Daily Respiratory Research Analysis

N6-methyladenosine (m6A) epitranscriptomic modifications play crucial roles in regulating both host and viral gene expression. Here, we revealed a novel mechanism by which paramyxoviruses exploit host m6A machinery to simultaneously enhance viral replication and suppress host immunity. Our results demonstrated that the viral matrix protein (M) of bovine parainfluenza virus type 3 (BPIV3) binds to the methyltransferase domain of METTL3 in the nucleus and facilitates its translocation to the cytoplasm through an exportin-1-dependent pathway. This mechanism is conserved across multiple paramyxoviruses, including human parainfluenza virus type 3, Sendai virus, Nipah virus, and measles virus, suggesting an evolutionarily conserved viral strategy. The relocated METTL3 catalyzes m6A modification at specific sites within viral nucleocapsid protein (N) mRNA, significantly enhancing its stability and protein expression. Using reverse genetics, we generated recombinant viruses harbouring mutations at these m6A acceptor sites, which exhibited markedly attenuated viral replication, confirming the critical role of these epitranscriptomic marks in the viral life cycle. Rescue experiments demonstrated that the expression of exogenous N protein partially restored the viral titer and concomitant genome/antigenome synthesis in m6A site mutant, indicating that reduced N protein abundance represents a key mechanism underlying impaired viral replication. Furthermore, M protein-mediated depletion of nuclear METTL3 significantly reduces m6A modification of host IFN-β mRNA, resulting in diminished interferon expression and compromised antiviral responses. Supporting this mechanism, infection with viruses bearing nuclear export signal mutations that prevent METTL3 cytoplasmic translocation, maintained IFN-β mRNA m6A modification and resulted in significantly elevated IFN-β expression. These findings provide direct mechanistic evidence that paramyxoviruses utilize M-driven METTL3 relocalization as a sophisticated immune evasion strategy. Our study illuminates how paramyxoviruses strategically manipulate epitranscriptomic regulation to create an environment conducive to viral propagation, thereby advancing our understanding of virus-host interactions and identifying potential targets for antiviral therapeutics.

BACKGROUND: Accurate risk stratification of pulmonary nodules is critical for early lung cancer detection. This study aimed to improve malignancy classification and invasiveness prediction using machine learning models integrating low-dose computed tomography (LDCT) radiomics and plasma cell-free DNA (cfDNA) fragmentomics. METHODS: This multicenter study enrolled 1356 participants across discovery (n = 1147) and external validation (n = 209) cohorts. A deep learning-based imaging model processed LDCT scans for automated lung nodule detection and malignancy classification. A parallel cfDNA model analyzed four whole-genome fragmentation features: copy number variation, fragment size ratio, fragment-based methylation, and mutation context and signature. The two models were integrated via a stacked ensemble algorithm. An invasion prediction model evaluated tumor aggressiveness. FINDINGS: The integrated imaging-cfDNA model outperformed individual models, with an AUC of 0.950 (95% CI: 0.926-0.975) in the internal test set and 0.966 (95% CI: 0.940-0.991) in the external validation. The combined model's specificity increased to 0.60 (95% CI: 0.49-0.71) while maintaining 95% sensitivity, compared to specificities of 0.50 (95% CI: 0.41-0.59) and 0.33 (95% CI: 0.23-0.44) at equivalent sensitivity levels for the imaging and cfDNA models, respectively. The combined model consistently outperformed the other two models across nodule characteristics, with particular improvement for 10-20 mm and pure solid nodules. The invasion prediction model stratified lung cancers with an AUC of 0.884 (internal) and 0.880 (external). Prediction scores increased stepwise with tumor aggressiveness, from adenocarcinoma in situ to minimally invasive adenocarcinoma, and were highest for invasive adenocarcinoma. INTERPRETATION: This multimodal approach enhances pulmonary nodule risk stratification by integrating radiomic and molecular biomarkers. The model significantly improves diagnostic accuracy, potentially reducing unnecessary procedures while minimizing missed diagnoses, supporting its clinical utility in lung cancer screening. FUNDING: Noncommunicable Chronic Diseases-National Science and Technology Major Project, National Key Research & Development Programme, China National Science Foundation, the Science and Technology Planning Project of Guangzhou, and Guangzhou National Laboratory.

BACKGROUND: The benefit-risk profile of systemic corticosteroids in non-COVID-19 pneumonia and acute respiratory distress syndrome (ARDS) remains debated. PURPOSE: To assess corticosteroid effects on mortality and infection-related complications in adults with severe pneumonia or ARDS. DATA SOURCES: MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, Web of Science, ClinicalTrials.gov, and World Health Organization International Clinical Trials Registry Platform through September 2025. STUDY SELECTION: Randomized controlled trials comparing systemic corticosteroids with placebo and usual care. Primary analysis: severe pneumonia or ARDS with corticosteroids 3 mg/kg DATA EXTRACTION: Paired reviewers; consensus for disagreements. DATA SYNTHESIS: From 16 831 screened records, 20 studies (15 severe pneumonia, 5 ARDS) including 3459 participants met criteria. Low-dose, short-course corticosteroids probably reduce short-term mortality in severe pneumonia (15 studies, 2445 participants; risk ratio [RR], 0.73 [95% CI, 0.57 to 0.93]; LIMITATION: Heterogeneous pneumonia severity classification limiting subgroup precision. CONCLUSION: In severe pneumonia and ARDS, adjunct corticosteroids probably reduce short-term mortality. In severe pneumonia, they may reduce secondary shock. In both conditions, corticosteroids may have little or no effect on hospital-acquired infections. PRIMARY FUNDING SOURCE: None. (PROSPERO: CRD42024536301).