Rapid quantification of DNA methylation through dNMP analysis following bisulfite-PCR

We report a novel method for rapid quantification of the degree of DNA methylation of a specific gene. Our method combined bisulfite-mediated PCR and quantification of deoxyribonucleoside monophosphate (dNMP) contents in the PCR product through capillary electrophoresis. A specific bisulfite-PCR product was enzymatically hydrolyzed to dNMP monomers which were quantitatively analyzed through subsequent capillary electrophoresis. PCR following bisulfite treatment converts unmethylated cytosines to thymines while leaving methyl-cytosines unchanged. Then the ratio of cytosine to thymine determined by capillary electrophoresis represents the ratio of methyl-cytosine to cytosine in genomic locus of interest. Pure oligonucleotides with known sequences were processed in parallel as standards for normalization of dNMP peaks in capillary electrophoresis. Sources of quantification uncertainty such as carryovers of dNTPs or primers and incomplete hydrolysis were examined and ruled out. When the method was applied to samples with known methylation levels (by bisulfite-mediated sequencing) as a validation, deviations were within ±5%. After bisulfite-PCR, the analytical procedure can be completed within 1.5 h

The anti-aging gene KLOTHO is a novel target for epigenetic silencing in human cervical carcinoma

<p>Abstract</p> <p>Background</p> <p><it>Klotho </it>was originally characterized as an anti-aging gene that predisposed Klotho-deficient mice to a premature aging-like syndrome. Recently, KLOTHO was reported to function as a secreted Wnt antagonist and as a tumor suppressor. Epigenetic gene silencing of secreted Wnt antagonists is considered a common event in a wide range of human malignancies. Abnormal activation of the canonical Wnt pathway due to epigenetic deregulation of Wnt antagonists is thought to play a crucial role in cervical tumorigenesis. In this study, we examined epigenetic silencing of <it>KLOTHO </it>in human cervical carcinoma.</p> <p>Results</p> <p>Loss of <it>KLOTHO </it>mRNA was observed in several cervical cancer cell lines and in invasive carcinoma samples, but not during the early, preinvasive phase of primary cervical tumorigenesis. <it>KLOTHO </it>mRNA was restored after treatment with either the DNA demethylating agent 2'-deoxy-5-azacytidine or histone deacetylase inhibitor trichostatin A. Methylation-specific PCR and bisulfite genomic sequencing analysis of the promoter region of <it>KLOTHO </it>revealed CpG hypermethylation in non-<it>KLOTHO</it>-expressing cervical cancer cell lines and in 41% (9/22) of invasive carcinoma cases. Histone deacetylation was also found to be the major epigenetic silencing mechanism for <it>KLOTHO </it>in the SiHa cell line. Ectopic expression of the secreted form of KLOTHO restored anti-Wnt signaling and anti-clonogenic activity in the CaSki cell line including decreased active β-catenin levels, suppression of T-cell factor/β-catenin target genes, such as <it>c-MYC </it>and <it>CCND1</it>, and inhibition of colony growth.</p> <p>Conclusions</p> <p>Epigenetic silencing of <it>KLOTHO </it>may occur during the late phase of cervical tumorigenesis, and consequent functional loss of KLOTHO as the secreted Wnt antagonist may contribute to aberrant activation of the canonical Wnt pathway in cervical carcinoma.</p

Quantification of Trace-Level DNA by Real-Time Whole Genome Amplification

Quantification of trace amounts of DNA is a challenge in analytical applications where the concentration of a target DNA is very low or only limited amounts of samples are available for analysis. PCR-based methods including real-time PCR are highly sensitive and widely used for quantification of low-level DNA samples. However, ordinary PCR methods require at least one copy of a specific gene sequence for amplification and may not work for a sub-genomic amount of DNA. We suggest a real-time whole genome amplification method adopting the degenerate oligonucleotide primed PCR (DOP-PCR) for quantification of sub-genomic amounts of DNA. This approach enabled quantification of sub-picogram amounts of DNA independently of their sequences. When the method was applied to the human placental DNA of which amount was accurately determined by inductively coupled plasma-optical emission spectroscopy (ICP-OES), an accurate and stable quantification capability for DNA samples ranging from 80 fg to 8 ng was obtained. In blind tests of laboratory-prepared DNA samples, measurement accuracies of 7.4%, −2.1%, and −13.9% with analytical precisions around 15% were achieved for 400-pg, 4-pg, and 400-fg DNA samples, respectively. A similar quantification capability was also observed for other DNA species from calf, E. coli, and lambda phage. Therefore, when provided with an appropriate standard DNA, the suggested real-time DOP-PCR method can be used as a universal method for quantification of trace amounts of DNA

INVESTIGATION AND COMPARISON OF ROUTE CHOICE MODELS

In this study, MNL and C-logit are investigated as GEV type model, and MNP as non-GEV type model. Three models are tested in a very simple network to compare their performance and usefulness in practice. The layout of the paper is as follows. Section 2 provides a theoretical aspect of MNL, C-logit and MNP. Section 3 shows implementation of the models in a simple network and comparison of the results. Section 4 provides conclusion

Concept of the synthetic reference materials for DNA methylation.

