bioRxivImmunologyPreprint
Long-Term Intestinal Epithelial Remodeling Induced by Acute Protein-Energy Malnutrition
Fenja Amrei Schuran, Neha MishraiD, Víctor A. López-AgudeloiD, Nina Sommer, Joana P. BernardesiD, Alesia WalkeriD, Finn HinrichseniD, Ting Gong, Felix Gilbert, Lena Schröder, Archana Bhardwaj, Sven KünzeliD, Saskia Weber‐Stiehl, Go ItoiD, Florian TraniD, Mathieu GroussiniD, Christoph Roecken, Juan David MatuteiD, Stefan SchreiberiD, Josef Martin PenningeriD, Richard S. BlumbergiD, Philippe Schmitt‐KoppliniD, John F. BainesiD, Felix SommeriD, Philip C RosenstieliD
Abstract
Protein-energy malnutrition (PEM) is a global health burden with lasting effects that extend well beyond the initial nutrient deficiency. To systematically investigate the long-term effects of a single episode of PEM on the structure and function of the intestinal epithelium and its associated microbiota, we employed a comprehensive multi-omics approach, including (spatial) transcriptomics, DNA methylation analysis, fecal metagenomics, and metabolomics. Our findings show that PEM persistently alters the intestinal epithelium by depleting Paneth cells and suppressing antimicrobial gene expression - changes linked to DNA methylation that persist despite dietary recovery. In germ-free mice, the sustained epithelial phenotype after was absent. We identified the microbial lipid metabolite 9-HODE and epigenetically deregulated PPAR-driven GDF15 expression as key molecular drivers of the persistent PEM-induced Paneth cell dysfunction. Targeting microbial lipid production and its link to the host GDF15 pathway could offer novel therapeutic strategies for long-term consequences of malnutrition and other Paneth cell-associated diseases.
- organism: mouse
- assay: spatial transcriptomics
- assay: DNA methylation
- assay: metagenomics
- assay: metabolomics
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: cell-cell communication
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 2
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