Effect of Malaria Infection on Epstein-Barr Virus Persistence in Kenyan Children.

BACKGROUND: The 2 cofactors in the etiology of Burkitt lymphoma (BL) are Epstein-Barr virus (EBV) and repeated Plasmodium falciparum malaria infections. This study evaluated EBV loads in mucosal and systemic compartments of children with malaria and controls. Age was analyzed as a covariate because immunity to malaria in endemic regions is age dependent. METHODS: Children (2-10 years) with clinical malaria from Western Kenya and community controls without malaria were enrolled. Saliva and blood samples were collected, EBV viral load was assessed by quantitative polymerase chain reaction, and EpiTYPER MassARRAY was used to assess methylation of 3 different EBV genes. RESULTS: Regardless of the compartment, we detected EBV more frequently in malaria cases compared to controls, although the difference was not significant. When EBV was detected, there were no differences in viral load between cases and controls. However, EBV methylation was significantly lower in the malaria group compared to controls in both plasma and saliva (P < .05), indicating increased EBV lytic replication. In younger children before development of immunity to malaria, there was a significant effect of malaria on EBV load in peripheral blood mononuclear cells (P = .04). CONCLUSIONS: These data suggest that malaria can directly modulate EBV persistence in children, increasing their risk for BL.

Genome-wide DNA methylation profiling of stomach cancer in the ethnic population of Mizoram, North East India.

Stomach cancer is the fifth most common cancer in terms of prevalence and incidence and the fourth leading cause of mortality in men and women worldwide. It is well-established that aberrant DNA methylation in cells can lead to carcinogenesis. The primary objective of our study was to investigate the aberrant DNA methylation status of genes associated with stomach cancer with a particular reference to the ethnic population of Mizoram, North East India. The site-level analysis identified 2883 CpG sites differentially methylated, representing ∼922 genes. Out of which 476 Differentially Methylated Positions (DMPs) were promoter-associated, 452 DMPs were hypermethylated, and 24 were hypomethylated. The region-level analysis identified 462 Differentially Methylated Regions (DMRs) corresponding to ∼320 genes, of which ∼281 genes were hypermethylated and âˆ¼40 genes were hypomethylated. TCGA analysis showed that some of the genes had been previously implicated in other cancers including stomach cancer. Five hypermethylated genes were selected as candidate genes for further investigations and they have shown to be novel and could serve as candidate hypermethylation biomarkers for stomach cancer in this particular ethnic group.

A common epigenetic clock from childhood to old age.

Forensic age estimation is a DNA intelligence tool that forms an important part of Forensic DNA Phenotyping. Criminal cases with no suspects or with unsuccessful matches in searches on DNA databases; human identification analyses in mass disasters; anthropological studies or legal disputes; all benefit from age estimation to gain investigative leads. Several age prediction models have been developed to date based on DNA methylation. Although different DNA methylation technologies as well as diverse statistical methods have been proposed, most of them are based on blood samples and mainly restricted to adult age ranges. In the current study, we present an extended age prediction model based on 895 evenly distributed Spanish DNA blood samples from 2 to 104 years old. DNA methylation levels were detected using Agena Bioscience EpiTYPER® technology for a total of seven CpG sites located at seven genomic regions: ELOVL2, ASPA, PDE4C, FHL2, CCDC102B, MIR29B2CHG and chr16:85395429 (GRCh38). The accuracy of the age prediction system was tested by comparing three statistical methods: quantile regression (QR), quantile regression neural network (QRNN) and quantile regression support vector machine (QRSVM). The most accurate predictions were obtained when using QRNN or QRSVM (mean absolute prediction error, MAE of ± 3.36 and ± 3.41, respectively). Validation of the models with an independent Spanish testing set (N = 152) provided similar accuracies for both methods (MAE: ± 3.32 and ± 3.45, respectively). The main advantage of using quantile regression statistical tools lies in obtaining age-dependent prediction intervals, fitting the error to the estimated age. An additional analysis of dimensionality reduction shows a direct correlation of increased error and a reduction of correct classifications as the training sample size is reduced. Results indicated that a minimum sample size of six samples per year-of-age covered by the training set is recommended to efficiently capture the most inter-individual variability..

AmpliconDesign - an interactive web server for the design of high-throughput targeted DNA methylation assays.

