Transcriptomic epidemiology of smoking: the effect of smoking on gene expression in lymphocytes

Differentially co-expressed myofibre transcripts associated with abnormal myofibre proportion in chronic obstructive pulmonary disease

BACKGROUND

Skeletal muscle dysfunction is a common extrapulmonary manifestation of chronic obstructive pulmonary disease (COPD). Alterations in skeletal muscle myosin heavy chain expression, with reduced type I and increased type II myosin heavy chain expression, are associated with COPD severity when studied in largely male cohorts. The objectives of this study were (1) to define an abnormal myofibre proportion phenotype in both males and females with COPD and (2) to identify transcripts and transcriptional networks associated with abnormal myofibre proportion in COPD.

METHODS

Forty-six participants with COPD were assessed for body composition, strength, endurance and pulmonary function. Skeletal muscle biopsies from the vastus lateralis were assayed for fibre-type distribution and cross-sectional area via immunofluorescence microscopy and RNA-sequenced to generate transcriptome-wide gene expression data. Sex-stratified k-means clustering of type I and IIx/IIax fibre proportions was used to define abnormal myofibre proportion in participants with COPD and contrasted with previously defined criteria. Single transcripts and weighted co-expression network analysis modules were tested for correlation with the abnormal myofibre proportion phenotype.

RESULTS

Abnormal myofibre proportion was defined in males with COPD (n = 29) as <18% type I and/or >22% type IIx/IIax fibres and in females with COPD (n = 17) as <36% type I and/or >12% type IIx/IIax fibres. Half of the participants with COPD were classified as having an abnormal myofibre proportion. Participants with COPD and an abnormal myofibre proportion had lower median handgrip strength (26.1 vs. 34.0 kg, P = 0.022), 6-min walk distance (300 vs. 353 m, P = 0.039) and forced expiratory volume in 1 s-to-forced vital capacity ratio (0.42 vs. 0.48, P = 0.041) compared with participants with COPD and normal myofibre proportions. Twenty-nine transcripts were associated with abnormal myofibre proportions in participants with COPD, with the upregulated NEB, TPM1 and TPM2 genes having the largest fold differences. Co-expression network analysis revealed that two transcript modules were significantly positively associated with the presence of abnormal myofibre proportions. One of these co-expression modules contained genes classically associated with muscle atrophy, as well as transcripts associated with both type I and type II myofibres, and was enriched for genetic loci associated with bone mineral density.

CONCLUSIONS

Our findings indicate that there are significant transcriptional alterations associated with abnormal myofibre proportions in participants with COPD. Transcripts canonically associated with both type I and type IIa fibres were enriched in a co-expression network associated with abnormal myofibre proportion, suggesting altered transcriptional regulation across multiple fibre types.

Heme Metabolism Mediates the Effects of Smoking on Gut Microbiome

INTRODUCTION

The number of smokers worldwide increased greatly during the past decades and reached 1.14 billion in 2019, becoming a leading risk factor for human health. Tobacco smoking has wide effects on human genetics, epigenetics, transcriptome, and gut microbiome. Although many studies have revealed effects of smoking on host transcriptome, research on the relationship between smoking, host gene expression, and the gut microbiome is limited.

AIMS AND METHODS

We first explored transcriptome and metagenome profile differences between smokers and nonsmokers. To evaluate the relationship between host gene expression and gut microbiome, we then applied bidirectional mediation analysis to infer causal relationships between smoking, gene expression, and gut microbes.

RESULTS

Metagenome and transcriptome analyses revealed 71 differential species and 324 differential expressed genes between smokers and nonsmokers. With smoking as an exposure variable, we identified 272 significant causal relationships between gene expression and gut microbes, among which there were 247 genes that mediate the effect of smoking on gut microbes. Pathway-based enrichment analysis showed that these genes were significantly enriched in heme metabolic pathway, which mainly mediated the changes of Bacteroides finegoldii and Lachnospiraceae bacterium 9_1_43BFAA. Additionally, by performing metabolome data analysis in the Integrated Human Microbiome Project (iHMP) database, we verified the correlation between the intermediate products of the heme metabolism pathway (porphobilinogen, bilirubin, and biliverdin) and gut microbiome.

CONCLUSIONS

By investigating the bidirectional interaction between smoking-related host gene expression and gut microbes, this study provided evidence for the mediation of smoking on gut microbes through co-involvement or interaction of heme metabolism.

IMPLICATIONS

By comparing the metagenome and transcriptome sequencing profiles between 34 smokers and 33 age- and gender-matched nonsmokers, we are the first to reveal causal relationships among tobacco smoking, host gene expression, and gut microbes. These findings offer insight into how smoking affects gut microbes through host gene expression and metabolism, which highlights the importance of heme metabolism in modulating the effects of smoking on gut microbiome.

Identification of the HNSC88 Molecular Signature for Predicting Subtypes of Head and Neck Cancer

Head and neck squamous cell carcinoma (HNSC) exhibits genetic heterogeneity in etiologies, tumor sites, and biological processes, which significantly impact therapeutic strategies and prognosis. While the influence of human papillomavirus on clinical outcomes is established, the molecular subtypes determining additional treatment options for HNSC remain unclear and inconsistent. This study aims to identify distinct HNSC molecular subtypes to enhance diagnosis and prognosis accuracy. In this study, we collected three HNSC microarrays (n = 306) from the Gene Expression Omnibus (GEO), and HNSC RNA-Seq data (n = 566) from The Cancer Genome Atlas (TCGA) to identify differentially expressed genes (DEGs) and validate our results. Two scoring methods, representative score (RS) and perturbative score (PS), were developed for DEGs to summarize their possible activation functions and influence in tumorigenesis. Based on the RS and PS scoring, we selected candidate genes to cluster TCGA samples for the identification of molecular subtypes in HNSC. We have identified 289 up-regulated DEGs and selected 88 genes (called HNSC88) using the RS and PS scoring methods. Based on HNSC88 and TCGA samples, we determined three HNSC subtypes, including one HPV-associated subtype, and two HPV-negative subtypes. One of the HPV-negative subtypes showed a relationship to smoking behavior, while the other exhibited high expression in tumor immune response. The Kaplan-Meier method was used to compare overall survival among the three subtypes. The HPV-associated subtype showed a better prognosis compared to the other two HPV-negative subtypes (log rank, p = 0.0092 and 0.0001; hazard ratio, 1.36 and 1.39). Additionally, within the HPV-negative group, the smoking-related subgroup exhibited worse prognosis compared to the subgroup with high expression in immune response (log rank, p = 0.039; hazard ratio, 1.53). The HNSC88 not only enables the identification of HPV-associated subtypes, but also proposes two potential HPV-negative subtypes with distinct prognoses and molecular signatures. This study provides valuable strategies for summarizing the roles and influences of genes in tumorigenesis for identifying molecular signatures and subtypes of HNSC.

Gene Set Based Integrated Methylome and Transcriptome Analysis Reveals Potential Molecular Mechanisms Linking Cigarette Smoking and Related Diseases

Advanced integrative analysis of DNA methylation and transcriptomics data may provide deeper insights into smoke-induced epigenetic alterations, their effects on gene expression and related biological processes, linking cigarette smoking and related diseases. We hypothesize that accumulation of DNA methylation changes in CpG sites across genomic locations of different genes might have biological significance. We tested the hypothesis by performing gene set based integrative analysis of blood DNA methylation and transcriptomics data to identify potential transcriptomic consequences of smoking via changes in DNA methylation in the Young Finns Study (YFS) participants (n = 1114, aged 34-49 years, women: 54%, men: 46%). First, we performed epigenome-wide association study (EWAS) of smoking. We then defined sets of genes based on DNA methylation status within their genomic regions, for example, sets of genes containing hyper- or hypomethylated CpG sites in their body or promoter regions. Gene set analysis was performed using transcriptomics data from the same participants. Two sets of genes, one containing 49 genes with hypomethylated CpG sites in their body region and the other containing 33 genes with hypomethylated CpG sites in their promoter region, were differentially expressed among the smokers. Genes in the two gene sets are involved in bone formation, metal ion transport, cell death, peptidyl-serine phosphorylation, and cerebral cortex development process, revealing epigenetic-transcriptomic pathways to smoking-related diseases such as osteoporosis, atherosclerosis, and cognitive impairment. These findings contribute to a deeper understanding of the pathophysiology of smoking-related diseases and may provide potential therapeutic targets.

