Cdc42. 4,5 In our study, the doubly lipidated R186C Cdc42 mutant was retained in the Golgi (see Fig E4 , B). This impaired its plasma membrane anchoring and resulted in actin polymerization defects and hyperactivation of NF-kB signaling.In conclusion, our study identifies a strong link between impaired cytosol/membrane cycling of Cdc42 resulting from abnormal double lipidation, partial defects in actin polymerization, and hyperactivation of NF-kB signaling, which can explain the pathophysiology of the disease. More broadly, our findings are consistent with reports in the literature that link inflammation with actin turnover 6 and membrane targeting of Rho GTPases. [7][8][9] Thus, further investigation of the various consequences of CDC42 mutations are required because these mutations arise in a broad spectrum of clinical phenotypes. Ultimately, it offers the possibility of designing specific therapeutic targeting of this pathway that is newly involved in autoinflammatory diseases.

No abstract

Gain-of-function mutations in STING1 cause the monogenic interferonopathy, SAVI, which presents with early-onset systemic inflammation, cold-induced vasculopathy and/or interstitial lung disease. We identified 5 patients (3 kindreds) with predominantly peripheral vascular disease who harbor 3 novel STING1 variants, p.H72N, p.F153V, and p.G158A. The latter two were predicted by a previous cryo-EM structure model to cause STING autoactivation. The p.H72N variant in exon 3, however, is the first SAVI-causing variant in the transmembrane linker region. Mutations of p.H72 into either charged residues or hydrophobic residues all led to dramatic loss of cGAMP response, while amino acid changes to residues with polar side chains were able to maintain the wild type status. Structural modeling of these novel mutations suggests a reconciled model of STING activation, which indicates that STING dimers can oligomerize in both open and closed states which would obliviate a high-energy 180° rotation of the ligand-binding head for STING activation, thus refining existing models of STING activation. Quantitative comparison showed that an overall lower autoactivating potential of the disease-causing mutations was associated with less severe lung disease, more severe peripheral vascular disease and the absence of a robust interferon signature in whole blood. Our findings are important in understanding genotype-phenotype correlation, designing targeted STING inhibitors and in dissecting differentially activated pathways downstream of different STING mutations.

Proteasome dysfunction can lead to autoinflammatory disease associated with elevated type I interferon (IFN-αβ) and NF-κB signaling; however, the innate immune pathway driving this is currently unknown. Here, we identified protein kinase R (PKR) as an innate immune sensor for proteotoxic stress. PKR activation was observed in cellular models of decreased proteasome function and in multiple cell types from patients with proteasome-associated autoinflammatory disease (PRAAS). Furthermore, genetic deletion or small-molecule inhibition of PKR in vitro ameliorated inflammation driven by proteasome deficiency. In vivo, proteasome inhibitor–induced inflammatory gene transcription was blunted in PKR-deficient mice compared with littermate controls. PKR also acted as a rheostat for proteotoxic stress by triggering phosphorylation of eIF2α, which can prevent the translation of new proteins to restore homeostasis. Although traditionally known as a sensor of RNA, under conditions of proteasome dysfunction, PKR sensed the cytoplasmic accumulation of a known interactor, interleukin-24 (IL-24). When misfolded IL-24 egress into the cytosol was blocked by inhibition of the endoplasmic reticulum–associated degradation pathway, PKR activation and subsequent inflammatory signaling were blunted. Cytokines such as IL-24 are normally secreted from cells; therefore, cytoplasmic accumulation of IL-24 represents an internal danger-associated molecular pattern. Thus, we have identified a mechanism by which proteotoxic stress is detected, causing inflammation observed in the disease PRAAS.

Spatial transcriptomics (ST) is a powerful approach for cancers molecular and cellular characterization. Pancreatic intraepithelial neoplasia (PanIN) is a pancreatic ductal adenocarcinoma (PDAC) premalignancy diagnosed from formalin-fixed and paraffin-embedded (FFPE) specimens limiting single-cell based investigations. We developed a new FFPE ST analysis protocol for PanINs complemented with novel transfer learning approaches. The first transfer learning approach, to assign cell types to ST spots and integrate the transcriptional signatures, shows that PanINs are surrounded by PDAC cancer associated fibroblasts (CAFs) subtypes, including the rare antigen-presenting CAFs. Furthermore, most PanINs are of the classical PDAC subtype while one sample expresses cancer stem cell markers. A second transfer learning approach, to integrate ST PanIN data with PDAC scRNA-seq data, identifies a shift between inflammatory and proliferative signaling as PanINs progress to PDAC. Our data support a model of inflammatory signaling and PanIN-CAF interactions promoting premalignancy progression and PDAC immunosuppressive characteristics.

