The 'ForensOMICS' approach for postmortem interval estimation from human bone by integrating metabolomics, lipidomics, and proteomics.
The 'ForensOMICS' approach for postmortem interval estimation from human bone by integrating metabolomics, lipidomics, and proteomics.
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DOI：
10.7554/elife.83658
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发表时间：
2022-12-30
期刊：
影响因子：
7.7
通讯作者：
Procopio N
中科院分区：
文献类型：
--
作者：
Bonicelli A;Mickleburgh HL;Chighine A;Locci E;Wescott DJ;Procopio N
多个组学的组合使用允许整体研究复杂的相互关联的生物过程。我们将代谢组学、脂质组学和蛋白质组学的组合应用于人类骨骼，以研究它们估计死亡后经过的时间的综合潜力（即，死亡时间（PMI）。这种“ForensOMICS”方法有可能提高PMI对被分解的人类遗骸估计的准确性和精确性，从而帮助法医调查人员建立死亡事件的时间轴。在将其放置在德克萨斯州法医人类学中心（FACTS）拥有的法医人类学研究设施之前，从四名女性尸体捐赠者中收集胫骨前中段骨。在选定的PMI（219-790-834- 872天）再次采集骨样本。使用液相色谱质谱法（LC-MS）从植入前和植入后骨样本中获得非靶向代谢组学、脂质组学和蛋白质组学特征。通过单变量和多变量分析独立地研究了三个组学块，然后使用组学研究的潜在变量方法（DIABLO）进行生物标志物发现的数据集成分析，以确定描述死后变化的标记物的减少数量，并基于其PMI区分个体。由此产生的模型表明，放置前的代谢组，脂质组和蛋白质组的配置文件是明确区分从放置后的。预放置样品中的代谢物表明能量代谢的消失和向另一种燃料来源的转换（例如，结构蛋白）。我们能够识别出某些具有极好的PMI估计潜力的生物分子，主要是来自代谢组学块的生物分子。我们的研究结果表明，通过靶向具有不同死后稳定性的化合物的组合，将来我们可以通过使用代谢物和脂质来估计短PMI，以及通过使用蛋白质来估计长PMI。
The combined use of multiple omics allows to study complex interrelated biological processes in their entirety. We applied a combination of metabolomics, lipidomics and proteomics to human bones to investigate their combined potential to estimate time elapsed since death (i.e., the postmortem interval [PMI]). This ‘ForensOMICS’ approach has the potential to improve accuracy and precision of PMI estimation of skeletonized human remains, thereby helping forensic investigators to establish the timeline of events surrounding death. Anterior midshaft tibial bone was collected from four female body donors before their placement at the Forensic Anthropology Research Facility owned by the Forensic Anthropological Center at Texas State (FACTS). Bone samples were again collected at selected PMIs (219-790-834-872days). Liquid chromatography mass spectrometry (LC-MS) was used to obtain untargeted metabolomic, lipidomic, and proteomic profiles from the pre- and post-placement bone samples. The three omics blocks were investigated independently by univariate and multivariate analyses, followed by Data Integration Analysis for Biomarker discovery using Latent variable approaches for Omics studies (DIABLO), to identify the reduced number of markers describing postmortem changes and discriminating the individuals based on their PMI. The resulting model showed that pre-placement metabolome, lipidome and proteome profiles were clearly distinguishable from post-placement ones. Metabolites in the pre-placement samples suggested an extinction of the energetic metabolism and a switch towards another source of fuelling (e.g., structural proteins). We were able to identify certain biomolecules with an excellent potential for PMI estimation, predominantly the biomolecules from the metabolomics block. Our findings suggest that, by targeting a combination of compounds with different postmortem stability, in the future we could be able to estimate both short PMIs, by using metabolites and lipids, and longer PMIs, by using proteins.