Identifying chronic obstructive pulmonary disease from integrative omics and clustering in lung tissue

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is a highly morbid and heterogenous disease. While COPD is defined by spirometry, many COPD characteristics are seen in cigarette smokers with normal spirometry. The extent to which COPD and COPD heterogeneity is captured in omics of lung tissue is not known.

METHODS

We clustered gene expression and methylation data in 78 lung tissue samples from former smokers with normal lung function or severe COPD. We applied two integrative omics clustering methods: (1) Similarity Network Fusion (SNF) and (2) Entropy-Based Consensus Clustering (ECC).

RESULTS

SNF clusters were not significantly different by the percentage of COPD cases (48.8% vs. 68.6%, p = 0.13), though were different according to median forced expiratory volume in one second (FEV1) % predicted (82 vs. 31, p = 0.017). In contrast, the ECC clusters showed stronger evidence of separation by COPD case status (48.2% vs. 81.8%, p = 0.013) and similar stratification by median FEV1% predicted (82 vs. 30.5, p = 0.0059). ECC clusters using both gene expression and methylation were identical to the ECC clustering solution generated using methylation data alone. Both methods selected clusters with differentially expressed transcripts enriched for interleukin signaling and immunoregulatory interactions between lymphoid and non-lymphoid cells.

CONCLUSIONS

Unsupervised clustering analysis from integrated gene expression and methylation data in lung tissue resulted in clusters with modest concordance with COPD, though were enriched in pathways potentially contributing to COPD-related pathology and heterogeneity.

The role of autoimmune antibodies to predict secondary autoimmunity in patients with relapsing-remitting multiple sclerosis treated with alemtuzumab: A nationwide prospective survey

Background

Alemtuzumab (ALZ) is an immune reconstitution therapy for treating relapsing-remitting multiple sclerosis (RRMS). However, ALZ increases the risk of secondary autoimmune diseases (SADs).

Objective

We explored whether the detection of autoimmune antibodies (auto-Abs) could predict the development of SADs.

Methods

We included all patients with RRMS in Sweden who initiated ALZ treatment (n = 124, 74 female subjects) from 2009 to 2019. The presence of auto-Abs was determined in plasma samples obtained at the baseline and at 6, 12, and 24 months of follow-up, as well as in a subgroup of patients (n = 51), it was determined in plasma samples obtained at the remaining 3-month intervals up to 24 months. Monthly blood tests, urine tests, and the assessment of clinical symptoms were performed for monitoring safety including that of SADs.

Results

Autoimmune thyroid disease (AITD) developed in 40% of patients, within a median follow-up of 4.5 years. Thyroid auto-Abs were detected in 62% of patients with AITD. The presence of thyrotropin receptor antibodies (TRAbs) at the baseline increased the risk of AITD by 50%. At 24 months, thyroid auto-Abs were detected in 27 patients, and 93% (25/27) developed AITD. Among patients without thyroid auto-Abs, only 30% (15/51) developed AITD (p &lt; 0.0001). In the subgroup of patients (n = 51) with more frequent sampling for auto-Abs, 27 patients developed ALZ-induced AITD, and 19 of them had detectable thyroid auto-Abs prior to the AITD onset, with a median interval of 216 days. Eight patients (6.5%) developed non-thyroid SAD, and none had detectable non-thyroid auto-Abs.

Conclusion

We conclude that monitoring thyroid auto-Abs, essentially TRAbs, may improve the surveillance of AITD associated with ALZ treatment. The risk for non-thyroid SADs was low, and monitoring non-thyroid auto-Abs did not seem to provide any additional information for predicting non-thyroid SADs.

Comparison of effects of tobacco cigarettes, electronic nicotine delivery systems and tobacco heating products on miRNA-mediated gene expression. A systematic review

OBJECTIVES

This work attempts to summarize current knowledge on the effects of cigarettes, electronic nicotine delivery systems and tobacco heating products on miRNA-mediated gene expression regulation and on their possible impact on smoking-related respiratory disease development.

MATERIALS AND METHODS

Literature search by terms combination: 'smoking', 'cigarette' 'THP', 'tobacco heating product', 'ENDS', 'electronic nicotine delivery system', 'e-cigarette', electronic cigarette' and 'miRNA-mediated gene expression' has been performed from October 2021 to February 2022. In this systematic review all relevant literature, including clinical trials, cellular and animal-based studies were included.

RESULTS

Cigarette smoke (CS) significantly altered transcriptome, including miRNAs expression profile. MiRNA-mediated gene expression is mentioned as one of the mechanisms associated with smoking-related respiratory disease development. Differential expression of miRNAs was reduced in aerosol from e-cigarettes (EC) and tobacco heating products (THP) when compared to CS. However, there was a significant alteration of some miRNAs expression when compared to air-controls in both EC and THP.

DISCUSSION

CS negatively affects transcriptome and miRNA-mediated gene expression regulation because of a huge number of hazardous substances which predispose to smoking-related diseases. Despite the reduced effect of ENDS and THP on miRNAs profile compared to CS, differences in expression of miRNAs when compared to air-control were observed, which may be harmful to never-smokers who may perceive such alternative smoking products as non-hazardous. To clearly indicate the role of ENDS and THP in the alteration of miRNA-mediated gene expression and the development of smoking-related respiratory diseases associated with this mechanism, more long-term studies should be performed in the future.

Peripheral blood RNA biomarkers for cardiovascular disease from bench to bedside: a position paper from the EU-CardioRNA COST action CA17129

Despite significant advances in the diagnosis and treatment of cardiovascular diseases, recent calls have emphasized the unmet need to improve precision-based approaches in cardiovascular disease. Although some studies provide preliminary evidence of the diagnostic and prognostic potential of circulating coding and non-coding RNAs, the complex RNA biology and lack of standardization have hampered the translation of these markers into clinical practice. In this position paper of the CardioRNA COST action CA17129, we provide recommendations to standardize the RNA development process in order to catalyse efforts to investigate novel RNAs for clinical use. We list the unmet clinical needs in cardiovascular disease, such as the identification of high-risk patients with ischaemic heart disease or heart failure who require more intensive therapies. The advantages and pitfalls of the different sample types, including RNAs from plasma, extracellular vesicles, and whole blood, are discussed in the sample matrix, together with their respective analytical methods. The effect of patient demographics and highly prevalent comorbidities, such as metabolic disorders, on the expression of the candidate RNA is presented and should be reported in biomarker studies. We discuss the statistical and regulatory aspects to translate a candidate RNA from a research use only assay to an in-vitro diagnostic test for clinical use. Optimal planning of this development track is required, with input from the researcher, statistician, industry, and regulatory partners.

