DOI: 10.5061/dryad.dbrv15f9z
title: Data from: Early life microbial succession in the gut follows common patterns in humans across the globe

description: Characterizing the dynamics of microbial community succession in the
 infant gut microbiome is crucial for understanding child health and
 development, but no normative model currently exists. Here, we estimate
 child age using gut microbial taxonomic relative abundances from
 metagenomes, with high temporal resolution (±3 months) for the first 1.5
 years of life. Using 3,154 samples from 1,827 infants across 12 countries,
 we trained a random forest model, achieving a root mean square error of
 2.61 months. We identified key taxonomic predictors of age, including
 declines in Bifidobacterium spp. and increases in Faecalibacterium
 prausnitzii and Lachnospiraceae. Microbial succession patterns are
 conserved across infants from diverse human populations, suggesting
 universal developmental trajectories. Functional analysis confirmed trends
 in key microbial genes involved in feeding transitions and dietary
 exposures. This model provides a normative benchmark of “microbiome age”
 for assessing early gut maturation that can be used alongside other
 measures of child development.
description: Raw metagenomic sequence reads were processed using tools from
 the bioBakery suite, following already-established protocols [1].
 Initially, KneadData v0.10.0 was employed with default settings to trim
 low-quality reads and eliminate human sequences, using the hg37 reference
 database. Subsequently, MetaPhlAn v3.1.0, utilizing the
 mpa_v31_CHOCOPhlAn_201901 database, was applied with default parameters to
 map microbial marker genes and generate taxonomic profiles. The taxonomic
 profiles, along with the same reads obtained in the initial step, were
 then processed with HUMAnN v3.7 to produce stratified functional
 profiles. [1] - Beghini, F.
 <em>et al.</em> Integrating taxonomic, functional, and
 strain-level profiling of diverse microbial communities with bioBakery 3.
 <em>Elife</em> 10, (2021).
description: # Data from: Early life microbial succession in the gut follows common
 patterns in humans across the globe
 [https://doi.org/10.5061/dryad.dbrv15f9z](https://doi.org/10.5061/dryad.dbrv15f9z) This dataset contains: * Pre-formatted selected Taxonomic profiles from stool metagenomic sequencing obtaine from a combination of: * 8 previously-published and publicly-available studies: **Asnicar, F. et al. (2017)** [1], **Backhed, F. et al. (2015)** [2], **Kostic, A. D. et al. (2015)** [3], **Pehrsson, E. et al. (2016)** [4], **Shao, Y. et. al (2019)** [5], **Vatanen, T. et al. (2016)** [6], **Yassour, M. et al. (2018)** [7], **Bonham, K. et al. (2023)** [8]; and * 4 studies from the Wellcome Leap 1kD program: **Fatori, D. et al. (2024)** [9], **Hemmingway, A. et al. (2020)** [10], **O'Sullivan, J. et al. (2024)** [11] and the publication accompanying this dataset, **Bottino, G. et al. (2024)**, which introduces the samples from the Khula study, **Zieff, M. et al. (2024)** [12] * Pre-formatted selected functional EC profiles for the samples from the Khula study ## Description of the data and file structure This dataset contains 4 files: * **`full_taxonomic_inputs.csv`** is the file containing all taxonomic profiles prior to prevalence filtering and removal of samples with no reads assigned to prevalence-filtered taxa. **`filtered_taxonomic_inputs.csv`** is the post-filter version of the same file, which contains only the samples and features utilized to train the Machine Learning models and to perform downstream analysis. These files have the following structure: * **`study_name`**: String, identified by publication , i.e. `BonhamK_2023`. * **`datagroup`**: String, meta-detagroup of samples. Options are `ECHO`, `LEAP` and `CMD`. * **`site`**: String, ISO 3166-1 alpha-3 three-letter code for country of origin of samples. * **`datacolor`**: String, manuscript-wide color adopter for data source. * **`datasource`**: String, major data source for the sample. Options are `ECHO-RESONANCE`, `CMD-DIABIMMUNE`, `CMD-OTHER`, `1kDLEAP-KHULA`, `1kDLEAP-GERMINA`, `1kDLEAP-COMBINE`, `1kdLEAP-M4EFAD`. * `CMD-DIABIMMUNE` and `CMD-OTHER` are combined into the single `CMD` meta category on most downstream analyses. * **`visit`**: String, nominal timepoint of stool sample collection, when available, on the original annotation format of each study. *For a better understanding of acronyms in this column, please refer to the **`visit_annotation_glossary.xlsx`** file*. * **`westernized_cat`**: String, categorization of `site` as a westernized country. * **`subject_id`**: String, unique study participant identifier * **`ageMonths`**: