Untargeted metabolomics reveals multiple metabolites influencing smoking-related DNA methylation

Redox Metabolomics of Menthol in Children's Plasma with Second-Hand Cigarette and Electronic Cigarette Exposures

Background

Cigarettes and electronic cigarettes generate many redox-active materials which could impact children's health through second-hand exposures. High-resolution metabolomics methods enable use of non-targeted mass spectrometry of plasma to test for redox consequences of second-hand exposures.

Objectives

Our objectives were to test for oxidative stress metabolites and altered metabolic pathways associated with second-hand exposure to redox-active flavorants and flavorant metabolites in plasma of infants and children.

Methods

Untargeted plasma metabolomics data for infants and children in a population known to include individuals with second-hand exposures to cigarettes and electronic cigarettes were analyzed for cotinine and metabolites of flavorants. A metabolome-wide association study (MWAS) was performed separately for cotinine and menthol glucuronide, derived from the redox-active flavorant, menthol. Pathway enrichment analysis was used to identify metabolic pathways, and xMWAS was used to detect metabolic communities associated with flavorant metabolites.

Results

Menthol glucuronide was one of several flavorant metabolites positively correlated with cotinine. MWAS and pathway enrichment analysis revealed that some pathways associated with both menthol glucuronide and cotinine, while others only associated with menthol glucuronide, including sphingolipid, glycerophospholipid, antioxidant, N-glycan and mitochondrial energy metabolism. 4-hydroxynonenal and other oxidized lipids positively correlated with menthol glucuronide.

Discussion

The results show that flavorants from second-hand electronic cigarette and cigarette exposures in infants and children are associated with changes in redox metabolism which are known to associate with human lung diseases.

Metabolomics efficiently discriminates monozygotic twins in peripheral blood

Monozygotic (MZ) twins cannot be distinguished using conventional forensic STR typing because they present identical STR genotypings. However, MZ twins do not always live in the same environment and often have different dietary and other lifestyle habits. Metabolic profiles are deyermined by individual characteristics and are also influenced by the environment in which they live. Therefore, they are potential markers capable of identifying MZ twins. Moreover, the production of proteins varies from organism to organism and is influenced by both the physiological state of the body and the external environment. Hence, we used metabolomics and proteomics to identify metabolites and proteins in peripheral blood to discriminate MZ twins. We identified 1749 known metabolites and 622 proteins in proteomic analysis. The metabolic profiles of four pairs of MZ twins revealed minor differences in intra-MZ twins and major differences in inter-MZ twins. Each pair of MZ twins exhibited distinct characteristics, and four metabolites-methyl picolinate, acesulfame, paraxanthine, and phenylbenzimidazole sulfonic acid-were observed in all four MZ twin pairs. These four differential exogenous metabolites conincidently show that the different external environments and life styles can be well distinguished by metabolites, considering that twins do not all have the same eating habits and living environments. Moreover, MZ twins showed different protein profiles in serum but not in whole blood. Thus, our results indicate that differential metabolites provide potential biomarkers for the personal identification of MZ twins in forensic medicine.

LncRNA H19 promotes tumor angiogenesis in smokers by targeting anti-angiogenic miRNAs

A key concept in drug discovery is the identification of candidate therapeutic targets such as long noncoding RNAs (lncRNAs) because of their extensive involvement in neoplasms, and impressionability by smoking. Induced by exposure to cigarette smoke, lncRNA H19 targets and inactivates miR-29, miR-30a, miR-107, miR-140, miR-148b, miR-199a and miR-200, which control the rate of angiogenesis by inhibiting BiP, DLL4, FGF7, HIF1A, HIF1B, HIF2A, PDGFB, PDGFRA, VEGFA, VEGFB, VEGFC, VEGFR1, VEGFR2 and VEGFR3. Nevertheless, these miRNAs are often dysregulated in bladder cancer, breast cancer, colorectal cancer, glioma, gastric adenocarcinoma, hepatocellular carcinoma, meningioma, non-small-cell lung carcinoma, oral squamous cell carcinoma, ovarian cancer, prostate adenocarcinoma and renal cell carcinoma. As such, the present perspective article seeks to establish an evidence-based hypothetical model of how a smoking-related lncRNA known as H19 might aggravate angiogenesis by interfering with miRNAs that would otherwise regulate angiogenesis in a nonsmoking individual.

