Identification of a small optimal subset of CpG sites as bio-markers from high-throughput DNA methylation profiles

Novel feature selection methods for construction of accurate epigenetic clocks

Epigenetic clocks allow us to accurately predict the age and future health of individuals based on the methylation status of specific CpG sites in the genome and are a powerful tool to measure the effectiveness of longevity interventions. There is a growing need for methods to efficiently construct epigenetic clocks. The most common approach is to create clocks using elastic net regression modelling of all measured CpG sites, without first identifying specific features or CpGs of interest. The addition of feature selection approaches provides the opportunity to optimise the identification of predictive CpG sites. Here, we apply novel feature selection methods and combinatorial approaches including newly adapted neural networks, genetic algorithms, and 'chained' combinations. Human whole blood methylation data of ~470,000 CpGs was used to develop clocks that predict age with R2 correlation scores of greater than 0.73, the most predictive of which uses 35 CpG sites for a R2 correlation score of 0.87. The five most frequent sites across all clocks were modelled to build a clock with a R2 correlation score of 0.83. These two clocks are validated on two external datasets where they maintain excellent predictive accuracy. When compared with three published epigenetic clocks (Hannum, Horvath, Weidner) also applied to these validation datasets, our clocks outperformed all three models. We identified gene regulatory regions associated with selected CpGs as possible targets for future aging studies. Thus, our feature selection algorithms build accurate, generalizable clocks with a low number of CpG sites, providing important tools for the field.

DNA methylation and histone acetylation are involved in Wnt10b expression during the secondary hair follicle cycle in Angora rabbits

Secondary hair follicles (SHFs) in the Angora rabbit exhibit classic cyclic hair development, but the multiple molecular signals involved in hair cycling are yet to be explored in detail. In the present study, we investigated the expression pattern, methylation and histone H3 acetylation status of Wnt10b, as a molecular signal participating in hair cycling, during the SHF cycle in the Angora rabbit. Expression of Wnt10b at the anagen phase was significantly higher than that at both the telogen and catagen phases, suggesting that Wnt10b might serve as a critical activator during cyclic transition of SHFs. Methylation frequency of the fifth CpG site (CpG5-175 bp) in CpG islands at the anagen phase was lower than that at both the catagen and telogen phases. The methylation status of the CpG5 site was negatively correlated with Wnt10b expression. This indicated that the methylation of CpG5 might participate in Wnt10b transcriptional suppression in SHFs. Furthermore, histone H3 acetylation status in the regions-256~-11 bp and 98 ~ 361 bp were significantly lower at both the catagen and telogen phases than at the anagen phase. The histone H3 acetylation level was significantly positively correlated with Wnt10b expression. This confirmed that histone acetylation was likely involved in upregulating Wnt10b transcription in SHFs. Additionally, potential binding to the transcription factors ZF57 and HDBP was predicted within the CpG5 site. In conclusion, our findings reveal the epigenetic mechanism of Wnt10b transcription and provide a new insight into epigenetic regulation during the SHF cycle in the Angora rabbit.

methCancer-gen: a DNA methylome dataset generator for user-specified cancer type based on conditional variational autoencoder

Background

Recently, DNA methylation has drawn great attention due to its strong correlation with abnormal gene activities and informative representation of the cancer status. As a number of studies focus on DNA methylation signatures in cancer, demand for utilizing publicly available methylome dataset has been increased. To satisfy this, large-scale projects were launched to discover biological insights into cancer, providing a collection of the dataset. However, public cancer data, especially for certain cancer types, is still limited to be used in research. Several simulation tools for producing epigenetic dataset have been introduced in order to alleviate the issue, still, to date, generation for user-specified cancer type dataset has not been proposed.

Results

In this paper, we present methCancer-gen, a tool for generating DNA methylome dataset considering type for cancer. Employing conditional variational autoencoder, a neural network-based generative model, it estimates the conditional distribution with latent variables and data, and generates samples for specified cancer type.

Conclusions

To evaluate the simulation performance of methCancer-gen for the user-specified cancer type, our proposed model was compared to a benchmark method and it could successfully reproduce cancer type-wise data with high accuracy helping to alleviate the lack of condition-specific data issue. methCancer-gen is publicly available at https://github.com/cbi-bioinfo/methCancer-gen.

Correlation analyses of CpG island methylation of cluster of differentiation 4 protein with gene expression and T lymphocyte subpopulation traits

Objective

Cluster of differentiation 4 protein (CD4) gene is an important immune related gene which plays a significant role in T cell development and host resistance during viral infection.

Methods

In order to unravel the relationship of CpG island methylation level of CD4 gene with its gene expression and T lymphocyte subpopulation traits, we used one typical Chinese indigenous breed (Dapulian, DP) and one commercial breed (Landrace), then predicted the CpG island of CD4 gene, determined the methylation status of CpG sites by bisulfite sequencing polymerase chain reaction (BSP), and carried out the correlation analyses of methylation frequencies of CpG sites with mRNA expression and T lymphocyte subpopulation traits.

