Post-mortem changes in the metabolomic compositions of rabbit blood, aqueous and vitreous humors View Full Text


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Article Info

DATE

2016-09-20

AUTHORS

Ekaterina A. Zelentsova, Lyudmila V. Yanshole, Olga A. Snytnikova, Vadim V. Yanshole, Yuri P. Tsentalovich, Renad Z. Sagdeev

ABSTRACT

IntroductionThe analysis of post-mortem metabolomic changes in biological fluids opens the way to develop new methods for the estimation of post-mortem interval (PMI). It may also help in the analysis of disease-induced metabolomic changes in human tissues when the postoperational samples are compared to the post-mortem samples from healthy donors.ObjectivesThe goals of this study are to observe and classify the post-mortem changes occurring in the rabbit blood, aqueous and vitreous humors (AH and VH), to identify the potential PMI markers among a wide range of metabolites, and also to determine which biological fluid—blood, AH or VH—is more suitable for the PMI estimation.MethodsThe quantitative metabolomic profiling of samples of the rabbit serum, AH and VH taken at different PMIs has been performed with the combined use of high-frequency NMR and high-resolution LC–MS methods.ResultsThe quantitative levels of 61 metabolites in the rabbit serum, AH and VH at different PMIs have been measured. It has been found that the post-mortem metabolomic changes in AH and VH proceed slower than in blood, and the data scattering is lower. Among the metabolites whose concentrations increase with time, the most significant and linear growth is found for hypoxanthine, choline and glycerol.ConclusionThe obtained results suggest that the ocular fluids AH and VH may have some advantages over blood serum for the search of potential biochemical markers for the PMI estimation. Among the compounds studied in the present work, hypoxanthine, choline and glycerol give the biggest promise as the potential PMI biomarkers. More... »

PAGES

172

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http://scigraph.springernature.com/pub.10.1007/s11306-016-1118-2

DOI

http://dx.doi.org/10.1007/s11306-016-1118-2

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https://app.dimensions.ai/details/publication/pub.1021468189


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