Quantifying the bystander effect of antimicrobial use on the gut microbiome and resistome in Malawian adults

Domain: biology
Published: 2025-12-21
Editorial outcome: major_revision

Abstract
Antibiotic treatment for sepsis has an unintended yet crucial consequence: it exerts a bystander effect on the microbiome, changing its bacterial composition and resistome. Antimicrobial stewardship aims, in part, to minimise this effect to prevent development of subsequent drug-resistant infection, but data evaluating and quantifying these changes are largely lacking, especially in low-income settings which are disproportionately affected by antimicrobial resistance. Such data are critical to creating evidence-based stewardship protocols. Here, we address this data gap in Blantyre, Malawi. We use longitudinal sampling of human stool and metagenomic deep sequencing to describe microbiome composition and resistome pre-, during- and post-antimicrobial exposure. We develop Bayesian regression models to link these changes to individual antimicrobial agents. We find that ceftriaxone, in particular, exerts strong off-target effects, both increasing abundance of Enterobacterales, and the prevalence of macrolide and aminoglycoside resistance genes. Simulation from the fitted models allows exploration of different stewardship strategies and can inform practice in Malawi and elsewhere.

Introduction
Antimicrobial resistance (AMR) is a global public health threat, and a key strategy to prevent it is antimicrobial stewardship. Stewardship aims to avoid unnecessary antimicrobials, to reduce the duration of antimicrobial use to the minimum necessary, and to select an antimicrobial agent with as narrow a spectrum of antibacterial activity as possible1. These principles aim in part to minimise antimicrobial pressure for promotion of AMR in bacteria other than the intended pathogenic target-the so-called bystander effect2. Even though minimising the bystander effect is a key factor in the selection of an antimicrobial agent for a given infection, data quantifying the magnitude and duration of this effect for a given antimicrobial at the individual level are lacking.

Addressing this is key to designing and implementing antimicrobial strategies that can minimise the development of AMR. In sepsis, for example, it is recognised that early antimicrobials confer a survival advantage and global efforts to improve sepsis care prioritise early antimicrobials in suspected sepsis3. But this strategy may result in widespread administration of broad-spectrum antimicrobials, including to people who are ultimately found not to have sepsis or an infectious cause of their illness. This is particularly the case in low-resource settings where diagnostics to inform targeted antimicrobial treatment are frequently unavailable. To truly assess the impact of such a strategy requires not only descriptions of clinical outcomes but tools to quantify the unintended effects of antimicrobials in promoting AMR, both in pathogens and in bystander bacteria.

Shotgun metagenomic sequencing provides a method to achieve this, enabling a quantitative assessment of bacterial taxa in a sample (microbiota) and antimicrobial resistance genes (resistome), and linking changes to antimicrobial exposures. Most work has focussed on the development of the gut microbiome in the first days to years of life, where antimicrobial exposure delays development of a mature microbiome4, reduces diversity5 and promotes colonisation with resistant organisms6,7, with evidence that narrower-spectrum treatment may reduce this effect8. In adults, data are largely restricted to healthy volunteers9,10 or specific cohorts11 (e.g. inflammatory bowel disease) in high resource settings—where data suggest that a major determinant of antimicrobial effect on the microbiome is the pre-exposure composition12. Data from low-resource settings, like much of sub-Saharan Africa, is very scanty, particularly in people with febrile illness. This group may be expected to have both a significant broad-spectrum antimicrobial exposure plus different pre-exposure microbiota composition to healthy controls, and so may well have different antimicrobial-induced microbiota changes than healthy volunteers in high-resource settings—but data describing this are largely absent. Given that the highest burden of AMR falls in low-resource settings13 this is an important data gap to address.

This present work aims to address this by leveraging our prior study which described the aetiology and clinical outcomes of a cohort of adults admitted to hospital in Blantyre, Malawi with sepsis14. In that study, we described the development of gut mucosal colonisation with Extended-spectrum beta-lactamase producing Enterobacterales (ESBL-E), as defined by longitudinal sampling and selective culture15. We linked antimicrobial exposure to ESBL-E carriage with a Bayesian modelling approach, demonstrating that antimicrobial exposure acted to promote ESBL-E carriage. Here, we build on this: we describe the dynamics of gut microbiota and resistome of the cohort under antimicrobial pressure, expanding our models to quantify the bystander effect of individual antimicrobial agents. We find that ceftriaxone, in particular, exerts strong off-target effects, both increasing the abundance of Enterobacterales and the prevalence of macrolide and aminoglycoside resistance genes.

Results
Quantifying the bystander effect on microbiome: ceftriaxone and ciprofloxacin exposure are associated with increased Enterobacterales abundance

The original study recruited 425 adults between February 2017 and October 2018 into three study arms: 225 participants with sepsis admitted to Queen Elizabeth Central Hospital, Blantyre Malawi, and exposed to antimicrobials, 100 age- and sex-matched antimicrobial-unexposed hospital inpatients and 100 community members. Stool or rectal swab samples were collected at recruitment (within 24h of hospitalisation for hospitalised participants), then at four subsequent visits: 7, 28, 90 and 180 days later, except for community members who did not have day 7 or 90 samples. For the current analysis we carried out shotgun metagenomic sequencing on a subset of samples: 426 samples from 162 participants passed QC and were incorporated. Table 1 shows the demographics of included participants. Most of the cohort (109/162, 67%) received at least one antimicrobial, most commonly ceftriaxone (94/109 of antimicrobial-exposed, 86%), followed by co-trimoxazole (56/109, 51%), ciprofloxacin (30/109, 28%) and amoxicillin (25/162, 15%, Supplementary Table 1). Co-trimoxazole had a prolonged course length compared to other antimicrobials (Supplementary Table 1) because use was commonly as preventative therapy (CPT) in the context of HIV (52/56 co-trimoxazole prescriptions, 93% were CPT); this also had a reduced dose compared to treatment (480 mg once daily versus 960 mg twice daily). Antibiotic combination therapy was unusual (Supplementary Fig. 1, with 1,418/9,559 [15%] person-days of antibacterial exposure) but where it occurred, it was most commonly co-trimoxazole with another antimicrobial (1,034/1,418 [73%] person-days of antibacterial combination therapy). Sequential antibacterial exposure was common: 27/109 (25%) participants received one antibacterial, 48/109 (44%) two, and 34/109 (32%) three or more antibacterials.

We first examined microbiome composition stratified by study arm and visit. There were shifts in alpha diversity (Shannon diversity), most marked at visit 1 (day 7) in the hospitalised/antimicrobial-exposed group (Fig. 1A), corresponding to the time of maximal antimicrobial exposure. We explored this effect with linear modelling of Shannon diversity. A within-participant correlation structure accounted for repeated sampling, and we included the top four antimicrobials—ceftriaxone, ciprofloxacin, cotrimoxazole and amoxicillin and hospitalisation as covariates (hence accounting for different durations of antibiotic exposure or hospitalisation), with an exponential decay of effect following cessation. Stool (versus rectal swab) was included as a covariate to account for any differences introduced by different sampling methods. This identified that ceftriaxone exposure (Fig. 1B, D) was most strongly associated with a decrease in diversity; cotrimoxazole had the least effect. Hospitalisation itself was also associated with a reduction in diversity (Fig. 1B, D). Sampling stool (versus rectal swab) did not have an association with diversity (Fig. 1C). Shannon diversity returned to baseline by ~50 days in simulations following perturbation (Fig. 1D).

We assessed beta diversity with all-against-all Bray-Curtis dissimilarity and principal components analysis; there was overlap of all three arms of the study in PCA space, but 61 hospitalised/antimicrobial exposed samples fell outside the 95% percentile of the distribution of the antimicrobial unexposed samples, consistent with different microbiome composition (Fig. 1E). Hospitalised/antimicrobial exposed participants had persistent differences in within-participant Bray-Curtis dissimilarity between baseline and subsequent samples (Fig. 1E), compared to controls. Hence, though Shannon diversity returned to baseline, there is evidence for changes in microbiome composition due to antimicrobial exposure, which persist out to 6 months following exposure.

At all time points and across all arms the most abundant Phylum was Bacteroidetes (Fig. 2A); Proteobacteria (largely Enterobacterales) were more abundant at visit 1 (day 7) in the hospitalised/antimicrobial exposed group (Fig. 2A–C), the time point which corresponds to maximal antimicrobial exposure. To quantify this, we fit negative-binomial Bayesian mixed effects models to the absolute number of reads assigned to a given taxon with a per-participant random effect with a multivariate-normal correlation structure to account for repeated measurements, a per-sample read depth offset to account for varying sampling depth and covariates (antimicrobial exposure, hospitalisation, and stool versus swab) as above. A separate model was fit for each of the top three phyla, the top 10 orders of phylum Proteobacteria, and the top 10 genera of order Enterobacterales.

The results of these models are shown in Fig. 2 and Supplementary Fig. 2. Ceftriaxone and ciprofloxacin are associated (95% Credible Interval [CrI] effect >1 on log scale) with increase in Proteobacteria abundance, driven by increase in Order Enterobacterales and, within that order, the genera Escherichia and Shigella (though credible intervals on genus-level parameter estimates are wide and 95% CrI cross 1). Across all models, the posterior median half-life of the exponential decay of exposure effect was 11 (range 4–156) days (Supplementary Fig. 2). Simulation of the fitted models (Fig. 2E, F) allows comparison of the effect of different antimicrobial strategies on microbiome composition; generally, antimicrobial-associated effects return to baseline by 30 days following exposure. Rectal swabs (versus stool) had a higher abundance of Proteobacteria, driven by the genera Enterobacterales, Campylobacterales, and Pseudomonadales, and within Enterobacterales, a higher abundance of Escherichia/Shigella, Klebsiella, and Salmonella.

Ceftriaxone exerts a bystander effect on the resistome, increasing prevalence of aminoglycoside and macrolide resistance

We used metagenome assembly followed by AMRFinderPlus to define each individual’s resistome over time. Across the 426 samples, we identified 369 unique AMR genes of 25 classes and 60 subclasses (Fig. 3A, Supplementary Fig. 3); trimethoprim, tetracycline, beta-lactam, sulphonamide, and lincosamide/macrolide/streptogramin resistance genes were present in almost all samples, as were aminoglycoside resistance genes despite low levels of intestinal excretion of these drugs in humans. The plasmid-mediated colistin resistance gene mcr was detected twice (mcr10 and mcr10.1): in two different participants, neither of whom had hospital admission within the previous 3 months, despite colistin being prohibited in Malawi, as seen in a recent large E. coli genome collection from Blantyre16. Many of the cephalosporin and carbapenem resistance genes were Bacteroides-specific and less clinically relevant in terms of causing drug-resistant infection in our setting (Supplementary Fig. 4); excluding these (which we defined as defined as cfiA, cblA, crxA, cepA or cfxA beta-lactamases), cephalosporin and carbapenem resistance genes were identified in 74% (102/138) and 7% (10/138) of samples from hospitalised participants respectively (Supplementary Table 2), most commonly blaCTX-M-15 and blaOXA-818 (Supplementary Fig. 4).

Five AMRFinderPlus defined gene subclasses were more prevalent in hospitalised/antimicrobial exposed group at visit 1 (day 7) than other study arms or visits, corresponding to time of maximal antimicrobial exposure (Supplementary Fig. 5): they conferred aminoglycoside, macrolide and cephalosporin resistance, consistent with these subclasses being associated with antimicrobial exposure. To quantify this effect and relate it to individual antimicrobial agents we fit models identical to the taxonomy models but including the presence/absence of AMRFinderPlus genes subclass in a mixed-effect logistic regression model (Fig. 3, Supplementary Figs. 6, 7). These confirmed that ceftriaxone exposure was associated (95% CrI of odds ratio >1) with increased prevalence of genes conferring resistance to aminoglycosides (primarily aac(6’)-I, aac(3)-II and aph(3’)-II alleles), macrolides (mphA, msrC, ermB) and rifamycins (arr). The effect of ciprofloxacin was similar: it was associated with the presence of aminoglycoside (aac(6’)-I) and macrolide (mphA, ermC) genes. Amoxicillin and co-trimoxazole were associated with fewer resistance subclasses than ceftriaxone (amoxicillin with mphA macrolide aac(6’)-I aminoglycoside resistance genes and co-trimoxazole with msrD and mefA macrolide resistance and qnr plasmid-mediated quinolone resistance).

Stool (versus rectal swab) did not have a strong effect on the presence of resistance gene subclass: spectinomycin resistance genes were more likely to be identified in rectal swab samples, and trimethoprim resistance genes more likely to be identified in stool samples, but in both cases, confidence intervals were wide (Supplementary Fig. 6).

