Data from: Social and environmental predictors of gut microbiome age in wild baboons
Data files
Dec 26, 2024 version files 20.19 MB
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IDTAXA_silva_assignment.rds
2.16 MB
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merged_baboon_data_with_metadata_preset_more_blanks_greater_1000.rds
16.37 MB
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metadata_full_descriptions.csv
6.30 KB
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metadata_full.rds
1.65 MB
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README.md
2.20 KB
Abstract
Understanding why some individuals age faster than others is essential to evolutionary biology and geroscience, but measuring variation in biological age is difficult. One solution may lie in measuring gut microbiome composition because microbiota change with many age-related factors (e.g., immunity and behavior). Here we create a microbiome-based age predictor using 13,563 gut microbial profiles from 479 wild baboons collected over 14 years. The resulting “microbiome clock” predicts host chronological age. Deviations from the clock’s predictions are linked to demographic and socio-environmental factors that predict baboon health and survival: animals who appear old-for-age tend to be male, sampled in the dry season (for females), and high social status (both sexes). However, an individual’s “microbiome age” does not predict the attainment of developmental milestones or lifespan. Hence, the microbiome clock accurately reflects age and some social and environmental conditions, but not the pace of development or mortality risk.
README: Social and environmental predictors of gut microbiome age in wild baboons
https://doi.org/10.5061/dryad.b2rbnzspv
This dataset consists of anonymized baboon data originally used to understand how to predict host age from 16S rRNA gut microbiome data.
Description of the data and file structure
I simplified the main data required for this paper into the following four files:
- IDTAXA_silva_assignment.rds is all the taxonomic assignments from SILVA_SSU_r132_March2018.RData. The specific ASVs are the rows and the columns are the taxonomic classification for those ASVs.
- merged_baboon_data_with_metadata_preset_more_blanks_greater_1000.rds is a lightly processed ASV table. The rows correspond to the sample ID (named DADA_id in the metadata file), and the columns correspond to specific ASVs.
- metadata_full.rds is an extremely wide metadata table (13524x83) that should include all the associated demographic, social, and environmental data required to recreate these analyses. Each row is a sample and each column is a descriptive variable.
- metadata_full_descriptions.csv is a README file for the metadata_full.rds datafile, consisting of descriptions of the columns.
Sharing/Access information
Data represented in this dataset is a subset of that used in Grieneisen et al. 2021 and Bjork et al. 2022. Raw 16*S* rRNA gene sequences are deposited on EBI-ENA (project ERP119849) and Qiita [study 12949].
Data derived from this raw data and its corresponding R and Python code is available at https://github.com/maunadasari/Dasari_etal-GutMicrobiomeAge.
Code/Software
This code was written in RMarkdown and Jupyter Notebook and simplified for publication at https://github.com/maunadasari/Dasari_etal-GutMicrobiomeAge. All questions related to the data and code in this repository should be directed to Mauna Dasari (mauna.dasari@gmail.com).