Chronic Hypersensitivity Pneumonitis, an Interstitial Lung Disease with Distinct Molecular Signatures.

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

Gene expression associations with body mass index in the Multi-Ethnic Study of Atherosclerosis

Background/objectivesObesity, defined as excessive fat accumulation that represents a health risk, is increasing in adults and children, reaching global epidemic proportions. Body mass index (BMI) correlates with body fat and future health risk, yet differs in prediction by fat distribution, across populations and by age. Nonetheless, few genetic studies of BMI have been conducted in ancestrally diverse populations. Gene expression association with BMI was assessed in the Multi-Ethnic Study of Atherosclerosis (MESA) in four self-identified race and ethnicity (SIRE) groups to identify genes associated with obesity.Subjects/methodsRNA-sequencing was performed on 1096 MESA participants (37.8% white, 24.3% Hispanic, 28.4% African American, and 9.5% Chinese American) and linear models were used to assess the association of expression from each gene for its effect on BMI, adjusting for age, sex, sequencing center, study site, five expression and four genetic principal components in each self-identified race group. Sample-size-weighted meta-analysis was performed to identify genes with BMI-associated expression across ancestry groups.ResultsWithin individual SIRE groups, there were zero to three genes whose expression is significantly (p < 1.97 × 10-6) associated with BMI. Across all groups, 45 genes were identified by meta-analysis whose expression was significantly associated with BMI, explaining 29.7% of BMI variation. The 45 genes are expressed in a variety of tissues and cell types and are enriched for obesity-related processes including erythrocyte function, oxygen binding and transport, and JAK-STAT signaling.ConclusionsWe have identified genes whose expression is significantly associated with obesity in a multi-ethnic cohort. We have identified novel genes associated with BMI as well as confirmed previously identified genes from earlier genetic analyses. These novel genes and their biological pathways represent new targets for understanding the biology of obesity as well as new therapeutic intervention to reduce obesity and improve global public health

Host methylation predicts SARS-CoV-2 infection and clinical outcome.

BackgroundSince 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.MethodsWe 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.ResultsEpigenome-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.ConclusionsIn 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

Large scale proteomic studies create novel privacy considerations.

Privacy protection is a core principle of genomic but not proteomic research. We identified independent single nucleotide polymorphism (SNP) quantitative trait loci (pQTL) from COPDGene and Jackson Heart Study (JHS), calculated continuous protein level genotype probabilities, and then applied a naïve Bayesian approach to link SomaScan 1.3K proteomes to genomes for 2812 independent subjects from COPDGene, JHS, SubPopulations and InteRmediate Outcome Measures In COPD Study (SPIROMICS) and Multi-Ethnic Study of Atherosclerosis (MESA). We correctly linked 90-95% of proteomes to their correct genome and for 95-99% we identify the 1% most likely links. The linking accuracy in subjects with African ancestry was lower (~ 60%) unless training included diverse subjects. With larger profiling (SomaScan 5K) in the Atherosclerosis Risk Communities (ARIC) correct identification was > 99% even in mixed ancestry populations. We also linked proteomes-to-proteomes and used the proteome only to determine features such as sex, ancestry, and first-degree relatives. When serial proteomes are available, the linking algorithm can be used to identify and correct mislabeled samples. This work also demonstrates the importance of including diverse populations in omics research and that large proteomic datasets (> 1000 proteins) can be accurately linked to a specific genome through pQTL knowledge and should not be considered unidentifiable

Large scale proteomic studies create novel privacy considerations

Abstract Privacy protection is a core principle of genomic but not proteomic research. We identified independent single nucleotide polymorphism (SNP) quantitative trait loci (pQTL) from COPDGene and Jackson Heart Study (JHS), calculated continuous protein level genotype probabilities, and then applied a naïve Bayesian approach to link SomaScan 1.3K proteomes to genomes for 2812 independent subjects from COPDGene, JHS, SubPopulations and InteRmediate Outcome Measures In COPD Study (SPIROMICS) and Multi-Ethnic Study of Atherosclerosis (MESA). We correctly linked 90–95% of proteomes to their correct genome and for 95–99% we identify the 1% most likely links. The linking accuracy in subjects with African ancestry was lower (~ 60%) unless training included diverse subjects. With larger profiling (SomaScan 5K) in the Atherosclerosis Risk Communities (ARIC) correct identification was > 99% even in mixed ancestry populations. We also linked proteomes-to-proteomes and used the proteome only to determine features such as sex, ancestry, and first-degree relatives. When serial proteomes are available, the linking algorithm can be used to identify and correct mislabeled samples. This work also demonstrates the importance of including diverse populations in omics research and that large proteomic datasets (> 1000 proteins) can be accurately linked to a specific genome through pQTL knowledge and should not be considered unidentifiable

Tevatron Run II combination of the effective leptonic electroweak mixing angle

International audienceDrell-Yan lepton pairs produced in the process pp¯→ℓ+ℓ-+X through an intermediate γ*/Z boson have an asymmetry in their angular distribution related to the spontaneous symmetry breaking of the electroweak force and the associated mixing of its neutral gauge bosons. The CDF and D0 experiments have measured the effective-leptonic electroweak mixing parameter sin2θefflept using electron and muon pairs selected from the full Tevatron proton-antiproton data sets collected in 2001-2011, corresponding to 9–10  fb-1 of integrated luminosity. The combination of these measurements yields the most precise result from hadron colliders, sin2θefflept=0.23148±0.00033. This result is consistent with, and approaches in precision, the best measurements from electron-positron colliders. The standard model inference of the on-shell electroweak mixing parameter sin2θW, or equivalently the W-boson mass MW, using the zfitter software package yields sin2θW=0.22324±0.00033 or equivalently, MW=80.367±0.017  GeV/c2