Differences in antimicrobial resistance gene abundance and microbial diversity of the gut microbiome in patients on antibiotics enrolled in a clinical trial

Abstract

Background:

Understanding how the gut microbiome adapts on exposure to individual antibiotics, with respect to antimicrobial resistance gene (ARG) enrichment, is important.

Objectives:

To characterise the changes that occur in the gut microbiome of patients enrolled in an antibiotic clinical trial and to propose methods in which to embed gut microbiome analysis into clinical trials.

Design:

This was a prospective cohort study of hospitalised patients who were successfully enrolled and randomised into two clinical trials between January 2021 to December 2021.

Methods:

Adult patients admitted to the hospital with a bloodstream infection have been randomised to receive either benzylpenicillin, ampicillin, cefazolin, ceftriaxone, piperacillin-tazobactam or meropenem at a single institution. Faecal specimens were collected at enrolment and every second day until discharge. Each specimen underwent DNA sequencing to determine microbial diversity and ARG abundance.

Results:

Ten patients (including six females) were included. DNA concentration and sampling quality were markedly lower for rectal swabs compared to stool samples. Relative abundance of Enterococcus faecium was increased in individual patients where treatment included ampicillin, meropenem and piperacillin-tazobactam. Piperacillin-tazobactam also increased the abundance of key beta-lactamase genes (bla_SHV-100, bla_OXA-392, bla_CMY-18). Ampicillin increased the abundance of bla_TEM-1A. There were no extended-spectrum beta-lactamase (ESBL) or carbapenemase genes detected in our study. The presence of key anaerobes such as Clostridium and Bifidobacterium species appeared to play an important role in colonisation resistance of E. faecium and Clostridioides difficile.

Conclusion:

Differential changes in anaerobic bacterial genera on exposure to antibiotics may be a key determinant of colonisation resistance. The pre-analytical phase of microbiome analysis is a critical factor in data quality and interpretation.

Keywords

antibiotic resistance antibiotics clinical trial gut microbiome

Introduction

The global burden of antimicrobial resistance continues to rise and is largely driven by antimicrobial use.¹ There exists a dire need for new management strategies to mitigate this growing risk to patient and public health outcomes. Despite being crucial for the treatment of severe infections, antimicrobial use leads to unintended consequences on the human microbiome.² Indeed, inappropriate and excessive use of antimicrobials also contributes to antimicrobial resistance. Within the gut microbiome, administration of broad-spectrum antimicrobials can cause a rapid and significant drop in taxonomic richness, diversity and evenness.^2,3 Moreover, these compositional changes can remain long after cessation of antimicrobials.⁴ In addition to changes in diversity, an increase in the abundance of antibiotic-resistance genes belonging to the resistome is observed.⁵ This includes problematic antimicrobial resistance determinants such as those encoding extended-spectrum beta-lactamases (ESBL) and carbapenemases in Gram-negative bacteria, as well as vancomycin resistance genes (vanA and vanB) in Enterococcus spp. Colonisation resistance is the reverse process whereby the microbiota inhibits the expansion of Enterobacterales and enterococci in the gut lumen and is promoted by taxonomic diversity (including the presence of anaerobic bacteria).⁶

It remains unclear what effect each antimicrobial has on the gut microbiome of individual patients with respect to bacterial community profiling, taxonomic diversity and antimicrobial resistance gene (ARG) abundance. Difficulties exist in carrying out such in vivo studies with no agreed-upon methodology (including statistical analysis) for the assessment and comparison of clinical specimens undergoing metagenomic sequencing.⁷ Significant differences in richness and diversity have been observed within and between healthy patient stool specimens. Gaining a more refined understanding of the effect of the individual antibiotic over time, on the type and degree of change in the gut microbiome, will better inform decision making on antimicrobial prescribing. Moreover, developing an agreed-upon framework in which to embed gut microbiome analysis into contemporary clinical trials will help to compare clinically relevant antibiotic-related adverse events.⁸ Here, we aim to characterise the changes that occur in the gut microbiome of patients enrolled in an interventional clinical trial receiving a single antibiotic, in addition to proposing a methods structure in which to embed gut microbiome analysis into clinical trials.

