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

Expression patterns and clinical value of key m6A RNA modification regulators in smoking patients with coronary artery disease

Even though N6-methyladenosine (m6A) RNA modifications are increasingly being implicated in human disease, their mechanisms are not fully understood in smokers with coronary artery disease (CAD). Thirty m6A-related regulators' expression (MRRE) in CAD individuals (smokers and non-smokers) were analyzed from GEO. Support Vector Machine, random forest, and nomogram models were constructed to assess its clinical value. Consensus clustering, principal component analysis, and ssGSEA were used to construct a full picture of m6A-related regulators in smokers with CAD. Oxygen-glucose deprivation (OGD) and qRT-PCR were used to validate hypoxia's effect on MRRE. A comparison between smokers with CAD and controls revealed lower expression levels of RBM15B, YTHDC2, and ZC3H13. Based on three key MRREs, all models showed good clinical value, and smokers with CAD were divided into two distinct molecular subgroups. The correlations were found between key MRRE and the degree of immune infiltration. Three key MRREs in HUVECs and FMC84 mouse cardiomyocytes were reduced in the OGD group. Through hypoxia, smoking might reduce the expression levels of RBM15B, YTHDC2, and ZC3H13 in smokers with CAD. Our findings provide an important theoretical basis for the treatment of smokers with CAD.

Phenotype prediction using biologically interpretable neural networks on multi-cohort multi-omics data

Integrating multi-omics data into predictive models has the potential to enhance accuracy, which is essential for precision medicine. In this study, we developed interpretable predictive models for multi-omics data by employing neural networks informed by prior biological knowledge, referred to as visible networks. These neural networks offer insights into the decision-making process and can unveil novel perspectives on the underlying biological mechanisms associated with traits and complex diseases. We tested the performance, interpretability and generalizability for inferring smoking status, subject age and LDL levels using genome-wide RNA expression and CpG methylation data from the blood of the BIOS consortium (four population cohorts, Ntotal = 2940). In a cohort-wise cross-validation setting, the consistency of the diagnostic performance and interpretation was assessed. Performance was consistently high for predicting smoking status with an overall mean AUC of 0.95 (95% CI: 0.90-1.00) and interpretation revealed the involvement of well-replicated genes such as AHRR, GPR15 and LRRN3. LDL-level predictions were only generalized in a single cohort with an R2 of 0.07 (95% CI: 0.05-0.08). Age was inferred with a mean error of 5.16 (95% CI: 3.97-6.35) years with the genes COL11A2, AFAP1, OTUD7A, PTPRN2, ADARB2 and CD34 consistently predictive. For both regression tasks, we found that using multi-omics networks improved performance, stability and generalizability compared to interpretable single omic networks. We believe that visible neural networks have great potential for multi-omics analysis; they combine multi-omic data elegantly, are interpretable, and generalize well to data from different cohorts.

Association of the methionine sulfoxide reductase A rs10903323 gene polymorphism with functional activity and oxidative modification of alpha-1-antitrypsin in COPD patients

OBJECTIVE

Chronic obstructive pulmonary disease (COPD) is multi-factorial disorder which results from environmental influences and genetic factors. We aimed to investigate whether methionine sulfoxide reductase A (MSRA) rs10903323 gene polymorphism is associated with COPD development and severity in Serbian adult population.

METHODS

The study included 155 patients with COPD and 134 healthy volunteers. Genotyping was determined performing home-made polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). The difference between the inhibitory activities of normal and oxidized Alpha-1-Antitrypsin (A1AT) against elastase and trypsin was used for determination of Oxidized Alpha-1-Antitrypsin (OxyA1AT) (expressed as % and g/L). Functional activity of A1AT was presented as a specific inhibitor activity to elastase (SIA-Elastase, kU/g).

RESULTS

Frequencies of the genotypes AA, AG and GG were 80.0%, 20.0%, 0% in COPD patients and 80.5%, 18.5% and 1.5% in the control group, and there was no significant difference in genotype or allele distributions between groups. Serum level of A1AT (g/L) and OxyA1AT was significantly higher in COPD patients than in the control group, but functional activity of A1AT (SIA-Elastase) was significantly lower in COPD patients than in the control group. In COPD group, increased level of OxyA1AT was present in G allele carriers who were smokers relative to G allele carriers who were not smokers. In the smoker group of patients with severe and very severe COPD (GOLD3+4), significant increase in OxyA1AT level was present in G allele carriers compared to AA homozygotes.

CONCLUSION

These findings suggest that MSRA rs10903323 gene polymorphism is probably not a risk for COPD by itself but could represent a COPD modifier, since minor, G allele, is associated with an increased level of oxidized A1AT, indicating impaired ability of MSRA to repair oxidized A1AT in COPD-smokers, and in severe form of COPD.

Longitudinal multi-omics study reveals common etiology underlying association between plasma proteome and BMI trajectories in adolescent and young adult twins

BACKGROUND

The influence of genetics and environment on the association of the plasma proteome with body mass index (BMI) and changes in BMI remains underexplored, and the links to other omics in these associations remain to be investigated. We characterized protein-BMI trajectory associations in adolescents and adults and how these connect to other omics layers.

METHODS

Our study included two cohorts of longitudinally followed twins: FinnTwin12 (N = 651) and the Netherlands Twin Register (NTR) (N = 665). Follow-up comprised 4 BMI measurements over approximately 6 (NTR: 23-27 years old) to 10 years (FinnTwin12: 12-22 years old), with omics data collected at the last BMI measurement. BMI changes were calculated in latent growth curve models. Mixed-effects models were used to quantify the associations between the abundance of 439 plasma proteins with BMI at blood sampling and changes in BMI. In FinnTwin12, the sources of genetic and environmental variation underlying the protein abundances were quantified by twin models, as were the associations of proteins with BMI and BMI changes. In NTR, we investigated the association of gene expression of genes encoding proteins identified in FinnTwin12 with BMI and changes in BMI. We linked identified proteins and their coding genes to plasma metabolites and polygenic risk scores (PRS) applying mixed-effects models and correlation networks.

RESULTS

We identified 66 and 14 proteins associated with BMI at blood sampling and changes in BMI, respectively. The average heritability of these proteins was 35%. Of the 66 BMI-protein associations, 43 and 12 showed genetic and environmental correlations, respectively, including 8 proteins showing both. Similarly, we observed 7 and 3 genetic and environmental correlations between changes in BMI and protein abundance, respectively. S100A8 gene expression was associated with BMI at blood sampling, and the PRG4 and CFI genes were associated with BMI changes. Proteins showed strong connections with metabolites and PRSs, but we observed no multi-omics connections among gene expression and other omics layers.

CONCLUSIONS

Associations between the proteome and BMI trajectories are characterized by shared genetic, environmental, and metabolic etiologies. We observed few gene-protein pairs associated with BMI or changes in BMI at the proteome and transcriptome levels.

