Identification of gene expression signature for cigarette smoke exposure response--from man to mouse

Blood and urine multi-omics analysis of the impact of e-vaping, smoking, and cessation: from exposome to molecular responses

Cigarette smoking is a major preventable cause of morbidity and mortality. While quitting smoking is the best option, switching from cigarettes to non-combustible alternatives (NCAs) such as e-vapor products is a viable harm reduction approach for smokers who would otherwise continue to smoke. A key challenge for the clinical assessment of NCAs is that self-reported product use can be unreliable, compromising the proper evaluation of their risk reduction potential. In this cross-sectional study of 205 healthy volunteers, we combined comprehensive exposure characterization with in-depth multi-omics profiling to compare effects across four study groups: cigarette smokers (CS), e-vapor users (EV), former smokers (FS), and never smokers (NS). Multi-omics analyses included metabolomics, transcriptomics, DNA methylomics, proteomics, and lipidomics. Comparison of the molecular effects between CS and NS recapitulated several previous observations, such as increased inflammatory markers in CS. Generally, FS and EV demonstrated intermediate molecular effects between the NS and CS groups. Stratification of the FS and EV by combustion exposure markers suggested that this position on the spectrum between CS and NS was partially driven by non-compliance/dual use. Overall, this study highlights the importance of in-depth exposure characterization before biological effect characterization for any NCA assessment study.

Cigarette smoke stimulates clonal expansion of Jak2V617F and Tet2-/- cells

Introduction

Somatic mutations in myeloid growth factor pathway genes, such as JAK2, and genes involved in epigenetic regulation, such as TET2, in hematopoietic stem cells (HSCs) leads to clonal hematopoiesis of indeterminate potential (CHIP) which presents a risk factor for hematologic malignancy and cardiovascular disease. Smoking behavior has been repeatedly associated with the occurrence of CHIP but whether smoking is an environmental inflammatory stressor in promoting clonal expansion has not been investigated.

Methods

We performed in vivo smoke exposures in both wildtype (WT) mice and transplanted mice carrying Jak2V617F mutant and Tet2 knockout (Tet-/-) cells to determine the impact of cigarette smoke (CS) in the HSC compartment as well as favoring mutant cell expansion.

Results

WT mice exposed to smoke displayed increased oxidative stress in long-term HSCs and suppression of the hematopoietic stem and progenitor compartment but smoke exposure did not translate to impaired hematopoietic reconstitution in primary bone marrow transplants. Gene expression analysis of hematopoietic cells in the bone marrow identified an imbalance between Th17 and Treg immune cells suggesting a local inflammatory environment. We also observed enhanced survival of Jak2V617F cells exposed to CS in vivo and cigarette smoke extract (CSE) in vitro. WT bone marrow hematopoietic cells from WT/Jak2V617F chimeric mice exposed to CS demonstrated an increase in neutrophil abundance and distinct overexpression of bone marrow stromal antigen 2 (Bst2) and retinoic acid early transcript 1 (Raet1) targets. Bst2 and Raet1 are indicative of increased interferon signaling and cellular stress including oxidative stress and DNA damage, respectively. In chimeric mice containing both WT and Tet2-/- cells, we observed an increased percentage of circulating mutant cells in peripheral blood post-cigarette smoke exposure when compared to pre-exposure levels while this difference was absent in air-exposed controls.

Conclusion

Altogether, these findings demonstrate that CS results in an inflamed bone marrow environment that provides a selection pressure for existing CHIP mutations such as Jak2V617F and Tet2 loss-of-function.

Identification of Smoking-Associated Transcriptome Aberration in Blood with Machine Learning Methods

Long-term cigarette smoking causes various human diseases, including respiratory disease, cancer, and gastrointestinal (GI) disorders. Alterations in gene expression and variable splicing processes induced by smoking are associated with the development of diseases. This study applied advanced machine learning methods to identify the isoforms with important roles in distinguishing smokers from former smokers based on the expression profile of isoforms from current and former smokers collected in one previous study. These isoforms were deemed as features, which were first analyzed by the Boruta to select features highly correlated with the target variables. Then, the selected features were evaluated by four feature ranking algorithms, resulting in four feature lists. The incremental feature selection method was applied to each list for obtaining the optimal feature subsets and building high-performance classification models. Furthermore, a series of classification rules were accessed by decision tree with the highest performance. Eventually, the rationality of the mined isoforms (features) and classification rules was verified by reviewing previous research. Features such as isoforms ENST00000464835 (expressed by LRRN3), ENST00000622663 (expressed by SASH1), and ENST00000284311 (expressed by GPR15), and pathways (cytotoxicity mediated by natural killer cell and cytokine-cytokine receptor interaction) revealed by the enrichment analysis, were highly relevant to smoking response, suggesting the robustness of our analysis pipeline.