What genes are differentially expressed in individuals with schizophrenia? A systematic review

Schizophrenia is a severe, complex mental disorder characterized by a combination of positive symptoms, negative symptoms, and impaired cognitive function. Schizophrenia is highly heritable (~80%) with multifactorial etiology and complex polygenic genetic architecture. Despite the large number of genetic variants associated with schizophrenia, few causal variants have been established. Gaining insight into the mechanistic influences of these genetic variants may facilitate our ability to apply these findings to prevention and treatment. Though there have been more than 300 studies of gene expression in schizophrenia over the past 15 years, none of the studies have yielded consistent evidence for specific genes that contribute to schizophrenia risk. The aim of this work is to conduct a systematic review and synthesis of case-control studies of genome-wide gene expression in schizophrenia. Comprehensive literature searches were completed in PubMed, EmBase, and Web of Science, and after a systematic review of the studies, data were extracted from those that met the following inclusion criteria: human case-control studies comparing the genome-wide transcriptome of individuals diagnosed with schizophrenia to healthy controls published between January 1, 2000 and June 30, 2020 in the English language. Genes differentially expressed in cases were extracted from these studies, and overlapping genes were compared to previous research findings from the genome-wide association, structural variation, and tissue-expression studies. The transcriptome-wide analysis identified different genes than those previously reported in genome-wide association, exome sequencing, and structural variation studies of schizophrenia. Only one gene, GBP2, was replicated in five studies. Previous work has shown that this gene may play a role in immune function in the etiology of schizophrenia, which in turn could have implications for risk profiling, prevention, and treatment. This review highlights the methodological inconsistencies that impede valid meta-analyses and synthesis across studies. Standardization of the use of covariates, gene nomenclature, and methods for reporting results could enhance our understanding of the potential mechanisms through which genes exert their influence on the etiology of schizophrenia. Although these results are promising, collaborative efforts with harmonization of methodology will facilitate the identification of the role of genes underlying schizophrenia.

Transcriptomic analyses of humans and mice provide insights into depression

Accumulating studies have been conducted to identify risk genes and relevant biological mechanisms underlying major depressive disorder (MDD). In particular, transcriptomic analyses in brain regions engaged in cognitive and emotional processes, e.g., the dorsolateral prefrontal cortex (DLPFC), have provided essential insights. Based on three independent DLPFC RNA-seq datasets of 79 MDD patients and 75 healthy controls, we performed differential expression analyses using two alternative approaches for cross-validation. We also conducted transcriptomic analyses in mice undergoing chronic variable stress (CVS) and chronic social defeat stress (CSDS). We identified 12 differentially expressed genes (DEGs) through both analytical methods in MDD patients, the majority of which were also dysregulated in stressed mice. Notably, the mRNA level of the immediate early gene FOS ( Fos proto-oncogene) was significantly decreased in both MDD patients and CVS-exposed mice, and CSDS-susceptible mice exhibited a greater reduction in Fos expression compared to resilient mice. These findings suggest the potential key roles of this gene in the pathogenesis of MDD related to stress exposure. Altered transcriptomes in the DLPFC of MDD patients might be, at least partially, the result of stress exposure, supporting that stress is a primary risk factor for MDD.

Cigarette smoke-induced alterations in blood: A review of research on DNA methylation and gene expression

Worldwide, smoking remains a threat to public health, causing preventable diseases and premature mortality. Cigarette smoke is a powerful inducer of DNA methylation and gene expression alterations, which have been associated with negative health consequences. Here, we review the current knowledge on smoking-related changes in DNA methylation and gene expression in human blood samples. We identified 30 studies focused on the association between active smoking, DNA methylation modifications, and gene expression alterations. Overall, we identified 1,758 genes with differentially methylated sites (DMS) and differentially expressed genes (DEG) between smokers and nonsmokers, of which 261 were detected in multiple studies (≥4). The most frequently (≥10 studies) reported genes were AHRR, GPR15, GFI1, and RARA. Functional enrichment analysis of the 261 genes identified the aryl hydrocarbon receptor repressor and T cell pathways (T helpers 1 and 2) as influenced by smoking status. These results highlight specific genes for future mechanistic and translational research that may be associated with cigarette smoke exposure and smoking-related diseases. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

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.

Smoking-related changes in DNA methylation and gene expression are associated with cardio-metabolic traits

BACKGROUND

Tobacco smoking is a well-known modifiable risk factor for many chronic diseases, including cardiovascular disease (CVD). One of the proposed underlying mechanism linking smoking to disease is via epigenetic modifications, which could affect the expression of disease-associated genes. Here, we conducted a three-way association study to identify the relationship between smoking-related changes in DNA methylation and gene expression and their associations with cardio-metabolic traits.

RESULTS

We selected 2549 CpG sites and 443 gene expression probes associated with current versus never smokers, from the largest epigenome-wide association study and transcriptome-wide association study to date. We examined three-way associations, including CpG versus gene expression, cardio-metabolic trait versus CpG, and cardio-metabolic trait versus gene expression, in the Rotterdam study. Subsequently, we replicated our findings in The Cooperative Health Research in the Region of Augsburg (KORA) study. After correction for multiple testing, we identified both cis- and trans-expression quantitative trait methylation (eQTM) associations in blood. Specifically, we found 1224 smoking-related CpGs associated with at least one of the 443 gene expression probes, and 200 smoking-related gene expression probes to be associated with at least one of the 2549 CpGs. Out of these, 109 CpGs and 27 genes were associated with at least one cardio-metabolic trait in the Rotterdam Study. We were able to replicate the associations with cardio-metabolic traits of 26 CpGs and 19 genes in the KORA study. Furthermore, we identified a three-way association of triglycerides with two CpGs and two genes (GZMA; CLDND1), and BMI with six CpGs and two genes (PID1; LRRN3). Finally, our results revealed the mediation effect of cg03636183 (F2RL3), cg06096336 (PSMD1), cg13708645 (KDM2B), and cg17287155 (AHRR) within the association between smoking and LRRN3 expression.

CONCLUSIONS

Our study indicates that smoking-related changes in DNA methylation and gene expression are associated with cardio-metabolic risk factors. These findings may provide additional insights into the molecular mechanisms linking smoking to the development of CVD.

Use of an Exposome Approach to Understand the Effects of Exposures From the Natural, Built, and Social Environments on Cardio-Vascular Disease Onset, Progression, and Outcomes

Obesity, diabetes, and hypertension have increased by epidemic proportions in recent years among African Americans in comparison to Whites resulting in significant adverse cardiovascular disease (CVD) disparities. Today, African Americans are 30% more likely to die of heart disease than Whites and twice as likely to have a stroke. The causes of these disparities are not yet well-understood. Improved methods for identifying underlying risk factors is a critical first step toward reducing Black:White CVD disparities. This article will focus on environmental exposures in the external environment and how they can lead to changes at the cellular, molecular, and organ level to increase the personal risk for CVD and lead to population level CVD racial disparities. The external environment is defined in three broad domains: natural (air, water, land), built (places you live, work, and play) and social (social, demographic, economic, and political). We will describe how environmental exposures in the natural, built, and social environments "get under the skin" to affect gene expression though epigenetic, pan-omics, and related mechanisms that lead to increased risk for adverse CVD health outcomes and population level disparities. We also will examine the important role of metabolomics, proteomics, transcriptomics, genomics, and epigenomics in understanding how exposures in the natural, built, and social environments lead to CVD disparities with implications for clinical, public health, and policy interventions. In this review, we apply an exposome approach to Black:White CVD racial disparities. The exposome is a measure of all the exposures of an individual across the life course and the relationship of those exposures to health effects. The exposome represents the totality of exogenous (external) and endogenous (internal) exposures from conception onwards, simultaneously distinguishing, characterizing, and quantifying etiologic, mediating, moderating, and co-occurring risk and protective factors and their relationship to disease. Specifically, it assesses the biological mechanisms and underlying pathways through which chemical and non-chemical environmental exposures are associated with CVD onset, progression and outcomes. The exposome is a promising approach for understanding the complex relationships among environment, behavior, biology, genetics, and disease phenotypes that underlie population level, Black: White CVD disparities.