Socioeconomic Inequalities and Molecular Risk for Aging in Young Adulthood

Diverse manifestations of biological aging often reflect disparities in socioeconomic status (SES). In this paper, we examine associations between indicators of SES and an mRNA-based aging signature during young adulthood, before clinical indications of aging are common. We use data from wave V (2016-2018) of the National Longitudinal Study of Adolescent to Adult Health, a nationally representative study of adults aged 33-43 years, with transcriptomic data from a subset of 2,491 participants. Biological aging is measured using 1) a composite transcriptomic aging signature previously identified by Peters et al.'s out-of-sample meta-analysis (Nat Commun. 2015;6:8570) and 2) 9 subsets that represent functional pathways of coexpressed genes. SES refers to income, education, occupation, subjective social status, and a composite measure combining these 4 dimensions. We examine hypothesized mechanisms through which SES could affect aging: body mass index, smoking, health insurance status, difficulty paying bills, and psychosocial stress. We find that SES-especially the composite measure and income-is associated with transcriptomic aging and immune, mitochondrial, ribosomal, lysosomal, and proteomal pathways. Counterfactual mediational models suggest that the mediators partially account for these associations. The results thus reveal that numerous biological pathways associated with aging are already linked to SES in young adulthood.

A Transcriptomic Analysis of Smoking-Induced Gene Expression Alterations in Coronary Artery Disease Patients

Smoking is a well established risk factor for coronary artery disease (CAD). Despite this, there have been no previous studies investigating the effects of smoking on blood gene expression in CAD patients. This single-centre cross-sectional study was designed with clearly defined inclusion criteria to address this gap. We conducted a high-throughput approach using next generation sequencing analysis with a single-end sequencing protocol and a read length of 75-cycles. Sixty-one patients with a median age of 67 years (range: 28-88 years) were recruited, and only 44 subjects were included for further analyses. Our investigation revealed 120 differentially expressed genes (DEGs) between smokers and nonsmokers, with a fold change (FC) of ≥1.5 and a p-value < 0.05. Among these DEGs, 15 were upregulated and 105 were downregulated. Notably, when applying a more stringent adjusted FC ≥ 2.0, 31 DEGs (5 upregulated, annotated to immune response pathways, and 26 downregulated, involving oxygen and haem binding or activity, with FDR ≤ 0.03) remained statistically significant at an alpha level of <0.05. Our results illuminate the molecular mechanisms underlying CAD, fortifying existing epidemiological evidence. Of particular interest is the unexplored overexpression of RCAN3, TRAV4, and JCHAIN genes, which may hold promising implications for the involvement of these genes in CAD among smokers.

Role of untargeted omics biomarkers of exposure and effect for tobacco research

Tobacco research remains a clear priority to improve individual and population health, and has recently become more complex with emerging combustible and noncombustible tobacco products. The use of omics methods in prevention and cessation studies are intended to identify new biomarkers for risk, compared risks related to other products and never use, and compliance for cessation and reinitation. to assess the relative effects of tobacco products to each other. They are important for the prediction of reinitiation of tobacco use and relapse prevention. In the research setting, both technical and clinical validation is required, which presents a number of complexities in the omics methodologies from biospecimen collection and sample preparation to data collection and analysis. When the results identify differences in omics features, networks or pathways, it is unclear if the results are toxic effects, a healthy response to a toxic exposure or neither. The use of surrogate biospecimens (e.g., urine, blood, sputum or nasal) may or may not reflect target organs such as the lung or bladder. This review describes the approaches for the use of omics in tobacco research and provides examples of prior studies, along with the strengths and limitations of the various methods. To date, there is little consistency in results, likely due to small number of studies, limitations in study size, the variability in the analytic platforms and bioinformatic pipelines, differences in biospecimen collection and/or human subject study design. Given the demonstrated value for the use of omics in clinical medicine, it is anticipated that the use in tobacco research will be similarly productive.

Associations of gene expression in blood with BMI and weight changes among women in the Norwegian Women and Cancer postgenome cohort

OBJECTIVE

This study aimed to evaluate associations between blood gene expression profiles and (1) current BMI and (2) past weight changes (WCs) among women who had never been diagnosed with cancer in the Norwegian Women and Cancer (NOWAC) postgenome cohort.

METHODS

This cross-sectional study (N = 1694) used gene expression profiles and information from three questionnaires: Q1 (baseline), Q2 (follow-up), and Q3 (blood collection). The authors performed gene-wise linear regression models to identify differentially expressed genes (DEGs) and functional enrichment analyses to identify their biological functions.

RESULTS

When assessing BMIQ3 , the study observed 2394, 769, and 768 DEGs for the obesity-versus-normal weight, obesity-versus-overweight, and overweight-versus-normal weight comparisons, respectively. Up to 169 DEGs were observed when investigating WCQ3-Q1 (mean = 7 years, range = 5.5-14 years) and WCQ3-Q2 (mean = 1 year, range = <1 month-9 years) in interaction models with BMI categories, of which 1 to 169 genes were associated with WCs and 0 to 9 were associated with interaction effects of BMI and WCs. Biological functions of BMI-associated DEGs were linked to metabolism, erythrocytes, oxidative stress, and immune processes, whereas WC-associated DEGs were linked to signal transduction.

CONCLUSIONS

Many BMI-associated but few WC-associated DEGs were identified in the blood of women in Norway. The biological functions of BMI-associated DEGs likely reflect systemic impacts of obesity, especially blood reticulocyte-erythrocyte ratio shifts.

Effects of smoking on genome-wide DNA methylation profiles: A study of discordant and concordant monozygotic twin pairs

Background

Smoking-associated DNA methylation levels identified through epigenome-wide association studies (EWASs) are generally ascribed to smoking-reactive mechanisms, but the contribution of a shared genetic predisposition to smoking and DNA methylation levels is typically not accounted for.

Methods

We exploited a strong within-family design, that is, the discordant monozygotic twin design, to study reactiveness of DNA methylation in blood cells to smoking and reversibility of methylation patterns upon quitting smoking. Illumina HumanMethylation450 BeadChip data were available for 769 monozygotic twin pairs (mean age = 36 years, range = 18-78, 70% female), including pairs discordant or concordant for current or former smoking.

Results

In pairs discordant for current smoking, 13 differentially methylated CpGs were found between current smoking twins and their genetically identical co-twin who never smoked. Top sites include multiple CpGs in CACNA1D and GNG12, which encode subunits of a calcium voltage-gated channel and G protein, respectively. These proteins interact with the nicotinic acetylcholine receptor, suggesting that methylation levels at these CpGs might be reactive to nicotine exposure. All 13 CpGs have been previously associated with smoking in unrelated individuals and data from monozygotic pairs discordant for former smoking indicated that methylation patterns are to a large extent reversible upon smoking cessation. We further showed that differences in smoking level exposure for monozygotic twins who are both current smokers but differ in the number of cigarettes they smoke are reflected in their DNA methylation profiles.