The 95% CrI of effect estimates for the association of all antibiotics with cephalosporin resistance genes crossed the null (Fig. 3C), but cephalosporin resistance genes were very commonly detected across all samples. Bacteroides-specific genes were included in this analysis (Fig. 3B) which were present in almost all samples-this could obscure an effect on other beta lactamases in the modelling analysis. Hence, we stratified all beta lactamases as Bacteroides-associated (defined as cfiA, cblA, crxA, cepA or cfxA beta-lactamases) or not (all other beta lactamases) and refitted the models (Fig. 3F). This showed that, consistent with our previous culture-based analysis, there was an association of ceftriaxone exposure with presence of non-Bacteroides cephalosporin resistance genes (largely blaCTX-M-15); hospitalisation and co-trimoxazole exposure also showed an association with cephalosporin resistance, as did amoxicillin and ciprofloxacin, though in the case of the latter two antimicrobials the 95% CrI crossed the null. Co-trimoxazole was also associated with the presence of narrow spectrum beta lactamases (most commonly blaTEM and blaOXA).

Overall, and across all models, the half-life of the effect of antimicrobial exposure was more prolonged in resistome that taxonomy models, with a posterior median (range) 76 (8–245) days; simulations from the posterior quantify the effect of ceftriaxone in particular in driving aminoglycoside and macrolide resistance genes (Fig. 3D, E), and again show that antibiotic-induced perturbations in resistome return to baseline by around 30 days.

Because AMRFinderPlus is likely biased towards pathogenic/culturable bacteria, we repeated the above analysis using ResFinderFG, which contains antibiotic resistance genes identified from functional metagenomic studies17. In contrast to the AMRFinderPlus analysis there was no clear relationship between prevalence of resistance genes expected to confer resistance a given antimicrobial agent and study arm or visit; prevalence of aminoglycoside resistance genes was higher in the hospitalised/antimicrobial exposed, but confidence intervals were wide and overlapping (Supplementary Fig. 8). In the modelling analysis, only presence of genes expected to confer resistance to gentamicin were associated with ceftriaxone exposure, in contrast to AMRFinderPlus-identified genes (Supplementary Fig. 9).

Resistance is associated with clinically relevant Enterobacterales, including E. coli

In the AMRFinderPlus analysis, presence of genes of the macrolide and aminoglycoside resistance subclasses (as well as chloramphenicol and the fluoroquinolones subclasses) correlated with Proteobacteria abundance (Supplementary Fig. 10), particularly Enterobacterales, and Escherichia within this order. To explicitly link AMR gene presence to clinically relevant Enterobacterales, we focussed on E. coli. We binned all assembled contigs, identified bins comprising E. coli (defined as Average Nucleotide Identity to an E. coli reference >95%) and identified AMR genes that could be attributed to E. coli using AMRFinder using the E. coli specific models which allowed identification of point mutations conferring resistance (e.g. in quinolone-resistance determining region, QRDR) as well as AMR gene presence. We compared the diversity of the genomes identified using this approach to our previous analysis of E. coli genomes we identified in the same samples using ESBL-selective media by clustering the genomes using popPUNK v2.7.2. As expected, the genomes from the metagenomic approach were more diverse (33 unique popPUNK clusters in culture-derived genomes versus 95 in the metagenome-derived genomes, Fig. 4A). We identified 93 unique AMR associated genes/mutations in 260 E. coli bins: predicted fluoroquinolone resistance was common (128/260 [49%] samples, commonly gyrA and parC mutations), as was trimethoprim (75/260 [29%], all dfr alleles) and cephalosporin resistance (68/260 [26%], most commonly blaCTX-M-15, Supplementary Fig. 11).

Fitting this E. coli specific AMR Finder subclass presence/absence data using the models described above (Fig. 4, Supplementary Figs. 12, 13) revealed that the association of ceftriaxone exposure with aminoglycoside and macrolide resistance was at least partly mediated via resistance in E. coli: presence of aminoglycoside (aac(3)-IId and aac(6’)-Ib) and macrolide (mphA) genes in E. coli was associated (95% CrI or odds ratio >1) with ceftriaxone exposure (Fig. 4, Supplementary Fig. 13). Ceftriaxone exposure was associated with AMR determinants of multiple other subclasses including cephalosporin (largely blaCTX-M-15 and blaCMY-2 genes), but also of the quinolone (largely QRDR point mutations) and sulphonamide (largely sul alleles) subclasses, and point mutations associated with fosfomycin, colistin and aztreonam resistance (Fig. 4, Supplementary Fig. 13). Quinolone exposure was associated with presence of trimethoprim resistance determinants in E. coli; amoxicillin exposure with quinolone and fosfomycin resistance mutations in E. coli, whereas co-trimoxazole exposure was not associated with clinically relevant antimicrobial resistance determinants in E. coli. Hospitalisation, independent of exposure to antimicrobials of these four classes, was associated with the presence of resistance determinants of the cephalosporin and aztreonam subclass in these E. coli genomes. Stool (versus rectal swab) did not show an association with the presence of resistance genes in this E. coli focussed analysis.

Methods
Inclusion and ethics statement
The study was approved by the research ethics committees of the Liverpool School of Tropical Medicine (16-062) and Malawi College of Medicine (P.11/16/2063). The study has included local researchers throughout the research process and took place in the context of the Malawi Liverpool Wellcome clinical research project, which has ongoing capacity-building programmes. The research does not result in stigmatisation, incrimination, discrimination or otherwise personal risk to participants and does not involve health, safety, security or other risk to researchers. Written informed consent was obtained from all participants.

Study design and setting
The design of the observational clinical cohort study on which this analysis is based is described in detail elsewhere15. The study was approved by the research ethics committees of the Liverpool School of Tropical Medicine (16-062) and Malawi College of Medicine (P.11/16/2063).

Samples were collected between February 19th, 2017, and 1st May 2019. In arm one, adults at least 16 years old with sepsis-defined as fever (or history of fever within 72h) and evidence of organ dysfunction (oxygen saturation <90%, systolic blood pressure <90mmHg, respiratory rate >30 breaths/minute or Glasgow Coma Score <15) were enroled. Two comparator cohorts were also enroled: arm 2 was composed of age- and sex-matched adults being admitted to hospital via the Emergency Department but with no current plan for antibiotic therapy from the attending clinical team, and arm 3 was a group of age-, sex- and location-matched community controls. Participants were excluded from the comparator groups if they had received antibiotics within the past four weeks. Hospitalised participants were followed up daily by a member of the study team to record exposure to antibiotics. Treatment decisions were taken by clinicians who were independent from the study team. On discharge, patients were followed up at approximately days 7, 28, 90 and 180, corresponding to visits 1, 2, 3 and 4. Day 7 and 90 (1 and 3) visits were omitted for the community group. At baseline (visit 0, within 24h of hospital admission for hospitalised participants) and at each study visit, a stool sample or rectal swab was collected. Missing samples were unusual (13% of visits) and distributed across all study visits; because of participant deaths more samples were collected in the earlier visits, Supplementary Fig. 14); full flow of participants through the study is described in the original study publication15. Following incubation in enrichment broth, culture and identification (using analytical profile index) for the presence of ESBL-E organisms was carried out, and primary samples were stored.

Isolate sequencing
Isolate selected for whole genome sequencing and their extraction have been described previously28,29, and these data are available at the European Nucleotide Archive under project IDs PRJEB26677, PRJEB28522, PRJEB36486 and PRJNA869071 with linked metadata available as the RblantyreESBL package30. Briefly, DNA was extracted from overnight nutrient broth cultures using the Qiagen DNA Mini Kit (Qiagen, Germany) according to the manufacturer’s instructions. Sequencing was performed at the Wellcome Sanger Institute using the Illumina HiSeq X10 system (Illumina Inc., USA), producing 150bp paired-end reads.

Metagenomic sequencing
Samples for metagenomic sequencing were stored at 4 degrees Celsius immediately following collection then at −20 degrees Celsius within 24h, and DNA extracted within 2 weeks. A subset of 450 stored extracted DNA samples from 163 participants were selected for metagenomic sequencing. These were selected on pragmatic grounds to maximise longitudinal representation. Samples were not sequenced from individuals who had only completed one study visit. Due to the process of selecting samples for sequencing, age and sex matching of the comparator cohorts was not maintained. Genomic material was extracted using the Qiagen DNA stool mini kit (Hilden, Germany), with the addition of a bead-beating step. A sterile saline negative control sample for each extraction run was included (25 in total), which were also prepared and sequenced as below. Library preparation was performed with the NEBNExt Ultra II FS kit (New England Biolabs, Ipswich, Massachusetts, Unites States) on the Mosquito Ultra II platform (Qiagen), using a 1/10 reduced volume protocol. Metagenomic sequencing was performed at the University of Liverpool Centre for Genomic Research (Liverpool, U.K.), using the Illumina NovaSeq platform with an S4 flow cell (San Diego, California, United States). Sequencing was multiplexed and aimed at a depth of 100 million reads per sample. Reads were deposited in the European Nucleotide Archive, under project accession PRJEB86881.

Bioinformatic analysis
Quality control of reads. Modules from the MetaWRAP (v1.3.2) pipeline31 were used to standardise metagenome analysis. All paired-end reads underwent quality control using the MetaWRAP “read_qc” module to remove low-quality, adapter, and human sequence reads. The T2T consortium complete human genome (GCF_009914755.1) and human mitochondrial genome (NC_012920.1) were used as references for the removal of human reads. Samples with sequencing failure (fewer than 0.5 million reads were excluded from further analysis). Excluding these, the samples had a median (IQR) of 133 (107–150) million reads for stool samples and 82 (58–128) million reads for rectal swab samples, with median (IQR) proportion of human reads 0.1% (01–0.6%) for stool samples and 4.8% (0.4–20.2%) for rectal swab samples.

Read-based taxonomy. Reads were assigned taxonomy with Kraken232 (v2.1.2) using a custom database comprising all RefSeq complete genomes and proteins for archaea, bacteria, fungi, viruses, plants, and protozoa downloaded on 14 March 2023. This database also included complete RefSeq plasmid nucleotide and protein sequences, as well as a version of the NCBI UniVec database minimised for false positives. A confidence threshold of 0.1 was applied for read assignments, and reports were generated for downstream analysis.

Assembly of metagenomes and E. coli bins. Metagenomic reads were assembled using metaSPAdes33 (v3.1.3) under default settings. To generate the best bins possible, three binning strategies (MetaBAT2, CONCOCT and MaxBin2) were employed across all samples via MetaWRAP. The reference genome for the E. coli type strain (ATCC 11775, Accession: GCF_003697165.2) was then used as a reference for FastANI34 (v1.3.3) to identify any bins that were E. coli to the species level (>95% ANI). CheckM235 (v1.0.2) was then used to select bins that meet MIMAG standards (>50% completion, <10% contamination). For each sample, the most complete bin was then selected for downstream analysis, yielding 261 bins, representing the same number of samples.

Calling antimicrobial resistance from metagenomes and quantifying the bystander effect. To understand the general contribution of the microbiome to antimicrobial resistance, metagenomes were analysed to find acquired antimicrobial resistance genes using AMRFinderPlus36 (v3.11.20) under default settings (coverage 50%, identity 90%). Once E. coli was determined to be a primary organism of interest, a second analysis was conducted using an E. coli-specific model within AMRFinderPlus, allowing detection of point mutations associated with resistance. This refined analysis was applied to the E. coli metagenome-derived genome bins, and E. coli isolates. This approach aimed to compare the diversity of AMR genes between cultured and uncultured samples and to quantify the extent to which specific AMR determinants could be attributed to the E. coli metagenomic bins. Isolate reads were assembled using SPAdesSPA37 (v3.1.3). Where genes that may be associated with unculturable bacteria, we performed the same analysis using the ResFinderFG v2.0 database, which collates antimicrobial resistance genes from functional metagenomic studies17. We generated a binary presence/absence matrix of ResFinderFG v2.0 antibiotic resistance genes across samples; to align the methods with the AMRFinderPlus analysis, hits were retained if BLAST alignments had ≥90% identity and ≥50% coverage of the reference gene. Gene names and antibiotic classes were extracted directly from FASTA headers.

Cultured vs uncultured diversity using PopPUNK. In order to characterised diversity in a comparable way, PopPUNK38 (v2.7.2) was used to assign clusters to E. coli bins and isolates, querying the “reference only” Escherichia coli v2 database. The resulting PopPUNK clusters were visualised in Microreact39 and summarised as frequency tables to identify the dominant clusters observed when comparing metagenomic versus culture-based approaches.

Statistical analysis and reproducibility
Study design and sample size are detailed above. No data were excluded from the analyses. The study was not randomised and there was no blinding.

All analysis was carried out in R40 v4.4.2 and all plots were generated with ggplot41 v3.5.1. Unless otherwise stated, summary statistics are presented as medians with interquartile ranges or proportions with exact binomial confidence intervals. Shannon Diversity and Bray-Curtis Dissimilarity were used to quantify alpha and beta diversity, and principal components analysis on alpha diversity were carried out using the PhyloSeq42 v1.5.0 R package. Kruskall-Wallis test to compare Bray-Curtis dissimilarity to baseline across the three study arms was carried out with ggstatsplot v0.13.0. Linear models as implemented in lm function in R were used to compare Shannon diversity between stool and rectal swab samples and PERMANOVA as implemented in the adonis2 function in the R package vegan v2.6.8 used to compare Bray-Curtis dissimilarity between stool and rectal swab samples.