Methods

Clinical data

Adult patients admitted to the hospital with bloodstream infection at the Royal Brisbane & Women’s Hospital who had been enrolled and randomised into an interventional antibiotic clinical trial were identified prospectively for inclusion in the study between January 2021 to December 2021. Two such trials were enrolled during the study period, GAMECHANGER (ClinicalTrials.gov NCT03869437) and BALANCE (Clinical Trials.gov NCT03005145). All patients enrolled in either of these trials at our centre were eligible for inclusion in our study. Stool samples or rectal swabs were collected at baseline and every 2–3 days thereafter until discharge from the hospital. Collection of stool samples was given preference over rectal swabs by the study team. Patient demographic, microbiological and clinical data were collected prospectively. This study was approved by the Royal Brisbane & Women’s Hospital Human Research Ethics Committee (LNR/2020/QRBW/65029). Written informed consent for inclusion in our study was obtained from participants.

Sample handling and storage

Fresh faeces specimens were collected in a non-sterile container, and rectal swabs were collected in a commercial system (MWE DRYSWAB™). Faeces specimens were visually inspected, and their appearance was recorded (e.g., watery). Rectal swabs were visually inspected for discolouration immediately post-sampling (re-collection was performed if no discolouration was evident). Samples were stored at 4°C for <72 h before being transported to our reference laboratory and ultimately stored at −80°C prior to DNA extraction.

Sequencing methods

DNA extraction was performed using the QIAamp Power Fecal Pro DNA kit (Qiagen, Clayton, Victoria). Briefly, each sample was mixed with lysis buffer and sterile zirconia beads as per the manufacturer’s instructions. After bead-beating, using a modified Vortex Genie vortex mixer for 10 min (faeces) or 30 min (swabs) at room temperature, the samples were centrifuged for 5 min at 13,200 rpm and the supernatant was further treated as per the manufacturer’s recommendations. A blank control was included with each extraction run and subsequently sequenced for quality control and monitoring of possible contamination. Library preparation and sequencing were performed as previously described by Schlebusch et al.⁹

Bioinformatic analysis

Human reads were quantified and removed by mapping trimmed reads to the human genome using bowtie2.¹⁰ The raw reads were trimmed which were then used as input to MGmapper.¹¹ An acquired AMR gene database (ResFinder version 8 (1)) was used to annotate properly paired reads (MGmapper option fullmode) where each read pair had sequence coverage of at least 80% compared with the length of the trimmed reads. This was the only filter that was applied to discard a read pair. An AMR gene database (ResFinder) was used to annotate properly paired reads.¹² To enable multiple database hits, AMR genes were mapped using the fullmode option in MGmapper for the most optimal abundance calculation. Genomic annotation was performed by identifying the best hit (MGmaper option bestmode) for a pair of reads when aligned against a range of reference sequence databases. The total bacterial read count for a sample was calculated as the sum of read counts from each of the bacteria-related databases. The total fraction of unmapped reads for all samples was used to translate the percentage of unmapped reads into Z-scores (i.e., the number of standard deviations from the mean). Community profiling was performed using Kraken.¹³ Relative abundance of AMR genes and bacterial genera was calculated as fragments per kilobase of transcript per million mapped reads (FPKM). This was done to account for both sample-wise sequencing depth differences and size-dependent probability of observing a reference. For AMR genes, FPKM was calculated on each individual ResFinder reference sequence. FPKMs were subsequently summed up across categories to bacteria genera, drug class level and highly homologous AMR gene groups. Alpha and beta diversity and richness were calculated on rarefied versions of the resistance and bacterial count matrices.

Results

Clinical data

Ten patients were successfully enrolled and randomised into two clinical trials (Table 1). Age ranged from 20 to 78 years old, and 6 of 10 patients were female. The bloodstream isolates encountered were Escherichia coli, Streptococcus agalactiae, Klebsiella pneumoniae and Citrobacter koseri. The trial antibiotics administered for directed therapy for bloodstream infection were ampicillin, ceftriaxone, cefazolin, benzylpenicillin, piperacillin-tazobactam and meropenem. Empiric antimicrobial therapy was heterogeneous among patients, and there was no washout period. These antibiotic regimens included ampicillin plus gentamicin, clindamycin plus benzylpenicillin, piperacillin-tazobactam, piperacillin plus gentamicin and ampicillin plus gentamicin plus metronidazole (Figure 1). One patient was pregnant (Patient 10). No patient received nutrition via an enteral feeding tube. The patient timeline of gut microbiome antimicrobial exposure and faecal sampling is outlined in Figure 1.