Differential gene expression of 3D primary human airway cultures exposed to cigarette smoke and electronic nicotine delivery system (ENDS) preparations

BACKGROUND

Acute exposure to cigarette smoke alters gene expression in several biological pathways such as apoptosis, immune response, tumorigenesis and stress response, among others. However, the effects of electronic nicotine delivery systems (ENDS) on early changes in gene expression is relatively unknown. The objective of this study was to evaluate the early toxicogenomic changes using a fully-differentiated primary normal human bronchial epithelial (NHBE) culture model after an acute exposure to cigarette and ENDS preparations.

RESULTS

RNA sequencing and pathway enrichment analysis identified time and dose dependent changes in gene expression and several canonical pathways when exposed to cigarette preparations compared to vehicle control, including oxidative stress, xenobiotic metabolism, SPINK1 general cancer pathways and mucociliary clearance. No changes were observed with ENDS preparations containing up to 28 µg/mL nicotine. Full model hierarchical clustering revealed that ENDS preparations were similar to vehicle control.

CONCLUSION

This study revealed that while an acute exposure to cigarette preparations significantly and differentially regulated many genes and canonical pathways, ENDS preparations containing the same concentration of nicotine had very little effect on gene expression in fully-differentiated primary NHBE cultures.

Metal exposure from additive manufacturing and its effect on the nasal lavage fluid proteome - a pilot study

The use of metal additive manufacturing (AM) is steadily increasing and is an emerging concern regarding occupational exposure. In this study, non-invasive sampled nasal lavage fluid (NLF) from the upper airways was collected from metal AM operators at the beginning and end of a workweek during two consecutive years with preventive interventions in the occupational setting in-between (n = 5 year 1, n = 9 year 2). During year one, NLF was also collected from welders (n = 6) from the same company to get a comparison with a traditional manufacturing technique with known exposure and health risks. The samples were investigated using untargeted proteomics, as well as using multi-immunoassay to analyze a panel of 71 inflammatory protein markers. NLF in AM operators from year 1 showed decreased levels of Immunoglobulin J and WAP four-disulfide core domain protein 2 and increased levels of Golgi membrane protein 1, Uteroglobin and Protein S100-A6 at the end of the workweek. At year two, after preventive interventions, there were no significant differences at the end of the workweek. In welders, Annexin A1 and Protein S100-A6 were increased at the end of the workweek. The analysis of 71 inflammatory biomarkers showed no significant differences between the beginning and the end of workweek year 1 in AM operators. We identified several proteins of interest in the AM operators that could serve as possible markers for exposure in future studies with a larger cohort for validation.

Epigenetics and pulmonary diseases in the horizon of precision medicine: a review

Epigenetic mechanisms represent potential molecular routes which could bridge the gap between genetic background and environmental risk factors contributing to the pathogenesis of pulmonary diseases. In patients with COPD, asthma and pulmonary arterial hypertension (PAH), there is emerging evidence of aberrant epigenetic marks, mainly including DNA methylation and histone modifications which directly mediate reversible modifications to the DNA without affecting the genomic sequence. Post-translational events and microRNAs can be also regulated epigenetically and potentially participate in disease pathogenesis. Thus, novel pathogenic mechanisms and putative biomarkers may be detectable in peripheral blood, sputum, nasal and buccal swabs or lung tissue. Besides, DNA methylation plays an important role during the early phases of fetal development and may be impacted by environmental exposures, ultimately influencing an individual's susceptibility to COPD, asthma and PAH later in life. With the advances in omics platforms and the application of computational biology tools, modelling the epigenetic variability in a network framework, rather than as single molecular defects, provides insights into the possible molecular pathways underlying the pathogenesis of COPD, asthma and PAH. Epigenetic modifications may have clinical applications as noninvasive biomarkers of pulmonary diseases. Moreover, combining molecular assays with network analysis of epigenomic data may aid in clarifying the multistage transition from a "pre-disease" to "disease" state, with the goal of improving primary prevention of lung diseases and its subsequent clinical management.We describe epigenetic mechanisms known to be associated with pulmonary diseases and discuss how network analysis could improve our understanding of lung diseases.

