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PRJEB38352 ERP121770

IBD Surgery Effects on Gut Microbiome and Metabolome (UCSD)

Gastrointestinal Surgery for Inflammatory Bowel Disease Persistently Lowers Microbiome and Metabolome Diversity

382 stool samples from IBD patients (Crohn's disease, ulcerative colitis, and unspecified IBD) at UC San Diego, studying surgery (ileocolonic resection, colectomy) effects on microbiome and metabolome in a longitudinal design. machine-written summary

382 samples · 382 runs · US · Age unknown · 2021 · 8.68 Gbp / sample

Sources
✓Sequence archive 3,044✓Abstract / description 114✓Full text 1,528○Supplementary tables–External resources–User contribution–Human verified

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

what each source gave
✓ Sequence archive3,044 sample values (antibiotic exposure, collection date, health condition, health condition detail, intervention, intervention detail, medication, sex)
✓ Abstract / description114 sample values (health condition, health condition detail)
✓ Full text1,528 sample values (country, location locality, location region, location site) · PMC8047854
○ Supplementary tablesfiles read; no table could be matched to the samples
– External resourcesnot covered by the external resources we ingest
– User contributionnothing uploaded yet
– Human verifiednot yet checked by a person · I checked this project — verify it
Interventions

intervention · arms resolved

ESurgical procedure studied before/after (bariatric surgery: 42 people · 105 samplessurgery 42 people · 105 samplesControl / pre-intervention baseline (sample level only): 86 people · 220 samplesno intervention 86 people · 220 samples
exposed vs. not exposed (observational)

6 people have samples in more than one arm.

Surgical procedure studied before/after (bariatric surgery design observational_with_procedure · population ibd_unspecified · Ileocolonic resection or colectomy in IBD patients

“ileocolonic resections, and 17 had colectomies” — bioproject.description (study level; per-sample arms are in the intervention column below)

Samples at a glance
1 country
Age
unknown382
Condition
crohns_disease196
ulcerative_colitis129
ibd_unspecified57
Sex
female157
male157
Country
US382
Body site
primary382
Collected 2014–2017
2014: 78 samples2015: 189 samples2016: 58 samples2017: 57 samples20142017
Sequencing depth per sample · this project all projects
all projects, 0–0.03 Gbp: 25,080 samples (4 %)this project, 0–0.03 Gbp: 52 samplesall projects, 0.03–0.1 Gbp: 17,066 samples (3 %)this project, 0.03–0.1 Gbp: 9 samplesall projects, 0.1–0.3 Gbp: 29,179 samples (5 %)this project, 0.1–0.3 Gbp: 15 samplesall projects, 0.3–1 Gbp: 47,044 samples (8 %)this project, 0.3–1 Gbp: 14 samplesall projects, 1–3 Gbp: 124,983 samples (22 %)this project, 1–3 Gbp: 23 samplesall projects, 3–10 Gbp: 253,728 samples (44 %)this project, 3–10 Gbp: 114 samplesall projects, 10–30 Gbp: 72,533 samples (13 %)this project, 10–30 Gbp: 155 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 8.68 Gbp per sample0130Gbp
! Self-contradictory metadata

antibiotic exposure: two sequence archives disagree for 4 samples (e.g. no vs yes) — withheld; sex differs between samples of the same subject id (3 subjects, 11 samples) — sex withheld; subject ids shared by samples of both sexes (1 ids, 2 samples) — ids withheld.

The sources disagree on 15 samples. Where two incompatible values exist for one sample (or one person's samples disagree on sex), neither value is published: the field is left empty here, in the sample sheet and in the downloads. Both sides, with their sources, are in gut_metadata_contradictions.parquet of the data package (what counts as a contradiction).

