Electronic cigarette (e-cig) vaping has increased in the past decade in the US, and e-cig use is misleadingly marketed as a safe cessation for quitting smoking. The main constituents in e-liquid are ...humectants, such as propylene glycol (PG) and vegetable glycerine (VG), but different flavoring chemicals are also used. However, the toxicology profile of flavored e-cigs in the pulmonary tract is lacking. We hypothesized that menthol and tobacco-flavored e-cig (nicotine-free) exposure results in inflammatory responses and dysregulated repair in lung fibroblast and epithelium.
We exposed lung fibroblast (HFL-1) and epithelium (BEAS-2B) to Air, PG/VG, menthol flavored, or tobacco-flavored e-cig, and determined the cytotoxicity, inflammation, and wound healing ability in 2D cells and 3D microtissue chip models.
After exposure, HFL-1 showed decreased cell number with increased IL-8 levels in the tobacco flavor group compared to air. BEAS-2B also showed increased IL-8 secretion after PG/VG and tobacco flavor exposure, while menthol flavor exposure showed no change. Both menthol and tobacco-flavored e-cig exposure showed decreased protein abundance of type 1 collagen α 1 (COL1A1), α-smooth-muscle actin (αSMA), and fibronectin as well as decreased gene expression level of αSMA (Acta2) in HFL-1. After tobacco flavor e-cig exposure, HFL-1 mediated wound healing and tissue contractility were inhibited. Furthermore, BEAS-2B exposed to menthol flavor showed significantly decreased tight junction gene expressions, such as CDH1, OCLN, and TJP1.
Overall, tobacco-flavored e-cig exposure induces inflammation in both epithelium and fibroblasts, and tobacco-flavored e-cig inhibits wound healing ability in fibroblasts.
Electronic cigarette (e-cig) vaping is increasing rapidly in the United States, as e-cigs are considered less harmful than combustible cigarettes. However, limited research has been conducted to ...understand the possible mechanisms that mediate toxicity and pulmonary health effects of e-cigs. We hypothesized that sub-chronic e-cig exposure induces inflammatory response and dysregulated repair/extracellular matrix (ECM) remodeling, which occur through the α7 nicotinic acetylcholine receptor (nAChRα7). Adult wild-type (WT), nAChRα7 knockout (KO), and lung epithelial cell-specific KO (nAChRα7 CreCC10) mice were exposed to e-cig aerosol containing propylene glycol (PG) with or without nicotine. Bronchoalveolar lavage fluids (BALF) and lung tissues were collected to determine e-cig induced inflammatory response and ECM remodeling, respectively. Sub-chronic e-cig exposure with nicotine increased inflammatory cellular influx of macrophages and T-lymphocytes including increased pro-inflammatory cytokines in BALF and increased SARS-Cov-2 Covid-19 ACE2 receptor, whereas nAChRα7 KO mice show reduced inflammatory responses associated with decreased ACE2 receptor. Interestingly, matrix metalloproteinases (MMPs), such as MMP2, MMP8 and MMP9, were altered both at the protein and mRNA transcript levels in female and male KO mice, but WT mice exposed to PG alone showed a sex-dependent phenotype. Moreover, MMP12 was increased significantly in male mice exposed to PG with or without nicotine in a nAChRα7-dependent manner. Additionally, sub-chronic e-cig exposure with or without nicotine altered the abundance of ECM proteins, such as collagen and fibronectin, significantly in a sex-dependent manner, but without the direct role of nAChRα7 gene. Overall, sub-chronic e-cig exposure with or without nicotine affected lung inflammation and repair responses/ECM remodeling, which were mediated by nAChRα7 in a sex-dependent manner.
A non-parametric Bayesian factor model is proposed for joint analysis of multi-platform genomics data. The approach is based on factorizing the latent space (feature space) into a shared component ...and a data-specific component with the dimensionality of these components (spaces) inferred via a beta-Bernoulli process. The proposed approach is demonstrated by jointly analyzing gene expression/copy number variations and gene expression/methylation data for ovarian cancer patients, showing that the proposed model can potentially uncover key drivers related to cancer.
Availability and implementation: The source code for this model is written in MATLAB and has been made publicly available at https://sites.google.com/site/jointgenomics/
Contact:
catherine.ll.zheng@gmail.com
Supplementary information:
Supplementary data are available at Bioinformatics online.