Conclusions

In conclusion, by analysing data from monozygotic twins, we robustly demonstrate that DNA methylation level in human blood cells is reactive to cigarette smoking.

Funding

We acknowledge funding from the National Institute on Drug Abuse grant DA049867, the Netherlands Organization for Scientific Research (NWO): Biobanking and Biomolecular Research Infrastructure (BBMRI-NL, NWO 184.033.111) and the BBRMI-NL-financed BIOS Consortium (NWO 184.021.007), NWO Large Scale infrastructures X-Omics (184.034.019), Genotype/phenotype database for behaviour genetic and genetic epidemiological studies (ZonMw Middelgroot 911-09-032); Netherlands Twin Registry Repository: researching the interplay between genome and environment (NWO-Groot 480-15-001/674); the Avera Institute, Sioux Falls (USA), and the National Institutes of Health (NIH R01 HD042157-01A1, MH081802, Grand Opportunity grants 1RC2 MH089951 and 1RC2 MH089995); epigenetic data were generated at the Human Genomics Facility (HuGe-F) at ErasmusMC Rotterdam. Cotinine assaying was sponsored by the Neuroscience Campus Amsterdam. DIB acknowledges the Royal Netherlands Academy of Science Professor Award (PAH/6635).

Longitudinal multi-omics study reveals common etiology underlying association between plasma proteome and BMI trajectories in adolescent and young adult twins

Background

The influence of genetics and environment on the association of the plasma proteome with body mass index (BMI) and changes in BMI remain underexplored, and the links to other omics in these associations remain to be investigated. We characterized protein-BMI trajectory associations in adolescents and adults and how these connect to other omics layers.

Methods

Our study included two cohorts of longitudinally followed twins: FinnTwin12 (N=651) and the Netherlands Twin Register (NTR) (N=665). Follow-up comprised four BMI measurements over approximately 6 (NTR: 23-27 years old) to 10 years (FinnTwin12: 12-22 years old), with omics data collected at the last BMI measurement. BMI changes were calculated using latent growth curve models. Mixed-effects models were used to quantify the associations between the abundance of 439 plasma proteins with BMI at blood sampling and changes in BMI. The sources of genetic and environmental variation underlying the protein abundances were quantified using twin models, as were the associations of proteins with BMI and BMI changes. In NTR, we investigated the association of gene expression of genes encoding proteins identified in FinnTwin12 with BMI and changes in BMI. We linked identified proteins and their coding genes to plasma metabolites and polygenic risk scores (PRS) using mixed-effect models and correlation networks.

Results

We identified 66 and 14 proteins associated with BMI at blood sampling and changes in BMI, respectively. The average heritability of these proteins was 35%. Of the 66 BMI-protein associations, 43 and 12 showed genetic and environmental correlations, respectively, including 8 proteins showing both. Similarly, we observed 6 and 4 genetic and environmental correlations between changes in BMI and protein abundance, respectively. S100A8 gene expression was associated with BMI at blood sampling, and the PRG4 and CFI genes were associated with BMI changes. Proteins showed strong connections with many metabolites and PRSs, but we observed no multi-omics connections among gene expression and other omics layers.

Conclusions

Associations between the proteome and BMI trajectories are characterized by shared genetic, environmental, and metabolic etiologies. We observed few gene-protein pairs associated with BMI or changes in BMI at the proteome and transcriptome levels.

Emerging roles of a chemoattractant receptor GPR15 and ligands in pathophysiology

Chemokine receptors play a central role in the maintenance of immune homeostasis and development of inflammation by directing leukocyte migration to tissues. GPR15 is a G protein-coupled receptor (GPCR) that was initially known as a co-receptor for human immunodeficiency virus (HIV) and simian immunodeficiency virus (SIV), with structural similarity to other members of the chemoattractant receptor family. Since the discovery of its novel function as a colon-homing receptor of T cells in mice a decade ago, GPR15 has been rapidly gaining attention for its involvement in a variety of inflammatory and immune disorders. The recent identification of its natural ligand C10orf99, a chemokine-like polypeptide strongly expressed in gastrointestinal tissues, has established that GPR15-C10orf99 is a novel signaling axis that controls intestinal homeostasis and inflammation through the migration of immune cells. In addition, it has been demonstrated that C10orf99-independent functions of GPR15 and GPR15-independent activities of C10orf99 also play significant roles in the pathophysiology. Therefore, GPR15 and its ligands are potential therapeutic targets. To provide a basis for the future development of GPR15- or GPR15 ligand-targeted therapeutics, we have summarized the latest advances in the role of GPR15 and its ligands in human diseases as well as the molecular mechanisms that regulate GPR15 expression and functions.

Exploring causal effects of smoking and alcohol related lifestyle factors on self-report tiredness: A Mendelian randomization study

Self-reported tiredness or low energy, often referred to as fatigue, has been linked to lifestyle factors, although data from randomized-controlled trials are lacking. We investigate whether modifiable lifestyle factors including smoking and alcohol intake related exposures (SAIEs) are causal factors for fatigue using Mendelian randomization (MR). A two-sample MR study was performed by using genome-wide association summary results from UK Biobank (UKBB), and each of the sample size is more than 100,000. We used the inverse variance weighted method, and sensitivity analyses (MR Egger, weighted median, penalized median estimators, and multivariable MR) to account for pleiotropy. The two-sample MR analyses showed inverse causal effect of never-smoking status and positive effect of current smoking status on the risk of fatigue. Similarly, genetically predicted alcoholic intake was positively associated with fatigue. The results were consistent across the different MR methods. Our Mendelian randomization analyses do support that the cessation of smoking and alcohol can decrease the risk of fatigue, and limit alcohol intake frequency can also reduce the risk.

Maximizing the value of twin studies in health and behaviour

In the classical twin design, researchers compare trait resemblance in cohorts of identical and non-identical twins to understand how genetic and environmental factors correlate with resemblance in behaviour and other phenotypes. The twin design is also a valuable tool for studying causality, intergenerational transmission, and gene-environment correlation and interaction. Here we review recent developments in twin studies, recent results from twin studies of new phenotypes and recent insights into twinning. We ask whether the results of existing twin studies are representative of the general population and of global diversity, and we conclude that stronger efforts to increase representativeness are needed. We provide an updated overview of twin concordance and discordance for major diseases and mental disorders, which conveys a crucial message: genetic influences are not as deterministic as many believe. This has important implications for public understanding of genetic risk prediction tools, as the accuracy of genetic predictions can never exceed identical twin concordance rates.

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.

