Application of novel and existing methods to identify genes with evidence of epigenetic association: results from GAW20

Background

The rise in popularity and accessibility of DNA methylation data to evaluate epigenetic associations with disease has led to numerous methodological questions. As part of GAW20, our working group of 8 research groups focused on gene searching methods.

Results

Although the methods were varied, we identified 3 main themes within our group. First, many groups tackled the question of how best to use pedigree information in downstream analyses, finding that (a) the use of kinship matrices is common practice, (b) ascertainment corrections may be necessary, and (c) pedigree information may be useful for identifying parent-of-origin effects. Second, many groups also considered multimarker versus single-marker tests. Multimarker tests had modestly improved power versus single-marker methods on simulated data, and on real data identified additional associations that were not identified with single-marker methods, including identification of a gene with a strong biological interpretation. Finally, some of the groups explored methods to combine single-nucleotide polymorphism (SNP) and DNA methylation into a single association analysis.

Conclusions

A causal inference method showed promise at discovering new mechanisms of SNP activity; gene-based methods of summarizing SNP and DNA methylation data also showed promise. Even though numerous questions still remain in the analysis of DNA methylation data, our discussions at GAW20 suggest some emerging best practices.

     

Integrative pathway-based survival prediction utilizing the interaction between gene expression and DNA methylation in breast cancer

Background

Integrative analysis on multi-omics data has gained much attention recently. To investigate the interactive effect of gene expression and DNA methylation on cancer, we propose a directed random walk-based approach on an integrated gene-gene graph that is guided by pathway information.

Methods

Our approach first extracts a single pathway profile matrix out of the gene expression and DNA methylation data by performing the random walk over the integrated graph. We then apply a denoising autoencoder to the pathway profile to further identify important pathway features and genes. The extracted features are validated in the survival prediction task for breast cancer patients.

Results

The results show that the proposed method substantially improves the survival prediction performance compared to that of other pathway-based prediction methods, revealing that the combined effect of gene expression and methylation data is well reflected in the integrated gene-gene graph combined with pathway information. Furthermore, we show that our joint analysis on the methylation features and gene expression profile identifies cancer-specific pathways with genes related to breast cancer.

Conclusions

In this study, we proposed a DRW-based method on an integrated gene-gene graph with expression and methylation profiles in order to utilize the interactions between them. The results showed that the constructed integrated gene-gene graph can successfully reflect the combined effect of methylation features on gene expression profiles. We also found that the selected features by DA can effectively extract topologically important pathways and genes specifically related to breast cancer.

     

An optimized rapid bisulfite conversion method with high recovery of cell-free DNA

Background

Methylation analysis of cell-free DNA is a encouraging tool for tumor diagnosis, monitoring and prognosis. Sensitivity of methylation analysis is a very important matter due to the tiny amounts of cell-free DNA available in plasma. Most current methods of DNA methylation analysis are based on the difference of bisulfite-mediated deamination of cytosine between cytosine and 5-methylcytosine. However, the recovery of bisulfite-converted DNA based on current methods is very poor for the methylation analysis of cell-free DNA.

Results

We optimized a rapid method for the crucial steps of bisulfite conversion with high recovery of cell-free DNA. A rapid deamination step and alkaline desulfonation was combined with the purification of DNA on a silica column. The conversion efficiency and recovery of bisulfite-treated DNA was investigated by the droplet digital PCR. The optimization of the reaction results in complete cytosine conversion in 30 min at 70 °C and about 65% of recovery of bisulfite-treated cell-free DNA, which is higher than current methods.

Conclusions

The method allows high recovery from low levels of bisulfite-treated cell-free DNA, enhancing the analysis sensitivity of methylation detection from cell-free DNA.

     

Quality control for Illumina 450K methylation data in the absence of iDat files using correlation structure in pedigrees and repeated measures

Background

An important feature in many genomic studies is quality control and normalization. This is particularly important when analyzing epigenetic data, where the process of obtaining measurements can be bias prone. The GAW20 data was from the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN), a study with multigeneration families, where DNA cytosine-phosphate-guanine (CpG) methylation was measured pre- and posttreatment with fenofibrate. We performed quality control assessment of the GAW20 DNA methylation data, including normalization, assessment of batch effects and detection of sample swaps.

Results

We show that even after normalization, the GOLDN methylation data has systematic differences pre- and posttreatment. Through investigation of (a) CpGs sites containing a single nucleotide polymorphism, (b) the stability of breeding values for methylation across time points, and (c) autosomal gender-associated CpGs, 13 sample swaps were detected, 11 of which were posttreatment.