Table 1.

Summary of clinical data.

Patient Number	Age	Sex	Bloodstream isolate	Source of bacteraemia	Trial	Trial antibiotic	Comorbidities	Relevant medications
Patient 1	55	Female	E. coli	Urinary tract infection	GAMECHANGER	Ceftriaxone	Malignancy	Chemotherapy
Patient 2	59	Female	E. coli	Urinary tract infection	BALANCE	Cefazolin	Diabetes mellitus Alcoholism	Pantoprazole
Patient 3	70	Female	K. pneumoniae	Urinary tract infection	BALANCE	Ceftriaxone	Diabetes mellitus	Empagliflozin
Patient 4	61	Female	S. agalactiae	Cellulitis	BALANCE	Benzylpenicillin	—	Pantoprazole
Patient 5	78	Male	E. cloacae	Diabetic foot ulcer	GAMECHANGER	Meropenem	Diabetes mellitus	Metformin
Patient 6	72	Female	E. coli	Urinary tract infection	BALANCE	Ampicillin	—	—
Patient 7	67	Male	C. koseri	Urinary tract infection	GAMECHANGER	Piperacillin-tazobactam	Malignancy	Rosuvastatin
Patient 8	69	Male	E. coli	Line-related infection	BALANCE	Ampicillin	Chronic kidney disease Diabetes mellitus	—
Patient 9	58	Male	E. coli	Urinary tract infection	BALANCE	Ampicillin	Diabetes mellitus	Empagliflozin Metformin
Patient 10	20	Female	S. agalactiae	Endometritis	BALANCE	Ampicillin	—	—

Figure 1.

Patient timeline of gut microbiome antimicrobial exposure and faecal sampling.

Faecal sampling quality

Four patients produced four faecal specimens, three produced three specimens, two produced two specimens and one produced one specimen during their inpatient stay in the hospital whilst enrolled in the clinical trial. Of a total of 30 specimens, 15 were rectal swabs and 15 were stool samples. One patient had their stool specimens described as watery, whilst the rest were formed. Faecal sampling quality was determined for all 30 specimens (Table 2). Overall, DNA concentration was lower for rectal swabs compared to stool samples (16.35 vs 29.70 ng/µL). We determined that stool specimens had higher biomass than rectal swabs and, on average, yielded a much lower percentage of human (0.22% vs 24.78%) and skin flora (4.36% vs 16.37%) reads.

Table 2.

Quality of faecal specimens among patients enrolled in a clinical trial.

