Geographic variation of mutagenic exposures in kidney cancer genomes.

International differences in the incidence of many cancer types indicate the existence of carcinogen exposures that have not yet been identified by conventional epidemiology make a substantial contribution to cancer burden1. In clear cell renal cell carcinoma, obesity, hypertension and tobacco smoking are risk factors, but they do not explain the geographical variation in its incidence2. Underlying causes can be inferred by sequencing the genomes of cancers from populations with different incidence rates and detecting differences in patterns of somatic mutations. Here we sequenced 962 clear cell renal cell carcinomas from 11 countries with varying incidence. The somatic mutation profiles differed between countries. In Romania, Serbia and Thailand, mutational signatures characteristic of aristolochic acid compounds were present in most cases, but these were rare elsewhere. In Japan, a mutational signature of unknown cause was found in more than 70% of cases but in less than 2% elsewhere. A further mutational signature of unknown cause was ubiquitous but exhibited higher mutation loads in countries with higher incidence rates of kidney cancer. Known signatures of tobacco smoking correlated with tobacco consumption, but no signature was associated with obesity or hypertension, suggesting that non-mutagenic mechanisms of action underlie these risk factors. The results of this study indicate the existence of multiple, geographically variable, mutagenic exposures that potentially affect tens of millions of people and illustrate the opportunities for new insights into cancer causation through large-scale global cancer genomics.

Evaluating interactions of polygenic risk scores and NAT2 genotypes with tobacco smoking in bladder cancer risk.

Tobacco smoking is the most important risk factor for bladder cancer. Previous studies have identified the N-acetyltransferase (NAT2) gene in association with bladder cancer risk. The NAT2 gene encodes an enzyme that metabolizes aromatic amines, carcinogens commonly found in tobacco smoke. In our study, we evaluated potential interactions of tobacco smoking with NAT2 genotypes and polygenic risk score (PRS) for bladder cancer, using data from the UK Biobank, a large prospective cohort study. We used Cox proportional hazards models to measure the strength of the association. The PRS was derived using genetic risk variants identified by genome-wide association studies for bladder cancer. With an average of 10.1 years of follow-up of 390 678 eligible participants of European descent, 769 incident bladder cancer cases were identified. Current smokers with a PRS in the highest tertile had a higher risk of developing bladder cancer (HR: 6.45, 95% CI: 4.51-9.24) than current smokers with a PRS in the lowest tertile (HR: 2.41, 95% CI: 1.52-3.84; P for additive interaction = <.001). A similar interaction was found for genetically predicted metabolizing NAT2 phenotype and tobacco smoking where current smokers with the slow NAT2 phenotype had an increased risk of developing bladder cancer (HR: 5.70, 95% CI: 2.64-12.30) than current smokers with the fast NAT2 phenotype (HR: 3.61, 95% CI: 1.14-11.37; P for additive interaction = .100). Our study provides support for considering both genetic and lifestyle risk factors in developing prevention measures for bladder cancer.

Smoking, blood DNA methylation sites and lung cancer risk.

Altered DNA methylation (DNAm) might be a biological intermediary in the pathway from smoking to lung cancer. In this study, we investigated the contribution of differential blood DNAm to explain the association between smoking and lung cancer incidence. Blood DNAm was measured in 2321 Strong Heart Study (SHS) participants. Incident lung cancer was assessed as time to event diagnoses. We conducted mediation analysis, including validation with DNAm and paired gene expression data from the Framingham Heart Study (FHS). In the SHS, current versus never smoking and pack-years single-mediator models showed, respectively, 29 and 21 differentially methylated positions (DMPs) for lung cancer with statistically significant mediated effects (14 of 20 available, and five of 14 available, positions, replicated, respectively, in FHS). In FHS, replicated DMPs showed gene expression downregulation largely in trans, and were related to biological pathways in cancer. The multimediator model identified that DMPs annotated to the genes AHRR and IER3 jointly explained a substantial proportion of lung cancer. Thus, the association of smoking with lung cancer was partly explained by differences in baseline blood DNAm at few relevant sites. Experimental studies are needed to confirm the biological role of identified eQTMs and to evaluate potential implications for early detection and control of lung cancer.

Genetic mutations in smoking-associated prostate cancer.