Gaussian factor models have proven widely useful for parsimoniously characterizing dependence in multivariate data. There is rich literature on their extension to mixed categorical and continuous ...variables, using latent Gaussian variables or through generalized latent trait models accommodating measurements in the exponential family. However, when generalizing to non-Gaussian measured variables, the latent variables typically influence both the dependence structure and the form of the marginal distributions, complicating interpretation and introducing artifacts. To address this problem, we propose a novel class of Bayesian Gaussian copula factor models that decouple the latent factors from the marginal distributions. A semiparametric specification for the marginals based on the extended rank likelihood yields straightforward implementation and substantial computational gains. We provide new theoretical and empirical justifications for using this likelihood in Bayesian inference. We propose new default priors for the factor loadings and develop efficient parameter-expanded Gibbs sampling for posterior computation. The methods are evaluated through simulations and applied to a dataset in political science. The models in this article are implemented in the R package bfa (available from http://stat.duke.edu/jsm38/software/bfa). Supplementary materials for this article are available online.
Many steps in gene expression and mRNA biosynthesis are coupled to transcription elongation and organized through the C-terminal domain (CTD) of the large subunit of RNA polymerase II (RNAPII). We ...showed recently that Spt6, a transcription elongation factor and histone H3 chaperone, binds to the Ser2P CTD and recruits Iws1 and the REF1/Aly mRNA export adaptor to facilitate mRNA export. Here we show that Iws1 also recruits the HYPB/Setd2 histone methyltransferase to the RNAPII elongation complex and is required for H3K36 trimethylation (H3K36me3) across the transcribed region of the c-Myc, HIV-1, and PABPC1 genes in vivo. Interestingly, knockdown of either Iws1 or HYPB/Setd2 also enhanced H3K27me3 at the 5' end of the PABPC1 gene, and depletion of Iws1, but not HYPB/Setd2, increased histone acetylation across the coding regions at the HIV-1 and PABPC1 genes in vivo. Knockdown of HYPB/Setd2, like Iws1, induced bulk HeLa poly(A)+ mRNAs to accumulate in the nucleus. In vitro, recombinant Spt6 binds selectively to a stretch of uninterrupted consensus repeats located in the N-terminal half of the CTD and recruits Iws1. Thus Iws1 connects two distinct CTD-binding proteins, Spt6 and HYPB/Setd2, in a megacomplex that affects mRNA export as well as the histone modification state of active genes.
Molecular clock REV-ERBα is central to regulating lung injuries, and decreased REV-ERBα abundance mediates sensitivity to pro-fibrotic insults and exacerbates fibrotic progression. In this study, we ...determine the role of REV-ERBα in fibrogenesis induced by bleomycin and Influenza A virus (IAV). Bleomycin exposure decreases the abundance of REV-ERBα, and mice dosed with bleomycin at night display exacerbated lung fibrogenesis. Rev-erbα agonist (SR9009) treatment prevents bleomycin induced collagen overexpression in mice. Rev-erbα global heterozygous (Rev-erbα Het) mice infected with IAV showed augmented levels of collagens and lysyl oxidases compared with WT-infected mice. Furthermore, Rev-erbα agonist (GSK4112) prevents collagen and lysyl oxidase overexpression induced by TGFβ in human lung fibroblasts, whereas the Rev-erbα antagonist exacerbates it. Overall, these results indicate that loss of REV-ERBα exacerbates the fibrotic responses by promoting collagen and lysyl oxidase expression, whereas Rev-erbα agonist prevents it. This study provides the potential of Rev-erbα agonists in the treatment of pulmonary fibrosis.
HMG-CoA reductase inhibitors (or "statins") are important and commonly used medications to lower cholesterol and prevent cardiovascular disease. Nearly half of patients stop taking statin medications ...one year after they are prescribed leading to higher cholesterol, increased cardiovascular risk, and costs due to excess hospitalizations. Identifying which patients are at highest risk for not adhering to long-term statin therapy is an important step towards individualizing interventions to improve adherence. Electronic health records (EHR) are an increasingly common source of data that are challenging to analyze but have potential for generating more accurate predictions of disease risk. The aim of this study was to build an EHR based model for statin adherence and link this model to biologic and clinical outcomes in patients receiving statin therapy. We gathered EHR data from the Military Health System which maintains administrative data for active duty, retirees, and dependents of the United States armed forces military that receive health care benefits. Data were gathered from patients prescribed their first statin prescription in 2005 and 2006. Baseline billing, laboratory, and pharmacy claims data were collected from the two years leading up to the first statin prescription and summarized using non-negative matrix factorization. Follow up statin prescription refill data was used to define the adherence outcome (> 80 percent days covered). The subsequent factors to emerge from this model were then used to build cross-validated, predictive models of 1) overall disease risk using coalescent regression and 2) statin adherence (using random forest regression). The predicted statin adherence for each patient was subsequently used to correlate with cholesterol lowering and hospitalizations for cardiovascular disease during the 5 year follow up period using Cox regression. The analytical dataset included 138 731 individuals and 1840 potential baseline predictors that were reduced to 30 independent EHR "factors". A random forest predictive model taking patient, statin prescription, predicted disease risk, and the EHR factors as potential inputs produced a cross-validated c-statistic of 0.736 for classifying statin non-adherence. The addition of the first refill to the model increased the c-statistic to 0.81. The predicted statin adherence was independently associated with greater cholesterol lowering (correlation = 0.14, p < 1e-20) and lower hospitalization for myocardial infarction, coronary artery disease, and stroke (hazard ratio = 0.84, p = 1.87E-06). Electronic health records data can be used to build a predictive model of statin adherence that also correlates with statins' cardiovascular benefits.
pathway-based classification of human breast cancer Gatza, Michael L; Lucas, Joseph E; Barry, William T ...
