Defect Detection in Printed Circuit Boards Using Semi-Supervised Learning.

Defect inspection is essential in the semiconductor industry to fabricate printed circuit boards (PCBs) with minimum defect rates. However, conventional inspection systems are labor-intensive and time-consuming. In this study, a semi-supervised learning (SSL)-based model called PCB_SS was developed. It was trained using labeled and unlabeled images under two different augmentations. Training and test PCB images were acquired using automatic final vision inspection systems. The PCB_SS model outperformed a completely supervised model trained using only labeled images (PCB_FS). The performance of the PCB_SS model was more robust than that of the PCB_FS model when the number of labeled data is limited or comprises incorrectly labeled data. In an error-resilience test, the proposed PCB_SS model maintained stable accuracy (error increment of less than 0.5%, compared with 4% for PCB_FS) for noisy training data (with as much as 9.0% of the data labeled incorrectly). The proposed model also showed superior performance when comparing machine-learning and deep-learning classifiers. The unlabeled data utilized in the PCB_SS model helped with the generalization of the deep-learning model and improved its performance for PCB defect detection. Thus, the proposed method alleviates the burden of the manual labeling process and provides a rapid and accurate automatic classifier for PCB inspections.

MoDentify: phenotype-driven module identification in metabolomics networks at different resolutions.

Summary: Associations of metabolomics data with phenotypic outcomes are expected to span functional modules, which are defined as sets of correlating metabolites that are coordinately regulated. Moreover, these associations occur at different scales, from entire pathways to only a few metabolites; an aspect that has not been addressed by previous methods. Here, we present MoDentify, a free R package to identify regulated modules in metabolomics networks at different layers of resolution. Importantly, MoDentify shows higher statistical power than classical association analysis. Moreover, the package offers direct interactive visualization of the results in Cytoscape. We present an application example using complex, multifluid metabolomics data. Due to its generic character, the method is widely applicable to other types of data. Availability and implementation: https://github.com/krumsieklab/MoDentify (vignette includes detailed workflow). Supplementary information: Supplementary data are available at Bioinformatics online.

Inhibition of the hexosamine biosynthesis pathway potentiates cisplatin cytotoxicity by decreasing BiP expression in non-small-cell lung cancer cells.

Platinum anticancer agents are essential components in chemotherapeutic regimens for non-small-cell lung cancer (NSCLC) patients ineligible for targeted therapy. However, platinum-based regimens have reached a plateau of therapeutic efficacy; therefore, it is critical to implement novel approaches for improvement. The hexosamine biosynthesis pathway (HBP), which produces amino-sugar N-acetyl-glucosamine for protein glycosylation, is important for protein function and cell survival. Here we show a beneficial effect by the combination of cisplatin with HBP inhibition. Expression of glutamine:fructose-6-phosphate amidotransferase (GFAT), the rate-limiting enzyme of HBP, was increased in NSCLC cell lines and tissues. Pharmacological inhibition of GFAT activity or knockdown of GFATimpaired cell proliferation and exerted synergistic or additive cytotoxicity to the cells treated with cisplatin. Mechanistically, GFAT positively regulated the expression of binding immunoglobulin protein (BiP; also known as glucose-regulated protein 78, GRP78), an endoplasmic reticulum chaperone involved in unfolded protein response (UPR). Suppressing GFAT activity resulted in downregulation of BiP that activated inositol-requiring enzyme 1α, a sensor protein of UPR, and exacerbated cisplatin-induced cell apoptosis. These data identify GFAT-mediated HBP as a target for improving platinum-based chemotherapy for NSCLC.

Characterization of missing values in untargeted MS-based metabolomics data and evaluation of missing data handling strategies.