Gene expression in blood reflects smoking exposure among cancer-free women in the Norwegian Women and Cancer (NOWAC) postgenome cohort

Active smoking has been linked to modulated gene expression in blood. However, there is a need for a more thorough understanding of how quantitative measures of smoking exposure relate to differentially expressed genes (DEGs) in whole-blood among ever smokers. This study analysed microarray-based gene expression profiles from whole-blood samples according to smoking status and quantitative measures of smoking exposure among cancer-free women (n = 1708) in the Norwegian Women and Cancer postgenome cohort. When compared with never smokers and former smokers, current smokers had 911 and 1082 DEGs, respectively and their biological functions could indicate systemic impacts of smoking. LRRN3 was associated with smoking status with the lowest FDR-adjusted p-value. When never smokers and all former smokers were compared, no DEGs were observed, but LRRN3 was differentially expressed when never smokers were compared with former smokers who quit smoking ≤ 10 years ago. Further, LRRN3 was positively associated with smoking intensity, pack-years, and comprehensive smoking index score among current smokers; and negatively associated with time since cessation among former smokers. Consequently, LRRN3 expression in whole-blood is a molecular signal of smoking exposure that could supplant self-reported smoking data in further research targeting blood-based markers related to the health effects of smoking.

Transcriptomic analysis of monocytes from HIV-positive men on antiretroviral therapy reveals effects of tobacco smoking on interferon and stress response systems associated with depressive symptoms

Background

Tobacco smoking induces immunomodulatory and pro-inflammatory effects associated with transcriptome changes in monocytes and other immune cell types. While smoking is prevalent in HIV-infected (HIV+) individuals, few studies have investigated its effects on gene expression in this population. Here, we report whole-transcriptome analyses of 125 peripheral blood monocyte samples from ART-treated HIV+ and uninfected (HIV−) men enrolled in the Multicenter AIDS Cohort Study (MACS) (n = 25 HIV+ smokers, n = 60 HIV+ non-smokers, n = 40 HIV− non-smoking controls). Gene expression profiling was performed using Illumina HumanHT-12 Expression BeadChip microarrays. Differential expression analysis was performed with weighted linear regression models using the R limma package, followed by functional enrichment and Ingenuity Pathway analyses.

Results

A total of 286 genes were differentially expressed in monocytes from HIV+ smokers compared with HIV− non-smokers; upregulated genes (n = 180) were enriched for immune and interferon response, chemical/stress response, mitochondria, and extracellular vesicle gene ontology (GO) terms. Expression of genes related to immune/interferon responses (AIM2, FCGR1A-B, IFI16, SP100), stress/chemical responses (APAF1, HSPD1, KLF4), and mitochondrial function (CISD1, MTHFD2, SQOR) was upregulated in HIV+ non-smokers and further increased in HIV+ smokers. Gene expression changes associated with smoking in previous studies of human monocytes were also observed (SASH1, STAB1, PID1, MMP25). Depressive symptoms (CES-D scores ≥ 16) were more prevalent in HIV+ tobacco smokers compared with HIV+ and HIV− non-smokers (50% vs. 26% and 13%, respectively; p = 0.007), and upregulation of immune/interferon response genes, including IFI35, IFNAR1, OAS1-2, STAT1, and SP100, was associated with depressive symptoms in logistic regression models adjusted for HIV status and smoking (p < 0.05). Network models linked the Stat1-mediated interferon pathway to transcriptional regulator Klf4 and smoking-associated toll-like receptor scaffolding protein Sash1, suggesting inter-relationships between smoking-associated genes, control of monocyte differentiation, and interferon-mediated inflammatory responses.

Conclusions

This study characterizes immune, interferon, stress response, and mitochondrial-associated gene expression changes in monocytes from HIV+ tobacco smokers, and identifies augmented interferon and stress responses associated with depressive symptoms. These findings help to explain complex interrelationships between pro-inflammatory effects of HIV and smoking, and their combined impact on comorbidities prevalent in HIV+ individuals.

