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PRJEB20800 ERP022986

Gut resistome recovery after antibiotics in Danish adults

Gut resistome modulates resilience and recovery of gut microbiota after broad-spectrum antibiotic treatment

57 fecal samples from healthy adult men in Denmark, collected in an intervention study examining gut microbiota and resistome changes following a broad-spectrum antibiotic treatment. machine-written summary

57 samples · 114 runs · DK · Adult · 2018 · 6.57 Gbp / sample

Sources
✓Sequence archive 348○Abstract / description○Full text✓Supplementary tables 165✓External resources 171–User contribution–Human verified

Checked this project's papers and archive records yourself? Mark it human-verified.

what each source gave
✓ Sequence archive348 sample values (antibiotic exposure, collection date, country, intervention, latitude, location region, longitude, subject id)
○ Abstract / descriptionread; no usable statement
○ Full textread; no usable statement or table · PMC7338343
✓ Supplementary tables165 sample values (intervention, subject id, timepoint)
✓ External resources171 sample values (health condition, health condition detail, sex) · from curatedMetagenomicData 3 (cMD 3)
– User contributionnothing uploaded yet
– Human verifiednot yet checked by a person · I checked this project — verify it
Interventions

intervention · arms resolved

before · no interventionbefore · no interventi12 samplesantibioticantibiotic45 samples
single arm: samples before and on the intervention

12 people have samples in more than one arm.

Antibiotic given as the study intervention (incl. eradication therapy design single_arm_before_after · population healthy_control · 4-day broad-spectrum triple antibiotic intervention (meropenem, vancomycin, gentamicin)

“4-day intervention with three broad-spectrum antibiotics” — bioproject.description (study level; per-sample arms are in the intervention column below)

Samples at a glance
1 sampling locations · 1 country
Age
adult57
Condition
healthy_control57
Sex
male57
Country
DK57
Body site
primary57
Collected 2012–2013
2012: 53 samples2013: 4 samples20122013
Sequencing depth per sample · this project all projects
all projects, 0–0.03 Gbp: 25,080 samples (4 %)all projects, 0.03–0.1 Gbp: 17,066 samples (3 %)all projects, 0.1–0.3 Gbp: 29,179 samples (5 %)all projects, 0.3–1 Gbp: 47,044 samples (8 %)all projects, 1–3 Gbp: 124,983 samples (22 %)all projects, 3–10 Gbp: 253,728 samples (44 %)this project, 3–10 Gbp: 56 samplesall projects, 10–30 Gbp: 72,533 samples (13 %)this project, 10–30 Gbp: 1 samplesall projects, 30–100 Gbp: 3,024 samples (1 %)all projects, 100–300 Gbp: 106 samples (0 %)all projects, ≥ 300 Gbp: 9 samples (0 %)project median 6.57 Gbp per sample0130Gbp
Sampling design and relatedness

longitudinal

12 of 12 people sampled more than once (57 samples; median 5, up to 5 per person) · follow-up median 6 mo (max 9 mo) · median interval between samples 31 d (time from collection dates). Schedule stated by the source: “Fecal samples at Day 0 (pre-treatment), Day 4, Day 8, Day 42, Day 180 post antibiotic treatment”; stated follow-up 6 mo.

ERAS1ERAS1: day 0ERAS1: day 8ERAS1: day 42ERAS1: day 183ERAS10ERAS10: day 0ERAS10: day 8ERAS10: day 42ERAS10: day 191ERAS11ERAS11: day 0ERAS11: day 4ERAS11: day 8ERAS11: day 39ERAS11: day 186ERAS12ERAS12: day 0ERAS12: day 4ERAS12: day 8ERAS12: day 43ERAS12: day 184ERAS2ERAS2: day 0ERAS2: day 4ERAS2: day 8ERAS2: day 41ERAS2: day 181ERAS3ERAS3: day 0ERAS3: day 4ERAS3: day 8ERAS3: day 41ERAS3: day 187ERAS4ERAS4: day 0ERAS4: day 4ERAS4: day 8ERAS4: day 42ERAS4: day 183ERAS5ERAS5: day 0ERAS5: day 4ERAS5: day 8ERAS5: day 39ERAS5: day 184ERAS6ERAS6: day 0ERAS6: day 4ERAS6: day 8ERAS6: day 27ERAS6: day 264ERAS7ERAS7: day 0ERAS7: day 4ERAS7: day 8ERAS7: day 41ERAS7: day 187ERAS8ERAS8: day 0ERAS8: day 8ERAS8: day 42ERAS8: day 182ERAS9ERAS9: day 0ERAS9: day 4ERAS9: day 8ERAS9: day 42ERAS9: day 1810 d2 mo4 mo7 mo9 mo

Each row is one person (12 shown); dots are samples at days since that person's first sample.