OBJECTIVES: Tobacco smoking is known to cause cancers potentially predisposed by genetic risks. We compared the frequency of gene mutations using a next generation sequencing database of smokers and nonsmokers with prostate cancer (PCa) to identify subsets of patients with potential genetic risks. MATERIALS AND METHODS: Data from the American Association for Cancer Research Project Genomics Evidence Neoplasia Information Exchange (GENIE) registry was analyzed. The GENIE registry contains clinically annotated sequenced tumor samples. We included 1832 men with PCa in our cohort, categorized as smokers and nonsmokers, and compared the frequency of mutations (point mutations, copy number variations, and structural variants) of 47 genes with more than 5% mutation rate between the two categories and correlated with overall survival using logistic regression analysis. RESULTS: Overall, 1007 (55%) patients were nonsmokers, and 825 (45%) were smokers. The mutation frequency was significantly higher in smokers compared to nonsmokers, 47.6% and 41.3%, respectively (p = 0.02). The median tumor mutational burden was also significantly higher in the samples from smokers (3.59 mut/MB) compared to nonsmokers (1.87 mut/MB) (p < 0.001). Patients with a smoking history had a significantly higher frequency of PREX2, PTEN, AGO2, KMT2C, and a lower frequency of adenomatous polyposis coli (APC) and KMT2A mutations than compared to nonsmokers. The overall mortality rate (28.5% vs. 22.8%) was significantly higher among smokers (p = 0.006). On a multivariate logistic regression analysis, the presence of metastatic disease at the time of diagnosis (OR: 2.26, 95% CI: 1.78-2.89, p < 0.001), smoking history (OR: 1.32, 95% CI: 1.05-1.65, p = 0.02), and higher frequency of PTEN somatic gene mutation (OR: 1.89, 95% CI: 1.46-2.45, p < 0.001) were independent predictors of increased overall mortality among patients with PCa. Patients with PTEN mutation had poorer overall survival compared to men without PTEN mutations: 96.00 (95% CI: 65.36-113.98) and 120.00 (95% CI: 115.05-160.00) months, respectively (p < 0.001) irrespective of smoking history although the G129R PTEN mutation was characteristically detected in smokers. CONCLUSIONS: PCa patients with a tobacco smoking history demonstrated a significantly higher frequency of somatic genetic mutations. Whereas mutations of PREX2, KMT2C, AGO2, and PTEN genes were higher in smokers, the APC and KMT2A mutations were higher in nonsmokers. The PTEN somatic gene mutation was associated with increased overall mortality among patients with PCa irrespective of smoking history. We found that G129R PTEN mutation known to reduce the PTEN phosphatase activity and K267Rfs*9 a frameshift deletion mutation in the C2 domain of PTEN associated with membrane binding exclusively detected in smokers and nonsmokers, respectively. These findings may be used to further our understanding of PCa associated with smoking.

Tobacco Smoking-Related Mutational Signatures in Classifying Smoking-Associated and Nonsmoking-Associated NSCLC.

INTRODUCTION: Patient-reported smoking history is frequently used as a stratification factor in NSCLC-directed clinical research. Nevertheless, this classification does not fully reflect the mutational processes in a tumor. Next-generation sequencing can identify mutational signatures associated with tobacco smoking, such as single-base signature 4 and indel-based signature 3. This provides an opportunity to redefine the classification of smoking- and nonsmoking-associated NSCLC on the basis of individual genomic tumor characteristics and could contribute to reducing the lung cancer stigma. METHODS: Whole genome sequencing data and clinical records were obtained from three prospective cohorts of metastatic NSCLC (N = 316). Relative contributions and absolute counts of single-base signature 4 and indel-based signature 3 were combined with relative contributions of age-related signatures to divide the cohort into smoking-associated ("smoking high") and nonsmoking-associated ("smoking low") clusters. RESULTS: The smoking high (n = 169) and smoking low (n = 147) clusters differed considerably in tumor mutational burden, signature contribution, and mutational landscape. This signature-based classification overlapped considerably with smoking history. Yet, 26% of patients with an active smoking history were included in the smoking low cluster, of which 52% harbored an EGFR/ALK/RET/ROS1 alteration, and 4% of patients without smoking history were included in the smoking high cluster. These discordant samples had similar genomic contexts to the rest of their respective cluster. CONCLUSIONS: A substantial subset of metastatic NSCLC is differently classified into smoking- and nonsmoking-associated tumors on the basis of smoking-related mutational signatures than on the basis of smoking history. This signature-based classification more accurately classifies patients on the basis of genome-wide context and should therefore be considered as a stratification factor in clinical research.

