Animal models in tuberculosis metabolomics: a systematic review of current evidence and the road to translational relevance
Rochelle Caudron, Ilse du Preez, Laneke Luies, Monique Opperman
Frontiers in Molecular Biosciences · 2025-10
Abstract
Background: Animal models are important for tuberculosis (TB) research, offering controlled settings to study disease mechanisms. However, their ability to replicate TB-induced metabolic responses in humans is uncertain. This systematic review evaluated the current use of animal models in metabolomics studies aimed at characterising active pulmonary TB. Methods: PubMed, Scopus, and Web of Science were systematically searched for metabolomics studies of pulmonary TB in humans and animal models, following PRISMA guidelines. Eligible studies were screened, and quality was assessed using QUDOMICS and STAIR tools. Data were synthesised by species, sample matrix, experimental design, and reported differential metabolites. Differential metabolite names were compared between species and subjected to pathway analysis in MetaboAnalyst 6.0. Results: Of the 80 eligible studies, nine involved animal models, predominantly mice. These models captured only 4.7% of human TB-associated differential metabolites, with the highest overlap (3.8%) in mouse lung tissue. Despite low concordance at metabolite level, conserved disruptions were observed in amino acid, glutathione, and one-carbon metabolism pathways. Interspecies variation was evident, influenced by host species, sample matrix, infection protocol, and analytical method. Conclusion: Animal models partially replicated key metabolic features of human TB, particularly at the pathway level. However, variability across studies hampers current translational interpretation. Broader model use, standardised protocols, and integrated multi-platform omics approaches are needed to improve the relevance and comparability of animal models in TB metabolomics research.
MeSH terms
- Relevance (law)
- Translational research
- Comparability
- Metabolomics
- Medicine
- Tuberculosis
- Computational biology
- Current (fluid)
- Animal model
- Bioinformatics
- Risk analysis (engineering)
- Data science
- Human studies
- Biology
- Intensive care medicine
- Clinical significance
- Computer science