Gene expression microarray public dataset reanalysis in chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) was classified by the Centers for Disease Control and Prevention in 2014 as the 3^rd leading cause of death in the United States (US). The main cause of COPD is exposure to tobacco smoke and air pollutants. Problems associated with COPD include under-diagnosis of the disease and an increase in the number of smokers worldwide. The goal of our study is to identify disease variability in the gene expression profiles of COPD subjects compared to controls, by reanalyzing pre-existing, publicly available microarray expression datasets. Our inclusion criteria for microarray datasets selected for smoking status, age and sex of blood donors reported. Our datasets used Affymetrix, Agilent microarray platforms (7 datasets, 1,262 samples). We re-analyzed the curated raw microarray expression data using R packages, and used Box-Cox power transformations to normalize datasets. To identify significant differentially expressed genes we used generalized least squares models with disease state, age, sex, smoking status and study as effects that also included binary interactions, followed by likelihood ratio tests (LRT). We found 3,315 statistically significant (Storey-adjusted q-value <0.05) differentially expressed genes with respect to disease state (COPD or control). We further filtered these genes for biological effect using results from LRT q-value <0.05 and model estimates’ 10% two-tailed quantiles of mean differences between COPD and control), to identify 679 genes. Through analysis of disease, sex, age, and also smoking status and disease interactions we identified differentially expressed genes involved in a variety of immune responses and cell processes in COPD. We also trained a logistic regression model using the common array genes as features, which enabled prediction of disease status with 81.7% accuracy. Our results give potential for improving the diagnosis of COPD through blood and highlight novel gene expression disease signatures.

A Meta-Analysis of the Performance of a Blood-Based Exposure Response Gene Signature Across Clinical Studies on the Tobacco Heating System 2.2 (THS 2.2)

As part of emerging tobacco harm reduction strategies, modified risk tobacco products (MRTP) are being developed to offer alternatives that have the potential to reduce the individual risk and population harm compared with smoking cigarettes for adult smokers who want to continue using tobacco and nicotine products. MRTPs are defined as any tobacco products that are distributed for use to reduce harm or the risk of tobacco-related disease associated with commercially marketed tobacco products. One such candidate MRTP is the Tobacco Heating System (THS) 2.2, which does not burn tobacco but instead heats it, thus producing significantly reduced levels of harmful and potentially harmful constituents compared with cigarettes. The clinical assessment of candidate MRTPs requires the development of exposure-response markers to distinguish current smokers from either nonsmokers or former smokers with high specificity and sensitivity. Toward this end, a whole blood-derived gene signature was previously developed and reported. Four randomized, controlled, open-label, three-arm parallel group reduced exposure clinical studies have been conducted with subjects randomized to three arms: switching from cigarettes to THS 2.2, continuous use of cigarettes, or smoking abstinence. These clinical studies had an investigational period of 5 days in confinement, which was followed by an 85-day ambulatory period in two studies. Here we tested the previously developed blood-derived signature on the samples derived from those clinical studies. We showed that in all four studies, the signature scores were reduced consistently in subjects who either stopped smoking or switched to THS 2.2 compared with subjects who continued smoking cigarettes.

Whole transcriptome targeted gene quantification provides new insights on pulmonary sarcomatoid carcinomas

Pulmonary sarcomatoid carcinomas (PSC) are a rare group of lung cancer with a median overall survival of 9–12 months. PSC are divided into five histotypes, challenging to diagnose and treat. The identification of PSC biomarkers is warranted, but PSC molecular profile remains to be defined. Herein, a targeted whole transcriptome analysis was performed on 14 PSC samples, evaluated also for the presence of the main oncogene mutations and rearrangements. PSC expression data were compared with transcriptome data of lung adenocarcinomas (LUAD) and squamous cell carcinomas (LUSC) from The Cancer Genome Atlas. Deregulated genes were used for pathway enrichment analysis; the most representative genes were tested by immunohistochemistry (IHC) in an independent cohort (30 PSC, 31 LUAD, 31 LUSC). All PSC cases were investigated for PD-L1 expression. Thirty-eight genes deregulated in PSC were identified, among these IGJ and SLMAP were confirmed by IHC. Moreover, Forkhead box signaling and Fanconi anemia pathways were specifically enriched in PSC. Finally, some PSC harboured alterations in genes targetable by tyrosine kinase inhibitors, as EGFR and MET. We provide a deep molecular characterization of PSC; the identification of specific molecular profiles, besides increasing our knowledge on PSC biology, might suggest new strategies to improve patients management.