Comparing stool and rectal swab samples
Comparing rectal swab and stool samples in terms of Shannon diversity, Bray-Curtis dissimilarity and taxonomy (Supplementary Fig. 15) showed that, though they were similar (stool vs rectal swab effect on Shannon diversity −0.15 (95% CI −0.36–0.07, p=0.17, R2=0.002 from linear model) there were some differences in beta-diversity (PERMANOVA p=0.001, R2=0.02); we accounted for this by including stool versus rectal swab as a covariate in the modelling analysis.

Linear regression: Shannon diversity. To quantify the effect of antimicrobial exposure on Shannon diversity, taxa abundance and presence of AMR genes, and account for any differences from including stool and rectal swab samples together, we constructed Bayesian regression models using the Stan probabilistic programming language, accessed via cmdstanR v0.8.1 using cmdstan v2.35. All the models had the same correlation structure to account for repeated measurements on individuals; the simplest model with the linear regression model for Shannon diversity, where the sample y is given by

μ i = β 0 + x ij β j + f i (1)

y i ~ Normal(μ, σ) (2)

Where μ is the mean and σ the standard deviation of the normal distribution. i=1, 2, :::n where n is the number of samples. There are j covariates in the model; x is a matrix with n rows: row i gives the covariate values for sample y; β are the regression coefficients. f encodes the within-participant correlation; it is 0 for different participants, but within-participant for a given sample is defined by

f k ~ MVN(0, Σ) (3)

i.e. a multivariate normal distribution with a k-by-k covariance matrix for k within-participant samples. This matrix uses an exponentiated quadratic kernel:

Σ = α 2 exp(−t ij 2 / 2l 2) (4)

Where t is the difference in time between two samples. α encodes the magnitude of the within-participant correlation and l, the length scale, the temporal correlation.

The effect of antimicrobials was encoded in a time-dependent manner, β(t).

If antimicrobials are administered between t and t then during exposure, the effect of a given antimicrobial j is

β(t > t > t ) = β (5)

b a j

The effect after exposure is

β(t) = β exp(−(t − t ) / τ) (6)

j j b

And the effect before any exposure (or for a participant with no exposure) is

β(t < t ) = 0 (7)

a

This allows the effect of antimicrobial exposure to decay exponentially on a timescale fit to the data, with a rate of decay defined by the parameter tau. Stool versus rectal swab is included in the model as a binary covariate without this time-dependent structure (i.e. a standard linear model covariate).

Priors were Student t distribution with 3 degrees of freedom and mean 0, sd 3 for β, τ, α: l used an inverse gamma distribution with both parameters set to 3; the mean of the prior on β 0 was 3.

Negative binomial regression: taxon abundance. Modelling absolute read numbers assigned to a given taxa used the same linear predictor, but assumed that the read number was negatively binomially distributed with an overdispersion parameter ϕ, and added an offset to account for varying read depths

log(μNB i) = β 0 + x ij β j + f i + log d i (8)

y i ~ NB(μNB, ϕ) (9)

Where d is the number of reads for sample i, and y is the number of reads assigned to the taxa of choice. A different model was fit for the top 10 phyla, orders, and genera. Priors were the same as the linear model except the prior on the intercept (β 0) was set to a mean of −3. Antimicrobial covariates had the same time-varying effect, and stool vs rectal the same non-time varying effect as the linear Shannon diversity model.

Logistic regression: AMR gene presence. Modelling presence or absence of AMR gene used the same linear predictor as above, but a logistic regression model and logit link function

logit(μLR i) = β 0 + x ij β j + f i (10)

y i ~ Bernoulli(μLR) (11)

Priors were the same as the linear model except the intercept (β 0) was set to a mean of 0. Antimicrobial covariates had the same time-varying effect, and stool vs rectal the same non-time varying effect as the linear Shannon diversity model.