Contradicted values (17)
samplefieldvalueother valuehandling
SAMEA6844973antibiotic exposureno (sequence archive)yes (sequence archive)withheld: neither value published
SAMEA6844989antibiotic exposureno (sequence archive)yes (sequence archive)withheld: neither value published
SAMEA6845187antibiotic exposureno (sequence archive)yes (sequence archive)withheld: neither value published
SAMEA6845256antibiotic exposureno (sequence archive)yes (sequence archive)withheld: neither value published
SAMEA6845002sexfemale (sequence archive)subject 100033: female in 1 sample, male in 1 samplewithheld: neither value published
SAMEA6845010sexmale (sequence archive)subject 100033: female in 1 sample, male in 1 samplewithheld: neither value published
SAMEA6845018sexmale (sequence archive)subject 100046: male in 4 samples, female in 1 samplewithheld: neither value published
SAMEA6845019sexmale (sequence archive)subject 100046: male in 4 samples, female in 1 samplewithheld: neither value published
SAMEA6845020sexmale (sequence archive)subject 100046: male in 4 samples, female in 1 samplewithheld: neither value published
SAMEA6845021sexmale (sequence archive)subject 100046: male in 4 samples, female in 1 samplewithheld: neither value published
SAMEA6845023sexfemale (sequence archive)subject 100046: male in 4 samples, female in 1 samplewithheld: neither value published
SAMEA6845113sexfemale (sequence archive)subject 100089: female in 3 samples, male in 1 samplewithheld: neither value published
SAMEA6845114sexfemale (sequence archive)subject 100089: female in 3 samples, male in 1 samplewithheld: neither value published
SAMEA6845115sexfemale (sequence archive)subject 100089: female in 3 samples, male in 1 samplewithheld: neither value published
SAMEA6845120sexmale (sequence archive)subject 100089: female in 3 samples, male in 1 samplewithheld: neither value published
SAMEA6845002subject id100033female (sequence archive)subject id withheld (samples of both sexes share it)
SAMEA6845010subject id100033male (sequence archive)subject id withheld (samples of both sexes share it)
Sampling design and relatedness

longitudinal

102 of 179 people sampled more than once (303 samples; median 3, up to 5 per person) · follow-up median 12 mo (max 2.0 y) · median interval between samples 6 mo (time from timepoint numbers, unit from the stated sampling schedule). Schedule stated by the source: “Stool collected at baseline and follow-up months up to 24 months post initial sampling (e.g., 0,1,3,6,9,12,15,18,19,24)”.

100007100007: day 0100007: day 183100007: day 365100007: day 730100008100008: day 0100008: day 183100008: day 365100009100009: day 0100009: day 183100009: day 365100009: day 548100013100013: day 0100013: day 30100013: day 183100013: day 365100013: day 548100017100017: day 0100017: day 183100017: day 365100018100018: day 0100018: day 365100018: day 548100018: day 730100019100019: day 0100019: day 91100019: day 274100019: day 457100021100021: day 0100021: day 183100021: day 365100024100024: day 0100024: day 183100024: day 365100029100029: day 0100029: day 183100029: day 365100029: day 548100031100031: day 0100031: day 183100031: day 365100034100034: day 0100034: day 183100034: day 365100034: day 548100038100038: day 0100038: day 183100038: day 365100040100040: day 0100040: day 365100040: day 548100046100046: day 0100046: day 0100046: day 183100046: day 365100046: day 548100051100051: day 0100051: day 183100051: day 365100051: day 548100053100053: day 0100053: day 183100053: day 365100054100054: day 0100054: day 183100054: day 365100054: day 548100055100055: day 0100055: day 183100055: day 365100056100056: day 0100056: day 183100056: day 365100056: day 548100064100064: day 0100064: day 183100064: day 365100064: day 548100064: day 578100065100065: day 0100065: day 183100065: day 365100065: day 548100067100067: day 0100067: day 183100067: day 365100067: day 548100067: day 730100068100068: day 0100068: day 183100068: day 365100068: day 548100080100080: day 0100080: day 183100080: day 365100080: day 548100082100082: day 0100082: day 183100082: day 365100082: day 548100082: day 639100083100083: day 0100083: day 183100083: day 365100083: day 548100089100089: day 0100089: day 0100089: day 183100089: day 548100090100090: day 0100090: day 30100090: day 183100090: day 365100092100092: day 0100092: day 183100092: day 365100092: day 548100104100104: day 0100104: day 183100104: day 365100104: day 548100108100108: day 0100108: day 183100108: day 365100108: day 548100112100112: day 0100112: day 183100112: day 365100112: day 548100165100165: day 0100165: day 365100165: day 548100165: day 609100168100168: day 0100168: day 365100168: day 548100168: day 639100181100181: day 0100181: day 91100181: day 183100181: day 274100181: day 3650 d6 mo12 mo18 mo2.0 y

Each row is one person (36 shown of 102); dots are samples at days since that person's first sample.