BACKGROUND: Untargeted mass spectrometry (MS)-based metabolomics data often contain missing values that reduce statistical power and can introduce bias in biomedical studies. However, a systematic assessment of the various sources of missing values and strategies to handle these data has received little attention. Missing data can occur systematically, e.g. from run day-dependent effects due to limits of detection (LOD); or it can be random as, for instance, a consequence of sample preparation. METHODS: We investigated patterns of missing data in an MS-based metabolomics experiment of serum samples from the German KORA F4 cohort (n = 1750). We then evaluated 31 imputation methods in a simulation framework and biologically validated the results by applying all imputation approaches to real metabolomics data. We examined the ability of each method to reconstruct biochemical pathways from data-driven correlation networks, and the ability of the method to increase statistical power while preserving the strength of established metabolic quantitative trait loci. RESULTS: Run day-dependent LOD-based missing data accounts for most missing values in the metabolomics dataset. Although multiple imputation by chained equations performed well in many scenarios, it is computationally and statistically challenging. K-nearest neighbors (KNN) imputation on observations with variable pre-selection showed robust performance across all evaluation schemes and is computationally more tractable. CONCLUSION: Missing data in untargeted MS-based metabolomics data occur for various reasons. Based on our results, we recommend that KNN-based imputation is performed on observations with variable pre-selection since it showed robust results in all evaluation schemes.

Facilitators and barriers to students' learning in an obesity prevention graduate program.

Childhood obesity is a major public health concern with underpinnings at the individual, family, community and societal levels. The Transdisciplinary Childhood Obesity Prevention Graduate Certificate Program (TOP) is an innovative graduate-level certificate program developed to train professionals to understand and address obesity from multiple perspectives using an interprofessional education (IPE) approach. Currently, there is limited knowledge on what promotes or hinders learning in IPE approaches dealing with obesity prevention. The goal of this report is to address this gap by describing facilitators and barriers to learning in a graduate-level training program. Using a qualitative research design, semi-structured interviews were collected from 23 professional students, as part of a larger program evaluation project for TOP. Thematic analysis revealed the challenges and strengths of the program that relate specifically to: its interprofessional approach, its structure, and its activities. Interprofessional exchanges were reported to expand students' learning, but adequate interprofessional representation must be maintained, and the complexity of interprofessional collaborations must also be well-coordinated. Standardising the program structure and courses for consistency across professions, and clear communication are critical to program success. Findings add to the existing literature on what promotes effective learning in a professional obesity prevention program using an IPE approach.

Methylation in benign prostate and risk of disease progression in men subsequently diagnosed with prostate cancer.

In DNA from prostate tumors, methylation patterns in gene promoter regions can be a biomarker for disease progression. It remains unclear whether methylation patterns in benign prostate tissue--prior to malignant transformation--may provide similar prognostic information. To determine whether early methylation events predict prostate cancer outcomes, we evaluated histologically benign prostate specimens from 353 men who eventually developed prostate cancer and received "definitive" treatment [radical prostatectomy (58%) or radiation therapy (42%)]. Cases were drawn from a large hospital-based cohort of men with benign prostate biopsy specimens collected between 1990 and 2002. Risk of disease progression associated with methylation was estimated using time-to-event analyses. Average follow-up was over 5 years; biochemical recurrence (BCR) occurred in 91 cases (26%). In White men, methylation of the APC gene was associated with increased risk of BCR, even after adjusting for standard clinical risk factors for prostate cancer progression (adjusted hazard ratio (aHR) = 2.26; 95%CI 1.23-4.16). APC methylation was most strongly associated with a significant increased risk of BCR in White men with low prostate specific antigen at cohort entry (HR = 3.66; 95%CI 1.51-8.85). In additional stratified analyses, we found that methylation of the RARB gene significantly increased risk of BCR in African American cases who demonstrated methylation of at least one of the other four genes under study (HR = 3.80; 95%CI 1.07-13.53). These findings may have implications in the early identification of aggressive prostate cancer as well as reducing unnecessary medical procedures and emotional distress for men who present with markers of indolent disease.

LocTree3 prediction of localization.

