bioRxivMicrobiologyPreprint
Computational metabolic modeling unveils gut microbiomes role in metabolic shifts during murine cancer cachexia
Torben KuehnastiD, Isabella PototschnigiD, Manuela Träger, Christian DieneriD, Hansjörg HabischiD, Andrea VogeliD, Lisa WinkiD, Alexander MahnertiD, Tobias MadliD, Thomas WeichhartiD, Martina SchweigeriD, Christine Moissl‐EichingeriD
Abstract
Cancer cachexia is a multifactorial syndrome characterized by involuntary weight loss, muscle wasting, systemic inflammation, and metabolic alterations, affecting up to 87% of pancreatic and gastric cancer patients. Unlike simple starvation, cachexia is driven by metabolic disruption involving both host physiology and the gut microbiome. While microbiome changes in cachexia have been documented, a coherent understanding of how these changes translate into functional metabolic shifts remains elusive. In this study, we combined in vivo fecal and plasma metabolomic analyses with a novel computational microbiome simulation pipeline to identify cachexia-associated microbial metabolites. Using the murine MCA207 tumor line and its cachectic derivative CHX207, we differentiated microbiome changes driven by cachexia from those induced by tumor growth. Our computational tool, McMurGut, a murine-tailored extension of MICOM, enabled simulation of microbial metabolic interactions specific to the mouse microbiome, covering 91% of identified genera. We identified significant abundance changes in 35 microbial genera and corresponding shifts in metabolite production, including reductions in short-chain fatty acids (SCFAs) like acetate and butyrate, alongside increased production of galactose, formate, and propionate. Notably, decreases in SCFA production, particularly by genera such as Faecalibaculum and Dubosiella, correlated with exacerbated cachectic symptoms. Additionally, the elevated production of formate and galactose, primarily by Bacteroides and Lactobacillus, suggested altered fermentation pathways in cachexia, potentially linked to increased mucus degradation. Validation of our computational predictions via NMR metabolomics highlighted key congruencies between predicted and experimentally observed metabolites, supporting the role of microbiome-driven metabolic shifts in cachexia pathology. These findings provide crucial insights into the microbiomes involvement in cachexia and suggest future avenues for therapeutic interventions aimed at modulating microbial taxa and their metabolic outputs to improve patient outcomes.
- organism: mouse
- assay: metabolomics
- research focus: cell type annotation
- research focus: differential expression
- approach: deep learning
- approach: statistical model
- Citations
- 1
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