Metabolic profiling of femoral muscle from rats at different periods of time after death

• Published in: PloS one 2018-09, Vol.13 (9), p.e0203920-e0203920
• Main Authors: Du, Tieshuai, Lin, Zebin, Xie, Yaling, Ye, Xing, Tu, Chunyan, Jin, Kaidi, Xie, Jianhui, Shen, Yiwen
• Format: Article
• Language: English
• Subjects: Biodegradation; Biological activity; Biology and Life Sciences; Carbon; Carbon cycle; Chromatography; Death; Discriminant analysis; Femur; Forensic engineering; Forensic medicine; Forensic science; Ionization; Laboratory animals; Lipids; Liquid chromatography; Mass spectrometry; Medicine and Health Sciences; Metabolism; Metabolites; Metabolomics; Mortality; Muscles; NMR; Nuclear magnetic resonance; Physical Sciences; Potassium; Principal components analysis; Research and Analysis Methods; Scientific imaging; Spectrometry
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0203920

Clarification of postmortem metabolite changes can help characterize the process of biological degradation and facilitate investigations of forensic casework, especially in the estimation of postmortem interval (PMI). Metabolomics can provide information on the molecular profiles of tissues, which can aid in investigating postmortem metabolite changes. In this study, liquid chromatography-mass spectrometric (LC-MS) analysis was performed to identify the metabolic profiles of rat femoral muscle at ten periods of time after death within 168 h. The results obtained by reversed-phase liquid chromatography (RPLC)- and hydrophilic interaction liquid chromatography (HILIC)- electrospray ionization (ESI±) have revealed more than 16,000 features from all four datasets. Furthermore, 915 of these features were identified using an in-house database. Principal component analysis (PCA) demonstrated the time-specific features of molecular profiling at each period of time after death. Moreover, results from partial least squares projection to latent structures-discriminant analysis (PLS-DA) disclosed a strong association of metabolic alterations of at least 59 metabolites with the time since death, especially within 48 h after death, which expounds these metabolites as potential indicators in PMI estimation. Altogether, our results illustrate the potentiality of metabolic profiling in the evaluation of PMI and provide candidate metabolite markers with strong correlation with time since death for forensic purpose.