Biology subjects

Meijer, O. C.

Publications and source records attributed to Meijer, O. C..

6 recordsLinked to original sources

bioRxiv2023

Harnessing PROTAC technology to combat stress hormone receptor activation

Counteracting the overactivation of glucocorticoid receptors (GR) is an important therapeutic goal in stress-related psychiatry and beyond. The only clinically approved GR antagonist lacks selectivity and induces unwanted side effects. To complement existing tools of small-molecule-based inhibitors, we present a highly potent, novel catalytically-driven GR degrader, KH-103, based on proteolysis-targeting chimera technology. This selective degrader enables immediate and reversible GR depletion that is independent of genetic manipulation and circumvents transcriptional adaptations to inhibition. KH-103 achieves passive inhibition, preventing agonistic induction of gene expression, and significantly averts the GRs genomic effects compared to two currently available inhibitors. Application in primary-neuron cultures revealed the dependency of a glucocorticoid-induced increase in spontaneous calcium activity on GR. Finally, we present a proof of concept for application in-vivo. KH-103 opens opportunities for a more lucid interpretation of GR functions with translational potential.

molecular biology↗

bioRxiv2023

Multimodal Data Integration Advances Longitudinal Prediction of the Naturalistic Course of Depression and Reveals a Multimodal Signature of Disease Chronicity

The ability to individually predict disease course of major depressive disorder (MDD) is essential for optimal treatment planning. Here, we use a data-driven machine learning approach to assess the predictive value of different sets of biological data (whole-blood proteomics, lipid-metabolomics, transcriptomics, genetics), both separately and added to clinical baseline variables, for the longitudinal prediction of 2-year MDD chronicity (defined as presence of MDD diagnosis after 2 years) at the individual subject level. Prediction models were trained and cross-validated in a sample of 643 patients with current MDD (2-year chronicity n = 318) and subsequently tested for performance in 161 MDD individuals (2-year chronicity n = 79). Proteomics data showed best unimodal data predictions (AUROC = 0.68). Adding proteomic to clinical data at baseline significantly improved 2-year MDD chronicity predictions (AUROC = 0.63 vs AUROC = 0.78, p = 0.013), while the addition of other -omics data to clinical data did not yield significantly increased model performance. SHAP and enrichment analysis revealed proteomic analytes involved in inflammatory response and lipid metabolism, with fibrinogen levels showing the highest variable importance, followed by symptom severity. Machine learning models outperformed psychiatrists ability to predict two-year chronicity (balanced accuracy = 71% vs 55%). This study showed the added predictive value of combining proteomic, but not other -omic data, with clinical data. Adding other -omic data to proteomics did not further improve predictions. Our results reveal a novel multimodal signature of MDD chronicity that shows clinical potential for individual MDD disease course predictions from baseline measurements.

genomics↗

bioRxiv2022

Novel assays monitoring direct glucocorticoid receptor protein activity exhibit high predictive power for ligand activity on endogenous gene targets

Exogenous glucocorticoids are widely used in the clinic for the treatment of inflammatory disorders and auto-immune diseases. Unfortunately, their use is hampered by many side effects and therapy resistance. Efforts to find more selective glucocorticoid receptor (GR) agonists and modulators (called SEGRAMs) that are able to separate anti-inflammatory effects via gene repression from metabolic effects via gene activation, have been unsuccessful so far. In this study, we characterized a set of functionally diverse GR ligands in A549 cells, first using a panel of luciferase-based reporter gene assays evaluating GR-driven gene activation and gene repression. We expanded this minimal assay set with novel luciferase-based read-outs monitoring GR protein levels, GR dimerization and GR Serine 211 (Ser211) phosphorylation status and compared their outcomes with compound effects on the mRNA levels of known GR target genes in A549 cells and primary hepatocytes. We found that luciferase reporters evaluating GR-driven gene activation and gene repression were not always reliable predictors for effects on endogenous target genes. Remarkably, our novel assay monitoring GR Ser211 phosphorylation levels proved to be the most reliable predictor for compound effects on almost all tested endogenous GR targets, both driven by gene activation and repression. The integration of this novel assay in existing screening platforms running both in academia and industry may therefore boost chances to find novel GR ligands with an actual improved therapeutic benefit.

pharmacology and toxicology↗

bioRxiv2022

Transcriptional and cell type profiles of cortical brain regions showing ultradian cortisol rhythm dependent responses to emotional face stimulation

The characteristic endogenous circadian rhythm of plasma glucocorticoid concentrations is made up from an underlying ultradian pulsatile secretory pattern. Recent evidence has indicated that this ultradian cortisol pulsatility is crucial for normal emotional response in man. In this study, we investigate the anatomical transcriptional and cell type signature of brain regions sensitive to a loss of ultradian rhythmicity in the context of emotional processing. We combine human cell type and transcriptomic atlas data of high spatial resolution with functional magnetic resonance imaging (fMRI) data. We show that the loss of cortisol ultradian rhythm alters emotional processing response in cortical brain areas that are characterized by transcriptional and cellular profiles of GABAergic function. We find that two previously identified key components of rapid non-genomic GC signaling - the ANXA1 gene and retrograde endocannabinoid signaling - show top differential expression and the most significant enrichment. Our results further indicate that specific cell types, including a specific NPY-expressing GABAergic neuronal cell type, and specific G protein signaling cascades underly the cerebral effects of a loss of ultradian cortisol rhythm. Our results provide a biological mechanistic underpinning of our fMRI findings, indicating specific cell types and cascades as a target for manipulation in future experimental studies.

neuroscience↗

bioRxiv2021

Brain Areas Affected by Intranasal Oxytocin Show Higher Oxytocin Receptor Expression

Neuroimaging studies suggest that intranasal oxytocin (IN-OXT) may modulate emotional and social processes by altering neural activity patterns. The extent of brain penetration after IN-OXT is unclear, and it is currently under debate whether IN-OXT can directly bind central oxytocin receptors (OXTRs). We investigated oxytocin pathway gene expression in regions affected by IN-OXT on task-based fMRI. We found that OXTR is more highly expressed in affected than unaffected subcortical regions; this effect did not vary by task-type or sex. Cortical results revealed higher OXTR expression in regions affected by IN-OXT in emotional processing tasks and in male-only data. No significant differences were found in expression of the closely related vasopressin receptors. Our findings suggest that the mechanism by which IN-OXT may alter brain functionality involves direct activation of central OXTRs.

neuroscience↗

bioRxiv2019

Molecular characterization of the stress network in the human brain

The biological mechanisms underlying inter-individual differences in human stress reactivity remain poorly understood. We aimed to identify the molecular underpinning of neural stress sensitivity. Linking mRNA expression data from the Allen Human Brain Atlas to task-based fMRI revealed 201 differentially expressed genes in cortex-specific brain regions differentially activated by stress in individuals with low or high stress sensitivity. These genes are associated with stress-related psychiatric disorders (e.g. schizophrenia and anxiety) and include markers for specific neuronal populations (e.g. ADCYAP1, GABRB1, SSTR1, and TNFRSF12A), neurotransmitter receptors (e.g. GRIN3A, SSTR1, GABRB1, and HTR1E), and signaling factors that interact with the corticosteroid receptor and hypothalamic-pituitary-adrenal axis (e.g. ADCYAP1, IGSF11, and PKIA). Overall, the identified genes potentially underlie altered stress reactivity in individuals at risk for psychiatric disorders and play a role in mounting an adaptive stress response, making them potentially druggable targets for stress-related diseases.

neuroscience↗