Investigation of genomic methylation status using methylation-specific and bisulfite sequencing polymerase chain reaction. Methods Mol Biol. 2009;523:217-234. [PMID: 19381936 DOI: 10.1007/978-1-59745-190-1_15]

meHOLMES: A CRISPR-cas12a-based method for rapid detection of DNA methylation in a sequence-independent manner

Aberrant DNA methylation is closely associated with various diseases, particularly cancer, and its precise detection plays an essential role in disease diagnosis and monitoring. In this study, we present a novel DNA methylation detection method (namely meHOLMES), which integrates both the TET2/APOBEC-mediated cytosine deamination step and the CRISPR-Cas12a-based signal readout step. TET2/APOBEC efficiently converts unmethylated cytosine to uracil, which is subsequently changed to thymine after PCR amplification. Utilizing a rationally designed crRNA, Cas12a specifically identifies unconverted methylated cytosines and generates detectable signals using either fluorescent reporters or lateral flow test strips. meHOLMES quantitatively detects methylated CpG sites with or without Protospacer Adjacent Motif (PAM) sequences in both artificial and real biological samples. In addition, meHOLMES can complete the whole detection process within 6 h, which is much faster than traditional bisulfite-based sample pre-treatment method. Above all, meHOLMES provides a simpler, faster, more accurate, and cost-effective approach for quantitation of DNA methylation levels in a sequence-independent manner.

Early detection of hepatocellular carcinoma via no end-repair enzymatic methylation sequencing of cell-free DNA and pre-trained neural network

BACKGROUND

Early detection of hepatocellular carcinoma (HCC) is important in order to improve patient prognosis and survival rate. Methylation sequencing combined with neural networks to identify cell-free DNA (cfDNA) carrying aberrant methylation offers an appealing and non-invasive approach for HCC detection. However, some limitations exist in traditional methylation detection technologies and models, which may impede their performance in the read-level detection of HCC.

METHODS

We developed a low DNA damage and high-fidelity methylation detection method called No End-repair Enzymatic Methyl-seq (NEEM-seq). We further developed a read-level neural detection model called DeepTrace that can better identify HCC-derived sequencing reads through a pre-trained and fine-tuned neural network. After pre-training on 11 million reads from NEEM-seq, DeepTrace was fine-tuned using 1.2 million HCC-derived reads from tumor tissue DNA after noise reduction, and 2.7 million non-tumor reads from non-tumor cfDNA. We validated the model using data from 130 individuals with cfDNA whole-genome NEEM-seq at around 1.6X depth.

RESULTS

NEEM-seq overcomes the drawbacks of traditional enzymatic methylation sequencing methods by avoiding the introduction of unmethylation errors in cfDNA. DeepTrace outperformed other models in identifying HCC-derived reads and detecting HCC individuals. Based on the whole-genome NEEM-seq data of cfDNA, our model showed high accuracy of 96.2%, sensitivity of 93.6%, and specificity of 98.5% in the validation cohort consisting of 62 HCC patients, 48 liver disease patients, and 20 healthy individuals. In the early stage of HCC (BCLC 0/A and TNM I), the sensitivity of DeepTrace was 89.6 and 89.5% respectively, outperforming Alpha Fetoprotein (AFP) which showed much lower sensitivity in both BCLC 0/A (50.5%) and TNM I (44.7%).

CONCLUSIONS

By combining high-fidelity methylation data from NEEM-seq with the DeepTrace model, our method has great potential for HCC early detection with high sensitivity and specificity, making it potentially suitable for clinical applications. DeepTrace: https://github.com/Bamrock/DeepTrace.

Novel Identification of Bacterial Epigenetic Regulations Would Benefit From a Better Exploitation of Methylomic Data

DNA methylation can be part of epigenetic mechanisms, leading to cellular subpopulations with heterogeneous phenotypes. While prokaryotic phenotypic heterogeneity is of critical importance for a successful infection by several major pathogens, the exact mechanisms involved in this phenomenon remain unknown in many cases. Powerful sequencing tools have been developed to allow the detection of the DNA methylated bases at the genome level, and they have recently been extensively applied on numerous bacterial species. Some of these tools are increasingly used for metagenomics analysis but only a limited amount of the available methylomic data is currently being exploited. Because newly developed tools now allow the detection of subpopulations differing in their genome methylation patterns, it is time to emphasize future strategies based on a more extensive use of methylomic data. This will ultimately help to discover new epigenetic gene regulations involved in bacterial phenotypic heterogeneity, including during host-pathogen interactions.

Integrating heterogeneous high-throughput data for meta-dimensional pharmacogenomics and disease-related studies

The current paradigm of human genetics research is to analyze variation of a single data type (i.e., DNA sequence or RNA levels) to detect genes and pathways that underlie complex traits such as disease state or drug response. While these studies have detected thousands of variations that associate with hundreds of complex phenotypes, much of the estimated heritability, or trait variability due to genetic factors, remain unexplained. We may be able to account for a portion of the missing heritability if we incorporate a systems biology approach into these analyses. Rapid technological advances will make it possible for scientists to explore this hypothesis via the generation of high-throughput omics data - transcriptomic, proteomic and methylomic to name a few. Analyzing this 'meta-dimensional' data will require clever statistical techniques that allow for the integration of qualitative and quantitative predictor variables. For this article, we examine two major categories of approaches for integrated data analysis, give examples of their use in experimental and in silico datasets, and assess the limitations of each method.

Detection of p16 and MGMT promoter methylation in gastric carcinomas by nested methylation-specific polymerase chain reaction

AIM: To analyze aberrant promoter methylation of the p16 and O-6-methylguanine-DNA methyltransferase (MGMT) genes in serum DNA samples from patients with gastric carcinoma.

METHODS: Nested methylation-specific polymerase chain reaction (nMSP) was adopted to detect the promoter methylation of the p16 and MGMT genes in serum DNA samples from 69 patients with gastric carcinoma. Serum DNA samples from 16 healthy individuals were used as normal controls.

RESULTS: The frequencies of p16 and MGMT promoter methylation in patients with gastric carcinoma were 30.4% and 17.4%, respectively. In contrast, no p16 and MGMT promoter methylation was detected in normal controls. The frequencies of p16 and MGMT promoter methylation were significantly higher in patients with gastric carcinoma than in normal controls (both P < 0.05).

CONCLUSION: Detection of p16 and MGMT promoter methylation in serum DNA samples can provide valuable information for molecular diagnosis of early gastric carcinoma.