Conclusions

This paper demonstrates several ways to perform quality control of methylation data in the absence of raw data files and highlights the importance of normalization and quality control of the GAW20 methylation data from the GOLDN study.

     

Identification of prognostic signature in cancer based on DNA methylation interaction network

Background

The identification of prognostic biomarkers for cancer patients is essential for cancer research. These days, DNA methylation has been proved to be associated with cancer prognosis. However, there are few methods which identify the prognostic markers based on DNA methylation data systematically, especially considering the interaction among DNA methylation sites.

Methods

In this paper, we first evaluated the stabilities of microRNA, mRNA, and DNA methylation data in prognosis of cancer. After that, a rank-based method was applied to construct a DNA methylation interaction network. In this network, nodes with the largest degrees (10% of all the nodes) were selected as hubs. Cox regression was applied to select the hubs as prognostic signature. In this prognostic signature, DNA methylation levels of each DNA methylation site are correlated with the outcomes of cancer patients. After obtaining these prognostic genes, we performed the survival analysis in the training group and the test group to verify the reliability of these genes.

Results

We applied our method in three cancers (ovarian cancer, breast cancer and Glioblastoma Multiforme).In all the three cancers, there are more common ones of prognostic genes selected from different samples in DNA methylation data, compared with gene expression data and miRNA expression data, which indicates the DNA methylation data may be more stable in cancer prognosis. Power-law distribution fitting suggests that the DNA methylation interaction networks are scale-free. And the hubs selected from the three networks are all enriched by cancer related pathways. The gene signatures were obtained for the three cancers respectively, and survival analysis shows they can distinguish the outcomes of tumor patients in both the training data sets and test data sets, which outperformed the control signatures.

Conclusions

A computational method was proposed to construct DNA methylation interaction network and this network could be used to select prognostic signatures in cancer.

     

A streamlined method for analysing genome-wide DNA methylation patterns from low amounts of FFPE DNA

Background

Formalin fixed paraffin embedded (FFPE) tumor samples are a major source of DNA from patients in cancer research. However, FFPE is a challenging material to work with due to macromolecular fragmentation and nucleic acid crosslinking. FFPE tissue particularly possesses challenges for methylation analysis and for preparing sequencing-based libraries relying on bisulfite conversion. Successful bisulfite conversion is a key requirement for sequencing-based methylation analysis.

Methods

Here we describe a complete and streamlined workflow for preparing next generation sequencing libraries for methylation analysis from FFPE tissues. This includes, counting cells from FFPE blocks and extracting DNA from FFPE slides, testing bisulfite conversion efficiency with a polymerase chain reaction (PCR) based test, preparing reduced representation bisulfite sequencing libraries and massively parallel sequencing.

Results

The main features and advantages of this protocol are:

• An optimized method for extracting good quality DNA from FFPE tissues.
• An efficient bisulfite conversion and next generation sequencing library preparation protocol that uses 50 ng DNA from FFPE tissue.
• Incorporation of a PCR-based test to assess bisulfite conversion efficiency prior to sequencing.

Conclusions

We provide a complete workflow and an integrated protocol for performing DNA methylation analysis at the genome-scale and we believe this will facilitate clinical epigenetic research that involves the use of FFPE tissue.

     

Using recursive feature elimination in random forest to account for correlated variables in high dimensional data

Background

Random forest (RF) is a machine-learning method that generally works well with high-dimensional problems and allows for nonlinear relationships between predictors; however, the presence of correlated predictors has been shown to impact its ability to identify strong predictors. The Random Forest-Recursive Feature Elimination algorithm (RF-RFE) mitigates this problem in smaller data sets, but this approach has not been tested in high-dimensional omics data sets.

Results

We integrated 202,919 genotypes and 153,422 methylation sites in 680 individuals, and compared the abilities of RF and RF-RFE to detect simulated causal associations, which included simulated genotype–methylation interactions, between these variables and triglyceride levels. Results show that RF was able to identify strong causal variables with a few highly correlated variables, but it did not detect other causal variables.

Conclusions

Although RF-RFE decreased the importance of correlated variables, in the presence of many correlated variables, it also decreased the importance of causal variables, making both hard to detect. These findings suggest that RF-RFE may not scale to high-dimensional data.

