacteria. Arch Biochem Biophys. 2012;525:161–9.

    Article  CAS  PubMed  Google Scholar 

  56. Mahdi AA, Lloyd RG. Identification of the recR locus of Escherichia coli K-12 and analysis of its role in recombination and DNA repair. Mol Gen Genet. 1989;216:503–10.

  57. Zhang C, Gu H, Ren Y, Lu L. GlcA-mediated glycerol-3-phosphate synthesis contributes to the oxidation resistance of Aspergillus fumigatus via decreasing the cellular ROS. Fungal Genet Biol. 2021;149: 103531.

    Article  CAS  PubMed  Google Scholar 

  58. Cerullo F, Filbeck S, Patil PR, Hung H-C, Xu H, Vornberger J, et al. Bacterial ribosome collision sensing by a MutS DNA repair ATPase paralogue. Nature. 2022;603:509–14.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Koonin EV, Aravind L. Dynein light chains of the Roadblock/LC7 group belong to an ancient protein superfamily implicated in NTPase regulation. Curr Biol. 2000;10:R774–6.

    Article  CAS  PubMed  Google Scholar 

  60. McLoon AL, Wuichet K, Häsler M, Keilberg D, Szadkowski D, Søgaard-Andersen L. MglC, a paralog of Myxococcus xanthus GTPase-activating protein MglB, plays a divergent role in motility regulation. J Bacteriol. 2016;198:510–20.

  61. Kwong WK, Moran NA. Gut microbial communities of social bees. Nat Rev Microbiol. 2016;14:374–84.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Baranwal J, Lhospice S, Kanade M, Chakraborty S, Gade PR, Harne S, et al. Allosteric regulation of a prokaryotic small Ras-like GTPase contributes to cell polarity oscillations in bacterial motility. PLOS Biol. 2019;17:e3000459.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Muñoz-Dorado J, Marcos-Torres FJ, García-Bravo E, Moraleda-Muñoz A, Pérez J. Myxobacteria: moving, killing, feeding, and surviving together. Front Microbiol. 2016;7:781.

  64. Conrad JC. Physics of bacterial near-surface motility using flagella and type IV pili: implications for biofilm formation. Res Microbiol. 2012;163:619–29.

    Article  CAS  PubMed  Google Scholar 

  65. Salzer R, Joos F, Averhoff B. Different effects of MglA and MglB on pilus-mediated functions and natural competence in Thermus thermophilus. Extremophiles. 2015;19:261–7.

    Article  CAS  PubMed  Google Scholar 

  66. Powell JE, Leonard SP, Kwong WK, Engel P, Moran NA. Genome-wide screen identifies host colonization determinants in a bacterial gut symbiont. Proc Natl Acad Sci. 2016;113:13887–92.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Friedrich C, Bulyha I, Søgaard-Andersen L. Outside-in assembly pathway of the type IV pilus system in Myxococcus xanthus. J Bacteriol. 2014;196:378–90.

    Article  PubMed  PubMed Central  Google Scholar 

  68. Li Y, Leonard SP, Powell JE, Moran NA. Species divergence in gut-restricted bacteria of social bees. Proc Natl Acad Sci. 2022;119:e2115013119.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

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Funding

National Natural Science Foundation of China (U2032210).

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HZ, XX, and CZ designed the research. YM and XZ collected the samples. YL, ZH, and SL performed the amplicon sequencing. YM, YZ, and HZ analyzed the data. YM, CZ, XX, and HZ wrote the manuscript. All authors revised the manuscript and approved the final version.

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Correspondence to Hao Zheng.

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Supplementary Information

Additional file 1:

Dataset S1. Strain-level composition of samples from different groups.

Additional file 2:

Dataset S2. Polymorphic variant calling on the Snodgrassella populations during the serial colonization in vivo of the mutant library.

Additional file 3:

Fig. S1. (A-B) Pictures of the ARTP mutation breeding system, the front view (A), and the operation chamber (B). The equipment used to generate the helium radio-frequency ARTP jet consists of a 13.56-MHz power supply, a co-axial-type plasma generator, a helium gas supply and control subsystem, and a stainless-steel sample plate that can be moved smoothly in the vertical direction to adjust the stand-off distance between the plasma torch nozzle exit and the sample plate. (C-D) The procedure of mutagenic treatment consisted of several steps. Firstly, an aliquot of pretreating liquid was taken and coated uniformly on the surface of the slide and placed in the chamber of the ARTP mutagenesis operation instrument. Then, the distance between the slide and the plasma emitter jet orifice was adjusted, the constant power output power was 120 W, and the gas flow rate was 10 SLM. The processing time of mutation was chosen and adjusted. Bacterial cells are exposed to plasma for random mutagenesis. Fig. S2. Mutations in the three strains used in colony expansion experiments and in vivo competition experiments. Fig. S3. Design of primers (A) and results of sequence alignment (B) for allele identification by Sanger sequencing. Fig. S4. (A) Whole-genome phylogenetic tree based on representative isolates’ genomes of Snodgrassella. The tree was rooted with the sequence of Simonsiella muelleri ATCC 29453. The phylogenetic clusters are noted based on the results of Cornet et al. (2022). (B) Heatmaps show the values of pairwise ANI of representative isolates’ genomes of Snodgrassella. Fig. S5. Mutation numbers (A) and the average mutation frequencies (B) in different groups. Each data point indicates an independent lineage. Statistical analysis was performed using oneway ANOVA. Fig. S6. Maximum-likelihood phylogeny of the MglB from Snodgrassella strains. Fig. S7. Visual representation of MglB-3 sequences from (A) Apis mellifera and (B) Bombus species, prepared using WebLogo (https://weblogo.berkeley.edu/logo.cgi). Fig. S8. Colony expansion assay of the WT (A), the background mutant SA01065’ (B), and mutant SA01065 (C) on 1.5% agar. Fig. S9. The absolute bacterial density of Apis mellifera (A) and Bombus terrestris (B) for the in vivo competition assays. Fig. S10. In vivo competition assays in the non-native Apis mellifera (A) and the native host Bombus terrestris (B). Two sets of experiments were conducted, including WT versus SA01065 and SA01065’ versus SA01065. The amplicon sequencing was performed to identify the different variant types of Snodgrassella communis B10998 in the population.

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Meng, Y., Zhang, X., Zhai, Y. et al. Identification of the mutual gliding locus as a factor for gut colonization in non-native bee hosts using the ARTP mutagenesis. Microbiome 12, 93 (2024). https://doi.org/10.1186/s40168-024-01813-0

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