Biology subjects

Dudek, S.

Publications and source records attributed to Dudek, S..

7 recordsLinked to original sources

bioRxiv2026

Mechanical stress-induced release of mitochondrial double-stranded RNA activates RIG-I to drive osteogenic differentiation in aortic disease

Background: Gain-of-function mutations in RIG-I and MDA5 (RIG-I-like receptors, RLR) cause Singleton-Merten syndrome, an interferonopathy with early aortic calcification. Here we set out to study a possible involvement of RIG-I-like receptor signaling in the formation of aortic valve stenosis in general. Methods and results: We investigated whether mechanical stress-induced RLR activation promotes osteogenic differentiation and calcification of primary human aortic cells, whether such RLR activation drives the development of aortic valve stenosis in vivo, and whether inhibition of downstream JAK/STAT activation ameliorates aortic valve stenosis. We found that activation of primary human aortic interstitial cells by exogenous RIG-I- and MDA5 agonists induced a type I interferon response and an osteogenic program including increased expression of BMP-2 and BSP2, and promoted calcification. These effects were RIG-I/MDA5 or MAVS-dependent, as assessed by CRISPR/Cas9 knockouts. The stress-granule protein G3BP1 and the Wnt receptor FZD5 were induced downstream of RLR activation and were required for osteogenic gene expression and calcification. To assess a potential endogenous source of RIG-I activation, mechanical stretch mimicking valvular tissue stress was applied in cell cuture, which induced a significant RIG-I-dependent interferon response. Sequencing of highly purified preparations of double-stranded RNA from stretched cells demonstrated a predominant mitochondrial origin of double-stranded RNA, and depleting mitochondrial RNA abolished the stretch-dependent response. The JAK/STAT inhibitor ruxolitinib suppressed the RIG-I-induced response including the expression of osteogenic factors in vitro. In a murine wire-injury model of aortic stenosis, MAVS deficiency attenuated valvular morphological changes and improved hemodynamic parameters. Ruxolitinib attenuated hemodynamic progression, prevented aortic stenosis development, and improved survival. Conclusions: Altogether, our results demonstrate that the mechanical-stress-induced release of mitochondrial double-stranded RNA activates RIG-I signaling in aortic interstitial cells, thereby driving osteogenic differentiation and calcification via a MAVS- and G3BP1-dependent pathway. JAK/STAT inhibition with ruxolitinib mitigates this process in a mouse model of aortic valve stenosis, identifying a candidate therapeutic strategy.

cell biology↗

bioRxiv2026

Innate Immune Function of Neutrophil Cytoplasts Generated Post-Vital NETosis

The migration of polymorphonuclear neutrophils (PMN) into the site of infection such as lungs during pneumonia is a canonical feature of innate immunity. Formation of neutrophil-derived extracellular traps (NETs), web-like strands of varying lengths comprising DNA, histones, elastase, and myeloperoxidase, is an important determinant of PMN-mediated innate immunity. NETs form in microvessels, entrap bacteria and effete matter, and dampen PMN-mediated inflammatory injury at specific sites. However, studies have largely focused on NET release secondary to lytic NETosis and lysis of PMN. Far less is known about vital NETosis occurring in the absence of PMN rupture. As vital NETosis is characterized by generation of anuclear PMN termed cytoplasts (PMNcyto), we addressed the function of PMNcyto as a critical determinant of PMN-mediated innate immunity. Studies were made in mice challenged with live Pseudomonas aeruginosa (PA) i.t. to induce fulminant pneumonia characterized by tissue injury in which we determined the role of generated PMNcyto population. Using Tomato Red (tDTomato) transgenic mice to mark PMN, we observed PA pneumonia induced PMN transmigration leading to PMNcyto generation in the airspace. In contrast, PMNcyto transmigration, was minimal. PMNcyto accumulating in lung tissue actively phagocytosed and killed PA. Instillation of ex vivo generated PMNcyto also prevented PA-induced inflammatory lung injury and reduced mortality as compared to control mice. We demonstrated that the salutary effects of PMNcyto required functional microchondria. Proteomic analysis revealed that PMNcyto retained bactericidal and ROS generating pathways, consistent with an intact plasma membrane. Genetic deletion of peptidyl arginine deaminase 4 (PAD4), which mediates histone citrullination and promotes NETosis, facilitates PMNcyto generation and thereby abrogated pneumonia-induced mortality. Thus, we have identified the crucial host defense function of PMNcyto generated post-vital NETosis, suggesting that PMNcyto hold promise as cell based anti-bacterial therapy in pneumonia.