Evaluating the causal effect of tobacco smoking on white matter brain aging: a two-sample Mendelian randomization analysis in UK Biobank.

BACKGROUND AND AIMS: Tobacco smoking is a risk factor for impaired brain function, but its causal effect on white matter brain aging remains unclear. This study aimed to measure the causal effect of tobacco smoking on white matter brain aging. DESIGN: Mendelian randomization (MR) analysis using two non-overlapping data sets (with and without neuroimaging data) from UK Biobank (UKB). The group exposed to smoking and control group consisted of current smokers and never smokers, respectively. Our main method was generalized weighted linear regression with other methods also included as sensitivity analysis. SETTING: United Kingdom. PARTICIPANTS: The study cohort included 23 624 subjects [10 665 males and 12 959 females with a mean age of 54.18 years, 95% confidence interval (CI) = 54.08, 54.28]. MEASUREMENTS: Genetic variants were selected as instrumental variables under the MR analysis assumptions: (1) associated with the exposure; (2) influenced outcome only via exposure; and (3) not associated with confounders. The exposure smoking status (current versus never smokers) was measured by questionnaires at the initial visit (2006-10). The other exposure, cigarettes per day (CPD), measured the average number of cigarettes smoked per day for current tobacco users over the life-time. The outcome was the 'brain age gap' (BAG), the difference between predicted brain age and chronological age, computed by training machine learning model on a non-overlapping set of never smokers. FINDINGS: The estimated BAG had a mean of 0.10 (95% CI = 0.06, 0.14) years. The MR analysis showed evidence of positive causal effect of smoking behaviors on BAG: the effect of smoking is 0.21 (in years, 95% CI = 6.5 × 10-3 , 0.41; P-value = 0.04), and the effect of CPD is 0.16 year/cigarette (UKB: 95% CI = 0.06, 0.26; P-value = 1.3 × 10-3 ; GSCAN: 95% CI = 0.02, 0.31; P-value = 0.03). The sensitivity analyses showed consistent results. CONCLUSIONS: There appears to be a significant causal effect of smoking on the brain age gap, which suggests that smoking prevention can be an effective intervention for accelerated brain aging and the age-related decline in cognitive function.

A polygenic approach to the association between smoking and schizophrenia.

Smoking prevalence in schizophrenia is considerably larger than in general population, playing an important role in early mortality. We compared the polygenic contribution to smoking in schizophrenic patients and controls to assess if genetic factors may explain the different prevalence. Polygenic risk scores (PRSs) for smoking initiation and four genetically correlated traits were calculated in 1108 schizophrenic patients (64.4% smokers) and 1584 controls (31.1% smokers). PRSs for smoking initiation, educational attainment, body mass index and age at first birth were associated with smoking in patients and controls, explaining a similar percentage of variance in both groups. Attention-deficit hyperactivity disorder (ADHD) PRS was associated with smoking only in schizophrenia. This association remained significant after adjustment by psychiatric cross-disorder PRS. A PRS combining all the traits was more explanative than smoking initiation PRS alone, indicating that genetic susceptibility to the other traits plays an additional role in smoking behaviour. Smoking initiation PRS was also associated with schizophrenia in the whole sample, but the significance was lost after adjustment for smoking status. This same pattern was observed in the analysis of specific SNPs at the CHRNA5-CHRNA3-CHRNB4 cluster associated with both traits. Overall, the results indicate that the same genetic factors are involved in smoking susceptibility in schizophrenia and in general population and are compatible with smoking acting, directly or indirectly, as a risk factor for schizophrenia that contributes to the high prevalence of smoking in these patients. The contrasting results for ADHD PRS may be related to higher ADHD symptomatology in schizophrenic patients.

Predictors of Smoking in Older Adults and an Epigenetic Validation of Self-Report.