Dynamic changes in lung responses after single and repeated exposures to cigarette smoke in mice

Cigarette smoke is well recognized to cause injury to the airways and the alveolar walls over time. This injury usually requires many years of exposure, suggesting that the lungs may rapidly develop responses that initially protect it from this repetitive injury. Our studies tested the hypotheses that smoke induces an inflammatory response and changes in mRNA profiles that are dependent on sex and the health status of the lung, and that the response of the lungs to smoke differs after 1 day compared to 5 days of exposure. Male and female wildtype (WT) and Scnn1b-transgenic (βENaC) mice, which have chronic bronchitis and emphysematous changes due to dehydrated mucus, were exposed to cigarette smoke or sham air conditions for 1 or 5 days. The inflammatory response and gene expression profiles were analyzed in lung tissue. Overall, the inflammatory response to cigarette smoke was mild, and changes in mediators were more numerous after 1 than 5 days. βENaC mice had more airspace leukocytes than WT mice, and smoke exposure resulted in additional significant alterations. Many genes and gene sets responded similarly at 1 and 5 days: genes involved in oxidative stress responses were upregulated while immune response genes were downregulated. However, certain genes and biological processes were regulated differently after 1 compared to 5 days. Extracellular matrix biology genes and gene sets were upregulated after 1 day but downregulated by 5 days of smoke compared to sham exposure. There was no difference in the transcriptional response to smoke between WT and βENaC mice or between male and female mice at either 1 or 5 days. Taken together, these studies suggest that the lungs rapidly alter gene expression after only one exposure to cigarette smoke, with few additional changes after four additional days of repeated exposure. These changes may contribute to preventing lung damage.

Distinct gene expression changes in human peripheral blood mononuclear cells treated with different tobacco product preparations

Cigarette smoking exerts diverse physiological effects including immune suppression. To better characterize the biological effects of different categories of tobacco products, a genome-wide gene expression study was performed. Transcriptomic profiling was performed in PBMCs treated with different equi-nicotine units of aqueous extracts of cigarette smoke (termed Whole Smoke-Conditioned Medium, or WS-CM), or a single dose smokeless tobacco extract (STE) prepared from reference tobacco products. WS-CM induced dose-dependent changes in the expression of several genes. No significant expression differences between low WS-CM and media control were detected. However, transcripts were significantly affected by medium WS-CM (479), high WS-CM (2, 703), and STE (2, 156). The overlap between medium WS-CM and STE, and high WS-CM and STE, was minimal (34 and 65 transcripts, respectively). Hierarchical clustering revealed that gene expression profiles for STE and medium WS-CM co-clustered, while those affected by the high dose of WS-CM clustered distinctly. Functional analysis revealed that WS-CM, but not STE, uniquely affected genes involved in immune cell development and inflammatory response. Cascades of upstream regulators (e.g., TNF, IL1β, NFƙB) were identified for the observed gene expression changes and generally suppressed by WS-CM, but not by STE. Collectively, these findings demonstrate that combustible and non-combustible tobacco products elicit distinct biological effects, which could explain the observed chronic immune suppression in smokers.

Interplay between genome-wide implicated genetic variants and environmental factors related to childhood antisocial behavior in the UK ALSPAC cohort

We investigated gene-environment (G × E) interactions related to childhood antisocial behavior between polymorphisms implicated by recent genome-wide association studies (GWASs) and two key environmental adversities (maltreatment and smoking during pregnancy) in a large population cohort (ALSPAC). We also studied the MAOA candidate gene and addressed comorbid attention-deficit/hyperactivity disorder (ADHD). ALSPAC is a large, prospective, ethnically homogeneous British cohort. Our outcome consisted of mother-rated conduct disorder symptom scores at age 7;9 years. G × E interactions were tested in a sex-stratified way (α = 0.0031) for four GWAS-implicated variants (for males, rs4714329 and rs9471290; for females, rs2764450 and rs11215217), and a length polymorphism near the MAOA-promoter region. We found that males with rs4714329-GG (P = 0.0015) and rs9471290-AA (P = 0.0001) genotypes were significantly more susceptible to effects of smoking during pregnancy in relation to childhood antisocial behavior. Females with the rs11215217-TC genotype (P = 0.0018) were significantly less susceptible to effects of maltreatment, whereas females with the MAOA-HL genotype (P = 0.0002) were more susceptible to maltreatment effects related to antisocial behavior. After adjustment for comorbid ADHD symptomatology, aforementioned G × E's remained significant, except for rs11215217 × maltreatment, which retained only nominal significance. Genetic variants implicated by recent GWASs of antisocial behavior moderated associations of smoking during pregnancy and maltreatment with childhood antisocial behavior in the general population. While we also found a G × E interaction between the candidate gene MAOA and maltreatment, we were mostly unable to replicate the previous results regarding MAOA-G × E's. Future studies should, in addition to genome-wide implicated variants, consider polygenic and/or multimarker analyses and take into account potential sex stratification.

