Fecal gdh/A. hallii & PhIP metabolism in healthy adults
20 fecal samples from healthy adult donors in Switzerland, cross-sectional, studying glycerol/diol dehydratase (gdh)-harboring taxa, Anaerobutyricum hallii abundance, and gut microbiota capacity to transform the dietary carcinogen PhIP. machine-written summary
20 samples · 20 runs · CH · Age unknown · 2025 · 69.17 Gbp / sample
Checked this project's papers and archive records yourself? Mark it human-verified.
what each source gave
| ✓ Sequence archive | 120 sample values (collection date, country, latitude, location region, longitude, subject id) |
| ○ Abstract / description | read; no usable statement |
| ✓ Full text | 60 sample values (antibiotic exposure, health condition, health condition detail) · PMC12993610 |
| ○ 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 |
| – Human verified | not yet checked by a person · I checked this project — verify it |
Genera, mean relative abundance over 14 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 | 20 | 100% | ||||
| country | 20 | 100% | 20 | |||
| health_condition | 20 | 100% | 20 | |||
| subject_id | 20 | 100% | 20 | |||
| antibiotic_exposure | 20 | 100% | 20 | |||
| collection_date | 20 | 100% | 20 | |||
| detailed_location | 20 | 100% | ||||
| health_condition_detail | 20 | 100% | 20 | |||
| latitude | 20 | 100% | 20 | |||
| location_region | 20 | 100% | 20 | |||
| longitude | 20 | 100% | 20 |
- <i>Anaerobutyricum hallii</i> promotes the functional depletion of a food carcinogen in diverse healthy fecal microbiota. 2023 PMID 41853383 PMC12993610 own_data
Archive description
Metagenome analysis has proven to be a useful tool to identify markers associated with CRC, as previous studies have identified microbial species and genes which were associated with CRC. These predictions were then successfully validated against external metagenomic cohorts (Thomas et al., 2019 Wirbel et al., 2019 Zeller et al., 2014). Particularly, one study inferred elevated production of secondary bile acids in CRC microbiota, suggesting an association between CRC and fat- and meat-rich diet, which in turn might me correlated with high intake of HCAs and lower potential of the gut microbiota to detoxify or higher potential to reactivate them.</p><p>However, metagenome analysis only identifies the genetic potential and not the functional activity. Having identified the functional activity of two selected enzymatic activities, the dataset generated with the human cohort recruited gives the opportunity to link genetic potential and functional activity for two connected enzymatic activities, which will help to be able to predict activity in vivo.</p><p>The specific objectives are</p><p>1. Generate metagenomes of 20 fecal samples tested for GDH and B-GUS activity</p><p>2. Generate a gene catalogue to report abundance of gdh and b-gus</p><p>3. Assemble metagenomes from assembled genomes to identify gdh and b-gus contributing taxa</p><p>4. Apply machine learning to use the dataset generated with the human cohort recruited (metagenome and activity data) to generate a model able to predict key activities from additional cohort data, as described in Wirbel et al. (2020).
How this project entered the registry
Classified as human gut / stool shotgun DNA metagenome of age unknown by sonnet_x2 with confidence 0.75. Assay shotgun_dna.
- host run.host_tax_id · “9606”
- body site study.description · “Generate metagenomes of 20 fecal samples”
Evidence examples (one per field × route)
| sample | field | value | route | conf. | source | quote |
|---|---|---|---|---|---|---|
| SAMN18644686 | antibiotic_exposure | no | R3 | 0.70 | paper.fulltext.methods | had not received antibiotics in the previous 3 months |
| SAMN18644686 | collection_date | 2020-01 | R1 | 0.90 | biosample.attribute:collection_date | 2020-01 |
| SAMN18644686 | country | CH | R1 | 0.90 | sample.attr.geo_loc_name | Switzerland |
| SAMN18644686 | health_condition | healthy_control | R3 | 0.70 | paper.fulltext.methods | 20 healthy adult individuals |
| SAMN18644686 | health_condition_detail | healthy adult individuals | R3 | 0.70 | paper.fulltext.methods | 20 healthy adult individuals |
| SAMN18644686 | latitude | 47.3600 | R1 | 0.90 | biosample.attribute:lat_lon | 47.36 N 8.55 E |
| SAMN18644686 | location_region | Zurich | R1 | 0.60 | biosample.attribute:lat_lon | 47.36 N 8.55 E |
| SAMN18644686 | longitude | 8.5500 | R1 | 0.90 | biosample.attribute:lat_lon | 47.36 N 8.55 E |
| SAMN18644686 | subject_id | D44 | R1 | 0.85 | sample.attr.host_subject_id | D44 |
Sequencing
20 runs over 20 samples · mean 70.46 Gbp per run (median 69.17 Gbp, total 1,409.22 Gbp; 20 runs with a base count) · layout PAIRED 20 · platform ILLUMINA 20 · models NextSeq 500 (20) · 70% of runs with a Sandpiper profile · first public 2025-04-27
Authors (9)
Ramirez Garcia A first · Greppi A · Constancias F · Ruscheweyh HJ · Gasser J · Hurley K · Sturla SJ · Schwab C · Lacroix C last
Downloads
PRJNA720271.csv.gz (1.4 KB) · parquet (70.7 KB) — all 20 samples, every field with route and confidence · PRJNA720271_determinations.csv.gz (1.7 KB) — 180 evidence rows