DNA Methylation of TXNIP Independently Associated with Inflammation and Diabetes Mellitus in Twins

Thioredoxin-interacting protein (TXNIP) plays a key role in diabetes development and prognosis through its role in pancreatic β-cell dysfunction and death as well as in upregulating the inflammatory response in hyperglycemia. DNA methylation (DNAm) of TXNIP (TXNIP-cg19693031) is associated with the prevalence and incidence of type 2 diabetes (T2D); however, its role in inflammation and its relationship with T2D remain unclear. We aimed to investigate the epigenetic associations of TXNIP-cg19693031 with a panel of inflammatory biomarkers and to examine whether these inflammatory biomarkers modify the association between TXNIP-cg19693031 methylation and diabetes in 218 middle-aged male twins from the Emory Twin Study. We confirmed the association of TXNIP-cg19693031 DNAm with T2D, as well as with HbA1c, insulin and fasting glucose. We found that hypomethylation at TXNIP-cg19693031 is strongly associated with both type 2 diabetes and higher levels of inflammatory biomarkers (VCAM-1, ICAM-1, MMP-2, sRAGE and P-selectin); however, the relationship between TXNIP-cg19693031 and T2D is independent of the levels of these inflammatory biomarkers. Our results suggest that DNA methylation of TXNIP is linked with multiple biological processes, through which the TXNIP may have broad influence on chronic disease risk.

Connecting the epigenome, metabolome and proteome for a deeper understanding of disease

Epigenome-wide association studies (EWAS) identify genes that are dysregulated by the studied clinical endpoints, thereby indicating potential new diagnostic biomarkers, drug targets and therapy options. Combining EWAS with deep molecular phenotyping, such as approaches enabled by metabolomics and proteomics, allows further probing of the underlying disease-associated pathways. For instance, methylation of the TXNIP gene is associated robustly with prevalent type 2 diabetes and further with metabolites that are short-term markers of glycaemic control. These associations reflect TXNIP's function as a glucose uptake regulator by interaction with the major glucose transporter GLUT1 and suggest that TXNIP methylation can be used as a read-out for the organism's exposure to glucose stress. Another case is the association between DNA methylation of the AHRR and F2RL3 genes with smoking and a protein that is involved in the reprogramming of the bronchial epithelium. These examples show that associations between DNA methylation and intermediate molecular traits can open new windows into how the body copes with physiological challenges. This knowledge, if carefully interpreted, may indicate novel therapy options and, together with monitoring of the methylation state of specific methylation sites, may in the future allow the early diagnosis of impending disease. It is essential for medical practitioners to recognize the potential that this field holds in translating basic research findings to clinical practice. In this review, we present recent advances in the field of EWAS with metabolomics and proteomics and discuss both the potential and the challenges of translating epigenetic associations, with deep molecular phenotypes, to biomedical applications.

A Mediation Approach to Discovering Causal Relationships between the Metabolome and DNA Methylation in Type 1 Diabetes

Environmental factors including viruses, diet, and the metabolome have been linked with the appearance of islet autoimmunity (IA) that precedes development of type 1 diabetes (T1D). We measured global DNA methylation (DNAm) and untargeted metabolomics prior to IA and at the time of seroconversion to IA in 92 IA cases and 91 controls from the Diabetes Autoimmunity Study in the Young (DAISY). Causal mediation models were used to identify seven DNAm probe-metabolite pairs in which the metabolite measured at IA mediated the protective effect of the DNAm probe measured prior to IA against IA risk. These pairs included five DNAm probes mediated by histidine (a metabolite known to affect T1D risk), one probe (cg01604946) mediated by phostidyl choline p-32:0 or o-32:1, and one probe (cg00390143) mediated by sphingomyelin d34:2. The top 100 DNAm probes were over-represented in six reactome pathways at the FDR <0.1 level (q = 0.071), including transport of small molecules and inositol phosphate metabolism. While the causal pathways in our mediation models require further investigation to better understand the biological mechanisms, we identified seven methylation sites that may improve our understanding of epigenetic protection against T1D as mediated by the metabolome.

Characterizing the properties of bisulfite sequencing data: maximizing power and sensitivity to identify between-group differences in DNA methylation

BACKGROUND

The combination of sodium bisulfite treatment with highly-parallel sequencing is a common method for quantifying DNA methylation across the genome. The power to detect between-group differences in DNA methylation using bisulfite-sequencing approaches is influenced by both experimental (e.g. read depth, missing data and sample size) and biological (e.g. mean level of DNA methylation and difference between groups) parameters. There is, however, no consensus about the optimal thresholds for filtering bisulfite sequencing data with implications for the reproducibility of findings in epigenetic epidemiology.

