Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2023.03.14.532697

Platelet Activating Factor Activity Modulates Hyperoxic Neonatal Lung Injury Severity

Yee, A.·Kandasamy, J.·Ambalavanan, N.·Ren, C.·Halloran, B.·Olave, N.·Nicola, T.·Jilling, T.
View original source ↗Export metadata ↓

Abstract

Hyperoxia-induced inflammation contributes significantly to developmental lung injury and bronchopulmonary dysplasia (BPD) in preterm infants. Platelet activating factor (PAF) is known to be a major driver of inflammation in lung diseases such as asthma and pulmonary fibrosis, but its role in BPD has not been previously investigated. Therefore, to determine whether PAF signaling independently modulates neonatal hyperoxic lung injury and BPD pathogenesis, lung structure was assessed in 14 day-old C57BL/6 wild-type (WT) and PAF receptor knockout (PTAFR KO) mice that were exposed to 21% (normoxia) or 85% O2 (hyperoxia) from postnatal day 4. Lung morphometry showed that PTAFR KO mice had attenuated hyperoxia-induced alveolar simplification when compared to WT mice. Functional analysis of gene expression data from hyperoxia-exposed vs. normoxia-exposed lungs of WT and PTAFR KO showed that the most upregulated pathways were the hypercytokinemia/hyperchemokinemia pathway in WT mice, NAD signaling pathway in PTAFR KO mice, and agranulocyte adhesion and diapedesis as well as other pro-fibrotic pathways such as tumor microenvironment and oncostatin-M signaling in both mice strains, indicating that PAF signaling may contribute to inflammation but may not be a significant mediator of fibrotic processes during hyperoxic neonatal lung injury. Gene expression analysis also indicated increased expression of pro-inflammatory genes such as CXCL1, CCL2 and IL-6 in the lungs of hyperoxia-exposed WT mice and metabolic regulators such as HMGCS2 and SIRT3 in the lungs of PTAFR KO mice, suggesting that PAF signaling may modulate BPD risk through changes in pulmonary inflammation and/or metabolic reprogramming in preterm infants.

Explore related subjects

pathologyPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Yee, A., Kandasamy, J., Ambalavanan, N., Ren, C., Halloran, B., Olave, N., Nicola, T., Jilling, T.. 2023-03-15. Platelet Activating Factor Activity Modulates Hyperoxic Neonatal Lung Injury Severity. https://doi.org/10.1101/2023.03.14.532697

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

NAE1-Dependent Protein Neddylation Preserves Endothelial Identity and Vascular Integrity

Background: Endothelial dysfunction is a central driver of cardiovascular and inflammatory diseases, yet the post-translational mechanisms that preserve endothelial homeostasis remain incompletely understood. Protein neddylation, the covalent conjugation of a ubiquitin-like modifier, regulates diverse cellular processes, yet its physiological role in the vascular endothelium remains unknown. This study investigated whether protein neddylation is required to preserve endothelial identity and vascular homeostasis. Methods: We generated tamoxifen-inducible endothelial-specific Nae1 knockout mice to inhibit neddylation and combined bulk RNA sequencing, single-cell and single-nucleus transcriptomics, quantitative proteomics, biochemical analyses, and gain- and loss-of-function approaches to define the role of endothelial neddylation in vascular homeostasis and inflammatory injury. Results: Endothelial-specific Nae1 deletion caused rapid mortality associated with vascular leakage, platelet accumulation, inflammation, and multi-organ injury. Multi-omics analyses demonstrated profound loss of endothelial identity, characterized by suppression of core endothelial programs and activation of inflammatory, procoagulant, and pyroptotic pathways. Single-cell analyses revealed progressive endothelial dysfunction culminating in depletion of the endothelial population and remodeling of the vascular niche. Mechanistically, endothelial neddylation deficiency activated gasdermin D (GSDMD)- and gasdermin E (GSDME)-dependent pyroptosis, whereas dual inhibition of GSDMD and GSDME markedly attenuated inflammatory transcriptomic remodeling, vascular injury, hepatocyte death, immune cell infiltration, and platelet accumulation. Translational analyses demonstrated reduced endothelial neddylation in experimental endotoxemia and decreased expression of neddylation pathway components in human atherosclerosis and COVID-19 datasets. Conversely, restoration of endothelial neddylation partially reversed inflammatory endothelial transcriptomic reprogramming in vivo. Conclusions: NAE1-dependent protein neddylation is an essential regulator of endothelial identity and vascular integrity. Loss of endothelial neddylation promotes gasdermin-dependent pyroptosis and thrombo-inflammatory vascular injury, whereas restoration of the neddylation pathway mitigates inflammatory endothelial dysfunction. These findings identify endothelial neddylation as a fundamental mechanism maintaining vascular homeostasis and a potential therapeutic target for cardiovascular and inflammatory diseases.

pathology↗
bioRxiv2024

Unsupervised learning for labeling global glomerulosclerosis

Current deep learning models for classifying glomeruli in nephropathology are trained almost exclusively in a supervised manner, requiring expert-labeled images. Very little is known about the potential for unsupervised learning to overcome this bottleneck. To address this open question in a proof-of-concept, the project focused on the most fundamental classification task: globally sclerosed versus non-globally sclerosed glomeruli. The performance of clustering between the two classes was extensively studied across a variety of labeled datasets with diverse compositions and histological stains, and across the feature embeddings produced by 34 different pre-trained CNN models. As demonstrated by the study, clustering of globally and non-globally sclerosed glomeruli is generally highly feasible, yielding accuracies of over 95% in most datasets. Further work will be required to expand these experiments towards the clustering of additional glomerular lesion categories. We are convinced that these efforts (i) will open up opportunities for semi-automatic labeling approaches, thus alleviating the need for labor-intensive manual labeling, and (ii) illustrate that glomerular classification models can potentially be trained even in the absence of expert-derived class labels.

pathology↗
bioRxiv2024

Weakly Supervised Vector Quantization for Whole Slide Images Classification

Whole Slide Images (WSIs) are gigapixel, high-resolution digital scans of microscope slides, providing detailed tissue profiles for pathological analysis. Due to their gigapixel size and lack of detailed annotations, Multiple Instance Learning (MIL) becomes the primary technique for WSI analysis. However, current MIL methods for WSIs directly use embeddings extracted by a pretrained vision encoder, which are not task-specific and often exhibit high variability. To address this, we introduce a novel method, VQ-MIL, which maps the embeddings to a discrete space using weakly supervised vector quantization to refine the embeddings and reduce the variability. Additionally, the discrete embeddings from our methods provides clearer visualizations compared to other methods. Our experiments show that VQ-MIL achieves state-of-the-art classification results on two benchmark datasets. The source code is available at https://github.com/aCoalBall/VQMIL.

pathology↗