Number	Sample	Description	DNA concentration (ng/µL)	Total number of reads	Human reads	Human reads (% total)	Skin flora reads*	Skin flora reads* (% total)	Unassigned reads	Analysed reads
Patient 1	Faeces	—	2.40	11,832,836	47,579	0.40	4265	0.06	5,041,588	6,743,669
Patient 1	Faeces	Watery	1.47	12,568,571	132,100	1.05	3850	0.05	5,036,626	7,399,845
Patient 1	Faeces	Mucous	0.92	10,203,057	90,508	0.89	421,934	4.88	1,564,803	8,547,746
Patient 1	Faeces	Watery	1.04	—	—	—	—	—	—	—
Patient 2	Faeces	—	23.95	10,901,394	1410	0.01	364	0	658,176	10,241,808
Patient 2	Swab	—	17.71	11,245,386	3,260,199	28.99	6089	0.1	4,984,714	3,000,473
Patient 2	Faeces	—	78.28	7,002,617	910	0.01	447	0.01	1,065,392	5,936,315
Patient 3	Swab	—	67.16	9,747,874	7,303,746	74.93	1389	0.01	413,321	2,030,807
Patient 3	Faeces	—	13.54	22,693,170	6897	0.03	4869	0.03	6,261,248	16,425,025
Patient 3	Swab	—	23.65	18,326,408	15,485,673	84.50	1,482,455	8.3	472,688	2,368,047
Patient 4	Swab	Nothing	<Min	1,874,789	245,709	13.11	365,064	23.29	307,294	1,321,786
Patient 4	Faeces	—	121.00	15,926,500	7980	0.05	3560	0.03	5,234,170	10,684,350
Patient 4	Faeces	—	54.17	14,372,247	18,651	0.13	3285	0.03	2,667,210	11,686,386
Patient 4	Faeces	—	90.59	15,543,356	4426	0.03	1940	0.01	2,484,702	13,054,228
Patient 5	Swab	—	98.34	29,150,406	44,421	0.15	421,153	2.80	14,094,521	15,011,464
Patient 5	Swab	—	12.39	16,469,346	73,096	0.44	3,134,027	43.73	9,302,320	7,093,930
Patient 5	Swab	—	12.08	13,338,803	2,934,736	22.00	1,204,425	13.89	4,667,158	5,736,909
Patient 5	Swab	—	0.33	13,578,053	6,827,123	50.28	2,287,295	18.56	1,255,906	5,495,024
Patient 6	Swab	—	0.34	3,869,873	24,521	0.63	17,816	1.13	2,298,294	1,547,058
Patient 6	Faeces	—	17.55	59,576,581	28,831	0.05	6530	0.02	16,496,019	43,051,731
Patient 6	Swab	—	1.421	23,929,022	5,397,020	22.55	9205	0.05	4,808,883	13,723,119
Patient 7	Swab	—	3.526	3,998,469	1,646,437	41.2	1,203,200	34.24	484,575	1,867,457
Patient 7	Swab	—	0.6462	5,747,781	1,472,373	25.6	998,711	35.79	2,957,495	1,317,913
Patient 7	Swab	—	1.063	13,431,765	601,784	4.48	6,647,482	55.15	1,378,979	11,451,002
Patient 7	Faeces	—	9.259	24,626,061	25,896	0.11	12,686,718	55.78	1,882,951	22,717,214
Patient 8	Faeces	—	8.363	7,976,046	13,852	0.17	2265	0.04	1,697,467	6,264,727
Patient 8	Faeces	—	8.284	7,759,316	17,069	0.22	1690	0.03	1,464,189	6,278,058
Patient 9	Swab	—	1.91	7,534,548	38,058	0.51	217,567	3.74	1,719,016	5,777,474
Patient 10	Swab	—	4.617	7,389,252	167,742	2.27	191,717	4.80	3,397,662	3,823,848
Patient 10	Faeces	—	14.62	10,109,086	3240	0.03	4284	0.09	5,121,055	4,984,791

Skin flora contains bacterial genera Staphylococcus, Propionibacterium, Micrococcus, Cutibacterium and Corynebacterium.

Community profiling and antimicrobial resistance gene abundance

All stool specimens collected underwent community profiling and ARG abundance determination (Figures 2 and 3). Of trial participants receiving ampicillin (Patients 6, 8, 9 and 10), Patients 6, 9 and 10 demonstrated preservation of key anaerobic bacteria (Parabacteroides spp., Bifidobacterium spp., Bacteroides spp.). These patients also showed a marked reduction in Prevotella spp., Streptococcus spp., Lactobacillus spp. and Actinomyces spp. Enterococcus faecium became present or more abundant in Patients 8 and 10. Clostridioides difficile became more abundant in Patient 10. A reduction in the abundance of yeast (Saccharomyces spp.) was observed in Patient 8. An increase in abundance of bla_TEM—1A was seen in Patient 6. Of trial participants receiving ceftriaxone (Patients 1 and 3), Patient 3 demonstrated a marked reduction in Enterobacterales (including E. coli) in addition to the beta-lactamase gene bla_TEM-1A. Clostridioides difficile and E. faecalis became present or more abundant during antimicrobial administration. Patient 1 had no beta-lactamase genes detected and exhibited a marked reduction in anaerobic species (Bifidobacterium spp., Clostridium spp., Bacteroides spp.) in the last specimen collection which coincided with receipt of amoxicillin-clavulanate 24 h prior. There were no ESBL genes detected in trial participants receiving ceftriaxone. Of trial participants receiving piperacillin-tazobactam (Patient 7), E. faecium and yeast species (Candida spp. and Saccharomyces spp.) became present or increased in abundance, coincident with a reduction in key anaerobic species (Clostridium spp. and Bifidobacterium spp.) and the abundance of bla_Z gene (Penicillinase of Staphylococcus aureus). Of trial participants receiving meropenem (Patient 5), there was an increased abundance of E. faecium and Bacteroides spp. with a coincident reduction in Enterobacterales and other anaerobic species (Parabacteroides spp., Clostridium spp. and Bifidobacterium spp.). Of note, this patient also received piperacillin-tazobactam for 3 days prior to commencement of meropenem. A marked reduction in the abundance of certain beta-lactamase genes (bla_SHV-100, bla_OXA-392, bla_CMY-18) when transitioning from piperacillin-tazobactam to meropenem. A marked increase in abundance of the cfxA gene (encoding CfxA beta-lactamase) was also noted. There were no carbapenemase genes detected. Of trial participants receiving cefazolin (Patient 2), an increase in key anaerobic species (Bifidobacterium spp., Clostridium spp., Bacteroides spp. and Anaerococcus spp.) and abundance of bla_TEM-1A was observed. Of trial participants receiving benzylpenicillin (Patient 4), an increased abundance of Enterobacterales and anaerobic species (Parabacteroides spp., Clostridium spp., Bacteroides spp.) and of certain beta-lactamase genes (bla_Z, bla_SHV) and the penA gene (encoding PLP2, which is associated with decreased susceptibilities to extended-spectrum cephalosporins in gonococcal strains). A reduction in Bifidobacterium spp., Lactococcus spp. and Streptococcus spp. was also seen in this patient. Of patients who received at least one dose empiric gentamicin (Patients 1, 2, 3, 6, 7, 8, 9 and 10), an increase in abundance of aminoglycoside resistance genes (aph(6)-Id, aph(3’)-Ia, aph(3’)-Ib, aac(3)-XI) were observed in faecal specimens following exposure.