Patients with schizophrenia and bipolar disorder display a similar global gene expression signature in whole blood that reflects elevated proportion of immature neutrophil cells with association to lipid changes

Schizophrenia (SCZ) and bipolar disorder (BD) share clinical characteristics, genetic susceptibility, and immune alterations. We aimed to identify differential transcriptional patterns in peripheral blood cells of patients with SCZ or BD versus healthy controls (HC). We analyzed microarray-based global gene expression data in whole blood from a cohort of SCZ (N = 329), BD (N = 203) and HC (N = 189). In total, 65 genes were significantly differentially expressed in SCZ and 125 in BD, as compared to HC, with similar ratio of up- and downregulated genes in both disorders. Among the top differentially expressed genes, we found an innate immunity signature that was shared between SCZ and BD, consisting of a cluster of upregulated genes (e.g., OLFM4, ELANE, BPI and MPO) that indicate an increased fraction of immature neutrophils. Several of these genes displayed sex differences in the expression pattern, and post-hoc analysis demonstrated a positive correlation with triglyceride and a negative correlation with HDL cholesterol. We found that many of the downregulated genes in SCZ and BD were associated with smoking. These findings of neutrophil granulocyte-associated transcriptome signatures in both SCZ and BD point at altered innate immunity pathways with association to lipid changes and potential for clinical translation.

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

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

Dissecting the epigenomic differences between smoking and nicotine dependence in a veteran cohort

Smoking is a serious public health issue linked to more than 8 million deaths per year worldwide and may lead to nicotine dependence (ND). Although the epigenomic literature on smoking is well established, studies evaluating the role of epigenetics in ND are limited. In this study, we examined the epigenomic signatures of ND and how these differ from smoking exposure to identify biomarkers specific to ND. We investigated the peripheral epigenetic profile of smoking status (SS) and ND in a US male veteran cohort. DNA from saliva was collected from 1135 European American (EA) male US military veterans. DNAm was assessed using the Illumina Infinium Human MethylationEPIC BeadChip array. SS was evaluated as current smokers (n = 137; 12.1%) and non-current smokers (never and former; n = 998; 87.9%). NDFTND was assessed as a continuous variable using the Fagerström Test for ND (FTND; n = 1135; mean = 2.54 ± 2.29). Epigenome-wide association studies (EWAS) and co-methylation analyses were conducted for SS and NDFTND . A total of 450 and 22 genome-wide significant differentially methylated sites (DMS) were associated with SS and NDFTND , respectively (15 overlapped DMS). We identified 97 DMS (43 genes) in SS-EWAS previously reported in the literature, including AHRR and F2RL3 genes (p-value: 1.95 × 10-83 to 4.55 × 10-33 ). NDFTND novel DMS mapped to NEUROG1, ANPEP, and SLC29A1. Co-methylation analysis identified 386 modules (11 SS-related and 19 NDFTND -related). SS-related modules showed enrichment for alcoholism, while NDFTND -related modules were enriched for nicotine addiction. This study confirms previous findings associated with SS and identifies novel and-potentially specific-epigenetic biomarkers of ND that may inform prognosis and novel treatment strategies.

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.

Integrating human brain proteomic data with genome-wide association study findings identifies novel brain proteins in substance use traits

Despite the identification of a growing number of genetic risk loci for substance use traits (SUTs), the impact of these loci on protein abundance and the potential utility of relevant proteins as therapeutic targets are unknown. We conducted a proteome-wide association study (PWAS) in which we integrated human brain proteomes from discovery (Banner; N = 152) and validation (ROSMAP; N = 376) datasets with genome-wide association study (GWAS) summary statistics for 4 SUTs. The 4 samples comprised GWAS of European-ancestry individuals for smoking initiation [Smk] (N = 1,232,091), alcohol use disorder [AUD] (N = 313,959), cannabis use disorder [CUD] (N = 384,032), and opioid use disorder [OUD] (N = 302,585). We conducted transcriptome-wide association studies (TWAS) with human brain transcriptomic data to examine the overlap of genetic effects at the proteomic and transcriptomic levels and characterize significant genes through conditional, colocalization, and fine-mapping analyses. We identified 27 genes (Smk = 21, AUD = 3, CUD = 2, OUD = 1) that were significantly associated with cis-regulated brain protein abundance. Of these, 7 showed evidence for causality (Smk: NT5C2, GMPPB, NQO1, RHOT2, SRR and ACTR1B; and AUD: CTNND1). Cis-regulated transcript levels for 8 genes (Smk = 6, CUD = 1, OUD = 1) were associated with SUTs, indicating that genetic loci could confer risk for these SUTs by modulating both gene expression and proteomic abundance. Functional studies of the high-confidence risk proteins identified here are needed to determine whether they are modifiable targets and useful in developing medications and biomarkers for these SUTs.

Impact of long-term exposure to PM2.5 on peripheral blood gene expression pathways involved in cell signaling and immune response

BACKGROUND

Exposure to ambient air pollution, even at low levels, is a major environmental health risk. The peripheral blood transcriptome provides a potential avenue for the elucidation of ambient air pollution related biological perturbations. We assessed the association between long-term estimates for seven priority air pollutants and perturbations in peripheral blood transcriptomics data collected in the Dutch National Twin Register (NTR) and Netherlands Study of Depression and Anxiety (NESDA) cohorts.

METHODS

In both the discovery (n = 2438) and replication (n = 1567) cohort, outdoor concentration of 7 air pollutants (NO₂, NO_x, particulate matter (PM_2.5, PM_2.5abs, PM₁₀, PM_coarse)_, and ultrafine particles) was predicted with land use regression models. Gene expression was assessed by Affymetrix U219 arrays. Multi-variable univariate mixed-effect models were applied to test for an association between the air pollutants and the transcriptome. Functional analysis was conducted in DAVID.

RESULTS

In the discovery cohort, we observed for 335 genes (374 probes with FDR < 5 %) a perturbation in peripheral blood gene expression that was associated with long-term average levels of PM_2.5. For 69 genes pooled effect estimates from the NTR and NESDA cohorts were significant. Identified genes play a role in biological pathways related to cell signaling and immune response. Sixty-two out of 69 genes had a similar direction of effect in an analysis in which we regressed the probes on differential PM_2.5 exposure within monozygotic twin pairs, indicating that the observed differences in gene expression were likely driven by differences in air pollution, rather than by confounding by genetic factors.

CONCLUSION

Our results indicate that PM_2.5 can elicit a response in cell signaling and the immune system, both hallmarks of environmental diseases. The differential effect that we observed between air pollutants may aid in the understanding of differential health effects that have been observed with these exposures.

