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Biology subjects

Augsburger, M.

Publications and source records attributed to Augsburger, M..

2 recordsLinked to original sources

Activity based proteome profiling of serum serinehydrolases: application in pediatric abusive head trauma

PurposeTraumatic brain injury (TBI), including pediatric abusive head trauma (AHT), is the leading cause of death and disability in children and young adults worldwide. The current understanding of trauma-induced molecular changes in the brain of human subjects with intracranial haemorrhage (ICH) remains inadequate and requires further investigation to improve the outcome and management of TBI in the clinic. Calcium-mediated damage at the site of brain injury has been shown to activate several catalytic enzymes. Experimental designSerine hydrolases (SHs) are major catalytic enzymes involved in the biochemical pathways of blood coagulation, systemic inflammation and neuronal signaling. Here we investigated activity-based protein profiling (ABPP) by measuring the activity status of SH enzymes in the serum of infants with severe ICH as a consequence of AHT or atraumatic infants who died of sudden infant death syndrome (SIDS). ResultsOur proof-of-principle study revealed significantly reduced physiological activity of dozens of metabolic SHs in the serum of infants with severe AHT compared to the SIDS group, with some of the enzymes being related to neurodevelopment and basic brain metabolism. Conclusions and clinical relevanceTo our knowledge, this is the first study to investigate the ABPP of the SHs enzyme family to detect changes in their physiological activity in blood serum in severe TBI. We used antemortem (AM) serum from infants under the age of 2 years who were victims of AHT with a severe form of ICH. The analytical approach used in the proof-of-principle study shows reduced activities of serum serine lipases in AHT cases and could be further investigated in mild forms of AHT, which currently show 30% of misdiagnosed cases in clinics.

neuroscience↗

Visualization and normalization of drift effect across batches in metabolome-wide association studies

As a powerful phenotyping technology, metabolomics provides new opportunities in biomarker discovery through metabolome-wide association studies (MWAS) and identification of metabolites having regulatory effect in various biological processes. While MS-based metabolomics assays are endowed with high-throughput and sensitivity, large-scale MWAS are doomed to long-term data acquisition generating an overtime-analytical signal drift that can hinder the uncovering of true biologically relevant changes. We developed "dbnorm", a package in R environment, which allows visualization and removal of signal heterogeneity from large metabolomics datasets. "dbnorm" integrates advanced statistical tools to inspect dataset structure, at both macroscopic (sample batch) and microscopic (metabolic features) scales. To compare model performance on data correction, "dbnorm" assigns a score, which allows the straightforward identification of the best fitting model for each dataset. Herein, we show how "dbnorm" efficiently removes signal drift among batches to capture the true biological heterogeneity of data in two large-scale metabolomics studies.

bioinformatics↗