<p>(<b>A</b>) Structures of a plasmid pair each of which harbors bisulfite-converted sequence of either 0%—(M0) or 100%- (M100) methylation status of a gene. A plasmid construct is composed of a common backbone (pGEM) and a synthetic insert which represents bisulfite-converted sequence of either 0%- or 100%- methylation status of gene. (<b>B</b>) Use of the reference materials as working standards in DNA methylation analysis. qPCRs targeting common backbone sequences such as <i>bla</i> and <i>ori</i> regions were performed to determine relative quantities of the plasmid pair of a gene.</p

Calibration of analytical biases and accurate measurements of gene methylation of in-lab unknown samples.

<p>Raw results without calibration (<b>A</b>) and calibrated results using the template-type reference materials (<b>B</b>) are comparatively shown. Underestimating raw results for <i>P16</i> from both melting- and NGS-based analyses were successfully calibrated to correctly estimate the reference value of unknown sample. Overestimations for <i>MLH1</i> were also successfully calibrated. Results for <i>P14</i> with no detectable bias were correctly estimating the reference value without calibration. Error bars for reference values and calibrated values represent expanded uncertainties while those for uncalibrated values represent standard deviations.</p

Comparative validation of methylation controls from commercial sources with the synthetic reference materials.

<p>Methylation levels of commercial DNA methylation controls and synthetic reference materials were measured by melting- (<b>A</b>) and NGS-based analyses (<b>B</b>).</p

Gene-dependent PCR biases in DNA methylation analyses.

<p>Normalized melting profiles (left), standard curves from melting analyses (center) and standard curves from NGS analyses (right) from analysis of working standards of <i>P14</i> (<b>A</b>), <i>P16</i> (<b>B</b>) and <i>MLH1</i> (<b>C</b>) genes. Standard curves for NGS analyses were plotted based on the read counts for unmethylated and methylated DNA from amplicon sequencing as provided in the <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0137006#pone.0137006.s006" target="_blank">S5 Table</a>. No PCR bias for <i>P14</i>, preferential amplification of unmethylated templates for <i>P16</i> and preferential amplification of methylated templates for <i>MLH1</i> were consistently observed from both melting- and NGS-based analyses. Bias values (b) were calculated based on a previously described equation [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0137006#pone.0137006.ref019" target="_blank">19</a>].</p

A Deep Learning Approach for Estimating Traffic Density Using Data Obtained from Connected and Autonomous Probes.

The focus of this research is on the estimation of traffic density from data obtained from Connected and Autonomous Probes (CAPs). CAPs pose an advantage over expensive and invasive infrastructure such as loop detectors. CAPs maneuver their driving trajectories, sensing the presence of adjacent vehicles and distances to them by means of several electronic sensors, whose data can be used for more sophisticated traffic density estimation techniques. Traffic density has a highly nonlinear nature during on-congestion and queue-clearing conditions. Closed-mathematical forms of the traditional density estimation techniques are incapable of dealing with complex nonlinearities, which opens the door for data-driven approaches such as machine learning techniques. Deep learning algorithms excel in data-rich contexts, which recognize nonlinear and highly situation-dependent patterns. Our research is based on an LSTM (Long short-term memory) neural network for the nonlinearity associated with time dynamics of traffic flow. The proposed method is designed to learn the input-output relation of Edie's definition. At the same time, the method recognizes a temporally nonlinear pattern of traffic. We evaluate our algorithm by using a microscopic simulation program (PARAMICS) and demonstrate that our model accurately estimates traffic density in Free-flow, Transition, and Congested conditions

A Deep Learning Approach for Estimating Traffic Density Using Data Obtained from Connected and Autonomous Probes

The focus of this research is on the estimation of traffic density from data obtained from Connected and Autonomous Probes (CAPs). CAPs pose an advantage over expensive and invasive infrastructure such as loop detectors. CAPs maneuver their driving trajectories, sensing the presence of adjacent vehicles and distances to them by means of several electronic sensors, whose data can be used for more sophisticated traffic density estimation techniques. Traffic density has a highly nonlinear nature during on-congestion and queue-clearing conditions. Closed-mathematical forms of the traditional density estimation techniques are incapable of dealing with complex nonlinearities, which opens the door for data-driven approaches such as machine learning techniques. Deep learning algorithms excel in data-rich contexts, which recognize nonlinear and highly situation-dependent patterns. Our research is based on an LSTM (Long short-term memory) neural network for the nonlinearity associated with time dynamics of traffic flow. The proposed method is designed to learn the input-output relation of Edie&rsquo;s definition. At the same time, the method recognizes a temporally nonlinear pattern of traffic. We evaluate our algorithm by using a microscopic simulation program (PARAMICS) and demonstrate that our model accurately estimates traffic density in Free-flow, Transition, and Congested conditions