Longitudinal change in blood DNA epigenetic signature after smoking cessation

Cigarette smoking is associated with epigenetic changes that may be reversible following smoking cessation. Whole blood DNA methylation was evaluated in Framingham Heart Study Offspring (n = 169) and Third Generation (n = 30) cohort participants at two study visits 6 years apart and in Atherosclerosis Risk in Communities (ARIC) study (n = 222) participants at two study visits 20 years apart. Changes in DNA methylation (delta β values) at 483,565 cytosine-phosphate-guanine (CpG) sites and differentially methylated regions (DMRs) were compared between participants who were current, former, or never smokers at both visits (current-current, former-former, never-never, respectively), versus those who quit in the interim (current-former). Interim quitters had more hypermethylation at four CpGs annotated to AHRR, one CpG annotated to F2RL3, and one intergenic CpG (cg21566642) compared with current-current smokers (FDR < 0.02 for all), and two significant DMRs were identified. While there were no significant differentially methylated CpGs in the comparison of interim quitters and former-former smokers, 106 DMRs overlapping with small nucleolar RNA were identified. As compared with all non-smokers, current-current smokers additionally had more hypermethylation at two CpG sites annotated to HIVEP3 and TMEM126A, respectively, and another intergenic CpG (cg14339116). Gene transcripts associated with smoking cessation were implicated in immune responses, cell homoeostasis, and apoptosis. Smoking cessation is associated with early reversion of blood DNA methylation changes at CpG sites annotated to AHRR and F2RL3 towards those of never smokers. Associated gene expression suggests a role of longitudinal smoking-related DNA methylation changes in immune response processes.

DNA Methylation Abnormalities and Altered Whole Transcriptome Profiles after Switching from Combustible Tobacco Smoking to Heated Tobacco Products

BACKGROUND

The use of heated tobacco products (HTP) has increased exponentially in Japan since 2016; however, their effects on health remain a major concern.

METHODS

Tsuruoka Metabolome Cohort Study participants (n = 11,002) were grouped on the basis of their smoking habits as never smokers (NS), past smokers (PS), combustible tobacco smokers (CS), and HTP users for <2 years. Peripheral blood mononuclear cells were collected from 52 participants per group matched to HTP users using propensity scores, and DNA and RNA were purified from the samples. DNA methylation (DNAm) analysis of the 17 smoking-associated DNAm biomarker genes (such as AHRR, F2RL3, LRRN3, and GPR15), as well as whole transcriptome analysis, was performed.

RESULTS

Ten of the 17 genes were significantly hypomethylated in CS and HTP users compared with NS, among which AHRR, F2RL3, and RARA showed intermediate characteristics between CS and NS; nonetheless, AHRR expression was significantly higher in CS than in the other three groups. Conversely, LRRN3 and GPR15 were more hypomethylated in HTP users than in NS, and GPR15 expression was markedly upregulated in all the groups when compared with that in NS.

CONCLUSIONS

HTP users (switched from CS <2 years) display abnormal DNAm and transcriptome profiles, albeit to a lesser extent than the CS. However, because the molecular genetic effects of long-term HTP use are still unknown, long-term molecular epidemiologic studies are needed.

IMPACT

This study provides new insights into the molecular genetic effects on DNAm and transcriptome profiles in HTP users who switched from CS.

Genetics of substance use disorders in the era of big data

Substance use disorders (SUDs) are conditions in which the use of legal or illegal substances, such as nicotine, alcohol or opioids, results in clinical and functional impairment. SUDs and, more generally, substance use are genetically complex traits that are enormously costly on an individual and societal basis. The past few years have seen remarkable progress in our understanding of the genetics, and therefore the biology, of substance use and abuse. Various studies - including of well-defined phenotypes in deeply phenotyped samples, as well as broadly defined phenotypes in meta-analysis and biobank samples - have revealed multiple risk loci for these common traits. A key emerging insight from this work establishes a biological and genetic distinction between quantity and/or frequency measures of substance use (which may involve low levels of use without dependence), versus symptoms related to physical dependence.

Cigarette smoking-associated isoform switching and 3' UTR lengthening via alternative polyadenylation

Cigarette smoking induces a profound transcriptomic and systemic inflammatory response. Previous studies have focused on gene level differential expression of smoking, but the genome-wide effects of smoking on alternative isoform regulation have not yet been described. We conducted RNA sequencing in whole-blood samples of 454 current and 767 former smokers in the COPDGene Study, and we analyzed the effects of smoking on differential usage of isoforms and exons. At 10% FDR, we detected 3167 differentially expressed genes, 945 differentially used isoforms and 160 differentially used exons. Isoform switch analysis revealed widespread 3' UTR lengthening associated with cigarette smoking. The lengthening of these 3' UTRs was consistent with alternative usage of distal polyadenylation sites, and these extended 3' UTR regions were significantly enriched with functional sequence elements including microRNA and RNA-protein binding sites. These findings warrant further studies on alternative polyadenylation events as potential biomarkers and novel therapeutic targets for smoking-related diseases.

The Role of GPR15 Function in Blood and Vasculature

Since the first prominent description of the orphan G protein-coupled receptor 15 (GPR15) on lymphocytes as a co-receptor for the human immunodeficiency virus (HIV) type 1 and 2 and the first report about the GPR15-triggered cytoprotective effect on vascular endothelial cells by recombinant human thrombomodulin, several decades passed before the GPR15 has been recently deorphanized. Because of new findings on GPR15, this review will summarize the consequences of GPR15 signaling considering the variety of GPR15-expressing cell types and of GPR15 ligands, with a focus on blood and vasculature.

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).

Association between tobacco substance usage and a missense mutation in the tumor suppressor gene P53 in the Saudi Arabian population

The tumor suppressor gene TP53 and its downstream genes P21 and MDM2 play crucial roles in combating DNA damage at the G1/S cell cycle checkpoint. Polymorphisms in these genes can lead to the development of various diseases. This study was conducted to examine a potential association between tobacco substance usage (TSU) and single-nucleotide polymorphism (SNP) at the exon regions of the P53, P21, and MDM2 genes by comparing populations of smokers and non-smokers from Saudi Arabia. P53 rs1042522 (C/G), P21 rs1801270 (A/C), and MDM2 rs769412 (A/G) were investigated by genotyping 568 blood specimens: 283 from male/female smokers and 285 from male/female non-smokers. The results obtained from the smokers and their control non-smokers were compared according to age, sex, duration of smoking, and type of TSU. Heterozygous CG, homozygous GG, and CG+GG genotypes, as well as the G allele of rs1042522 were significantly associated with TSU in Saudi smokers compared with non-smokers. The C allele frequency of rs1801270 was also associated with TSU in smokers (OR = 1.33, p = 0.049) in comparison with non-smokers, in younger smokers (≤29 years) (OR = 1.556, p = 0.03280) in comparison with non-smokers of the same age, in smokers who had smoked cigarettes for seven years or less (OR = 1.596, p = 0.00882), and in smokers who had consumed shisha (OR = 1.608, p = 0.04104) in comparison with the controls. However, the genotypic and allelic frequencies for rs769412 did not show significant associations with TSU in Saudis. The selected SNP of P53 was strongly associated with TSU and may be linked to TSU-induced diseases in the Saudi Arabian population.

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.