There are several established predictors of smoking, but it is unknown if these predictors operate similarly for young and old smokers. We examined clinical data from the National Lung Screening Trial (NLST) to determine the predictive ability of gender, body mass index (BMI), marital status, and race on smoking behavior, with emphasis on gender interactions. In addition, we validated the self-report of smoking behaviors for a subgroup that had available epigenetic data in the form of cg05575921 methylation. Participants were N=9572 current or former smokers from the NLST biofluids database, age 55-74, minimum of 30 pack years, and mostly White. A subgroup of N=3084 who had DNA were used for the self-report validation analysis. The predictor analysis was based on the larger group and used penalized logistic regression to predict the self-report of being a former or current smoker at baseline. Cg05575921 methylation showed a moderate ability to discriminate among former and current smokers, AUC = 0.85 (95% confidence interval = [0.83, 0.86]). The final selected variables for the prediction model were BMI, gender, BMI by gender, age, divorced (vs. married), education, and race. The gender by BMI interaction was such that males had a higher probability of current smoking for lower BMI, but this switched to females having higher current smoking for overweight to obese. There is evidence that the self-reported smoking behavior in NLST is moderately accurate. The results of the primary analysis are consistent with the general smoking literature, and our results provide additional specificity regarding the gender by BMI interaction. Body weight issues might play a role in smoking cessation for older established smokers in a similar manner as younger smokers. It could be that women have less success with cessation when their BMI increases.

Genome-wide methylation and expression analyses reveal the epigenetic landscape of immune-related diseases for tobacco smoking.

BACKGROUND: Smoking is a major causal risk factor for lung cancer, chronic obstructive pulmonary disease (COPD), cardiovascular disease (CVD), and is the main preventable cause of deaths in the world. The components of cigarette smoke are involved in immune and inflammatory processes, which may increase the prevalence of cigarette smoke-related diseases. However, the underlying molecular mechanisms linking smoking and diseases have not been well explored. This study was aimed to depict a global map of DNA methylation and gene expression changes induced by tobacco smoking and to explore the molecular mechanisms between smoking and human diseases through whole-genome bisulfite sequencing (WGBS) and RNA-sequencing (RNA-seq). RESULTS: We performed WGBS on 72 samples (36 smokers and 36 nonsmokers) and RNA-seq on 75 samples (38 smokers and 37 nonsmokers), and cytokine immunoassay on plasma from 22 males (9 smokers and 13 nonsmokers) who were recruited from the city of Jincheng in China. By comparing the data of the two groups, we discovered a genome-wide methylation landscape of differentially methylated regions (DMRs) associated with smoking. Functional enrichment analyses revealed that both smoking-related hyper-DMR genes (DMGs) and hypo-DMGs were related to synapse-related pathways, whereas the hypo-DMGs were specifically related to cancer and addiction. The differentially expressed genes (DEGs) revealed by RNA-seq analysis were significantly enriched in the "immunosuppression" pathway. Correlation analysis of DMRs with their corresponding gene expression showed that genes affected by tobacco smoking were mostly related to immune system diseases. Finally, by comparing cytokine concentrations between smokers and nonsmokers, we found that vascular endothelial growth factor (VEGF) was significantly upregulated in smokers. CONCLUSIONS: In sum, we found that smoking-induced DMRs have different distribution patterns in hypermethylated and hypomethylated areas between smokers and nonsmokers. We further identified and verified smoking-related DMGs and DEGs through multi-omics integration analysis of DNA methylome and transcriptome data. These findings provide us a comprehensive genomic map of the molecular changes induced by smoking which would enhance our understanding of the harms of smoking and its relationship with diseases.

Smoking induces sex-specific changes in the small airway proteome.

INTRODUCTION: Cigarette smoke triggers many cellular and signaling responses in the lung and the resulting inflammation plays a central role in smoke-related lung diseases, such as COPD. We explored the effects of smoking on the small airway proteome in samples obtained by collection of exhaled particles with the aim to identify specific proteins dysregulated by smoking. METHODS: Exhaled particles were obtained from 38 current smokers, 47 former smokers and 22 healthy controls with the PExA method. 120 ng of sample was collected from individual subjects and analyzed with the SOMAscan proteomics platform. General linear model-based statistics were performed. RESULTS: Two hundred and three proteins were detected in at least half of 107 total samples. Active smoking exerted a significant impact on the protein composition of respiratory tract lining fluid (RTLF), with 81 proteins altered in current smokers compared to never smokers (p < 0.05, q < 0.124). Among the proteins most clearly discriminating between current and never smokers were sRAGE, FSTL3, SPOCK2 and protein S, all of them being less abundant in current smokers. Analysis stratified for sex unveiled sex differences with more pronounced proteomic alterations due to active smoking in females than males. Proteins whose abundance was altered by active smoking in women were to a larger extent related to the complement system. The small airway protein profile of former smokers appeared to be more similar to that observed in never smokers. CONCLUSIONS: The study shows that smoking has a strong impact on protein expression in the small airways, and that smoking affects men and women differently, suggesting PExA sampling combined with high sensitivity protein analysis offers a promising platform for early detection of COPD and identification of novel COPD drug targets.