The prediction of protein sub-cellular localization is an important step toward elucidating protein function. For each query protein sequence, LocTree2 applies machine learning (profile kernel SVM) to predict the native sub-cellular localization in 18 classes for eukaryotes, in six for bacteria and in three for archaea. The method outputs a score that reflects the reliability of each prediction. LocTree2 has performed on par with or better than any other state-of-the-art method. Here, we report the availability of LocTree3 as a public web server. The server includes the machine learning-based LocTree2 and improves over it through the addition of homology-based inference. Assessed on sequence-unique data, LocTree3 reached an 18-state accuracy Q18=80±3% for eukaryotes and a six-state accuracy Q6=89±4% for bacteria. The server accepts submissions ranging from single protein sequences to entire proteomes. Response time of the unloaded server is about 90 s for a 300-residue eukaryotic protein and a few hours for an entire eukaryotic proteome not considering the generation of the alignments. For over 1000 entirely sequenced organisms, the predictions are directly available as downloads. The web server is available at http://www.rostlab.org/services/loctree3.

Network-based approach for analyzing intra- and interfluid metabolite associations in human blood, urine, and saliva.

Most studies investigating human metabolomics measurements are limited to a single biofluid, most often blood or urine. An organism's biochemical pool, however, comprises complex transboundary relationships, which can only be understood by investigating metabolic interactions and physiological processes spanning multiple parts of the human body. Therefore, we here propose a data-driven network-based approach to generate an integrated picture of metabolomics associations over multiple fluids. We performed an analysis of 2251 metabolites measured in plasma, urine, and saliva, from 374 participants of the Qatar Metabolomics Study on Diabetes (QMDiab). Gaussian graphical models (GGMs) were used to estimate metabolite-metabolite interactions on different subsets of the data set. First, we compared similarities and differences of the metabolome and the association networks between the three fluids. Second, we investigated the cross-talk between the fluids by analyzing correlations occurring between them. Third, we propose a framework for the analysis of medically relevant phenotypes by integrating type 2 diabetes, sex, age, and body mass index into our networks. In conclusion, we present a generic, data-driven network-based approach for structuring and visualizing metabolite correlations within and between multiple body fluids, enabling unbiased interpretation of metabolomics multifluid data.

A systems view of type 2 diabetes-associated metabolic perturbations in saliva, blood and urine at different timescales of glycaemic control.

AIMS/HYPOTHESIS: Metabolomics has opened new avenues for studying metabolic alterations in type 2 diabetes. While many urine and blood metabolites have been associated individually with diabetes, a complete systems view analysis of metabolic dysregulations across multiple biofluids and over varying timescales of glycaemic control is still lacking. METHODS: Here we report a broad metabolomics study in a clinical setting, covering 2,178 metabolite measures in saliva, blood plasma and urine from 188 individuals with diabetes and 181 controls of Arab and Asian descent. Using multivariate linear regression we identified metabolites associated with diabetes and markers of acute, short-term and long-term glycaemic control. RESULTS: Ninety-four metabolite associations with diabetes were identified at a Bonferroni level of significance (p < 2.3 × 10(-5)), 16 of which have never been reported. Sixty-five of these diabetes-associated metabolites were associated with at least one marker of glycaemic control in the diabetes group. Using Gaussian graphical modelling, we constructed a metabolic network that links diabetes-associated metabolites from three biofluids across three different timescales of glycaemic control. CONCLUSIONS/INTERPRETATION: Our study reveals a complex network of biochemical dysregulation involving metabolites from different pathways of diabetes pathology, and provides a reference framework for future diabetes studies with metabolic endpoints.

Increased methylation of lung cancer-associated genes in sputum DNA of former smokers with chronic mucous hypersecretion.