Objective. To identify novel heterozygous LPIN2 mutations in a patient with Majeed syndrome and characterize the pathomechanisms that lead to the development of sterile osteomyelitis.Methods. Targeted genetic analysis and functional studies assessing monocyte responses, macrophage differentiation, and osteoclastogenesis were conducted to compare the pathogenesis of Majeed syndrome to interleukin-1 (IL-1)-mediated diseases including neonatal-onset multisystem inflammatory disease (NOMID) and deficiency of the IL-1 receptor antagonist (DIRA).Results. A 4-year-old girl of mixed ethnic background presented with sterile osteomyelitis and elevated acute-phase reactants. She had a 17.8-kb deletion on the maternal LPIN2 allele and a splice site mutation, p.R517H, that variably spliced out exons 10 and 11 on the paternal LPIN2 allele. The patient achieved long-lasting remission receiving IL-1 blockade with canakinumab. Compared to controls, monocytes and monocyte-derived M1-like macrophages from the patient with Majeed syndrome and those with NOMID or DIRA had elevated caspase 1 activity and IL-1β secretion. In contrast, lipopolysaccharide-stimulated, monocyte-derived, M2-like macrophages from the patient with Majeed syndrome released higher levels of osteoclastogenic mediators (IL-8, IL-6, tumor necrosis factor, CCL2, macrophage inflammatory protein 1α/β, CXCL8, and CXCL1) compared to NOMID patients and healthy controls. Accelerated osteoclastogenesis in the patient with Majeed syndrome was associated with higher NFATc1 levels, enhanced JNK/MAPK, and reduced Src kinase activation, and partially responded to JNK inhibition and IL-1 (but not IL-6) blockade.Conclusion. We report 2 novel compound heterozygous disease-causing mutations in LPIN2 in an American patient with Majeed syndrome. LPIN2 deficiency drives differentiation of proinflammatory M2-like macrophages and enhances intrinsic osteoclastogenesis. This provides a model for the pathogenesis of sterile osteomyelitis which differentiates Majeed syndrome from other IL-1-mediated autoinflammatory diseases.ClinicalTrials.gov identifier: NCT02974595.

Pancreatic ductal adenocarcinoma (PDAC) is a devastating malignancy driven by a heterogeneous tumor microenvironment enriched with cancer associated fibroblasts (CAFs) that influence its overall immunosuppressive composition. We examined inflammation in PDAC by leveraging our existing patient-derived organoid (PDO) model and a novel PDO-CAF co-culture. We first identified induction of major histocompatibility complex class II expression following treatment with interferon gamma. In parallel, we collated an atlas of 6 published single-cell RNA-sequencing datasets (174,394 cells) combining 61 PDAC (142,807 cells) and 16 non-malignant samples. By combining in silico modeling and in vitro PDO co-culture, we define a gene expression pattern of inflammatory processes in epithelial tumor cells. Following computational inferences, we examined interactions between epithelial tumor cells and CAFs, focusing on VEGF-A and ITGB1 pathways. This work, integrating computational and biological approaches, highlights the value of convergence to accelerate our understanding of key drivers of PDAC.

Non-negative matrix factorization (NMF) is an unsupervised learning method well suited to high-throughput biology. Still, inferring biological processes requires additional post hoc statistics and annotation for interpretation of features learned from software packages developed for NMF implementation. Here, we aim to introduce a suite of computational tools that implement NMF and provide methods for accurate, clear biological interpretation and analysis. A generalized discussion of NMF covering its benefits, limitations, and open questions in the field is followed by three vignettes for the Bayesian NMF algorithm CoGAPS (Coordinated Gene Activity across Pattern Subsets). Each vignette will demonstrate NMF analysis to quantify cell state transitions in public domain single-cell RNA-sequencing (scRNA-seq) data of malignant epithelial cells in 25 pancreatic ductal adenocarcinoma (PDAC) tumors and 11 control samples. The first uses PyCoGAPS, our new Python interface for CoGAPS that we developed to enhance runtime of Bayesian NMF for large datasets. The second vignette steps through the same analysis using our R CoGAPS interface, and the third introduces two new cloud-based, plug-and-play options for running CoGAPS using GenePattern Notebook and Docker. By providing Python support, cloud-based computing options, and relevant example workflows, we facilitate user-friendly interpretation and implementation of NMF for single-cell analyses.