Skip to main content
Download PDF

Microbiome recovery in adult females with uncomplicated urinary tract infections in a randomised phase 2A trial of the novel antibiotic gepotidacin (GSK2140944)

BMC Microbiology volume 21, Article number: 181 (2021) Cite this article

A Correction to this article was published on 26 October 2021

This article has been updated

Abstract

Background

With increasing concerns about the impact of frequent antibiotic usage on the human microbiome, it is important to characterize the potential for such effects in early antibiotic drug development clinical trials. In a randomised Phase 2a clinical trial study that evaluated the pharmacokinetics of repeated oral doses of gepotidacin, a first-in-chemical-class triazaacenaphthylene antibiotic with a distinct mechanism of action, in adult females with uncomplicated urinary tract infections for gepotidacin (GSK2140944) we evaluated the potential changes in microbiome composition across multiple time points and body-sites (ClinicalTrials.gov: NCT03568942).

Results

Samples of gastrointestinal tract (GIT), pharyngeal cavity and vaginal microbiota were collected with consent from 22 patients at three time points relative to the gepotidacin dosing regimen; Day 1 (pre-dose), Day 5 (end of dosing) and Follow-up (Day 28 ± 3 days). Microbiota composition was determined by DNA sequencing of 16S rRNA gene variable region 4 amplicons. By Day 5, significant changes were observed in the microbiome diversity relative to pre-dose across the tested body-sites. However, by the Follow-up visit, microbiome diversity changes were reverted to compositions comparable to Day 1. The greatest range of microbiome changes by body-site were GIT followed by the pharyngeal cavity then vagina. In Follow-up visit samples we found no statistically significant occurrences of pathogenic taxa.

Conclusion

Our findings suggest that gepotidacin alteration of the human microbiome after 5 days of dosing is temporary and rebound to pre-dosing states is evident within the first month post-treatment. We recommend that future antibiotic drug trials include similar exploratory investigations into the duration and context of microbiome modification and recovery.

Trial registration

NCT03568942. Registered 26 June 2018.

Background

Gepotidacin (GSK2140944) is a novel first-in-class triazaacenaphthylene antibiotic that selectively inhibits type IIA topoisomerases (DNA gyrase and topoisomerase IV) through a previously unexploited mechanism which is distinct from existing fluorquinolone inhibitors of this complex [1]. Phase 1 and 2 clinical studies show that gepotidacin is well-tolerated and has demonstrated efficacy in patients with acute bacterial skin infections [2, 3] and uncomplicated urogenital gonorrhea caused by Neisseria gonorrhoeae [4].

An increasing area of clinical interest is the potential effects that antibiotics have on the composition of endogenous human microbiota and its pan-genome, the microbiome. One specific concern is the impact of antibiotics on increased risk of severe secondary infections such as recurrent Clostridium difficile [5]. Epidemiological studies have also associated frequent usage of oral antibiotics to increased risk for certain chronic diseases such as inflammatory bowel disease [6] and celiac disease [7]. Given the critical role of the microbiota in maintaining immune homeostasis, the impact of pharmacologic agents is an emerging consideration in patient care [8]. Although antibiotic effects on the microbiome have been well-studied in animal models [9], healthy volunteers under antibiotics regimes [10] and retrospective analyses of patient cohorts [11], to the best of our knowledge, few studies have measured the effects of antibiotics on the human microbiome and its recovery in patients with bacterial infections, as typically enrolled in Phase 2 or 3 clinical trials.

In support of the clinical development of gepotidacin, we report on the spatial and longitudinal changes in the microbiome as an exploratory endpoint in a Phase 2a single-center, open label clinical study that evaluated the pharmacokinetics of repeated oral doses of gepotidacin in adult females with uncomplicated urinary tract infection (uUTI) [12]. Our results suggest that gepotidacin is associated with temporary, yet significant reduction in patients’ microbiome diversity by end of its dosing 5 day regimen. However, there is significant rebound or recovery of the microbiota to near baseline levels within a 4-week period post-treatment which suggests that gepotidacin related effects on the three body sites’ microbiome are temporary and transient.

Results

Study design and sample collection

The microbiome analysis was an exploratory endpoint in a Phase 2A clinical trial (ClinicalTrials.gov: NCT03568942). Study design, protocols and primary findings for this trial were previously reported [12] (see Methods). Samples for microbiome analyses were collected from 22 subjects with informed written consent in accordance with study protocols at three time-points: Day 1 (pre-dose); Day 5 (end of dosing or post-dose) and; Follow-up (visit around Day 28 ± 3 days). Three different body sites were sampled, namely the gastro-intestinal tract or GIT (stool sample), pharyngeal cavity (saliva sterile swab) and vagina (vaginal sterile swab). A total of 156 samples were collected with consent from 22 individuals (Additional file 1: Supplementary Table S1). Microbiota composition was determined by Illumina miSeq DNA sequencing of 16S rRNA gene variable region 4 amplicons along with the appropriate positive and negative experimental controls. After strigent quality control evaluation, 141 samples were used for subsequent microbiome analyses (see Methods for complete laboratory and data analysis protocols). Overall DNA read quality was high with average sequencing depth of 132 ± 69 K reads per sample. The relative abundances of assigned bacterial taxa for each body site and time-point are given in Additional file 2: Supplementary Data File S1.

Microbiota relative abundance and diversity

The three sampled body sites showed time-point related changes at the phylum level, represented for clarity as relative abundance in Fig. 1a. The greatest changes relative to time-points were observed for the pharyngeal cavity followed by the GIT and, finally, vagina. Proteobacteria, the predominant phylum of bacteria known to cause UTIs [13], was detected across all body sites at Day 1 with the greatest depletion occurring in GIT samples at Day 5.