BACKGROUND: Chronic mucous hypersecretion (CMH) contributes to COPD exacerbations and increased risk for lung cancer. Because methylation of gene promoters in sputum has been shown to be associated with lung cancer risk, we tested whether such methylation was more common in persons with CMH. METHODS: Eleven genes commonly silenced by promoter methylation in lung cancer and associated with cancer risk were selected. Methylation specific PCR (MSP) was used to profile the sputum of 900 individuals in the Lovelace Smokers Cohort (LSC). Replication was performed in 490 individuals from the Pittsburgh Lung Screening Study (PLuSS). RESULTS: CMH was significantly associated with an overall increased number of methylated genes, with SULF2 methylation demonstrating the most consistent association. The association between SULF2 methylation and CMH was significantly increased in males but not in females both in the LSC and PLuSS (OR = 2.72, 95% CI = 1.51-4.91, p = 0.001 and OR = 2.97, 95% CI = 1.48-5.95, p = 0.002, respectively). Further, the association between methylation and CMH was more pronounced among 139 male former smokers with persistent CMH compared to current smokers (SULF2; OR = 3.65, 95% CI = 1.59-8.37, p = 0.002). CONCLUSIONS: These findings demonstrate that especially male former smokers with persistent CMH have markedly increased promoter methylation of lung cancer risk genes and potentially could be at increased risk for lung cancer.

Methylation of the RARB gene increases prostate cancer risk in black Americans.

PURPOSE: Gene promoter hypermethylation may be useful as a biomarker for cancer risk in histopathologically benign prostate specimens. MATERIALS AND METHODS: We performed a nested case-control study of gene promoter methylation status for 5 genes (APC, RARB, CCND2, RASSF1 and MGMT) measured in benign biopsy specimens from 511 prostate cancer case-control pairs. We estimated the overall and race stratified risk of subsequent prostate cancer associated with methylation status. RESULTS: On race stratified analysis RARB methylation was associated with a higher cancer risk in black American men (OR 2.18, 95% CI 1.39-3.44). APC methylation was associated with an increased risk of high grade tumors (OR 2.43, 95% CI 1.20-4.90), which was higher in black than in white men (OR 3.21 vs 2.04). In cases RARB and APC gene methylation in benign prostate samples persisted in matched malignant specimens. In black cases the combined risk associated with RARB and APC methylation (OR 3.04, 95% CI 1.44-6.42) was greater than the individual risk of each gene and significantly different from that in white cases (OR 1.14, 95% CI 0.56-2.30). CONCLUSIONS: RARB gene methylation in histopathologically benign prostate samples was associated with a statistically significant increased risk of subsequent prostate cancer in black men. Methylation data on additional genes may improve risk stratification and clinical decision making algorithms for cancer screening and diagnosis.

Flavored E-cigarette product aerosols induce transformation of human bronchial epithelial cells.

OBJECTIVES: E-cigarettes are the most commonly used nicotine containing products among youth. In vitro studies support the potential for e-cigarettes to cause cellular stress in vivo; however, there have been no studies to address whether exposure to e-liquid aerosols can induce cell transformation, a process strongly associated with pre-malignancy. We examined whether weekly exposure of human bronchial epithelial cell (HBEC) lines to e-cigarette aerosols would induce transformation and concomitant changes in gene expression and promoter hypermethylation. MATERIALS AND METHODS: An aerosol delivery system exposed three HBEC lines to unflavored e-liquid with 1.2% nicotine, 3 flavored products with nicotine, or the Kentucky reference cigarette once a week for 12 weeks. Colony formation in soft agar, RNA-sequencing, and the EPIC Beadchip were used to evaluate transformation, genome-wide expression and methylation changes. RESULTS: Jamestown e-liquid aerosol induced transformation of HBEC2 and HBEC26, while unflavored and Blue Pucker transformed HBEC26. Cigarette smoke aerosol transformed HBEC4 and HBEC26 at efficiencies up to 3-fold greater than e-liquids. Transformed clones exhibited extensive reprogramming of the transcriptome with common and distinct gene expression changes observed between the cigarette and e-liquids. Transformation by e-liquids induced alterations in canonical pathways implicated in lung cancer that included axonal guidance and NRF2. Gene methylation, while prominent in cigarette-induced transformed clones, also affected hundreds of genes in HBEC2 transformed by Jamestown. Many genes with altered expression or epigenetic-mediated silencing were also affected in lung tumors from smokers. CONCLUSIONS: These studies show that exposure to e-liquid aerosols can induce a pre-malignant phenotype in lung epithelial cells. While the Food and Drug Administration banned the sale of flavored cartridge-based electric cigarettes, consumers switched to using flavored products through other devices. Our findings clearly support expanding studies to evaluate transformation potency for the major categories of e-liquid flavors to better inform risk from these complex mixtures.

