Biology subjects

Wilhelmsen, K.

Publications and source records attributed to Wilhelmsen, K..

5 recordsLinked to original sources

bioRxiv2025

Inhibition of NLRP3 by a CNS-penetrating indazole scaffold

Low-grade inflammation is a hallmark of ageing and a key cause of age-related impairments and diseases1. The NOD-like receptor NLRP3 senses a variety of danger signals and environmental insults, resulting in pro-inflammatory response, inflammasome formation and pyroptosis2,3. Its aberrant activation has been linked to many acute and chronic diseases ranging from atherosclerosis to Alzheimers disease and cancer, making NLRP3 an attractive therapeutic target4,5. Here we report the discovery, characterization, and structure of an indazole-based NLRP3 antagonist, BAL-1516, which potently inhibits inflammasome formation in monocytes and microglia. The cryo-electron microscopy structure of BAL-1516 bound to NLRP3 reveals a previously undescribed compound binding site at a surface groove of the nucleotide-binding domain with contacts to the FISNA and WHD subdomains. The characteristic feature of BAL compound binding is the formation of three hydrogen bonds to the peripheral {beta}-strand of the triple-ATPase; two from the indazoles nitrogen atoms and a third from the compounds linker region. Additional phenyl and thiazole moieties render the compound hydrophobic, allowing excellent blood-brain barrier penetration. The compound binding site is highly specific for NOD-like receptors, and the optimized compound BAL-1516 is able to directly bind mouse NLRP3 despite two conservative residue changes in the binding interface. The BAL compounds represent a first-in-class family of NLRP3 inhibitors, providing a broad design space, including covalent and degradative properties, for the development of NLRP3-directed therapeutics. The innate immune system contains cytosolic proteins that sense cellular stress caused by bacterial, viral and fungal infections or sterile inflammation, to control cellular integrity2. NLRP3 is a well-studied member of the nucleotide-binding oligomerization domain (NOD)-like receptors (NLRs) that is involved in the activation of the inflammasome, a multiprotein complex that mediates inflammation6. Upon detection of stress or pathogen-associated signals, NLRP3 triggers the activation of caspase-1, which leads to the production of pro-inflammatory cytokines such as IL-1{beta} and IL-18, driving inflammatory responses and ultimately pyroptotic cell death. In the context of neuroinflammation, NLRP3 plays a significant role in the pathogenesis of various neurodegenerative diseases, including Alzheimers disease, Parkinsons disease, and multiple sclerosis7. Research into targeting NLRP3 signalling with CNS-penetrating molecules holds potential for developing therapeutic strategies to alleviate neuroinflammatory conditions and to slow the progression of neurodegenerative diseases.

immunology↗

bioRxiv2024

Discovery of a Potent and Selective Inhibitor of Human NLRP3 with a Novel Binding Modality and Mechanism of Action

The NLRP3 inflammasome is an intracellular protein complex that causes inflammation via the release of IL-1{beta} and pyroptosis. NLRP3 activation is associated with many age-related inflammatory diseases, and NLRP3 inhibition is a promising therapeutic strategy. We previously performed a DNA encoded library screen to identify novel NLRP3 binding molecules. Herein we describe the characterization of BAL-0028 as a potent and specific inhibitor of NLRP3 signaling. Notably, BAL-0028 is a poor inhibitor of mouse NLRP3 but inhibits human and primate NLRP3 with nanomolar potency. Using cellular and biochemical analyses we demonstrate that BAL-0028 binds to the NLRP3 NACHT domain at a site that is distinct from the MCC950 binding pocket. Using humanized NLRP3 mice we show that a derivative of BAL-0028 inhibits NLRP3 activation in vivo in a peritonitis model. Finally, we demonstrate that BAL-0028 inhibits select hyperactive NLRP3 mutations associated with autoinflammatory diseases more potently than does MCC950. BAL-0028 thus represents a new modality for NLRP3 inhibition in inflammatory diseases. SUMMARYNLRP3 is a target for anti-inflammatory therapies and can be inhibited by the tool compound MCC950. We describe the characterization of a new small molecule inhibitor of NLRP3 BAL-0028 that has a distinct mechanism of action and binding site.

immunology↗

bioRxiv2021

Eicosanoid signaling as a therapeutic target in middle-aged mice with severe COVID-19