Fig. 1
figure 1

Overview of microbiome dynamics during gepotidacin Phase 2a clinical trial. (A) Phylum level changes in the relative abundance of microbiota across different body sites and time points. (B) Changes in microbiota community as measured by different indices of alpha diversity. The initial, lower placed value in each comparison is from the overall ANOVA (* P value ≤0.05; ** P value ≤0.005, ns = nonsignificant)

Full size image

Changes within microbial communities were measured by comparing different diversity indexes which gave comparable results (Fig. 1b and Fig. S1). Focusing on the Berger-Parker diversity index, the pharyngeal cavity showed notable declines in alpha-diversity (False Discovery Rate [FDR]-adjusted P value ≤0.05) at Day 5 and Follow-up relative to Day 1 but with a slight rebound trend at Follow-up. GIT microbiome showed a significant decline in alpha-diversity at Day 5 relevant to Day 1. Alpha-diversity values in GIT samples at Follow-up visit rose to levels that were non-significantly different from Day 1. In vaginal samples, alpha-diversity values trended lower at Day 5 and then higher at Follow-up with non-significant changes across all time-points. Similar conclusions could be drawn using different alpha diversity measures (Additional file 3: Supplementary Fig. S1a). An attempt to perform pairwise analysis resulted in loss of significant differences for GIT, but mainly due to patient drop-out during the trial, which reduced the statistical power (Additional file 3: Supplementary Fig. S1b).

The beta diversity index reflects differences between microbial communities across time-points or body sites. PCoA (Fig. 2a) and NMDS (Fig. 2b) of weighted UniFrac distances varied across the tested body sites with differences being less pronounced for Day 5 compared to Day 1 or Follow-up (Fig. 2c). This trend reflects the overall reduction in microbiome diversity at those body sites caused by gepotidacin up to Day 5 with a partial rebound of microbial communities by Follow-up, after cessation of dosing.

Fig. 2
figure 2

Beta diversity index of microbial communities using unweighted UniFrac distances with PCoA (A) or NMDS (B) projections on different body sites over time. (C) Violin plots showing the distribution of the first CCA scores for each visit and body type (* P value ≤0.05; ** P value ≤0.005; *** P value ≤0.0001 ns = nonsignificant, Wilcoxon test with Benjamini-Hochberg FDR correction)

Full size image

PERMANOVA (multivariate analysis with permutations) [14] confirmed that differences were significant (P value ≤0.001) between samples based on Visit, Type and Visit:Type variables. Constrained correspondence analysis (CCA), which explains the variability by selected variables, shows that microbial communities from different body sites were very distinct at Day 1 (Additional file 4 Supplementary Fig. S2). Those differences were reduced when patients received gepotidacin, then re-established following cessation of dosing. The greatest to least changes in microbiota community structure across time among the body sites were GIT, pharyngeal cavity and vagina, which is congruent with alpha diversity variation.

Changes in detected abundances of bacterial genera

We evaluated changes in specific bacteria genera at Day 5 and Follow-up compared to Day 1 for each sampled body site. Among the three tested body sites, the GIT had the most variety of changed genera between Day 1 and Day 5. The greatest log-fold decreases in observed abundances were observed for the genera Tyzzerella, Parabacteriodes, Enterococcus, Selenomonas, Treponema, and several species of Prevotella, Veillonella and Fusobacterium (Fig. 3a). Lactobacillus, a core member of the GIT microbiota, rose in abundance. At Follow-up there was an increase in Neisseria spp. and a decrease in some core microbiota, such as Lactobacillus, compared to Day 1 which might be a consequence of the GIT microbiome returning to pre-dosing conditions. Other genera which showed the largest increases at Follow-up compated to Day 1 were Phascolarctobacterium, Sutterella, Prevotella, Bifidobacterium, Dialister, Veillonella and Actinomyces as well as members of the families Ruminococcaceae and Lachnospiraceae. There was no statistically significant change in the genus Clostridioides which includes the GIT pathogen C. difficile.

Fig. 3
figure 3

Changes in specific microbiota genera at Day 5 and Follow-up compared to Day 1 for the (A) gastro-intestinal tract (GIT); (B) pharyngeal cavity and (C) vagina. Size represents -log10 of FDR-adjusted P-value and lines represent CI at 95%

Full size image

The pharyngeal cavity microbiome showed intermediate level changes at Day 5 and Follow-up relative to Day 1 (Fig. 3b). Genera negatively impacted at Day 5 and Follow-up include Prevotella, Bacteriodes and the oral specific genus Porphyromonas. The pharyngeal cavity microbiome also had a specific decrease of Neisseria at Day 5. Both Day 5 and Follow-up samples were characterized by increases of Faecalibacterium and Ruminococcaceae UCG-014.

The vaginal microbiome showed the least changes in diversity at Day 5 and Follow-up relative to Day 1 (Fig. 3c). However, at Day 5, significant reduction of the genus Escherichia-Shigella occurred, which is congruent with the antibacterial spectrum of gepotidacin, as well as minor reductions of the genus Haemophilus and the vaginal predominant genus Lactococcus. Escherichia-Shigella remained depleted at Follow-up vs Day 1 comparison, while Lactococcus was no longer significantly depleted, suggesting a recovery to Day 1 levels. The genus Bacteriodes showed depletion at Day 5 and an increase at Follow-up compared to Day 1.

Changes in the detected abundances of specific bacterial pathogens

Next, we attempted to determine if in vitro susceptibility of specific pathogens to gepotidacin corresponded to the detectable abundances of similar genera in the microbiome as indirect evidence for in vivo drug effects. Previous in vitro studies reported minimum inhibitory concentrations (MIC) of gepotidacin needed to inhibit 90% of the bacteria tested as MIC90 ≤ 4 μg/mL [15, 16]. Tested species included the genera Bacillus, Clostridioides, Escherichia-Shigella, Haemophilus, Neisseria, Staphylococcus or Streptococcus.

