After influenza infection, lineage-negative epithelial progenitors (LNEPs) exhibit a binary response to reconstitute epithelial barriers: activating a Notch-dependent ΔNp63/cytokeratin 5 (Krt5) remodelling program or differentiating into alveolar type II cells (AEC2s). Here we show that local lung hypoxia, through hypoxia-inducible factor (HIF1α), drives Notch signalling and Krt5 basal-like cell expansion. Single-cell transcriptional profiling of human AEC2s from fibrotic lungs revealed a hypoxic subpopulation with activated Notch, suppressed surfactant protein C (SPC), and transdifferentiation toward a Krt5 basal-like state. Activated murine Krt5 LNEPs and diseased human AEC2s upregulate strikingly similar core pathways underlying migration and squamous metaplasia. While robust, HIF1α-driven metaplasia is ultimately inferior to AEC2 reconstitution in restoring normal lung function. HIF1α deletion or enhanced Wnt/β-catenin activity in Sox2 LNEPs blocks Notch and Krt5 activation, instead promoting rapid AEC2 differentiation and migration and improving the quality of alveolar repair.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5600325 | PMC |
| http://dx.doi.org/10.1038/ncb3580 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Elucidating the Causal Link Between Treg-Related Immune Traits and Atherosclerosis-Related Cardiovascular Diseases: A Bidirectional Mendelian Randomisation Analysis.
Heart Lung Circ
January 2025
Department of Cardiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China. Electronic address:
Aim: Regulatory T cells (Tregs) play a crucial role in the development and progression of atherosclerosis. However, the specific association between Treg immune traits and atherosclerosis and related cardiovascular diseases remains unclear, impeding their potential for clinical therapeutic application.
Method: Fifty-eight Treg-related immune traits were obtained from the latest summary level genome-wide association study, which included 3,757 individuals from Sardinia.
MCF-DTI: Multi-Scale Convolutional Local-Global Feature Fusion for Drug-Target Interaction Prediction.
Molecules
January 2025
School of Computer, Guangdong University of Education, Guangzhou 510310, China.
Predicting drug-target interactions (DTIs) is a crucial step in the development of new drugs and drug repurposing. In this paper, we propose a novel drug-target prediction model called MCF-DTI. The model utilizes the SMILES representation of drugs and the sequence features of targets, employing a multi-scale convolutional neural network (MSCNN) with parallel shared-weight modules to extract features from the drug side.
View Article and Find Full Text PDFStandardized Mortality Ratios (SMRs) and Radon Exposure Analysis for Lung Cancer and All-Cause Mortality in Locorotondo, Southern Italy.
Medicina (Kaunas)
December 2024
Interdisciplinary Department of Medicine, University of Bari, Piazza G. Cesare, 11, 70124 Bari, Italy.
: Radon is a known risk factor for lung cancer, and residential radon exposure is the leading cause of lung cancer in never smokers; however, in Italy, there is still a lack of public awareness regarding the risk caused by residential radon exposure. In this mortality study, which was carried out in an Italian Apulian town (Locorotondo) of the Bari province, we aimed to analyze lung cancer mortality and all-cause mortality in a population highly exposed to radon. : The study period was 1998-2021.
View Article and Find Full Text PDFPrecision in Motion Management: Long-Term Local Control and Prognostic Insights in SBRT for Oligometastatic Lung and Liver Metastases.
Cancers (Basel)
January 2025
Department of Radiotherapy, Research Centre for Digital Medicine, VUB-UZ Brussel, 1090 Brussels, Belgium.
: Inadequate dosing and respiratory motion contribute to local recurrence for oligometastatic disease (OMD). While short-term LC rates are well-documented, data on long-term LC remain limited. This study investigated long-term LC after stereotactic body radiotherapy (SBRT), using respiratory motion management techniques.
View Article and Find Full Text PDFEnhanced Lung Cancer Survival Prediction Using Semi-Supervised Pseudo-Labeling and Learning from Diverse PET/CT Datasets.
Cancers (Basel)
January 2025
BC Cancer Research Institute, Vancouver, BC V5Z 1L3, Canada.
Objective: This study explores a semi-supervised learning (SSL), pseudo-labeled strategy using diverse datasets such as head and neck cancer (HNCa) to enhance lung cancer (LCa) survival outcome predictions, analyzing handcrafted and deep radiomic features (HRF/DRF) from PET/CT scans with hybrid machine learning systems (HMLSs).
Methods: We collected 199 LCa patients with both PET and CT images, obtained from TCIA and our local database, alongside 408 HNCa PET/CT images from TCIA. We extracted 215 HRFs and 1024 DRFs by PySERA and a 3D autoencoder, respectively, within the ViSERA 1.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!