Promoter Hypermethylation of LATS1 Gene in Oral Squamous Cell Carcinoma (OSCC) among North Indian Population.

BACKGROUND: LATS1 (Large Tumor Suppressor, isoform 1) is a gene that forms a complex with the cyclin-dependent kinase, CDK1, and regulates cell cycle progression. Genetic modifications lead to a loss in the activity of LATS1 gene. OSCC is the most commonly emerging cancer caused by genetic as well as epigenetic changes. Epigenetics changes vary from one population to another because these are influenced by dietary factors and environmental factors.  Tobacco chewing and smoking has been reported as major risk factors in OSCC. No report was found in the previous literature showing promoter hypermethylation of LATS1 gene. METHODS: A total of 50 OSCC patients and 20 normal individuals were recruited in this study. Blood samples (50) from OSCC patients and blood samples (20) from healthy individuals as controls were used in the present study. Isolation of genomic DNA was carried out from blood using the standard phenol-chloroform extraction. Further Isolated DNA was modified with sodium bisulfite using the agarose bead method and finally, the methylation studies of LATS1 gene were carried out using Methylation-Specific PCR (MSP-PCR). RESULTS: 19 out of 50 patients (38.0%) were found to be methylated for LATS1 gene.; a statistically significant result was obtained (p -value= < 0.05) with an odds ratio of 0.37 in cases compared to controls. The status of methylation of LATS1 genes was also found to be statistically significantly associated with smokers and tobacco chewers (p-value = < 0.05). The methylation of LATS1 gene showed a significant risk of developing OSCC in patients. CONCLUSION: These results suggest that the LATS1 gene may provide a better alternative as a diagnostic biomarker. This is the first report on the promoter hypermethylation of LATS1 gene in OSCC patients among the North Indian population.
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Novel DNA methylation signatures of tobacco smoking with trans-ethnic effects.

BACKGROUND: Smoking remains one of the leading preventable causes of death. Smoking leaves a strong signature on the blood methylome as shown in multiple studies using the Infinium HumanMethylation450 BeadChip. Here, we explore novel blood methylation smoking signals on the Illumina MethylationEPIC BeadChip (EPIC) array, which also targets novel CpG-sites in enhancers. METHOD: A smoking-methylation meta-analysis was carried out using EPIC DNA methylation profiles in 1407 blood samples from four UK population-based cohorts, including the MRC National Survey for Health and Development (NSHD) or 1946 British birth cohort, the National Child Development Study (NCDS) or 1958 birth cohort, the 1970 British Cohort Study (BCS70), and the TwinsUK cohort (TwinsUK). The overall discovery sample included 269 current, 497 former, and 643 never smokers. Replication was pursued in 3425 trans-ethnic samples, including 2325 American Indian individuals participating in the Strong Heart Study (SHS) in 1989-1991 and 1100 African-American participants in the Genetic Epidemiology Network of Arteriopathy Study (GENOA). RESULTS: Altogether 952 CpG-sites in 500 genes were differentially methylated between smokers and never smokers after Bonferroni correction. There were 526 novel smoking-associated CpG-sites only profiled by the EPIC array, of which 486 (92%) replicated in a meta-analysis of the American Indian and African-American samples. Novel CpG sites mapped both to genes containing previously identified smoking-methylation signals and to 80 novel genes not previously linked to smoking, with the strongest novel signal in SLAMF7. Comparison of former versus never smokers identified that 37 of these sites were persistently differentially methylated after cessation, where 16 represented novel signals only profiled by the EPIC array. We observed a depletion of smoking-associated signals in CpG islands and an enrichment in enhancer regions, consistent with previous results. CONCLUSION: This study identified novel smoking-associated signals as possible biomarkers of exposure to smoking and may help improve our understanding of smoking-related disease risk.

Performance Characteristics of Mutational Signature Analysis in Targeted Panel Sequencing.

