DNA methylation signatures of illicit drug injection and hepatitis C are associated with HIV frailty

Zhang, Xin Yu; Hu, Ying; Justice, Amy C.; Li, Boyang; Wang, Zuoheng; Zhao, Hongyu; Krystal, John H.

doi:10.1038/s41467-017-02326-1

Cited by 36 publications

(46 citation statements)

References 53 publications

(49 reference statements)

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“…The In nium Methylation EPIC array (Illumina, San Diego, CA, USA) was used to measure PBMC DNA methylation pro les from the same four participants. These four samples with DNA input of 1000 ng were preprocessed using standard procedures as previously described (18). Brie y, the predicted sex based on methylome was consistent with self-reported sex for all samples.…”

Section: Epic Array Data Preprocessingmentioning

confidence: 80%

Comparison of Methylation Capture Sequencing and Infinium EPIC Methylation Array in Peripheral Blood Mononuclear Cells

Shu

Zhang

Aouizerat

2020

Preprint

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Background: Epigenome-wide association studies (EWAS) have been widely applied to identify methylation CpG sites associated with human disease. To date, the Infinium Methylation EPIC array (EPIC) is commonly used for high-throughput DNA methylation profiling. However, the EPIC array covers only 30% of the human methylome. Methylation Capture bisulfite sequencing (MC-seq) captures target regions of methylome and has advantages of extensive coverage in the methylome at an affordable price. Methods: Epienome-wide DNA methylation in four peripheral blood mononuclear cell samples was profiled by using SureSelectXT Methyl-Seq for MC-seq and EPIC platforms separately. CpG site-based reproducibility of MC-seq was assessed with DNA sample inputs ranging in quantity of high (> 1000ng), medium (300-1000ng), and low (150ng-300ng). To compare the performance of MC-seq and the EPIC arrays, we conducted a Pearson correlation and methylation value difference at each CpG site that was detected by both MC-seq and EPIC. We compared the percentage and counts in each CpG island and gene annotation between MC-seq and the EPIC array.Results: After quality control, an average of 3,708,550 CpG sites per sample was detected by MC-seq with DNA quantity >1000ng. Reproducibility of MC-seq detected CpG sites was high with strong correlation estimates for CpG methylation among samples with high, medium, and low DNA inputs (r > 0.96). The EPIC array captured an average of 846,464 CpG sites per sample. Compared with the EPIC array, MC-seq detected more CpGs in coding regions and CpG islands. Among the 472,540 CpG sites captured by both platforms, methylation of a majority of CpG sites was highly correlated in the same sample (r: 0.98~0.99). However, methylation for a small proportion of CpGs (N=235) differed significantly between the two platforms, with differences in beta values of greater than 0.5.Conclusions: Our results show that MC-seq is an efficient and reliable platform for methylome profiling with a broader coverage of the methylome than the array-based platform. Although methylation measurements in majority of CpGs are highly correlated, a number of CpG sites show large discrepancy between the two platforms, which warrants further investigation and needs cautious interpretation.

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Section: Epic Array Data Preprocessingmentioning

confidence: 80%

Comparison of Methylation Capture Sequencing and Infinium EPIC Methylation Array in Peripheral Blood Mononuclear Cells

Shu

Zhang

Aouizerat

2020

Preprint

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“…We applied all six statistical tests to three publicly available DNA methylation data sets (GSE37020 [ 16 ], GSE20080 [ 17 ] and GSE107080 [ 18 ]) from Gene Expression Omnibus (GEO)( www.ncbi.nlm.nih.gov/geo ).…”

Section: Resultsmentioning

confidence: 99%

Robust joint score tests in the application of DNA methylation data analysis

Wang

et al. 2018

BMC Bioinformatics

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BackgroundRecently differential variability has been showed to be valuable in evaluating the association of DNA methylation to the risks of complex human diseases. The statistical tests based on both differential methylation level and differential variability can be more powerful than those based only on differential methylation level. Anh and Wang (2013) proposed a joint score test (AW) to simultaneously detect for differential methylation and differential variability. However, AW’s method seems to be quite conservative and has not been fully compared with existing joint tests.ResultsWe proposed three improved joint score tests, namely iAW.Lev, iAW.BF, and iAW.TM, and have made extensive comparisons with the joint likelihood ratio test (jointLRT), the Kolmogorov-Smirnov (KS) test, and the AW test. Systematic simulation studies showed that: 1) the three improved tests performed better (i.e., having larger power, while keeping nominal Type I error rates) than the other three tests for data with outliers and having different variances between cases and controls; 2) for data from normal distributions, the three improved tests had slightly lower power than jointLRT and AW. The analyses of two Illumina HumanMethylation27 data sets GSE37020 and GSE20080 and one Illumina Infinium MethylationEPIC data set GSE107080 demonstrated that three improved tests had higher true validation rates than those from jointLRT, KS, and AW.ConclusionsThe three proposed joint score tests are robust against the violation of normality assumption and presence of outlying observations in comparison with other three existing tests. Among the three proposed tests, iAW.BF seems to be the most robust and effective one for all simulated scenarios and also in real data analyses.Electronic supplementary materialThe online version of this article (10.1186/s12859-018-2185-3) contains supplementary material, which is available to authorized users.