How longitudinal and relatedness information is derived

Community composition
Bacteroides 12.0%Prevotella 8.2%Phocaeicola 5.6%CAG-288 5.4%Roseburia 4.5%Faecalibacterium 3.9%Alistipes 3.8%CAG-345 3.7%Escherichia 2.9%Veillonella 2.7%Dialister 2.7%Borkfalkia 2.6%not assigned to a genus 4.1%other genera 37.8%

Genera, mean relative abundance over 53 profiled gut samples of this project (Sandpiper SingleM profiles, GTDB R232; low-depth and non-metagenome profiles left out).

Field coverage
fieldsamplescoverageR1R2R3R4
age_category57 100%
country57 100%57
health_condition57 100%57
subject_id57 100%255
antibiotic_exposure57 100%57
collection_date57 100%57
detailed_location57 100%
health_condition_detail57 100%57
intervention57 100%255
latitude57 100%57
location_region57 100%57
longitude57 100%57
sex57 100%57
timepoint_label57 100%255
Papers (12)
  • Mobilization of Antibiotic Resistance: Are Current Approaches for Colocalizing Resistomes and Mobilomes Useful? 2020 PMID 32695079 PMC7338343 own_data
  • Individualized recovery of gut microbial strains post antibiotics. 2019 PMID 31632686 PMC6789009 unsure
  • Metagenomic growth rate inferences of strains in situ. 2020 PMID 32494636 PMC7176420 unsure
  • Dispersal strategies shape persistence and evolution of human gut bacteria. 2021 PMID 34111423 PMC8288446 unsure
  • Functional profile of host microbiome indicates Clostridioides difficile infection. 2022 PMID 36289064 PMC9621045 unsure
7 more
  • Identification of robust and generalizable biomarkers for microbiome-based stratification in lifestyle interventions. 2023 PMID 37553697 PMC10408196
  • Uncovering Predictive Factors and Interventions for Restoring Microecological Diversity after Antibiotic Disturbance. 2023 PMID 37764709 PMC10536327
  • A metagenomics pipeline reveals insertion sequence-driven evolution of the microbiota. 2024 PMID 38565143 PMC11081829
  • Bacteroidales-Specific Antimicrobial Genes Can Influence the Selection of the Dominant Fecal Strain of Bacteroides vulgatus and Bacteroides uniformis from the Gastrointestinal Tract Microbial Community. 2024 PMID 38792577 PMC11121782
  • Extensively acquired antimicrobial-resistant bacteria restructure the individual microbial community in post-antibiotic conditions. 2025 PMID 40360555 PMC12075632
  • Microbes with higher metabolic independence are enriched in human gut microbiomes under stress. 2025 PMID 40377187 PMC12084026
  • Metagenomic estimation of absolute bacterial biomass in the mammalian gut through host-derived read normalization. 2025 PMID 40742180 PMC12363224
Details
Archive description

Antibiotics have profound impacts on the microbial communities living in the human gut, where bacterial species harbor and exchange antibiotic resistance genes collectively called resistome. Human population surveys have demonstrated that gut microbial resistomes reflect local antibiotics usage patterns, while previous small cohort studies based on single antibiotic exposures have shown varying degrees of antibiotic impact on the gut microbiota and their associated resistome. However, the role of resistomes of particular gut species in their ability to persist in the face of antibiotic treatment has not been systematically studied.Applying shotgun sequencing and quantitative metagenomics, we analyzed the partial eradication and subsequent regrowth of gut microbiota in 12 healthy young men over a 6 month period following a 4-day intervention with three broad-spectrum antibiotics. The gut microbial communities of the study subjects underwent profound compositional changes due to the treatment but slowly recovered to near-baseline compositions. Immediately after the treatment, the gut microbiota featured an increased relative abundance of opportunistic and proinflammatory bacterial species, and a decreased relative abundance of short chain fatty acid producers, while exhibiting an increased functional potential for multidrug efflux pumps and virulence factors. These enrichment trends were weaker after 8 days and mostly cleared out after 42 days. However, 9 common intestinal commensal species found at baseline and associated with short chain fatty production or efficient digestion of polysaccharides remained undetectable in most of the subjects even after 180 days.Carrying genes that confer resistance to the antibiotics used in this multidrug study had specific consequences for the survival and de novo colonization potential of microbial species. While species harboring beta-lactam resistance genes were positively selected during and after the intervention, those harboring glycopeptide or aminoglycoside resistance exhibited increased odds of de novo colonization but decreased odds of survival. Despite a mild long-lasting imprint following exposure to three antibiotics, the gut microbiota of healthy young adults are resilient to a broad-spectrum antibiotic intervention, and their recovery process is modulated by antibiotic resistance gene carriage.

