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In the context of data science, data projection and clustering are common procedures. The chosen analysis method is crucial to avoid faulty pattern recognition. It is therefore necessary to know the properties and especially the limitations of projection and clustering algorithms. This report describes a collection of datasets that are grouped together in the Fundamental Clustering and Projection Suite (FCPS). The FCPS contains 10 datasets with the names "Atom", "Chainlink", "EngyTime", "Golfball", "Hepta", "Lsun", "Target", "Tetra", "TwoDiamonds", and "WingNut". Common clustering methods occasionally identified non-existent clusters or assigned data points to the wrong clusters in the FCPS suite. Likewise, common data projection methods could only partially reproduce the data structure correctly on a two-dimensional plane. In conclusion, the FCPS dataset collection addresses general challenges for clustering and projection algorithms such as lack of linear separability, different or small inner class spacing, classes defined by data density rather than data spacing, no cluster structure at all, outliers, or classes that are in contact. This report describes a collection of datasets that are grouped together in the Fundamental Clustering and Projection Suite (FCPS). It is designed to address specific problems of structure discovery in high-dimensional spaces.
Nuclear receptor related 1 (Nurr1) is an orphan ligand-activated transcription factor and considered as neuroprotective transcriptional regulator with great potential as therapeutic target for neurodegenerative diseases. However, the collection of available Nurr1 modulators and mechanistic understanding of Nurr1 are limited. Here, we report the discovery of several structurally diverse non-steroidal anti-inflammatory drugs as inverse Nurr1 agonists demonstrating that Nurr1 activity can be regulated bidirectionally. As chemical tools, these ligands enable unraveling the co-regulatory network of Nurr1 and the mode of action distinguishing agonists from inverse agonists. In addition to its ability to dimerize, we observe an ability of Nurr1 to recruit several canonical nuclear receptor co-regulators in a ligand-dependent fashion. Distinct dimerization states and co-regulator interaction patterns arise as discriminating factors of Nurr1 agonists and inverse agonists. Our results contribute a valuable collection of Nurr1 modulators and relevant mechanistic insights for future Nurr1 target validation and drug discovery.
Hepatocyte nuclear factor 4α (HNF4α) is a ligand-sensing transcription factor and presents as a potential drug target in metabolic diseases and cancer. In humans, mutations in the HNF4α gene cause maturity-onset diabetes of the young (MODY), and the elevated activity of this protein has been associated with gastrointestinal cancers. Despite the high therapeutic potential, available ligands and structure–activity relationship knowledge for this nuclear receptor are scarce. Here, we disclose a chemically diverse collection of orthogonally validated fragment-like activators as well as inverse agonists, which modulate HNF4α activity in a low micromolar range. These compounds demonstrate the druggability of HNF4α and thus provide a starting point for medicinal chemistry as well as an early tool for chemogenomics.
The retinoid X receptor (RXR) is a ligand-sensing transcription factor acting mainly as a universal heterodimer partner for other nuclear receptors. Despite presenting as a potential therapeutic target for cancer and neurodegeneration, adverse effects typically observed for RXR agonists, likely due to the lack of isoform selectivity, limit chemotherapeutic application of currently available RXR ligands. The three human RXR isoforms exhibit different expression patterns; however, they share high sequence similarity, presenting a major obstacle toward the development of subtype-selective ligands. Here, we report the discovery of the saturated fatty acid, palmitic acid, as an RXR ligand and disclose a uniform set of crystal structures of all three RXR isoforms in an active conformation induced by palmitic acid. A structural comparison revealed subtle differences among the RXR subtypes. We also observed an ability of palmitic acid as well as myristic acid and stearic acid to induce recruitment of steroid receptor co-activator 1 to the RXR ligand-binding domain with low micromolar potencies. With the high, millimolar endogenous concentrations of these highly abundant lipids, our results suggest their potential involvement in RXR signaling.
5‐Lipoxygenase (5‐LO) is the initial enzyme in the biosynthesis of leukotrienes, which are mediators involved in pathophysiological conditions such as asthma and certain cancer types. Knowledge of proteins involved in 5‐LO pathway regulation, including gene regulatory proteins, is needed to evaluate all options for therapeutic intervention in these diseases. Here, we present a mass spectrometric screening of ALOX5 promoter‐interacting proteins, obtained by DNA pulldown and label‐free quantitative mass spectrometry. Protein preparations from myeloid and B‐lymphocytic cell lines were screened for promoter DNA interactors. Through statistical analysis, 66 proteins were identified as specific ALOX5 promotor binding proteins. Among those, the 15 most likely candidates for a prominent role in ALOX5 gene regulation are the known ALOX5 interactors Sp1 and Sp3, the related factor Sp2, two Krüppel‐like factors (KLF13 and KLF16) and six other zinc finger proteins (MAZ, PRDM10, VEZF1, ZBTB7A, ZNF281 and ZNF579). Intriguingly, we also identified two helicases (BLM and DHX36) and the proteins hnRNPD and hnRNPK, which are, together with the protein MAZ, known to interact with DNA G‐quadruplex structures. As G‐quadruplexes are implicated in gene regulation, spectroscopic and antibody‐based methods were used to confirm their presence within the GC‐rich sequence of the ALOX5 promoter. In summary, we have systematically characterized the interactome of the ALOX5 promoter, identifying several zinc finger proteins as novel potential ALOX5 gene regulators. Further, we have shown that the ALOX5 promoter can form DNA G‐quadruplex structures, which may play a functional role in ALOX5 gene regulation.