A scalable software solution for anonymizing high-dimensional biomedical data.

BACKGROUND: Data anonymization is an important building block for ensuring privacy and fosters the reuse of data. However, transforming the data in a way that preserves the privacy of subjects while maintaining a high degree of data quality is challenging and particularly difficult when processing complex datasets that contain a high number of attributes. In this article we present how we extended the open source software ARX to improve its support for high-dimensional, biomedical datasets. FINDINGS: For improving ARX's capability to find optimal transformations when processing high-dimensional data, we implement 2 novel search algorithms. The first is a greedy top-down approach and is oriented on a formally implemented bottom-up search. The second is based on a genetic algorithm. We evaluated the algorithms with different datasets, transformation methods, and privacy models. The novel algorithms mostly outperformed the previously implemented bottom-up search. In addition, we extended the GUI to provide a high degree of usability and performance when working with high-dimensional datasets. CONCLUSION: With our additions we have significantly enhanced ARX's ability to handle high-dimensional data in terms of processing performance as well as usability and thus can further facilitate data sharing.

Comparative Genotoxicity and Mutagenicity of Cigarette, Cigarillo, and Shisha Tobacco Products in Epithelial and Cardiac Cells.

Epidemiology studies link cigarillos and shisha tobacco (delivered through a hookah waterpipe) to increased risk for cardiopulmonary diseases. Here we performed a comparative chemical constituent analysis between 3 cigarettes, 3 cigarillos, and 8 shisha tobacco products. The potency for genotoxicity and oxidative stress of each product's generated total particulate matter (TPM) was also assessed using immortalized oral, lung, and cardiac cell lines to represent target tissues. Levels of the carcinogenic carbonyl formaldehyde were 32- to 95-fold greater, while acrolein was similar across the shisha aerosols generated by charcoal heating compared to cigarettes and cigarillos. Electric-mediated aerosol generation dramatically increased acrolein to levels exceeding those in cigarettes and cigarillos by up to 43-fold. Equivalent cytotoxic-mediated cell death and dose response for genotoxicity through induction of mutagenicity and DNA strand breaks was seen between cigarettes and cigarillos, while minimal to no effect was observed with shisha tobacco products. In contrast, increased potency of TPM from cigarillos compared to cigarettes for inducing oxidative stress via reactive oxygen radicals and lipid peroxidation across cell lines was evident, while positivity was seen for shisha tobacco products albeit at much lower levels. Together, these studies provide new insight into the potential harmful effects of cigarillos for causing tobacco-associated diseases. The high level of carbonyls in shisha products, that in turn is impacted by the heating mechanism, reside largely in the gas phase which will distribute throughout the respiratory tract and systemic circulation to likely increase genotoxic stress.

Chromatin remodeling by the histone methyltransferase EZH2 drives lung pre-malignancy and is a target for cancer prevention.

