Shifts in bacterial communities to antibiotic exposure
To explore the alterations in the resistome in time dimension following the antibiotic administration, we first describe the microbial community changes in the guts of crayfishes and sediments. To assess these dynamic microbiota variances, gut content and sediment samples were collected from 8 time-points (3 repetitions) during the whole experiment for 16S rRNA sequencing (Fig. 1a) and the sequencing results were listed in Additional file1: Table S1.
The data revealed that the application of antibiotics resulted in a sharp decline of the microbial community as indicated by the alpha diversity in the guts, (Chao1 and dominance index, P=0.0083) and there was no restoration to baseline levels even after 28 days following antibiotic cessation (Fig. 1b). Although the microbial diversity was reduced, the reduction was not significant in the sediment samples (Fig. 1b). In addition, the sequencing data demonstrated a temporal change and segregation in microbial structure between different phases (Fig. 1c).
The antibiotic affected both the microbial composition in the guts and sediments, with persistent incomplete recovery at 42 days in the guts (Fig. 1d). Furthermore, Enterrococaceae family demonstrated the highest sensitivity to the antibiotic treatment in the guts and the relative abundance was reduced from 29% to 0.2%. In contrast, administration of antibiotics for 14 days led to increase in Enterobacteriaceae from 20% to 34% in the gut (Fig. 1d). The data showed that Citrobacter genus, an environmental contaminant[18] was the most dominant genus and had similar trends as Enterobacteriaceae in the guts (Fig. 1e). In addition, a partial sequence that was identified as an uncultured bacterium, which was initially reported in the intestinal bacteria in wild and domesticated adult black tiger shrimp [19], was hardly affected in the guts (Fig. 1d). The findings showed that resistance may exist in some bacteria that cannot be cultured in vitro. There was led variation in the family level of bacteria in the sediments, and the microbiome was primarily dominated by Rhodocyclaceae (18%), Lactobacillaceae (18%) and Geobacteraceae (12%) throughout the entire experimental period. Enterobacteriaceae was the only family common to the guts and sediments and it was more stable in the sediments (Fig. 1d). In summary, the application of antibiotic altered the composition of the bacterial community and diversity in both the guts and sediments. Besides, the microbiome determinants in the guts were more affected than those in the sediments and demonstrated a marked delay in recovery.
Longitudinal changes of the ARGs in the gut and sediment microbiome in response to antibiotic administration
To understand dynamic changes of the ARGs between samples in different periods, we employed shotgun metagenomic sequencing on the gut contents and sediment samples to test the presence of ARGs. A total of 480 Gb of Illumina sequencing data was produced from the samples, and then we acquired 14.74 million 150 bp paired-end reads per sample (on average) after host-subtraction and trimming (Additional file2: Table S2).
The data showed that there was a substantial increase in the total abundance of the ARGs in the crayfish guts and sediments microbiome, which reached a maximum value at 14 day. There was 6.97 times and 6.56 times enrichment in the guts and sediments, respectively, following the antibiotic administration (Fig. 2a). On the other hand, the relative abundance of ARGs in 99 different subtypes in the gut bacteria was higher than that in the sediments (85 subtypes) and the recovery time was 14 days later (Additional file1: Fig. S2). Most of the ARGs observed in the gut microbiome were predicted to confer multidrug, β-lactam and quinolone phenotypes which increased by 6.95, 6.31and 7.49 times, respectively. In the sediments, sulfonamide, chloramphenicol, tetracycline, quinolone, and aminoglycoside resistance genes were the primary ARGs which were enhanced by over 8 times. In addition, the sediments contained more varieties of the ARGs which included 3 extra classes of rifamycin, bleomycin and trimethoprim (Fig. 2a).
Our data showed that the relative abundance of genes predicted to confer resistance to quinolone were dramatically increased after application of enrofloxacin which was mainly produced by qnrB and qnrS genes (Fig. 2b). Among these quinolone resistance genes, qnrB accounted for more than 95% in all samples of guts (Fig. 2b). The qnrB gene was associated with the highest risk in the ARG family as reported by the World Health Organization and as identified in various Enterobacterial species. The dominant quinolone resistance gene was qnrS which accounted for 95.9% in the sediments (Fig. 2b). The application of the enrofloxacin was associated with little changes in the proportions of quinolone resistance genes in total ARGs (Fig. 2b). Therefore, these data illustrated that enrofloxacin did not only potentially enrich quinolone resistance genes but also promoted emergence of other resistance genes to different antibiotics.