Results

There was one CpG island predicted in the upstream −2 kb region and exon one of porcine CD4 gene, which located 333 bp upstream from the start site of gene and contained nine CpG sites. The correlation analysis results indicated that the methylation frequency of CpG_2 significantly correlated with CD4 mRNA expression in the DP and Landrace combined population, though it did not reach significance level in DP and Landrace separately. Additionally, 15 potential binding transcription factors (TFs) were predicted within the CpG island, and one of them (Jumonji) contained CpG_2 site, suggesting that it may influence the CD4 gene expression through the potential binding TFs. We also found methylation frequency of CpG_2 negatively correlated with T lymphocyte subpopulation traits CD4+CD8−CD3−, CD4−CD8+CD3− and CD4+/CD8+, and positively correlated with CD4−CD8+CD3+ and CD4+CD8+CD3+ (for all correlation, p<0.01) in DP and Landrace combined population. Thus, the CpG_2 was a critical methylation site for porcine CD4 gene expression and T lymphocyte subpopulation traits.

Conclusion

We speculated that increased methylation frequency of CpG_2 may lead to the decreased expression of CD4, which may have some kind of influence on T lymphocyte subpopulation traits and the immunity of DP population.

A robust biomarker of differential correlations improves the diagnosis of cytologically indeterminate thyroid cancers

The fine-needle aspiration of thyroid nodules and subsequent cytological analysis is unable to determine the diagnosis in 15 to 30% of thyroid cancer cases; patients with indeterminate cytological results undergo diagnostic surgery which is potentially unnecessary. Current gene expression biomarkers based on well-determined cytology are complex and their accuracy is inconsistent across public datasets. In the present study, we identified a robust biomarker using the differences in gene expression values specifically from cytologically indeterminate thyroid tumors and a powerful multivariate search tool coupled with a nearest centroid classifier. The biomarker is based on differences in the expression of the following genes: CCND1, CLDN16, CPE, LRP1B, MAGI3, MAPK6, MATN2, MPPED2, PFKFB2, PTPRE, PYGL, SEMA3D, SERGEF, SLC4A4 and TIMP1. This 15-gene biomarker exhibited superior accuracy independently of the cytology in six datasets, including The Cancer Genome Atlas (TCGA) thyroid dataset. In addition, this biomarker exhibited differences in the correlation coefficients between benign and malignant samples that indicate its discriminatory power, and these 15 genes have been previously related to cancer in the literature. Thus, this 15-gene biomarker provides advantages in clinical practice for the effective diagnosis of thyroid cancer.

Epigenetic biomarker development

Epigenetic mechanisms control gene expression in a way that is stably propagated over multiple cell divisions, but which is also flexible enough to respond to environmental influences. This intermediate position between stability and plasticity renders epigenetic information highly useful for monitoring cellular states in the context of personalized medicine. Epigenetic alterations have also been identified as causal events for common diseases such as cancer and autoimmune disorders. The goal of epigenetic biomarker development is to design experimental assays that produce relevant information for diagnosis, prognosis and therapy optimization in routine clinical treatment and drug discovery. Here, I outline a systematic approach to epigenetic biomarker development and highlight key bioinformatic tools that facilitate discovery, optimization and validation of novel biomarkers.

A beta-mixture model for dimensionality reduction, sample classification and analysis

Background

Patterns of genome-wide methylation vary between tissue types. For example, cancer tissue shows markedly different patterns from those of normal tissue. In this paper we propose a beta-mixture model to describe genome-wide methylation patterns based on probe data from methylation microarrays. The model takes dependencies between neighbour probe pairs into account and assumes three broad categories of methylation, low, medium and high. The model is described by 37 parameters, which reduces the dimensionality of a typical methylation microarray significantly. We used methylation microarray data from 42 colon cancer samples to assess the model.

Results

Based on data from colon cancer samples we show that our model captures genome-wide characteristics of methylation patterns. We estimate the parameters of the model and show that they vary between different tissue types. Further, for each methylation probe the posterior probability of a methylation state (low, medium or high) is calculated and the probability that the state is correctly predicted is assessed. We demonstrate that the model can be applied to classify cancer tissue types accurately and that the model provides accessible and easily interpretable data summaries.

Conclusions

We have developed a beta-mixture model for methylation microarray data. The model substantially reduces the dimensionality of the data. It can be used for further analysis, such as sample classification or to detect changes in methylation status between different samples and tissues.

An epigenetic signature in peripheral blood predicts active ovarian cancer

Background

Recent studies have shown that DNA methylation (DNAm) markers in peripheral blood may hold promise as diagnostic or early detection/risk markers for epithelial cancers. However, to date no study has evaluated the diagnostic and predictive potential of such markers in a large case control cohort and on a genome-wide basis.

Principal Findings

By performing genome-wide DNAm profiling of a large ovarian cancer case control cohort, we here demonstrate that active ovarian cancer has a significant impact on the DNAm pattern in peripheral blood. Specifically, by measuring the methylation levels of over 27,000 CpGs in blood cells from 148 healthy individuals and 113 age-matched pre-treatment ovarian cancer cases, we derive a DNAm signature that can predict the presence of active ovarian cancer in blind test sets with an AUC of 0.8 (95% CI (0.74–0.87)). We further validate our findings in another independent set of 122 post-treatment cases (AUC = 0.76 (0.72–0.81)). In addition, we provide evidence for a significant number of candidate risk or early detection markers for ovarian cancer. Furthermore, by comparing the pattern of methylation with gene expression data from major blood cell types, we here demonstrate that age and cancer elicit common changes in the composition of peripheral blood, with a myeloid skewing that increases with age and which is further aggravated in the presence of ovarian cancer. Finally, we show that most cancer and age associated methylation variability is found at CpGs located outside of CpG islands.

Significance

Our results underscore the potential of DNAm profiling in peripheral blood as a tool for detection or risk-prediction of epithelial cancers, and warrants further in-depth and higher CpG coverage studies to further elucidate this role.