Single-cell analyses identify dysfunctional CD16+ CD8 T cells in smokers

Tobacco smoke exposure contributes to the global burden of communicable and chronic diseases. To identify the immune cells affected by smoking, we use single-cell RNA sequencing on peripheral blood from smokers and nonsmokers. Transcriptomes reveal a subpopulation of FCGR3A (CD16)-expressing natural killer (NK)-like CD8 T lymphocytes that increase in smokers. Mass cytometry confirms elevated CD16⁺ CD8 T cells in smokers. Inferred as highly differentiated by pseudotime analysis, NK-like CD8 T cells express markers that are characteristic of effector memory re-expressing CD45RA T (T_EMRA) cells. Indicative of immune aging, smokers’ CD8 T cells are biased toward differentiated cells, and smokers have fewer naive cells than nonsmokers. DNA methylation-based models show that smoking dose is associated with accelerated aging and decreased telomere length, a biomarker of T cell senescence. Immune aging accompanies T cell senescence, which can ultimately lead to impaired immune function. This suggests a role for smoking-induced, senescence-associated immune dysregulation in smoking-mediated pathologies.

Smoking shifts the composition of CD8 T cells from naive to differentiated states

NK-like CD16⁺ CD8 T_EMRA cells are elevated in smokers and express GZMB and PRF1

DNA methylation links smoking dose with age acceleration and shortened telomeres

CD8 T, CD4 T, NKT, NK, and monocytes express senescence-linked genes in smokers

Novel and Known Gene-Smoking Interactions With cIMT Identified as Potential Drivers for Atherosclerosis Risk in West-African Populations of the AWI-Gen Study

Introduction

Atherosclerosis is a key contributor to the burden of cardiovascular diseases (CVDs) and many epidemiological studies have reported on the effect of smoking on carotid intima-media thickness (cIMT) and its subsequent effect on CVD risk. Gene-environment interaction studies have contributed towards understanding some of the missing heritability of genome-wide association studies. Gene-smoking interactions on cIMT have been studied in non-African populations (European, Latino-American, and African American) but no comparable African research has been reported. Our aim was to investigate smoking-SNP interactions on cIMT in two West African populations by genome-wide analysis.

Materials and methods

Only male participants from Burkina Faso (Nanoro = 993) and Ghana (Navrongo = 783) were included, as smoking was extremely rare among women. Phenotype and genotype data underwent stringent QC and genotype imputation was performed using the Sanger African Imputation Panel. Smoking prevalence among men was 13.3% in Nanoro and 42.5% in Navrongo. We analyzed gene-smoking interactions with PLINK after adjusting for covariates: age and 6 PCs (Model 1); age, BMI, blood pressure, fasting glucose, cholesterol levels, MVPA, and 6 PCs (Model 2). All analyses were performed at site level and for the combined data set.

Results

In Nanoro, we identified new gene-smoking interaction variants for cIMT within the previously described RCBTB1 region (rs112017404, rs144170770, and rs4941649) (Model 1: p = 1.35E-07; Model 2: p = 3.08E-08). In the combined sample, two novel intergenic interacting variants were identified, rs1192824 in the regulatory region of TBC1D8 (p = 5.90E-09) and rs77461169 (p = 4.48E-06) located in an upstream region of open chromatin. In silico functional analysis suggests the involvement of genes implicated in biological processes related to cell or biological adhesion and regulatory processes in gene-smoking interactions with cIMT (as evidenced by chromatin interactions and eQTLs).

Discussion

This is the first gene-smoking interaction study for cIMT, as a risk factor for atherosclerosis, in sub-Saharan African populations. In addition to replicating previously known signals for RCBTB1, we identified two novel genomic regions (TBC1D8, near BCHE) involved in this gene-environment interaction.

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.

OSCA: a tool for omic-data-based complex trait analysis

The rapid increase of omic data has greatly facilitated the investigation of associations between omic profiles such as DNA methylation (DNAm) and complex traits in large cohorts. Here, we propose a mixed-linear-model-based method called MOMENT that tests for association between a DNAm probe and trait with all other distal probes fitted in multiple random-effect components to account for unobserved confounders. We demonstrate by simulations that MOMENT shows a lower false positive rate and more robustness than existing methods. MOMENT has been implemented in a versatile software package called OSCA together with a number of other implementations for omic-data-based analyses.

Gene expression profile of human lung in a relatively early stage of COPD with emphysema

Purpose

As only some smokers develop COPD with emphysema, we explored the molecular pathogenesis of early-stage COPD with emphysema using gene expression profiling of human lung tissues.

Patients and methods

First, 110 subjects who had smoked more than ten pack-years were classified into three groups: COPD with emphysema, COPD without emphysema, and healthy smokers. COPD and emphysema were confirmed by post-bronchodilator forced expiratory volume in 1 second/forced vital capacity <0.7 and by chest computed tomography. Lung tissues obtained surgically from the 110 subjects were processed and used for RNA-Seq analysis.

Results

Among the 110 subjects, 29 had COPD with emphysema, 21 had COPD without emphysema, and 60 were healthy smokers; their mean post-bronchodilator forced expiratory volume in 1 second values were 78%, 80%, and 94%, respectively. Using RNA-Seq, we evaluated 16,676 genes expressed in lung tissues. Among them, 1,226 genes in the COPD with emphysema group and 434 genes in the COPD without emphysema group were differentially expressed genes compared to the expression in healthy smokers. In the COPD with emphysema group, ACER2 and LMAN2L were markedly increased and decreased, respectively. In the COPD without emphysema group, the CHRM3 gene, previously reported to be associated with COPD, and HDAC10 were markedly increased and decreased, respectively.

Conclusion

Our study identified differences in gene expression in subjects with COPD according to emphysema status using RNA-Seq transcriptome analysis. These findings may have mechanistic implications in COPD.

Infectious flexor hand tenosynovitis: State of knowledge. A study of 120 cases

INTRODUCTION

Since Kanavel in 1905, knowledge of phlegmon of flexor tendon sheaths of the fingers have evolved over the twentieth century. This serious infection is 20% of infections of the hand and may have adverse consequences for the function of the finger and even beyond, of the hand. Amputation is always a risk. Frequently face this type of infection, we conducted a retrospective study and made an inventory of knowledge in order to consolidate and improve the overall care.

MATERIALS & METHODS

The study was retrospective and cross, focused on 120 patients operated on at Hand Surgery Unit, during 4 years. Inclusion criteria were primary or secondary infection of the sheath of the flexor tendons of the fingers.The evaluation focused on clinical and paraclinical perioperative parameters. At last follow, digital mobility (Total Active Motion), the functional score of QuickDASH and the socio-professional consequences were evaluated.

RESULTS

The mean age was 40 years, with a male predominance. The hospital stay was 17 days on average (3 days to 80 days). From the classification of Michon, as amended by Sokolow, we found 60 Stage I, 48 stage II, 12 stage III. The Total Active Motion was respectively 240 °, 140 °, 40 °. QuickDASH scores were respectively 20, 56 and 90 out of 100. The time for return to work was 1 month for stage I, 4 months for stage II and 12 months for stage III.

DISCUSSION

The long-term functional outcome was generally poor, with stiffness or digital amputation. The poor prognostic factors were: the initial advanced stage of infection, infection beta-haemolytic Streptococcus group A, and delayed surgical management. Smoking was identified as a new risk factor in this disease, as well as diabetes or immunodeficiency. This study confirmed the predominance of Staphylococcus, and scalability of the infection depending on the mode of contamination, and / or type of germ that is to say, scalability schedule for β-hemolytic streptococci group A chronic and scalability for intracellular bacteria (mycobacteria).

