Rationale: Chronic hypersensitivity pneumonitis (CHP) is caused by an immune response to antigen inhalation and is characterized by variable histopathological and clinical features. A subset of subjects with CHP have usual interstitial pneumonia and appear to be clinically similar to subjects with idiopathic pulmonary fibrosis (IPF).Objectives: To determine the common and unique molecular features of CHP and IPF.Methods: Transcriptome analysis of lung samples from CHP (n = 82), IPF (n = 103), and unaffected controls (n = 103) was conducted. Differential gene expression was determined adjusting for sex, race, age, and smoking history and using false discovery rate to control for multiple comparisons.Measurements and Main Results: When compared with controls, we identified 413 upregulated and 317 downregulated genes in CHP and 861 upregulated and 322 downregulated genes in IPF.Concordantly upregulated or downregulated genes in CHP and IPF were related to collagen catabolic processes and epithelial development, whereas genes specific to CHP (differentially expressed in CHP when compared with control and not differentially expressed in IPF) were related to chemokine-mediated signaling and immune responsiveness. Using weighted gene coexpression network analysis, we found that among subjects with CHP, genes involved in adaptive immunity or epithelial cell development were associated with improved or reduced lung function, respectively, and that MUC5B expression was associated with epithelial cell development. MUC5B expression was also associated with lung fibrosis and honeycombing.Conclusions: Gene expression analysis of CHP and IPF identified signatures common to CHP and IPF, as well as genes uniquely expressed in CHP. Select modules of gene expression are characterized by distinct clinical and pathological features of CHP.

Background Since the onset of the SARS-CoV-2 pandemic, most clinical testing has focused on RT-PCR1. Host epigenome manipulation post coronavirus infection2–4 suggests that DNA methylation signatures may differentiate patients with SARS-CoV-2 infection from uninfected individuals, and help predict COVID-19 disease severity, even at initial presentation. Methods We customized Illumina’s Infinium MethylationEPIC array to enhance immune response detection and profiled peripheral blood samples from 164 COVID-19 patients with longitudinal measurements of disease severity and 296 patient controls. Results Epigenome-wide association analysis revealed 13,033 genome-wide significant methylation sites for case-vs-control status. Genes and pathways involved in interferon signaling and viral response were significantly enriched among differentially methylated sites. We observe highly significant associations at genes previously reported in genetic association studies (e.g.IRF7, OAS1). Using machine learning techniques, models built using sparse regression yielded highly predictive findings: cross-validated best fit AUC was 93.6% for case-vs-control status, and 79.1%, 80.8%, and 84.4% for hospitalization, ICU admission, and progression to death, respectively. Conclusions In summary, the strong COVID-19-specific epigenetic signature in peripheral blood driven by key immune-related pathways related to infection status, disease severity, and clinical deterioration provides insights useful for diagnosis and prognosis of patients with viral infections.

Our study highlights the significance of epigenetic marks in lung immune response in granulomatous lung disease.

TEF, IVY, and DAS conceived and designed the study. RB, ADW, FM and SMH collected the data. IRK, RB, JC, and FM analyzed the data. MR performed clinical phenotyping of the subjects.

Because errors at DNA level power pathogen evolution, a systematic understanding of the rate and molecular spectrum of mutations could guide the avoidance and treatment of infectious diseases. We thus accumulated tens of thousands of spontaneous mutations in 768 repeatedly bottlenecked lineages of 18 strains from various geographical sites, temporal spread, and genetic backgrounds. Entailing over ∼1.36 million generations, the resultant data yield an average mutation rate of ∼0.0005 per genome per generation, with significant within-species variation. This is one of the lowest bacterial mutation rates reported, giving direct support for a high genome stability in this pathogen resulting from high DNA-mismatch-repair efficiency and replication-machinery fidelity. Pathogenicity genes do not exhibit an accelerated mutation rate, and thus elevated mutation rates may not be the major determinant for the diversification of toxin and secretion systems. Intriguingly, a low error rate at the transcript level is not observed, suggesting distinct fidelity of the replication and transcription machineries. This work urges more attention on the most basic evolutionary processes of even the best-known human pathogens, and deepens the understanding of their genome evolution.