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Published April 23, 2024 | Version 1.0.0
Journal article Open

Metabolic modeling reveals the aging-associated decline of host-microbiome metabolic interactions in mice

  • 1. ROR icon Kiel University
  • 2. ROR icon University Hospital Schleswig-Holstein

Contributors

Project leader:

  • 1. ROR icon Kiel University
  • 2. ROR icon University Hospital Schleswig-Holstein

Description

Aging is the predominant cause of morbidity and mortality in industrialized countries. The specific molecular mechanisms that drive aging are poorly understood, especially the contribution of the microbiota in these processes. Here, we combined multi-omics with metabolic modeling in mice to comprehensively characterize host–microbiome interactions and how they are affected by aging. Our findings reveal a complex dependency of host metabolism on microbial functions, including previously known as well as novel interactions. We observed a pronounced reduction in metabolic activity within the aging microbiome, which we attribute to reduced beneficial interactions in the microbial community and a reduction in its metabolic output. These microbial changes coincided with a corresponding downregulation of key host pathways predicted by our model to be dependent on the microbiome that are crucial for maintaining intestinal barrier function, cellular replication, and homeostasis. Our results elucidate microbiome–host interactions that potentially influence host aging processes, focusing on microbial nucleotide metabolism as a pivotal factor in aging dynamics. These pathways could serve as future targets for the development of microbiome-based therapies against aging.

 

Notes (English)

Metagenomic raw read and MAG assembly data was deposited in the European Nucleotide Archive (ENA) under BioProject PRJEB73981 (ebi.ac.uk/ena/browser/view/PRJEB73981). Individual accession numbers for each MAG were listed in Supplementary Table S1.2. Gene expression data was published in the GEO database under record GSE262290 (ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE262290).  Metabolomics data has been made available at the MassIVE database (massive.ucsd.edu) with identifiers MSV000094409, MSV000094410 and MSV000094436. The metamodel can be found under accession MODEL2310020001 in the BioModels database (ebi.ac.uk/biomodels/MODEL2310020001). Detailed sample metadata, the microbial metabolic models and supplementary resources as well as source code used for data analysis (github.com/sciwitch/MouseMicrobiomeAging) were deposited in a zenodo record (doi.org/10.5281/zenodo.10844503).

Table of contents (English)

FileName Folder
Description

AgingDeclineHostMicrobiomeInteractions.pdf

. Full research article "Metabolic modeling reveals the aging-associated decline of host-microbiome metabolic interactions in mice"
SupplFigures.pdf . Supplementary figures related to main Figures 1-6
supplTables1_2024-03.ods . Supplementary data tables related to main Figure 1 in manuscript
supplTables2_2024-02.ods . Supplementary data tables related to main Figure 2 in manuscript
supplTables3_2024-03.ods . Supplementary data tables related to main Figure 3 in manuscript
supplTables4_2024-02.ods . Supplementary data tables related to main Figure 4 in manuscript
supplTables5_2024-03.ods . Supplementary data tables related to main Figure 5 in manuscript
supplTables6_2024-03.ods . Supplementary data tables related to main Figure 6 in manuscript
obj_metamouse-2023-05-10.rds databases gapseq microbiome metabolic models for use with R
df_rxn2subsys20230510MetaMouse.rds databases Reactions mapped to subsystems/pathways
df_rxnAnnotation.rds databases RXN-IDs to Reaction Names
gapseqPathwayIDsToNames.csv databases gapseq and MetaCyc Pathway IDs to names
df_mouseGOannotaion_2023-05-16.rds databases mouse gene symbols relation to GeneOntology pathways and terms
GRCm38.102.EnsemblToGeneSymbol.tsv.gz databases Mouse Ensembl gene ids converted to gene symbols for reference genome version GRCm38.102
metaboliteAnnotation.csv databases translation table from model metabolite/reaction ID to metabolite name
df_transcriptMetaData.rds mouseTranscriptome Mouse metadata for RNA-Seq. data
mtx_CountsColonAllAges.rds mouseTranscriptome RNA-Seq. gene expression read counts for Colon
mtx_CountsLiverAllAges.rds mouseTranscriptome RNA-Seq. gene expression read counts for Liver
mtx_CountsBrainAllAges.rds mouseTranscriptome RNA-Seq. gene expression read counts for Brain
simple_phylogeny_fromGTDBTKmsa_2023-05-09.tree mouseMetagenome Phylogenetic tree of 181 mouse gut bacteria MAGs from multiple sequence alignment by GTDB-Tk
df_MAGabundances.rds mouseMetagenome Read depth coverage of each MAG per Sample
df_metadataMAG.rds mouseMetagenome Mouse metadata for MAG data
FVAactiveReactions.rds mouseMetagenome Predicted active reactions for each microbial metabolic model based on FVA
df_rxnAbundancesMM.rds mouseMetagenome FVA predicted active reaction abundances of mouse microbiome metabolic models
df_MMRxnsByAge.rds mouseMetagenome FVA predicted active reaction abundances of mouse microbiome metabolic models, analyzed with linear models by Age
microbiotaModelsSBML.zip mouseMetagenome Metabolic models reconstructed from MAGs of the mouse microbiota
df_colonRNAnormFiltered.rds combineDataLayers Variance stabilized and near zero variance filtered gene expression data for Colon
df_liverRNAnormFiltered.rds combineDataLayers Variance stabilized and near zero variance filtered gene expression data for Liver
df_brainRNAnormFiltered.rds combineDataLayers Variance stabilized and near zero variance filtered gene expression data for Brain
df_colonMetaData.rds combineDataLayers Mouse metadata for partial correlations of microbiome reactions with colon gene expression data
df_liverMetaData.rds combineDataLayers Mouse metadata for partial correlations of microbiome reactions with liver gene expression data
df_brainMetaData.rds combineDataLayers Mouse metadata for partial correlations of microbiome reactions with brain gene expression data
df_MMrxnFiltered.rds combineDataLayers Abundance of microbiome active reactions per mouse, near-zero variance filtered
df_pcorRxn2ColonRNATop.rds combineDataLayers Significance filtered top correlation pairs of microbiome reactions to colon gene expression
df_pcorRxn2LiverRNATop.rds combineDataLayers Significance filtered top correlation pairs of microbiome reactions to liver gene expression
df_pcorRxn2BrainRNATop.rds combineDataLayers Significance filtered top correlation pairs of microbiome reactions to brain gene expression
df_pcorRxn2ColonRNA.rds . Full table of all correlation pairs of microbiome reactions to colon gene expression
df_pcorRxn2LiverRNA.rds . Full table of all correlation pairs of microbiome reactions to liver gene expression
df_pcorRxn2BrainRNA.rds . Full table of all correlation pairs of microbiome reactions to brain gene expression
metamodel_analysis.zip

metaModel

Source code and data files for running the metamodel and related analysis

 

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Additional details

Related works

Is supplement to
Preprint: 10.1101/2024.03.28.587009 (DOI)

Dates

Created
2024-03

Software

Repository URL
https://github.com/sciwitch/MouseMicrobiomeAging
Programming language
R
Development Status
Inactive