Targeted analysis of DNA methylation patterns based on bisulfite-treated genomic DNA (BT-DNA) is considered as a gold-standard for epigenetic biomarker development. Existing software tools facilitate primer design, primer quality control or visualization of primer localization. However, high-throughput design of primers for BT-DNA amplification is hampered by limits in throughput and functionality of existing tools, requiring users to repeatedly perform specific tasks manually. Consequently, the design of PCR primers for BT-DNA remains a tedious and time-consuming process. To bridge this gap, we developed AmpliconDesign, a webserver providing a scalable and user-friendly platform for the design and analysis of targeted DNA methylation studies based on BT-DNA, e.g. deep amplicon bisulfite sequencing (ampBS-seq) or EpiTYPER MassArray. Core functionality of the web server includes high-throughput primer design and binding site validation based on in silico bisulfite-converted DNA sequences, prediction of fragmentation patterns for EpiTYPER MassArray, an interactive quality control as well as a streamlined analysis workflow for ampBS-seq.

A Comparison of Forensic Age Prediction Models Using Data From Four DNA Methylation Technologies.

Individual age estimation can be applied to criminal, legal, and anthropological investigations. DNA methylation has been established as the biomarker of choice for age prediction, since it was observed that specific CpG positions in the genome show systematic changes during an individual's lifetime, with progressive increases or decreases in methylation levels. Subsequently, several forensic age prediction models have been reported, providing average age prediction error ranges of ±3-4 years, using a broad spectrum of technologies and underlying statistical analyses. DNA methylation assessment is not categorical but quantitative. Therefore, the detection platform used plays a pivotal role, since quantitative and semi-quantitative technologies could potentially result in differences in detected DNA methylation levels. In the present study, we analyzed as a shared sample pool, 84 blood-based DNA controls ranging from 18 to 99 years old using four different technologies: EpiTYPER®, pyrosequencing, MiSeq, and SNaPshotTM. The DNA methylation levels detected for CpG sites from ELOVL2, FHL2, and MIR29B2 with each system were compared. A restricted three CpG-site age prediction model was rebuilt for each system, as well as for a combination of technologies, based on previous training datasets, and age predictions were calculated accordingly for all the samples detected with the previous technologies. While the DNA methylation patterns and subsequent age predictions from EpiTYPER®, pyrosequencing, and MiSeq systems are largely comparable for the CpG sites studied, SNaPshotTM gives bigger differences reflected in higher predictive errors. However, these differences can be reduced by applying a z-score data transformation.

Tracking age-correlated DNA methylation markers in the young.

DNA methylation is the most extensively studied epigenetic signature, with a large number of studies reporting age-correlated CpG sites in overlapping genes. However, most of these studies lack sample coverage of individuals under 18 years old and therefore little is known about the progression of DNA methylation patterns in children and adolescents. In the present study we aimed to select candidate age-correlated DNA methylation markers based on public datasets from Illumina BeadChip arrays and previous publications, then to explore the resulting markers in 209 blood samples from donors aged between 2 to 18 years old using the EpiTYPER® DNA methylation analysis system. Results from our analyses identified six genes highly correlated with age in the young, in particular the gene KCNAB3, which indicates its potential as a highly informative and specific age biomarker for childhood and adolescence. We outline a preliminary age prediction model based on quantile regression that uses data from the six CpG sites most strongly correlated with age ranges extended to include children and adolescents.

DNA methylation analysis from saliva samples for epidemiological studies.

Saliva is a non-invasive, easily accessible tissue, which is regularly collected in large epidemiological studies to examine genetic questions. Recently, it is becoming more common to use saliva to assess DNA methylation. However, DNA extracted from saliva is a mixture of both bacterial and human DNA derived from epithelial and immune cells in the mouth. Thus, there are unique challenges to using salivary DNA in methylation studies that can influence data quality. This study assesses: (1) quantification of human DNA after extraction; (2) delineation of human and bacterial DNA; (3) bisulfite conversion (BSC); (4) quantification of BSC DNA; (5) PCR amplification of BSC DNA from saliva and; (6) quantitation of DNA methylation with a targeted assay. The framework proposed will allow saliva samples to be more widely used in targeted epigenetic studies.