RESULTS

We used a large reduced representation bisulfite sequencing (RRBS) dataset to assess the distribution of read depth across DNA methylation sites and the extent of missing data. To investigate how various study variables influence power to identify DNA methylation differences between groups, we developed a framework for simulating bisulfite sequencing data. As expected, sequencing read depth, group size, and the magnitude of DNA methylation difference between groups all impacted upon statistical power. The influence on power was not dependent on one specific parameter, but reflected the combination of study-specific variables. As a resource to the community, we have developed a tool, POWEREDBiSeq, which utilizes our simulation framework to predict study-specific power for the identification of DNAm differences between groups, taking into account user-defined read depth filtering parameters and the minimum sample size per group.

CONCLUSIONS

Our data-driven approach highlights the importance of filtering bisulfite-sequencing data by minimum read depth and illustrates how the choice of threshold is influenced by the specific study design and the expected differences between groups being compared. The POWEREDBiSeq tool, which can be applied to different types of bisulfite sequencing data (e.g. RRBS, whole genome bisulfite sequencing (WGBS), targeted bisulfite sequencing and amplicon-based bisulfite sequencing), can help users identify the level of data filtering needed to optimize power and aims to improve the reproducibility of bisulfite sequencing studies.

Cardiovascular disease risk and pathophysiology in South Asians: can longitudinal multi-omics shed light?

Cardiovascular disease (CVD) is the leading cause of mortality in South Asia, with rapidly increasing prevalence of hypertension, type 2 diabetes (T2DM) and hyperlipidemia over the last two decades. Atherosclerotic CVD (ASCVD) affects South Asians earlier in life and at lower body weights, which is not fully explained by differential burden of conventional risk factors. Heart failure (HF) is a complex clinical syndrome of heterogeneous structural phenotypes including two major clinical subtypes, HF with preserved (HFpEF) and reduced ejection fraction (HFrEF). The prevalence of HF in South Asians is also rising with other metabolic diseases, and HFpEF develops at younger age and leaner body mass index in South Asians than in Whites. Recent genome-wide association studies, epigenome-wide association studies and metabolomic studies of ASCVD and HF have identified genes, metabolites and pathways associated with CVD traits. However, these findings were mostly driven by samples of European ancestry, which may not accurately represent the CVD risk at the molecular level, and the unique risk profile of CVD in South Asians. Such bias, while formulating hypothesis-driven research studies, risks missing important causal or predictive factors unique to South Asians. Importantly, a longitudinal design of multi-omic markers can capture the life-course risk and natural history related to CVD, and partially disentangle putative causal relationship between risk factors, multi-omic markers and subclinical and clinical ASCVD and HF. In conclusion, combining high-resolution untargeted metabolomics with epigenomics of rigorous, longitudinal design will provide comprehensive unbiased molecular characterization of subclinical and clinical CVD among South Asians. A thorough understanding of CVD-associated metabolomic profiles, together with advances in epigenomics and genomics, will lead to more accurate estimates of CVD progression and stimulate new strategies for improving cardiovascular health.

PepT1-knockout mice harbor a protective metabolome beneficial for intestinal wound healing

Genetic knockout (KO) of peptide transporter-1 (PepT1) protein is known to provide resistance to acute colitis and colitis-associated cancer (CAC) in mouse models. However, it was unclear which molecule(s) or pathway(s) formed the basis for these protective effects. Recently, we demonstrated that the PepT1^-/- microbiota is sufficient to protect against colitis and CAC. Given that PepT1 KO alters the gut microbiome and thereby changes the intestinal metabolites that are ultimately reflected in the feces, we investigated the fecal metabolites of our PepT1 KO mice. Using a liquid chromatography-mass spectrometry (LC-MS)-based untargeted-metabolomics technique, we found that the fecal metabolites were significantly different between the KO and normal wild-type (WT) mice. Among the altered fecal metabolites, tuberonic acid (TA) was sevenfold higher in KO mouse feces than in WT mouse feces. Accordingly, we studied whether the increased TA could direct an anti-inflammatory effect. Using in vitro models, we discovered that TA not only prevented lipopolysaccharide (LPS)-induced inflammation in macrophages but also improved the epithelial cell healing processes. Our results suggest that TA, and possibly other fecal metabolites, play a crucial role in the pathway(s) associated with the anticolitis effects of PepT1 KO.NEW & NOTEWORTHY Fecal metabolites were significantly different between the KO and normal wild-type (WT) mice. One fecal metabolite, tuberonic acid (TA), was sevenfold higher in KO mouse feces than in WT mouse feces. TA prevented lipopolysaccharide (LPS)-induced inflammation in macrophages and improved the epithelial cell healing process.

