Gut mycobiome in end-stage renal disease
292 gut metagenome samples from Beijing, China (223 ESRD patients, 69 healthy controls), studying gut mycobiome dysbiosis and its association with serum and fecal metabolites, in a case-control design. machine-written summary
292 samples · 292 runs · CN · Adult · 2019 · 11.32 Gbp / sample
Checked this project's papers and archive records yourself? Mark it human-verified.
what each source gave
| ✓ Sequence archive | 1,569 sample values (collection date, country, latitude, location locality, location region, longitude) |
| ✓ Abstract / description | 292 sample values (subject id) |
| ○ Full text | read; no usable statement or table · PMC10894479, PMC11253615, PMC7677483, PMC8178816 |
| ✓ Supplementary tables | 1,230 sample values (age, bmi, health condition, health condition detail, sex) |
| ✓ External resources | 515 sample values (antibiotic exposure, health condition) · from BugSigDB, 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 272 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 | 292 | 100% | ||||
| country | 292 | 100% | 292 | |||
| health_condition | 292 | 100% | 292 | |||
| subject_id | 292 | 100% | 292 | |||
| age_at_collection_days | 292 | 100% | 292 | |||
| antibiotic_exposure | 292 | 100% | 292 | |||
| collection_date | 292 | 100% | 292 | |||
| detailed_location | 292 | 100% | ||||
| health_condition_detail | 292 | 100% | 292 | |||
| latitude | 292 | 100% | 292 | |||
| location_locality | 292 | 100% | 292 | |||
| longitude | 292 | 100% | 292 | |||
| sex | 292 | 100% | 292 | |||
| bmi | 285 | 98% | 285 | |||
| location_region | 109 | 37% | 109 |
- Aberrant gut microbiota alters host metabolome and impacts renal failure in humans and rodents. 2020 PMID 32241904 PMC7677483 own_data
- Diet posttranslationally modifies the mouse gut microbial proteome to modulate renal function. 2020 PMID 32943527 PMC8178816 own_data
- Altered gut mycobiome in patients with end-stage renal disease and its correlations with serum and fecal metabolomes. 2024 PMID 38403655 PMC10894479 own_data
- Guild-level signature of gut microbiome for diabetic kidney disease. 2024 PMID 38819146 PMC11253615 own_data
- Impact of Antibiotic Resistance Genes in Gut Microbiome of Patients With Cirrhosis. 2021 PMID 33857456 PMC9069394 unsure
4 more
- Statistical modeling of gut microbiota for personalized health status monitoring. 2023 PMID 37596617 PMC10436630
- A metagenome-wide study of the gut virome in chronic kidney disease. 2025 PMID 39897560 PMC11780533
- gutMSNP: a comprehensive database for human gut microbial single-nucleotide polymorphisms. 2026 PMID 41359024 PMC12807719
- MMETHANE: interpretable AI for predicting host status from microbial composition and metabolomics data. 2025 PMID 41361854 PMC12802134
Archive description
Gut microbiota drives an adverse metabolic landscape in end-stage renal disease
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 · “human gut metagenome Raw sequence”
- body site study.description · “Gut microbiota drives an”
- life stage external_curation.infant_catalog · “infant triage reason_code age_adult_only”
Evidence examples (one per field × route)
| sample | field | value | route | conf. | source | quote |
|---|---|---|---|---|---|---|
| SAMN08918041 | age_at_collection_days | 22646 | R2 | 0.85 | paper.supp.12967_2024_5004_MOESM1_ESM.xlsx[ST1!Age] | 62 |
| SAMN08918041 | antibiotic_exposure | no | R2 | 0.60 | external.bugsigdb source | Antibiotics exclusion: 1 month |
| SAMN08918041 | bmi | 19.6 | R2 | 0.85 | paper.supp.12967_2024_5004_MOESM1_ESM.xlsx[ST1!BMI] | 19.57 |
| SAMN08918041 | collection_date | 2016-01-06 | R1 | 0.90 | biosample.attribute:collection_date | 2016-01-06 |
| SAMN08918041 | country | CN | R1 | 0.90 | sample.attr.geo_loc_name | China: Beijing |
| SAMN08918041 | health_condition | healthy_control | R2 | 0.80 | paper.supp.12967_2024_5004_MOESM1_ESM.xlsx[ST1!Disease] | HC |
| SAMN08918110 | health_condition | kidney_disease | R2 | 0.75 | external.gmrepo source | phenotype: Kidney Failure, Chronic |
| SAMN08918041 | health_condition_detail | HC | R2 | 0.85 | paper.supp.12967_2024_5004_MOESM1_ESM.xlsx[ST1!Disease] | HC |
| SAMN08918041 | latitude | 39.9000 | R1 | 0.90 | biosample.attribute:lat_lon | 39.9 N 116.23 E |
| SAMN08918041 | location_locality | Beijing | R1 | 0.85 | biosample.attribute:geo_loc_name | China: Beijing |
| SAMN08918110 | location_region | Beijing | R1 | 0.60 | biosample.attribute:lat_lon | 39.97 N 116.38 E |
| SAMN08918041 | longitude | 116.2300 | R1 | 0.90 | biosample.attribute:lat_lon | 39.9 N 116.23 E |
| SAMN08918041 | sex | female | R2 | 0.85 | paper.supp.12967_2024_5004_MOESM1_ESM.xlsx[ST1!Gender] | female |
| SAMN08918041 | subject_id | SAMN08918041 | R4 | 0.60 | design.one_sample_per_subject:abstract | a cohort of 223 patients with ESRD and 69 healthy controls |
Sequencing
292 runs over 292 samples · mean 11.22 Gbp per run (median 11.32 Gbp, total 3,276.03 Gbp; 292 runs with a base count) · layout PAIRED 292 · platform ILLUMINA 292 · models HiSeq X Ten (292) · 93% of runs with a Sandpiper profile · first public 2019-06-01
Authors (84)
Ormaasen I first · Rudi K · Diep DB · Snipen L last · Ren Y first · Chen L · Guo R · Ma S · Li S · Zhang Y · Jiang H · Shi H · Zhang P last · Argentini C first · Lugli GA · Tarracchini C · Fontana F · Mancabelli L · Viappiani A · Anzalone R · Angelini L · Alessandri G · Longhi G · Bianchi MG · Taurino G · Bussolati O · Milani C · van Sinderen D · Turroni F · Ventura M last · De Biase R · Bocchio F · De Conto F · Nouvenne A · Ticinesi A · Meschi T · Cecchi R · Tang S first · Wu G · Liu Y · Xue B · Zhang S · Zhang W · Jia Y · Xie Q · Liang C · Wang L · Heng H · Wei W · Shi X · Hu Y · Yang J · Zhao L · Wang X · Yuan H last · Yan Q · Wang K · Deng J · Wang G · Li L · Guo X · Zhao G · Yang L · Wang Y · Kang J · Sha S · Fan S · Cheng L · Meng J · Yu H · Chen F · He D · Wang J · Liu S · Qian K first · Du M · Tan S · Ding M · Song M · Lu M · Qi C · Zhang X last · Dawkins JJ first · Gerber GK last
Downloads
PRJNA449784.csv.gz (10.0 KB) · parquet (85.2 KB) — all 292 samples, every field with route and confidence · PRJNA449784_determinations.csv.gz (27.7 KB) — 3,606 evidence rows