CONTEXT.­: Mutational signatures have been described in the literature and a few centers have implemented pipelines for clinical reporting. OBJECTIVE.­: To describe the performance of a mutational signature caller with clinical samples sequenced on a targeted next-generation sequencing panel with a small genomic footprint. DESIGN.­: One thousand six hundred eighty-two clinical samples were analyzed for the presence of mutational signatures using deconstructSigs on variant calls with at least 20 variant reads. RESULTS.­: Signature 10 (associated with POLe mutation) achieved separation of cases and controls in hypermutated samples. Signatures 4 (associated with tobacco smoking) and 7 (associated with ultraviolet radiation) as an indicator of pulmonary or cutaneous primary sites showed moderate sensitivity and high specificity at optimal cutpoints. Mutational signatures in malignancies with unknown primaries were somewhat consistent with the clinically suspected primary site, with an apparent dose-response relationship between the number of variants analyzed and the ability of mutational signature analysis to correctly suggest a primary site. CONCLUSIONS.­: Mutational signatures represent an opportunity for orthogonal testing of primary site, which may be particularly useful in supporting cutaneous or pulmonary sites in poorly differentiated neoplasms. Tobacco smoking, ultraviolet radiation, and POLe mutational signatures are the most appropriate signatures for implementation. Even relatively small numbers of variants appear capable of supporting a clinically suspected primary.

Tumor Mutational Burden Is Polygenic and Genetically Associated with Complex Traits and Diseases.

Tumor mutational burden (TMB) is an emerging biomarker of response to immunotherapy in solid tumors. However, the extent to which variation in TMB between patients is attributable to germline genetic variation remains elusive. Here, using 7,004 unrelated patients of European descent across 33 cancer types from The Cancer Genome Atlas, we show that pan-cancer TMB is polygenic with approximately 13% of its variation explained by approximately 1.1 million common variants altogether. We identify germline variants that affect TMB in stomach adenocarcinoma through altering the expression levels of BAG5 and KLC1. Further analyses provide evidence that TMB is genetically associated with complex traits and diseases, such as smoking, rheumatoid arthritis, height, and cancers, and some of the associations are likely causal. Overall, these results provide new insights into the genetic basis of somatic mutations in tumors and may inform future efforts to use genetic variants to stratify patients for immunotherapy. SIGNIFICANCE: This study provides evidence for a polygenic architecture of tumor mutational burden and opens an avenue for the use of whole-genome germline genetic variations to stratify patients with cancer for immunotherapy.

Differential plasma exosomal long non-coding RNAs expression profiles and their emerging role in E-cigarette users, cigarette, waterpipe, and dual smokers.

Long non-coding RNAs (lncRNAs) are the varied set of transcripts that play a critical role in biological processes like gene regulation, transcription, post-transcriptional modification, and chromatin remodeling. Recent studies have reported the presence of lncRNAs in the exosomes that are involved in regulating cell-to-cell communication in lung pathologies including lung cancer, chronic obstructive pulmonary disease (COPD), asthma, and idiopathic pulmonary fibrosis (IPF). In this study, we compared the lncRNA profiles in the plasma-derived exosomes amongst non-smokers (NS), cigarette smokers (CS), E-cig users (E-cig), waterpipe smokers (WP) and dual smokers (CSWP) using GeneChip™ WT Pico kit for transcriptional profiling. We found alterations in a distinct set of lncRNAs among subjects exposed to E-cig vapor, cigarette smoke, waterpipe smoke and dual smoke with some overlaps. Gene enrichment analyses of the differentially expressed lncRNAs demonstrated enrichment in the lncRNAs involved in crucial biological processes including steroid metabolism, cell differentiation and proliferation. Thus, the characterized lncRNA profiles of the plasma-derived exosomes from smokers, vapers, waterpipe users, and dual smokers will help identify the biomarkers relevant to chronic lung diseases such as COPD, asthma or IPF.

Exosomal microRNAs are novel circulating biomarkers in cigarette, waterpipe smokers, E-cigarette users and dual smokers.