Overall trends suggested that gepotidacin in vitro MIC90 results are generally predictive of detectable abundances of these bacteria in the microbiome as measured clinically (Fig. 4). Haemophilus spp. and Streptococcus spp. were heavily impacted at Day 5 in the pharyngeal cavity and vagina but recovered at Follow-up. Neisseria spp., generally not found in the GIT microbiota, were reduced in the pharyngeal cavity and vagina at Day 5 and then rebounded at Follow-up. Streptococcus spp. showed transient increases only at Day 5 in the GIT. Bacillus spp. had low abundances across body sites and did not significantly change over time. Low abundances of Clostridioides spp. appeared in some Follow-up stool samples as discussed below.

Fig. 4
figure 4

Overall trends in changes for specific pathogenic genera including Bacillus, Clostridioides, Escherichia-Shigella, Haemophilus, Neisseria, Staphylococcus and Streptococcus. (* P value ≤0.05; ** P value ≤0.005; *** P value ≤0.0001 ns = nonsignificant)

Full size image

We also looked at species-level changes in detected abundances for three specific pathogens, Escherichia coli, Neisseria gonorrhoeae and Clostridioides difficile, using phylogenetic analyses of actual 16S rRNA V4 sequences. For those terminal branches in the tree with sequences from our microbiome analyses, the log-transformed abundances were analyzed for each body site and time-point. A major caveat of this approach is that the average read length (~ 252 nucleotides) might be insufficient for robust taxonomic affiliation at the species or strain levels.

Certain members of the Enterobacterales genus, which includes uropathogenic E. coli (UPEC), Escherichia-Shigella and other affiliated species, were detected across the three body sites at all time points (Fig. 5). While we could not resolve to the strain level using the available sequence data, Escherichia-Shigella reads were initially found at Day 1 in the GIT and vagina but greatly reduced or undetectable at Day 5 and Follow-up. The Escherichia-Shigella species cluster was not detected at any time points in the pharyngeal cavity, except for a minor occurrence of Serratia spp. at Day 5.

Fig. 5
figure 5

Species level changes in abundance for E. coli species using phylogenetic analyses of their 16S rRNA-V4 sequences and closely related sequences from the NCBI public database

Full size image

N. gonorrhoeae was detected at medium to high abundance at Day 1 in the pharyngeal cavity of a single patient (Additional file 5: Supplementary Fig. S3). The observed variation of Neisseria genus across the sampled body sites might be explained by changes of an unknown Neisseria spp. Finally, throughout our study, C. difficile-related sequences were only detected in low abundances in the GIT of four different patients at Follow-up (Additional file 6: Supplementary Fig. S4) but none of these patients presented clinically with infections caused by C. difficile.

Discussion

In an exploratory analysis arm of a Phase 2a gepotidacin clinical trial in female subjects with uUTIs, we show the potential impact of gepotidacin on the microbiome at three different body sites across three time-points when patients were dosed orally at 1500 mg BID for 5 days. Significant declines in microbiome diversity relative to Day 1 were observed by Day 5 in microbial communities of the GIT, pharyngeal cavity and vagina as determined from DNA sequencing of 16S rRNA-V4 region amplicons from stool samples and pharyngeal and vaginal swabs, respectively. The three microbial communities showed significant recovery in diversity at Follow-up (collected at 28 ± 3 days). Our analyses suggest that the overall effects of gepotidacin on these body sites’ microbiome are relatively transient and reversible.

Although gepotidacin was dosed orally and, therefore, expected to mainly affect the GIT microbiome, we also observed changes in the distal pharyngeal and vaginal microbiomes. The overall magnitude of changes in microbiome alpha diversity were greatest for the pharyngeal cavity, followed by GIT. Vaginal microbiome diversity did not significantly change although some pathogenic genera including Haemophilus and Streptococcus, remained depleted at Follow-up. During the same Ph2a clinical trial, localized concentrations of gepotidacin were measured using swab samples collected over a 4 day period which revealed the rank-order of body-sites, in terms of high-to-low drug concentrations, as rectal > vaginal > pharyngeal [12]. The vaginal microbiome might be less affected by gepotidacin due to its naturally lower diversity skewed towards Gram-positive species such as lactic-acid producing Lactobacillus spp. [17]. In vitro susceptibility testing of nine different Lactobacillus spp. suggested variable susceptibility to gepotidacin (MIC range ≤ 0.015–2 μg/mL) (unpublished data). Collectively, these findings suggest that differences in microbiota dynamics at these three specific body sites might be due to multiple factors including localized drug concentrations and the intrinsic composition of the microbiome at those locations. Additional in vitro studies involving individual microbiota species and strains are necessary to understand the lineage specific factors impacting susceptibility to gepotidacin and other antibiotics.

Analyses of impacted microbiota genera suggest that gepotidacin has a distinct and selective in vivo spectrum of activity which might partially explain the observed rebound of the microbial community post-treatment. Our study suggests that gepotidacin potentially affects the abundance of several genera, including those of known pathogens, the Bacillus, Escherichia, Clostridioides, Haemophilus, Neisseria, Staphylococcus, and Streptococcus. Although C. difficile reads could be detected at low concentration in stool samples of four patients at Follow-up, these were not statistically significant with respect to other time points or body sites and no patient in the study presented with clinically relevant C. difficile infections. Gepotidacin did affect the abundance of some genera considered part of the core healthy microbiome, such as Prevotella spp. However, other proposed beneficial taxa, including Lactococcus and Lactobacillus, were mostly unchanged. Utilization of qPCR and metagenomics assays would have provided higher resolution of antibiotic effects at the species and strain level.