Quantitative Region-Specific DNA Methylation Analysis by the EpiTYPER™ Technology.

DNA methylation plays a profound role in development and health as well as development and progression of disease. High-throughput quantitative DNA methylation analysis is therefore crucial for the study of the normal physiology of the epigenome and its dysregulation in disease. Many target areas are identified by a range of emerging genome-wide cytosine methylation techniques, but these whole genome scans usually only provide methylation data for a few individual CpG sites (CpGs) within a region. The EpiTYPER™ assay is a region-specific method for the detection and quantitative analysis of DNA methylation that allows performing a high-resolution scan of selected regions. It thus enables a more detailed analysis of single CpGs and the surrounding area and can, in addition to candidate gene methylation analysis, be used to validate CpGs detected by genome wide techniques. The EpiTYPER™ assay allows a fast and reproducible targeted quantification of individual CpGs in a high throughput manner and is based on base-specific cleavage of bisulfite-converted genomic DNA and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). Up to 85% of the CpGs within a target region can be analyzed and the detection precision allows quantifying methylation differences as low as 5-7%.

Comparative assessment of DNA methylation patterns between reduced representation bisulfite sequencing and Sequenom EpiTyper methylation analysis.

AIM: Validation of sequencing-based DNA methylation data is an important step for meaningful translation of findings. However, there has been limited assessment of different platforms to validate methylation data from next generation sequencing. METHODS: We performed a comparative methylation analysis between the genome-wide platform of reduced representation bisulfite sequencing with a targeted, Sequenom EpiTyper platform (four genes were analyzed in 15 cell lines covering 52 CpG sites). RESULTS: We show that the accuracy of validation substantially improves if results from multiple and adjacent CpG sites are combined rather than at single CpG sites. We demonstrate increased read number improves accuracy of reduced representation bisulfite sequencing results. Further, by using series of replicates, we document variation in samples analyzed by Sequenom EpiTyper, indicating the importance of including replicates to increase precision. CONCLUSION: The results reveal potential sources of bias and provide a guideline for refining study design for DNA methylation analysis.

Development of a methylation marker set for forensic age estimation using analysis of public methylation data and the Agena Bioscience EpiTYPER system.

Individual age estimation has the potential to provide key information that could enhance and extend DNA intelligence tools. Following predictive tests for externally visible characteristics developed in recent years, prediction of age could guide police investigations and improve the assessment of age-related phenotype expression patterns such as hair colour changes and early onset of male pattern baldness. DNA methylation at CpG positions has emerged as the most promising DNA tests to ascertain the individual age of the donor of a biological contact trace. Although different methodologies are available to detect DNA methylation, EpiTYPER technology (Agena Bioscience, formerly Sequenom) provides useful characteristics that can be applied as a discovery tool in localized regions of the genome. In our study, a total of twenty-two candidate genomic regions, selected from the assessment of publically available data from the Illumina HumanMethylation 450 BeadChip, had a total of 177 CpG sites with informative methylation patterns that were subsequently investigated in detail. From the methylation analyses made, a novel age prediction model based on a multivariate quantile regression analysis was built using the seven highest age-correlated loci of ELOVL2, ASPA, PDE4C, FHL2, CCDC102B, C1orf132 and chr16:85395429. The detected methylation levels in these loci provide a median absolute age prediction error of ±3.07years and a percentage of prediction error relative to the age of 6.3%. We report the predictive performance of the developed model using cross validation of a carefully age-graded training set of 725 European individuals and a test set of 52 monozygotic twin pairs. The multivariate quantile regression age predictor, using the CpG sites selected in this study, has been placed in the open-access Snipper forensic classification website.

Design, measurement and processing of region-specific DNA methylation assays: the mass spectrometry-based method EpiTYPER.

EpiTYPER® is a mass spectrometry-based bisulfite sequencing method that enables region-specific DNA methylation analysis in a quantitative and high-throughput fashion. The technology targets genomic regions of 100-600 base pairs and results in the quantitative measurement of DNA methylation levels largely at single-nucleotide resolution. It is particularly suitable for larger scale efforts to study candidate regions or to validate regions from genome-wide DNA methylation studies. Here, we describe in detail how to design and perform EpiTYPER measurements and preprocess the data, providing details for high quality measurements not provided in the standard EpiTYPER protocol.