ABC transporters fulfill diverse physiological functions in different cellularlocalizations ranging from the plasma membrane to intracellular membranouscompartments. Several ABC transporters have been spotted in the endolyso-somal system, which consists of endosomes, autophagosomes, lysosomes, andlysosome-related organelles. In this review, we present an overview of lysoso-mal ABC transporters including ABCA2, ABCA3, ABCA5, ABCB6,ABCB9, and ABCD4, discussing their trafficking routes, putative substrates,potential physiological functions, and associated diseases. In addition, weoffer a critical evaluation of the literature linking ABC transporters to lyso-somal drug sequestration, examining pitfalls associated with in vitro modelsof drug resistance.
Background: Zolpidem is a non-benzodiazepine hypnotic agent which has been shown to be effective in inducing and maintaining sleep in adults and is one of the most frequently prescribed hypnotics in the world. For drugs that are used to treat sleeping disorders, the time to reach the maximum concentration (Tmax) of the drug in plasma is important to achieving a fast onset of action and this must be maintained when switching from one product to another.
Objectives: The main objective of the present work was to create a PBPK/PD model for zolpidem and establish a clinically relevant “safe space” for dissolution of zolpidem from the commercial immediate release (IR) formulation. A second objective was to analyze literature pharmacokinetic data to verify the negative food effect ascribed to zolpidem and consider its ramifications in terms of the “safe space” for dissolution.
Methods: Using dissolution, pharmacokinetic and pharmacodynamic data, an integrated PBPK/PD model for immediate release zolpidem tablets was constructed in Simcyp®. This model was used to identify the clinically relevant dissolution specifications necessary to ensure efficacy.
Results: According to the simulations, as long as 85% of the drug is released in 45 minutes or less, the impact on the PK and PD profiles of zolpidem would be minimal. According to the FDA, the drug has to dissolve from the test and reference products at a similar rate and to an extent of 85% in not more than 30 minutes to pass bioequivalence via the BCS-biowaiver test. Thus, the BCS-biowaiver specifications are somewhat more stringent than the “safe space” based on the PBPK/PD model. Published data from fasted and fed state pharmacokinetic studies suggest but do not prove a negative food effect of zolpidem.
Conclusions: A PBPK/PD model indicates that current BCS biowaiver criteria are more restrictive for immediate release zolpidem tablets than they need to be. In view of the close relationship between PK and PD, it remains advisable to avoid taking zolpidem tablets with or immediately after a meal, as indicated by the Stilnox® labeling.
Central cholinergic function and metabolic changes in streptozotocin‐induced rat brain injury
(2020)
As glucose hypometabolism in the brain is an early sign of Alzheimer´s dementia (AD), the diabetogenic drug streptozotocin (STZ) has been used to induce Alzheimer‐like pathology in rat brain by intracereboventricular injection (icv‐STZ). However, many details of the pathological mechanism of STZ in this AD model remain unclear. Here, we report metabolic and cholinergic effects of icv‐STZ using microdialysis in freely moving animals. We found that icv‐STZ at a dose of 3 mg/kg (2 × 1.5 mg/kg) causes overt toxicity reflected in body weight loss. Three weeks after STZ administration, histological examination revealed a high number of glial fibrillary acidic protein reactive cells in the hippocampus, accompanied by Fluoro‐Jade C‐positive cells in the CA1 region. Glucose and lactate levels in microdialysates were unchanged, but mitochondrial respiration measured ex vivo was reduced by 9%–15%. High‐affinity choline uptake, choline acetyltransferase, and acetylcholine esterase (AChE) activities in the hippocampus were reduced by 16%, 28%, and 30%, respectively. Importantly, extracellular acetylcholine (ACh) levels in the hippocampus were unchanged and responded to behavioral and pharmacological challenges. In comparison, extracellular ACh levels and cholinergic parameters in the striatum were unchanged or slightly increased. We conclude that the icv‐STZ model poorly reflects central cholinergic dysfunction, an important characteristic of dementia. The icv‐STZ model may be more aptly described as an animal model of hippocampal gliosis.
A plethora of data has highlighted the role of epigenetics in the development of cancer. Initiation and progression of different cancer types are associated with a variety of changes of epigenetic mechanisms, including aberrant DNA methylation, histone modifications, and miRNA expression. At the same time, advances in the available epigenetic tools allow to investigate and reverse these epigenetic changes and form the basis for the development of anticancer drugs in human oncology. Although human and canine cancer shares several common features, only recently that studies emerged investigating the epigenetic landscape in canine cancer and applying epigenetic modulators to canine cancer. This review focuses on the existing studies involving epigenetic changes in different types of canine cancer and the use of small-molecule inhibitors in canine cancer cells.
Alzheimer’s disease (AD) is the most prevalent neurodegenerative disease causing dementia and poses significant health risks to middle-aged and elderly people. Brain magnetic resonance imaging (MRI) is the most widely used diagnostic method for AD. However, it is challenging to collect sufficient brain imaging data with high-quality annotations. Weakly supervised learning (WSL) is a machine learning technique aimed at learning effective feature representation from limited or low-quality annotations. In this paper, we propose a WSL-based deep learning (DL) framework (ADGNET) consisting of a backbone network with an attention mechanism and a task network for simultaneous image classification and image reconstruction to identify and classify AD using limited annotations. The ADGNET achieves excellent performance based on six evaluation metrics (Kappa, sensitivity, specificity, precision, accuracy, F1-score) on two brain MRI datasets (2D MRI and 3D MRI data) using fine-tuning with only 20% of the labels from both datasets. The ADGNET has an F1-score of 99.61% and sensitivity is 99.69%, outperforming two state-of-the-art models (ResNext WSL and SimCLR). The proposed method represents a potential WSL-based computer-aided diagnosis method for AD in clinical practice.