Metagenomic AMR surveillance in gut samples
Cross-sectional study of 1197 pooled human faecal samples from Cambodia, Kenya, and the United Kingdom, profiled by metagenomics to derive taxonomy-adjusted antimicrobial resistance metrics for Enterobacterales. machine-written summary
1,197 samples · 1,197 runs · KH, GB, KE · Age unknown · 2020 · 2.17 Gbp / sample
Checked this project's papers and archive records yourself? Mark it human-verified.
what each source gave
| ✓ Sequence archive | 5,967 sample values (collection date, country, latitude, location region, longitude) |
| ○ Abstract / description | read; no usable statement |
| ✓ Full text | 18 sample values (collection date) · PMC8173267 |
| ○ Supplementary tables | files read; no table could be matched to the samples |
| – External resources | not covered by the external resources we ingest |
| – User contribution | nothing uploaded yet · share metadata |
| – Human verified | not yet checked by a person · I checked this project — verify it |
Genera, mean relative abundance over 1,079 profiled gut samples of this project (Sandpiper SingleM profiles, GTDB R232; low-depth and non-metagenome profiles left out).
| field | samples | coverage | R1 | R2 | R3 | R4 |
|---|---|---|---|---|---|---|
| age_category | 0 | 0% | ||||
| country | 1,197 | 100% | 1,197 | |||
| health_condition | 0 | 0% | ||||
| subject_id | 0 | 0% | ||||
| collection_date | 1,197 | 100% | 1,179 | 18 | ||
| detailed_location | 1,197 | 100% | ||||
| latitude | 1,197 | 100% | 1,197 | |||
| location_region | 1,197 | 100% | 1,197 | |||
| longitude | 1,197 | 100% | 1,197 |
- Population-level faecal metagenomic profiling as a tool to predict antimicrobial resistance in <i>Enterobacterales</i> isolates causing invasive infections: An exploratory study across Cambodia, Kenya, and the UK. 2021 PMID 34124634 PMC8173267 own_data
- A Pipeline for Constructing Reference Genomes for Large Cohort-Specific Metagenome Compression. 2023 PMID 37894218 PMC10609127 unsure
Archive description
Antimicrobial resistance (AMR) is a global health threat, especially in low-/middle-income countries (LMICs), where there is limited surveillance to inform empiric antibiotic treatment guidelines. Enterobacterales are a particular AMR problem, and a common cause of disease. We developed a novel approach for AMR surveillance in Enterobacterales by profiling metagenomes of pooled human faecal material from three sites (n=563 individuals Cambodia, Kenya, UK) to derive a taxonomy-adjusted AMR metric which could be used to predict the aggregate burden of resistant Enterobacterales infections within each setting. Samples were sequenced (Illumina) taxonomic and resistance gene profiling was performed using ResPipe (https://gitlab.com/hsgweon/ResPipe). Data on organisms causing bacteraemia and meningitis and susceptibility results from 2010-2017 were collated for each site. Bayesian generalised linear models with a binomial likelihood were fitted to determine the capacity of the metric to predict AMR in Enterobacterales infections in each setting. The most informative model predicted the proportion of resistant infections in the target populations for 14/14 of antibiotics in the UK, 12/12 in Kenya, and 8/12 in Cambodia. Intermittent metagenomics of pooled human samples could be a powerful tool in determining and monitoring the burden of AMR in clinical infections, especially in resource-limited settings.
How this project entered the registry
Classified as human gut / stool shotgun DNA metagenome of age unknown by sonnet_x2 with confidence 0.68. Assay shotgun_dna.
- host run.scientific_name · “gut metagenome”
- host study.description · “pooled human faecal material from three sites”
- body site study.description · “pooled human faecal material from three sites”
Evidence examples (one per field × route)
| sample | field | value | route | conf. | source | quote |
|---|---|---|---|---|---|---|
| SAMEA6084797 | collection_date | 2013-09-12 | R1 | 0.90 | biosample.attribute:collection_date | 2013-09-12 |
| SAMEA6085275 | collection_date | 2014-09/2016-04 | R3 | 0.70 | paper.fulltext.abstract | collected between September 2014 and April 2016 |
| SAMEA6084797 | country | KH | R1 | 0.90 | sample.attr.geographic_location_country_and_or_sea | Cambodia |
| SAMEA6084797 | latitude | 13.3550 | R1 | 0.90 | biosample.attribute:geographic_location_latitude+geographic_location_longitude | 13.355009 103.855171 |
| SAMEA6084797 | location_region | Siem Reap | R1 | 0.60 | biosample.attribute:geographic_location_latitude+geographic_location_longitude | 13.355009 103.855171 |
| SAMEA6084797 | longitude | 103.8552 | R1 | 0.90 | biosample.attribute:geographic_location_latitude+geographic_location_longitude | 13.355009 103.855171 |
Sequencing
1,197 runs over 1,197 samples · mean 2.43 Gbp per run (median 2.17 Gbp, total 2,905.93 Gbp; 1,197 runs with a base count) · layout PAIRED 1,197 · platform ILLUMINA 1,197 · models Illumina HiSeq 4000 (1,197) · 91% of runs with a Sandpiper profile · first public 2020-05-02
Authors (22)
Auguet OT first · Niehus R · Gweon HS · Berkley JA · Waichungo J · Njim T · Edgeworth JD · Batra R · Chau K · Swann J · Walker SA · Peto TEA · Crook DW · Lamble S · Turner P · Cooper BS · Stoesser N last · Wang L first · Ding R · He S · Wang Q · Zhou Y last
Help complete this project
3 of the 4 core fields are below 50% coverage: age_category, health_condition, subject_id. Share metadata
Downloads
PRJEB34871.csv.gz (24.9 KB) · parquet (114.0 KB) — all 1,197 samples, every field with route and confidence · PRJEB34871_determinations.csv.gz (34.2 KB) — 5,985 evidence rows