How longitudinal and relatedness information is derived

Community composition
Bacteroides 15.5%Phocaeicola 10.7%Escherichia 7.2%Faecalibacterium 4.6%Roseburia 3.5%Prevotella 2.8%Alistipes 2.7%Enterocloster 2.1%Akkermansia 2.0%Blautia_A 2.0%Mediterraneibacter 1.9%Bifidobacterium 1.8%not assigned to a genus 6.0%other genera 37.0%

Genera, mean relative abundance over 259 profiled gut samples of this project (Sandpiper SingleM profiles, GTDB R232; low-depth and non-metagenome profiles left out).

Field coverage
fieldsamplescoverageR1R2R3R4
age_category0 0%
country382 100%382
health_condition382 100%32557
subject_id380 99%380
collection_date382 100%382
detailed_location382 100%
health_condition_detail382 100%32557
location_locality382 100%382
location_region382 100%382
location_site382 100%382
intervention325 85%325
medication325 85%325
timepoint_label325 85%325
antibiotic_exposure321 84%321
sex314 82%314
intervention_detail22 6%22
Papers (1)
  • Gastrointestinal Surgery for Inflammatory Bowel Disease Persistently Lowers Microbiome and Metabolome Diversity. 2021 PMID 33026068 PMC8047854 own_data
Details
Archive description

Background Many studies have investigated the role of the microbiome in inflammatory bowel disease (IBD), but few have focused on surgery specifically or its consequences on the metabolome that may differ by surgery type and require longitudinal sampling. Our objective was to characterize and contrast microbiome and metabolome changes after different surgeries for IBD, including ileocolonic resection and colectomy. Methods The UC San Diego IBD Biobank was used to prospectively collect 332 stool samples from 129 subjects (50 ulcerative colitis 79 Crohn’s disease). Of these, 21 with Crohn’s disease had ileocolonic resections, and 17 had colectomies. We used shotgun metagenomics and untargeted liquid chromatography followed by tandem mass spectrometry metabolomics to characterize the microbiomes and metabolomes of these patients up to 24 months after the initial sampling. Results The species diversity and metabolite diversity both differed significantly among groups (species diversity: Mann-Whitney U test P value = 7.8e-17 metabolomics, P-value = 0.0043). Escherichia coli in particular expanded dramatically in relative abundance in subjects undergoing surgery. The species profile was better able to classify subjects according to surgery status than the metabolite profile (average precision 0.80 vs 0.68). Conclusions Intestinal surgeries seem to reduce the diversity of the gut microbiome and metabolome in IBD patients, and these changes may persist. Surgery also further destabilizes the microbiome (but not the metabolome) over time, even relative to the previously established instability in the microbiome of IBD patients. These long-term effects and their consequences for health outcomes need to be studied in prospective longitudinal trials linked to microbiome-involved phenotypes.

How this project entered the registry

Classified as human gut / stool shotgun DNA metagenome of age unknown by opus_adjudicated with confidence 0.65. Assay shotgun_dna.