Figure 2.

Community profiling of stool specimens collected from hospitalised patients enrolled in a clinical trial.

Figure 3.

Antimicrobial resistance gene abundance in stool specimens collected from hospitalised patients enrolled in a clinical trial.

Diversity and richness

The Shannon index for alpha diversity was calculated for each specimen (Figure 4). The most dramatic decline in the Shannon index (4.286 to 1.880) was seen in Patient 5 receiving meropenem. Patients exposed to antimicrobials with negligible anaerobic activity (cefazolin, ceftriaxone; Patients 1, 2 and 3) demonstrated good preservation of diversity and richness indices. Similar high Shannon indices were noted for patients exposed to ampicillin, except for Patient 8 who received 2 days of piperacillin-tazobactam prior to enrolment into the clinical trial. Despite being exposed to metronidazole, Patient 10 had the highest recorded Shannon index values (both above 4.30).

Figure 4.

Intra- and inter-patient differences in the Shannon index among clinical trial patients receiving different antimicrobials.

Discussion

This nested sub-study was undertaken from within two clinical trials and sought to identify key changes in vivo that occur in the gut microbiome on exposure to different antimicrobials in a controlled setting. In addition, we aimed to establish an appropriate workflow and methodology for integrating microbiome analysis into clinical trials. There was a large amount of variability in DNA concentration values between specimens, which likely reflected differences in specimen volume, collection technique, staff training, as well as specimen type. Optimisation of the pre-analytical phase of microbiome analysis would require stool samples collected by (or with direct oversight from) a trained trial coordinator. Because specimens were batch tested, we were unable to accurately assess the concentration of DNA yielded in relation to time from collection to DNA extraction. Enhanced degradation of nucleic acid resulting from improper or prolonged storage has been well documented in the literature.^14,15

Our study was able to successfully link clinically important gut microbiome changes to individual antibiotic exposure in a clinical trial setting. Relative abundance of E. faecium, which is an ESKAPE pathogen (acronym of: E. faecium, S. aureus, K. pneumoniae, A. baumannii, P. aeruginosa and Enterobacter species) responsible for many healthcare-associated infections¹⁶ was observed to increase in individual patients that received ampicillin, meropenem and piperacillin-tazobactam.¹⁶ There were no van genes detected. Although thought to cause comparatively less collateral damage, ampicillin may target specific anaerobes, Enterobacterales and E. faecalis, culminating in an environment that enables the expansion of E. faecium.¹⁷ This finding has important antimicrobial stewardship and infection control implications and thus requires further investigation. C. difficile was detected in two patients, one who received a combination of ampicillin, gentamicin and metronidazole and the other with ceftriaxone. Although metronidazole has been described as a protective factor against C. difficile infection, colonisation resistance from ampicillin and gentamicin, favouring expansion of C. difficile, may have overcome this effect.¹⁸ Pre-clinical models have been able to shed light on the precise gut microbiome perturbations that lead to C. difficile infection.¹⁹ Porphyromonadaceae, Lachnospiraceae, Lactobacillus and Alistipes inhibit intestinal colonisation by C. difficile, whereas the genera Escherichia and Streptococcus have the opposite effect.¹⁹ C. difficile was not detected from stool samples in patients receiving piperacillin-tazobactam or meropenem, although the analytical sensitivity of targeted molecular assays (e.g., singleplex real-time PCR) is likely to be greater than for metagenomic sequencing.²⁰ The differences between C. difficile nucleic acid detection (and quantitation) from metagenomic sequencing versus conventional testing (e.g., stool EIA and toxin PCR) with respect to clinical disease and infection control implications require investigation.

