No abstract

Glucocorticoids (GCs) regulate a myriad of physiological systems, such as the immune and nervous systems. Systemic GC levels in blood are often measured as an indicator of local GC levels in target organs. However, several extra-adrenal organs can produce and metabolize GCs locally. More sensitive and specific methods for GC analysis (i.e., mass spectrometry) allow measurement of local GC levels in small tissue samples with low GC concentrations. Consequently, is it now apparent that systemic GC levels often do not reflect local GC levels. Here, we review the use of systemic GC measurements in clinical and research settings, discuss instances where systemic GC levels do not reflect local GC levels, and present evidence that local GC levels provide useful insights, with a focus on local GC production in the thymus (immunosteroids) and brain (neurosteroids). Lastly, we suggest key areas for further research, such as the roles of immunosteroids and neurosteroids in neonatal programming and the potential clinical relevance of local GC modulators.

Corticosterone is produced by the adrenal glands and also produced locally by other organs, such as the brain. Local levels of corticosterone in specific brain regions during development are not known. Here, we microdissected brain tissue and developed a novel liquid chromatography tandem mass spectrometry method (LC‐MS/MS) to measure a panel of seven steroids (including 11‐deoxycorticosterone (DOC), corticosterone, and 11‐dehydrocorticosterone (DHC) in the blood, hippocampus (HPC), cerebral cortex (CC), and hypothalamus (HYP) of mice at postnatal day (PND) 5, 21, and 90. In a second cohort of mice, we measured the expression of three genes that code for steroidogenic enzymes that regulate corticosterone levels (Cyp11b1, Hsd11b1, and Hsd11b2) in the HPC, CC, and HYP. There were region‐specific patterns of steroid levels across development, including higher corticosterone levels in the HPC and HYP than in the blood at PND5. In contrast, corticosterone levels were higher in the blood than in all brain regions at PND21 and PND90. Brain corticosterone levels were not positively correlated with blood corticosterone levels, and correlations across brain regions increased with age. Local corticosterone levels were best predicted by local DOC levels at PND5, but by local DHC levels at PND21 and PND90. Transcripts for the three enzymes were detectable in all samples (with highest expression of Hsd11b1) and showed region‐specific changes with age. These data demonstrate that individual brain regions fine‐tune local levels of corticosterone during early development and that coupling of glucocorticoid levels across regions increases with age.

Despite the vast amount of metabolic information that can be captured in untargeted metabolomics, many biological applications are looking for a biology-driven metabolomics platform that targets a set of metabolites that are relevant to the given biological question. Steroids are a class of important molecules that play critical roles in many physiological systems and diseases. Besides known steroids, there are a large number of unknown steroids that have not been reported in the literature. The ability to rapidly detect and quantify both known and unknown steroid molecules in a biological sample can greatly accelerate a broad range of steroid-focused life science research. This work describes the development and application of SteroidXtract, a convolutional neural network (CNN)-based bioinformatics tool that can recognize steroid molecules in mass spectrometry (MS)-based untargeted metabolomics using their unique tandem MS (MS2) spectral patterns. SteroidXtract was trained using a comprehensive set of standard MS2 spectra from MassBank of North America (MoNA) and an in-house steroid library. Data augmentation strategies, including intensity thresholding and Gaussian noise addition, were created and applied to minimize data overfitting caused by the limited number of standard steroid MS2 spectra. The CNN model embedded in SteroidXtract was further compared with random forest and XGBoost using nested cross-validations to demonstrate its performance. Finally, SteroidXtract was applied in several metabolomics studies to demonstrate its sensitivity, specificity, and robustness. Compared to conventional statistics-driven metabolomics data interpretation, our work offers a novel automated biology-driven approach to interpreting untargeted metabolomics data, prioritizing biologically important molecules with high throughput and sensitivity.