Fitting and simulating from the models. Time was scaled by the standard deviation. All models were run with 4 chains for 1000 iterations with a warmup of 500 iterations. Convergence was assessed by the Gelman-Rubin statistic being close to 1, and inspection of traceplots. Posteriors were summarised using median and 95% credible intervals, unless otherwise stated. For simulations, covariate values were fixed and the whole posterior (excluding warmup) used to generate predictions of the population mean outcomes of interest (i.e. ignoring within-participant correlations), which were then summarised with medians and 95% quantiles, unless otherwise stated.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The study is an observational clinical cohort study from the Liverpool School of Tropical Medicine and Malawi College of Medicine. Conducted between February 19, 2017, and May 1, 2019, it involved 425 adults into three arms: sepsis patients exposed to antimicrobials, unexposed hospital inpatients, and community members. Comparator groups excluded those who had received antibiotics in the past four weeks. Hospitalized participants were followed daily for antibiotic exposure, with follow-ups at approximately days 7, 28, 90, and 180 post-discharge. Stool samples or rectal swabs were collected at baseline and each visit, cultured, and tested for ESBL-E organisms. Selected isolates underwent whole genome sequencing, with DNA extraction and sequencing performed using specific kits and the Illumina HiSeq X10 system. A subset of 450 samples from 163 participants underwent metagenomic sequencing using the Illumina NovaSeq platform. Quality control of reads was performed using MetaWRAP, and taxonomy assignment was done using Kraken2. Metagenomic reads were assembled using metaSPAdes. Antimicrobial resistance (AMR) genes were identified using AMRFinderPlus, with E. coli-specific models used for refined analysis. PopPUNK was used to assign clusters to E. coli bins and isolates, with diversity visualized using Microreact. Statistical analysis was conducted in R, using Bayesian regression models to analyze Shannon Diversity, taxon abundance, and AMR gene presence. Linear regression quantified the effect of antimicrobial exposure on Shannon diversity, while negative binomial regression modeled absolute read numbers assigned to taxa. Logistic regression modeled the presence or absence of AMR genes. Diversity shifts were noted, particularly in the antimicrobial-exposed group. Ceftriaxone exposure was strongly associated with diversity changes. Beta diversity analysis showed persistent differences in microbiome composition due to antimicrobial exposure. Proteobacteria, particularly Enterobacterales, were more abundant in the antimicrobial-exposed group. Ceftriaxone and ciprofloxacin were associated with increased Proteobacteria abundance. The study also found that ceftriaxone exposure drove the prevalence of aminoglycoside and macrolide resistance genes. Resistance genes correlated with Enterobacterales abundance, particularly E. coli.\n\nMAJOR COMMENTS\nPopulation: Many patients were already exposed to cotrimoxazole, which may have already affected the composition of the microbiota. In L139 Fig 2, there is a very high proportion of Proteobacteria even in the arms not exposed to antibiotics for sepsis. Authors may also consider the hypothesis that before reaching a healthcare structure, patients may have already taken antibiotics (easily accessible over the counter) even though they were supposed not to (but did this exclude cotrimoxazole?).\nAlso, the antibiotic exposure is not detailed (L99, Table 1, and Supp Table 1): 109/1062 patients received at least one antimicrobial; what about antibiotic switches and combination therapy? This should be specified as the effects observed on the gut microbiota are directly connected to the type of exposure.\nTechnical issues: The authors have considered both rectal swabs and fecal samples, which may strongly differ in terms of content. Swabs typically yield more human reads and may bring some bacteria associated with skin and mucosa colonization. A comparison between results obtained with swabs and fecal samples would be expected to ensure that they can be assumed to be similar. An overgrowth of opportunistic pathogens may also be seen in samples not immediately stored at low temperature. The authors should clarify these technical aspects and how they have been tackled.\nLack of novelty: The effect of ceftriaxone on the gut microbiota has been well-documented in several studies, yet not in the specific population studied here with high baseline resistance levels for 3GC and FQ-resistant E. coli. Nonetheless, the significant impact on the gut microbiota and the selection of antibiotic-resistant Enterobacterales could be expected from previous findings from other studies. This also applies to the other antibiotics.\nThe resistome part focused on ARG carried by culturable/pathogenic bacteria and not on ARG carried by commensal bacteria (a specific database would have been required). Hence, the findings are highly connected to the selection of opportunistic pathogens (Enterobacterales, Enterococci) under antibiotics, which again does not add much to the field. As highlighted in the discussion, connecting the dynamics of ARG to antibiotic exposure is highly difficult, especially because ARG from commensals have not yet been characterized in terms of spectrum and expression. This study does not bring novelty in addressing the resistome dynamics under antibiotic exposure.\nImpact: The authors stress that their results may guide AMS. AMS is also based on local epidemiology of resistance, aiming at maximizing the chances that empirical therapy would work on the most expected pathogens, while sparing at best the microbiota. From their results, what would be the impact of this work on changing the AMS practices? Also, authors may mix the spectrum (related to the clinical outcome) and the impact on the microbiota (of which the spectrum is a component among others).\n\nMINOR COMMENTS\nL116: How can the authors explain the effect of hospitalization on the microbiota diversity? Could this be explained by the exposure to other drugs such as other antimicrobials? Could patients in the non-sepsis cohorts receive non-antibiotic antimicrobials (e.g., antimicrobials in the Supp Table 1)?\nL140: Ciprofloxacin is associated with the disappearance of Enterobacterales in non-FQ-R carriers. Do the authors have an estimation of FQ-R carriage in their cohort at baseline?\nL154: Figure 3A and not 2A.\nL165: Non-Bacteroides carbapenemases: OXA-181 is not in the Supp Figure 3 (mix with OXA-818?). OXA-1 may not be considered as a cephalosporin-hydrolyzing enzyme.\nL170: Aminoglycosides-modifying enzymes (AMEs) encoding genes are prevalent regardless of AG exposition. AG are indeed known to have no effect on the microbiota (no intestinal excretion). This should be discussed.\nL231: CMY-2 (with a hyphen).\nL236: Fosfomycin.\nL248: The authors should mitigate that the association with an increase of Escherichia under ceftriaxone is associated with a high baseline rate of resistance (same for ciprofloxacin), otherwise susceptible E. coli decrease.\nL248", "reviewer_250": "Italicize the genera.\nL595-596: No more age-sex matching for group comparison due to the selection of samples may have introduced some biases in the comparison between groups; this should be discussed in a limitation section.\nL620: Kraken with custom database, which date for RefSeq downloading?\nL630: E. coli bin reconstruction: How have the authors delineated the potential multiple lineages? The phylogroups? Was there any QC applied (e.g., Total bin size)?\nL640: AMRfinder+ settings, please specify identity and coverage.\nL644: Issue of subdominance of resistant E. coli captured by culture and not by metagenomic sequencing. Were resistant E. coli quantified by culture? It would be interesting to assess a concentration where ARG from cultured E. coli become undetectable by metagenomic sequencing.\nL650: E. coli italicized.", "reviewer_2": "In this article, the authors use longitudinal sampling to quantify the impact of antibiotic use on the gut microbiome. They compare three populations: hospitalised individuals exposed to antibiotics, hospitalised individuals not exposed to antibiotics, and community members. Overall, the study is interesting and relevant in the context of understanding the complex link between antibiotic exposure and resistance. I only have a few questions (addressed to the authors below), but some relate to methodological points which I think would be important to clarify.\nMy major worry is related to the number of samples available for each visit. You mention that you analysed 426 samples from 162 participants. However, if all individuals had one sample for each visit, according to the protocol, I think there should be a total of 8 682 samples (5 visits for 109 “Hospitalised/antibiotic exposed” individuals + 5 visits for 29 “Hospitalised/antibiotic unexposed” individuals + 3 visits for 29 “Community/antibiotic unexposed” individuals). Could you please clarify this point? If samples are missing, how does this impact your conclusions? Particularly since you try to account for within-participant correlation.\nMy second major question is on variations due to bacteria acquired during hospitalisation, instead of selected for by antibiotics. Have you looked at hospital length of stay in this analysis? For example, I suspect the impact of hospitalisation on gut microbiome would not be the same for an individual hospitalised for 5 days compared to several weeks.\nMy third major question is on the timing for samples in hospitalised patients. You mention that a sample was taken at recruitment (i.e. visit 0, line 94), but that visit 1 was approximately 7 days after discharge. When exactly does recruitment take place? If it is at patient admission, then there is potentially substantial variability in the duration between visit 0 and visit 1, as this would depend on patient hospital length of stay, and would in any case be greater than 7 days. In that case, it is not clear to me how you account for this in your models. Alternatively, if this is at patient discharge, then patients would have already been exposed to antibiotics at that time, therefore you are not measuring a baseline microbiome but one already impacted by antibiotics.\nSome minor comments below:\nLine 95: “except for community members who did not have day 28 or 90 samples”, I think this is a typo, community members don’t have samples at 7 and 90 days (visits 1 and 3).\nTable 1: how are the 95% CIs obtained? These are characteristics relating to the study population, so there is no uncertainty there.\nFigures 1 and 2: it is not clear to me why the confidence intervals are always smaller for study visit 4. One interpretation could be that there is less between-individual variability at this visit than precedent ones, but this should not be the case for community members (since the hypothesis is that their gut microbiome is not substantially affected during the study period), and in any case should be mentioned.\nFigure 2e-f: you should correct the x-axis title to “Time (days)”.\nFrom line 154 onwards, you refer multiple times to figure 2 but I think this is a typo and should be figure 3, since that is the one presenting the resistome results.\nFigure 4a: I recommend you extend the figure width so that the x-axis labels are readable.\nLine 272: you suggest balancing the effect of ceftriaxone on the microbiota against its activity on locally prevalent pathogens. Could you comment on the feasibility of such a strategy? This is related to a comment you make in your introduction (lines 57-59): “this strategy may result in widespread administration of broad-spectrum antimicrobials, including to people who are ultimately found not to have sepsis or an infectious cause of their illness”. I agree that antibiotic stewardship is important, but improving patient outcomes would bear much more weight than the potential effect of the antibiotic on the microbiota when making the decision of whether or not to prescribe antibiotics in such cases.", "reviewer_3": "The authors explore the bystander effects of antibiotic use in sepsis patients in Malawi to shed light on bystander effects in LMICs and among individuals with severe infections. Although the findings are neither unexpected nor novel (the bystander effect of antibiotics has been demonstrated multiple times in other contexts), there is merit in carrying out this research in LMICs with patients experiencing severe infections. This study also benefits from employing longitudinal data that enables individuals to act as their own controls, along with the use of Bayesian modeling.\nThe authors use appropriate bioinformatic and statistical methods to investigate the data to answer questions. His work supports the conclusions and claims. There is adequate detail provided for the methods. The study provides new scientific knowledge on this topic, which has not yet been explored elsewhere.\nSpecific comments\nThe figures are easy to interpret and straightforward.\nThe introduction is clear and well written.\nThe Results section does not fully adhere to the appropriate scientific style and could benefit from revision in certain areas. I suggest carefully checking whether sufficient detail is provided when reporting results and whether non-scientific language (i.e., use of vague phrases such as \"some\" or unnecessary descriptive terms lacking clear numeric statistical interpretation) is present. A few examples, though not an exhaustive list, are provided below.\nPlease provide more information for observed changes, ie, increase or decrease in the results section.\nPlease avoid using descriptive words such as \"convincingly\" when reporting results of statistical tests. Similarly \"exposures acted with a prolonged effect\", \"apparently driving\" are not the appropriate scientific style of writing.\nDefine abbreviations, when first used, such as CrI.\nLine 188: What does \"which could attenuate an effect in other Genera\" mean?\nCode:\nThe Code repo must be cleaned so that scripts and files irrelevant to the paper are removed."}, "author_response": ""}, {"round": 2, "reviewer_comments": {"reviewer_1": "MAJOR COMMENTS\nPopulation: Many patients were already exposed to cotrimoxazole, which may have already affected the composition of the microbiota. In L139 Fig 2, there is a very high proportion of Proteobacteria even in the arms not exposed to antibiotics for sepsis. Authors may also consider the hypothesis that before reaching a healthcare structure, patients may have already taken antibiotics (easily accessible over the counter) even though they were supposed not to (but did this exclude cotrimoxazole?).\nResponse 2.1 - This is possible: though participants were specifically asked whether they had taken antibiotics and excluded if they had done so within 4 weeks (for the non-sepsis arms), it is possible that they were mistaken. We have added a sentence to the limitations section:\nLine 371 “It is also possible – in this setting where antimicrobials can be easily available without a prescription - that participants received antimicrobials prior to hospital admission, which could affect microbiome/resistome composition”\nR: the authors may add that this is particularly suspect especially in the sepsis – antibiotic arm (high proportion of Proteobacteria at time 0). What would be the explanation for such a high abundance of Proteobacteria, otherwise?\nAlso, the antibiotic exposure is not detailed (L99, Table 1, and Supp Table 1): 109/1062 patients received at least one antimicrobial; what about antibiotic switches and combination therapy? This should be specified as the effects observed on the gut microbiota are directly connected to the type of exposure.\nResponse 2.2 - We draw the reviewer’s attention to Supp. Table 1 which includes the number of participants who received a given agent and the median (IQR) range of exposure. To provide more detail on combination therapies and switches we have added Supplementary Figure 1 and the following text to the manuscript:\nLine 111: “Antibiotic combination therapy was unusual (1,418/9,559 [15%] persondays of antibacterial exposure) and most commonly cotrimoxazole with another antimicrobial (1,034/1,418 [73%] person-days of combination therapy)”\nMore broadly, linking given exposures to microbiota is one of the main motivations of the modelling analysis, which accounts (in the inclusion of time varying covariates) for switches and combinations of antimicrobials.