How this project entered the registry

Classified as human gut / stool shotgun DNA metagenome of adult by deterministic_prior with confidence 0.90. Assay shotgun_dna.

  • host external_curation.infant_catalog · “infant triage reason_code age_adult_only”
  • body site study.title · “Gut resistome modulates resilience”
  • body site study.description · “in the human gut , where bacterial”
  • life stage external_curation.infant_catalog · “infant triage reason_code age_adult_only”
  • life stage study.description · “12 healthy young men over a 6”
Evidence examples (one per field × route)
samplefieldvaluerouteconf.sourcequote
SAMEA104062398antibiotic_exposurenoR10.70sample.name_rule:aliasERAS1_Dag0
SAMEA104062398collection_date2012-04-23R10.90biosample.attribute:collection_date2012-04-23
SAMEA104062398countryDKR10.90sample.attr.geographic_location_country_and_or_seaDenmark
SAMEA104062398health_conditionhealthy_controlR20.75external.cmd sourcedisease: Healthy
SAMEA104062398health_condition_detailHealthyR20.75external.cmd sourcedisease: Healthy
SAMEA104062402interventionno_interventionR10.70sample.name_rule:aliasERAS2_Dag0
SAMEA104062398interventionno_interventionR20.85paper.supp.Data_Sheet_1.zip/Data Sheet 2.XLSX[Sheet1!TREATMENT]ERR1995225: TREATMENT=Pre-treatment
SAMEA104062398latitude55.6500R10.90biosample.attribute:geographic_location_latitude+geographic_location_longitude55.65 12.47
SAMEA104062398location_regionCapital RegionR10.60biosample.attribute:geographic_location_latitude+geographic_location_longitude55.65 12.47
SAMEA104062398longitude12.4700R10.90biosample.attribute:geographic_location_latitude+geographic_location_longitude55.65 12.47
SAMEA104062398sexmaleR20.75external.cmd sourcesex: Male
SAMEA104062402subject_idERAS2R10.70sample.name_rule:aliasERAS2_Dag0
SAMEA104062398subject_idERAS1R20.85paper.supp.Data Sheet 2.XLSX[Sheet1!PATIENT ID]ERAS1
SAMEA104062402timepoint_labelday 0R10.70sample.name_rule:aliasERAS2_Dag0
SAMEA104062398timepoint_labelDAY_0R20.85paper.supp.Data Sheet 2.XLSX[Sheet1!TIME]DAY_0
Sequencing

114 runs over 57 samples · mean 3.33 Gbp per run (median 1.70 Gbp, total 379.74 Gbp; 114 runs with a base count) · layout PAIRED 57, SINGLE 57 · platform ILLUMINA 114 · models Illumina HiSeq 2000 (114) · 46% of runs with a Sandpiper profile · first public 2018-05-24

Authors (43)

Slizovskiy IB first · Mukherjee K · Dean CJ · Boucher C · Noyes NR last · Chen J first · Zhu J · Lu W · Wang H · Pan M · Tian P · Zhao J · Zhang H · Chen W last · Koo H first · Morrow CD last · Tarracchini C first · Lugli GA · Mancabelli L · van Sinderen D · Turroni F · Ventura M · Milani C last · Baek JW first · Lim S · Park N · Song B · Kirtipal N · Nielsen J · Mardinoglu A · Shoaie S · Kim JI · Son JW · Koh A · Lee S last · Tang G first · Carr AV · Perez C · Ramos Sarmiento K · Levy L · Lampe JW · Diener C · Gibbons SM last

Downloads

PRJEB20800.csv.gz (2.7 KB) · parquet (73.1 KB) — all 57 samples, every field with route and confidence · PRJEB20800_determinations.csv.gz (7.2 KB) — 684 evidence rows

Samples (57 · every field this project has values for · hover a cell for its source)
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