Several studies have reported the effects of fluoroquinolones on the GIT microbiome. Fluoroquinolones also target bacterial DNA gyrase and topoisomerase IV, although gepotidacin has distinct molecular interactions with these proteins which avoid known amino acids associated with bacterial resistance to fluoroquinolones [1]. Dethlefsen and Relman measured microbiota changes using 16S rRNA amplicons in three healthy individuals given two courses of the fluoroquinolone ciprofloxacin over a period of 10 months [18]. They observed a rapid loss of diversity within 3–4 days of initial dosing. The microbiome began to recover within 1 week after last dose although not to the baseline, pre-dose situation. Willmann et al. performed a longitudinal, microbiome metagenomics analyses of two hospital patient cohorts treated prophylactically with the antibiotics cotrimoxazole or ciprofloxacin [19]. At the end of the observation period, 6 days post-last dose, the ciprofloxacin cohort showed a significantly greater reduction in microbiota species diversity and richness compared to the cotrimoxazole cohort. However, multi-variate analyses which integrated various clinical measures, suggested that higher dosing of ciprofloxacin relative to cotrimoxazole, nearly four-fold greater, might be a key contributing factor. Focusing solely on the enterococci, de Lastours et al. found that stool samples from healthy volunteers had an increase in Enterococcus species with fluoroquinolone resistant loci relative to baseline 42 days after a 2 week dosing regimen of ciprofloxacin [20]. As for other antibiotic classes, Cannon et al. [21] compared the effects of vancomycin and surotomycin, a bactericidal cyclic lipopeptide, on the microbiome in a Phase 2 trial against C. difficile infections. Using qPCR, they assessed changes among 10 microbiota species in fecal samples collected from 8 control patients dosed orally with 125 mg of vancomycin or surotomycin for 10 days at 7 timepoints (3 pre-dose and dosed and 4 follow-up samplings). Vancomycin-treated patients were found to have notable suppression of microbiota abundances at 42 days compared to surotomycin, with Veillonella spp., Bacteriodes spp. and Prevotella spp. failing to recover to baseline values. In comparison to fluoroquinolones, the microbiome seems to recover to near pre-dose status within a few weeks post-dosing of gepotidacin.

Chng et al. [11] recently published a meta-analysis of more than 500 microbiome profiles from 117 individuals across four different continents receiving antibiotic therapy. They identified 21 bacterial species associated with post-treatment GIT microbiome recovery which included members of the genera Bacteroides, Bifidobacterium and Ruminococcus, also seen elevated in our Follow-up stool samples. However, several genera differed between the two studies which might be attributed dissimilarities in administered drug classes and study subject cohorts. In Chng et al. cohorts, most patients received antiobiotics from amoxicillin-clavulanic acid, lincosamides or macrolides classes while the sole fluroquinolone, ciprofloxacin, was administered to only a few healthy volunteers. There was no sub-cohort analysis of microbiota changes by drug class. Thus further clinical studies involving head-to-head comparisons of antibiotic induced microbiome changes in patients would be useful in elucidating the specific effects of different compound classes on human microbiota.

One caveat of our study is that all subjects had a bacterial infection at enrollment thus their baseline microbiome composition might be affected by pathogen-induced dysbiosis. Therefore, we have no information about the impact of gepotidacin on re-establishing a healthy microflora. However, since clearing of the infection was confirmed, the remaining differences in microbiota composition between Day 1 and Follow-up might hint of reversion to a non-dysbiotic state at the sampled body sites. A second caveat of our study is that the microbiome data reflects changes in the detectable abundances of microbiota taxa and does include measures of absolute bacterial load. We did show that previously determined in vitro susceptibility of specific pathogens to gepotidacin corresponded to the abundances of similar genera in the microbiome as indirect evidence for in vivo drug effects. While direct and more specific measures of bacterial abundance, for example using qPCR of universal bacterial specific loci might be interesting, their interpretation in the context of microbiome could be complicated by rapid niche-filling by microbiota with low susceptibility to gepotidacin which could result in sustained levels of overall bacterial load across time-points. A third caveat is that without metagenomic data, we are unable to assess the potential impact of gepotidacin on the occurrences of specific drug resistance gene loci, the so-called resistome, in the microbiota from GIT, pharyngeal or vaginal samples.

Conclusion

Our study provides evidence of relatively rapid microbiome recovery at multiple body sites for patients with uUTIs being treated with the novel antibiotic, gepotidacin. The clinical ramifications of facilitating microbiome rebound after antibiotic treatment needs further investigation using larger patient cohorts as well as multiple comparisons across different antibiotic regimens.

Methods

Study population and sample collection

The microbiome analysis was an exploratory endpoint in a Phase 2A clinical trial (ClinicalTrials.gov: NCT03568942). Study design, protocols and primary findings for this trial were previously published [12]. Eligible female subjects received twice daily (BID) dose of gepotidacin 1500 mg (mg) for 5 days via oral route. The total duration of the study was approximately 28 days. This trial is compliant with CONSORT guidelines. Samples for microbiome analyses were collected from 22 subjects with informed consent in accordance with study protocols at three time-points: Day 1 (pre-dose); Day 5 (end of dosing or post dose) and; Follow-up (visit around Day 28 ± 3 days). Three different body sites were sampled, namely gastro-intestinal tract (GIT; stool sample), pharyngeal cavity (saliva sterile swab) and vaginal (vaginal sterile swab).

DNA extraction and sequencing

All samples were stored at − 80 °C prior to DNA extraction and sequencing. For quality control purposes, all genomic extractions, sequencing and data analyses were performed in a single, central next generation sequencing (NGS) laboratory of GlaxoSmithKline Research and Development (GSK R&D) in Collegeville, Pennsylvania, USA.