immunology↗

bioRxiv2024

Endothelial ERG programs neutrophil transcriptome for sustained anti-inflammatory vascular niche

Neutrophils (PMNs) reside as a marginated pool within the vasculature, ready for deployment during infection. However, how endothelial cells (ECs) control PMN extravasation and activation to strengthen tissue homeostasis remains ill-defined. Here, we found that the vascular ETS-related gene (ERG) is a generalized mechanism regulating PMN activity in preclinical tissue injury models and human patients. We show that ERG loss in ECs rewired PMN-transcriptome, enriched for genes associated with the CXCR2-CXCR4 signaling. Rewired PMNs compromise mice survival after pneumonia and induced lung vascular inflammatory injury following adoptive transfer into naive mice, indicating their longevity and inflammatory activity memory. Mechanistically, EC-ERG restricted PMN extravasation and activation by upregulating the deubiquitinase A20 and downregulating the NF{kappa}B-IL8 cascade. Rescuing A20 in EC-Erg-/- endothelium or suppressing PMN-CXCR2 signaling rescued EC control of PMN activation. Findings deepen our understanding of EC control of PMN-mediated inflammation, offering potential avenues for targeting various inflammatory diseases. HighlightsO_LIERG regulates trans-endothelial neutrophil (PMN) extravasation, retention, and activation C_LIO_LILoss of endothelial (EC) ERG rewires PMN-transcriptome C_LIO_LIAdopted transfer of rewired PMNs causes inflammation in a naive mouse C_LIO_LIERG transcribes A20 and suppresses CXCR2 function to inactivate PMNs C_LI In brief/blurbThe authors investigated how vascular endothelial cells (EC) control polymorphonuclear neutrophil (PMN) extravasation, retention, and activation to strengthen tissue homeostasis. They showed that EC-ERG controls PMN transcriptome into an anti-adhesive and anti-inflammatory lineage by synthesizing A20 and suppressing PMNs-CXCR2 signaling, defining EC-ERG as a target for preventing neutrophilic inflammatory injury.

immunology↗

bioRxiv2023

Organism-Wide Analysis of Sepsis Reveals Mechanisms of Systemic Inflammation

Sepsis is a systemic response to infection with life-threatening consequences. Our understanding of the impact of sepsis across organs of the body is rudimentary. Here, using mouse models of sepsis, we generate a dynamic, organism-wide map of the pathogenesis of the disease, revealing the spatiotemporal patterns of the effects of sepsis across tissues. These data revealed two interorgan mechanisms key in sepsis. First, we discover a simplifying principle in the systemic behavior of the cytokine network during sepsis, whereby a hierarchical cytokine circuit arising from the pairwise effects of TNF plus IL-18, IFN-{gamma}, or IL-1{beta} explains half of all the cellular effects of sepsis on 195 cell types across 9 organs. Second, we find that the secreted phospholipase PLA2G5 mediates hemolysis in blood, contributing to organ failure during sepsis. These results provide fundamental insights to help build a unifying mechanistic framework for the pathophysiological effects of sepsis on the body.