Numeric, age at stool sample collection, in months. * **`sample`**: String, unique sample identifier * Multiple columns of taxa abundances encoded with species name in the form `Genus_species`, i.e. **`Escherichia_coli`** or **`Klebsiella_pneumoniae`**. Values in relative abundances on the scale `0.0:100.0` * **`Shannon_index`**: alpha-diversity of the sample measured as the Shannon index * `ecs.arrow `is the file containing all species-stratified functional EC profiles for the 426 prevalence-filtered ***Khula*** samples from the prefious `filtered_inputs.csv`. * Columns are samples and rows are ECs with the format `X.X.X.X: Function|Genus_species`, i.e. `1.1.1.88: Hydroxymethylglutaryl-CoA reductase|s__Staphylococcus_aureus` * Metadata can be obtained by joining on **`ecs_metadata.csv`** with column names matching the `sample` column * **`AgeModel_FullCV_Results.jld2`** are the results of the model training experiments with cross-validation, for full cross-cohort CV with hyperparameter grid search. Additional Leave-One-Source-Out crossvalidation results are also given on `.jld` format, for each of the data sources mentioned in the main text: **`regression_Age_LeaveCMDOut.jld`**, **`regression_Age_LeaveCombineOut.jld`**, **`regression_Age_LeaveEchoOut.jld`**, **`regression_Age_LeaveGerminaOut.jld`**, **`regression_Age_LeaveKhulaOut.jld`**, **`regression_Age_LeaveM4EFADOut.jld`**; to be read with the `JLD2.jl` library and the classes from the `Leap.jl` julia package [available on Zenodo (opens in new window)](https://doi.org/10.5281/zenodo.12822332) * We have also provided **`inputs_with_testdata.csv`**, which is a copy of **`full_taxonomic_inputs.csv`** with the held-out independent test samples (Ennis, 2024) [13] appended at the end. We similarly made available **`regression_Age_LeaveEnnisOut.jld`** as the test data results. ## Sharing/Access information Code for loading the data and performing analyses is [available on Zenodo (opens in new window)](https://doi.org/10.5281/zenodo.12822332) ## Code/Software See the Readme and setup instructions for using code [available on Zenodo (opens in new window)](https://doi.org/10.5281/zenodo.12822332) ###### Study references 1. Asnicar, F. et al. *Studying vertical microbiome transmission from mothers to infants by strain-level metagenomic profiling.* mSystems 2, (2017). 2. Bäckhed, F. et al. *Dynamics and stabilization of the human gut microbiome during the first year of life.* Cell Host Microbe 17, 690–703 (2015). 3. Kostic, A. D. et al. *The dynamics of the human infant gut microbiome in development and in progression toward type 1 diabetes.* Cell Host Microbe 17, 260–273 (2015). 4. Pehrsson, E. C. et al. *Interconnected microbiomes and resistomes in low-income human habitats.* Nature 533, 212–216 (2016). 5. Shao, Y. et al. *Stunted microbiota and opportunistic pathogen colonization in caesarean-section birth.* Nature 574, 117–121 (2019). 6. Vatanen, T. et al. *Variation in microbiome LPS immunogenicity contributes to autoimmunity in humans.* Cell 165, 842–853 (2016). 7. Yassour, M. et al. *Natural history of the infant gut microbiome and impact of antibiotic treatment on bacterial strain diversity and stability.* Sci. Transl. Med. 8, 343ra81 (2016). 8. Bonham, K. S. et al. *Gut-resident microorganisms and their genes are associated with cognition and neuroanatomy in children.* Sci Adv 9, eadi0497 (2023). 9. Fatori, D. et al. *Identifying biomarkers and trajectories of executive functions and language development in the first 3 years of life: design, methods and initial findings of the Germina cohort study.* Preprint at [https://doi.org/10.31219/osf.io/ed4fb](https://doi.org/10.31219/osf.io/ed4fb) (2024). 10. Hemmingway, A. et al. *A detailed exploration of early infant milk feeding in a prospective birth cohort study in Ireland: combination feeding of breast milk and infant formula and early breast-feeding cessation.* Br. J. Nutr. 124, 440–449 (2020). 11. O’Sullivan, J. et al. *Alterations in gut microbiota composition, plasma lipids, and brain activity, suggest inter-connected pathways influencing malnutrition-associated cognitive and neurodevelopmental changes.* Preprint at [https://doi.org/10.21203/rs.3.rs-4115616/v1](https://doi.org/10.21203/rs.3.rs-4115616/v1) (2024). 12. Zieff, M. R. et al. *Characterizing developing executive functions in the first 1000 days in South Africa and Malawi: The Khula Study.* Wellcome Open Research 9, (2024). 13. Ennis, D., Shmorak, S., Jantscher-Krenn, E. & Yassour, M. *Longitudinal quantification of Bifidobacterium longum subsp. infantis reveals late colonization in the infant gut independent of maternal milk HMO composition.* Nat. Commun. 15, 894 (2024).