Genomic DNA was isolated from stool samples using QIAamp PowerFecal DNA Kit (Qiagen, Hilden, Germany) according to the manufacturer’s instructions. Genomic DNA was isolated from saliva and vaginal samples by first concentrating the preservation solution using ultra centrifugal filters (Amicon, EMD Millipore, Darmstadt, Germany) followed by using QIAamp PowerSoil Pro Kit (Qiagen, Hilden, Germany) according to manufacturer’s instructions. Each genomic DNA sample was quantified by Qubit fluorometric kit (Invitrogen, Thermofisher, Waltham, MA). PCR amplification of the 16S rRNA V4 region was conducted with primers, 515f (5′- GTGCCAGCMGCCGCGGTAA-3′) and 806r (5′-GGACTACHVGGGTWTCTAAT-3′) including an 8-nt index sequence, a 10-nt pad sequence, a 2-nt linker, and the appropriate Illumina adapter [22, 23]. The index sequences were selected to be at least 2-nt different from all other indices in use, and when combined, they provide an equal intensity in the two light channels used by the sequencer (i.e., green channel [G/T] and red channel [A/C]) [24]. Each 25 μL PCR reaction containing on average 100 ng of genomic DNA, KAPA HiFi HotStart ReadyMix (KAPABIOSYSTEMS, Wilmington, MA), and 0.2 μM of each primer (Integrated DNA Technologies, Coralville, IA). PCR was performed on an ABI 9700 thermocycler and included the following cycling steps: Initial denaturing at 95 °C for 3 min followed by 35 cycles of 95 °C × 20 s, 60 °C × 15 s, and 72 °C × 30 s ending with a 72 °C 1 × minute extension. A 2ul aliquot of each resulting amplicon was run on a 2.0% E-gel 96 SYBR Safe Stain Precast agarose gel (Invitrogen, Waltham, MA) to check quality and quantity. Positive (ZymoBIOMICS Community and DNA Standards) and negative controls consisting of reagent-only isolation kit reactions along with no-template amplifications using Microbial DNA-Free Water (Qiagen, Hilden, Germany) were included for all isolation steps, PCR reactions and DNA sequencing runs. All PCR products were verified using both Qubit quantitation and gel electrophoresis for sensitive resolution of the amplicon of interest. All negative controls were shown to be free of DNA contamination by a combination of negative Qubit High Sensitivity results and a lack of detectable E-gel amplicon bands.

Amplicons were then purified using a magnetic bead capture kit (Ampure XP; Agencourt) and quantified using a fluorometric kit (Qubit; Invitrogen). The purified amplicons were then pooled in equimolar concentrations using a SequalPrep plate normalization kit (Invitrogen), and the final concentration of the library was determined using a SYBR green quantitative PCR (qPCR) assay with primers specific to the Illumina adapters (KAPABIOSYSTEMS, Wilmington, MA).

To check for proper cluster density and sample normalization, an Illumina MiSeq single-end 26 bp + 8 bp dual index sequencing run was performed using the MiSeq instrument. Unique dual-index barcodes were designed per Illumina recommendation to avoid sequencing artifacts due to index hopping. The pool was mixed with a PhiX library (Illumina, San Diego CA) at a ratio of .5:9.5 in order to increase the entropy of the library. A final MiSeq 2 × 150 bp + 8 bp dual index sequencing run was performed on the pooled samples.

Reads were first filtered to remove the PhiX library reads. All reads mapping to the Enterobacteria phage PhiX 174 reference genome (GenBank: NC_001422.1) using the software Bowtie (v1.0.1) [25] were removed from the analysis. The paired reads were next merged with the software PEAR (v0.9.5) [26]. DNA sequence data are available from the National Centre for Biotechnology Information Sequence Read Archive (SRA) under BioProject ID: PRJNA630295 and SRA submission: SUB7386163.

Data analysis

All statistical analyses were post-hoc (i.e. defined after unblinding the clinical study). Reads from 16S rRNA-V4 regions (≥19 M total) were analyzed using Qiime2, v2018.8 [27]. Samples from positive and negative control wells were analyzed separately. All 16S rRNA-V4 reads were trimmed where average quality score dropped below 25 (150 and 149 base pairs [bp] for forward and reverse reads, respectively) and dereplicated using DADA2 [28] with paired-end default settings (including quality control, trimming, pair-joining and chimera removals) resulting in 90.74% retained reads. The 16S rRNA-V4 representative amplicon sequence variants (ASVs) were assigned to the SILVA 132 database [29] by using a multinomial naïve Bayes classifier [30]. Phylogenetic trees were built in Qiime2 using MAFFT [31] and fasttree [32]. Finally, data were exported from Qiime2 and converted into the BIOM v1.5 format [33] for easier import into R.

Diversity analyses were performed using the R packages “Phyloseq” v1.34.0 [34] and “vegan” v2.5–7 [35], “microbiome” 1.12–0 [36] and “picante” 1.8.2 [37]. Alpha diversity was calculated using Berger-Parker, Faith’s PD, observed ASVs, Simpson’s and Shannon’s diversity indexes on non-normalized data [38]. Beta diversity analysis was performed on log-normalized data to minimize biases related to rarefaction [39] and included principal coordinate analysis (PCoA), non-metric multidimensional scaling (NMDS) and Constrained Correspondence Analysis (CCA), performed on either Bray-Curtis or unweighted UniFrac distances [40]. PERMANOVA was used to test for significance with default setting of 999 permutations. Differential abundance tests were performed on non-normalized ASVs pseudocounts with the DESeq2 package (parametric model, Wald’s test) [41]. Per-body-site contrasts were made for each time point against Day 1 (P ≤ 0.01%). The false discovery rate (FDR) method was used to adjust P-values for multiple tests where applicable [42].

Availability of data and materials

DNA sequence data and metadata are available from the National Centre for Biotechnology Information Sequence Read Archive (SRA) under BioProject ID: PRJNA630295]. Metadata are formatted following the MIMARKS standard and included in the BioProject repository. Additional files are available as Supplementary Electronic Material. Code to reproduce the analysis is available at https://github.com/andreanuzzo/gepotidacin_phIIa_microbiome. This study is registered at clinicaltrials.gov with the identifier NCT03568942. Anonymized individual participant data and study documents can be requested for further research from www.clinicalstudydatarequest.com.