  • host run.host_tax_id · “9606”
  • host run.scientific_name · “human gut metagenome”
  • body site study.description · “332 stool samples from 129 subjects”
Evidence examples (one per field × route)
samplefieldvaluerouteconf.sourcequote
SAMEA6844943antibiotic_exposureyesR10.90biosample_attry
SAMEA6844885collection_date2017R10.85biosample.attribute:collection_timestamp2017
SAMEA6844885countryUSR30.70paper.fulltext.methodsUniversity of California, San Diego (UCSD) at the Inflammatory Bowel Disease Center
SAMEA6844943health_conditioncrohns_diseaseR10.90biosample_attrcd
SAMEA6844885health_conditionibd_unspecifiedR40.50study.descriptioninflammatory bowel disease (IBD)
SAMEA6844943health_condition_detailcdR10.90sample.attr.diseasecd
SAMEA6844885health_condition_detailMixed IBD cohort: ulcerative colitis and Crohn's diseaseR40.50study.descriptioninflammatory bowel disease (IBD)
SAMEA6844943interventionsurgeryR10.80biosample.attribute:weeks_from_surgeryweeks_from_surgery: 74
SAMEA6844944interventionno_interventionR10.85biosample.attribute:colon_present;biosample.attribute:has_surgery;biosample.attribute:ic_valve_removed;biosample.attribute:interval_surgery;biosample.attribute:small_bowel_resectioncolon;n
SAMEA6845040interventionsurgeryR10.85biosample.attribute:ic_valve_removedy
SAMEA6845136interventionsurgeryR10.85biosample.attribute:has_surgery;biosample.attribute:small_bowel_resectiony
SAMEA6845243interventionsurgeryR10.85biosample.attribute:ic_valve_removed;biosample.attribute:interval_surgeryy
SAMEA6844943intervention_detailweeks_from_surgery = 74 (after intervention start)R10.80biosample.attribute:weeks_from_surgeryweeks_from_surgery: 74
SAMEA6844885location_localitySan DiegoR30.70paper.fulltext.methodsUniversity of California, San Diego (UCSD)
SAMEA6844885location_regionCaliforniaR30.70paper.fulltext.methodsUniversity of California, San Diego
SAMEA6844885location_siteUCSD Inflammatory Bowel Disease CenterR30.70paper.fulltext.methodsseen at the University of California, San Diego (UCSD)
SAMEA6844943medicationimmunosuppressant_or_biologicR10.85biosample.attribute:current_tnf_inhibitor;biosample.attribute:tnf_exposurey
SAMEA6844946medicationcorticosteroidR10.90biosample.attribute:current_steroid_usey
SAMEA6844948medicationother_medicationR10.85biosample.attribute:use_5asay
SAMEA6844952medicationimmunosuppressant_or_biologicR10.85biosample.attribute:current_integrin_inhibitor_use;biosample.attribute:immune_modulator_use;biosample.attribute:tnf_exposurey
SAMEA6844953medicationimmunosuppressant_or_biologicR10.85biosample.attribute:tnf_exposurey
SAMEA6844963medicationother_medication;probiotic_or_prebioticR10.85biosample.attribute:probiotics;biosample.attribute:use_5asay
SAMEA6844975medicationcorticosteroidR10.90biosample.attribute:steroid_coursey
SAMEA6845099medicationimmunosuppressant_or_biologic;other_medicationR10.85biosample.attribute:tnf_exposure;biosample.attribute:use_5asay
SAMEA6845166medicationimmunosuppressant_or_biologicR10.85biosample.attribute:immune_modulator_use;biosample.attribute:tnf_exposure;biosample.attribute:ustekinumab_usey
SAMEA6844943sexmaleR10.90sample.attr.sexmale
SAMEA6844885subject_idMG.BLANK.1.11DR10.85sample.attr.host_subject_idMG.BLANK.1.11D
SAMEA6844943timepoint_label24R10.85sample.attr.time_point_month24
Sequencing

382 runs over 382 samples · mean 8.19 Gbp per run (median 8.68 Gbp, total 3,126.76 Gbp; 382 runs with a base count) · layout PAIRED 382 · platform ILLUMINA 382 · models Illumina HiSeq 2500 (382) · 68% of runs with a Sandpiper profile · first public 2021-05-07

Authors (26)

Fang X first · Vázquez-Baeza Y · Elijah E · Vargas F · Ackermann G · Humphrey G · Lau R · Weldon KC · Sanders JG · Panitchpakdi M · Carpenter C · Jarmusch AK · Neill J · Miralles A · Dulai P · Singh S · Tsai M · Swafford AD · Smarr L · Boyle DL · Palsson BO · Chang JT · Dorrestein PC · Sandborn WJ · Knight R · Boland BS last

Downloads

PRJEB38352.csv.gz (12.6 KB) · parquet (88.1 KB) — all 382 samples, every field with route and confidence · PRJEB38352_determinations.csv.gz (30.6 KB) — 4,686 evidence rows

Samples (382 · every field this project has values for · hover a cell for its source)
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