Gut microbiome and resistome of animals and humans, Brazil
Cross-sectional One Health study of 321 fecal metagenome samples from humans, cattle, swine, and poultry across Brazil's five geographic regions, characterizing gut microbiome diversity and antimicrobial resistome. machine-written summary
96 samples · 321 runs · BR · Age unknown · 2022 · 1.89 Gbp / sample
Checked this project's papers and archive records yourself? Mark it human-verified.
what each source gave
| ✓ Sequence archive | 480 sample values (collection date, country, latitude, location region, longitude) |
| ○ Abstract / description | read; no usable statement |
| ○ Full text | read; no usable statement or table · PMC11536998, PMC9233704, PMC9602611 |
| ○ Supplementary tables | files read; no table could be matched to the samples |
| – 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 288 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 | 96 | 100% | 96 | |||
| health_condition | 0 | 0% | ||||
| subject_id | 0 | 0% | ||||
| collection_date | 96 | 100% | 96 | |||
| detailed_location | 96 | 100% | ||||
| latitude | 96 | 100% | 96 | |||
| location_region | 96 | 100% | 96 | |||
| longitude | 96 | 100% | 96 |
- Large Scale Genome-Centric Metagenomic Data from the Gut Microbiome of Food-Producing Animals and Humans. 2022 PMID 35752638 PMC9233704 own_data
- Exploring the Bacteriome and Resistome of Humans and Food-Producing Animals in Brazil. 2022 PMID 35993730 PMC9602611 own_data
- Uncovering new <i>Firmicutes</i> species in vertebrate hosts through metagenome-assembled genomes with potential for sporulation. 2024 PMID 39283121 PMC11536998 own_data
Archive description
Brazil is the fifth-largest country by area (3.2 million square miles) and represents 47% of the South American continent. It is also the sixth most populous country (210 million inhabitants), the first world's largest exporter of beef (2,314 million tonnes), poultry (3,811 million tonnes), and fourth-largest exporter of pork meat (861,000 tonnes), according to 2019 report(23), with Asian countries being the main importers (24). As part of a one-health based surveillance study, we characterized the fecal metagenomes of food-producing animals (swine, cattle, and poultry) and humans collected around the same timeframe from all five Brazilian geographic regions as well as the resistome encountered. To the best of our knowledge, this is the first study of this kind performed in South America.
How this project entered the registry
Classified as human and non-human (mixed) gut / stool mixed assays of age unknown by sonnet_x2 with confidence 0.55. Assay mixed.
- host study.description · “food-producing animals (swine, cattle, and poultry) and humans”
- body site study.description · “characterized the fecal metagenomes of food-producing animals”
Evidence examples (one per field × route)
| sample | field | value | route | conf. | source | quote |
|---|---|---|---|---|---|---|
| SAMN17225200 | collection_date | 2020 | R1 | 0.90 | biosample.attribute:collection_date | 2020 |
| SAMN17225200 | country | BR | R1 | 0.90 | sample.attr.geo_loc_name | Brazil |
| SAMN17225200 | latitude | -22.2211 | R1 | 0.90 | biosample.attribute:lat_lon | 22.22111 S 54.80556 W |
| SAMN17225200 | location_region | Mato Grosso do Sul | R1 | 0.60 | biosample.attribute:lat_lon | 22.22111 S 54.80556 W |
| SAMN17225200 | longitude | -54.8056 | R1 | 0.90 | biosample.attribute:lat_lon | 22.22111 S 54.80556 W |
Sequencing
321 runs over 96 samples · mean 2.52 Gbp per run (median 1.89 Gbp, total 810.03 Gbp; 321 runs with a base count) · layout PAIRED 321 · platform ILLUMINA 321 · models NextSeq 500 (321) · 90% of runs with a Sandpiper profile · first public 2022-05-08
Authors (42)
Lemos LN first · de Carvalho FM · Santos FF · Valiatti TB · Corsi DC · de Oliveira Silveira AC · Gerber A · Guimarães APC · de Oliveira Souza C · Brasiliense DM · Maia Castelo-Branco DSC · Anzai EK · Bessa-Neto FO · de Melo GM · de Souza GH · Ferraz LFC · de Nazaré Miranda Bahia M · Mattos MS · da Silva RGB · Veiga R · Simionatto S · Monteiro WAP · de Oliveira Lima WA · Kiffer CRV · Cayô R · Gales AC · de Vasconcelos ATR last · Silveira ACO · Gerber AL · Souza CO · Cassu Corsi D · Castelo-Branco DSCM · Guedes GMM · de Souza GHA · Bahia MNM · Vaz MSM · Lima WAO · Campos Pignatari AC · Machado DT first · Dias BdC · Marques de Carvalho F · Vasconcelos ATR last
Help complete this project
3 of the 4 core fields are below 50% coverage: age_category, health_condition, subject_id. Share metadata
Downloads
PRJNA684454.csv.gz (3.3 KB) · parquet (73.2 KB) — all 96 samples, every field with route and confidence · PRJNA684454_determinations.csv.gz (3.2 KB) — 480 evidence rows