Change history

Abbreviations

uUTI:

Uncomplicated urinary tract infection

GIT:

Gastrointestinal tract

MIC:

Minimum inhibitory concentrations

BID:

Two times a day dosing

References

  1. Bax BD, Chan PF, Eggleston DS, Fosberry A, Gentry DR, Gorrec F, et al. Type IIA topoisomerase inhibition by a new class of antibacterial agents. Nature. 2010;466(7309):935–40. https://0-doi-org.brum.beds.ac.uk/10.1038/nature09197.

    Article  PubMed  Google Scholar 

  2. O’riordan W, Tiffany C, Scangarella-Oman N, Perry C, Hossain M, Ashton T, et al. Efficacy, safety, and tolerability of gepotidacin (GSK2140944) in the treatment of patients with suspected or confirmed gram-positive acute bacterial skin and skin structure infections. Antimicrob Agents Chemother. 2017;61(6). https://0-doi-org.brum.beds.ac.uk/10.1128/AAC.02095-16.

  3. Scangarella-Oman NE, Ingraham KA, Tiffany CA, Tomsho L, van Horn SF, Mayhew DN, et al. In vitro activity and microbiological efficacy of gepotidacin from a phase 2, randomized, multicenter, dose-ranging study in patients with acute bacterial skin and skin structure infections. Antimicrob Agents Chemother. 2020;64.

  4. Scangarella-Oman NE, Hossain M, Dixon PB, Ingraham K, Min S, Tiffany CA, et al. Microbiological analysis from a phase 2 randomized study in adults evaluating single oral doses of gepotidacin in the treatment of uncomplicated urogenital gonorrhea caused by neisseria gonorrhoeae. Antimicrob Agents Chemother. 2018;62(12). https://0-doi-org.brum.beds.ac.uk/10.1128/AAC.01221-18.

  5. Abt MC, McKenney PT, Pamer EG. Clostridium difficile colitis: pathogenesis and host defence. Nat Rev Microbiol. 2016;14(10):609–20. https://0-doi-org.brum.beds.ac.uk/10.1038/nrmicro.2016.108.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Shaw SY, Blanchard JF, Bernstein CN. Association between the use of antibiotics in the first year of life and pediatric inflammatory bowel disease. Am J Gastroenterol. 2010;105(12):2687–92. https://0-doi-org.brum.beds.ac.uk/10.1038/ajg.2010.398.

    Article  PubMed  Google Scholar 

  7. Dydensborg Sander S, Nybo Andersen AM, Murray JA, Karlstad Ø, Husby S, Størdal K. Association between antibiotics in the first year of life and celiac disease. Gastroenterology. 2019;156(8):2217–29. https://0-doi-org.brum.beds.ac.uk/10.1053/j.gastro.2019.02.039.

    Article  PubMed  Google Scholar 

  8. Maier L, Pruteanu M, Kuhn M, Zeller G, Telzerow A, Anderson EE, et al. Extensive impact of non-antibiotic drugs on human gut bacteria. Nature. 2018;555(7698):623–8. https://0-doi-org.brum.beds.ac.uk/10.1038/nature25979.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Iizumi T, Taniguchi T, Yamazaki W, Vilmen G. Alekseyenko a v., Gao Z, et al. effect of antibiotic pre-treatment and pathogen challenge on the intestinal microbiota in mice. Gut Pathogens. 2016;8(1):1–10. https://0-doi-org.brum.beds.ac.uk/10.1186/s13099-016-0143-z.

    Article  CAS  Google Scholar 

  10. Arat S, Spivak A, van Horn S, Thomas E, Traini C, Sathe G, et al. Microbiome changes in healthy volunteers treated with GSK1322322, a novel antibiotic targeting bacterial peptide deformylase. Antimicrob Agents Chemother. 2015;59(2):1182–92. https://0-doi-org.brum.beds.ac.uk/10.1128/AAC.04506-14.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Chng KR, Ghosh TS, Tan YH, Nandi T, Lee IR, Ng AHQ, et al. Metagenome-wide association analysis identifies microbial determinants of post-antibiotic ecological recovery in the gut. Nature Ecology and Evolution. 2020;4(9):1256–67. https://0-doi-org.brum.beds.ac.uk/10.1038/s41559-020-1236-0.

    Article  PubMed  Google Scholar 

  12. Overcash JS, Tiffany CA, Scangarella-Oman NE, Perry CR, Tao Y, Hossain M, et al. Phase 2a pharmacokinetic, safety, and exploratory efficacy evaluation of oral gepotidacin (GSK2140944) in female participants with uncomplicated urinary tract infection (acute uncomplicated cystitis). Antimicrob Agents Chemother. 2020;64(7). https://0-doi-org.brum.beds.ac.uk/10.1128/AAC.00199-20.

  13. Guglietta A. Recurrent urinary tract infections in women: risk factors, etiology, pathogenesis and prophylaxis. Future Microbiol. 2017;12(3):239–46. https://0-doi-org.brum.beds.ac.uk/10.2217/fmb-2016-0145.

    Article  CAS  PubMed  Google Scholar 

  14. Tang ZZ, Chen G, Alekseyenko A v. PERMANOVA-S: Association test for microbial community composition that accommodates confounders and multiple distances. In: Bioinformatics. Oxford University Press; 2016. p. 2618–2625. doi:https://0-doi-org.brum.beds.ac.uk/10.1093/bioinformatics/btw311.

  15. Biedenbach DJ, Bouchillon SK, Hackel M, Miller LA, Scangarella-Oman NE, Jakielaszek C, et al. In vitro activity of gepotidacin, a novel triazaacenaphthylene bacterial topoisomerase inhibitor, against a broad spectrum of bacterial pathogens. Antimicrob Agents Chemother. 2016;60(3):1918–23. https://0-doi-org.brum.beds.ac.uk/10.1128/AAC.02820-15.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Jacobsson S, Golparian D, Scangarella-Oman N, Unemo M. In vitro activity of the novel triazaacenaphthylene gepotidacin (GSK2140944) against MDR Neisseria gonorrhoeae. J Antimicrob Chemother. 2018;73(8):2072–7. https://0-doi-org.brum.beds.ac.uk/10.1093/jac/dky162.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Smith SB, Ravel J. The vaginal microbiota, host defence and reproductive physiology. J Physiol. 2017;595(2):451–63. https://0-doi-org.brum.beds.ac.uk/10.1113/JP271694.