BACKGROUND: Electronic cigarettes (e-cigs) vaping, cigarette smoke, and waterpipe tobacco smoking are associated with various cardiopulmonary diseases. microRNAs are present in higher concentration in exosomes that play an important role in various physiological and pathological functions. We hypothesized that the non-coding RNAs transcript may serve as susceptibility to disease biomarkers by smoking and vaping. METHODS: Plasma exosomes/EVs from cigarette smokers, waterpipe smokers and dual smokers (cigarette and waterpipe) were characterized for their size, morphology and TEM, Nanosight and immunoblot analysis. Exosomal RNA was used for small RNA library preparation and the library was quantified using the High Sensitivity DNA Analysis on the Agilent 2100 Bioanalyzer system and sequenced using the Illumina NextSeq 500 and were converted to fastq format for mapping genes. RESULTS: Enrichment of various non-coding RNAs that include microRNAs, tRNAs, piRNAs, snoRNAs, snRNAs, Mt-tRNAs, and other biotypes are shown in exosomes. A comprehensive differential expression analysis of miRNAs, tRNAs and piRNAs showed significant changes across different pairwise comparisons. The seven microRNAs that were common and differentially expressed of when all the smoking and vaping groups were compared with non-smokers (NS) are hsa-let-7a-5p, hsa-miR-21-5p, hsa-miR-29b-3p, hsa-let-7f-5p, hsa-miR-143-3p, hsa-miR-30a-5p and hsa-let-7i-5p. The e-cig vs. NS group has differentially expressed 5 microRNAs (hsa-miR-224-5p, hsa-miR-193b-3p, hsa-miR-30e-5p, hsa-miR-423-3p, hsa-miR-365a-3p, and hsa-miR-365b-3p), which are not expressed in other three groups. Gene set enrichment analysis of microRNAs showed significant changes in the top six enriched functions that consisted of biological pathway, biological process, molecular function, cellular component, site of expression and transcription factor in all the groups. Further, the pairwise comparison of tRNAs and piRNA in all these groups revealed significant changes in their expressions. CONCLUSIONS: Plasma exosomes of cigarette smokers, waterpipe smokers, e-cig users and dual smokers have common differential expression of microRNAs which may serve to distinguish smoking and vaping subjects from NS. Among them has-let-7a-5p has high sensitivity and specificity to distinguish NS with the rest of the users, using ROC curve analysis. These findings will pave the way for the utilizing the potential of exosomes/miRNAs as a novel theranostic agents in lung injury and disease caused by tobacco smoking and vaping.

The effect of SLC6A3 variable number of tandem repeats and methylation levels on individual susceptibility to start tobacco smoking and on the ability of smokers to quit smoking.

OBJECTIVE: Nicotine acts through the dopamine pathway in the brain affecting reward processing through cigarette consumption. Thus, both genetic and epigenetic factors related to dopamine metabolism may influence individual's smoking behavior. MATERIALS AND METHODS: We studied variations of two variable numbers of tandem repeats (VNTRs), 40 and 30 bp in length, in SLC6A3 gene together with six DNA methylation sites located in a first intron of the gene in relation to several smoking-related phenotypes in a study population consisting of 1230 Whites of Russian origin. RESULTS: Both the 5R allele of 30 bp VNTR and the 9R allele of 40 bp VNTR in SLC6A3 were associated with a reduced risk to tobacco smoking [odds ratio (OR) 0.53, 95% confidence interval (CI) 0.37-0.75; OR 0.62, 95% CI 0.43-0.88]. Although the carriers of 9R allele also had high Fagerström test for nicotine dependence scores (OR 1.65, 95% CI 1.04-2.60), they were still more likely to succeed in smoking cessation (OR 0.59, 95% CI 0.40-0.88). Also, current smokers had more than 2.5-fold likelihood to have increased SLC6A3 methylation levels than former smokers (OR 2.72, 95% CI 1.63-4.53). CONCLUSION: The SLC6A3 5R of 30 bp and 9R of 40 bp VNTR variants may lead to a reduced risk to start smoking through decreased dopamine availability, and can also affect the success in subsequent smoking cessation attempts. Moreover, the elevated mean methylation values in the first intron of SLC6A3 may be related to nicotine dependence via a more active dopamine transporter.

Cigarette Smoke Exposure and Inflammatory Signaling Increase the Expression of the SARS-CoV-2 Receptor ACE2 in the Respiratory Tract.

The factors mediating fatal SARS-CoV-2 infections are poorly understood. Here, we show that cigarette smoke causes a dose-dependent upregulation of angiotensin converting enzyme 2 (ACE2), the SARS-CoV-2 receptor, in rodent and human lungs. Using single-cell sequencing data, we demonstrate that ACE2 is expressed in a subset of secretory cells in the respiratory tract. Chronic smoke exposure triggers the expansion of this cell population and a concomitant increase in ACE2 expression. In contrast, quitting smoking decreases the abundance of these secretory cells and reduces ACE2 levels. Finally, we demonstrate that ACE2 expression is responsive to inflammatory signaling and can be upregulated by viral infections or interferon treatment. Taken together, these results may partially explain why smokers are particularly susceptible to severe SARS-CoV-2 infections. Furthermore, our work identifies ACE2 as an interferon-stimulated gene in lung cells, suggesting that SARS-CoV-2 infections could create positive feedback loops that increase ACE2 levels and facilitate viral dissemination.