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“…The adherence to antiretroviral therapy (ART adherence) was obtained by a survey in the same time window as the blood draw for the measurement of DNA methylation. Genomic DNA was extracted from whole blood using a standard method 12 .…”

Section: Introductionmentioning

confidence: 99%

“…Finally, epigenetic modifications can be reliably detected in noninvasive fluids and biospecimens 11 . Thus, the utility of epigenetic alterations has motivated the biomarker research field to develop epigenetic signatures derived from easily accessible cells for clinical use [12][13][14] .DNAm markers are emerging as diagnostic biomarkers in many areas of medicine and are applied to predict complex diseases 15 . For example, DNAm markers on the promoters of several genes, including BMP3, NDRG4, and SPEPT9, in blood or stool samples have been approved by the Food and Drug Administration as biomarkers for colorectal cancer screening 16 .…”

mentioning

confidence: 99%

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DNA Methylation Signature on Phosphatidylethanol, not Self-Reported Alcohol Consumption, Predicts Hazardous Alcohol Consumption in Two Distinct Populations

Liang

Justice

So‐Armah

et al. 2019

Preprint

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The process of diagnosing hazardous alcohol drinking (HAD) is based on self-reported data and is thereby vulnerable to bias. There has been an interest in developing epigenetic biomarkers for HAD that might complement clinical assessment. Because alcohol consumption has been previously linked to DNA methylation (DNAm), here, we aimed to select DNAm signatures in blood to predict HAD from two demographically and clinically distinct populations (N total =1,549). We first separately conducted an epigenome-wide association study (EWAS) for phosphatidylethanol (PEth), an objective measure of alcohol consumption, and for self-reported alcohol consumption in Cohort 1.We identified 102 PEth-associated CpGs, including 32 CpGs previously associated with alcohol consumption or alcohol use disorders. In contrast, no CpG reached epigenomewide significance on self-reported alcohol consumption. Using a machine learning approach, two subsets of CpGs from EWAS on PEth and on self-reported alcohol consumption from Cohort 1 were separately tested for the prediction of HAD in Cohort 2. We found that a subset of 130 CpGs selected from the EWAS on PEth showed an excellent prediction of HAD with area under the ROC curve (AUC) of 91.31% in training set and 70.65% in validation set of Cohort 2. However, CpGs preselected from the EWAS on self-reported alcohol consumption showed a poor prediction of HAD with AUC 75.18% in the training set and 57.60% in the validation set. Our results demonstrate that an objective measure for alcohol consumption is a more informative phenotype than self-reported data for revealing epigenetic mechanism. The PEthassociated DNAm signature in blood is a robust biomarker for alcohol consumption.is approximately 4-7 days 6 . Thus, other more stable biomarkers for alcohol consumption are needed to inform clinical practice.Epigenetic signatures have emerged as attractive biomarkers for complex diseases such as cancers and neurodegenerative diseases 7 . Epigenetic markers may reflect environmental exposures, including alcohol consumption. Among these epigenetic markers, DNA methylation (DNAm) biomarkers are particularly attractive because they are relatively stable and capture an early stage of pathophysiological changes 8,9 . A recent longitudinal study on DNAm showed that most DNA methylome changes occurred 80-90 days before clinically detectable glucose elevation 10 , suggesting that DNAm is involved in an early stage of diabetes. Finally, epigenetic modifications can be reliably detected in noninvasive fluids and biospecimens 11 . Thus, the utility of epigenetic alterations has motivated the biomarker research field to develop epigenetic signatures derived from easily accessible cells for clinical use [12][13][14] .DNAm markers are emerging as diagnostic biomarkers in many areas of medicine and are applied to predict complex diseases 15 . For example, DNAm markers on the promoters of several genes, including BMP3, NDRG4, and SPEPT9, in blood or stool samples have been approved by the Food and Drug Administration...

show abstract

DNA methylation signatures of illicit drug injection and hepatitis C are associated with HIV frailty

Cited by 36 publications

References 53 publications

Comparison of Methylation Capture Sequencing and Infinium EPIC Methylation Array in Peripheral Blood Mononuclear Cells

Comparison of Methylation Capture Sequencing and Infinium EPIC Methylation Array in Peripheral Blood Mononuclear Cells

Robust joint score tests in the application of DNA methylation data analysis

DNA Methylation Signature on Phosphatidylethanol, not Self-Reported Alcohol Consumption, Predicts Hazardous Alcohol Consumption in Two Distinct Populations

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