Ensemble of rankers for efficient gene signature extraction in smoke exposure classification

Background

System toxicology aims at understanding the mechanisms used by biological systems to respond to toxicants. Such understanding can be leveraged to assess the risk of chemicals, drugs, and consumer products in living organisms. In system toxicology, machine learning techniques and methodologies are applied to develop prediction models for classification of toxicant exposure of biological systems. Gene expression data (RNA/DNA microarray) are often used to develop such prediction models.

Results

The outcome of the present work is an experimental methodology to develop prediction models, based on robust gene signatures, for the classification of cigarette smoke exposure and cessation in humans. It is a result of the participation in the recent sbv IMPROVER SysTox Computational Challenge. By merging different gene selection techniques, we obtain robust gene signatures and we investigate prediction capabilities of different off-the-shelf machine learning techniques, such as artificial neural networks, linear models and support vector machines. We also predict six novel genes in our signature, and firmly believe these genes have to be further investigated as biomarkers for tobacco smoking exposure.

Conclusions

The proposed methodology provides gene signatures with top-ranked performances in the prediction of the investigated classification methods, as well as new discoveries in genetic signatures for bio-markers of the smoke exposure of humans.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2035-3) contains supplementary material, which is available to authorized users.

Perceived Racial Discrimination and DNA Methylation Among African American Women in the InterGEN Study

INTRODUCTION

Experiences of racial discrimination have been associated with poor health outcomes. Little is known, however, about how perceived racial discrimination influences DNA methylation (DNAm) among African Americans (AAs). We examined the association of experiences of discrimination with DNAm among AA women in the Intergenerational Impact of Genetic and Psychological Factors on Blood Pressure (InterGEN) study.

METHODS

The InterGEN study examines the effects of genetic and psychological factors on blood pressure among AA women and their children. Measures include the Major Life Discrimination (MLD) and the Race-Related Events (RES) scales. In the present analysis, we examined discrimination and DNAm at baseline in the InterGEN study. The 850K EPIC Illumina BeadChip was used for evaluating DNAm in this epigenome-wide association study (EWAS).

RESULTS

One hundred and fifty-two women contributed data for the RES-EWAS analysis and 147 for the MLD-EWAS analysis. Most were 30-39 years old, nonsmokers, had some college education, and had incomes

CONCLUSION

We observed significant epigenetic associations between disease-associated genes (e.g., schizophrenia, bipolar disorder, and asthma) and perceived discrimination as measured by the MLD Scale. Future health disparities research should include epigenetics in high-risk populations to elucidate functional consequences induced by the psychosocial environment.

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Key Words

• African Americans
• DNA methylation
• epigenomics
• racism
• women

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MESH Headings

• Adult
• Black or African American/genetics
• Asthma/genetics
• Blood Pressure/genetics
• Child
• DNA/analysis
• DNA Methylation/genetics
• Epigenesis, Genetic
• Epigenomics
• Female
• Humans
• Male
• Racism
• Risk Factors
• United States

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Grants

• R01 NR013520 NINR NIH HHS
• UL1 TR001863 NCATS NIH HHS
• National Institute of Nursing Research

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Affiliation(s)

Veronica Barcelona de Mendoza 1 Yale School of Nursing, Orange, CT, USA
Yunfeng Huang 2 Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, USA
Cindy A Crusto 3 Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
Yan V Sun 2 Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, USA
Jacquelyn Y Taylor 1 Yale School of Nursing, Orange, CT, USA

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Tobacco exposure-related alterations in DNA methylation and gene expression in human monocytes: the Multi-Ethnic Study of Atherosclerosis (MESA)

Alterations in DNA methylation and gene expression in blood leukocytes are potential biomarkers of harm and mediators of the deleterious effects of tobacco exposure. However, methodological issues, including the use of self-reported smoking status and mixed cell types have made previously identified alterations in DNA methylation and gene expression difficult to interpret. In this study, we examined associations of tobacco exposure with DNA methylation and gene expression, utilizing a biomarker of tobacco exposure (urine cotinine) and CD14+ purified monocyte samples from 934 participants of the community-based Multi-Ethnic Study of Atherosclerosis (MESA). Urine cotinine levels were measured using an immunoassay. DNA methylation and gene expression were measured with microarrays. Multivariate linear regression was used to test for associations adjusting for age, sex, race/ethnicity, education, and study site. Urine cotinine levels were associated with methylation of 176 CpGs [false discovery rate (FDR)<0.01]. Four CpGs not previously identified by studies of non-purified blood samples nominally replicated (P value<0.05) with plasma cotinine-associated methylation in 128 independent monocyte samples. Urine cotinine levels associated with expression of 12 genes (FDR<0.01), including increased expression of P2RY6 (Beta ± standard error = 0.078 ± 0.008, P = 1.99 × 10⁻²²), a gene previously identified to be involved in the release of pro-inflammatory cytokines. No cotinine-associated (FDR<0.01) methylation profiles significantly (FDR<0.01) correlated with cotinine-associated (FDR<0.01) gene expression profiles. In conclusion, our findings i) identify potential monocyte-specific smoking-associated methylation patterns and ii) suggest that alterations in methylation may not be a main mechanism regulating gene expression in monocytes in response to cigarette smoking.