BACKGROUND: Trimethylation of lysine 27 and dimethylation of lysine 9 of histone-H3 catalyzed by the histone methyltransferases EZH2 and G9a impede gene transcription in cancer. Our human bronchial epithelial (HBEC) pre-malignancy model studied the role of these histone modifications in transformation. Tobacco carcinogen transformed HBEC lines were characterized for cytosine DNA methylation, transcriptome reprogramming, and the effect of inhibiting EZH2 and G9a on the transformed phenotype. The effects of targeting EZH2 and G9a on lung cancer prevention was assessed in the A/J mouse lung tumor model. RESULTS: Carcinogen exposure induced transformation and DNA methylation of 12-96 genes in the four HBEC transformed (T) lines that was perpetuated in malignant tumors. In contrast, 506 unmethylated genes showed reduced expression in one or more HBECTs with many becoming methylated in tumors. ChIP-on-chip for HBEC2T identified 327 and 143 genes enriched for H3K27me3 and H3K9me2. Treatment of HBEC2T and HBEC13T with DZNep, a lysine methyltransferase inhibitor depleted EZH2, reversed transformation, and induced transcriptional reprogramming. The EZH2 small molecule inhibitor EPZ6438 also affected transformation and expression in HBEC2T, while a G9a inhibitor, UNC0642 was ineffective. Genetic knock down of EZH2 dramatically reduced carcinogen-induced transformation of HBEC2. Only DZNep treatment prevented progression of hyperplasia to adenomas in the NNK mouse lung tumor model through reducing EZH2 and affecting the expression of genes regulating cell growth and invasion. CONCLUSION: These studies demonstrate a critical role for EZH2 catalyzed histone modifications for premalignancy and its potential as a target for chemoprevention of lung carcinogenesis.

Cytotoxicity and Genotoxicity of E-Cigarette Generated Aerosols Containing Diverse Flavoring Products and Nicotine in Oral Epithelial Cell Lines.

Electronic cigarettes are the most commonly used nicotine containing product among teenagers. The oral epithelium is the first site of exposure and our recent work revealed considerable diversity among e-liquids for composition and level of chemical constituents that impact nicotine deposition in a human oral-trachea cast and affect the formation of reactive carbonyls. Here, we evaluate the dose response for cytotoxicity and genotoxicity of e-cigarette-generated aerosols from 10 diverse flavored e-liquid products with and without nicotine compared with unflavored in 3 immortalized oral epithelial cell lines. Three e-liquids, Blue Pucker, Love Potion, and Jamestown caused ≥20% cell toxicity assessed by the neutral red uptake assay. Nine products induced significant levels of oxidative stress up to 2.4-fold quantified by the ROS-Glo assay in at least 1 cell line, with dose response seen for Love Potion with and without nicotine across all cell lines. Lipid peroxidation detected by the thiobarbituric acid reactive substances assay was less common among products; however, dose response increases up to 12-fold were seen for individual cell lines. Micronuclei formation indicative of genotoxicity was increased up to 5-fold for some products. Blue Pucker was the most genotoxic e-liquid, inducing micronuclei across all cell lines irrespective of nicotine status. A potency score derived from all assays identified Blue Pucker and Love Potion as the most hazardous e-liquids. These in vitro acute exposure studies provide new insight about the potential for some flavored vaping products to induce significant levels of oxidative stress and genotoxicity.

Identification of novel epigenetic abnormalities as sputum biomarkers for lung cancer risk among smokers and COPD patients.

OBJECTIVES: Smoking is a common risk factor for chronic obstructive pulmonary disease (COPD) and lung cancer. Although COPD patients have higher risk of lung cancer compared to non-COPD smokers, the molecular links between these diseases are not well-defined. This study aims to identify genes that are downregulated by cigarette smoke and commonly repressed in COPD and lung cancer. MATERIALS AND METHODS: Primary human airway epithelial cells (HAEC) were exposed to cigarette-smoke-extract (CSE) for 10-weeks and significantly suppressed genes were identified by transcriptome array. Epigenetic abnormalities of these genes in lung adenocarcinoma (LUAD) from patients with or without COPD were determined using genome-wide and gene-specific assays and by in vitro treatment of cell lines with trichostatin-A or 5-aza-2-deoxycytidine. RESULTS: The ten most commonly downregulated genes following chronic CSE exposure of HAEC and show promoter hypermethylation in LUAD were selected. Among these, expression of CCNA1, SNCA, and ZNF549 was significantly reduced in lung tissues from COPD compared with non-COPD cases while expression of CCNA1 and SNCA was further downregulated in tumors with COPD. The promoter regions of all three genes were hypermethylated in LUAD but not normal or COPD lungs. The reduced expression and aberrant promoter hypermethylation of these genes in LUAD were independently validated using data from the Cancer Genome Atlas project. Importantly, SNCA and ZNF549 methylation detected in sputum DNA from LUAD (52% and 38%) cases were more prevalent compared to cancer-free smokers (26% and 15%), respectively (p < 0.02). CONCLUSIONS: Our data show that suppression of CCNA1, SNCA, and ZNF549 in lung cancer and COPD occurs with or without promoter hypermethylation, respectively. Detecting methylation of these and previously identified genes in sputum of cancer-free smokers may serve as non-invasive biomarkers for early detection of lung cancer among high risk smokers including COPD patients.