\nR: Thank for adding Supp Fig 1. Unless I missed it, providing the number of antimicrobials administered per patient would be useful (eg. xx% received 1 antimicrobial, xx% 2 antimicrobials…during the inclusion period)\nTechnical issues: The authors have considered both rectal swabs and fecal samples, which may strongly differ in terms of content. Swabs typically yield more human reads and may bring some bacteria associated with skin and mucosa colonization. A comparison between results obtained with swabs and fecal samples would be expected to ensure that they can be assumed to be similar. An overgrowth of opportunistic pathogens may also be seen in samples not immediately stored at low temperature. The authors should clarify these technical aspects and how they have been tackled.\nResponse 2.3 - samples were stored immediately at low temperature; we have added the following text to clarify\nLine 631: “Samples for metagenomic sequencing were stored at 4 degrees Celsius immediately following collection then at -20 degrees Celsius within 24 hours, and DNA extracted within 2 weeks.”\nWe have added a comparison of rectal swabs and stool in terms of alpha and beta diversity and taxonomy (Supplementary Figure 13), demonstrating that they are similar, and the following text:\nLine 706:\n“Comparing stool and rectal swab samples\nComparing rectal swab and stool samples in terms of Shannon diversity, Bray-Curtis dissimilarity and taxonomy (Supplementary Figure 13) showed that they were similar; we combined them in all following analysis.”\nR: were all samples of the study considered for this analysis? With all time points? Could rectal swabs be favored in particular situations? (introducing biases). In the Bray-Curtis graph (panel B) swabs seem to be overrepresented in the right of the axis 1 (only 8 of the at the left). As for a validation, one would expect a comparison between a swab and a fecal sample taken from the same person at the same time. I understand the difficulties met to collect feces, yet I maintain this is a major issue for which the analysis shown in Supp Fig 13 does not provide a clear green light to allow a combination of feces and swabs in the same longitudinal analysis.\nLack of novelty: The effect of ceftriaxone on the gut microbiota has been well-documented in several studies, yet not in the specific population studied here with high baseline resistance levels for 3GC and FQ-resistant E. coli. Nonetheless, the significant impact on the gut microbiota and the selection of antibiotic-resistant Enterobacterales could be expected from previous findings from other studies. This also applies to the other antibiotics.\nResponse 2.4 -\nWe have edited the introduction to underline the uniqueness of the dataset:\nLine 71: “In adults, data are largely restricted to healthy volunteers or specific cohorts (e.g. inflammatory bowel disease) in high resource settings. , - where some data suggest that a major determinant of antimicrobial effect on the microbiome is the pre-exposure composition. Data from low resource settings like much of subSaharan is very scanty, particularly cohorts of adults within people with severe febrile illness. This group may be expected to both have significant broad-spectrum antimicrobial exposure plus different pre-exposure microbiota composition to healthy controls, and so may well have different antimicrobial-induced microbiota changes than healthy volunteers in high-resource settings – but data describing this are largely absent. Given that the highest burden of AMR falls in low-resource settings this is an important data gap to address.\nAnd the discussion\nLine 353: “The strengths of our study are our longitudinal sampling, and modelling approach, which together allow us to account for between-person microbiome variation, and describe the microbiome effect of antimicrobials in an under-studied low-resource setting.”\nReferences\nStewart AG, Harris PNA, Graham RMA, Jennison AV, Schlebusch S, Kakkanat A, Harris-Brown T, Paterson DL, Forde BM. Differences in antimicrobial resistance gene abundance and microbial diversity of the gut microbiome in patients on antibiotics enrolled in a clinical trial. Ther Adv Infect Dis. 2025 Jun 5;12:20499361251337597. doi: 10.1177/20499361251337597. PMID: 40487791; PMCID: PMC12144387.\nOlivares C, Ruppé E, Ferreira S, Corbel T, Andremont A, de Gunzburg J, Guedj J, Burdet C. A modelling framework to characterize the impact of antibiotics on the gut microbiota diversity. Gut Microbes. 2025 Dec;17(1):2442523. doi: 10.1080/19490976.2024.2442523. Epub 2024 Dec 22. PMID: 39711113.\nd'Humières C, Delavy M, Alla L, Ichou F, Gauliard E, Ghozlane A, Levenez F, Galleron N, Quinquis B, Pons N, Mullaert J, Bridier-Nahmias A, Condamine B, Touchon M, Rainteau D, Lamazière A, Lesnik P, Ponnaiah M, Lhomme M, Sertour N, Devente S, Docquier JD, Bougnoux ME, Tenaillon O, Magnan M, Ruppé E, Grall N, Duval X, Ehrlich D, Mentré F, Denamur E, Rocha EPC, Le Chatelier E, Burdet C; PrediRes study group. Perturbation and resilience of the gut microbiome up to 3 months after β-lactams exposure in healthy volunteers suggest an important role of microbial β-lactamases. Microbiome. 2024 Mar 12;12(1):50. doi: 10.1186/s40168- 023-01746-0. PMID: 38468305; PMCID: PMC10929231.\nRaymond F, Ouameur AA, Déraspe M, Iqbal N, Gingras H, Dridi B, Leprohon P, Plante PL, Giroux R, Bérubé È, Frenette J, Boudreau DK, Simard JL, Chabot I, Domingo MC, Trottier S, Boissinot M, Huletsky A, Roy PH, Ouellette M, Bergeron MG, Corbeil J. The initial state of the human gut microbiome determines its reshaping by antibiotics. ISME J. 2016 Mar;10(3):707-20. doi: 10.1038/ismej.2015.148. Epub 2015 Sep 11. PMID: 26359913; PMCID: PMC4817689\nR: I agree that such studies in sub-Saharan population is difficult to perform, yet some exist (PMC12272486, PMC11272098", "reviewer_4": "e931-e942) draws similar conclusions: exposure to amoxicillin in children with acute malnutrition leads to microbiome reduced diversity and richness and to an increase of ARG. In the present study, the antibiotic exposure is less controlled (with combinations -mostly cotrimoxazole, and switches -even if it does not seem too frequent).\nThe resistome part focused on ARG carried by culturable/pathogenic bacteria and not on ARG carried by commensal bacteria (a specific database would have been required). Hence, the findings are highly connected to the selection of opportunistic pathogens (Enterobacterales, Enterococci) under antibiotics, which again does not add much to the field. As highlighted in the discussion, connecting the dynamics of ARG to antibiotic exposure is highly difficult, especially because ARG from commensals have not yet been characterized in terms of spectrum and expression.\nResponse 2.5 - Our resistome analysis uses AMRFinder+, a curated database of all AMR genes; the aim of this database is to be comprehensive, rather than focus on ARG in culturable/pathogenic bacteria. Our analysis therefore is a comprehensive description of all genes contained in the database, whether they are present in culturable, nonculturable or nonpathogenic bacteria (we do also present an E. coli specific analysis because of the importance of this commensal as a pathogen; but this is presented alongside a total-resistome analysis)\nHowever, all AMR gene reference databases are, by their nature, biased towards to AMR genes in culturable and pathogenic bacteria, as these have been most studied. We accept that this is a limitation but it is not the aim in this study to address these biases in databases, and we suggest that i) addressing these biases is beyond the scope of this study and ii) this does not affect the novelty of the aspects of the study which we highlight above. We have added a sentence to the limitations section:\nLine 385: “Our identification of AMR genes in general is dependent on their presence in the databases we used, which are likely to be biased towards culturable and pathogenic bacteria ; but addressing these biases was not the aim of and is beyond the scope of our study.”\nR: I disagree. AMRFinder is not suitable for analysing ARG from commensal, intestinal bacteria which make up the dominant microbiota. This is known and accepted since the first functional metagenomic studies (see the work from Gautam Dantas, and more recently the organization of ARG obtained from functional metagenomic studies into ResFinderFG", "reviewer_novel\nr": "along with the two previous remarks (1/ resistome studies in similar population has been performed and 2/ the resistome characterization is not exhaustive), the novelty is limited in my opinion.\nImpact: The authors stress that their results may guide AMS. AMS is also based on local epidemiology of resistance, aiming at maximizing the chances that empirical therapy would work on the most expected pathogens, while sparing at best the microbiota. From their results, what would be the impact of this work on changing the AMS practices? Also, authors may mix the spectrum (related to the clinical outcome) and the impact on the microbiota (of which the spectrum is a component among others).\nResponse 2.7 - We strongly agree with the reviewer that part of stewardship is aiming to maximise the chance that empirical therapy will be active against local pathogens, whilst sparing as much as possible an effect on the microbiota. We suggest that it is critical to be able to quantify the effect of individual agents on the microbiota to be able to assess the effect of a given stewardship strategy – not just qualitatively describe effects - and we provide data to do this in this manuscript. Indeed we have already shown in this cohort that the pathogens causing sepsis are frequently non-suspectable to ceftriaxone; this current manuscript expands this analysis to quantify the effect of this unwarranted ceftriaxone exposure. We have added some text to the manuscript to clarify this point:\nLine 286: “Prior analysis of this cohort has demonstrated that 83% of participants with sepsis receive ceftriaxone but this agent would be expected to be an effective therapy in only 24%. There is clearly scope for expanded stewardship (both to reduce ceftriaxone exposure in those who do not require it, but also to expand access to alternate, effective, antimicrobials such as carbapenems, where required), but any strategy comes with resource implications, a significant consideration in this resource-limited setting. By quantifying the effect of antimicrobial exposure our modelling analysis paves the way for in silico simulation from the posterior of our fitted models to quantitatively compare the microbiota effect of competing stewardship strategies\nR: in this case the authors emphasize the need for rapid diagnostic tests in order to tailor the antibiotic therapy, rather than choosing between active antibiotics according to their impact on the gut microbiota. Here, the authors show that ceftriaxone has a strong impact while inactive in many cases, but what would be the alternative? It is still not clear to me how the results of this study may concretely contribute to AMS in this setting.\nMINOR COMMENTS\nL116: How can the authors explain the effect of hospitalization on the microbiota diversity? Could this be explained by the exposure to other drugs such as other antimicrobials? Could patients in the non-sepsis cohorts receive non-antibiotic antimicrobials (e.g., antimicrobials in the Supp Table 1)?\nResponse 2.7 All treatments were at the discretion of the treating clinicians so it is certainly possible that participants received treatments that may have an effect on the microbiota; it’s also possible that crowded wards with limited sanitation (one shared toilet per ward, no alcohol hand rub, very limited soap, limited running water, limited detergent available to the limited number of cleaning staff and unhygienic facilities for patient attendants) could have an impact. We’ve added some text to the manuscript:\nLine 332: “In our cohort, hospitalisation was associated with changes in microbiome and resistome, which could be due to ward crowding and sanitation (one shared toilet per ward limited handwashing facilities and availability of detergent and unhygienic facilities for patient attendants) – though confounding is also possible if hospitalisation was associated with receipt of other medications which could modulate the microbiota.\nL140: Ciprofloxacin is associated with the disappearance of Enterobacterales in non-FQ-R carriers. Do the authors have an estimation of FQ-R carriage in their cohort at baseline?\nResponse 2.7 - Baseline quinolone resistance gene prevalence is shown in Supplementary Figure 5\nR: resistance to FQ in Enterobacterales is mainly driven by mutations (potentially in association with acquired ARG – searched by AMRFinder+). I meant the carriage rate of FQ-R Enterobacterales (searched by culture).\nL154: Figure 3A and not 2A.\nResponse 2.8 - many thanks, corrected\nR: OK\nL165: Non-Bacteroides carbapenemases: OXA-181 is not in the Supp Figure 3 (mix with OXA-818?). OXA-1 may not be considered as a cephalosporin-hydrolyzing enzyme.\nResponse 2.9 - many thanks the typo (OXA 181 -> 818) has been corrected. The class/subclass of gene is from the AMRfinder+ tool – see\nhttps://www.ncbi.nlm.nih.gov/pathogens/genehierarchy/\nR: see PMID: 18031291, OXA-1 is a narrow-spectrum enzyme, which can affect extended-spectrum cephalosporins only when overexpressed.\nL170: Aminoglycosides-modifying enzymes (AMEs) encoding genes are prevalent regardless of AG exposition. AG are indeed known to have no effect on the microbiota (no intestinal excretion). This should be discussed.\nResponse 2.10 - We have added the following text:\nLine 162: “Across the 426 samples, we identified 369 unique AMR genes of 25 classes and 60 subclasses (Figure 3A, Supplementary Figure 2); trimethoprim, tetracycline, beta-lactam, sulphonamide and lincosamide/macrolide/streptogramin resistance genes were present in almost all samples, as were aminoglycoside resistance genes despite low levels of intestinal excretion of these drugs in humans.\nR: OK\nL231: CMY-2 (with a hyphen).\nResponse 2.11 - added\nR: OK\nL236: Fosfomycin.\nResponse 2.12 - typo corrected\nR: OK\nL248: The authors should mitigate that the association with an increase of Escherichia under ceftriaxone is associated with a high baseline rate of resistance (same for ciprofloxacin), otherwise susceptible E. coli decrease.\nResponse 2.13 - We have added the following text\nLine 257: “In this setting with a high prevenance of detectible baseline cephalosporin resistance” and\nLine 263: “Ciprofloxacin – another agent with high prevalence of baseline detectible resistance –\nR: this is in line with the previous comment about FQ-R Enterobacterales. Were Enterobacterales resistant to extended spectrum cephalosporins searcher in this study? Otherwise the high prevalence of cephalosporin and fluoroquinolone resistance shall be referred to as suspected.\nL248", "reviewer_250": "Italicize the genera.\nResponse 2.14 – done\nR: OK\nL595-596: No more age-sex matching for group comparison due to the selection of samples may have introduced some biases in the comparison between groups; this should be discussed in a limitation section.\nResponse 2.15 - We have added the following text:\nLine 365: “The sample selection and subsequent lack of age-sex matching may have resulted in confounding or other bias.\nR: OK\nL620: Kraken with custom database, which date for RefSeq downloading?\nResponse 2.16 – We have added the following Line 664: “…downloaded on 14 March 2023”\nR: OK\nL630: E. coli bin reconstruction: How have the authors delineated the potential multiple lineages? The phylogroups? Was there any QC applied (e.g., Total bin size)?