Pre-clinical studies from the recent past have indicated that senescent cells can negatively affect health and contribute to premature aging. Targeted eradication of these cells has been shown to improve the health of aged experimental animals, leading to a clinical interest in finding compounds that selectively eliminate senescent cells while sparing non-senescent ones. In our study, we identified a senolytic capacity of statins, which are lipid-lowering drugs prescribed to patients at high risk of cardiovascular events. Using two different models of senescence in human vascular endothelial cells (HUVECs), we found that statins preferentially eliminated senescent cells, while leaving non-senescent cells unharmed. We observed that the senolytic effect of statins could be negated with the co-administration of mevalonic acid and that statins induced cell detachment leading to anoikis-like apoptosis, as evidenced by real-time visualization of caspase-3/7 activation. Our findings suggest that statins possess a senolytic property, possibly also contributing to their described beneficial cardiovascular effects. Further studies are needed to explore the potential of short-term, high-dose statin treatment as a candidate senolytic therapy.

The glomerular basement membrane (GBM) and extra-cellular matrix (ECM) are essential to maintain a functional interaction between the glomerular podocytes and the fenestrated endothelial cells in the formation of the slit diaphragm for the filtration of blood. Dysregulation of ECM homeostasis can cause Focal segmental glomerulosclerosis (FSGS). Despite this central role, alterations in ECM composition during FSGS have not been analyzed in detail yet. Here, we characterized the ECM proteome changes in miR-193a-overexpressing mice, which suffer from FSGS due to suppression of Wilms’ tumor 1 (WT1). By mass spectrometry we identified a massive activation of the acute phase response, especially the complement and fibrinogen pathways. Several protease inhibitors (ITIH1, SERPINA1, SERPINA3) were also strongly increased. Complementary analysis of RNA expression data from both miR-193a mice and human FSGS patients identified additional candidate genes also mainly involved in the acute phase response. In total, we identified more than 60 dysregulated, ECM-associated genes with potential relevance for FSGS progression. Our comprehensive analysis of a murine FSGS model and translational comparison with human data offers novel targets for FSGS therapy.

Ulcerations of the lower extremities are a frequently encountered problem in clinical practice and are of significant interest in public health due to the high prevalence of underlying pathologies, including chronic venous disease, diabetes and peripheral arterial occlusive disease. However, leg ulcers can also present as signs and symptoms of various rare diseases and even as an adverse reaction to drugs. In such cases, correct diagnosis ultimately relies on histopathological examination. Apart from the macroscopic presentation, patient history and anatomic location, which are sometimes indicative, most ulcers have very distinct histopathological features. These features are found in different layers of the skin or even associated vessels. In this narrative review, we discuss and highlight the histopathological differences of several types of leg ulcers that can contribute to efficient and accurate diagnosis.

Prostate cancer (PCa) is a major concern in public health, with many genetically distinct subsets. Genomic alterations in PCa are extraordinarily complex, and both germline and somatic mutations are of great importance in the development of this tumor. The aim of this review is to provide an overview of genetic changes that can occur in the development of PCa and their role in potential therapeutic approaches. Various pathways and mechanisms proposed to play major roles in PCa are described in detail to provide an overview of current knowledge.

Key Points: Pauci-immune focal necrotizing glomerulonephritis (piFNGN) entails heterogeneous glomerular lesions in different stages of evolution. Spatial profiling of glomeruli in piFNGN identifies protein and mRNA signatures that correlate with morphologically distinct lesions. Profiling of individual glomeruli provides insights into the pathogenesis of piFNGN and may identify therapeutic targets or biomarkers. Pauci-immune focal necrotizing glomerulonephritis (piFNGN) involves asynchronous onset and progression of injurious lesions in biopsies. Pathologists can describe this heterogeneity within a biopsy, but translating the information into prognostic or expression analyses is challenging. Understanding the underlying molecular processes could improve treatment; however, bulk or single-cell transcriptomic analyses of dissociated tissue disregard the heterogeneity of glomerular injury. We characterize protein and mRNA expression of individual glomeruli in 20 biopsies from 18 patients with antineutrophil cytoplasmic antibody-associated piFNGN using the NanoString digital spatial profiling (DSP) platform. For this purpose, circular annotations of glomeruli were analyzed using protein, immuno-oncology RNA, and Cancer Transcriptome Atlas panels (n=120, 72, and 48 glomeruli, respectively). Histologic evaluation of glomerular patterns of injury was performed in adjacent serial sections. Expression data were processed by log₂ transformation, quantile normalization, and batch adjustment. DSP revealed distinct but overlapping gene expression profiles relating to the morphological evolution of injurious lesions, including dynamic expression of various immune checkpoint regulators. Enrichment analysis indicated deregulated pathways that underline known and highlight novel potential mechanisms of disease. Moreover, by capturing individual glomeruli, DSP describes heterogeneity between and within biopsies. We demonstrate the benefit of spatial profiling for characterization of heterogeneous glomerular injury, indicating novel molecular correlates of glomerular injury in piFNGN.

In this paper, we present a new evolution-based algorithm that optimizes cell detection image processing workflows in a self-adaptive fashion. We use evolution strategies to optimize the parameters for all steps of the image processing pipeline and improve cell detection results. The algorithm reliably produces good cell detection results without the need for extensive domain knowledge. Our algorithm also needs no labeled data to produce good cell detection results compared to the state-of-the-art neural network approaches. Furthermore, the algorithm can easily be adapted to different applications by modifying the processing steps in the pipeline and has high scalability since it supports multithreading and computation on graphical processing units (GPUs).