The differential depletion of anaerobic genera from the gut microbiome on exposure to antimicrobials with anti-anaerobic activity was notable. Bifidobacterium species are important to gut microbial diversity and were relatively preserved in patients exposed to ampicillin. Despite predictably depleting Clostridium and Bifidobacterium species, exposure to meropenem resulted in an increased abundance of Bacteroides species. Ambler class A beta-lactamase gene cfxA is associated with cephamycin resistance and is found on Bacteroides and Parabacteroides species.²¹ The cfiA gene in B. fragilis is a metallo-beta-lactamase that is responsible for carbapenem resistance and has been documented.²² There were no cfiA genes identified in our study.

With regards to beta-lactam antimicrobial resistance determinants, bla_TEM-1A abundance increased in the setting of ampicillin exposure. Ampicillin likely selected TEM-1A-producing Enterobacterales in the gut microbiota.²³ This has direct and immediate clinical consequences in that further bloodstream or healthcare-associated infection would likely be due to a TEM-1A producer and would require therapy with a further detrimental effect on the gut microbiome (e.g., amoxicillin-clavulanate, ceftriaxone). This was made evident with Patient 3 who received ceftriaxone and demonstrated a rapid reduction in abundance of bla_TEM-1A followed by a rise in C. difficile and E. faecalis. Patient 5 received piperacillin-tazobactam in the days prior to clinical trial enrolment, followed by meropenem. The first faecal specimen at the point of antimicrobial transition demonstrated an abundance of beta-lactamase genes (bla_SHV-100, bla_OXA-392 and bla_CMY-18), which were eradicated with subsequent meropenem exposure. Concerns over the clinical efficacy and in vitro activity of piperacillin-tazobactam against OXA-1 and CMY-2-producing Enterobacterales have been raised.^24,25 Also, exposure of the gut microbiome to a certain class of antibiotics may result in resistance development to a separate class of antibiotics (i.e., cross-resistance). This has been demonstrated in a pre-clinical model.²⁶

Yeast species (Candida spp. and Saccharomyces spp.) became most prevalent in the gut microbiome in the patient with piperacillin-tazobactam exposure. Although many broad-spectrum antibiotics have been associated with gastrointestinal tract colonisation with Candida species, piperacillin-tazobactam may be associated with a much higher comparative risk.²⁷ Monitoring the risk of invasive candidiasis with piperacillin-tazobactam exposure in a clinical trial setting is important.

Integration of gut microbiome analysis into contemporary antibiotic clinical trials has been proposed to better aid treatment decisions and guide the placement of new antimicrobials in the clinical space in the post-marketing phase of development.⁸ A microbiome nested sub-study was performed in the PIRATE trial, which assessed the non-inferiority of shorter antibiotic courses (7 vs 14 days) for Gram-negative bacteraemia.²⁸ Enrolled participants were followed up to 30 days, and there was no difference in ARG abundance (median counts per million) or microbial species diversity (Shannon species diversity) between the two groups. In healthy volunteers, gut microbiome diversity and richness have been reported in a study to return to pre-antibiotic treatment levels after approximately 2 months.⁴ Long-acting antibiotics (e.g., azithromycin) and pre-treatment microbiota resembling sick and hospitalised patients lead to more persistent antibiotic-related microbiome changes. Other studies have echoed the importance of pre-treatment microbiota composition.²⁹ Moreover, 20% of patients in our study were taking a proton pump inhibitor, which has been independently associated with lower abundance in gut commensals and lower microbial diversity.³⁰ This likely confounded findings in these patients.