\nResponse 2.17 - This is exactly why we used PopPUNK. It clusters bacterial genomes into lineages using variable-length k-mer comparisons, without requiring alignments or annotations. It’s designed to reflect underlying population structure and is widely used for delineating phylogroups (https://poppunk.readthedocs.io/en/latest/index.html). Regarding QC, we draw the reviewer’s attention to line 675 where we describe QC procedures such as CheckM, which includes total bin size, completeness, and contamination\nR: OK, yet how does Poppunk proceeds with incomplete genomes (met in metagenomic samples? The threshold L677 is 50%. I understand it was designed to process genomes. Or as you picked up the most complete bin, was it complete enough? Did you try to assess a phylogroup to the E. coli bins?\nL640: AMRfinder+ settings, please specify identity and coverage.\nResponse 2.18 - we used default settings; we have expanded the detail of this: Line 683: AMRFinderPLus under default settings (50% coverage, 90% identify)\nR: OK\nL644: Issue of subdominance of resistant E. coli captured by culture and not by metagenomic sequencing. Were resistant E. coli quantified by culture? It would be interesting to assess a concentration where ARG from cultured E. coli become undetectable by metagenomic sequencing.\nResponse 2.19 - We agree that this would be interesting but is not the aim of this analysis and is beyond the scope of this manuscript\nR: OK\nL650: E. coli italicized.\nResponse 2.20 - done\nR: OK\n(Remarks on code availability)", "reviewer_2": "Thank you for your responses to my previous comments. My main questions have mostly been answered appropriately (apologies for the typo in the expected number of samples, you are correct it was 777).\nThank you for the addition of Supplementary Figure 12. However, the issue of sample size is still not clear to me. In the reference you provided for the study flow diagram (https://www.nature.com/articles/s41564-022-01216-7 figure 1), I can see that for the community arm the sample size drops from 99 at enrollment to 83 at day 180 visit (approximately -17%). However, in your Supplementary Figure 12, the number of samples drops from approximately 25 to 6 for that same study arm (approximately -76%). I understand not all samples from the original study passed QC, so I expected a difference between the number of samples here and the number of samples in the original study (https://www.nature.com/articles/s41564-022-01216-7). But it's not clear to me how there can be such a substantial difference between the % of missing samples (17 vs 76); loss of participants does not explain this. Did fewer samples pass QC in the later visits? Could you please clarify this point, and the impact this could have on your results?\nMy other minor comment is that I still believe that 95% CIs in Table 1 are not needed and even potentially confusing for readers, since you are describing the included participants only. However as you have mentioned in your response, I am happy to defer the decision of whether to include these or not to the editorial office and their guidelines on statistics. If you do leave them, I would still strongly recommend you mention that they are binomial confidence intervals in the table caption, and not just on line 702.\n(Remarks on code availability)", "reviewer_3": "The authors have adequately responded to my comments in the rebuttal letter. However, the revised manuscript did not have the changes marked in red, which made it very difficult to review what had actually changed since the rebuttal letter also did not specify the exact lines where the text was modified. There are still vague phrases such as \"some\". The authors should also verify whether all instances of causal language, such as \"x increased y\", are appropriate, or whether \"x is associated with an increase in y\" is more accurate.\nIn the case of the comparison between rectal swabs and fecal samples, the provided additional analysis is not adequate. Although Figure S13 suggests that the samples are very similar, no actual statistical tests were conducted to investigate whether there is a difference in alpha and beta diversity between the sample types. It would also be good to report the average number of reads in both sample types as well as the average proportion of human reads in each.\nIt may also be possible to include an alternative analysis where the sample type (swab or fecal sample) is a covariate in the model to test whether the results change.\nI therefore suggest another round of review and request that the authors clearly mark the changes in the revised manuscript in red.\n(Remarks on code availability)"}, "author_response": "Pembroke Place,\nLiverpool, L3 5QA, UK\nTel: +44(0)151 705 3100\nFax: +44(0)151 705 3370\nwww.lstmed.ac.uk\n22nd July 2025\nThe Reviewers\nNature Communications\nDear Reviewers,\nRE: Revision and resubmission of NCOMMS-25-31277-T – point-by-point\nresponses to reviewers\nMany thanks to the reviewers for their comments, which we are pleased to address\npoint-by-point below – our responses are in red italics. All page numbers refer to the\nclean version with changes incorporated. Our numbering of responses here starts at\n2.1 – this is because responses to the editor (in a separate letter) are numbered\nstarting from 1.1.\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nThe study is an observational clinical cohort study from the Liverpool School of\nTropical Medicine and Malawi College of Medicine. Conducted between February\n19, 2017, and May 1, 2019, it involved 425 adults into three arms: sepsis patients\nexposed to antimicrobials, unexposed hospital inpatients, and community members.\nComparator groups excluded those who had received antibiotics in the past four\nweeks. Hospitalized participants were followed daily for antibiotic exposure, with\nfollow-ups at approximately days 7, 28, 90, and 180 post-discharge. Stool samples\nor rectal swabs were collected at baseline and each visit, cultured, and tested for\nESBL-E organisms. Selected isolates underwent whole genome sequencing, with\nDNA extraction and sequencing performed using specific kits and the Illumina HiSeq\nX10 system. A subset of 450 samples from 163 participants underwent metagenomic\nsequencing using the Illumina NovaSeq platform. Quality control of reads was\nperformed using MetaWRAP, and taxonomy assignment was done using Kraken2.\nMetagenomic reads were assembled using metaSPAdes. Antimicrobial resistance\n(AMR) genes were identified using AMRFinderPlus, with E. coli-specific models used\nfor refined analysis. PopPUNK was used to assign clusters to E. coli bins and\nisolates, with diversity visualized using Microreact. Statistical analysis was\nconducted in R, using Bayesian regression models to analyze Shannon Diversity,\ntaxon abundance, and AMR gene presence. Linear regression quantified the effect\nResearching and e ducating to save lives\nA Company Limited by Guarantee. Registered Number 83405, England and Wales. Registered Charity Number 222655.\nof antimicrobial exposure on Shannon diversity, while negative binomial regression\nmodeled absolute read numbers assigned to taxa. Logistic regression modeled the\npresence or absence of AMR genes. Diversity shifts were noted, particularly in the\nantimicrobial-exposed group. Ceftriaxone exposure was strongly associated with\ndiversity changes. Beta diversity analysis showed persistent differences in\nmicrobiome composition due to antimicrobial exposure. Proteobacteria, particularly\nEnterobacterales, were more abundant in the antimicrobial-exposed group.\nCeftriaxone and ciprofloxacin were associated with increased Proteobacteria\nabundance. The study also found that ceftriaxone exposure drove the prevalence of\naminoglycoside and macrolide resistance genes. Resistance genes correlated with\nEnterobacterales abundance, particularly E. coli.\nMAJOR COMMENTS Population: Many patients were already exposed to\ncotrimoxazole, which may have already affected the composition of the microbiota.\nIn L139 Fig 2, there is a very high proportion of Proteobacteria even in the arms not\nexposed to antibiotics for sepsis. Authors may also consider the hypothesis that\nbefore reaching a healthcare structure, patients may have already taken antibiotics\n(easily accessible over the counter) even though they were supposed not to (but did\nthis exclude cotrimoxazole?).\nResponse 2.1 - This is possible: though participants were specifically asked whether\nthey had taken antibiotics and excluded if they had done so within 4 weeks (for the\nnon-sepsis arms), it is possible that they were mistaken. We have added a sentence\nto the limitations section:\nLine 371 “It is also possible – in this setting where antimicrobials can be easily\navailable without a prescription - that participants received antimicrobials prior to\nhospital admission, which could affect microbiome/resistome composition”\nAlso, the antibiotic exposure is not detailed (L99, Table 1, and Supp Table 1):\n109/1062 patients received at least one antimicrobial; what about antibiotic switches\nand combination therapy? This should be specified as the effects observed on the\ngut microbiota are directly connected to the type of exposure.\nResponse 2.2 - We draw the reviewer’s attention to Supp. Table 1 which includes\nthe number of participants who received a given agent and the median (IQR) range\nof exposure. To provide more detail on combination therapies and switches we have\nadded Supplementary Figure 1 and the following text to the manuscript:\nLine 111: “Antibiotic combination therapy was unusual (1,418/9,559 [15%] person-\ndays of antibacterial exposure) and most commonly cotrimoxazole with another\nantimicrobial (1,034/1,418 [73%] person-days of combination therapy)”\nMore broadly, linking given exposures to microbiota is one of the main motivations of\nthe modelling analysis, which accounts (in the inclusion of time varying covariates)\nfor switches and combinations of antimicrobials.\nTechnical issues: The authors have considered both rectal swabs and fecal samples,\nwhich may strongly differ in terms of content. Swabs typically yield more human\nreads and may bring some bacteria associated with skin and mucosa colonization. A\ncomparison between results obtained with swabs and fecal samples would be\nexpected to ensure that they can be assumed to be similar. An overgrowth of\nopportunistic pathogens may also be seen in samples not immediately stored at low\ntemperature. The authors should clarify these technical aspects and how they have\nbeen tackled.\nResponse 2.3 - samples were stored immediately at low temperature; we have\nadded the following text to clarify\nLine 631: “Samples for metagenomic sequencing were stored at 4 degrees Celsius\nimmediately following collection then at -20 degrees Celsius within 24 hours, and\nDNA extracted within 2 weeks.”\nWe have added a comparison of rectal swabs and stool in terms of alpha and beta\ndiversity and taxonomy (Supplementary Figure 13), demonstrating that they are\nsimilar, and the following text:\nLine 706:\n“Comparing stool and rectal swab samples\nComparing rectal swab and stool samples in terms of Shannon diversity, Bray-Curtis\ndissimilarity and taxonomy (Supplementary Figure 13) showed that they were\nsimilar; we combined them in all following analysis.”\nLack of novelty: The effect of ceftriaxone on the gut microbiota has been well-\ndocumented in several studies, yet not in the specific population studied here with\nhigh baseline resistance levels for 3GC and FQ-resistant E. coli. Nonetheless, the\nsignificant impact on the gut microbiota and the selection of antibiotic-resistant\nEnterobacterales could be expected from previous findings from other studies. This\nalso applies to the other antibiotics.\nResponse 2.4 -\nWe have edited the introduction to underline the uniqueness of the dataset:\nLine 71: “In adults, data are largely restricted to healthy volunteers or specific\ncohorts (e.g. inflammatory bowel disease) in high resource settings. , - where some\ndata suggest that a major determinant of antimicrobial effect on the microbiome is\nthe pre-exposure composition. Data from low resource settings like much of sub-\nSaharan is very scanty, particularly cohorts of adults within people with severe febrile\nillness. This group may be expected to both have significant broad-spectrum\nantimicrobial exposure plus different pre-exposure microbiota composition to healthy\ncontrols, and so may well have different antimicrobial-induced microbiota changes\nthan healthy volunteers in high-resource settings – but data describing this are\nlargely absent. Given that the highest burden of AMR falls in low-resource settings\nthis is an important data gap to address.\nAnd the discussion\nLine 353: “The strengths of our study are our longitudinal sampling, and modelling\napproach, which together allow us to account for between-person microbiome\nvariation, and describe the microbiome effect of antimicrobials in an under-studied\nlow-resource setting.”\nReferences\nStewart AG, Harris PNA, Graham RMA, Jennison AV, Schlebusch S, Kakkanat A,\nHarris-Brown T, Paterson DL, Forde BM. Differences in antimicrobial resistance\ngene abundance and microbial diversity of the gut microbiome in patients on\nantibiotics enrolled in a clinical trial. Ther Adv Infect Dis. 2025 Jun\n5;12:20499361251337597. doi: 10.1177/20499361251337597. PMID: 40487791;\nPMCID: PMC12144387.\nOlivares C, Ruppé E, Ferreira S, Corbel T, Andremont A, de Gunzburg J, Guedj J,\nBurdet C. A modelling framework to characterize the impact of antibiotics on the gut\nmicrobiota diversity. Gut Microbes. 2025 Dec;17(1):2442523. doi:\n10.1080/19490976.2024.2442523. Epub 2024 Dec 22. PMID: 39711113.\nd'Humières C, Delavy M, Alla L, Ichou F, Gauliard E, Ghozlane A, Levenez F,\nGalleron N, Quinquis B, Pons N, Mullaert J, Bridier-Nahmias A, Condamine B,\nTouchon M, Rainteau D, Lamazière A, Lesnik P, Ponnaiah M, Lhomme M, Sertour\nN, Devente S, Docquier JD, Bougnoux ME, Tenaillon O, Magnan M, Ruppé E, Grall\nN, Duval X, Ehrlich D, Mentré F, Denamur E, Rocha EPC, Le Chatelier E, Burdet C;\nPrediRes study group. Perturbation and resilience of the gut microbiome up to 3\nmonths after β-lactams exposure in healthy volunteers suggest an important role of\nmicrobial β-lactamases. Microbiome. 2024 Mar 12;12(1):50. doi: 10.1186/s40168-\n023-01746-0. PMID: 38468305; PMCID: PMC10929231.\nRaymond F, Ouameur AA, Déraspe M, Iqbal N, Gingras H, Dridi B, Leprohon P,\nPlante PL, Giroux R, Bérubé È, Frenette J, Boudreau DK, Simard JL, Chabot I,\nDomingo MC, Trottier S, Boissinot M, Huletsky A, Roy PH, Ouellette M, Bergeron\nMG, Corbeil J. The initial state of the human gut microbiome determines its\nreshaping by antibiotics. ISME J. 2016 Mar;10(3):707-20. doi:\n10.1038/ismej.2015.148. Epub 2015 Sep 11. PMID: 26359913; PMCID:\nPMC4817689.\nThe resistome part focused on ARG carried by culturable/pathogenic bacteria and\nnot on ARG carried by commensal bacteria (a specific database would have been\nrequired). Hence, the findings are highly connected to the selection of opportunistic\npathogens (Enterobacterales, Enterococci) under antibiotics, which again does not\nadd much to the field. As highlighted in the discussion, connecting the dynamics of\nARG to antibiotic exposure is highly difficult, especially because ARG from\ncommensals have not yet been characterized in terms of spectrum and expression.\nResponse 2.5 - Our resistome analysis uses AMRFinder+, a curated database of all\nAMR genes; the aim of this database is to be comprehensive, rather than focus on\nARG in culturable/pathogenic bacteria. Our analysis therefore is a comprehensive\ndescription of all genes contained in the database, whether they are present in\nculturable, nonculturable or nonpathogenic bacteria (we do also present an E. coli\nspecific analysis because of the importance of this commensal as a pathogen; but\nthis is presented alongside a total-resistome analysis)\nHowever, all AMR gene reference databases are, by their nature, biased towards to\nAMR genes in culturable and pathogenic bacteria, as these have been most studied.\nWe accept that this is a limitation but it is not the aim in this study to address these\nbiases in databases, and we suggest that i) addressing these biases is beyond the\nscope of this study and ii) this does not affect the novelty of the aspects of the study\nwhich we highlight above. We have added a sentence to the limitations section:\nLine 385: “Our identification of AMR genes in general is dependent on their presence\nin the databases we used, which are likely to be biased towards culturable and\npathogenic bacteria ; but addressing these biases was not the aim of and is beyond\nthe scope of our study.”