The sbv IMPROVER Systems Toxicology Computational Challenge: Identification of Human and Species-Independent Blood Response Markers as Predictors of Smoking Exposure and Cessation Status

Cigarette smoking entails chronic exposure to a mixture of harmful chemicals that trigger molecular changes over time, and is known to increase the risk of developing diseases. Risk assessment in the context of 21^st century toxicology relies on the elucidation of mechanisms of toxicity and the identification of exposure response markers, usually from high-throughput data, using advanced computational methodologies. The sbv IMPROVER Systems Toxicology computational challenge (Fall 2015-Spring 2016) aimed to evaluate whether robust and sparse (≤40 genes) human (sub-challenge 1, SC1) and species-independent (sub-challenge 2, SC2) exposure response markers (so called gene signatures) could be extracted from human and mouse blood transcriptomics data of current (S), former (FS) and never (NS) smoke-exposed subjects as predictors of smoking and cessation status. Best-performing computational methods were identified by scoring anonymized participants' predictions. Worldwide participation resulted in 12 (SC1) and six (SC2) final submissions qualified for scoring. The results showed that blood gene expression data were informative to predict smoking exposure (i.e. discriminating smoker versus never or former smokers) status in human and across species with a high level of accuracy. By contrast, the prediction of cessation status (i.e. distinguishing FS from NS) remained challenging, as reflected by lower classification performances. Participants successfully developed inductive predictive models and extracted human and species-independent gene signatures, including genes with high consensus across teams. Post-challenge analyses highlighted "feature selection" as a key step in the process of building a classifier and confirmed the importance of testing a gene signature in independent cohorts to ensure the generalized applicability of a predictive model at a population-based level. In conclusion, the Systems Toxicology challenge demonstrated the feasibility of extracting a consistent blood-based smoke exposure response gene signature and further stressed the importance of independent and unbiased data and method evaluations to provide confidence in systems toxicology-based scientific conclusions.

Crowd-Sourced Verification of Computational Methods and Data in Systems Toxicology: A Case Study with a Heat-Not-Burn Candidate Modified Risk Tobacco Product

Systems toxicology intends to quantify the effect of toxic molecules in biological systems and unravel their mechanisms of toxicity. The development of advanced computational methods is required for analyzing and integrating high throughput data generated for this purpose as well as for extrapolating predictive toxicological outcomes and risk estimates. To ensure the performance and reliability of the methods and verify conclusions from systems toxicology data analysis, it is important to conduct unbiased evaluations by independent third parties. As a case study, we report here the results of an independent verification of methods and data in systems toxicology by crowdsourcing. The sbv IMPROVER systems toxicology computational challenge aimed to evaluate computational methods for the development of blood-based gene expression signature classification models with the ability to predict smoking exposure status. Participants created/trained models on blood gene expression data sets including smokers/mice exposed to 3R4F (a reference cigarette) or noncurrent smokers/Sham (mice exposed to air). Participants applied their models on unseen data to predict whether subjects classify closer to smoke-exposed or nonsmoke exposed groups. The data sets also included data from subjects that had been exposed to potential modified risk tobacco products (MRTPs) or that had switched to a MRTP after exposure to conventional cigarette smoke. The scoring of anonymized participants' predictions was done using predefined metrics. The top 3 performers' methods predicted class labels with area under the precision recall scores above 0.9. Furthermore, although various computational approaches were used, the crowd's results confirmed our own data analysis outcomes with regards to the classification of MRTP-related samples. Mice exposed directly to a MRTP were classified closer to the Sham group. After switching to a MRTP, the confidence that subjects belonged to the smoke-exposed group decreased significantly. Smoking exposure gene signatures that contributed to the group separation included a core set of genes highly consistent across teams such as AHRR, LRRN3, SASH1, and P2RY6. In conclusion, crowdsourcing constitutes a pertinent approach, in complement to the classical peer review process, to independently and unbiasedly verify computational methods and data for risk assessment using systems toxicology.

