Gut microbiome in SARS-CoV-2 infection, cross-sectional
Cross-sectional study of 146 gut microbiome samples from patients with SARS-CoV-2 infection, examining links between microbiome composition (including trypsin-degrading commensals) and diarrhoea severity. machine-written summary
146 samples · 146 runs · US · Age unknown · 2022 · 6.95 Gbp / sample
Checked this project's papers and archive records yourself? Mark it human-verified.
what each source gave
| ○ Sequence archive | records read; no usable sample values |
| ○ Abstract / description | read; no usable statement |
| ✓ Full text | 146 sample values (subject id) · PMC9477747 |
| ○ Supplementary tables | files read; no table could be matched to the samples |
| ✓ External resources | 438 sample values (country, health condition, health condition detail) · 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 136 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 | 146 | 100% | 146 | |||
| health_condition | 146 | 100% | 146 | |||
| subject_id | 146 | 100% | 146 | |||
| health_condition_detail | 146 | 100% | 146 |
- Identification of trypsin-degrading commensals in the large intestine. 2022 PMID 36071157 PMC9477747 own_data
- Identification of commensals and molecular components mediating trypsin degradation in the large intestine 2022 own_data
Archive description
Study of the gut microbiome composition in relation to complications associated with COVID-19 disease.
How this project entered the registry
Classified as human gut / stool shotgun DNA metagenome of age unknown by sonnet_x2 with confidence 0.72. Assay shotgun_dna.
- host run.host_tax_id · “9606”
- body site study.title · “Gut microbiome during COVID-19”
Evidence examples (one per field × route)
| sample | field | value | route | conf. | source | quote |
|---|---|---|---|---|---|---|
| SAMN29421612 | country | US | R2 | 0.75 | external.gmrepo source | country: United States of America |
| SAMN29421612 | health_condition | other_infection | R2 | 0.75 | external.gmrepo source | phenotype: COVID-19 |
| SAMN29421612 | health_condition_detail | COVID-19 | R2 | 0.75 | external.gmrepo source | phenotype: COVID-19 |
| SAMN29421612 | subject_id | SAMN29421612 | R3 | 0.70 | design.one_sample_per_subject:fulltext | Finally, we recruited 146 individuals who were diagnosed with COVID-19 and hospitalized at the Keio University hospital. |
Sequencing
146 runs over 146 samples · mean 7.08 Gbp per run (median 6.95 Gbp, total 1,033.27 Gbp; 146 runs with a base count) · layout PAIRED 146 · platform ILLUMINA 146 · models Illumina NovaSeq 6000 (146) · 93% of runs with a Sandpiper profile · first public 2022-07-06 – 2022-07-09
Authors (36)
Li Y first · Watanabe E · Kawashima Y · Plichta DR · Wang Z · Ujike M · Ang QY · Wu R · Furuichi M · Takeshita K · Yoshida K · Nishiyama K · Kearney SM · Suda W · Hattori M · Sasajima S · Matsunaga T · Zhang X · Watanabe K · Fujishiro J · Norman JM · Olle B · Matsuyama S · Namkoong H · Uwamino Y · Ishii M · Fukunaga K · Hasegawa N · Ohara O · Xavier RJ · Atarashi K · Honda K last · Norman J · Kearney S · Plichta D · Xavier R
Downloads
PRJNA821237.csv.gz (4.5 KB) · parquet (75.5 KB) — all 146 samples, every field with route and confidence · PRJNA821237_determinations.csv.gz (4.9 KB) — 584 evidence rows