Metabolomics and cardiovascular imaging: a combined approach for cardiovascular ageing

The purpose of this review is to explore how metabolomics can help uncover new biomarkers and mechanisms for cardiovascular ageing. Cardiovascular ageing refers to cardiovascular structural and functional alterations that occur with chronological ageing and that can lead to the development of cardiovascular disease. These alterations, which were previously only detectable on tissue histology or corroborated on blood samples, are now detectable with modern imaging techniques. Despite the emergence of powerful new imaging tools, clinical investigation into cardiovascular ageing is challenging because ageing is a life course phenomenon involving known and unknown risk factors that play out in a dynamic fashion. Metabolomic profiling measures large numbers of metabolites with diverse chemical properties. Metabolomics has the potential to capture changes in biochemistry brought about by pathophysiologic processes as well as by normal ageing. When combined with non-invasive cardiovascular imaging tools, metabolomics can be used to understand pathological consequences of cardiovascular ageing. This review will summarize previous metabolomics and imaging studies in cardiovascular ageing. These methods may be a clinically relevant and novel approach to identify mechanisms of cardiovascular ageing and formulate or personalize treatment strategies.

Cardiovascular disease risk and pathophysiology in South Asians: can longitudinal multi-omics shed light?

Cardiovascular disease (CVD) is the leading cause of mortality in South Asia, with rapidly increasing prevalence of hypertension, type 2 diabetes and hyperlipidemia over the last two decades. Atherosclerotic CVD (ASCVD) affects South Asians earlier in life and at lower body weights, which is not fully explained by differential burden of conventional risk factors. Heart failure (HF) is a complex clinical syndrome of heterogeneous structural phenotypes including two major clinical subtypes, HF with preserved (HFpEF) and reduced ejection fraction (HFrEF). The prevalence of HF in South Asians is also rising with other metabolic diseases, and HFpEF develops at younger age and leaner body mass index in South Asians than in Whites. Recent genome-wide association studies, epigenome-wide association studies and metabolomic studies of ASCVD and HF have identified genes, metabolites and pathways associated with CVD traits. However, these findings were mostly driven by samples of European ancestry, which may not accurately represent the CVD risk at the molecular level, and the unique risk profile of CVD in South Asians. Such bias, while formulating hypothesis-driven research studies, risks missing important causal or predictive factors unique to South Asians. Importantly, a longitudinal design of multi-omic markers can capture the life-course risk and natural history related to CVD, and partially disentangle putative causal relationship between risk factors, multi-omic markers and subclinical and clinical ASCVD and HF. In conclusion, combining high-resolution untargeted metabolomics with epigenomics of rigorous, longitudinal design will provide comprehensive unbiased molecular characterization of subclinical and clinical CVD among South Asians. A thorough understanding of CVD-associated metabolomic profiles, together with advances in epigenomics and genomics, will lead to more accurate estimates of CVD progression and stimulate new strategies for improving cardiovascular health.

Metabolomics in Radiation Biodosimetry: Current Approaches and Advances

Triage and medical intervention strategies for unanticipated exposure during a radiation incident benefit from the early, rapid and accurate assessment of dose level. Radiation exposure results in complex and persistent molecular and cellular responses that ultimately alter the levels of many biological markers, including the metabolomic phenotype. Metabolomics is an emerging field that promises the determination of radiation exposure by the qualitative and quantitative measurements of small molecules in a biological sample. This review highlights the current role of metabolomics in assessing radiation injury, as well as considerations for the diverse range of bioanalytical and sampling technologies that are being used to detect these changes. The authors also address the influence of the physiological status of an individual, the animal models studied, the technology and analysis employed in interrogating response to the radiation insult, and variables that factor into discovery and development of robust biomarker signatures. Furthermore, available databases for these studies have been reviewed, and existing regulatory guidance for metabolomics are discussed, with the ultimate goal of providing both context for this area of radiation research and the consideration of pathways for continued development.

Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources

As researchers are increasingly able to collect data on a large scale from multiple clinical and omics modalities, multi-omics integration is becoming a critical component of metabolomics research. This introduces a need for increased understanding by the metabolomics researcher of computational and statistical analysis methods relevant to multi-omics studies. In this review, we discuss common types of analyses performed in multi-omics studies and the computational and statistical methods that can be used for each type of analysis. We pinpoint the caveats and considerations for analysis methods, including required parameters, sample size and data distribution requirements, sources of a priori knowledge, and techniques for the evaluation of model accuracy. Finally, for the types of analyses discussed, we provide examples of the applications of corresponding methods to clinical and basic research. We intend that our review may be used as a guide for metabolomics researchers to choose effective techniques for multi-omics analyses relevant to their field of study.