    Article  CAS  PubMed  Google Scholar 

  18. Dethlefsen L, Relman DA. Incomplete recovery and individualized responses of the human distal gut microbiota to repeated antibiotic perturbation. Proc Natl Acad Sci U S A. 2011;108(SUPPL. 1):4554–61. https://0-doi-org.brum.beds.ac.uk/10.1073/pnas.1000087107.

    Article  PubMed  Google Scholar 

  19. Willmann M, Vehreschild MJGT, Biehl LM, Vogel W, Dörfel D, Hamprecht A, et al. Distinct impact of antibiotics on the gut microbiome and resistome: a longitudinal multicenter cohort study. BMC Biol. 2019;17(1):76. https://0-doi-org.brum.beds.ac.uk/10.1186/s12915-019-0692-y.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. de Lastours V, Maugy E, Mathy V, Chau F, Rossi B, Guérin F, et al. Ecological impact of ciprofloxacin on commensal enterococci in healthy volunteers. J Antimicrob Chemother. 2017;72(6):1574–80. https://0-doi-org.brum.beds.ac.uk/10.1093/jac/dkx043.

    Article  CAS  PubMed  Google Scholar 

  21. Cannon K, Byrne B, Happe J, Wu K, Ward L, Chesnel L, et al. Enteric microbiome profiles during a randomized phase 2 clinical trial of surotomycin versus vancomycin for the treatment of Clostridium difficile infection. J Antimicrob Chemother. 2017;72(12):3453–61. https://0-doi-org.brum.beds.ac.uk/10.1093/jac/dkx318.

    Article  CAS  PubMed  Google Scholar 

  22. Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Lozupone CA, Turnbaugh PJ, et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc Natl Acad Sci U S A. 2011;108(SUPPL. 1):4516–22. https://0-doi-org.brum.beds.ac.uk/10.1073/pnas.1000080107.

    Article  PubMed  Google Scholar 

  23. Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Huntley J, Fierer N, et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 2012;6(8):1621–4. https://0-doi-org.brum.beds.ac.uk/10.1038/ismej.2012.8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Kozich JJ, Westcott SL, Baxter NT, Highlander SK, Schloss PD. Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the miseq illumina sequencing platform. Appl Environ Microbiol. 2013;79(17):5112–20. https://0-doi-org.brum.beds.ac.uk/10.1128/AEM.01043-13.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Langmead B, Trapnell C, Pop M, Salzberg SL. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 2009;10(3):R25. https://0-doi-org.brum.beds.ac.uk/10.1186/gb-2009-10-3-r25.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Zhang J, Kobert K, Flouri T, Stamatakis A. PEAR: a fast and accurate Illumina paired-end reAd mergeR. Bioinformatics. 2014;30(5):614–20. https://0-doi-org.brum.beds.ac.uk/10.1093/bioinformatics/btt593.

    Article  CAS  PubMed  Google Scholar 

  27. Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol. 2019;37(8):852–7. https://0-doi-org.brum.beds.ac.uk/10.1038/s41587-019-0209-9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13(7):581–3. https://0-doi-org.brum.beds.ac.uk/10.1038/nmeth.3869.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41.

  30. Bokulich NA, Kaehler BD, Rideout JR, Dillon M, Bolyen E, Knight R, et al. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome. 2018;6(1):90. https://0-doi-org.brum.beds.ac.uk/10.1186/s40168-018-0470-z.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Katoh K, Misawa K, Kuma KI, Miyata T. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 2002;30(14):3059–66. https://0-doi-org.brum.beds.ac.uk/10.1093/nar/gkf436.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Price MN, Dehal PS, Arkin AP. FastTree 2 - approximately maximum-likelihood trees for large alignments. PLoS One. 2010;5(3):e9490. https://0-doi-org.brum.beds.ac.uk/10.1371/journal.pone.0009490.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. McDonald D, Clemente JC, Kuczynski J, Rideout JR, Stombaugh J, Wendel D, et al. The biological observation matrix (BIOM) format or: how I learned to stop worrying and love the ome-ome. GigaScience. 2012;464(1). https://0-doi-org.brum.beds.ac.uk/10.1186/2047-217X-1-7.

  34. McMurdie PJ, Holmes S. Phyloseq: a bioconductor package for handling and analysis of high-throughput phylogenetic sequence data. In: Pacific Symposium on Biocomputing; 2012. p. 235–46

    Google Scholar 

  35. Oksanen J, Blanchet FG, Kindt R, Legendre P, Minchin P, O’Hara RB, et al. vegan: Ordination methods, diversity analysis and other functions for community and vegetation ecologists. Community Ecology Package Vegan Available at: https://cranr-project.org, https://github.com/vegandevs/vegan. 2013.

  36. Lahti L, Shetty S. microbiome R package; 2019.

    Google Scholar 

  37. Kembel SW, Cowan PD, Helmus MR, Cornwell WK, Morlon H, Ackerly DD, et al. Picante: R tools for integrating phylogenies and ecology. Bioinformatics. 2010;26(11):1463–4. https://0-doi-org.brum.beds.ac.uk/10.1093/bioinformatics/btq166.

    Article  CAS  PubMed  Google Scholar 

  38. Knight R, Vrbanac A, Taylor BC, Aksenov A, Callewaert C, Debelius J, et al. Best practices for analysing microbiomes. Nat Rev Microbiol. 2018;16(7):410–22. https://0-doi-org.brum.beds.ac.uk/10.1038/s41579-018-0029-9.