There were many limitations to this study. First, the evaluable population was heterogeneous and was derived from two separate clinical trials that sought to answer two different clinical questions. Moreover, there were many trial antibiotics administered (six in total), with many having pre-trial exposure to different antimicrobials altogether. None of the patients received only a single antimicrobial, and the flow-on effect from a previous antimicrobial agent could have contributed to findings. In addition, no patients had a faecal specimen taken prior to antibiotic treatment, thus limiting our interpretations of the data. Although this represents a “real-world” setting, it creates difficulty in defining true antibiotic-associated effects on the gut microbiome. Second, faecal sampling number and duration were suboptimal, and specimens were batch tested. Gut microbiome adaptations to antimicrobials are known to evolve over time, which requires analysis over weeks to months to get an accurate picture. From a feasibility standpoint, it is much easier to collect faecal specimens from patients admitted to the hospital. This needs to be weighed up with every study where collecting quality stool samples from the community may cause additional issues with the study (i.e., participant response rates, storage and transport of specimens and additional costs). A proposed solution to this has been the development of commercial self-collection kits with in-built DNA/RNA stabilisers, which prevent nucleic acid degradation and enable prolonged storage and transport times. Third, half of all specimens collected were from rectal swabs, which proved to produce more human and skin flora sequences using the techniques and swab type in this study. Although easier to collect and perhaps more acceptable to patients, this needs to be weighed against study quality. Fourth, this study only recruited 10 patients and therefore did not have enough power to detect gut microbiome effects that may be evident with certain antimicrobials. By having large groups of individuals being exposed to the same antimicrobial in a clinical trial setting, this would allow for statistical analysis, which could result in a more refined understanding.

Conclusion

Understanding the determinants of antimicrobial resistance development at an individual level is of great clinical importance. Stool samples provide higher biomass samples for sequencing and should be considered the preferred specimen type obtained for these studies. A number of challenges exist with regards to integrating gut microbiome analysis into clinical trials (e.g., obtaining an antibiotic-free stool specimen at baseline). Narrow-spectrum antibiotics may have a greater, and more clinically important, effect on our gut microbiota than once thought. The presence of key anaerobes such as Clostridium and Bifidobacterium species may play an important role in colonisation resistance and may be suitable outcome measures for future clinical trials. Further refinement of clinical trial infrastructure, methodology and workflow is required before existing challenges can be overcome.

Footnotes

Acknowledgements

We acknowledge Ms. Tiffany Au for the assistance with the study submission to the HREC and with obtaining site-specific approval.

Declarations

ORCID iD

Adam G. Stewart

References

Antimicrobial Resistance Collaborators. Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. Lancet 2022; 399(10325): 629–655.

Fishbein

SRS

Mahmud

Dantas

Antibiotic perturbations to the gut microbiome. Nat Rev Microbiol 2023; 21: 772–788.

Maier

Goemans

Wirbel

, et al. Unravelling the collateral damage of antibiotics on gut bacteria. Nature 2021; 599(7883): 120–124.

Anthony

Wang

Sukhum

, et al. Acute and persistent effects of commonly used antibiotics on the gut microbiome and resistome in healthy adults. Cell Rep 2022; 39(2): 110649.

Patangia

Anthony Ryan

Dempsey

, et al. Impact of antibiotics on the human microbiome and consequences for host health. Microbiologyopen 2022; 11(1): e1260.

Sorbara

Pamer

EG.

Interbacterial mechanisms of colonization resistance and the strategies pathogens use to overcome them. Mucosal Immunol 2019; 12(1): 1–9.

Knight

Vrbanac

Taylor

, et al. Best practices for analysing microbiomes. Nat Rev Microbiol 2018; 16(7): 410–422.

Stewart

Satlin

Schlebusch

, et al. Completing the picture-capturing the resistome in antibiotic clinical trials. Clin Infect Dis 2021; 72(12): e1122–e1129.

Schlebusch

Graham

RMA

Jennison

, et al. Standard rectal swabs as a surrogate sample for gut microbiome monitoring in intensive care. BMC Microbiol 2022; 22(1): 99.

10.

Langmead

Salzberg

SL.

Fast gapped-read alignment with Bowtie 2. Nat Methods 2012; 9(4): 357–359.

11.

Petersen

Lukjancenko

Frølund Thomsen

, et al. MGmapper: reference based mapping and taxonomy annotation of metagenomics sequence reads. PLoS One 2017; 12(5): e0176469.

12.

