Cadmium, Smoking, and Human Blood DNA Methylation Profiles in Adults from the Strong Heart Study.

The epigenetic effects of individual environmental toxicants in tobacco remain largely unexplored. Cadmium (Cd) has been associated with smoking-related health effects, and its concentration in tobacco smoke is higher in comparison with other metals. We studied the association of Cd and smoking exposures with human blood DNA methylation (DNAm) profiles. We also evaluated the implication of findings to relevant methylation pathways and the potential contribution of Cd exposure from smoking to explain the association between smoking and site-specific DNAm. We conducted an epigenome-wide association study of urine Cd and self-reported smoking (current and former vs. never, and cumulative smoking dose) with blood DNAm in 790,026 CpGs (methylation sites) measured with the Illumina Infinium Human MethylationEPIC (Illumina Inc.) platform in 2,325 adults 45-74 years of age who participated in the Strong Heart Study in 1989-1991. In a mediation analysis, we estimated the amount of change in DNAm associated with smoking that can be independently attributed to increases in urine Cd concentrations from smoking. We also conducted enrichment analyses and in silico protein-protein interaction networks to explore the biological relevance of the findings. At a false discovery rate (FDR)-corrected level of 0.05, we found 6 differentially methylated positions (DMPs) for Cd; 288 and 17, respectively, for current and former smoking status; and 77 for cigarette pack-years. Enrichment analyses of these DMPs displayed enrichment of 58 and 6 Gene Ontology and Kyoto Encyclopedia of Genes and Genomes gene sets, respectively, including biological pathways for cancer and cardiovascular disease. In in silico protein-to-protein networks, we observed key proteins in DNAm pathways directly and indirectly connected to Cd- and smoking-DMPs. Among DMPs that were significant for both Cd and current smoking (annotated to PRSS23, AHRR, F2RL3, RARA, and 2q37.1), we found statistically significant contributions of Cd to smoking-related DNAm. Beyond replicating well-known smoking epigenetic signatures, we found novel DMPs related to smoking. Moreover, increases in smoking-related Cd exposure were associated with differential DNAm. Our integrative analysis supports a biological link for Cd and smoking-associated health effects, including the possibility that Cd is partly responsible for smoking toxicity through epigenetic changes. https://doi.org/10.1289/EHP6345.This work was supported by grants by the National Heart, Lung, and Blood Institute (NHLBI) (under contract numbers 75N92019D00027, 75N92019D00028, 75N92019D00029, & 75N92019D00030) and previous grants (R01HL090863, R01HL109315, R01HL109301, R01HL109284, R01HL109282, and R01HL109319 and cooperative agreements U01HL41642, U01HL41652, U01HL41654, U01HL65520, and U01HL65521), by the National Institute of Health Sciences (R01ES021367, R01ES025216, P42ES010349, P30ES009089), by the Spanish Funds for Research In Health Sciences, Carlos III Health Institute, co-funded by European Regional Development Fund (CP12/03080 and PI15/00071), by Chilean CONICYT/FONDECYT-POSTDOCTORADO Nº3180486 (A.L.R.-C) and a fellowship from “La Caixa” Foundation (ID 100010434). The fellowship code is “LCF/BQ/DR19/11740016.”S

Exosomal and Plasma Non-Coding RNA Signature Associated with Urinary Albumin Excretion in Hypertension

Non-coding RNA (ncRNA), released into circulation or packaged into exosomes, plays important roles in many biological processes in the kidney. The purpose of the present study is to identify a common ncRNA signature associated with early renal damage and its related molecular pathways. Three individual libraries (plasma and urinary exosomes, and total plasma) were prepared from each hypertensive patient (with or without albuminuria) for ncRNA sequencing analysis. Next, an RNA-based transcriptional regulatory network was constructed. The three RNA biotypes with the greatest number of differentially expressed transcripts were long-ncRNA (lncRNA), microRNA (miRNA) and piwi-interacting RNA (piRNAs). We identified a common 24 ncRNA molecular signature related to hypertension-associated urinary albumin excretion, of which lncRNAs were the most representative. In addition, the transcriptional regulatory network showed five lncRNAs (LINC02614, BAALC-AS1, FAM230B, LOC100505824 and LINC01484) and the miR-301a-3p to play a significant role in network organization and targeting critical pathways regulating filtration barrier integrity and tubule reabsorption. Our study found an ncRNA profile associated with albuminuria, independent of biofluid origin (urine or plasma, circulating or in exosomes) that identifies a handful of potential targets, which may be utilized to study mechanisms of albuminuria and cardiovascular damage