     

Downregulation of RdDM during strawberry fruit ripening

Background

Recently, DNA methylation was proposed to regulate fleshy fruit ripening. Fleshy fruits can be distinguished by their ripening process as climacteric fruits, such as tomatoes, or non-climacteric fruits, such as strawberries. Tomatoes undergo a global decrease in DNA methylation during ripening, due to increased expression of a DNA demethylase gene. The dynamics and biological relevance of DNA methylation during the ripening of non-climacteric fruits are unknown.

Results

Here, we generate single-base resolution maps of the DNA methylome in immature and ripe strawberry. We observe an overall loss of DNA methylation during strawberry fruit ripening. Thus, ripening-induced DNA hypomethylation occurs not only in climacteric fruit, but also in non-climacteric fruit. Application of a DNA methylation inhibitor causes an early ripening phenotype, suggesting that DNA hypomethylation is important for strawberry fruit ripening. The mechanisms underlying DNA hypomethylation during the ripening of tomato and strawberry are distinct. Unlike in tomatoes, DNA demethylase genes are not upregulated during the ripening of strawberries. Instead, genes involved in RNA-directed DNA methylation are downregulated during strawberry ripening. Further, ripening-induced DNA hypomethylation is associated with decreased siRNA levels, consistent with reduced RdDM activity. Therefore, we propose that a downregulation of RdDM contributes to DNA hypomethylation during strawberry ripening.

Conclusions

Our findings provide new insight into the DNA methylation dynamics during the ripening of non-climacteric fruit and suggest a novel function of RdDM in regulating an important process in plant development.

     

Identification of differential genomic DNA Methylation in the hypothalamus of pubertal rat using reduced representation Bisulfite sequencing

Background

There are many variables affecting the onset of puberty in animals, including genetic, nutritional, and environmental factors. Recent studies suggest that epigenetic regulation, especially DNA methylation, plays a majorrole in the regulation of puberty. However, there have been no reports on DNA methylation of the pubertal genome.

Methods

We investigated DNA methylation in the female rat hypothalamus at prepuberty and puberty using reduced representation bisulfite sequencing technology. The identified genes and signaling pathways exhibiting changes to DNA methylation in pubertal rats were determined by Gene Ontogeny and Kyoto Encyclopedia of Genes and Genomes analysis.

Results

The distribution of the three types of methylated C bases in promoter and CpG island (CGI) regions in the hypothalamus was as follows: 87.79% CG, 3.05% CHG, 9.16% CHH for promoters, and 88.35% CG, 3.21% CHG, 88.35% CHH for CGI in prepubertal rats; and 90.78% CG, 2.13% CHG, 7.09% CHH for promoters, and 88.59% CG, 88.59% CHG, 8.35% CHH for CGI in pubertal animals. CG showed the highest percentage of methylation, and was the highest methylation state in CGI. Compared to prepubertal hyoyhalamus samples, we identified ten genes with altered methylation in promoter regions in the pubertal hypothalamus samples, and 43 genes with altered methylation in the CGI. Changes in DNA methylation were found in gonadotropin-releasing hormone signaling pathways, and the oocyte meiosis pathway.

Conclusion

Our results demonstrate changes in DNA methylation occur in female rats from prepuberty to puberty suggestng DNA methylation may play a crucial role in the regulation of puberty onset. This study provides essential information for future studies on the role of epigenetics in the regulation of puberty.

     

Crystal structure of <Emphasis Type="Italic">Escherichia coli</Emphasis> protein ybgI,a toroidal structure with a dinuclear metal site

Background

The protein encoded by the gene ybgI was chosen as a target for a structural genomics project emphasizing the relation of protein structure to function.

Results

The structure of the ybgI protein is a toroid composed of six polypeptide chains forming a trimer of dimers. Each polypeptide chain binds two metal ions on the inside of the toroid.

Conclusion

The toroidal structure is comparable to that of some proteins that are involved in DNA metabolism. The di-nuclear metal site could imply that the specific function of this protein is as a hydrolase-oxidase enzyme.

     

A novel chimeric influenza virosome containing <Emphasis Type="Italic">Vesicular stomatitis</Emphasis> G protein as a more efficient gene delivery system

Objectives

To enhance the efficiency of influenza virosome-mediated gene delivery by engineering this virosome.

Results

A novel chimeric influenza virosome was constructed containing the glycoprotein of Vesicular stomatitis virus (VSV-G), along with its own hemagglutinin protein. To optimize the transfection efficiency of both chimeric and influenza cationic virosomes, HEK cells were transfected with plasmid DNA and virosomes and the transfection efficiency was assessed by FACS analysis. The chimeric virosome was significantly more efficient in mediating transfection for all amounts of DNA and virosomes compared to the influenza virosome.