Gene expression profiles associated with cigarette smoking and moist snuff consumption

Background

Among the different tobacco products that are available on the US market, cigarette smoking is shown to be the most harmful and the effects of cigarette smoking have been well studied. US epidemiological studies indicate that non-combustible tobacco products are less harmful than smoking and yet very limited biological and mechanistic information is available on the effects of these alternative tobacco products. For the first time, we characterized gene expression profiling in PBMCs from moist snuff consumers (MSC), compared with that from consumers of cigarettes (SMK) and non-tobacco consumers (NTC).

Results

Microarray analysis identified 100 differentially expressed genes (DEGs) between the SMK and NTC groups and 46 DEGs between SMK and MSC groups. However, we found no significant differences in gene expression between MSC and NTC. Both hierarchical clustering and principle component analysis revealed that MSC and NTC expression profiles were more similar than to SMK. Random forest classification identified a subset of DEGs which predicted SMK from either NTC or MSC with high accuracy (AUC 0.98).

Conclusions

PMBC gene expression profiles of NTC and MSC are similar to each other, while SMK exhibit distinct profiles with alterations in immune related pathways. In addition to discovering several biomarkers, these studies support further understanding of the biological effects of different tobacco products.

Trial registration

ClinicalTrials.gov. Identifier: NCT01923402. Date of Registration: August 14, 2013. Study was retrospectively registered.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-3565-1) contains supplementary material, which is available to authorized users.

Alterations in Serum Polyunsaturated Fatty Acids and Eicosanoids in Patients with Mild to Moderate Chronic Obstructive Pulmonary Disease (COPD)

Smoking is a major risk factor for several diseases including chronic obstructive pulmonary disease (COPD). To better understand the systemic effects of cigarette smoke exposure and mild to moderate COPD-and to support future biomarker development-we profiled the serum lipidomes of healthy smokers, smokers with mild to moderate COPD (GOLD stages 1 and 2), former smokers, and never-smokers (n = 40 per group) (ClinicalTrials.gov registration: NCT01780298). Serum lipidome profiling was conducted with untargeted and targeted mass spectrometry-based lipidomics. Guided by weighted lipid co-expression network analysis, we identified three main trends comparing smokers, especially those with COPD, with non-smokers: a general increase in glycero(phospho)lipids, including triglycerols; changes in fatty acid desaturation (decrease in ω-3 polyunsaturated fatty acids, and an increase in monounsaturated fatty acids); and an imbalance in eicosanoids (increase in 11,12- and 14,15-DHETs (dihydroxyeicosatrienoic acids), and a decrease in 9- and 13-HODEs (hydroxyoctadecadienoic acids)). The lipidome profiles supported classification of study subjects as smokers or non-smokers, but were not sufficient to distinguish between smokers with and without COPD. Overall, our study yielded further insights into the complex interplay between smoke exposure, lung disease, and systemic alterations in serum lipid profiles.

Periostin mediates cigarette smoke extract-induced proliferation and migration in pulmonary arterial smooth muscle cells

Cigarette smoking is an important risk factor for pulmonary arterial hypertension (PAH). Pulmonary arterial smooth muscle cells (PASMCs) play a critical role in the pathogenesis of PAH-associated arterial remodeling. This study was done to explore the expression and biological roles of periostin in PASMCs following exposure to cigarette smoke extract (CSE). PASMCs were exposed to different concentrations of CSE and tested for gene expression and reactive oxygen species (ROS) production. PASMCs were incubated with recombinant periostin protein or transfected with small interfering RNA targeting periostin before CSE exposure and then examined for cell proliferation and migration. Compared to control cells, exposure to CSE led to a significant upregulation of periostin. Pretreatment with 5mM N-acetyl-l-cysteine (an inhibitor of ROS formation) or 10μM U0126 (an inhibitor of ERK1/2) significantly prevented the induction of periostin in CSE-treated PASMCs. The addition of recombinant periostin protein significantly enhanced the proliferation and migration of PASMCs. In contrast, knockdown of endogenous periostin counteracted the proliferation and migration of PASMCs induced by CSE treatment. In conclusion, CSE induces the expression of periostin in PASMCs via promotion of ROS and activation of ERK1/2. Periostin mediates the effects of CSE on PASMC proliferation and migration. These findings warrant further exploration of the roles of periostin in cigarette smoking-associated pulmonary arterial remodeling.