Ordering of Omics Features Using Beta Distributions on Montecarlo p-Values

The current trend in genetic research is the study of omics data as a whole, either combining studies or omics techniques. This raises the need for new robust statistical methods that can integrate and order the relevant biological information. A good way to approach the problem is to order the features studied according to the different kinds of data so a key point is to associate good values to the features that permit us a good sorting of them. These values are usually the p-values corresponding to a hypothesis which has been tested for each feature studied. The Montecarlo method is certainly one of the most robust methods for hypothesis testing. However, a large number of simulations is needed to obtain a reliable p-value, so the method becomes computationally infeasible in many situations. We propose a new way to order genes according to their differential features by using a score defined from a beta distribution fitted to the generated p-values. Our approach has been tested using simulated data and colorectal cancer datasets from Infinium methylationEPIC array, Affymetrix gene expression array and Illumina RNA-seq platforms. The results show that this approach allows a proper ordering of genes using a number of simulations much lower than with the Montecarlo method. Furthermore, the score can be interpreted as an estimated p-value and compared with Montecarlo and other approaches like the p-value of the moderated t-tests. We have also identified a new expression pattern of eighteen genes common to all colorectal cancer microarrays, i.e., 21 datasets. Thus, the proposed method is effective for obtaining biological results using different datasets. Our score shows a slightly smaller type I error for small sizes than the Montecarlo p-value. The type II error of Montecarlo p-value is lower than the one obtained with the proposed score and with a moderated p-value, but these differences are highly reduced for larger sample sizes and higher false discovery rates. Similar performances from type I and II errors and the score enable a clear ordering of the features being evaluated

Gene Set Analysis Using Spatial Statistics

Gene differential expression consists of the study of the possible association between the gene expression, evaluated using different types of data as DNA microarray or RNA-Seq technologies, and the phenotype. This can be performed marginally for each gene (differential gene expression) or using a gene set collection (gene set analysis). A previous (marginal) per-gene analysis of differential expression is usually performed in order to obtain a set of significant genes or marginal p-values used later in the study of association between phenotype and gene expression. This paper proposes the use of methods of spatial statistics for testing gene set differential expression analysis using paired samples of RNA-Seq counts. This approach is not based on a previous per-gene differential expression analysis. Instead, we compare the paired counts within each sample/control using a binomial test. Each pair per gene will produce a p-value so gene expression profile is transformed into a vector of p-values which will be considered as an event belonging to a point pattern. This would be the first component of a bivariate point pattern. The second component is generated by applying two different randomization distributions to the correspondence between samples and treatment. The self-contained null hypothesis considered in gene set analysis can be formulated in terms of the associated point pattern as a random labeling of the considered bivariate point pattern. The gene sets were defined by the Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The proposed methodology was tested in four RNA-Seq datasets of colorectal cancer (CRC) patients and the results were contrasted with those obtained using the edgeR-GOseq pipeline. The proposed methodology has proved to be consistent at the biological and statistical level, in particular using Cuzick and Edwards test with one realization of the second component and between-pair distribution

Gene Set Analysis Using Spatial Statistics

Gene differential expression consists of the study of the possible association between the gene expression, evaluated using different types of data as DNA microarray or RNA-Seq technologies, and the phenotype. This can be performed marginally for each gene (differential gene expression) or using a gene set collection (gene set analysis). A previous (marginal) per-gene analysis of differential expression is usually performed in order to obtain a set of significant genes or marginal p-values used later in the study of association between phenotype and gene expression. This paper proposes the use of methods of spatial statistics for testing gene set differential expression analysis using paired samples of RNA-Seq counts. This approach is not based on a previous per-gene differential expression analysis. Instead, we compare the paired counts within each sample/control using a binomial test. Each pair per gene will produce a p-value so gene expression profile is transformed into a vector of p-values which will be considered as an event belonging to a point pattern. This would be the first component of a bivariate point pattern. The second component is generated by applying two different randomization distributions to the correspondence between samples and treatment. The self-contained null hypothesis considered in gene set analysis can be formulated in terms of the associated point pattern as a random labeling of the considered bivariate point pattern. The gene sets were defined by the Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The proposed methodology was tested in four RNA-Seq datasets of colorectal cancer (CRC) patients and the results were contrasted with those obtained using the edgeR-GOseq pipeline. The proposed methodology has proved to be consistent at the biological and statistical level, in particular using Cuzick and Edwards test with one realization of the second component and between-pair distribution

Emerging Role of miRNAs in the Drug Resistance of Gastric Cancer

Gastric cancer is the third leading cause of cancer mortality worldwide. Unfortunately, most gastric cancer cases are diagnosed in an advanced, non-curable stage and with a limited response to chemotherapy. Drug resistance is one of the most important causes of therapy failure in gastric cancer patients. Although the mechanisms of drug resistance have been broadly studied, the regulation of these mechanisms has not been completely understood. Accumulating evidence has recently highlighted the role of microRNAs in the development and maintenance of drug resistance due to their regulatory features in specific genes involved in the chemoresistant phenotype of malignancies, including gastric cancer. This review summarizes the current knowledge about the miRNAs' characteristics, their regulation of the genes involved in chemoresistance and their potential as targeted therapies for personalized treatment in resistant gastric cancer

Emerging Role of miRNAs in the Drug Resistance of Gastric Cancer

Gastric cancer is the third leading cause of cancer mortality worldwide. Unfortunately, most gastric cancer cases are diagnosed in an advanced, non-curable stage and with a limited response to chemotherapy. Drug resistance is one of the most important causes of therapy failure in gastric cancer patients. Although the mechanisms of drug resistance have been broadly studied, the regulation of these mechanisms has not been completely understood. Accumulating evidence has recently highlighted the role of microRNAs in the development and maintenance of drug resistance due to their regulatory features in specific genes involved in the chemoresistant phenotype of malignancies, including gastric cancer. This review summarizes the current knowledge about the miRNAs’ characteristics, their regulation of the genes involved in chemoresistance and their potential as targeted therapies for personalized treatment in resistant gastric cancer