\nThis study does not bring novelty in addressing the resistome dynamics under\nantibiotic exposure.\nResponse 2.6 - We disagree; as well as the importance of the setting (a low-income\nsub-Saharan African setting, from which data are sparse, as described above, and\nwhich is acknowledged by the reviewer) we draw the reviewer’s attention to our\nBayesian modelling strategy to assess the effect of antimicrobial agent on resistome\ndynamics, which we have developed, and is novel.\nImpact: The authors stress that their results may guide AMS. AMS is also based on\nlocal epidemiology of resistance, aiming at maximizing the chances that empirical\ntherapy would work on the most expected pathogens, while sparing at best the\nmicrobiota. From their results, what would be the impact of this work on changing the\nAMS practices? Also, authors may mix the spectrum (related to the clinical outcome)\nand the impact on the microbiota (of which the spectrum is a component among\nothers).\nResponse 2.7 - We strongly agree with the reviewer that part of stewardship is\naiming to maximise the chance that empirical therapy will be active against local\npathogens, whilst sparing as much as possible an effect on the microbiota. We\nsuggest that it is critical to be able to quantify the effect of individual agents on the\nmicrobiota to be able to assess the effect of a given stewardship strategy – not just\nqualitatively describe effects - and we provide data to do this in this manuscript.\nIndeed we have already shown in this cohort that the pathogens causing sepsis are\nfrequently non-suspectable to ceftriaxone; this current manuscript expands this\nanalysis to quantify the effect of this unwarranted ceftriaxone exposure. We have\nadded some text to the manuscript to clarify this point:\nLine 286: “Prior analysis of this cohort has demonstrated that 83% of participants\nwith sepsis receive ceftriaxone but this agent would be expected to be an effective\ntherapy in only 24%. There is clearly scope for expanded stewardship (both to\nreduce ceftriaxone exposure in those who do not require it, but also to expand\naccess to alternate, effective, antimicrobials such as carbapenems, where required),\nbut any strategy comes with resource implications, a significant consideration in this\nresource-limited setting. By quantifying the effect of antimicrobial exposure our\nmodelling analysis paves the way for in silico simulation from the posterior of our\nfitted models to quantitatively compare the microbiota effect of competing\nstewardship strategies.\nMINOR COMMENTS L116: How can the authors explain the effect of hospitalization\non the microbiota diversity? Could this be explained by the exposure to other drugs\nsuch as other antimicrobials? Could patients in the non-sepsis cohorts receive non-\nantibiotic antimicrobials (e.g., antimicrobials in the Supp Table 1)?\nResponse 2.7 All treatments were at the discretion of the treating clinicians so it is\ncertainly possible that participants received treatments that may have an effect on\nthe microbiota; it’s also possible that crowded wards with limited sanitation (one\nshared toilet per ward, no alcohol hand rub, very limited soap, limited running water,\nlimited detergent available to the limited number of cleaning staff and unhygienic\nfacilities for patient attendants) could have an impact. We’ve added some text to the\nmanuscript:\nLine 332: “In our cohort, hospitalisation was associated with changes in microbiome\nand resistome, which could be due to ward crowding and sanitation (one shared\ntoilet per ward limited handwashing facilities and availability of detergent and\nunhygienic facilities for patient attendants) – though confounding is also possible if\nhospitalisation was associated with receipt of other medications which could\nmodulate the microbiota.”\nL140: Ciprofloxacin is associated with the disappearance of Enterobacterales in non-\nFQ-R carriers. Do the authors have an estimation of FQ-R carriage in their cohort at\nbaseline?\nResponse 2.7 - Baseline quinolone resistance gene prevalence is shown in\nSupplementary Figure 5.\nL154: Figure 3A and not 2A.\nResponse 2.8 - many thanks, corrected.\nL165: Non-Bacteroides carbapenemases: OXA-181 is not in the Supp Figure 3 (mix\nwith OXA-818?). OXA-1 may not be considered as a cephalosporin-hydrolyzing\nenzyme.\nResponse 2.9 - many thanks the typo (OXA 181 -> 818) has been corrected. The\nclass/subclass of gene is from the AMRfinder+ tool – see\nhttps://www.ncbi.nlm.nih.gov/pathogens/genehierarchy/\nL170: Aminoglycosides-modifying enzymes (AMEs) encoding genes are prevalent\nregardless of AG exposition. AG are indeed known to have no effect on the\nmicrobiota (no intestinal excretion). This should be discussed.\nResponse 2.10 - We have added the following text:\nLine 162: “Across the 426 samples, we identified 369 unique AMR genes of 25\nclasses and 60 subclasses (Figure 3A, Supplementary Figure 2); trimethoprim,\ntetracycline, beta-lactam, sulphonamide and lincosamide/macrolide/streptogramin\nresistance genes were present in almost all samples, as were aminoglycoside\nresistance genes despite low levels of intestinal excretion of these drugs in humans.”\nL231: CMY-2 (with a hyphen).\nResponse 2.11 - added\nL236: Fosfomycin.\nResponse 2.12 - typo corrected\nL248: The authors should mitigate that the association with an increase of\nEscherichia under ceftriaxone is associated with a high baseline rate of resistance\n(same for ciprofloxacin), otherwise susceptible E. coli decrease.\nResponse 2.13 - We have added the following text\nLine 257: “In this setting with a high prevenance of detectible baseline cephalosporin\nresistance”\nand\nLine 263: “Ciprofloxacin – another agent with high prevalence of baseline detectible\nresistance -”\nL248"}, {"round": 3, "reviewer_comments": {"reviewer_1": "ORIGINAL COMMENT 2.1 Population: Many patients were already exposed to\ncotrimoxazole, which may have already affected the composition of the microbiota. In L139\nFig 2, there is a very high proportion of Proteobacteria even in the arms not exposed to\nantibiotics for sepsis. Authors may also consider the hypothesis that before reaching a\nhealthcare structure, patients may have already taken antibiotics (easily accessible over the\ncounter) even though they were supposed not to (but did this exclude cotrimoxazole?).\nOriginal Response 2.1 - This is possible: though participants were specifically asked\nwhether they had taken antibiotics and excluded if they had done so within 4 weeks (for the\nnon-sepsis arms), it is possible that they were mistaken. We have added a sentence to the\nlimitations section:\nLine 371 “It is also possible – in this setting where antimicrobials can be easily available\nwithout a prescription - that participants received antimicrobials prior to hospital admission,\nwhich could affect microbiome/resistome composition”\nR: the authors may add that this is particularly suspect especially in the sepsis – antibiotic\narm (high proportion of Proteobacteria at time 0). What would be the explanation for such a\nhigh abundance of Proteobacteria, otherwise?\nOUR FURTHER RESPONSE: We have added this suggestion (line 414) with the\nfollowing text:\n“It is also possible – in this setting where antimicrobials can be easily available without a\nprescription - that participants received antimicrobials, which could affect\nmicrobiome/resistome composition – especially in the hospitalised/antimicrobial exposed\ngroup who had an otherwise-unexplained high Proteobacteria prevalence.”\nCOMMENT 2.2 Also, the antibiotic exposure is not detailed (L99, Table 1, and Supp Table\n1): 109/1062 patients received at least one antimicrobial; what about antibiotic switches and\ncombination therapy? This should be specified as the effects observed on the gut microbiota\nare directly connected to the type of exposure.\nOriginal Response 2.2 - We draw the reviewer’s attention to Supp. Table 1 which includes\nthe number of participants who received a given agent and the median (IQR) range of\nexposure. To provide more detail on combination therapies and switches we have added\nSupplementary Figure 1 and the following text to the manuscript:\nLine 111: “Antibiotic combination therapy was unusual (1,418/9,559 [15%] person days of\nantibacterial exposure) and most commonly cotrimoxazole with another antimicrobial\n(1,034/1,418 [73%] person-days of combination therapy)”\nMore broadly, linking given exposures to microbiota is one of the main motivations of the\nmodelling analysis, which accounts (in the inclusion of time varying covariates) for switches\nand combinations of antimicrobials.\nR: Thank for adding Supp Fig 1. Unless I missed it, providing the number of antimicrobials\nadministered per patient would be useful (eg. xx% received 1 antimicrobial, xx% 2\nantimicrobials…during the inclusion period)\nOUR FURTHER RESPONSE: We have added the following text (line 115):\n“Sequential antibacterial exposure was common: 27/109 (25%) participants received one\nantibacterial, 48/109 (44%) two, and 34/109 (32%) three or more antibacterials.”\nORIGINAL COMMENT 2.3 Technical issues: The authors have considered both rectal\nswabs and fecal samples, which may strongly differ in terms of content. Swabs typically yield\nmore human reads and may bring some bacteria associated with skin and mucosa\ncolonization. A comparison between results obtained with swabs and fecal samples would be\nexpected to ensure that they can be assumed to be similar. An overgrowth of opportunistic\npathogens may also be seen in samples not immediately stored at low temperature. The\nauthors should clarify these technical aspects and how they have been tackled.\nOriginal Response 2.3 - samples were stored immediately at low temperature; we have\nadded the following text to clarify\nLine 631: “Samples for metagenomic sequencing were stored at 4 degrees Celsius\nimmediately following collection then at -20 degrees Celsius within 24 hours, and DNA\nextracted within 2 weeks.”\nWe have added a comparison of rectal swabs and stool in terms of alpha and beta diversity\nand taxonomy (Supplementary Figure 13), demonstrating that they are similar, and the\nfollowing text:\nLine 706: “Comparing stool and rectal swab samples\nComparing rectal swab and stool samples in terms of Shannon diversity, Bray-Curtis\ndissimilarity and taxonomy (Supplementary Figure 13) showed that they were similar; we\ncombined them in all following analysis.”\nR: were all samples of the study considered for this analysis? With all time points? Could\nrectal swabs be favored in particular situations? (introducing biases). In the Bray-Curtis\ngraph (panel B) swabs seem to be overrepresented in the right of the axis 1 (only 8 of the at\nthe left). As for a validation, one would expect a comparison between a swab and a fecal\nsample taken from the same person at the same time. I understand the difficulties met to\ncollect feces, yet I maintain this is a major issue for which the analysis shown in Supp Fig 13\ndoes not provide a clear green light to allow a combination of feces and swabs in the same\nlongitudinal analysis.\nOUR FURTHER RESPONSE: All time points and all samples were considered in this part\nof the analysis. We did not carry out a validation by comparing stool and rectal swabs from\nthe same participant though these comparisons have been carried out by others and support\nthe use of rectal swab as a proxy for stool (see e.g. https://www.nature.com/articles/s41598-\n022-27131-9 https://pubmed.ncbi.nlm.nih.gov/28359329/ ). Nevertheless, we agree that\nconfounding is possible, if (for example) more unwell patients were unable to provide stool.\nWe further address this point by adding stool (versus swab) as a covariate in all the presented\nmodels to account for putative confounding; see response below to reviewer 3 for full details.\nORIGINAL COMMENT 2.4: Lack of novelty: The effect of ceftriaxone on the gut\nmicrobiota has been well-documented in several studies, yet not in the specific\npopulation studied here with high baseline resistance levels for 3GC and FQ-resistant E. coli.\nNonetheless, the significant impact on the gut microbiota and the selection of antibiotic-\nresistant Enterobacterales could be expected from previous findings from other studies. This\nalso applies to the other antibiotics.\nOriginal Response 2.4 - We have edited the introduction to underline the uniqueness of the\ndataset:\nLine 71: “In adults, data are largely restricted to healthy volunteers or specific cohorts (e.g.\ninflammatory bowel disease) in high resource settings. , - where some data suggest that a\nmajor determinant of antimicrobial effect on the microbiome is the pre-exposure\ncomposition. Data from low resource settings like much of subSaharan is very scanty,\nparticularly cohorts of adults within people with severe febrile illness. This group may be\nexpected to both have significant broad-spectrum antimicrobial exposure plus different pre-\nexposure microbiota composition to healthy controls, and so may well have different\nantimicrobial-induced microbiota changes than healthy volunteers in high-resource settings –\nbut data describing this are largely absent. Given that the highest burden of AMR falls in\nlow-resource settings this is an important data gap to address.\nAnd the discussion\nLine 353: “The strengths of our study are our longitudinal sampling, and modelling approach,\nwhich together allow us to account for between-person microbiome variation, and describe\nthe microbiome effect of antimicrobials in an under-studied low-resource setting.”\nR: I agree that such studies in sub-Saharan population is difficult to perform, yet some exist\n(PMC12272486, PMC11272098", "reviewer_4": "e931-e942)\ndraws similar conclusions: exposure to amoxicillin in children with acute malnutrition\nleads to microbiome reduced diversity and richness and to an increase of ARG. In\nthe present study, the antibiotic exposure is less controlled (with combinations -\nmostly cotrimoxazole, and switches -even if it does not seem too frequent).\nOUR FURTHER RESPONSE: Many thanks for the comment and providing\nreferences; we believe that these support the novelty of our manuscript. The content\nof these references are summarised in the table below; they:\n• Either don’t present a resistome or microbiome analysis or don’t use metagenomic\nmethods at all eg PMID 31821452), use lower-resolution methods to describe\nresistome and microbiome composition (16s, probe-based technique)\n• Examine a narrow population of people\n• Only look at amoxicillin and azithromycin\nThe implication here is that it is valid to extrapolate the findings from these studies –\ne.g. largely the effect of amoxicillin on the microbiota of malnourished children – to\nthe effect of ceftriaxone on the microbiota adults with severe febrile illness. We\nsuggest that there is no a priori reason that this approach would be valid, and\ninstead provide novel data to empirically describe the effect, along with a modelling\nframework to address heterogeneity in antimicrobial exposure.\nPaper Method Population Resistome analysis Microbiome\ndiversity analysis\nPMID 31821452 Whole Malnourished No ❌ No❌\n“Increased risk of genome children\nacquisition and sequencing exposed to\ntransmission of ESBL- amoxicillin\nproducing\nEnterobacteriaceae in\nmalnourished children\nexposed to amoxicillin”\nPMCID 11272098 Probe Children in Limited to gene No❌\n“Minimal Impact on the enrichment Botswana probes used ⚠\nResistome of Children in sequencing exposed to\nBotswana After azithromycin\nAzithromycin Treatment\nfor Acute Severe\nDiarrheal Disease”\nGuitoer et al\nPMC12272486 “Effect 16S rRNA Adults in No❌ Limited (16s)⚠\nof long-term sequencing Zimbabwe\nazithromycin treatment living with HIV\non gut microbial exposed to\ndiversity in children and azithromycin\nadolescents with HIV-\nassociated chronic lung\ndisease” Flygel et al\n37866373 lancet ID Shotgun Children with Yes✅ Yes✅\n“Effect of amoxicillin on metagenomics acute severe\nthe gut microbiome of malnutrition\nchildren with severe exposed to\nacute malnutrition in amoxicillin\nMadarounfa", "reviewer_niger": "a\nretrospective\nmetagenomic analysis of\na placebo-controlled\ntrial”\nORIGINAL COMMENT 2.5 The resistome part focused on ARG carried by\nculturable/pathogenic bacteria and not on ARG carried by commensal bacteria (a\nspecific database would have been required). Hence, the findings are highly\nconnected to the selection of opportunistic pathogens (Enterobacterales,\nEnterococci) under antibiotics, which again does not add much to the field. As\nhighlighted in the discussion, connecting the dynamics of ARG to antibiotic exposure\nis highly difficult, especially because ARG from commensals have not yet been\ncharacterized in terms of spectrum and expression.