HIV-1 Productively Infects and Integrates in Bronchial Epithelial Cells.

Background: The role of lung epithelial cells in HIV-1-related lung comorbidities remains unclear, and the major hurdle in curing HIV is the persistence of latent HIV reservoirs in people living with HIV (PLWH). The advent of combined antiretroviral therapy has considerably increased the life span; however, the incidence of chronic lung diseases is significantly higher among PLWH. Lung epithelial cells orchestrate the respiratory immune responses and whether these cells are productively infected by HIV-1 is debatable. Methods: Normal human bronchial epithelial cells (NHBEs) grown on air-liquid interface were infected with X4-tropic HIV-1LAV and examined for latency using latency-reversing agents (LRAs). The role of CD4 and CXCR4 HIV coreceptors in NHBEs were tested, and DNA sequencing analysis was used to analyze the genomic integration of HIV proviral genes, Alu-HIVgag-pol, HIV-nef, and HIV-LTR. Lung epithelial sections from HIV-infected humans and SHIV-infected macaques were analyzed by FISH for HIV-gag-pol RNA and epithelial cell-specific immunostaining. Results and Discussion: NHBEs express CD4 and CXCR4 at higher levels than A549 cells. NHBEs are infected with HIV-1 basolaterally, but not apically, by X4-tropic HIV-1LAV in a CXCR4/CD4-dependent manner leading to HIV-p24 antigen production; however, NHBEs are induced to express CCR5 by IL-13 treatment. In the presence of cART, HIV-1 induces latency and integration of HIV provirus in the cellular DNA, which is rescued by the LRAs (endotoxin/vorinostat). Furthermore, lung epithelial cells from HIV-infected humans and SHIV-infected macaques contain HIV-specific RNA transcripts. Thus, lung epithelial cells are targeted by HIV-1 and could serve as potential HIV reservoirs that may contribute to the respiratory comorbidities in PLWH.

Dual promoter regulation of death-associated protein kinase gene leads to differentially silenced transcripts by methylation in cancer.

Death-associated protein kinase (DAPK), a mediator of apoptotic systems, is silenced by promoter hypermethylation in lung and breast tumors. This gene has a CpG island extending 2500 bp from the translational start site; however, studies characterizing its transcriptional regulation have not been conducted. Two transcripts for DAPK were identified that code for a single protein, while being regulated by two promoters. The previously identified DAPK transcript designated as exon 1 transcript was expressed at levels 3-fold greater than the alternate exon 1b transcript. Deletion constructs of promoter 1 identified a 332 bp region containing a functional CP2-binding site important for expression of the exon 1 transcript. While moderate reporter activity was seen in promoter 2, the region comprising intron 1 and containing a HNF3B-binding site sustained expression of the alternate transcript. Sequencing the DAPK CpG island in tumor cell lines revealed dense, but heterogenous methylation of CpGs that blocked access of the CP2 and HNF3B proteins that in turn, was associated with loss of transcription that was restored by treatment with 5-aza-2'-deoxycytidine. Prevalences were similar for methylation of promoter 1 and 2 and intron 1 in lung tumors, but significantly greater in promoter 2 and intron 1 in breast tumors, indicative of tissue-specific differences in silencing these two transcripts. These studies show for the first time dual promoter regulation of DAPK, a tumor suppressor gene silenced in many cancers, and substantiate the importance of screening for silencing of both transcripts in tumors.