CONCLUSION

Any suspicion of flexor hand tenosynovitis should lead to an emergency surgical exploration, not primary antibiotics prescription!

RNA sequencing to predict response to TNF-α inhibitors reveals possible mechanism for nonresponse in smokers

BACKGROUND

Several studies have employed microarray-based profiling to predict response to tumor necrosis factor-alpha inhibitors (TNFi) in rheumatoid arthritis (RA); yet efforts to validate these targets have failed to show predictive abilities acceptable for clinical practice.

METHODS

The eighty most extreme responders and nonresponders to TNFi therapy were selected from the observational BiOCURA cohort. RNA sequencing was performed on mRNA from peripheral blood mononuclear cells (PBMCs) collected before initiation of treatment. The expression of pathways as well as individual gene transcripts between responders and nonresponders was investigated. Promising targets were technically replicated and validated in n = 40 new patients using qPCR assays.

RESULTS

Before therapy initiation, nonresponders had lower expression of pathways related to interferon and cytokine signaling, while also showing higher levels of two genes, GPR15 and SEMA6B (p = 0.02). The two targets could be validated, however, additional analyses revealed that GPR15 and SEMA6B did not independently predict response, but were rather dose-dependent markers of smoking (p < 0.0001).

CONCLUSIONS

The study did not identify new transcripts ready to use in clinical practice, yet GPR15 and SEMA6B were recognized as candidate explanatory markers for the reduced treatment success in RA smokers.

Systematic correlation of environmental exposure and physiological and self-reported behaviour factors with leukocyte telomere length

Background

It is hypothesized that environmental exposures and behaviour influence telomere length, an indicator of cellular ageing. We systematically associated 461 indicators of environmental exposures, physiology and self-reported behaviour with telomere length in data from the US National Health and Nutrition Examination Survey (NHANES) in 1999-2002. Further, we tested whether factors identified in the NHANES participants are also correlated with gene expression of telomere length modifying genes.

Methods

We correlated 461 environmental exposures, behaviours and clinical variables with telomere length, using survey-weighted linear regression, adjusting for sex, age, age squared, race/ethnicity, poverty level, education and born outside the USA, and estimated the false discovery rate to adjust for multiple hypotheses. We conducted a secondary analysis to investigate the correlation between identified environmental variables and gene expression levels of telomere-associated genes in publicly available gene expression samples.

Results

After correlating 461 variables with telomere length, we found 22 variables significantly associated with telomere length after adjustment for multiple hypotheses. Of these varaibales, 14 were associated with longer telomeres, including biomarkers of polychlorinated biphenyls([PCBs; 0.1 to 0.2 standard deviation (SD) increase for 1 SD increase in PCB level, P  < 0.002] and a form of vitamin A, retinyl stearate. Eight variables associated with shorter telomeres, including biomarkers of cadmium, C-reactive protein and lack of physical activity. We could not conclude that PCBs are correlated with gene expression of telomere-associated genes.

Conclusions

Both environmental exposures and chronic disease-related risk factors may play a role in telomere length. Our secondary analysis found no evidence of association between PCBs/smoking and gene expression of telomere-associated genes. All correlations between exposures, behaviours and clinical factors and changes in telomere length will require further investigation regarding biological influence of exposure.

A whole-blood transcriptome meta-analysis identifies gene expression signatures of cigarette smoking

Cigarette smoking is a leading modifiable cause of death worldwide. We hypothesized that cigarette smoking induces extensive transcriptomic changes that lead to target-organ damage and smoking-related diseases. We performed a meta-analysis of transcriptome-wide gene expression using whole blood-derived RNA from 10,233 participants of European ancestry in six cohorts (including 1421 current and 3955 former smokers) to identify associations between smoking and altered gene expression levels. At a false discovery rate (FDR) <0.1, we identified 1270 differentially expressed genes in current vs. never smokers, and 39 genes in former vs. never smokers. Expression levels of 12 genes remained elevated up to 30 years after smoking cessation, suggesting that the molecular consequence of smoking may persist for decades. Gene ontology analysis revealed enrichment of smoking-related genes for activation of platelets and lymphocytes, immune response, and apoptosis. Many of the top smoking-related differentially expressed genes, including LRRN3 and GPR15, have DNA methylation loci in promoter regions that were recently reported to be hypomethylated among smokers. By linking differential gene expression with smoking-related disease phenotypes, we demonstrated that stroke and pulmonary function show enrichment for smoking-related gene expression signatures. Mediation analysis revealed the expression of several genes (e.g. ALAS2) to be putative mediators of the associations between smoking and inflammatory biomarkers (IL6 and C-reactive protein levels). Our transcriptomic study provides potential insights into the effects of cigarette smoking on gene expression in whole blood and their relations to smoking-related diseases. The results of such analyses may highlight attractive targets for treating or preventing smoking-related health effects.

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.

Cigarette smoking and hormones

PURPOSE OF REVIEW

The purpose is to provide a summary of the effects of cigarette smoking on steroid hormone metabolism and how it affects female fertility.

RECENT FINDINGS

Components of tobacco smoke such as polycyclic aromatic hydrocarbons lead to transcriptional upregulation of a number of genes, including members of the cytochrome P450 (CYP) family, in particular CYP1B1 and CYP1A1. In humans, CYP1A1 and CYP1A2 are the primary enzymes catalyzing the 2-hydroxylation of estradiol. This pathway shunts available estrogen away from the more estrogenically potent 16α-hydroxylation to the production of catechol estrogens, mainly 2 and 4 hydroxyestradiol.

SUMMARY

Smoking has multiple effects on hormone secretion and metabolism. These effects are mainly mediated by the pharmacological action of tobacco alkaloids (nicotine and its metabolite cotinine). A strong body of evidence indicates that the negative effects of cigarette smoking on fertility compromises nearly every system involved in the reproductive process.

The dynamics of smoking-related disturbed methylation: a two time-point study of methylation change in smokers, non-smokers and former smokers

Background

The evidence for epigenome-wide associations between smoking and DNA methylation continues to grow through cross-sectional studies. However, few large-scale investigations have explored the associations using observations for individuals at multiple time-points. Here, through the use of the Illumina 450K BeadChip and data collected at two time-points separated by approximately 7 years, we investigate changes in methylation over time associated with quitting smoking or remaining a former smoker, and those associated with continued smoking.

Results

Our results indicate that after quitting smoking the most rapid reversion of altered methylation occurs within the first two decades, with reversion rates related to the initial differences in methylation. For 52 CpG sites, the change in methylation from baseline to follow-up is significantly different for former smokers relative to the change for never smokers (lowest p-value 3.61 x 10^-39 for cg26703534, gene AHRR). Most of these sites’ respective regions have been previously implicated in smoking-associated diseases. Despite the early rapid change, dynamism of methylation appears greater in former smokers vs never smokers even four decades after cessation. Furthermore, our study reveals the heterogeneous effect of continued smoking: the methylation levels of some loci further diverge between smokers and non-smokers, while others re-approach. Though intensity of smoking habit appears more significant than duration, results remain inconclusive.

Conclusions

This study improves the understanding of the dynamic link between cigarette smoking and methylation, revealing the continued fluctuation of methylation levels decades after smoking cessation and demonstrating that continuing smoking can have an array of effects. The results can facilitate insights into the molecular mechanisms behind smoking-induced disturbed methylation, improving the possibility for development of biomarkers of past smoking behavior and increasing the understanding of the molecular path from exposure to disease.

Electronic supplementary material

The online version of this article (10.1186/s12864-017-4198-0) contains supplementary material, which is available to authorized users.