immunology↗

bioRxiv2018

Collective feature selection to identify crucial epistatic variants

BackgroundMachine learning methods have gained popularity and practicality in identifying linear and non-linear effects of variants associated with complex disease/traits. Detection of epistatic interactions still remains a challenge due to the large number of features and relatively small sample size as input, thus leading to the so-called \"short fat data\" problem. The efficiency of machine learning methods can be increased by limiting the number of input features. Thus, it is very important to perform variable selection before searching for epistasis. Many methods have been evaluated and proposed to perform feature selection, but no single method works best in all scenarios. We demonstrate this by conducting two separate simulation analyses to evaluate the proposed collective feature selection approach.\n\nResultsThrough our simulation study we propose a collective feature selection approach to select features that are in the \"union\" of the best performing methods. We explored various parametric, non-parametric, and data mining approaches to perform feature selection. We choose our top performing methods to select the union of the resulting variables based on a user-defined percentage of variants selected from each method to take to downstream analysis. Our simulation analysis shows that non-parametric data mining approaches, such as MDR, may work best under one simulation criteria for the high effect size (penetrance) datasets, while non-parametric methods designed for feature selection, such as Ranger and Gradient boosting, work best under other simulation criteria. Thus, using a collective approach proves to be more beneficial for selecting variables with epistatic effects also in low effect size datasets and different genetic architectures. Following this, we applied our proposed collective feature selection approach to select the top 1% of variables to identify potential interacting variables associated with Body Mass Index (BMI) in ~44,000 samples obtained from Geisingers MyCode Community Health Initiative (on behalf of DiscovEHR collaboration).\n\nConclusionsIn this study, we were able to show that selecting variables using a collective feature selection approach could help in selecting true positive epistatic variables more frequently than applying any single method for feature selection via simulation studies. We were able to demonstrate the effectiveness of collective feature selection along with a comparison of many methods in our simulation analysis. We also applied our method to identify non-linear networks associated with obesity.

bioinformatics↗

bioRxiv2017

CA2 Neuronal Activity Controls Hippocampal Oscillations and Social Behavior

Hippocampal oscillations arise from coordinated activity among distinct populations of neurons and are associated with cognitive functions and behaviors. Although much progress has been made toward identifying the relative contribution of specific neuronal populations in hippocampal oscillations, far less is known about the role of hippocampal area CA2, which is thought to support social aspects of episodic memory. Furthermore, the little existing evidence on the role of CA2 in oscillations has led to conflicting conclusions. Therefore, we sought to identify the specific contribution of CA2 pyramidal neurons to brain oscillations using a controlled experimental system. We used excitatory and inhibitory DREADDs in transgenic mice to acutely and reversibly manipulate CA2 pyramidal cell activity. Here, we report on the role of CA2 in hippocampal-prefrontal cortical network oscillations and social behavior. We found that excitation or inhibition of CA2 pyramidal cells bidirectionally regulated hippocampal and prefrontal cortical low gamma oscillations and inversely modulated hippocampal ripple oscillations. Further, CA2 inhibition impaired social approach behavior. These findings support a role for CA2 in low gamma generation and ripple modulation within the hippocampus and underscore the importance of CA2 neuronal activity in extrahippocampal oscillations and social behavior.

neuroscience↗

bioRxiv2017

A simulation study investigating power estimates in Phenome-Wide Association Studies

BackgroundPhenome-wide association studies (PheWAS) are a high-throughput approach to evaluate comprehensive associations between genetic variants and a wide range of phenotypic measures. PheWAS has varying sample sizes for quantitative traits, and variable numbers of cases and controls for binary traits across the many phenotypes of interest, which can affect the statistical power to detect associations. The motivation of this study is to investigate the various parameters which affect the estimation of statistical power in PheWAS, including sample size, case-control ratio, minor allele frequency, and disease penetrance.\n\nResultsWe performed a PheWAS simulation study, where we investigated variations in statistical power based on different parameters, such as overall sample size, number of cases, case-control ratio, minor allele frequency, and disease penetrance. The simulation was performed on both binary and quantitative phenotypic measures. Our simulation on binary traits suggests that the number of cases has more impact than the case to control ratio; also, we found that a sample size of 200 cases or more maintains the statistical power to identify associations for common variants. For quantitative traits, a sample size of 1000 or more individuals performed best in the power calculations. We focused on common genetic variants (MAF>0.01) in this study; however, in future studies, we will be extending this effort to perform similar simulations on rare variants.\n\nConclusionsThis study provides a series of PheWAS simulation analyses that can be used to estimate statistical power for some potential scenarios. These results can be used to provide guidelines for appropriate study design for future PheWAS analyses.

genomics↗