Pediatric Crohn's disease intestinal biopsy case-control study
40 intestinal biopsy samples from treatment-naïve pediatric Crohn's disease patients and healthy child controls (20 each), profiled by 16S rRNA and shotgun metagenomics in a case-control design. machine-written summary
40 samples · 75 runs · CA · Child · 2017 · 22.70 Gbp / sample
Checked this project's papers and archive records yourself? Mark it human-verified.
what each source gave
| ✓ Sequence archive | 80 sample values (health condition, health condition detail) |
| ○ Abstract / description | read; no usable statement |
| ✓ Full text | 40 sample values (location region) · PMC11650326, PMC5769311, PMC6087418 |
| ✓ Supplementary tables | 80 sample values (age, sex) |
| ✓ External resources | 40 sample values (country) · from GMrepo v3 |
| – User contribution | nothing uploaded yet |
| – Human verified | not yet checked by a person · I checked this project — verify it |
Genera, mean relative abundance over 33 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 | 40 | 100% | ||||
| country | 40 | 100% | 40 | |||
| health_condition | 40 | 100% | 40 | |||
| subject_id | 0 | 0% | ||||
| age_at_collection_days | 40 | 100% | 40 | |||
| detailed_location | 40 | 100% | ||||
| health_condition_detail | 40 | 100% | 40 | |||
| location_region | 40 | 100% | 40 | |||
| sex | 40 | 100% | 40 |
- Multi-omics differentially classify disease state and treatment outcome in pediatric Crohn's disease. 2018 PMID 29335008 PMC5769311 own_data
- Denoising the Denoisers: an independent evaluation of microbiome sequence error-correction approaches. 2018 PMID 30123705 PMC6087418 own_data
- Gut-microbiota-based ensemble model predicts prognosis of pediatric inflammatory bowel disease. 2024 PMID 39691780 PMC11650326 unsure
Archive description
Crohn’s disease (CD) has an unclear etiology, but there is growing evidence of a direct link with a dysbiotic microbiome. Many gut microbes have previously been associated with CD, but these have mainly been confounded with patients’ ongoing treatments. Additionally, most analyses of CD patients’ microbiomes have focused on microbes in stool samples, which yield different insights than profiling biopsy samples. We sequenced the 16S rRNA gene (16S) and carried out shotgun metagenomics (MGS) from the intestinal biopsies of 20 treatment-naïve CD and 20 control pediatric patients. We identified the abundances of microbial taxa and inferred functional categories within each dataset. We also identified known human genetic variants from the MGS data. We then used a machine learning approach to determine the classification accuracy when these datasets, collapsed to different hierarchical groupings, were used independently to classify patients by disease state and by CD patients’ response to treatment. We found that only 16S-identified microbes could classify patients by disease state, but that both sequencing technologies could classify patients by treatment response. Based on follow-ups with these patients we identified which microbes were best for predicting disease state and response to treatment, including several previously identified markers. By combining the top features from all significant models into a single model, we could compare the relative importance of these predictive features. We validated these top features with an independent cohort to demonstrate that they are general predictors of CD state. We demonstrate for the first time that useful predictors of CD treatment response can be produced from shotgun MGS sequencing of biopsy samples despite the complications related to large proportions of host DNA. The top predictive features that we identified in this study could be useful for building an improved classifier for CD and treatment response based on sufferers’ microbiome in the future.
How this project entered the registry
Classified as human gut / stool shotgun DNA metagenome of child by sonnet_x2 with confidence 0.62. Assay shotgun_dna.
- host run.scientific_name · “human gut metagenome”
- body site study.description · “intestinal biopsies of 20 treatment-naïve CD”
- life stage study.title · “Pediatric Crohn's Disease”
Evidence examples (one per field × route)
| sample | field | value | route | conf. | source | quote |
|---|---|---|---|---|---|---|
| SAMEA104174503 | age_at_collection_days | 5260.0 | R2 | 0.85 | paper.supp.40168_2018_398_MOESM2_ESM.xlsx[Sheet1!Age] | S1 | 14.4 |
| SAMEA104174503 | country | CA | R2 | 0.75 | external.gmrepo source | country: Canada |
| SAMEA104174503 | health_condition | healthy_control | R1 | 0.80 | biosample_attr | Control |
| SAMEA104174503 | health_condition_detail | Control | R1 | 0.90 | sample.attr.disease_state | Control |
| SAMEA104174503 | location_region | Scotland | R3 | 0.70 | paper.fulltext.methods | Bacteria in Inflammatory bowel disease in Scottish Children |
| SAMEA104174503 | sex | male | R2 | 0.85 | paper.supp.40168_2018_398_MOESM2_ESM.xlsx[Sheet1!Sex] | S1 | Male |
Sequencing
75 runs over 40 samples · mean 11.74 Gbp per run (median 11.88 Gbp, total 880.58 Gbp; 75 runs with a base count) · layout PAIRED 75 · platform ILLUMINA 75 · models Illumina HiSeq 2000 (75) · 88% of runs with a Sandpiper profile · first public 2017-07-27
Authors (19)
Douglas GM first · Hansen R · Jones CMA · Dunn KA · Comeau AM · Bielawski JP · Tayler R · El-Omar EM · Russell RK · Hold GL · Langille MGI · Van Limbergen J last · Nearing JT first · Ha SM first · Lee K · Kim GH · Hurych J · Cinek O · Shim JO last
Downloads
PRJEB21933.csv.gz (2.0 KB) · parquet (70.8 KB) — all 40 samples, every field with route and confidence · PRJEB21933_determinations.csv.gz (2.6 KB) — 240 evidence rows