Florensa

Sommer Kaas

Thomas Lanken Conradsen Clausen

, et al. ResFinder - an open online resource for identification of antimicrobial resistance genes in next-generation sequencing data and prediction of phenotypes from genotypes. Microb Genom 2022; 8(1): 000748.

13.

Wood

Salzberg

SL.

Kraken: ultrafast metagenomic sequence classification using exact alignments. Genome Biol 2014; 15(3): R46.

14.

Thomas

Clark

Doré

Fecal microbiota analysis: an overview of sample collection methods and sequencing strategies. Future Microbiol 2015; 10(9): 1485–1504.

15.

Fouhy

Deane

Rea

, et al. The effects of freezing on faecal microbiota as determined using MiSeq sequencing and culture-based investigations. PLoS One 2015; 10(3): e0119355.

16.

De Oliveira

DMP

Forde

Kidd

, et al. Antimicrobial resistance in ESKAPE pathogens. Clin Microbiol Rev 2020; 33(3): e00181–19.

17.

Dubin

Pamer

EG.

Enterococci and their interactions with the intestinal microbiome. Microbiol Spectr 2014; 5(6): bad-0014-2016.

18.

Cui

Dong

Zhang

, et al. Risk factors for Clostridioides difficile infection and colonization among patients admitted to an intensive care unit in Shanghai, China. BMC Infect Dis 2019; 19(1): 961.

19.

Schubert

Sinani

Schloss

PD.

Antibiotic-induced alterations of the murine gut microbiota and subsequent effects on colonization resistance against clostridium difficile. mBio 2015; 6(4): e00974.

20.

Ferreira

Otani

Møller Aarestrup

, et al. Quantitative PCR versus metagenomics for monitoring antibiotic resistance genes: balancing high sensitivity and broad coverage. FEMS Microbes 2023; 4: xtad008.

21.

Rong

SMM

Rodloff

Stingu

CS.

Diversity of antimicrobial resistance genes in bacteroides and parabacteroides strains isolated in Germany. J Glob Antimicrob Resist 2021; 24: 328–334.

22.

Kierzkowska

Majewska

Karłowicz

, et al. Phenotypic and genotypic identification of carbapenem resistance in Bacteroides fragilis clinical strains. Med Microbiol Immunol 2023; 212(3): 231–240.

23.

Livermore

Moosdeen

Lindridge

, et al. Behaviour of TEM-1 beta-lactamase as a resistance mechanism to ampicillin, mezlocillin and azlocillin in Escherichia coli. J Antimicrob Chemother 1986; 17(2): 139–146.

24.

Stewart

Paterson

Young

, et al. Meropenem versus piperacillin-tazobactam for definitive treatment of bloodstream infections caused by ampC β-lactamase-producing Enterobacter spp, Citrobacter freundii, Morganella morganii, Providencia spp, or Serratia marcescens: a pilot multicenter randomized controlled trial (MERINO-2). Open Forum Infect Dis 2021; 8(8): ofab387.

25.

Henderson

. Association with 30-day mortality and MIC in patients treated with piperacillin/tazobactam for Escherichia coli and Klebsiella pneumoniae bloodstream infections that are non-susceptible to ceftriaxone from patients enrolled in the MERINO trial. In: 29th European congress of clinical microbiology & infectious diseases, Amsterdam, Netherlands, 2019.

26.

Surathu

Raplee

, et al. The effect of antibiotics on the gut microbiome: a metagenomics analysis of microbial shift and gut antibiotic resistance in antibiotic treated mice. BMC Genomics 2020; 21(1): 263.

27.

Lin

Carmeli

Zumsteg

, et al. Prior antimicrobial therapy and risk for hospital-acquired Candida glabrata and Candida krusei fungemia: a case-case-control study. Antimicrob Agents Chemother 2005; 49(11): 4555–4560.

28.

Leo

Lazarevic

von Dach

, et al. Effects of antibiotic duration on the intestinal microbiota and resistome: the PIRATE RESISTANCE project, a cohort study nested within a randomized trial. EBioMedicine 2021; 71: 103566.

29.

Rashidi

Ebadi

Rehman

, et al. Gut microbiota response to antibiotics is personalized and depends on baseline microbiota. Microbiome 2021; 9(1): 211.

30.

Jackson

Goodrich

Maxan

M-E

, et al. Proton pump inhibitors alter the composition of the gut microbiota. Gut 2016; 65(5): 749–756.