scholarly journals Smoking induces coordinated DNA methylation and gene expression changes in adipose tissue with consequences for metabolic health

2018 ◽  
Author(s):  
Pei-Chien Tsai ◽  
Craig A Glastonbury ◽  
Melissa N Eliot ◽  
Sailalitha Bollepalli ◽  
Idil Yet ◽  
...  

AbstractTobacco smoking is a risk factor for multiple diseases, including cardiovascular disease and diabetes. Many smoking-associated signals have been detected in the blood methylome, but the extent to which these changes are widespread to metabolically relevant tissues, and impact gene expression or cardio-metabolic health, remains unclear.We investigated smoking-associated DNA methylation and gene expression variation in adipose tissue from 542 healthy female twins with available well-characterized cardio-metabolic phenotype profiles. We identified 42 smoking-methylation and 42 smoking-expression signals, where five genes (AHRR, CYP1A1, CYP1B1, CYTL1, F2RL3) were both hypo-methylated and up-regulated in smokers. We replicated and validated a proportion of the signals in blood, adipose, skin, and lung tissue datasets, identifying tissue-shared effects. Smoking leaves systemic imprints on DNA methylation after smoking cessation, with stronger but shorter-lived effects on gene expression. We tested for associations between the observed smoking signals and several adiposity phenotypes that constitute cardio-metabolic disease risk. Visceral fat and android/gynoid ratio were associated with methylation at smoking-markers with functional impacts on expression, such as CYP1A1, and in signals shared across tissues, such as NOTCH1. At smoking-signals BHLHE40 and AHRR DNA methylation and gene expression levels in current smokers were predictive of future gain in visceral fat upon smoking cessation.Our results provide the first comprehensive characterization of coordinated DNA methylation and gene expression markers of smoking in adipose tissue, a subset of which link to human cardio-metabolic health and may give insights into the wide ranging risk effects of smoking across the body.Author SummaryTobacco smoking is the strongest environmental risk factor for human disease. Here, we investigate how smoking systemically changes methylome and transcriptome signatures in multiple tissues in the human body. We observe strong and coordinated epigenetic and gene expression changes in adipose tissue, some of which are mirrored in blood, skin, and lung tissue. Smoking leaves a strong short-lived impact on gene expression levels, while methylation changes are long-lasting after smoking cessation. We investigated if these changes observed in a metabolically-relevant (adipose) tissue had impacts on human disease, and observed strong associations with cardio-metabolic disease traits. Some of the smoking signals could predict future gain in obesity and cardio-metabolic disease risk in current smokers who subsequently go on to quit smoking. Our results provide novel insights into understanding the widespread health consequence of smoking outside the lung.

Proceedings ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 27
Author(s):  
Jo Slater ◽  
Rozanne Kruger ◽  
Nikki Renall ◽  
Marilize Richter ◽  
Marine Corbin ◽  
...  

Background: This study investigates relationships between physical activity (PA) andbiomarkers of metabolic health in populations with different metabolic disease risk; Pacific and NewZealand European (NZE) women with different body composition profiles. [...]


2020 ◽  
Author(s):  
SAJ de With ◽  
APS Ori ◽  
T Wang ◽  
SL Pulit ◽  
E Strengman ◽  
...  

AbstractClozapine is an important antipsychotic drug. However, its use is often accompanied by metabolic adverse effects and, in rare instances, agranulocytosis. The molecular mechanisms underlying these adverse events are unclear. To gain more insights into the response to clozapine at the molecular level, we exposed lymphoblastoid cell lines (LCLs) to increasing concentrations of clozapine and measured genome-wide gene expression and DNA methylation profiles. We observed robust and significant changes in gene expression levels due to clozapine (n = 463 genes at FDR < 0.05) affecting cholesterol and cell cycle pathways. At the level of DNA methylation, we find significant changes upstream of the LDL receptor, in addition to global enrichments of regulatory, immune and developmental pathways. By integrating these data with human tissue gene expression levels obtained from the Genotype-Tissue Expression project (GTEx), we identified specific tissues, including liver and several tissues involved in immune, endocrine and metabolic functions, that clozapine treatment may disproportionately affect. Notably, differentially expressed genes were not enriched for genome-wide disease risk of schizophrenia or for known psychotropic drug targets. However, we did observe a nominally significant association of genetic signals related to total cholesterol and low-density lipoprotein levels. Together, these results shed light on the biological mechanisms through which clozapine functions. The observed associations with cholesterol pathways, its genetic architecture and specific tissue effects may be indicative of the metabolic adverse effects observed in clozapine users. LCLs may thus serve as a useful tool to study these molecular mechanisms further.


