Glucocorticoid receptor modulators

Diabetes mellitus: pathophysiological changes and therap

The development of novel glucocorticoid receptor antagonists: from rational chemical design to therapeutic efficacy in metabolic disease models

Glucocorticoids regulate numerous processes in human physiology, but deregulated or excessive glucocorticoid receptor (GR) signaling contributes to the development of various pathologies including metabolic syndrome. For this reason, GR antagonists have considerable therapeutic value. Yet, the only GR antagonist that is clinically approved to date - mifepristone - exhibits cross-reactivity with other nuclear steroid receptors like the progesterone receptor. In this study, we set out to identify novel selective GR antagonists by combining rational chemical design with an unbiased in vitro and in vivo screening approach. Using this pipeline, we were able to identify CORT125329 as the compound with the best overall profile from our octahydro series of novel GR antagonists, and demonstrated that CORT125329 does not exhibit cross-reactivity with the progesterone receptor. Further in vivo testing showed beneficial activities of CORT125329 in models for excessive corticosterone exposure and short- and long-term high-fat diet-induced metabolic complications. Upon CORT125329 treatment, most metabolic parameters that deteriorated upon high-fat diet feeding were similarly improved in male and female mice, confirming activity in both sexes. However, some sexually dimorphic effects were observed including male-specific antagonism of GR activity in brown adipose tissue and female-specific lipid lowering activities after short-term CORT125329 treatment. Remarkably, CORT125329 exhibits beneficial metabolic effects despite its lack of GR antagonism in white adipose tissue. Rather, we propose that CORT125329 treatment restores metabolic activity in brown adipose tissue by stimulating lipolysis, mitochondrial activity and thermogenic capacity. In summary, we have identified CORT125329 as a selective GR antagonist with strong beneficial activities in metabolic disease models, paving the way for further clinical investigation.Nephrolog

Hippocampal glucocorticoid target genes associated with enhancement of memory consolidation

Glucocorticoids enhance memory consolidation of emotionally arousing events via largely unknown molecular mechanisms. This glucocorticoid effect on the consolidation process also requires central noradrenergic neurotransmission. The intracellular pathways of these two stress mediators converge on two transcription factors: the glucocorticoid receptor (GR) and phosphorylated cAMP response element-binding protein (pCREB). We therefore investigated, in male rats, whether glucocorticoid effects on memory are associated with genomic interactions between the GR and pCREB in the hippocampus. In a two-by-two design, object exploration training or no training was combined with post-training administration of a memory-enhancing dose of corticosterone or vehicle. Genomic effects were studied by chromatin immunoprecipitation followed by sequencing (ChIP-seq) of GR and pCREB 45 min after training and transcriptome analysis after 3 hr. Corticosterone administration induced differential GR DNA-binding and regulation of target genes within the hippocampus, largely independent of training. Training alone did not result in long-term memory nor did it affect GR or pCREB DNA-binding and gene expression. No strong evidence was found for an interaction between GR and pCREB. Combination of the GR DNA-binding and transcriptome data identified a set of novel, likely direct, GR target genes that are candidate mediators of corticosterone effects on memory consolidation. Cell-specific expression of the identified target genes using single-cell expression data suggests that the effects of corticosterone reflect in part non-neuronal cells. Together, our data identified new GR targets associated with memory consolidation that reflect effects in both neuronal and non-neuronal cells.Development and application of statistical models for medical scientific researc

Minimizing errors in RT-PCR detection and quantification of SARS-CoV-2 RNA for wastewater surveillance

Wastewater surveillance for pathogens using reverse transcription-polymerase chain reaction (RT-PCR) is an effective and resource-efficient tool for gathering community-level public health information, including the incidence of coronavirus disease-19 (COVID-19). Surveillance of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) in wastewater can potentially provide an early warning signal of COVID-19 infections in a community. The capacity of the world's environmental microbiology and virology laboratories for SARS-CoV-2 RNA characterization in wastewater is increasing rapidly. However, there are no standardized protocols or harmonized quality assurance and quality control (QA/QC) procedures for SARS-CoV-2 wastewater surveillance. This paper is a technical review of factors that can cause false-positive and false-negative errors in the surveillance of SARS-CoV-2 RNA in wastewater, culminating in recommended strategies that can be implemented to identify and mitigate some of these errors. Recommendations include stringent QA/QC measures, representative sampling approaches, effective virus concentration and efficient RNA extraction, PCR inhibition assessment, inclusion of sample processing controls, and considerations for RT-PCR assay selection and data interpretation. Clear data interpretation guidelines (e.g., determination of positive and negative samples) are critical, particularly when the incidence of SARS-CoV-2 in wastewater is low. Corrective and confirmatory actions must be in place for inconclusive results or results diverging from current trends (e.g., initial onset or reemergence of COVID-19 in a community). It is also prudent to perform interlaboratory comparisons to ensure results' reliability and interpretability for prospective and retrospective analyses. The strategies that are recommended in this review aim to improve SARS-CoV-2 characterization and detection for wastewater surveillance applications. A silver lining of the COVID-19 pandemic is that the efficacy of wastewater surveillance continues to be demonstrated during this global crisis. In the future, wastewater should also play an important role in the surveillance of a range of other communicable diseases

MIBiG 3.0 : a community-driven effort to annotate experimentally validated biosynthetic gene clusters

With an ever-increasing amount of (meta)genomic data being deposited in sequence databases, (meta)genome mining for natural product biosynthetic pathways occupies a critical role in the discovery of novel pharmaceutical drugs, crop protection agents and biomaterials. The genes that encode these pathways are often organised into biosynthetic gene clusters (BGCs). In 2015, we defined the Minimum Information about a Biosynthetic Gene cluster (MIBiG): a standardised data format that describes the minimally required information to uniquely characterise a BGC. We simultaneously constructed an accompanying online database of BGCs, which has since been widely used by the community as a reference dataset for BGCs and was expanded to 2021 entries in 2019 (MIBiG 2.0). Here, we describe MIBiG 3.0, a database update comprising large-scale validation and re-annotation of existing entries and 661 new entries. Particular attention was paid to the annotation of compound structures and biological activities, as well as protein domain selectivities. Together, these new features keep the database up-to-date, and will provide new opportunities for the scientific community to use its freely available data, e.g. for the training of new machine learning models to predict sequence-structure-function relationships for diverse natural products. MIBiG 3.0 is accessible online at https://mibig.secondarymetabolites.org/