Conclusions

Chimeric influenza virosome, including VSV-G, is superior to the conventional influenza virosome for gene delivery.

     

Characterization of ankylosing spondylitis and rheumatoid arthritis using <Superscript>1</Superscript>H NMR-based metabolomics of human fecal extracts

Introduction

The differences in fecal metabolome between ankylosing spondylitis (AS)/rheumatoid arthritis (RA) patients and healthy individuals could be the reason for an autoimmune disorder.

Objectives

The study explored the fecal metabolome difference between AS/RA patients and healthy controls to clarify human immune disturbance.

Methods

Fecal samples from 109 individuals (healthy controls 34, AS 40, and RA 35) were analyzed by ¹H NMR spectroscopy. Data were analyzed with principal component analysis (PCA) and orthogonal projection to latent structure discriminant (OPLS-DA) analysis.

Results

Significant differences in the fecal metabolic profiles could distinguish AS/RA patients from healthy controls but could not distinguish between AS and RA patients. The significantly decreased metabolites in AS/RA patients were butyrate, propionate, methionine, and hypoxanthine. Significantly increased metabolites in AS/RA patients were taurine, methanol, fumarate, and tryptophan.

Conclusion

The metabolome variations in feces indicated AS and RA were two homologous diseases that could not be distinguished by ¹H NMR metabolomics.

     

PTEN at the interface of immune tolerance and tumor suppression

Background

PTEN is well known to function as a tumor suppressor that antagonizes oncogenic signaling and maintains genomic stability. The PTEN gene is frequently deleted or mutated in human cancers and the wide cancer spectrum associated with PTEN deficiency has been recapitulated in a variety of mouse models of Pten deletion or mutation. Pten mutations are highly penetrant in causing various types of spontaneous tumors that often exhibit resistance to anticancer therapies including immunotherapy. Recent studies demonstrate that PTEN also regulates immune functionality.

Objective

To understand the multifaceted functions of PTEN as both a tumor suppressor and an immune regulator.

Methods

This review will summarize the emerging knowledge of PTEN function in cancer immunoediting. In addition, the mechanisms underlying functional integration of various PTEN pathways in regulating cancer evolution and tumor immunity will be highlighted.

Results

Recent preclinical and clinical studies revealed the essential role of PTEN in maintaining immune homeostasis, which significantly expands the repertoire of PTEN functions. Mechanistically, aberrant PTEN signaling alters the interplay between the immune system and tumors, leading to immunosuppression and tumor escape.

Conclusion

Rational design of personalized anti-cancer treatment requires mechanistic understanding of diverse PTEN signaling pathways in modulation of the crosstalk between tumor and immune cells.

     

Methylated site display (MSD)-AFLP,a sensitive and affordable method for analysis of CpG methylation profiles

Background

It has been pointed out that environmental factors or chemicals can cause diseases that are developmental in origin. To detect abnormal epigenetic alterations in DNA methylation, convenient and cost-effective methods are required for such research, in which multiple samples are processed simultaneously. We here present methylated site display (MSD), a unique technique for the preparation of DNA libraries. By combining it with amplified fragment length polymorphism (AFLP) analysis, we developed a new method, MSD-AFLP.

Results

Methylated site display libraries consist of only DNAs derived from DNA fragments that are CpG methylated at the 5′ end in the original genomic DNA sample. To test the effectiveness of this method, CpG methylation levels in liver, kidney, and hippocampal tissues of mice were compared to examine if MSD-AFLP can detect subtle differences in the levels of tissue-specific differentially methylated CpGs. As a result, many CpG sites suspected to be tissue-specific differentially methylated were detected. Nucleotide sequences adjacent to these methyl-CpG sites were identified and we determined the methylation level by methylation-sensitive restriction endonuclease (MSRE)-PCR analysis to confirm the accuracy of AFLP analysis. The differences of the methylation level among tissues were almost identical among these methods. By MSD-AFLP analysis, we detected many CpGs showing less than 5% statistically significant tissue-specific difference and less than 10% degree of variability. Additionally, MSD-AFLP analysis could be used to identify CpG methylation sites in other organisms including humans.

Conclusion

MSD-AFLP analysis can potentially be used to measure slight changes in CpG methylation level. Regarding the remarkable precision, sensitivity, and throughput of MSD-AFLP analysis studies, this method will be advantageous in a variety of epigenetics-based research.