Gut microbiome in elite Irish and Brazilian athletes
Cross-sectional study of 40 faecal samples from elite adult athletes (Ireland and Brazil), examining gut microbiome composition and antimicrobial/bacteriocin-producing activity across sports classification groups. machine-written summary
40 samples · 40 runs · IE, BR · Adult · 2019 · 1.58 Gbp / sample
Checked this project's papers and archive records yourself? Mark it human-verified.
what each source gave
| ✓ Sequence archive | 240 sample values (collection date, country, latitude, location region, longitude, subject id) |
| ✓ Abstract / description | 40 sample values (lifestyle detail) |
| ○ Full text | read; no usable statement or table · PMC11684919, PMC9025905, PMC9045144, PMC9926762 |
| ✓ Supplementary tables | 40 sample values (lifestyle) |
| – External resources | not covered by the external resources we ingest |
| – User contribution | nothing uploaded yet |
| – Human verified | not yet checked by a person · I checked this project — verify it |
longitudinal
2 of 37 people sampled more than once (5 samples; median 2, up to 3 per person) · follow-up median 57 d (max 4 mo) · median interval between samples 2 d (time from collection dates).
Each row is one person (2 shown); dots are samples at days since that person's first sample.
Genera, mean relative abundance over 35 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 | 0 | 0% | ||||
| subject_id | 40 | 100% | 40 | |||
| collection_date | 40 | 100% | 40 | |||
| detailed_location | 40 | 100% | ||||
| latitude | 40 | 100% | 40 | |||
| lifestyle | 40 | 100% | 40 | |||
| lifestyle_detail | 40 | 100% | 40 | |||
| location_region | 40 | 100% | 40 | |||
| longitude | 40 | 100% | 40 |
- In Vitro and In Silico Based Approaches to Identify Potential Novel Bacteriocins from the Athlete Gut Microbiome of an Elite Athlete Cohort. 2022 PMID 35456752 PMC9025905 own_data
- Investigation of the Ecological Link between Recurrent Microbial Human Gut Communities and Physical Activity. 2022 PMID 35377222 PMC9045144 unsure
- The human gut microbiome of athletes: metagenomic and metabolic insights. 2023 PMID 36782241 PMC9926762 unsure
- Cataloging metagenome-assembled genomes and microbial genes from the athlete gut microbiome. 2024 PMID 39741946 PMC11684919 unsure
Archive description
The gut microbiome has begun to be characterised in athlete groups, albeit, to date, only across a subset of sports. This study aimed to determine if the gut microbiome and metabolome differed across sports classification groups among elite Irish athletes, many of whom were participating in the 2016 Summer Olympics. Faecal and urine samples were collected from 37 international level athletes. Faecal samples were prepared for shotgun metagenomic sequencing and faecal and urine samples underwent metabolomic profiling. Dietary intake data was collected using food frequency questionnaires and translated into daily intakes using FETA (Food Frequency Questionnaire European Prospective Investigation into Cancer and Nutrition Tool for Analysis). Differences were observed in the composition and functional capacity of the gut microbiome of athletes across sports groups that were distinguished on the basis of the level of dynamic and static components. The microbiomes of athletes participating in sports with a high dynamic component were found to be most distinct compositionally, while those of athletes participating in sports with high dynamic and static components were the most functionally distinct. Additionally, both microbial (faecal) and human (urine) derived metabolites were found to vary between sports classification groups. In particular cis-aconitate, succinic acid and lactate, in urine samples, and creatinine, in faeces, were found to be significantly different between groups. These differences were evident despite the absence of significant differences in diet. Differences in the gut microbiome and metabolome between groups, in the absence of dietary changes, indicate a role for training load or type as a contributory factor. Further exploration of this hypothesis has the potential to benefit athletes, aspiring athletes and the general public.
How this project entered the registry
Classified as human gut / stool shotgun DNA metagenome of adult by sonnet_x2 with confidence 0.78. Assay shotgun_dna.
- host run.scientific_name · “Homo sapiens”
- body site study.description · “Faecal samples were prepared for shotgun”
- life stage study.description · “37 international level athletes”
Evidence examples (one per field × route)
| sample | field | value | route | conf. | source | quote |
|---|---|---|---|---|---|---|
| SAMEA5665376 | collection_date | 2016-08-12 | R1 | 0.90 | biosample.attribute:collection_date | 2016-08-12 |
| SAMEA5665376 | country | BR | R1 | 0.90 | sample.attr.geographic_location_country_and_or_sea | Brazil |
| SAMEA5665376 | latitude | -18.8862 | R1 | 0.90 | biosample.attribute:geographic_location_latitude+geographic_location_longitude | -18.88623 -48.25581 |
| SAMEA5665376 | lifestyle | athlete | R2 | 0.85 | paper.supp.spectrum00420-22_supp_2_seq8.xlsx[Table S1!Participant_Type] | ERR3342944: Participant_Type=elite Irish athletes |
| SAMEA5665376 | lifestyle_detail | elite Irish athletes | R4 | 0.40 | ena.study_description | 37 international level athletes |
| SAMEA5665376 | location_region | Minas Gerais | R1 | 0.60 | biosample.attribute:geographic_location_latitude+geographic_location_longitude | -18.88623 -48.25581 |
| SAMEA5665376 | longitude | -48.2558 | R1 | 0.90 | biosample.attribute:geographic_location_latitude+geographic_location_longitude | -18.88623 -48.25581 |
| SAMEA5665376 | subject_id | OL002 | R1 | 0.70 | sample.name_rule:alias | OL002 |
Sequencing
40 runs over 40 samples · mean 1.76 Gbp per run (median 1.58 Gbp, total 70.36 Gbp; 40 runs with a base count) · layout PAIRED 40 · platform ILLUMINA 40 · models NextSeq 500 (40) · 88% of runs with a Sandpiper profile · first public 2019-08-01
Authors (18)
Tarracchini C first · Fontana F · Lugli GA · Mancabelli L · Alessandri G · Turroni F · Ventura M · Milani C last · Wosinska L first · Walsh CJ · O'Connor PM · Lawton EM · Cotter PD · Guinane CM · O'Sullivan O last · Longhi G · van Sinderen D · Walsh LH
Downloads
PRJEB32794.csv.gz (2.1 KB) · parquet (71.8 KB) — all 40 samples, every field with route and confidence · PRJEB32794_determinations.csv.gz (3.2 KB) — 320 evidence rows