Data from: Longitudinal changes in gut microbiota across reproductive states in wild baboons
Data files
Jun 15, 2026 version files 19.11 MB
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all_samples_asvlevel_filtered.rds
17.27 MB
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metadata_publication_README.xlsx
10.92 KB
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metadata_publication.csv
1.82 MB
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README.md
2.15 KB
Abstract
Background. In humans and other mammals, female reproduction is linked to extensive changes in physiology, immunity, hormones, and behavior. These changes likely shape, and may be shaped by, the composition of gut microbial communities. Characterizing the dynamics of gut microbial change across reproductive states, including its relationship to female physiology, is important for understanding how the gut microbiome influences female and offspring health.
Results. Here we characterize longitudinal changes in gut microbiota across reproduction by combining 16S rRNA gene sequencing data from 4,462 stool samples (spanning 14 years of sample collection) with life history data on multiple reproductive events in 169 female baboons. These baboons were members of a well-studied, natural baboon population in Kenya where reproductive state (ovarian cycling, pregnancy, and postpartum amenorrhea) is tracked daily and microbiota data could be paired with measurements of fecal-derived estrogen, progesterone, and glucocorticoid levels. We found extensive changes in baboon gut microbiota as females transitioned between reproductive states. Pregnancy was linked to distinct patterns of ASV richness, community composition, and taxonomic abundances compared to postpartum amenorrhea and ovarian cycling. The most dramatic shifts occurred as females transitioned from the first to second trimester of pregnancy, with altered abundances of taxa that have been linked, in humans and model systems, to host immunity, weight gain, or hormone levels. Host identity was consistently the strongest predictor of gut microbiota composition across states, and this individual signature was strongest during pregnancy. Estrogen and progesterone levels had robust associations with the gut microbiota overall, but the microbial taxa involved in these associations are reproductive state-dependent. Glucocorticoid concentrations were not a major predictor of gut microbiota composition in any state.
Conclusions. Together, our results support the idea that gut microbiota contribute to the complex physiological changes necessary during pregnancy, but that microbial changes during pregnancy are somewhat unique to each female. Variation in steroid hormones drives some, but not all, of these relationships, emphasizing the importance of considering steroid hormone levels in studies of gut microbiome variation. Our results motivate future work on how gut microbiota contribute to reproductive outcomes, including both maternal and offspring health.
Dataset DOI: 10.5061/dryad.5tb2rbphw
Description of the data and file structure
This dataset consists of anonymized baboon data originally used to understand connections between female reproduction and the gut microbiota (from 16S rRNA gene sequencing).
Files and variables
I simplified the main data required for this paper into the following three files:
- all_samples_asvlevel_filtered.rds is a phyloseq object containing data at the ASV level, filtered to ASVs present in >5% of all samples in this data set. The rows correspond to the sample ID (named sample_id in the metadata file), and the columns correspond to specific ASVs.
- metadata_publication.csv is a metadata table that includes all the associated reproductive state, hormone, host trait, host diet, rainfall, and technical data required to recreate these analyses. Each row is a sample and each column is a descriptive variable.
- metadata_publication_README.xlsx is a README file for the metadata_publication.csv data file, consisting of descriptions of the columns.
Code/software
This code was written in RStudio simplified for publication at https://github.com/CASouthworth/Southworthetal_GutMicrobiotaReproduction. All questions related to the data and code in this repository should be directed to Chelsea Southworth (csouthwo@nd.edu).
Access information
Data represented in this dataset are a subset of that used in Grieneisen et al. 2021 and Bjork et al. 2022. Raw 16S rRNA gene sequences are deposited on EBI-ENA (project ERP119849) and Qiita [study 12949].
Data derived from this raw data and the corresponding R and Python code are available at https://github.com/CASouthworth/Southworthetal_GutMicrobiotaReproduction