To characterize changes in the pattern of the ARG components, we specifically characterized core ARGs composition in the guts and sediments along the temporal continuum (Fig. 2c). The core ARGs were defined as ARGs with an average RPKM>10 in guts and a RPKM>1 in sediments. The core ARGs in the gut was substantially larger than that in sediments and included a wide range of genes of multidrug, beta-lactam. On the other hand, sulphonamides and tetracyclines were mainly in sediments. Interestingly, the core ARGs such as sul1, sul2, tetA, acrB, aadA and floR were both present in the gut and sediment bacteria (Fig. 2c). The bloom of the ARGs were largely driven by the core ARGs in all samples (Fig. 2d). In general, there was massive increase in diversity and relative abundance of total ARGs in the gut and sediment microbiome while the ARGs in the gut microbiome had a more marked delay in the recovery following the antibiotic administration
Spatial co-occurrence of MGEs and ARGs after antibiotic administration
Administration of antibiotics contributed to the enrichment of MGEs and the change of the relative abundance of MGEs showed similar tendency with the ARGs both in the guts and sediments (Fig. 3a). There was a clear increase in three types of MGEs (plasmid, intergase and IS), and the most abundant MGE group was IS at 14 days in the guts and intergase in the sediments, with differential patterns following antibiotic administration. At day 35, 21 days after the antibiotic exposure, the types of MGEs in guts and sediments were comparable to day 0 (Fig. 3b). In addition, there was positive correlation between the total abundance of ARGs and MGEs and which was stronger in sediments than in the guts. Besides, the abundance of ARGs was significantly correlated with the ISs in guts, but was correlated with plasmids, integrons and ISs in sediments (Fig. 3c). We also calculated the correlation between MGEs and the core ARGs, and showed that most of the core ARGs had significantly positive correlation with relevant MGEs in guts and sediments (r>0.9, Additional file1: Fig. S3). Therefore, tracking of such mobile genetic elements may provide a deeper insight into the extent of the spread of antibiotic resistance in the aquatic environment.
To determine which mobile ARGs were together with MGEs in a specific region and to what extent they were associated with the MGEs, we searched the 5-kb flanking regions of each mobile ARG for adjacent MGEs as integrase / recombinase and /or transposase. There was a tendency for an increased frequency of co-occurrence of MGEs and ARGs, with day 0 having 0.53% relative abundance of the total ARGs to 1.48% at day 14 in the guts. While there was no obvious change and the relative abundance of mobile resistant units maintained a high degree around 7.6% in all sediment samples, which may be due to the complicated environmental factors in sediments (Additional file1: Table S3). The main mobile resistant genes in the guts and sediments included clinically relevant genes which conferred resistance to antibiotic classes such as tetracyclines, sulphonamides, β-lactams, and aminoglycosides (Additional file1: Fig. S4a), which were widely disseminated genes. In addition, the most abundant ARGs forming mobile resistant units were sul2, floR and aadA (18.7%, 17.4% and 19% of mobile resistant units, respectively) in the guts while the domain mobile ARGs were aadA, tetA and sul2 (30.3%, 12%, 8.6%) in sediments. All these ARGs frequently coexisted with transposase and phage intergrase genes in the guts and sediments (Additional file1: Fig. S4a).
Similarly, there was a marked increase in the abundance of plasmids that carried the ARGs in the guts and sediments because of the antibiotic exposure (Fig. 3b). Besides, the percentage of ARGs in the plasmids increased from 2.78 to 9.41% in total ARGs in the guts and around 11.7-14.4% in the sediments (Additional file1: Table. S3). The aadA, tetA, tetE and sul2 genes in the plasmids were both present in the gut and sediment samples and there were MGEs in the neighborhood which demonstrated that these genes had the highest dissemination potential (Additional file1: Fig. S4). The association showed that the widespread MGEs were present in the neighboring resistance genes and were closely associated with emergence and spread of ARGs among bacteria, especially the transposase.