\nOriginal Response 2.5 - Our resistome analysis uses AMRFinder+, a curated\ndatabase of all AMR genes; the aim of this database is to be comprehensive, rather\nthan focus on ARG in culturable/pathogenic bacteria. Our analysis therefore is a\ncomprehensive description of all genes contained in the database, whether they are\npresent in culturable, nonculturable or nonpathogenic bacteria (we do also present\nan E. coli specific analysis because of th importance of this commensal as a\npathogen; but this is presented alongside a total-resistome analysis)\nHowever, all AMR gene reference databases are, by their nature, biased towards to\nAMR genes in culturable and pathogenic bacteria, as these have been most studied.\nWe accept that this is a limitation but it is not the aim in this study to address these\nbiases in databases, and we suggest that i) addressing these biases is beyond the\nscope of this study and ii) this does not affect the novelty of the aspects of the study\nwhich we highlight above. We have added a sentence to the limitations section:\nLine 385: “Our identification of AMR genes in general is dependent on their presence\nin the databases we used, which are likely to be biased towards culturable and\npathogenic bacteria ; but addressing these biases was not the aim of and is beyond\nthe scope of our study.”\nFURTHER REVIEWER COMMENT: I disagree. AMRFinder is not suitable for\nanalysing ARG from commensal, intestinal bacteria which make up the dominant\nmicrobiota. This is known and accepted since the first functional metagenomic\nstudies (see the work from Gautam Dantas, and more recently the organization of\nARG obtained from functional metagenomic studies into ResFinderFG,\nPMID: 37207327, see Figure 3). Using AMRFinder+ spans ARG from culturable\nbacteria (mostly enterococci and Enterobacterales) but barely ARG from anaerobic\nbacteria. The fact that ARG in this study correlate with E. coli is an illustration of this\nbias.\nOUR FURTHER RESPONSE: To address this comment we repeated our analysis\nusing the ResfinderFG database and added Supplementary Figures 8 and 9 and the\nfollowing text\nMethods Line 742; To identify genes that may be associated with unculturable\nbacteria, we performed the same analysis using the ResFinderFG v2.0 database,\nwhich collates antimicrobial resistance genes from functional metagenomic studies.\nWe generated a binary presence/absence matrix of ResFinderFG v2.0 antibiotic\nresistance genes across samples; to align with AMRFinderPlus, hits were retained if\nBLAST alignments had ≥90% identity and ≥50% coverage of the reference gene.\nGene names and antibiotic classes were extracted directly from FASTA headers.\nResults Line 228: Because AMRFinderPlus is likely biased towards\npathogenic/culturable bacteria we repeated the above analysis using ResFinderFG,\nwhich contains antibiotic resistance genes identified from functional metagenomic\nstudies. In contrast to the AMRFinderPlus analysis there was no clear relationship\nbetween prevalence of resistance genes expected to confer resistance a given\nantimicrobial agent and study arm or visit; prevalence of aminoglycoside resistance\ngenes was commonly higher in the hospitalised/antimicrobial exposed, but\nconfidence intervals were wide (Supplementary Figure 8). In the modelling analysis,\nonly presence of genes expected to confer resistance to gentamicin were associated\nwith ceftriaxone exposure, in contrast to AMRFinderPlus-identified genes\n(Supplementary Figure 9).\nDiscussion Line 281 “We present here a description of the bystander effect of\nantimicrobial treatment for sepsis in Blantyre, Malawi on microbiome and resistome\ncomposition, using two approaches to identifying resistance genes: AmrFinderPlus\nand ResFinderFG, to focus on culturable/pathogenic and unculturable/commensal\nbacteria, respectively”\nLine 307 We found that resistance genes as identified by ResFinderFG showed\nconsiderably less association with amicrobial exposure than those identified by\nAMRFinderPlus; this could be consistent with a relative stability of the non-culturable\nresistome as compared to the culturable/pathogenic resistome\nORIGINAL COMMENT 2.6: This study does not bring novelty in addressing the\nresistome dynamics under antibiotic exposure.\nOriginal Response 2.6 - We disagree; as well as the importance of the setting (a\nlow-income sub-Saharan African setting, from which data are sparse, as described\nabove, and which is acknowledged by the reviewer) we draw the reviewer’s attention\nto our Bayesian modelling strategy to assess the effect of antimicrobial agent on\nresistome dynamics, which we have developed, and is novel\nFURTHER REVIEWER COMMENT: along with the two previous remarks (1/\nresistome studies in similar population has been performed and 2/ the resistome\ncharacterization is not exhaustive), the novelty is limited in my opinion.\nOUR FURTHER RESPONSE: We disagree with point 1, especially the assertion that\nstudies have been done in a “similar population” – see above; point 2 is addressed\nby the addition of the ResFinderFG analysis\nORIGINAL COMMENT 2.7: Impact: The authors stress that their results may guide\nAMS. AMS is also based on local epidemiology of resistance, aiming at maximizing\nthe chances that empirical therapy would work on the most expected pathogens,\nwhile sparing at best the microbiota. From their results, what would be the impact of\nthis work on changing the AMS practices? Also, authors may mix the spectrum\n(related to the clinical outcome) and the impact on the microbiota (of which the\nspectrum is a component among others).\nOriginal Response 2.7 - We strongly agree with the reviewer that part of stewardship\nis aiming to maximise the chance that empirical therapy will be active against local\npathogens, whilst sparing as much as possible an effect on the microbiota. We\nsuggest that it is critical to be able to quantify the effect of individual agents on the\nmicrobiota to be able to assess the effect of a given stewardship strategy – not just\nqualitatively describe effects - and we provide data to do this in this manuscript.\nIndeed we have already shown in this cohort that the pathogens causing sepsis are\nfrequently non-suspectable to ceftriaxone; this current manuscript expands this\nanalysis to quantify the effect of this unwarranted ceftriaxone exposure. We have\nadded some text to the manuscript to clarify this point:\nLine 286: “Prior analysis of this cohort has demonstrated that 83% of participants\nwith sepsis receive ceftriaxone but this agent would be expected to be an effective\ntherapy in only 24%.\nThere is clearly scope for expanded stewardship (both to reduce ceftriaxone\nexposure in those who do not require it, but also to expand access to alternate,\neffective, antimicrobials such as carbapenems, where required), but any strategy\ncomes with resource implications, a significant consideration in this resource-limited\nsetting. By quantifying the effect of antimicrobial exposure our modelling analysis\npaves the way for in silico simulation from the posterior of our fitted models to\nquantitatively compare the microbiota effect of competing\nstewardship strategies\nORIGINAL REVIEWER RESPONSE: in this case the authors emphasize the need\nfor rapid diagnostic tests in order to tailor the antibiotic therapy, rather than choosing\nbetween active antibiotics according to their impact on the gut microbiota. Here, the\nauthors show that ceftriaxone has a strong impact while inactive in many cases, but\nwhat would be the alternative? It is still not clear to me how the results of this study\nmay concretely contribute to AMS in this setting.\nOUR FURTHER RESPONSE: Many thanks for the comment. We don’t mention\nrapid diagnostic tests in this response, or indeed in the manuscript, and it was not\nour intention to emphasize the need for rapid diagnostic tests. We agree with the\nreviewer that, all else being equal, choosing an active antibiotic according to the\nimpact on the gut microbiota is a good strategy. But how does the reviewer propose\nto quantify the “impact on the gut microbiota” to inform this decision? We suggest\nthat measuring it directly is optimal. Hence we present data to do just that and a\nmodelling framework data to inform this decision – not just in a qualitative sense, but\nquantifying the effect of different antimicrobials. Our point is not that a\nclinician should avoid ceftriaxone altogether, but rather that our analysis can be used\nto model the effect of different antimicrobial protocols on the microbiota, to aid in\ndeciding on the relative merit of these protocols. For example, we have previously\ndemonstrated that people presenting with sepsis in the Malawian setting are likely to\nhave disseminated TB, but are treated with ceftriaxone regardless. What would the\nimpact on colonisation with resistant organisms be of a rapid de-escalation of\nceftriaxone when the TB diagnosis is made? Our modelling framework can answer\nthis question with a simulation approach, to inform local\nantibiotic policies. Of course this cannot speak to any possible effect on clinical\noutcomes, but can help to inform decision making and/or further interventional\nstudies.\nWe’ve added this example to try and clarify (line 331)\n“For example, a high proportion of the 83% of people with sepsis in our prior study\nwho receive ceftriaxone have disseminated TB; what would the impact on\ncolonisation with resistant organisms be of a rapid de-escalation of ceftriaxone when\nthe TB diagnosis is made? Our modelling framework can answer this question with a\nsimulation approach, to inform local antibiotic policies and future interventional\nstudies.”\nORIGINAL COMMENT 2.7 L140: Ciprofloxacin is associated with the disappearance\nof Enterobacterales in non-FQ-R carriers. Do the authors have an estimation of FQ-\nR carriage in their cohort at baseline?\nOriginal Response 2.7 - Baseline quinolone resistance gene prevalence is shown in\nSupplementary Figure 5\nFURTHER REVIEWER COMMENT: resistance to FQ in Enterobacterales is mainly\ndriven by mutations (potentially in association with acquired ARG – searched by\nAMRFinder+). I meant the carriage rate of FQ-R Enterobacterales (searched by\nculture).\nOUR FURTHER RESPONSE: We did not culture fir FQ-R Enterobacterales; this is\nbeyond the scope of this study.\nORIGINAL COMMENT 2.8: L154: Figure 3A and not 2A.\nOriginal Response 2.8 - many thanks, corrected\nR: OK\nORIGINAL COMMENT 2.9: L165: Non-Bacteroides carbapenemases: OXA-181 is not in\nthe Supp Figure 3 (mix with OXA-818?). OXA-1 may not be considered as a cephalosporin-\nhydrolyzing enzyme.\nOriginal Response 2.9 - many thanks the typo (OXA 181 -> 818) has been corrected. The\nclass/subclass of gene is from the AMRfinder+ tool – see\nhttps://www.ncbi.nlm.nih.gov/pathogens/genehierarchy/\nR: see PMID: 18031291, OXA-1 is a narrow-spectrum enzyme, which can affect extended-spectrum cephalosporins only\nwhen overexpressed.\nORIGINAL COMMENT 2.10: L170: Aminoglycosides-modifying enzymes (AMEs) encoding\ngenes are prevalent regardless of AG exposition. AG are indeed known to have no effect on\nthe microbiota (no intestinal excretion). This should be discussed.\nOriginal Response 2.10 - We have added the following text:\nLine 162: “Across the 426 samples, we identified 369 unique AMR genes of 25 classes and\n60 subclasses (Figure 3A, Supplementary Figure 2); trimethoprim, tetracycline, beta-lactam,\nsulphonamide and lincosamide/macrolide/streptogramin resistance genes were present in\nalmost all samples, as were aminoglycoside resistance genes despite low levels of intestinal\nexcretion of these drugs in humans.\nR: OK\nORIGINAL COMMENT 2.11: L231: CMY-2 (with a hyphen).\nOriginal Response 2.11 - added\nR: OK\nORIGINAL COMMENT: L236: Fosfomycin.\nOriginal Response 2.12 - typo corrected\nR: OK\nORIGINAL COMMENT: L248: The authors should mitigate that the association with an\nincrease of Escherichia under ceftriaxone is associated with a high baseline rate of\nresistance (same for ciprofloxacin), otherwise susceptible E. coli decrease.\nOriginal Response 2.13 - We have added the following text\nLine 257: “In this setting with a high prevenance of detectible baseline cephalosporin\nresistance” and\nLine 263: “Ciprofloxacin – another agent with high prevalence of baseline detectible\nresistance –\nR: this is in line with the previous comment about FQ-R Enterobacterales. Were Enterobacterales resistant to extended\nspectrum cephalosporins searcher in this study? Otherwise the high prevalence of cephalosporin and fluoroquinolone\nresistance shall be referred to as suspected.\nORIGINAL COMMENT 2.14: L248", "reviewer_250": "Italicize the genera.\nOriginal Response 2.14 – done\nR: OK\nORIGINAL COMMENT: L595-596: No more age-sex matching for group comparison due\nto the selection of samples may have introduced some biases in the comparison between\ngroups; this should be discussed in a limitation section.\nOriginal Response 2.15 - We have added the following text:\nLine 365: “The sample selection and subsequent lack of age-sex matching may have resulted\nin confounding or other bias.\nR: OK\nORIGINAL COMMENT 2.16: L620: Kraken with custom database, which date for RefSeq\ndownloading?\nOriginal Response 2.16 – We have added the following Line 664: “…downloaded on 14\nMarch 2023”\nR: OK\nORIGINAL COMMENT 2.17: L630: E. coli bin reconstruction: How have the authors\ndelineated the potential multiple lineages? The phylogroups? Was there any QC applied\n(e.g., Total bin size)?\nOriginal Response 2.17 - This is exactly why we used PopPUNK. It clusters bacterial\ngenomes into lineages using variable-length k-mer comparisons, without requiring\nalignments or annotations. It’s designed to reflect underlying population structure and is\nwidely used for delineating phylogroups\n(https://poppunk.readthedocs.io/en/latest/index.html). Regarding QC, we draw the reviewer’s\nattention to line 675 where we describe QC procedures such as CheckM, which includes total\nbin size, completeness, and contamination\nR: OK, yet how does Poppunk proceeds with incomplete genomes (met in metagenomic\nsamples? The threshold L677 is 50%. I understand it was designed to process genomes. Or\nas you picked up the most complete bin, was it complete enough? Did you try to assess a\nphylogroup to the E. coli bins?\nORIGINAL COMMENT 2.18 L640: AMRfinder+ settings, please specify identity and\ncoverage.\nOriginal Response 2.18 - we used default settings; we have expanded the detail of this: Line\n683: AMRFinderPLus under default settings (50% coverage, 90% identify)\nR: OK\nORIGINAL COMMENT 2.19 L644: Issue of subdominance of resistant E. coli captured by\nculture and not by metagenomic sequencing. Were resistant E. coli quantified by culture? It\nwould be interesting to assess a concentration where ARG from cultured E. coli become\nundetectable by metagenomic sequencing.\nOriginal Response 2.19 - We agree that this would be interesting but is not the aim of this\nanalysis and is beyond the scope of this manuscript\nR: OK\nORIGINIAL COMMENT 2.20 L650: E. coli italicized.\nOriginal Response 2.20 - done\nR: OK\nReviewer #3 (Remarks to the Author):\nI reviewed the sections related to my previous comments, and I am satisfied with the\nresponses.\nComment: I request that you provide effect sizes, R2 values, and 95% CIs along\nwith the p-values regarding the stool versus rectal swab analysis. My impression is\nthat the statistical results of these analyses, as well as other analyses, do not fully\nfollow the Reporting guidelines. I trust that these issues will be corrected if the article\nproceeds to technical review.\nReviewer #3 (Remarks on code availability):\nComment: I did not review the code this time, as I had checked it before."}, "author_response": ""}]

Decision letter