**Background:** Metabolomics is a rapidly growing field that aims to identify and quantify metabolites in biological tissues and fluids. While extensive metabolomic databases exist for humans (e.g., the Human Metabolome Database), quantitative data for animal tissues are scarce, especially for wild species. Most animal studies use semi-quantitative methods (e.g., LC-MS or GC-MS) that report relative changes rather than absolute concentrations. Quantitative data, typically obtained via NMR spectroscopy, are more valuable for long-term reuse, cross-study comparisons, and data mining. The authors identified a critical need for a centralized, publicly accessible database that provides absolute metabolite concentrations in animal tissues, along with tools for comparison and data deposition. Such a resource would aid in modeling human diseases, understanding metabolic responses to environmental factors, and advancing evolutionary studies.
**Methods:** The Animal Metabolite Database (AMDB) was developed using Django v3.1.5 (Python web framework) and PostgreSQL v16.0 for data management. The frontend uses Bootstrap v4.6.2, and data visualization is powered by Plotly v2.16.1. The database currently contains data from 46 species, 14 tissues, 776 samples, 155 quantified metabolites, and over 44,000 measured concentration values. Samples were obtained from domestic, wild, and laboratory animals, primarily from Siberia and other regions of Russia. Tissue sampling followed strict guidelines (European Union Directive 2010/63/EU) and involved immediate freezing in liquid nitrogen and storage at −70 °C. Sample preparation included homogenization in methanol/water/chloroform, centrifugation, and separation of the aqueous layer. For NMR analysis, extracts were dissolved in D₂O with DSS as an internal standard and measured on a 700 MHz NMR spectrometer (Bruker AVANCE III HD) using a 5 mm TXI ATMA probe with 64–96 transients and a 20 s repetition time. For LC-MS, extracts were analyzed on an UltiMate 3000RS chromatograph coupled with ESI-Q-TOF mass spectrometers (maXis 4G or Impact II). Metabolite concentrations are reported in nanomoles per gram of wet tissue weight (nmol/g). The database is organized into samples, groups, and experiments, with each sample linked to metadata (species, age, weight, location, diseases). Users can browse by species, tissue, or metabolite, and use a 'Cart' and 'Sandbox' feature to compare groups from different experiments. Registered users can upload their own quantitative data via Excel templates, either for private comparison or public deposition after validation.
**Key Results:** The AMDB provides a comprehensive repository of quantitative metabolomic data, with a current focus on the eye lens and blood of vertebrates. Examples of applications include: (1) Studying age-related nuclear cataract in humans, where comparison of cataractous lenses with post-mortem controls revealed altered concentrations of antioxidants, UV filters, and osmolytes, suggesting lens epithelial cell dysfunction. (2) Using rat lenses as models, but finding that the metabolite set differs from humans due to rats' nocturnal lifestyle. (3) Discovering high concentrations of ovothiol A (a potent antioxidant) in fish lenses, previously thought absent in vertebrates. (4) Identifying NADH as a UV filter in bird lenses, improving visual acuity by reducing chromatic aberrations. (5) Demonstrating seasonal and ecological effects on fish lens metabolomes, including changes in osmolytes and antioxidants due to water acidity and dissolved oxygen levels. (6) Using metabolomic data to reconstruct vertebrate phylogeny via hierarchical clustering analysis, producing dendrograms that partially match genomic trees. The database also supports thanatochemistry applications, identifying metabolites (e.g., hypoxanthine, choline, creatine) that can estimate post-mortem interval.
**Clinical Implications:** The AMDB has significant clinical relevance, particularly in ophthalmology and disease modeling. By providing baseline metabolite concentrations in animal tissues, it enables researchers to select appropriate animal models for human diseases, such as age-related nuclear cataract. The database facilitates the identification of metabolic biomarkers for disease diagnosis and progression, and supports the development of therapeutic interventions targeting specific metabolic pathways. Additionally, the platform's ability to compare quantitative data across species and conditions can accelerate translational research, from basic science to clinical applications. The AMDB also promotes data sharing and reproducibility, aligning with FAIR principles, and has potential applications in forensic science (post-mortem interval estimation) and environmental health monitoring.