Aging-related changes in the gene expression profile of human lungs

Aging is a multifactorial process that leads to molecular and cellular changes, contributing to the susceptibility of most lung diseases. However, the molecular and genetic mechanism of lung aging remains poorly understood. Here, we performed RNA-seq transcriptome analysis of the lung tissues of 68 subjects and analyzed their gene expression profile to evaluate candidate genes related to lung aging. The subjects were classified into two groups (Younger group and Older group) based on their age. Lung tissues were obtained from surgically resected specimens, processed, and analyzed with RNA-seq. The median age of the subjects was 45 years in the Younger group and 74 years in the Older group. Around 71% and 53% of the subjects were female in the Younger and Older groups, respectively. After gene quality control and filtering, differentially expressed gene analysis showed that MAP3K15, CHRM2, and GALNT13 were upregulated in the Younger group, whereas COL17A1 and EDA2R were upregulated in the Older group. Multivariate analysis with adjustment for covariates showed that EDA2R was a risk factor for lung aging. Our study identified differences in the gene expression of the lungs of older subjects compared with younger subjects. These findings may have implications in lung aging.

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.

A Gene-Environment Interaction Between Smoking and Gene polymorphisms Provides a High Risk of Two Subgroups of Sarcoidosis

The influence and effect of cigarette smoking in sarcoidosis is unclear. Here, we evaluated gene-environment interaction between multiple genetic variants including HLA genes and smoking in sarcoidosis defined by two clinical phenotypes, Löfgren's syndrome (LS) and patients without Löfgren's syndrome (non-LS). To quantify smoking effects in sarcoidosis, we performed a gene-environment interaction study in a Swedish population-based case-control study consisting of 3,713 individuals. Cases and controls were classified according to their cigarette smoking status and genotypes by Immunochip platform. Gene-smoking interactions were quantified by an additive interaction model using a logistic regression adjusted by sex, age and first two principal components. The estimated attributable proportion (AP) was used to quantify the interaction effect. Assessment of smoking effects with inclusion of genetic information revealed 53 (in LS) and 34 (in non-LS) SNP-smoking additive interactions at false discovery rate (FDR) below 5%. The lead signals interacting with smoking were rs12132140 (AP = 0.56, 95% CI = 0.22-0.90), p = 1.28e-03) in FCRL1 for LS and rs61780312 (AP = 0.62, 95% CI = 0.28-0.90), p = 3e-04) in IL23R for non-LS. We further identified 16 genomic loci (in LS) and 13 (in non-LS) that interact with cigarette smoking. These findings suggest that sarcoidosis risk is modulated by smoking due to genetic susceptibility. Therefore, patients having certain gene variants, are at a higher risk for the disease. Consideration of individual's genetic predisposition is crucial to quantify effects of smoking in sarcoidosis.

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.

Evaluation of commonly used analysis strategies for epigenome- and transcriptome-wide association studies through replication of large-scale population studies

BACKGROUND

A large number of analysis strategies are available for DNA methylation (DNAm) array and RNA-seq datasets, but it is unclear which strategies are best to use. We compare commonly used strategies and report how they influence results in large cohort studies.

RESULTS

We tested the associations of DNAm and RNA expression with age, BMI, and smoking in four different cohorts (n = ~ 2900). By comparing strategies against the base model on the number and percentage of replicated CpGs for DNAm analyses or genes for RNA-seq analyses in a leave-one-out cohort replication approach, we find the choice of the normalization method and statistical test does not strongly influence the results for DNAm array data. However, adjusting for cell counts or hidden confounders substantially decreases the number of replicated CpGs for age and increases the number of replicated CpGs for BMI and smoking. For RNA-seq data, the choice of the normalization method, gene expression inclusion threshold, and statistical test does not strongly influence the results. Including five principal components or excluding correction of technical covariates or cell counts decreases the number of replicated genes.

CONCLUSIONS

Results were not influenced by the normalization method or statistical test. However, the correction method for cell counts, technical covariates, principal components, and/or hidden confounders does influence the results.

Current Smoking is Associated with Decreased Expression of miR-335-5p in Parenchymal Lung Fibroblasts

Cigarette smoking causes lung inflammation and tissue damage. Lung fibroblasts play a major role in tissue repair. Previous studies have reported smoking-associated changes in fibroblast responses and methylation patterns. Our aim was to identify the effect of current smoking on miRNA expression in primary lung fibroblasts. Small RNA sequencing was performed on lung fibroblasts from nine current and six ex-smokers with normal lung function. MiR-335-5p and miR-335-3p were significantly downregulated in lung fibroblasts from current compared to ex-smokers (false discovery rate (FDR) <0.05). Differential miR-335-5p expression was validated with RT-qPCR (p-value = 0.01). The results were validated in lung tissue from current and ex-smokers and in bronchial biopsies from non-diseased smokers and never-smokers (p-value <0.05). The methylation pattern of the miR-335 host gene, determined by methylation-specific qPCR, did not differ between current and ex-smokers. To obtain insights into the genes regulated by miR-335-5p in fibroblasts, we overlapped all proven miR-335-5p targets with our previously published miRNA targetome data in lung fibroblasts. This revealed Rb1, CARF, and SGK3 as likely targets of miR-335-5p in lung fibroblasts. Our study indicates that miR-335-5p downregulation due to current smoking may affect its function in lung fibroblasts by targeting Rb1, CARF and SGK3.

A characterization of cis- and trans-heritability of RNA-Seq-based gene expression

Insights into individual differences in gene expression and its heritability (h²) can help in understanding pathways from DNA to phenotype. We estimated the heritability of gene expression of 52,844 genes measured in whole blood in the largest twin RNA-Seq sample to date (1497 individuals including 459 monozygotic twin pairs and 150 dizygotic twin pairs) from classical twin modeling and identity-by-state-based approaches. We estimated for each gene h²_total, composed of cis-heritability (h²_cis, the variance explained by single nucleotide polymorphisms in the cis-window of the gene), and trans-heritability (h²_res, the residual variance explained by all other genome-wide variants). Mean h²_total was 0.26, which was significantly higher than heritability estimates earlier found in a microarray-based study using largely overlapping (>60%) RNA samples (mean h² = 0.14, p = 6.15 × 10^-258). Mean h²_cis was 0.06 and strongly correlated with beta of the top cis expression quantitative loci (eQTL, ρ = 0.76, p < 10^-308) and with estimates from earlier RNA-Seq-based studies. Mean h²_res was 0.20 and correlated with the beta of the corresponding trans-eQTL (ρ = 0.04, p < 1.89 × 10^-3) and was significantly higher for genes involved in cytokine-cytokine interactions (p = 4.22 × 10^-15), many other immune system pathways, and genes identified in genome-wide association studies for various traits including behavioral disorders and cancer. This study provides a thorough characterization of cis- and trans-h² estimates of gene expression, which is of value for interpretation of GWAS and gene expression studies.

