Ward, L. J. | bioRxiv Science

bioRxiv2025

The Human Metabolome and Machine Learning Improves Predictions of the Post-Mortem Interval

An accurate prediction of the time since death, known as the post-mortem interval (PMI), remains a critical research question in forensic and police investigations. Current methods, such as rectal temperature or vitreous potassium levels, only provide reliable PMI estimations up to 48-72 hours. In this study, we utilized metabolomic data from femoral whole blood samples of 4,876 individuals with known PMIs ranging from 1 to 67 days. We developed a neural network model that predicted PMI with a mean/median absolute error of 1.45/1.03 days in unseen test cases, outperforming six other machine learning architectures. To further highlight the biological signal, we performed pseudo time-series clustering of metabolic features used by the model, revealing 158 decreasing, 254 increasing, and 398 features with more complex patterns over the pseudo-time scale. Our findings also indicate that metabolomic data from approximately 256 individuals is sufficient to train a machine learning model for PMI prediction, making this approach widely applicable for researchers and forensic institutes worldwide.

systems biology↗

bioRxiv2022

Peptide location fingerprinting identifies species- and tissue-conserved structural remodelling of proteins as a consequence of ageing and disease

Extracellular matrix (ECM) in the intervertebral disc (IVD), lung and artery are thought to undergo the age-dependant accumulation of damage by chronic exposure to mechanisms such as reactive oxygen species, proteases and glycation. It is unknown whether this damage accumulation is species-dependant (via differing lifespans and hence cumulative exposures) or whether it can influence the progression of age-related diseases such as atherosclerosis. Peptide location fingerprinting (PLF) is a new proteomic analysis method, capable of the non-targeted identification of structure-associated changes within proteins. Here we applied PLF to publicly available ageing human IVD (outer annulus fibrosus), ageing mouse lung and human arterial atherosclerosis datasets and identified novel target proteins alongside common age-associated differences within protein structures which were conserved between tissue regions, organs, sexes and species and in age-related disease. We identify peptide yield differences across protein structures which coincide with biological regions, potentially reflecting the functional consequences of ageing or atherosclerosis for macromolecular assemblies (collagen VI and fibrin), enzyme/inhibitor activity (cathepsin B and alpha-2 macroglobulin), activation states (complement C3 and thrombin) and interaction states (laminins, perlecan, fibronectin, filamin-A, collagen XIV and apolipoprotein-B). Furthermore, we show that alpha-2 macroglobulin, prothrombin, collagen XIV and apolipoprotein-B all exhibit possible shared structural consequences in IVD ageing and arterial atherosclerosis, providing novel links between an age-related disease and intrinsic ageing. Crucially, we also demonstrate that fibronectin, laminin beta chains and filamin-A all exhibit conserved age-associated structural differences between mouse lung and human IVD, providing evidence that ECM, and their associating proteins, may be subjected to potentially similar mechanisms or consequences of ageing across species, irrespective of differences in lifespan and tissue function.

molecular biology↗