Mobile genetic context predicts dissemination potential of ARGs
Bacteria in the gut and sediment samples shared several ARGs such as sul1, sul2, tetA and tetR, flanking with the same MGEs (sul1 with phage integrase, sul2, tetA, tetR with transposase) (Additional file1: Fig. S4a), indicating a possible existence of a flow of genetic elements between the guts and sediments making the ARGs widely diffuse in the presence of the antibiotic. Two different contigs with the same sequence of 4,053 bps composed of ARGs group (tetA, tetR, MFS) and a IS91 transposase gene were found, and were classified as a plasmid sequence (identity=99.796%, evalue ≈0, bitscore = 5,272) with a chromosome sequence (identity=100%, evalue ≈0,bitscore=5303) of the species Pseudomonas aeruginosa in the sediment samples (Fig. 4a). We speculated that tetR and tetA genes were most likely to undergo intracellular transfer by transposon_IS91-mediated recombination between the plasmid and the chromosome in Pseudomonas aeruginosa in the sediment samples following the antibiotic pressure.
Furthermore, there was a contig with a sequence of 4127 bp harboring the tetR and tetA gene group and a nearby transposon of Tn3 family, had been classified as plasmid sequence of Salmonella enterica in the guts (identity=100%, evalue ≈0, bitscore=5303), and a different contig with the same sequence was observed in the sediments (identity=100%,evalue ≈0,bitscore=5303) (Fig. 4b). The transposon Tn3 has been shown to commonly carry antimicrobial passenger genes, recruit mobile integrons, and promote the exchange of genes [21]. This evidence proved that the tetR and tetA gene group had high intercellular transfer potential of Salmonella enterica through plasmids between the guts and sediments, which was a high prevalence species of antimicrobial resistance [22]. In addition, sul2 gene, defined as a highly disseminated gene in bacteria based on 42 genera that contained it, was reported to exist in small non-conjugative plasmids or large transmissible plasmids [23]. In our study, the sul2 gene was present in a gene fragment composed of two transposons of Tnp IS 1595 and a Tnp_1 in Flavobacterium sp plasmids I3-2 in guts (identity=99.95%, evalue ≈0, bitscore=3529). Similarly, the transposon of Tnp_IS 1595 with sul2 gene existed in the chromosomes of Myroides odoratimimus (identity=99.49%, evalue ≈0, bitscore=1039) which also belong to the Flavobacteriaceae in the sediments, showing that the sul2 gene may be transferred among Flavobacteriaceae between the guts and sediments since the Tnp_IS 1595 transposon could insert itself into a new genome by the activity of its transposase (Fig. 4b). Flavobacteriaceae is a potential ancestral source of the tigecycline resistance gene tet (X) [24]. In addition, the relative abundance of all the contigs with mobile resistant units had an increase in the gut and sediment bacteria following the antibiotic administration (Additional file 4: Fig. S5). All of the contigs mentioned above are listed (Additional file 3: Table S4). These results showed that the MGEs could be used to predict potential future transfer of neighboring ARGs and induce dissemination of ARGs between the guts and sediments through HGT under antibiotic pressure.
Correlation between the ARGs and bacterial taxa
Correlation analysis revealed possible hosts of target genes in complex environmental scenarios if the target genes and the coexisting microbial taxon had significantly positive correlations. A comparison of the correlation analysis between the core ARGs and the top 20 relative abundance of bacteria in the family level showed that there was a stronger and dense correlation in the guts of crayfishes than in the sediments (Fig. 6). Enterobacteriaceae family was highly correlated with 17 ARGs (r>0.8, p<0.05), followed by porphyromonadaceae (which was highly correlated with 16 ARGs) and Vibrionaceae (15 ARGs) in the guts (Fig. 6a). The family with the highest correlation with 11 ARGs was Sphingomonadaceae (r>0.8, p<0.05) in the sediments (Fig. 6b). Enterobacteriacea was the only family that had relatively high correlation with ARGs both in the guts and sediments (Fig. 6). Meanwhile, the total ARGs in the guts had a strong positive correlation with Citrobacter freundii (R2=0.7248) and Citrobacter braakii (R2=0.7102) (Additional file1: Fig. S6a). Furthermore, the Citrobacter maintained a relatively high proportion through the whole experiment. Our data also demonstrated that the ARGs were positively correlated with Escherichia coli (R2=0.7905) and Salmonella enterica (R2=0.7974) (Additional file1: Fig. S6b). The species had been recognized as significant pathogens in patients with underlying diseases and belong to Enterobacteriaceae family.