Cigarette smoke alters the transcriptome of non-involved lung tissue in lung adenocarcinoma patients

Alterations in the gene expression of organs in contact with the environment may signal exposure to toxins. To identify genes in lung tissue whose expression levels are altered by cigarette smoking, we compared the transcriptomes of lung tissue between 118 ever smokers and 58 never smokers. In all cases, the tissue studied was non-involved lung tissue obtained at lobectomy from patients with lung adenocarcinoma. Of the 17,097 genes analyzed, 357 were differentially expressed between ever smokers and never smokers (FDR < 0.05), including 290 genes that were up-regulated and 67 down-regulated in ever smokers. For 85 genes, the absolute value of the fold change was ≥2. The gene with the smallest FDR was MYO1A (FDR = 6.9 × 10⁻⁴) while the gene with the largest difference between groups was FGG (fold change = 31.60). Overall, 100 of the genes identified in this study (38.6%) had previously been found to associate with smoking in at least one of four previously reported datasets of non-involved lung tissue. Seven genes (KMO, CD1A, SPINK5, TREM2, CYBB, DNASE2B, FGG) were differentially expressed between ever and never smokers in all five datasets, with concordant higher expression in ever smokers. Smoking-induced up-regulation of six of these genes was also observed in a transcription dataset from lung tissue of non-cancer patients. Among the three most significant gene networks, two are involved in immunity and inflammation and one in cell death. Overall, this study shows that the lung parenchyma transcriptome of smokers has altered gene expression and that these alterations are reproducible in different series of smokers across countries. Moreover, this study identified a seven-gene panel that reflects lung tissue exposure to cigarette smoke.

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

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

The effect of e-cigarette aerosol emissions on respiratory health: a narrative review

Introduction: Due to the uptake in the use of e-cigarettes (ECs), evidence on their health effects is needed to inform health care and policy. Some regulators and health professionals have raised concerns that the respirable aerosols generated by ECs contain several constituents of potential toxicological and biological relevance to respiratory health. Areas covered: We critically assess published research on the respiratory system investigating the effects of ECs in preclinical models, clinical studies of people who switched to ECs from tobacco cigarettes, and population surveys. We assess the studies for the quality of their methodology and accuracy of their interpretation. To adequately assess the impact of EC use on human health, addressing common mistakes and developing robust and realistic methodological recommendations is an urgent priority. The findings of this review indicate that ECs under normal conditions of use demonstrate far fewer respiratory risks than combustible tobacco cigarettes. EC users and smokers considering ECs have the right to be informed about the relative risks of EC use, and to be made aware that findings of studies published by the media are not always reliable. Expert opinion: Growing evidence supports the relative safety of EC emission aerosols for the respiratory tract compared to tobacco smoke.

Co-expression network analysis of peripheral blood transcriptome identifies dysregulated protein processing in endoplasmic reticulum and immune response in recurrent MDD in older adults

The molecular factors involved in the pathophysiology of major depressive disorder (MDD) remain poorly understood. One approach to examine the molecular basis of MDD is co-expression network analysis, which facilitates the examination of complex interactions between expression levels of individual genes and how they influence biological pathways affected in MDD. Here, we applied an unsupervised gene-network based approach to a prospective experimental design using microarray genome-wide gene expression from the peripheral whole blood of older adults. We utilised the Sydney Memory and Ageing Study (sMAS, N = 521) and the Older Australian Twins Study (OATS, N = 186) as discovery and replication cohorts, respectively. We constructed networks using Weighted Gene Co-expression Network Analysis (WGCNA), and correlated identified modules with four subtypes of depression: single episode, current, recurrent, and lifetime MDD. Four modules of highly co-expressed genes were associated with recurrent MDD (N = 27) in our discovery cohort (FDR<0.2), with no significant findings for a single episode, current or lifetime MDD. Functional characterisation of these modules revealed a complex interplay between dysregulated protein processing in the endoplasmic reticulum (ER), and innate and adaptive immune response signalling, with possible involvement of pathogen-related pathways. We were underpowered to replicate findings at the network level in an independent cohort (OATS), however; we found a significant overlap for 9 individual genes with similar co-expression and dysregulation patterns associated with recurrent MDD in both cohorts. Overall, our findings support other reports on dysregulated immune response and protein processing in the ER in MDD and provide novel insights into the pathophysiology of depression.

Thyroid eye disease: what is new to know?

PURPOSE OF REVIEW

The pathophysiology of thyroid eye disease (TED) is still not fully understood. However, recently described risk factors and molecular findings have brought new insights into the mechanisms of TED and could lead to the emerging use of more targeted therapies. This article aims to review the clinical findings of TED, and the most recent advances in our understanding of the risk factors and therapeutic options for TED.

RECENT FINDINGS

Smoking has been recently shown to have an impact on specific gene expression involved in several disease-related pathways, which seems to be reversible with smoking cessation. This finding further emphasizes the importance of smoking cessation in the prevention and treatment of TED. Selenium deficiency and high-serum cholesterol have been described to be potential independent risk factors for TED and their management could decrease the incidence and severity of TED. In terms of therapeutic options, immunomodulatory medications have shown some promising results for disease control in TED over the past years, but further randomized prospective studies with larger sample sizes are still needed to prove their efficacy. A new technique of P brachytherapy was shown to have quick therapeutic effects on TED without significant side effects and could be a promising therapy for selected cases of TED.

SUMMARY

TED is one of the most common autoimmune inflammatory disorders of the orbit. Although its pathophysiology remains unclear, newly described genetic findings and risk factors could help in explaining its occurrence and guide future therapies. Immunosuppressant medications are increasingly used in the management of TED, but further studies are needed to confirm their effectiveness.

Characterizing the Relation Between Expression QTLs and Complex Traits: Exploring the Role of Tissue Specificity

Measurement of gene expression levels and detection of eQTLs (expression quantitative trait loci) are difficult in tissues with limited sample availability, such as the brain. However, eQTL overlap between tissues might be high, which would allow for inference of eQTL functioning in the brain via eQTLs detected in readily accessible tissues, e.g. whole blood. Applying Stratified Linkage Disequilibrium Score Regression (SLDSR), we quantified the enrichment in polygenic signal of blood and brain eQTLs in genome-wide association studies (GWAS) of 11 complex traits. We looked at eQTLs discovered in 44 tissues by the Genotype-Tissue Expression (GTEx) consortium and two other large representative studies, and found no tissue-specific eQTL effects. Next, we integrated the GTEx eQTLs with regions associated with tissue-specific histone modifiers, and interrogated their effect on rheumatoid arthritis and schizophrenia. We observed substantially enriched effects of eQTLs located inside regions bearing modification H3K4me1 on schizophrenia, but not rheumatoid arthritis, and not tissue-specific. Finally, we extracted eQTLs associated with tissue-specific differentially expressed genes and determined their effects on rheumatoid arthritis and schizophrenia, these analysis revealed limited enrichment of eQTLs associated with gene specifically expressed in specific tissues. Our results pointed to strong enrichment of eQTLs in their effect on complex traits, without evidence for tissue-specific effects. Lack of tissue-specificity can be either due to a lack of statistical power or due to the true absence of tissue-specific effects. We conclude that eQTLs are strongly enriched in GWAS signal and that the enrichment is not specific to the eQTL discovery tissue. Until sample sizes for eQTL discovery grow sufficiently large, working with relatively accessible tissues as proxy for eQTL discovery is sensible and restricting lookups for GWAS hits to a specific tissue for which limited samples are available might not be advisable.

