Long. stool metagenomes for bacterial growth phase modeling
80 stool samples from the USA, collected longitudinally, used to model gut bacterial population growth phases via stochastic logistic growth; no health condition or intervention specified. machine-written summary
80 samples · 80 runs · US · Age unknown · 2023 · 0.67 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, timepoint) |
| ○ Abstract / description | read; no usable statement |
| – 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 71 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 | 80 | 100% | 80 | |||
| health_condition | 0 | 0% | ||||
| subject_id | 0 | 0% | ||||
| collection_date | 80 | 100% | 80 | |||
| detailed_location | 80 | 100% | ||||
| latitude | 80 | 100% | 80 | |||
| location_region | 80 | 100% | 80 | |||
| longitude | 80 | 100% | 80 | |||
| timepoint_label | 80 | 100% | 80 |
- Growth phase estimation for abundant bacterial populations sampled longitudinally from human stool metagenomes. 2023 PMID 37709733 PMC10502120 unsure
Archive description
Longitudinal sampling of the stool has yielded important insights into the ecological dynamics of the human gut microbiome. However, due to practical limitations, the most densely sampled time series from the human gut are collected at a frequency of about once per day, while the population doubling times for gut commensals are on the order of minutes-to-hours. Despite this, much of the prior work on human gut microbiome time series modeling has, implicitly or explicitly, assumed that day-to-day fluctuations in taxon abundances are related to population growth or death rates, which is likely not the case. Here, we propose an alternative model of the human gut as a flow-through ecosystem at a dynamical steady state, where population dynamics occur internally and the bacterial population sizes measured in a bolus of stool represent an endpoint of these internal dynamics. We formalize this idea as stochastic logistic growth of a population in a system held at a semi-constant flow rate. We show how this model provides a path toward estimating the growth phases of gut bacterial populations in situ. We validate our model predictions using an in vitro Escherichia coli growth experiment. Finally, we show how this method can be applied to densely-sampled human stool metagenomic time series data. Consistent with our model, stool donors with slower defecation rates tended to harbor a larger proportion of taxa in later growth phases, while faster defecation rates were associated with more taxa in earlier growth phases. We discuss how these growth phase estimates may be used to better inform metabolic modeling in flow-through ecosystems, like animal guts or industrial bioreactors.</p><p>[This submission covers the E. coli in vitro data]
How this project entered the registry
Classified as human gut / stool shotgun DNA metagenome of age unknown by sonnet_x2 with confidence 0.53. Assay shotgun_dna.
- host study.title · “human stool metagenomes”
- body site study.description · “human gut microbiome time series”
Evidence examples (one per field × route)
| sample | field | value | route | conf. | source | quote |
|---|---|---|---|---|---|---|
| SAMN33685825 | collection_date | 2023-01-08 | R1 | 0.90 | biosample.attribute:collection_date | 2023-01-08 |
| SAMN33685825 | country | US | R1 | 0.90 | sample.attr.geo_loc_name | USA |
| SAMN33685825 | latitude | 42.3600 | R1 | 0.90 | biosample.attribute:lat_lon | 42.36 N 71.0941 W |
| SAMN33685825 | location_region | Massachusetts | R1 | 0.60 | biosample.attribute:lat_lon | 42.36 N 71.0941 W |
| SAMN33685825 | longitude | -71.0941 | R1 | 0.90 | biosample.attribute:lat_lon | 42.36 N 71.0941 W |
| SAMN33685825 | timepoint_label | 0 | R1 | 0.70 | sample.name_rule:sample_alias | A1_0_A_CKDL220033466-1A_HMLGCDSX5 |
Sequencing
80 runs over 80 samples · mean 0.66 Gbp per run (median 0.67 Gbp, total 52.72 Gbp; 80 runs with a base count) · layout PAIRED 80 · platform ILLUMINA 80 · models Illumina MiSeq (80) · 89% of runs with a Sandpiper profile · first public 2023-03-09
Authors (6)
Lim JJ first · Diener C · Wilson J · Valenzuela JJ · Baliga NS · Gibbons SM last
Help complete this project
3 of the 4 core fields are below 50% coverage: age_category, health_condition, subject_id. Share metadata
Downloads
PRJNA942341.csv.gz (2.6 KB) · parquet (72.7 KB) — all 80 samples, every field with route and confidence · PRJNA942341_determinations.csv.gz (3.8 KB) — 480 evidence rows