Endocrine Abstracts

The ‘central dogma of biology’ - a term coined by Francis Crick in 1957 - describes the flow of sequential information from genome via transcriptome, proteome and metabolome to the phenotype. Associated with each stage is the respective systems biology ‘-omics tool’, e.g. metabolomics. The metabolome represents the complete set of metabolites (low-molecular weight molecules) in a biological sample and metabolomics describes the systematic study of these small molecules present in this biological sample (Daviss and Bennett 2005). A typical workflow of metabolomics studies implies: i) collection of a biological sample (e.g. a biofluid), ii) sample preparation, iii) multicomponent analysis by analytical platform techniques consisting of a powerful separation technique (gas chromatography (GC) or liquid chromatography (LC)) combined with mass spectrometric (MS) or nuclear magnetic resonance (NMR) based detection, iv) data acquisition, v) data analysis by sophisticated bioinformatics tools (e.g. principal component analysis), and vi) interpretation to finally recognize patterns, characterize disease signatures or biomarkers. If all components in a complex mixture are determined in a non-discriminatory (unbiased) way, such an approach is called ‘untargeted’. In case only selected components are recorded, this approach is a ‘targeted’ one. Early attempts in characterizing normal and pathological human steroid metabolomes (steroid metabolomics, steroidomics) started from the 7^th decade of the last century onwards. GC and GC-MS were the dominating techniques pioneered by Sjövall (2004) and Shackleton (2006). From the eighties of last century onwards, LC-MS techniques joined and soon ascended impressively (Wudy and Choi 2016). Focusing on the currently prevailing analytical techniques GC-MS and LC-MS (Wudy et al. 2018; Shackleton et al. 2018), this talk will demonstrate the roles of these techniques in various examples from steroid research and clinical endocrinology with respect to delineation of steroid metabolomes, characterization of disease signatures and new biomarkers.