Determination of Global DNA Methylation in Biological Samples by Liquid Chromatography-Tandem Mass Spectrometry

A relative quantitation method for measuring DNA methylation and hydroxymethylation using guanine as an internal standard

Global DNA methylation and hydroxymethylation play an important role in gene expression. They can be connected with several diseases. The modification status could be a biomarker to determine the status of disease. A fast, easy and accurate liquid chromatography - tandem mass spectrometry method has been developed for the precise quantitation of 5-methylcytosine and 5-hydroxymethylcytosine. Formic acid was used for the hydrolysis of the DNA strand resulting in nucleobases. These polar hydrolysis products were separated on a normal phase column using reversed phase eluents in inverse gradient mode. Multiple reaction monitoring was applied to achieve high selectivity and sensitivity for the quantitation. A new relative quantitation model was developed by using guanine, as an internal standard, present in samples. The new method was successfully validated with excellent accuracy and precision values in the range of 0.005-0.5% for 5hmC and 1-15% for 5mC. The main advantages of this quantitation method are that, due to relative quantitation, calibration curves can be used without reacquiring the calibration points and no additional isotope labeled internal standards are required. The method was tested to identify the concentrations of 5mC and 5hmC in various sample types. The lowest level of DNA sample required in the case of 0.005% 5hmC is 0.5 μg.

Human Age Prediction Based on DNA Methylation Using a Gradient Boosting Regressor

All tissues of organisms will become old as time goes on. In recent years, epigenetic investigations have found that there is a close correlation between DNA methylation and aging. With the development of DNA methylation research, a quantitative statistical relationship between DNA methylation and different ages was established based on the change rule of methylation with age, it is then possible to predict the age of individuals. All the data in this work were retrieved from the Illumina HumanMethylation BeadChip platform (27K or 450K). We analyzed 16 sets of healthy samples and 9 sets of diseased samples. The healthy samples included a total of 1899 publicly available blood samples (0–103 years old) and the diseased samples included 2395 blood samples. Six age-related CpG sites were selected through calculating Pearson correlation coefficients between age and DNA methylation values. We built a gradient boosting regressor model for these age-related CpG sites. 70% of the data was randomly selected as training data and the other 30% as independent data in each dataset for 25 runs in total. In the training dataset, the healthy samples showed that the correlation between predicted age and DNA methylation was 0.97, and the mean absolute deviation (MAD) was 2.72 years. In the independent dataset, the MAD was 4.06 years. The proposed model was further tested using the diseased samples. The MAD was 5.44 years for the training dataset and 7.08 years for the independent dataset. Furthermore, our model worked well when it was applied to saliva samples. These results illustrated that the age prediction based on six DNA methylation markers is very effective using the gradient boosting regressor.

Low-dose carbon-based nanoparticle-induced effects in A549 lung cells determined by biospectroscopy are associated with increases in genomic methylation

Nanotechnology has introduced many manufactured carbon-based nanoparticles (CNPs) into our environment, generating a debate into their risks and benefits. Numerous nanotoxicology investigations have been carried, and nanoparticle-induced toxic effects have been reported. However, there remain gaps in our knowledge, primarily regarding mechanism. Herein, we assessed the global alterations induced by CNPs in A549 lung cells using biospectroscopy techniques, including attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy and surface-enhanced Raman spectroscopy (SERS). A549 cells were treated with fullerene (C₆₀), long or short multi-walled carbon nanotubes, or single-walled carbon nanotubes at concentrations of 0.1 mg/L, 0.01 mg/L and 0.001 mg/L. Exposed cells were then analysed by ATR-FTIR spectroscopy and SERS. Spectra were pre-processed via computational analysis, and information on biochemical alterations in exposed cells were identified. Additionally, global DNA methylation levels in cells exposed to CNPs at 0.1 mg/L were determined using HPLC-MS and genetic regulators (for DNA methylation) were checked by quantitative real-time RT-PCR. It was found that CNPs exert marked effects in A549 cells and also contribute to increases in global DNA methylation. For the first time, this study highlights that real-world levels of nanoparticles can alter the methylome of exposed cells; this could have enormous implications for their regulatory assessment.

A novel strategy for forensic age prediction by DNA methylation and support vector regression model

High deviations resulting from prediction model, gender and population difference have limited age estimation application of DNA methylation markers. Here we identified 2,957 novel age-associated DNA methylation sites (P < 0.01 and R(2) > 0.5) in blood of eight pairs of Chinese Han female monozygotic twins. Among them, nine novel sites (false discovery rate < 0.01), along with three other reported sites, were further validated in 49 unrelated female volunteers with ages of 20-80 years by Sequenom Massarray. A total of 95 CpGs were covered in the PCR products and 11 of them were built the age prediction models. After comparing four different models including, multivariate linear regression, multivariate nonlinear regression, back propagation neural network and support vector regression, SVR was identified as the most robust model with the least mean absolute deviation from real chronological age (2.8 years) and an average accuracy of 4.7 years predicted by only six loci from the 11 loci, as well as an less cross-validated error compared with linear regression model. Our novel strategy provides an accurate measurement that is highly useful in estimating the individual age in forensic practice as well as in tracking the aging process in other related applications.