DIMet: an open-source tool for differential analysis of targeted isotope-labeled metabolomics data

MOTIVATION

Many diseases, such as cancer, are characterized by an alteration of cellular metabolism allowing cells to adapt to changes in the microenvironment. Stable isotope-resolved metabolomics (SIRM) and downstream data analyses are widely used techniques for unraveling cells' metabolic activity to understand the altered functioning of metabolic pathways in the diseased state. While a number of bioinformatic solutions exist for the differential analysis of SIRM data, there is currently no available resource providing a comprehensive toolbox.

RESULTS

In this work, we present DIMet, a one-stop comprehensive tool for differential analysis of targeted tracer data. DIMet accepts metabolite total abundances, isotopologue contributions, and isotopic mean enrichment, and supports differential comparison (pairwise and multi-group), time-series analyses, and labeling profile comparison. Moreover, it integrates transcriptomics and targeted metabolomics data through network-based metabolograms. We illustrate the use of DIMet in real SIRM datasets obtained from Glioblastoma P3 cell-line samples. DIMet is open-source, and is readily available for routine downstream analysis of isotope-labeled targeted metabolomics data, as it can be used both in the command line interface or as a complete toolkit in the public Galaxy Europe and Workfow4Metabolomics web platforms.

AVAILABILITY AND IMPLEMENTATION

DIMet is freely available at https://github.com/cbib/DIMet, and through https://usegalaxy.eu and https://workflow4metabolomics.usegalaxy.fr. All the datasets are available at Zenodo https://zenodo.org/records/10925786.

Shotgun metagenomics and systemic targeted metabolomics highlight indole-3-propionic acid as a protective gut microbial metabolite against influenza infection

The gut-to-lung axis is critical during respiratory infections, including influenza A virus (IAV) infection. In the present study, we used high-resolution shotgun metagenomics and targeted metabolomic analysis to characterize influenza-associated changes in the composition and metabolism of the mouse gut microbiota. We observed several taxonomic-level changes on day (D)7 post-infection, including a marked reduction in the abundance of members of the Lactobacillaceae and Bifidobacteriaceae families, and an increase in the abundance of Akkermansia muciniphila. On D14, perturbation persisted in some species. Functional scale analysis of metagenomic data revealed transient changes in several metabolic pathways, particularly those leading to the production of short-chain fatty acids (SCFAs), polyamines, and tryptophan metabolites. Quantitative targeted metabolomics analysis of the serum revealed changes in specific classes of gut microbiota metabolites, including SCFAs, trimethylamine, polyamines, and indole-containing tryptophan metabolites. A marked decrease in indole-3-propionic acid (IPA) blood level was observed on D7. Changes in microbiota-associated metabolites correlated with changes in taxon abundance and disease marker levels. In particular, IPA was positively correlated with some Lactobacillaceae and Bifidobacteriaceae species (Limosilactobacillus reuteri, Lactobacillus animalis) and negatively correlated with Bacteroidales bacterium M7, viral load, and inflammation markers. IPA supplementation in diseased animals reduced viral load and lowered local (lung) and systemic inflammation. Treatment of mice with antibiotics targeting IPA-producing bacteria before infection enhanced viral load and lung inflammation, an effect inhibited by IPA supplementation. The results of this integrated metagenomic-metabolomic analysis highlighted IPA as an important contributor to influenza outcomes and a potential biomarker of disease severity.