RNA sequencing identifies novel non-coding RNA and exon-specific effects associated with cigarette smoking

BACKGROUND

Cigarette smoking is the leading modifiable risk factor for disease and death worldwide. Previous studies quantifying gene-level expression have documented the effect of smoking on mRNA levels. Using RNA sequencing, it is possible to analyze the impact of smoking on complex regulatory phenomena (e.g. alternative splicing, differential isoform usage) leading to a more detailed understanding of the biology underlying smoking-related disease.

METHODS

We used whole-blood RNA sequencing to describe gene and exon-level expression differences between 229 current and 286 former smokers in the COPDGene study. We performed differential gene expression and differential exon usage analyses using the voom/limma and DEXseq R packages. Samples from current and former smokers were compared while controlling for age, gender, race, lifetime smoke exposure, cell counts, and technical covariates.

RESULTS

At an adjusted p-value <0.05, 171 genes were differentially expressed between current and former smokers. Differentially expressed genes included 7 long non-coding RNAs that have not been previously associated with smoking: LINC00599, LINC01362, LINC00824, LINC01624, RP11-563D10.1, RP11-98G13.1, AC004791.2. Secondary analysis of acute smoking (having smoked within 2-h) revealed 5 of the 171 smoking genes demonstrated an acute response above the baseline effect of chronic smoking. Exon-level analyses identified 9 exons from 8 genes with significant differential usage by smoking status, suggesting smoking-induced changes in isoform expression.

CONCLUSIONS

Transcriptomic changes at the gene and exon levels from whole blood can refine our understanding of the molecular mechanisms underlying the response to smoking.

The effect of statins on cardiovascular outcomes by smoking status: A systematic review and meta-analysis of randomized controlled trials

Smoking is an important risk factor for cardiovascular disease (CVD) morbidity and mortality. The impact of statin therapy on CVD risk by smoking status has not been fully investigated. Therefore we assessed the impact of statin therapy on CVD outcomes by smoking status through a systematic review of the literature and meta-analysis of available randomized controlled trials (RCTs). The literature search included EMBASE, ProQuest, CINAHL and PUBMED databases to 30 January 2016 to identify RCTs that investigated the effect of statin therapy on cumulative incidence of major CVD endpoints (e.g. non-fatal myocardial infarction, revascularization, unstable angina, and stroke). Relative risks (RR) ratios were calculated from the number of events in different treatment groups for both smokers and non-smokers. Finally 11 trials with 89,604 individuals were included. The number of smokers and non-smokers in the statin groups of the analyzed studies was 8826 and 36,090, respectively. The RR for major CV events was 0.73 (95% confidence interval [CI]: 0.67-0.81; p<0.001) in nonsmokers and 0.72 (95%CI: 0.64-0.81; p<0.001) in smokers. Moderate to high heterogeneity was observed both in non-smokers (I²=77.1%, p<0.001) and in smokers (I²=51.6%, p=0.024) groups. Smokers seemed to benefit slightly more from statins than non-smokers according to the number needed to treat (NNT) analysis (23.5 vs 26.8) based on RRs applied to the control event rates. The number of avoided events per 1000 individuals was 42.5 (95%CI: 28.9-54.6) in smokers and 37.3 (95%CI: 27.2-46.4) in non-smokers. In conclusion, this meta-analysis suggests that the effect of statins on CVD is similar for smokers and non-smokers, but in terms of NNTs and number of avoided events, smokers seem to benefit more although non-significantly.

Differential gene expression patterns between smokers and non-smokers: cause or consequence?

The molecular mechanisms causing smoking‐induced health decline are largely unknown. To elucidate the molecular pathways involved in cause and consequences of smoking behavior, we conducted a genome‐wide gene expression study in peripheral blood samples targeting 18 238 genes. Data of 743 smokers, 1686 never smokers and 890 ex‐smokers were available from two population‐based cohorts from the Netherlands. In addition, data of 56 monozygotic twin pairs discordant for ever smoking were used. One hundred thirty‐two genes were differentially expressed between current smokers and never smokers (P < 1.2 × 10⁻⁶, Bonferroni correction). The most significant genes were G protein‐coupled receptor 15 (P < 1 × 10⁻¹⁵⁰) and leucine‐rich repeat neuronal 3 (P < 1 × 10⁻⁴⁴). The smoking‐related genes were enriched for immune system, blood coagulation, natural killer cell and cancer pathways. By taking the data of ex‐smokers into account, expression of these 132 genes was classified into reversible (94 genes), slowly reversible (31 genes), irreversible (6 genes) or inconclusive (1 gene). Expression of 6 of the 132 genes (three reversible and three slowly reversible) was confirmed to be reactive to smoking as they were differentially expressed in monozygotic pairs discordant for smoking. Cis‐expression quantitative trait loci for GPR56 and RARRES3 (downregulated in smokers) were associated with increased number of cigarettes smoked per day in a large genome‐wide association meta‐analysis, suggesting a causative effect of GPR56 and RARRES3 expression on smoking behavior. In conclusion, differential gene expression patterns in smokers are extensive and cluster in several underlying disease pathways. Gene expression differences seem mainly direct consequences of smoking, and largely reversible after smoking cessation. However, we also identified DNA variants that may influence smoking behavior via the mediating gene expression.

Aerosol from Tobacco Heating System 2.2 has reduced impact on mouse heart gene expression compared with cigarette smoke

Experimental studies clearly demonstrate a causal effect of cigarette smoking on cardiovascular disease. To reduce the individual risk and population harm caused by smoking, alternative products to cigarettes are being developed. We recently reported on an apolipoprotein E-deficient (Apoe-/-) mouse inhalation study that compared the effects of exposure to aerosol from a candidate modified risk tobacco product, Tobacco Heating System 2.2 (THS2.2), and smoke from the reference cigarette (3R4F) on pulmonary and vascular biology. Here, we applied a transcriptomics approach to evaluate the impact of the exposure to 3R4F smoke and THS2.2 aerosol on heart tissues from the same cohort of mice. The systems response profiles demonstrated that 3R4F smoke exposure led to time-dependent transcriptomics changes (False Discovery Rate (FDR) < 0.05; 44 differentially expressed genes at 3-months; 491 at 8-months). Analysis of differentially expressed genes in the heart tissue indicated that 3R4F exposure induced the downregulation of genes involved in cytoskeleton organization and the contractile function of the heart, notably genes that encode beta actin (Actb), actinin alpha 4 (Actn4), and filamin C (Flnc). This was accompanied by the downregulation of genes related to the inflammatory response. None of these effects were observed in the group exposed to THS2.2 aerosol.

Impact of environmental volatile organic compounds on otitis media in children: Correlation between exposure and urinary metabolites

INTRODUCTION

Volatile organic compounds (VOCs) induce inflammatory responses. Tobacco smoke contains numerous VOCs and is a risk factor for otitis media effusion (OME); however, no previous studies have investigated the association between VOCs and OME.

OBJECTIVES

We used urinary metabolites and exposure to environmental risk factors to investigate the association between VOC and polycyclic aromatic hydrocarbon exposure and recurrent OME in children.

METHODS

Children with recurrent OME who visited the Otorhinolaryngology Department of Seoul National University Hospital between November 2014 and June 2015 were prospectively enrolled in the study. Recurrent OME was defined as more than two OME episodes over a 6-month period lasting longer than 2 months. The control group consisted of children without OME in the last year. Demographic information, including age, sex, and previous medical history was obtained, and endoscopic examinations of the tympanic membrane were performed. Urinary concentrations of 1-hydroxypyrene, 2-naphthol, hippuric acid, trans, trans-muconic acid (t,t-MA), mandelic acid, phenyl glyoxylic acid, and methyl hippuric acid were analyzed using high-performance liquid chromatography/tandem mass spectroscopy. Environmental factors assessed included house type, age, renovations, the presence of furniture <6 months old, proximity to a road, and exposure to passive smoking.