Coronavirus disease 2019 (COVID-19) is especially severe in aged populations1. Resolution of the COVID-19 pandemic has been advanced by the recent development of SARS-CoV-2 vaccines, but vaccine efficacy is partly compromised by the recent emergence of SARS-CoV-2 variants with enhanced transmissibility2. The emergence of these variants emphasizes the need for further development of anti-SARS-CoV-2 therapies, especially in aged populations. Here, we describe the isolation of a new set of highly virulent mouse-adapted viruses and use them to test a novel therapeutic drug useful in infections of aged animals. Initially, we show that many of the mutations observed in SARS-CoV-2 during mouse adaptation (at positions 417, 484, 501 of the spike protein) also arise in humans in variants of concern (VOC)2. Their appearance during mouse adaptation indicates that immune pressure is not required for their selection. Similar to the human infection, aged mice infected with mouse-adapted SARS-CoV-2 develop more severe disease than young mice. In murine SARS, in which severity is also age-dependent, we showed that elevated levels of an eicosanoid, prostaglandin D2 (PGD2) and of a phospholipase, PLA2G2D, contributed to poor outcomes in aged mice3,4. Using our virulent mouse-adapted SARS-CoV-2, we show that infection of middle-aged mice lacking expression of DP1, a PGD2 receptor, or PLA2G2D are protected from severe disease. Further, treatment with a DP1 antagonist, asapiprant, protected aged mice from a lethal infection. DP1 antagonism is one of the first interventions in SARS-CoV-2-infected animals that specifically protects aged animals, and demonstrates that the PLA2G2D-PGD2/DP1 pathway is a useful target for therapeutic interventions. (Words: 254)

microbiology↗

bioRxiv2021

TWO-SIGMA-G: A New Competitive Gene Set Testing Framework for scRNA-seq Data Accounting for Inter-Gene and Cell-Cell Correlation

We propose TWO-SIGMA-G, a competitive gene set test for scRNA-seq data. TWO-SIGMA-G uses a mixed-effects regression model based on our previously published TWO-SIGMA to test for differential expression at the gene-level. This regression-based model provides flexibility and rigor at the gene-level in (1) handling complex experimental designs, (2) accounting for the correlation between biological replicates, and (3) accommodating the distribution of scRNA-seq data to improve statistical inference. Moreover, TWO-SIGMA-G uses a novel approach to adjust for inter-gene-correlation (IGC) at the set-level to control the set-level false positive rate. Simulations demonstrate that TWO-SIGMA-G preserves type-I error and increases power in the presence of IGC compared to other methods. Application to two datasets identified HIV-associated Interferon pathways in xenograft mice and pathways associated with Alzheimers disease progression in humans.

genomics↗

bioRxiv2019

SMNN: Batch Effect Correction for Single-cell RNA-seq data via Supervised Mutual Nearest Neighbor Detection

Batch effect correction has been recognized to be indispensable when integrating single-cell RNA sequencing (scRNA-seq) data from multiple batches. State-of-the-art methods ignore single-cell cluster label information, but such information can improve effectiveness of batch effect correction, particularly under realistic scenarios where biological differences are not orthogonal to batch effects. To address this issue, we propose SMNN for batch effect correction of scRNA-seq data via supervised mutual nearest neighbor detection. Our extensive evaluations in simulated and real datasets show that SMNN provides improved merging within the corresponding cell types across batches, leading to reduced differentiation across batches over MNN, Seurat v3, and LIGER. Furthermore, SMNN retains more cell type-specific features, partially manifested by differentially expressed genes identified between cell types after SMNN correction being biologically more relevant, with precision improving by up to 841%. Key PointsO_LIBatch effect correction has been recognized to be critical when integrating scRNA-seq data from multiple batches due to systematic differences in time points, generating laboratory and/or handling technician(s), experimental protocol, and/or sequencing platform. C_LIO_LIExisting batch effect correction methods that leverages information from mutual nearest neighbors across batches (for example, implemented in SC3 or Seurat) ignore cell type information and suffer from potentially mismatching single cells from different cell types across batches, which would lead to undesired correction results, especially under the scenario where variation from batch effects is non-negligible compared with biological effects. C_LIO_LITo address this critical issue, here we present SMNN, a supervised machine learning method that first takes cluster/cell-type label information from users or inferred from scRNA-seq clustering, and then searches mutual nearest neighbors within each cell type instead of global searching. C_LIO_LIOur SMNN method shows clear advantages over three state-of-the-art batch effect correction methods and can better mix cells of the same cell type across batches and more effectively recover cell-type specific features, in both simulations and real datasets. C_LI

bioinformatics↗