Bioenergetic Failure in Rat Oligodendrocyte Progenitor Cells Treated with Cerebrospinal Fluid Derived from Multiple Sclerosis Patients

In relapsing-remitting multiple sclerosis (RRMS) subtype, the patient’s brain itself is capable of repairing the damage, remyelinating the axon and recovering the neurological function. Cerebrospinal fluid (CSF) is in close proximity with brain parenchyma and contains a host of proteins and other molecules, which influence the cellular physiology, that may balance damage and repair of neurons and glial cells. The purpose of this study was to determine the pathophysiological mechanisms underpinning myelin repair in distinct clinical forms of MS and neuromyelitis optica (NMO) patients by studying the effect of diseased CSF on glucose metabolism and ATP synthesis. A cellular model with primary cultures of oligodendrocyte progenitor cells (OPCs) from rat cerebrum was employed, and cells were treated with CSF from distinct clinical forms of MS, NMO patients and neurological controls. Prior to comprehending mechanisms underlying myelin repair, we determine the best stably expressed reference genes in our experimental condition to accurately normalize our target mRNA transcripts. The GeNorm and NormFinder algorithms showed that mitochondrial ribosomal protein (Mrpl19), hypoxanthine guanine phosphoribosyl transferase (Hprt), microglobulin β2 (B2m), and transferrin receptor (Tfrc) were identified as the best reference genes in OPCs treated with MS subjects and were used for normalizing gene transcripts. The main findings on microarray gene expression profiling analysis on CSF treated OPCs cells revealed a disturbed carbohydrate metabolism and ATP synthesis in MS and NMO derived CSF treated OPCs. In addition, using STRING program, we investigate whether gene–gene interaction affected the whole network in our experimental conditions. Our findings revealed downregulated expression of genes involved in carbohydrate metabolism, and that glucose metabolism impairment and reduced ATP availability for cellular damage repair clearly differentiate more benign forms from the most aggressive forms and worst prognosis in MS patients

Plasma Exosomal Non-Coding RNA Profile Associated with Renal Damage Reveals Potential Therapeutic Targets in Lupus Nephritis

Despite considerable progress in our understanding of systemic lupus erythematosus (SLE) pathophysiology, patient diagnosis is often deficient and late, and this has an impact on disease progression. The aim of this study was to analyze non-coding RNA (ncRNA) packaged into exosomes by next-generation sequencing to assess the molecular profile associated with renal damage, one of the most serious complications of SLE, to identify new potential targets to improve disease diagnosis and management using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. The plasma exosomes had a specific ncRNA profile associated with lupus nephritis (LN). The three ncRNA types with the highest number of differentially expressed transcripts were microRNAs (miRNAs), long non-coding RNAs (lncRNAs) and piwi-interacting RNAs (piRNAs). We identified an exosomal 29-ncRNA molecular signature, of which 15 were associated only with LN presence; piRNAs were the most representative, followed by lncRNAs and miRNAs. The transcriptional regulatory network showed a significant role for four lncRNAs (LINC01015, LINC01986, AC087257.1 and AC022596.1) and two miRNAs (miR-16-5p and miR-101-3p) in network organization, targeting critical pathways implicated in inflammation, fibrosis, epithelial–mesenchymal transition and actin cytoskeleton. From these, a handful of potential targets, such as transforming growth factor-β (TGF-β) superfamily binding proteins (activin-A, TGFB receptors, etc.), WNT/β-catenin and fibroblast growth factors (FGFs) have been identified for use as therapeutic targets of renal damage in SLE

Biofluid Specificity of Long Non-Coding RNA Profile in Hypertension: Relevance of Exosomal Fraction

Non-coding RNA (ncRNA)-mediated targeting of various genes regulates the molecular mechanisms of the pathogenesis of hypertension (HTN). However, very few circulating long ncRNAs (lncRNAs) have been reported to be altered in essential HTN. The aim of our study was to identify a lncRNA profile in plasma and plasma exosomes associated with urinary albumin excretion in HTN by next-generation sequencing and to assess biological functions enriched in response to albuminuria using GO and KEGG analysis. Plasma exosomes showed higher diversity and fold change of lncRNAs than plasma, and low transcript overlapping was found between the two biofluids. Enrichment analysis identified different biological pathways regulated in plasma or exosome fraction, which were implicated in fatty acid metabolism, extracellular matrix, and mechanisms of sorting ncRNAs into exosomes, while plasma pathways were implicated in genome reorganization, interference with RNA polymerase, and as scaffolds for assembling transcriptional regulators. Our study found a biofluid specific lncRNA profile associated with albuminuria, with higher diversity in exosomal fraction, which identifies several potential targets that may be utilized to study mechanisms of albuminuria and cardiovascular damage