Integrative single-cell transcriptome analysis provides new insights into post-COVID-19 pulmonary fibrosis and potential therapeutic targets.

The global COVID-19 pandemic caused by the severe acute respiratory syndrome coronavirus 2 virus has resulted in a significant number of patients experiencing persistent symptoms, including post-COVID pulmonary fibrosis (PCPF). This study aimed to identify novel therapeutic targets for PCPF using single-cell RNA-sequencing data from lung tissues of COVID-19 patients, idiopathic pulmonary fibrosis (IPF) patients, and a rat transforming growth factor beta-1-induced fibrosis model treated with antifibrotic drugs. Patients with COVID-19 had lower alveolar macrophage counts than healthy controls, whereas patients with COVID-19 and IPF presented with elevated monocyte-derived macrophage counts. A comparative transcriptome analysis showed that macrophages play a crucial role in IPF and COVID-19 development and progression, and fibrosis- and inflammation-associated genes were upregulated in both conditions. Functional enrichment analysis revealed the upregulation of inflammation and proteolysis and the downregulation of ribosome biogenesis. Cholesterol efflux and glycolysis were augmented in both macrophage types. The study suggests that antifibrotic drugs may reverse critical lung fibrosis mediators in COVID-19. The results help clarify the molecular mechanisms underlying pulmonary fibrosis in patients with severe COVID-19 and IPF and highlight the potential efficacy of antifibrotic drugs in COVID-19 therapy. Collectively, all these findings may have significant implications for the development of new treatment strategies for PCPF.

Next-generation large-scale binary protein interaction network for Drosophila melanogaster.

Generating reference maps of interactome networks illuminates genetic studies by providing a protein-centric approach to finding new components of existing pathways, complexes, and processes. We apply state-of-the-art methods to identify binary protein-protein interactions (PPIs) for Drosophila melanogaster. Four all-by-all yeast two-hybrid (Y2H) screens of > 10,000 Drosophila proteins result in the 'FlyBi' dataset of 8723 PPIs among 2939 proteins. Testing subsets of data from FlyBi and previous PPI studies using an orthogonal assay allows for normalization of data quality; subsequent integration of FlyBi and previous data results in an expanded binary Drosophila reference interaction network, DroRI, comprising 17,232 interactions among 6511 proteins. We use FlyBi data to generate an autophagy network, then validate in vivo using autophagy-related assays. The deformed wings (dwg) gene encodes a protein that is both a regulator and a target of autophagy. Altogether, these resources provide a foundation for building new hypotheses regarding protein networks and function.

Extracellular Vesicle Proteomes Shed Light on the Evolutionary, Interactive, and Functional Divergence of Their Biogenesis Mechanisms.

Extracellular vesicles (EVs) are membranous structures containing bioactive molecules, secreted by most cells into the extracellular environment. EVs are classified by their biogenesis mechanisms into two major subtypes: ectosomes (enriched in large EVs; lEVs), budding directly from the plasma membrane, which is common in both prokaryotes and eukaryotes, and exosomes (enriched in small EVs; sEVs) generated through the multivesicular bodies via the endomembrane system, which is unique to eukaryotes. Even though recent proteomic analyses have identified key proteins associated with EV subtypes, there has been no systematic analysis, thus far, to support the general validity and utility of current EV subtype separation methods, still largely dependent on physical properties, such as vesicular size and sedimentation. Here, we classified human EV proteomic datasets into two main categories based on distinct centrifugation protocols commonly used for isolating sEV or lEV fractions. We found characteristic, evolutionarily conserved profiles of sEV and lEV proteins linked to their respective biogenetic origins. This may suggest that the evolutionary trajectory of vesicular proteins may result in a membership bias toward specific EV subtypes. Protein-protein interaction (PPI) network analysis showed that vesicular proteins formed distinct clusters with proteins in the same EV fraction, providing evidence for the existence of EV subtype-specific protein recruiters. Moreover, we identified functional modules enriched in each fraction, including multivesicular body sorting for sEV, and mitochondria cellular respiration for lEV proteins. Our analysis successfully captured novel features of EVs embedded in heterogeneous proteomics studies and suggests specific protein markers and signatures to be used as quality controllers in the isolation procedure for subtype-enriched EV fractions.