RESULTS

We enrolled 11 children with OME and 39 controls. Age and sex did not differ between groups. Exposure to passive smoking was significantly more common in the OME group than in the controls (P < 0.001). Urinary concentrations of t.t.-MA were significantly higher in the OME group (126.33 μg/g cr) than in controls (52.661 μg/g cr; P = 0.003). Other metabolites including 1-hydroxypyrene, 2-naphthol, hippuric acid, mandelic acid, phenyl glyoxylic acid, and methyl hippuric acid did not demonstrated significant relation with the OME.

CONCLUSIONS

Levels of t,t-MA, a biomarker of benzene exposure, were significantly higher in the OME group than in controls. Passive smoking was significantly more common in the OME group. Our findings suggest that high t,t-MA levels which were probably originated from passive smoking and other pollutants could be indicative OME in children.

Persistent alterations of gene expression profiling of human peripheral blood mononuclear cells from smokers

The number of validated biomarkers of tobacco smoke exposure is limited, and none exist for tobacco-related cancer. Additional biomarkers for smoke, effects on cellular systems in vivo are needed to improve early detection of lung cancer, and to assist the Food and Drug Administration in regulating exposures to tobacco products. We assessed the effects of smoking on the gene expression using human cell cultures and blood from a cross-sectional study. We profiled global transcriptional changes in cultured smokers' peripheral blood mononuclear cells (PBMCs) treated with cigarette smoke condensate (CSC) in vitro (n = 7) and from well-characterized smokers' blood (n = 36). ANOVA with adjustment for covariates and Pearson correlation were used for statistical analysis in this study. CSC in vitro altered the expression of 1 178 genes (177 genes with > 1.5-fold-change) at P < 0.05. In vivo, PBMCs of heavy and light smokers differed for 614 genes (29 with > 1.5-fold-change) at P < 0.05 (309 remaining significant after adjustment for age, race, and gender). Forty-one genes were persistently altered both in vitro and in vivo, 22 having the same expression pattern reported for non-small cell lung cancer. Our data provides evidence that persistent alterations of gene expression in vitro and in vivo may relate to carcinogenic effects of cigarette smoke, and the identified genes may serve as potential biomarkers for cancer. The use of an in vitro model to corroborate results from human studies provides a novel way to understand human exposure and effect. © 2015 Wiley Periodicals, Inc.

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

Gene expression profiling data can be used in toxicology to assess both the level and impact of toxicant exposure, aligned with a vision of 21st century toxicology. Here, we present a whole blood-derived gene signature that can distinguish current smokers from either nonsmokers or former smokers with high specificity and sensitivity. Such a signature that can be measured in a surrogate tissue (whole blood) may help in monitoring smoking exposure as well as discontinuation of exposure when the primarily impacted tissue (e.g., lung) is not readily accessible. The signature consisted of LRRN3, SASH1, PALLD, RGL1, TNFRSF17, CDKN1C, IGJ, RRM2, ID3, SERPING1, and FUCA1. Several members of this signature have been previously described in the context of smoking. The signature translated well across species and could distinguish mice that were exposed to cigarette smoke from ones exposed to air only or had been withdrawn from cigarette smoke exposure. Finally, the small signature of only 11 genes could be converted into a polymerase chain reaction-based assay that could serve as a marker to monitor compliance with a smoking abstinence protocol.

A haplotype-based normalization technique for the analysis and detection of allele specific expression

Background

Allele specific expression (ASE) has become an important phenotype, being utilized for the detection of cis-regulatory variation, nonsense mediated decay and imprinting in the personal genome, and has been used to both identify disease loci and consider the penetrance of damaging alleles. The detection of ASE using high throughput technologies relies on aligning short-read sequencing data, a process that has inherent biases, and there is still a need to develop fast and accurate methods to detect ASE given the unprecedented growth of sequencing information in big data projects.

Results

Here, we present a new approach to normalize RNA sequencing data in order to call ASE events with high precision in a short time-frame. Using simulated datasets we find that our approach dramatically improves reference allele quantification at heterozygous sites versus default mapping methods and also performs well compared to existing techniques for ASE detection, such as filtering methods and mapping to parental genomes, without the need for complex and time consuming manipulation. Finally, by sequencing the exomes and transcriptomes of 96 well-phenotyped individuals of the CARTaGENE cohort, we characterise the levels of ASE across individuals and find a significant association between the proportion of sites undergoing ASE within the genome and smoking.

Conclusions

The correct treatment and analysis of RNA sequencing data is vital to control for mapping biases and detect genuine ASE signals. By normalising RNA sequencing information after mapping, we show that this approach can be used to identify biologically relevant signals in personal genomes.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1238-8) contains supplementary material, which is available to authorized users.

The Effect of Different Case Definitions of Current Smoking on the Discovery of Smoking-Related Blood Gene Expression Signatures in Chronic Obstructive Pulmonary Disease

INTRODUCTION

Smoking is the number one modifiable environmental risk factor for chronic obstructive pulmonary disease (COPD). Clinical, epidemiological and increasingly "omics" studies assess or adjust for current smoking status using only self-report, which may be inaccurate. Objective measures such as exhaled carbon monoxide (eCO) may also be problematic owing to limitations in the measurements and the relatively short half life of the molecule. In this study, we determined the impact of different case definitions of current cigarette smoking on gene expression in peripheral blood of patients with COPD.

METHODS

Peripheral blood gene expression from 573 former- and current-smokers with COPD in the ECLIPSE study was used to find genes whose expression was associated with smoking status. Current smoking was defined using self-report, eCO concentrations, or both. Linear regression was used to determine the association of current smoking status with gene expression adjusting for age, sex and propensity score. Pathway enrichment analyses were performed on genes with P < .001.

RESULT

Using self-report or eCO, only two genes were differentially expressed between current and ex-smokers, with no enrichment in biological processes. When current smoking was defined using both eCO and self-report, four genes were differentially expressed (LRRN3, PID1, FUCA1, GPR15) with enrichment in 40 biological pathways related to metabolic processes, response to hypoxia and hormonal stimulus. Additionally, the combined definition provided better distributions of test statistics for differential gene expression.

CONCLUSION

A combined phenotype of eCO and self report allows for better discovery of genes and pathways related to current smoking.

IMPLICATIONS

Studies relying only on self report of smoking status to assess or adjust for the impact of smoking may not fully capture its effect and will lead to residual confounding of results.

Self-reported smoking, serum cotinine, and blood DNA methylation

BACKGROUND

Epigenome-wide profiling of DNA methylation pattern with respect to tobacco smoking has given rise to a new measure of smoking exposure. We investigated the relationships of methylation markers with both cotinine, an established marker of internal smoking exposure, and self-reported smoking.

METHODS

Blood DNA methylation levels across the genome and serum cotinine were measured in 1000 older adults aged 50-75 years. Epigenome-wide scans were performed to identify methylation markers associated with cotinine. The inter-dose-response relationships between the number of cigarettes smoked per day, cotinine concentration, and DNA methylation were modeled by restricted cubic spline regression.

