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Simple Summary: Biological responses to rugged ecological factors causes changes in the metabolic pathways in aquatic organisms, which may manifest as changes in the tissue metabolomic composition. Therefore, establishing a correlation between the metabolite concentrations in aquatic animals and ecological factors is important for understanding the molecular mechanisms of biological responses to ecological stresses. In this work, we determined the concentrations of 57 major metabolites in the lenses of fish from three locations in Siberia (Russia) that differed in levels of dissolved oxygen (LDO) and water purity. We found that the increased due to CO2 emissions water acidity and the reduced LDO caused significant changes in the fish lenses' metabolomic compositions, including amino acids, organic acids, and energy metabolites. The obtained results can be used in monitoring the ecological state of water bodies. Multiple stressors related to changes in environmental conditions (such as water temperature, salinity, and natural and anthropogenic pollution) may cause biological responses of aquatic organisms that lead to significant variations in the biochemical reactions in their tissues and thereby change the concentrations of metabolites. We used a quantitative NMR-based metabolomic analysis of the fish lens for the evaluation of the influence of environmental factors on metabolic processes in aquatic animals. For this purpose, three species of freshwater fish—Perca fluviatilis, Rutilus rutilus lacustris, and Gymnocephalus cernua—were caught at approximately the same time at three locations in Siberia (Russia) that differed in levels of dissolved oxygen (LDO) and water purity, and the concentrations of 57 major metabolites in the fish lenses were determined. We found that the metabolomic profiles of the fish lenses strongly depended on the location. The obtained data demonstrated that two typical stressors for aquatic animals—a reduced LDO and anthropogenic water pollution—caused a largely similar metabolic response in the fish lenses that led to an increase in the concentrations of several amino acids and a decrease in sarcosine and phosphoethanolamine. At the same time, the composition of the major lens osmolytes depended mostly on the oxygen level, while variations in AMP (decrease) and NAD (increase) corresponded to the water pollution. We suggest that the eye lens is a very convenient tissue for studying the impact of ecological factors on the metabolic state of aquatic animals, fish in particular. [ABSTRACT FROM AUTHOR]

Simple Summary: Modern evolutionary biology offers a wide variety of methods to explore the evolution of species and to describe their relationships. The methods of DNA/RNA sequence analysis have been developing for decades and have become increasingly popular and reasonably reliable. Nevertheless, final phylogenetic trees for many taxa are still under debate because both classical and genomics-based approaches have their own limitations for phylogenetic tree reconstruction. Here, we propose the use of younger 'omics' methods, namely quantitative metabolomics, to aid the phylogeny reconstruction of vertebrates. We show that metabolomics-based hierarchical clustering analysis trees match, although not perfectly, to the genomics-based trees. In the current pilot study, we propose the use of quantitative metabolomics to reconstruct the phylogeny of vertebrates, namely birds. We determined the concentrations of the 67 most abundant metabolites in the eye lenses of the following 14 species from 6 orders of the class Aves (Birds): the Black kite (Milvus migrans), Eurasian magpie (Pica pica), Northern raven (Corvus corax), Eurasian coot (Fulica atra), Godlewski's bunting (Emberiza godlewskii), Great crested grebe (Podiceps cristatus), Great tit (Parus major), Hawfinch (Coccothraustes coccothraustes), Hooded crow (Corvus cornix), House sparrow (Passer domesticus), Rock dove (Columba livia), Rook (Corvus frugilegus), Short-eared owl (Asio flammeus) and Ural owl (Strix uralensis). Further analysis shows that the statistical approaches generally used in metabolomics can be applied for differentiation between species, and the most fruitful results were obtained with hierarchical clustering analysis (HCA). We observed the grouping of conspecific samples independently of the sampling place and date. The HCA tree structure supports the key role of genomics in the formation of the lens metabolome, but it also indicates the influence of the species lifestyle. A combination of genomics-based and metabolomics-based phylogeny could potentially resolve arising issues and yield a more reliable tree of life. [ABSTRACT FROM AUTHOR]

Introduction: Application of metabolomic methods to forensic studies may expand the limits of the post-mortem interval (PMI) estimation, and improve the accuracy of the estimation. To this end, it is important to determine which tissue is the most suitable for analysis, and which compounds are the most promising candidates for PMI estimation. Objectives: This work is aimed at the comparison of human serum, aqueous humor (AH), and vitreous humor (VH) as perspective tissues for metabolomic-based PMI estimation, at the determination of most promising PMI biomarkers, and at the development of method of PMI estimation based on the measurement of concentrations of PMI biomarkers. Methods: Quantitative metabolomic profiling of samples of the human serum, AH, and VH taken at different PMIs has been performed with the use of NMR spectroscopy. Results: It is found that the metabolomic changes in anatomically isolated ocular fluids are slower and smoother than that in blood. A good positive time correlation (Pearson coefficient r > 0.5) was observed for several metabolites, including hypoxanthine, choline, creatine, betaine, glutamate, and glycine. A model for PMI estimation based on concentrations of several metabolites in AH and VH is proposed. Conclusions: The obtained results demonstrate that the metabolomic analysis of AH and VH is more suitable for the PMI estimation than that of serum. The compounds with good positive time correlation can be considered as potential PMI biomarkers. [ABSTRACT FROM AUTHOR]