To analyze potential host for the mobile resistant units, metagenomic sequences of bacteria in the guts and sediments were used to predict co-occurrence of ARGs with bacteria patterns. The network inference modeling demonstrated that the exchanges of the mobile resistant units were mainly detected in 31 genera of Proteobacteria, Firmicutes and Bacteroidetes, and the exchanges were more active in Proteobacteria. Importantly, our data showed that the majority of these highly disseminated genes such as sul1, sul2 and tetA and improved flanking with MGEs, were strongly associated with more than 5 genera of Proteobacteria (Fig. 6c). The main host ranges at genus levels such as Klebsiella, Salmonella, Escherichia and Aeromonas belong to Enterobacteriaceae family, Proteobacteria phylum (Fig. 6c). Klebsiella, an invasive and resistant zoonotic pathogenic genus, had the most of mobile ARGs such as aadA, aph(3')-I, floR, qnrS, sul1 and tetA[25], followed by Escherichia and Salmonella. These mobile ARGs appeared to be stably transferrable between genus of Enterobacteriaceae in their respective hosts. Previous data reported that Enterobacteriaceae were adapted to sharing genetic material and much important resistance due to mobile resistance genes [26]. Furthermore, recent demonstration of interphylum gene transfer further supported this phenomenon [27]. However, our study showed that the genes exchange network was only between different classes (Fig. 6c).
Metagenomic reconstruction and distribution of ARGs in MAGs
Reconstruction of bacterial genomes from metagenomic sequence provided a snapshot of taxonomic distribution of the ARGs among members of the gut and sediment microbial communities. In our study, a total of 96 genomic bins were obtained from all the samples and enabled a genome-based investigation of ARGs distribution. The average size of the bins was 2.07 Mb while the average length of N50 was 32,152 bp. As shown in Fig .6, there was taxonomic identification of bins with completeness of > 70% and contamination of < 5%. Out of the identified bins, 37.5% were assigned to Firmicutes, 15.6% to Proteobacteria while 20.3% bins were identified as Bacteroidetes. Similarly, 65.7% of the metagenomic bins were identified at the genus or species level, while the remaining bins did not match to genome-sequenced reference strains (Additional file 4: Table S5). All the bins showed differences in the abundance in different days. 61 bins showed a higher abundance at the 18th day while 18 new bins were comparable to day 0 of antibiotic treatment.
Most core ARGs with high relative abundance conferring multidrug, β-lactamase, MLS and tetracycline were detected in the metagenomics bins in limited members of the bacterial phylogeny. These bins with ARGs were all assigned to Proteobacteria (4 bins) and Firmicutes (2 bins), with a dramatic enrichment following the antibiotic treatment while restoring to baseline at day 42 (Fig. 6a). In addition, the bins carrying ARGs such as Citrobacter braakii, Morganella morganii_A and Hafnia paralvei belonging to Enterobacteriaceae, and Aeromonas veronii belonging to Aeromonadaceae were all zoonotic pathogens of Proteobacteria phylum and were associated with a higher rate of in-hospital mortality (Fig. 6a). Similarly, there was emergence of multiple antibiotics resistant bacteria (MRB) of Citrobacter braakii (7 ARGs), Morganella morganii_A (2 ARGs) and Anaerolineae bacterium CG2 (2 ARGs). The Citrobacter braakii accounted for 16% in all bins of the gut, and carried multidrug resistance genes (acrB, mdtC, tolC, emrB and emrA), MLS resistance genes (macB) and bata-lactam resistance genes (blaCMY-100, blaCMY-74), which mostly belonged to core ARGs (Fig. 6b). Our study showed that genomic bins of sediment7_2r.29 belonging to Anaerolineae bacterium CG_30 family and gut28_1.57 belonging to Clostridium genus were new ARBs, which suggested that these resistance genes may also be present in the hosts that we had not identified. In addition, some core ARGs such as ampC, acrB, bla_CMY-100, bla_CMY-74 and emrB on bins showed an obvious high abundance at day 28, albeit temporarily (Fig. 6b), Therefore, the application of enrofloxacin did not only promote resistance to different antibiotics but also potentially enriched multiple antibiotics resistance pathogenic bacteria, thus contributing to the changes in community composition. The proliferating of these multiple resistant pathogenic bacteria played a crucial role in augmenting various ARGs under antibiotic pressure.