bioRxivSystems BiologyPreprint
Multi-omics analysis highlights the link of aging-related cognitive decline with systemic inflammation and alterations of tissue-maintenance
Stefano FloriD, Thomas DostiD, Madlen Haase, Rowena SimoniD, Simone Ederer, A. Samer KadibalbaniD, Jan TaubenheimiD, Maja OleckaiD, Alesia WalkeriD, Johannes ZimmermanniD, Γεώργιος ΜαρίνοςiD, Sören FranzenburgiD, Philippe Schmitt‐KoppliniD, John F. BainesiD, Konstantin RiegeiD, Steve HoffmanniD, Lena BestiD, Christiane FrahmiD, Christoph KaletaiD
Abstract
Aging-related cognitive decline is associated with changes across different tissues and the gut microbiome, including dysfunction of the gut-brain axis. However, only few studies have linked multi-organ alterations to cognitive decline during aging. Here we report a multi-omics analysis integrating metabolomics, transcriptomics, DNA methylation, and metagenomics data from hippocampus, liver, colon, and fecal samples of mice, correlated with cognitive performance in the Barnes Maze spatial learning task across different age groups. We identified 734 molecular features associated with cognitive rank within individual data layers, of which 227 features remain when integrating all data layers with each other. Among the single-layer predictors, several host and microbial features were highlighted, with host-associated markers being predominant. Host features associated with cognitive function mainly belong to innate and adaptive inflammatory activity (inflammaging) and developmental processes. Our findings suggest that cognitive decline in aging is tightly coupled to systemic, age-associated inflammation, potentially initiated by microbiome-driven gastrointestinal inflammatory activity, emphasizing a link between peripheral tissue alterations and brain function.
- organism: mouse
- assay: metabolomics
- assay: metagenomics
- assay: DNA methylation
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: cell-cell communication
- approach: statistical model
- Citations
- 1
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