2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Pei-Chien Tsai ◽  
Craig A. Glastonbury ◽  
Melissa N. Eliot ◽  
Sailalitha Bollepalli ◽  
Idil Yet ◽  
...  

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Guillermo Palou-Márquez ◽  
Isaac Subirana ◽  
Lara Nonell ◽  
Alba Fernández-Sanlés ◽  
Roberto Elosua

Abstract Background The integration of different layers of omics information is an opportunity to tackle the complexity of cardiovascular diseases (CVD) and to identify new predictive biomarkers and potential therapeutic targets. Our aim was to integrate DNA methylation and gene expression data in an effort to identify biomarkers related to cardiovascular disease risk in a community-based population. We accessed data from the Framingham Offspring Study, a cohort study with data on DNA methylation (Infinium HumanMethylation450 BeadChip; Illumina) and gene expression (Human Exon 1.0 ST Array; Affymetrix). Using the MOFA2 R package, we integrated these data to identify biomarkers related to the risk of presenting a cardiovascular event. Results Four independent latent factors (9, 19, 21—only in women—and 27), driven by DNA methylation, were associated with cardiovascular disease independently of classical risk factors and cell-type counts. In a sensitivity analysis, we also identified factor 21 as associated with CVD in women. Factors 9, 21 and 27 were also associated with coronary heart disease risk. Moreover, in a replication effort in an independent study three of the genes included in factor 27 were also present in a factor identified to be associated with myocardial infarction (CDC42BPB, MAN2A2 and RPTOR). Factor 9 was related to age and cell-type proportions; factor 19 was related to age and B cells count; factor 21 pointed to human immunodeficiency virus infection-related pathways and inflammation; and factor 27 was related to lifestyle factors such as alcohol consumption, smoking and body mass index. Inclusion of factor 21 (only in women) improved the discriminative and reclassification capacity of the Framingham classical risk function and factor 27 improved its discrimination. Conclusions Unsupervised multi-omics data integration methods have the potential to provide insights into the pathogenesis of cardiovascular diseases. We identified four independent factors (one only in women) pointing to inflammation, endothelium homeostasis, visceral fat, cardiac remodeling and lifestyles as key players in the determination of cardiovascular risk. Moreover, two of these factors improved the predictive capacity of a classical risk function.


Genes ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 854
Author(s):  
Yishu Wang ◽  
Lingyun Xu ◽  
Dongmei Ai

DNA methylation is an important regulator of gene expression that can influence tumor heterogeneity and shows weak and varying expression levels among different genes. Gastric cancer (GC) is a highly heterogeneous cancer of the digestive system with a high mortality rate worldwide. The heterogeneous subtypes of GC lead to different prognoses. In this study, we explored the relationships between DNA methylation and gene expression levels by introducing a sparse low-rank regression model based on a GC dataset with 375 tumor samples and 32 normal samples from The Cancer Genome Atlas database. Differences in the DNA methylation levels and sites were found to be associated with differences in the expressed genes related to GC development. Overall, 29 methylation-driven genes were found to be related to the GC subtypes, and in the prognostic model, we explored five prognoses related to the methylation sites. Finally, based on a low-rank matrix, seven subgroups were identified with different methylation statuses. These specific classifications based on DNA methylation levels may help to account for heterogeneity and aid in personalized treatments.


2021 ◽  
Vol 69 (37) ◽  
pp. 10907-10919
Author(s):  
Hao Suo ◽  
Mohammad Rezaul Islam Shishir ◽  
Jianbo Xiao ◽  
Mingfu Wang ◽  
Feng Chen ◽  
...  

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