Machine Learning Approach for Predicting Past Environmental Exposures From Molecular Profiling of Post-Exposure Human Serum Samples

OBJECTIVE

To develop an approach for a retrospective analysis of post-exposure serum samples using diverse molecular profiles.

METHODS

The 236 molecular profiles from 800 de-identified human serum samples from the Department of Defense Serum Repository were classified as smokers or non-smokers based on direct measurement of serum cotinine levels. A machine-learning pipeline was used to classify smokers and non-smokers from their molecular profiles.

RESULTS

The refined supervised support vector machines with recursive feature elimination predicted smokers and non-smokers with 78% accuracy on the independent held-out set. Several of the identified classifiers of smoking status have previously been reported and four additional miRNAs were validated with experimental tobacco smoke exposure in mice, supporting the computational approach.

CONCLUSIONS

We developed and validated a pipeline that shows retrospective analysis of post-exposure serum samples can identify environmental exposures.

Novel DNA methylation sites associated with cigarette smoking among African Americans

INTRODUCTION

Cigarette smoking has been associated with adverse health outcomes for mothers and children and is a major contributor to heart disease. Although cigarette smoking is known to affect the epigenome, few studies have been done in African American populations. In this study, we investigated the association between cigarette smoking and DNA methylation (DNAm) among African Americans from the Intergenerational Impact of Genetic and Psychological Factors on Blood Pressure Study (InterGEN), and the Genetic Epidemiology Network of Arteriopathy (GENOA).

METHODS

The InterGEN study aims to examine the effects of genetic and psychological factors on blood pressure among African American women and their children. Current cigarette smoking was assessed at baseline. DNAm of saliva was assessed using the 850K EPIC Illumina BeadChip for Epigenome-Wide Association analyses. A replication study was conducted among 1100 participants in the GENOA study using the same BeadChip.

RESULTS

After controlling for age, body mass index, population structure and cell composition, 26 epigenome-wide significant sites (FDR q < 0.05) were identified, including the AHRR and PHF14 genes associated with atherosclerosis and lung disease, respectively. Six novel CpG sites were discovered in the InterGEN sample and replicated in the GENOA sample. Genes mapped include RARA, FSIP1, ALPP, PIK3R5, KIAA0087, and MGAT3, which were largely associated with cancer development.

CONCLUSION

We observed significant epigenetic associations between smoking and disease-associated genes (e.g., cardiovascular disease, lung cancer). Six novel CpG sites were identified and replicated across saliva and blood samples.

Distinctive pattern of AHNAK methylation level in peripheral blood mononuclear cells and the association with HBV-related liver diseases

The purpose of this study was to investigate the correlation between AHNAK methylation level in peripheral blood mononuclear cells (PBMC) and the progression of hepatitis B virus (HBV)‐related liver disease. Bioinformatics methods were applied to evaluate the AHNAK methylation level in PBMC and T cells at different stages of HBV related liver disease, to investigate the correlation between AHNAK methylation and clinical features, as well as to compare the methylation site of AHNAK in cancer tissues and adjacent tissues. Subsequently, the differentially expressed gene analysis technique was used to analyze the liver disease‐related genes and immune‐related pathways in hepatitis B patients with different pathological changes. Finally, promoter methylation and mRNA expression of AHNAK gene in liver cancer and adjacent tissues were determined by quantitative polymerase chain reaction (Q‐PCR), and the diagnostic value of AHNAK methylation level in hepatopathy was evaluated by receiver operating characteristic (ROC) curve. The promoter methylation level of AHNAK gene in PBMCs decreased with the progression of HBV‐related liver disease, and showed significant difference among the patients with various HBV‐related liver diseases (P = 0.0001). The AHNAK methylation level in PBMCs and T cells was negatively associated with age, white blood cell count, CREA, drinking, and positively associated with APTT and HbsAg. Higher mRNA expression of AHNAK was found in liver cancer tissues than that of adjacent tissues (P < 0.001), and the methylation level in PBMC decreased with the progression of hepatitis B‐related liver disease. The area under the ROC curve (ROC) was 0.883 (P < 0.001) in diagnosis of chronic hepatitis B (CHB), 0.885 (P < 0.001) in diagnosis of compensatory liver cirrhosis, 0.955 (P < 0.001) in diagnosis of decompensated liver cirrhosis, 0.981 (P < 0.001) in diagnosis of hepatocellular carcinoma. Our results revealed that AHNAK methylation level in peripheral blood decreases with the progression of hepatitis B‐related liver disease. This provided a potential differential diagnostic method for HBV‐related hepatopathies, and thus an early detective tool for liver cancer.