    Article  CAS  PubMed  Google Scholar 

  39. McMurdie PJ, Holmes S. Waste not, want not: why rarefying microbiome data is inadmissible. PLoS Comput Biol. 2014;10(4):e1003531. https://0-doi-org.brum.beds.ac.uk/10.1371/journal.pcbi.1003531.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Lozupone CA, Hamady M, Kelley ST, Knight R. Quantitative and qualitative β diversity measures lead to different insights into factors that structure microbial communities. Appl Environ Microbiol. 2007;73(5):1576–85. https://0-doi-org.brum.beds.ac.uk/10.1128/AEM.01996-06.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550. https://0-doi-org.brum.beds.ac.uk/10.1186/s13059-014-0550-8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Yoav B, Yosef H. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Royal Statistical Soc Series B. 1995;72:405–16.

    Google Scholar 

Download references

Acknowledgments

We thank James Yang for assisting with laboratory work and Brett Mahon for his comments on the manuscript, both from GlaxoSmithKline.

Funding

Microbiome sequencing and computational biology analyses was funded by GSK (study code, clinicaltrials.gov ID NCT03568942) and, in whole or in part, with Federal funds from the Office of the Assistant Secretary for Preparedness and Response, Biomedical Advanced Research and Development Authority, under OTA Agreement No. HHSO100201300011C. The funders had no influence on the scientific direction of the study nor the interpretation of results.

Author information

Authors and Affiliations

  1. Human Genetics, GlaxoSmithKline R&D, Medicines Research Centre, Gunnels Wood Road, Stevenage, SG1 2NY, UK

    Andrea Nuzzo

  2. Functional Genomics, GlaxoSmithKline R&D, Collegeville, PA, USA

    Stephanie Van Horn & Christopher Traini

  3. Development, GlaxoSmithKline R&D, Collegeville, PA, USA

    Caroline R. Perry, Etienne F. Dumont & David F. Gardiner

  4. Medicines Opportunities Research Unit, GlaxoSmithKline R&D, Collegeville, PA, USA

    Nicole E. Scangarella-Oman

  5. Human Genetics, GlaxoSmithKline R&D, Collegeville, PA, USA

    James R. Brown

  6. Present Address: Kaleido Biosciences, 65 Hayden Avenue, Lexington, MA, 02421, USA

    James R. Brown

Authors
  1. Andrea Nuzzo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Stephanie Van Horn
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Christopher Traini
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Caroline R. Perry
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Etienne F. Dumont
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Nicole E. Scangarella-Oman
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. David F. Gardiner
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. James R. Brown
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

C.R.P., E.F.D., N.E.S-O., D.F.G. and J.R.B. conceived the study. S.V.H. and C.T. performed DNA extractions and DNA sequencing. A.N. analyzed the data. A.N., J.R.B. and S.V.H. wrote the manuscript. All  authors have read and approved the final manuscript.

Corresponding author

Correspondence to Andrea Nuzzo.

Ethics declarations

Ethics approval and consent to participate

The microbiome analysis was an exploratory endpoint in a Phase 2A clinical trial (ClinicalTrials.gov: NCT03568942). Subjects provided written consent in accordance with GCP (Good Clinical Practice) guidelines. Protocols and primary findings for this trial were previously published [13]. The clinical trial was performed at the GSK Investigational Site. La Mesa, California, United States, 91942 and IRB approved. The central IRB for this study (GSK 206899) is available from Aspire IRB, 11491 Woodside Avenue, Santee, CA 92071.

Consent for publication

Not applicable.

Competing interests

A.N., S.V.H., C.T., C.R.P., and N.E.S-O., are employees of GlaxoSmithKline and hold restricted shares. E.F.D, D.F.G. and J.R.B. were employees and restricted shareholder when this work was completed.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1: Supplementary Table S1.

Summary of samples collected and passing quality control (QC) for subsequent analyses.

Additional file 2: Supplementary Data File S1:

Relative abundances of assigned bacterial taxa for each body site and time-point.

Additional file 3: Supplementary Figure S1.

Changes in microbiota community as measured by different indices of alpha diversity (A) and pairwise statistical analysis of alpha diversity values for the subset of patients having the full set of three visit throughout the trial (n = 16) (B). The initial, lower placed value in each comparison is from the overall ANOVA (* P value ≤0.05; ** P value ≤0.005, ns = nonsignificant).

Additional file 4: Supplementary Figure S2.

Multivariate analyses of changes in microbial community using constrained correspondence analysis (CCA) on Bray-Curtis distances showing recovery at Follow-up (A) and violin plots showing the distribution of the first CCA scores for each visit and body type (B) (* P value ≤0.05; ** P value ≤0.005; *** P value ≤0.0001 ns = nonsignificant, Wilcoxon test with Benjamini-Hochberg FDR correction).

Additional file 5: Supplementary Figure S3.

Species-level changes in abundance for Neisseria gonorrhoeae related species using phylogenetic analyses of their 16S rRNA gene -V4 sequences as well as related sequences from NCBI public database.

Additional file 6: Supplementary Figure S4.

Species-level changes in abundance for Clostridioides species using phylogenetic analyses of their 16S rRNA-V4 sequences as well as related sequences from NCBI public database. Occurrence of C. difficile in the GIT samples from four subjects at Follow-up is labeled.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nuzzo, A., Van Horn, S., Traini, C. et al. Microbiome recovery in adult females with uncomplicated urinary tract infections in a randomised phase 2A trial of the novel antibiotic gepotidacin (GSK2140944). BMC Microbiol 21, 181 (2021). https://0-doi-org.brum.beds.ac.uk/10.1186/s12866-021-02245-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://0-doi-org.brum.beds.ac.uk/10.1186/s12866-021-02245-8

Keywords

BMC Microbiology

ISSN: 1471-2180

Contact us