Smoking-Related DNA Methylation is Differentially Associated with Cadmium Concentration in Blood.

Tobacco smoking, a risk factor for several human diseases, can lead to alterations in DNA methylation. Smoking is a key source of cadmium exposure; however, there are limited studies examining DNA methylation alterations following smoking-related cadmium exposure. To identify such cadmium exposure-related DNA methylation, we performed genome-wide DNA methylation profiling using DNA samples from 50 smokers and 50 non-smokers. We found that a total of 136 CpG sites (including 70 unique genes) were significantly differentially methylated in smokers as compared to that in non-smokers. The CpG site cg05575921 in the AHRR gene was hypomethylated (Δ ß > - 0.2) in smokers, which was in accordance with previous studies. The rs951295 (within RNA gene LOC105370802) and cg00587941 sites were under-methylated by > 15% in smokers, whereas cg11314779 (within CELF6) and cg02126896 were over-methylated by ≥ 15%. We analyzed the association between blood cadmium concentration and DNA methylation level for 50 smokers and 50 non-smokers. DNA methylation rates of 307 CpG sites (including 207 unique genes) were significantly correlated to blood cadmium concentration (linear regression P value < 0.001). The four significant loci (cg05575921 and cg23576855 in AHRR, cg03636183 in F2RL3, and cg21566642) were under-methylated by > 10% in smokers compared to that in non-smokers. In conclusion, our study demonstrated that DNA methylation levels of rs951295, cg00587941, cg11314779, and cg02126896 sites may be new putative indicators of smoking status. Furthermore, we showed that these four loci may be differentially methylated by cadmium exposure due to smoking.

Epigenetic prediction of complex traits and mortality in a cohort of individuals with oropharyngeal cancer.

BACKGROUND: DNA methylation (DNAm) variation is an established predictor for several traits. In the context of oropharyngeal cancer (OPC), where 5-year survival is ~ 65%, DNA methylation may act as a prognostic biomarker. We examined the accuracy of DNA methylation biomarkers of 4 complex exposure traits (alcohol consumption, body mass index [BMI], educational attainment and smoking status) in predicting all-cause mortality in people with OPC. RESULTS: DNAm predictors of alcohol consumption, BMI, educational attainment and smoking status were applied to 364 individuals with OPC in the Head and Neck 5000 cohort (HN5000; 19.6% of total OPC cases in the study), followed up for median 3.9 years; inter-quartile range (IQR) 3.3 to 5.2 years (time-to-event-death or censor). The proportion of phenotypic variance explained in each trait was as follows: 16.5% for alcohol consumption, 22.7% for BMI, 0.4% for educational attainment and 51.1% for smoking. We then assessed the relationship between each DNAm predictor and all-cause mortality using Cox proportional-hazard regression analysis. DNAm prediction of smoking was most consistently associated with mortality risk (hazard ratio [HR], 1.38 per standard deviation (SD) increase in smoking DNAm score; 95% confidence interval [CI] 1.04 to 1.83; P 0.025, in a model adjusted for demographic, lifestyle, health and biological variables). Finally, we examined the accuracy of each DNAm predictor of mortality. DNAm predictors explained similar levels of variance in mortality to self-reported phenotypes. Receiver operator characteristic (ROC) curves for the DNAm predictors showed a moderate discrimination of alcohol consumption (area under the curve [AUC] 0.63), BMI (AUC 0.61) and smoking (AUC 0.70) when predicting mortality. The DNAm predictor for education showed poor discrimination (AUC 0.57). Z tests comparing AUCs between self-reported phenotype ROC curves and DNAm score ROC curves did not show evidence for difference between the two (alcohol consumption P 0.41, BMI P 0.62, educational attainment P 0.49, smoking P 0.19). CONCLUSIONS: In the context of a clinical cohort of individuals with OPC, DNAm predictors for smoking, alcohol consumption, educational attainment and BMI exhibit similar predictive values for all-cause mortality compared to self-reported data. These findings may have translational utility in prognostic model development, particularly where phenotypic data are not available.