Novel DNA Methylation Sites Influence GPR15 Expression in Relation to Smoking

Smoking is a major risk factor for cardiovascular diseases and has been implicated in the regulation of the G protein-coupled receptor 15 (GPR15) by affecting CpG methylation. The G protein-coupled receptor 15 is involved in angiogenesis and inflammation. An effect on GPR15 gene regulation has been shown for the CpG site CpG3.98251294. We aimed to analyze the effect of smoking on GPR15 expression and methylation sites spanning the GPR15 locus. DNA methylation of nine GPR15 CpG sites was measured in leukocytes from 1291 population-based individuals using the EpiTYPER. Monocytic GPR15 expression was measured by qPCR at baseline and five-years follow up. GPR15 gene expression was upregulated in smokers (beta (ß) = −2.699, p-value (p) = 1.02 × 10⁻⁷⁷) and strongly correlated with smoking exposure (ß = −0.063, p = 2.95 × 10⁻³⁴). Smoking cessation within five years reduced GPR15 expression about 19% (p = 9.65 × 10⁻⁵) with decreasing GPR15 expression over time (ß = 0.031, p = 3.81 × 10⁻⁶). Additionally, three novel CpG sites within GPR15 affected by smoking were identified. For CpG3.98251047, DNA methylation increased steadily after smoking cessation (ß = 0.123, p = 1.67 × 10⁻³) and strongly correlated with changes in GPR15 expression (ß = 0.036, p = 4.86 × 10⁻⁵). Three novel GPR15 CpG sites were identified in relation to smoking and GPR15 expression. Our results provide novel insights in the regulation of GPR15, which possibly linked smoking to inflammation and disease progression.

Association of current and former smoking with body mass index: A study of smoking discordant twin pairs from 21 twin cohorts

Background

Smokers tend to weigh less than never smokers, while successful quitting leads to an increase in body weight. Because smokers and non-smokers may differ in genetic and environmental family background, we analysed data from twin pairs in which the co-twins differed by their smoking behaviour to evaluate if the association between smoking and body mass index (BMI) remains after controlling for family background.

Methods and findings

The international CODATwins database includes information on smoking and BMI measured between 1960 and 2012 from 156,593 twin individuals 18–69 years of age. Individual-based data (230,378 measurements) and data of smoking discordant twin pairs (altogether 30,014 pairwise measurements, 36% from monozygotic [MZ] pairs) were analysed with linear fixed-effects regression models by 10-year periods. In MZ pairs, the smoking co-twin had, on average, 0.57 kg/m² lower BMI in men (95% confidence interval (CI): 0.49, 0.70) and 0.65 kg/m² lower BMI in women (95% CI: 0.52, 0.79) than the never smoking co-twin. Former smokers had 0.70 kg/m² higher BMI among men (95% CI: 0.63, 0.78) and 0.62 kg/m² higher BMI among women (95% CI: 0.51, 0.73) than their currently smoking MZ co-twins. Little difference in BMI was observed when comparing former smoking co-twins with their never smoking MZ co-twins (0.13 kg/m², 95% CI 0.04, 0.23 among men; -0.04 kg/m², 95% CI -0.16, 0.09 among women). The associations were similar within dizygotic pairs and when analysing twins as individuals. The observed series of cross-sectional associations were independent of sex, age, and measurement decade.

Conclusions

Smoking is associated with lower BMI and smoking cessation with higher BMI. However, the net effect of smoking and subsequent cessation on weight development appears to be minimal, i.e. never more than an average of 0.7 kg/m².

Genome-wide interaction study of smoking behavior and non-small cell lung cancer risk in Caucasian population

Non-small cell lung cancer is the most common type of lung cancer. Both environmental and genetic risk factors contribute to lung carcinogenesis. We conducted a genome-wide interaction analysis between single nucleotide polymorphisms (SNPs) and smoking status (never- versus ever-smokers) in a European-descent population. We adopted a two-step analysis strategy in the discovery stage: we first conducted a case-only interaction analysis to assess the relationship between SNPs and smoking behavior using 13336 non-small cell lung cancer cases. Candidate SNPs with P-value <0.001 were further analyzed using a standard case-control interaction analysis including 13970 controls. The significant SNPs with P-value <3.5 × 10-5 (correcting for multiple tests) from the case-control analysis in the discovery stage were further validated using an independent replication dataset comprising 5377 controls and 3054 non-small cell lung cancer cases. We further stratified the analysis by histological subtypes. Two novel SNPs, rs6441286 and rs17723637, were identified for overall lung cancer risk. The interaction odds ratio and meta-analysis P-value for these two SNPs were 1.24 with 6.96 × 10-7 and 1.37 with 3.49 × 10-7, respectively. In addition, interaction of smoking with rs4751674 was identified in squamous cell lung carcinoma with an odds ratio of 0.58 and P-value of 8.12 × 10-7. This study is by far the largest genome-wide SNP-smoking interaction analysis reported for lung cancer. The three identified novel SNPs provide potential candidate biomarkers for lung cancer risk screening and intervention. The results from our study reinforce that gene-smoking interactions play important roles in the etiology of lung cancer and account for part of the missing heritability of this disease.

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.

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

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

MicroRNAs as biomarkers for psychiatric disorders with a focus on autism spectrum disorder: Current progress in genetic association studies, expression profiling, and translational research

MicroRNAs (miRNAs) are a group of small noncoding RNA molecules, 18-25 nucleotides in length, which can negatively regulate gene expression at the post-transcriptional level by binding to messenger RNAs. About half of all identified miRNAs in humans are expressed in the brain and display regulatory functions important for many biological processes related to the development of the central nervous system (CNS). Disruptions in miRNA biogenesis and miRNA-target interaction have been related to CNS diseases, including psychiatric disorders. In this review, we focus on the role of miRNAs in autism spectrum disorder (ASD) and summarize recent findings about ASD-associated genetic variants in miRNA genes, in miRNA biogenesis genes, and miRNA targets. We discuss deregulation of miRNA expression in ASD and functional validation of ASD-related miRNAs in animal models. Including miRNAs in studies of ASD will contribute to our understanding of its etiology and pathogenesis and facilitate the discrimination between different disease subgroups. Autism Res 2017. © 2017 International Society for Autism Research, Wiley Periodicals, Inc. Autism Res 2017, 10: 1184-1203. © 2017 International Society for Autism Research, Wiley Periodicals, Inc.

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.