RESULTS

Of 61 CpGs that passed the genome-wide significance threshold (p<1.13×10(-7)), 40 CpGs in 25 chromosomal regions were successfully replicated, showing 0.2-3% demethylation per 10ng/ml increases in cotinine. The strongest associations were observed for several loci at AHRR, F2RL3, 2q37.1, 6p21.33, and GFI1 that were previously identified to be related to self-reported smoking. One locus at RAB34 was newly discovered. Both cotinine and methylation markers exhibited non-linear relationships with the number of cigarettes smoked per day, where the highest rates of increase in cotinine and decreases in methylation were observed at low smoking intensity (1-15 cigarettes/day) and plateaued at high smoking intensity (>15-20 cigarettes/day). A clear linear relationship was observed between cotinine concentration and methylation level. Both cotinine and methylation markers showed similar accuracy in distinguishing current from never smoker, but only methylation markers distinguished former from never smoker with high accuracy.

CONCLUSIONS

Our study corroborates and expands the list of smoking-associated DNA methylation markers. Methylation levels were linearly related to cotinine concentration and provided accurate measures for both current and past smoking exposure.

Gene expression in major depressive disorder

The search for genetic variants underlying major depressive disorder (MDD) has not yet provided firm leads to its underlying molecular biology. A complementary approach is to study gene expression in relation to MDD. We measured gene expression in peripheral blood from 1848 subjects from The Netherlands Study of Depression and Anxiety. Subjects were divided into current MDD (N=882), remitted MDD (N=635) and control (N=331) groups. MDD status and gene expression were measured again 2 years later in 414 subjects. The strongest gene expression differences were between the current MDD and control groups (129 genes at false-discovery rate, FDR<0.1). Gene expression differences across MDD status were largely unrelated to antidepressant use, inflammatory status and blood cell counts. Genes associated with MDD were enriched for interleukin-6 (IL-6)-signaling and natural killer (NK) cell pathways. We identified 13 gene expression clusters with specific clusters enriched for genes involved in NK cell activation (downregulated in current MDD, FDR=5.8 × 10(-5)) and IL-6 pathways (upregulated in current MDD, FDR=3.2 × 10(-3)). Longitudinal analyses largely confirmed results observed in the cross-sectional data. Comparisons of gene expression results to the Psychiatric Genomics Consortium (PGC) MDD genome-wide association study results revealed overlap with DVL3. In conclusion, multiple gene expression associations with MDD were identified and suggest a measurable impact of current MDD state on gene expression. Identified genes and gene clusters are enriched with immune pathways previously associated with the etiology of MDD, in line with the immune suppression and immune activation hypothesis of MDD.

Omics for prediction of environmental health effects: Blood leukocyte-based cross-omic profiling reliably predicts diseases associated with tobacco smoking

The utility of blood-based omic profiles for linking environmental exposures to their potential health effects was evaluated in 649 individuals, drawn from the general population, in relation to tobacco smoking, an exposure with well-characterised health effects. Using disease connectivity analysis, we found that the combination of smoking-modified, genome-wide gene (including miRNA) expression and DNA methylation profiles predicts with remarkable reliability most diseases and conditions independently known to be causally associated with smoking (indicative estimates of sensitivity and positive predictive value 94% and 84%, respectively). Bioinformatics analysis reveals the importance of a small number of smoking-modified, master-regulatory genes and suggest a central role for altered ubiquitination. The smoking-induced gene expression profiles overlap significantly with profiles present in blood cells of patients with lung cancer or coronary heart disease, diseases strongly associated with tobacco smoking. These results provide proof-of-principle support to the suggestion that omic profiling in peripheral blood has the potential of identifying early, disease-related perturbations caused by toxic exposures and may be a useful tool in hazard and risk assessment.

Gene Expression Profiles of Early Implant Adherent Cells in Smokers and Nonsmokers

The objective of this study was to evaluate the impact of smoking on the early molecular events involved in peri-implant healing at either a micro-roughened or a micro-roughened with superimposed nanofeatures surface implant in humans. Twenty-one subjects, 10 smokers and 11 nonsmokers received 4 mini-implants (2.2 × 5.0 mm; 2 of each surface). After 3 and 7 days, paired mini-implants were retrieved by reverse threading and RNA isolated from implant adherent cells. Whole genome microarrays were used interrogate the gene expression profiles. The study failed to identify differences in the gene expression profiles of implant adherent cells at this early stage of osseointegration (up to day 7) comparing smoker and nonsmoker individuals.

Prediction of the gene expression in normal lung tissue by the gene expression in blood

BACKGROUND

Comparative analysis of gene expression in human tissues is important for understanding the molecular mechanisms underlying tissue-specific control of gene expression. It can also open an avenue for using gene expression in blood (which is the most easily accessible human tissue) to predict gene expression in other (less accessible) tissues, which would facilitate the development of novel gene expression based models for assessing disease risk and progression. Until recently, direct comparative analysis across different tissues was not possible due to the scarcity of paired tissue samples from the same individuals.

METHODS

In this study we used paired whole blood/lung gene expression data from the Genotype-Tissue Expression (GTEx) project. We built a generalized linear regression model for each gene using gene expression in lung as the outcome and gene expression in blood, age and gender as predictors.

RESULTS

For ~18 % of the genes, gene expression in blood was a significant predictor of gene expression in lung. We found that the number of single nucleotide polymorphisms (SNPs) influencing expression of a given gene in either blood or lung, also known as the number of quantitative trait loci (eQTLs), was positively associated with efficacy of blood-based prediction of that gene's expression in lung. This association was strongest for shared eQTLs: those influencing gene expression in both blood and lung.

CONCLUSIONS

In conclusion, for a considerable number of human genes, their expression levels in lung can be predicted using observable gene expression in blood. An abundance of shared eQTLs may explain the strong blood/lung correlations in the gene expression.

Bayesian Network Inference Enables Unbiased Phenotypic Anchoring of Transcriptomic Responses to Cigarette Smoke in Humans

Microarray-based transcriptomic analysis has been demonstrated to hold the opportunity to study the effects of human exposure to, e.g., chemical carcinogens at the whole genome level, thus yielding broad-ranging molecular information on possible carcinogenic effects. Since genes do not operate individually but rather through concerted interactions, analyzing and visualizing networks of genes should provide important mechanistic information, especially upon connecting them to functional parameters, such as those derived from measurements of biomarkers for exposure and carcinogenic risk. Conventional methods such as hierarchical clustering and correlation analyses are frequently used to address these complex interactions but are limited as they do not provide directional causal dependence relationships. Therefore, our aim was to apply Bayesian network inference with the purpose of phenotypic anchoring of modified gene expressions. We investigated a use case on transcriptomic responses to cigarette smoking in humans, in association with plasma cotinine levels as biomarkers of exposure and aromatic DNA-adducts in blood cells as biomarkers of carcinogenic risk. Many of the genes that appear in the Bayesian networks surrounding plasma cotinine, and to a lesser extent around aromatic DNA-adducts, hold biologically relevant functions in inducing severe adverse effects of smoking. In conclusion, this study shows that Bayesian network inference enables unbiased phenotypic anchoring of transcriptomics responses. Furthermore, in all inferred Bayesian networks several dependencies are found which point to known but also to new relationships between the expression of specific genes, cigarette smoke exposure, DNA damaging-effects, and smoking-related diseases, in particular associated with apoptosis, DNA repair, and tumor suppression, as well as with autoimmunity.

Combining genetic and nongenetic biomarkers to realize the promise of pharmacogenomics for inflammatory diseases

Many drugs used to treat inflammatory diseases are ineffective in a substantial proportion of patients. Identifying patients that are likely to respond to specific therapies would facilitate personalized treatment strategies that could improve outcomes while reducing costs and risks of adverse events. Despite these clear benefits, there are limited examples of predictive biomarkers of drug efficacy currently implemented into clinical practice for inflammatory diseases. We review efforts to identify genetic and nongenetic biomarkers of drug response in these diseases and consider potential benefits from combining multiple sources of biological data into multifeature predictive models.