Fecal metagenomics in hyperuricemia, Dongguan, China
166 fecal metagenome samples from an adult population in Dongguan, China, studying gut microbiota in relation to hyperuricemia, using a case-control design. machine-written summary
166 samples · 166 runs · CN · Adult · 2024 · 8.76 Gbp / sample
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what each source gave
| ✓ Sequence archive | 830 sample values (collection date, country, latitude, location locality, longitude) |
| ✓ Abstract / description | 166 sample values (health condition detail) |
| – Full text | no open-access full text linked |
| – Supplementary tables | no supplementary tables available (or none open access) |
| – External resources | not covered by the external resources we ingest |
| – User contribution | nothing uploaded yet · share metadata |
| – Human verified | not yet checked by a person · I checked this project — verify it |
Genera, mean relative abundance over 149 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 | 166 | 100% | ||||
| country | 166 | 100% | 166 | |||
| health_condition | 0 | 0% | ||||
| subject_id | 0 | 0% | ||||
| collection_date | 166 | 100% | 166 | |||
| detailed_location | 166 | 100% | ||||
| health_condition_detail | 166 | 100% | 166 | |||
| latitude | 166 | 100% | 166 | |||
| location_locality | 166 | 100% | 166 | |||
| longitude | 166 | 100% | 166 |
- Alistipes indistinctus-derived hippuric acid promotes intestinal urate excretion to alleviate hyperuricemia. 2024 PMID 38412863 own_data
Archive description
Hyperuricemia, characterized by an imbalance between uric acid (UA) generation and excretion, has become the second most common metabolic disease. In addition to gout, hyperuricemia has also been linked to the development of inflammatory arthritis, renal and cardiovascular diseases, posing a major threat to all societies. Despite a clear pharmacological mechanism for existing hypouricemic agents, severe side effects remain a major concern in their clinical implementation. Clearly, identifying safe and effective alternatives has the potential to significantly impact the quality of life for millions, and ameliorate the global burden of hyperuricemia and associated diseases.</p><p>Recently, a growing body of evidence suggests that dysbiosis of gut microbiota plays an important role in the pathogenesis of hyperuricemia. Altered compositions of microbiota, characterized by a reduced abundance of genus Coprococcus, were found in subjects with hyperuricemia compared to those with normouricemia. Similarly, levels of some genera, such as Prevotella, Dehalobacterium, Ruminococcus, and Lactobacillus, were remarkably lower in rat model of hyperuricemia. Moreover, the phenotype of elevated UA was transmissible with fecal microbial transplantation from donors with hyperuricemia, whereas, supplementation of Bifidobacteria and Lactobacilli, two well-known probiotics, was effective to reduce UA in mice. However, murine models failed to fully replicate UA metabolism in humans, and the cross-sectional nature of existing human studies with several comorbidities but no biological validation precluded casual inference. Therefore, though emerging studies suggest that gut microbiota might be a novel therapeutic target for the management of hyperuricemia, specific species involved and their exact roles remain unclear.</p><p>To address the questions above, an integrative analysis of metagenomics and metabolomics in medication-naive subjects with or without hyperuricemia was performed.
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 · “sequence reads of fecal metagenomics in HUA”
- body site study.description · “that dysbiosis of gut microbiota plays 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 |
|---|---|---|---|---|---|---|
| SAMN39241102 | collection_date | 2018-03-01 | R1 | 0.90 | biosample.attribute:collection_date | 2018-03-01 |
| SAMN39241102 | country | CN | R1 | 0.90 | sample.attr.geo_loc_name | China: Dongguan |
| SAMN39241102 | health_condition_detail | mixed_case_control: subjects with hyperuricemia vs those with normouricemia | R4 | 0.50 | study.description | subjects with hyperuricemia compared to those with normouricemia |
| SAMN39241102 | latitude | 23.0000 | R1 | 0.90 | biosample.attribute:lat_lon | 23 N 114 E |
| SAMN39241102 | location_locality | Dongguan | R1 | 0.85 | biosample.attribute:geo_loc_name | China: Dongguan |
| SAMN39241102 | longitude | 114.0000 | R1 | 0.90 | biosample.attribute:lat_lon | 23 N 114 E |
Sequencing
166 runs over 166 samples · mean 8.77 Gbp per run (median 8.76 Gbp, total 1,455.47 Gbp; 166 runs with a base count) · layout PAIRED 166 · platform ILLUMINA 166 · models Illumina NovaSeq 6000 (166) · 90% of runs with a Sandpiper profile · first public 2024-01-05 – 2024-01-11
Authors (0)
No author record.
Help complete this project
2 of the 4 core fields are below 50% coverage: health_condition, subject_id. Share metadata
Downloads
PRJNA1060633.csv.gz (4.6 KB) · parquet (76.0 KB) — all 166 samples, every field with route and confidence · PRJNA1060633_determinations.csv.gz (5.7 KB) — 996 evidence rows