Proceedings of the National Academy of Sciences - PNAS,
04/2010, Volume:
107, Issue:
15
Journal Article
Peer reviewed
Open access
The hallmark of human cancer is heterogeneity, reflecting the complexity and variability of the vast array of somatic mutations acquired during oncogenesis. An ability to dissect this heterogeneity, ...to identify subgroups that represent common mechanisms of disease, will be critical to understanding the complexities of genetic alterations and to provide a framework to develop rational therapeutic strategies. Here, we describe a classification scheme for human breast cancer making use of patterns of pathway activity to build on previous subtype characterizations using intrinsic gene expression signatures, to provide a functional interpretation of the gene expression data that can be linked to therapeutic options. We show that the identified subgroups provide a robust mechanism for classifying independent samples, identifying tumors that share patterns of pathway activity and exhibit similar clinical and biological properties, including distinct patterns of chromosomal alterations that were not evident in the heterogeneous total population of tumors. We propose that this classification scheme provides a basis for understanding the complex mechanisms of oncogenesis that give rise to these tumors and to identify rational opportunities for combination therapies.
Abstract Introduction The Alzheimer's Disease Research Summits of 2012 and 2015 incorporated experts from academia, industry, and nonprofit organizations to develop new research directions to ...transform our understanding of Alzheimer's disease (AD) and propel the development of critically needed therapies. In response to their recommendations, big data at multiple levels are being generated and integrated to study network failures in disease. We used metabolomics as a global biochemical approach to identify peripheral metabolic changes in AD patients and correlate them to cerebrospinal fluid pathology markers, imaging features, and cognitive performance. Methods Fasting serum samples from the Alzheimer's Disease Neuroimaging Initiative (199 control, 356 mild cognitive impairment, and 175 AD participants) were analyzed using the AbsoluteIDQ-p180 kit. Performance was validated in blinded replicates, and values were medication adjusted. Results Multivariable-adjusted analyses showed that sphingomyelins and ether-containing phosphatidylcholines were altered in preclinical biomarker-defined AD stages, whereas acylcarnitines and several amines, including the branched-chain amino acid valine and α-aminoadipic acid, changed in symptomatic stages. Several of the analytes showed consistent associations in the Rotterdam, Erasmus Rucphen Family, and Indiana Memory and Aging Studies. Partial correlation networks constructed for Aβ1–42 , tau, imaging, and cognitive changes provided initial biochemical insights for disease-related processes. Coexpression networks interconnected key metabolic effectors of disease. Discussion Metabolomics identified key disease-related metabolic changes and disease-progression-related changes. Defining metabolic changes during AD disease trajectory and its relationship to clinical phenotypes provides a powerful roadmap for drug and biomarker discovery.
Cigarette smoke (CS) is the main etiological factor in the pathogenesis of emphysema/chronic obstructive pulmonary disease (COPD), which is associated with abnormal epithelial-mesenchymal transition ...(EMT). Previously, we have shown an association among circadian rhythms, CS-induced lung inflammation, and nuclear heme receptor α (REV-ERBα), acting as an antiinflammatory target in both pulmonary epithelial cells and fibroblasts. We hypothesized that molecular clock REV-ERBα plays an important role in CS-induced circadian dysfunction and EMT alteration. C57BL/6J WT and REV-ERBα heterozygous (Het) and -KO mice were exposed to CS for 30 days (subchronic) and 4 months (chronic), and WT mice were exposed to CS for 10 days with or without REV-ERBα agonist (SR9009) administration. Subchronic/chronic CS exposure caused circadian disruption and dysregulated EMT in the lungs of WT and REV-ERBα-KO mice; both circadian and EMT dysregulation were exaggerated in the REV-ERBα-KO condition. REV-ERBα agonist, SR9009 treatment reduced acute CS-induced inflammatory response and abnormal EMT in the lungs. Moreover, REV-ERBα agonist (GSK4112) inhibited TGF-β/CS-induced fibroblast differentiation in human fetal lung fibroblast 1 (HFL-1). Thus, CS-induced circadian gene alterations and EMT activation are mediated through a Rev-erbα-dependent mechanism, which suggests activation of REV-ERBα as a novel therapeutic approach for smoking-induced chronic inflammatory lung diseases.
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