Identification of genes with consistent methylation levels across different human tissues

A supervised learning method for classifying methylation disorders

BACKGROUND

DNA methylation is one of the most stable and well-characterized epigenetic alterations in humans. Accordingly, it has already found clinical utility as a molecular biomarker in a variety of disease contexts. Existing methods for clinical diagnosis of methylation-related disorders focus on outlier detection in a small number of CpG sites using standardized cutoffs which differentiate healthy from abnormal methylation levels. The standardized cutoff values used in these methods do not take into account methylation patterns which are known to differ between the sexes and with age.

RESULTS

Here we profile genome-wide DNA methylation from blood samples drawn from within a cohort composed of healthy controls of different age and sex alongside patients with Prader-Willi syndrome (PWS), Beckwith-Wiedemann syndrome, Fragile-X syndrome, Angelman syndrome, and Silver-Russell syndrome. We propose a Generalized Additive Model to perform age and sex adjusted outlier analysis of around 700,000 CpG sites throughout the human genome. Utilizing z-scores among the cohort for each site, we deployed an ensemble based machine learning pipeline and achieved a combined prediction accuracy of 0.96 (Binomial 95% Confidence Interval 0.868[Formula: see text]0.995).

CONCLUSION

We demonstrate a method for age and sex adjusted outlier detection of differentially methylated loci based on a large cohort of healthy individuals. We present a custom machine learning pipeline utilizing this outlier analysis to classify samples for potential methylation associated congenital disorders. These methods are able to achieve high accuracy when used with machine learning methods to classify abnormal methylation patterns.

Counteracting aged DNA methylation states to combat ageing and age-related diseases

DNA methylation (DNAm) overwrites information about multiple extrinsic factors on the genome. Age is one of these factors. Age causes characteristic DNAm changes that are thought to be not only major drivers of normal ageing but also precursors to diseases, cancer being one of these. Although there is still much to learn about the relationship between ageing, age-related diseases and DNAm, we now know how to interpret some of the effects caused by age in the form of changes in methylation marks at specific loci. In fact, these changes form the basis of the so called "epigenetic clocks", which translate the genomic methylation profile into an "epigenetic age". Epigenetic age does not only estimate chronological age but can also predict the risk of chronic diseases and mortality. Epigenetic age is believed to be one of the most accurate metrics of biological age. Initial evidence has recently been gathered pointing to the possibility that the rate of epigenetic ageing can be slowed down or even reversed. In this review, we discuss some of the most relevant advances in this field. Expected outcome is that this approach can provide insights into how to preserve health and reduce the impact of ageing diseases in humans.

Down-regulation of EGFL8 regulates migration, invasion and apoptosis of hepatocellular carcinoma through activating Notch signaling pathway

BACKGROUND

Our previous studies have reported the down-regulation of EGFL8 correlates to the development and prognosis of colorectal and gastric cancer. The present study is carried out to explore the expression pattern and role of EGFL8 in hepatocellular carcinoma (HCC).

METHODS AND MATERIALS

EGFL8 expression in 102 cases of HCC tissues matched with adjacent non-tumorous liver tissues, a normal liver cell line and three liver cancer cell lines with different metastatic capacity was detected by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and Western blot. Moreover, the clinicopathological features and prognosis of HCC patients were correlated with expression of EGFL8. Subsequently, the gain-and loss-of-function experiments were carried out to investigate the biological function of EGFL8 in HCC. We also used N-[N-(3,5-Difluorophenacetyl-L-alanyl)]-(S)- phenylglycine t-butyl ester (DAPT), an inhibitor for Notch signaling pathway, in these experiments to verify the involvement of Notch signaling pathway in the effects of EGFL8. Additionally, a mouse model was established to investigate the effect of EGFL8 on metastasis of HCC cells. The expression of Notch signaling pathway in HCC cells and xenograft mouse tumors were detected by Western blot and immunohistochemistory.

RESULTS

The expression of EGFL8 was significantly decreased in HCC tissues and cell lines and EGFL8 down-regulation correlated to multiple nodules, vein invasion, high TNM stage and poor prognosis of HCC. Interestingly, the expression levels of EGFL8 in three liver cancer cell lines were negatively associated with their metastatic capacity. In vitro and in vivo experiments indicated that EGFL8 obviously suppressed metastasis and invasion of HCC cells but slightly promoted apoptosis. Meanwhile, the expression of Notch signaling pathway was obviously suppressed in EGFL8 overexpressed HCCLM3 cells and xenograft mouse tumors generated from these cells but markedly elevated in EGFL8 depleted Hep3B cells. Furthermore, the up-regulated expression of Notch signaling pathway and effects induced by EGFL8 knockdown in Hep3B cells could be counteracted by DAPT treatment.

CONCLUSION

The down-regulation of EGFL8 was correlated to progression and poor prognosis of HCC and regulates HCC cell migration, invasion and apoptosis through activating the Notch signaling pathway, suggesting EGFL8 as a novel therapeutic target and a potential prognostic marker for HCC.

Integrated genome-wide methylation and expression analyses reveal functional predictors of response to antidepressants

Major depressive disorder (MDD) is primarily treated with antidepressants, yet many patients fail to respond adequately, and identifying antidepressant response biomarkers is thus of clinical significance. Some hypothesis-driven investigations of epigenetic markers for treatment response have been previously made, but genome-wide approaches remain unexplored. Healthy participants (n = 112) and MDD patients (n = 211) between 18-60 years old were recruited for an 8-week trial of escitalopram treatment. Responders and non-responders were identified using differential Montgomery-Åsberg Depression Rating Scale scores before and after treatment. Genome-wide DNA methylation and gene expression analyses were assessed using the Infinium MethylationEPIC Beadchip and HumanHT-12 v4 Expression Beadchip, respectively, on pre-treatment peripheral blood DNA and RNA samples. Differentially methylated positions (DMPs) located in regions of differentially expressed genes between responders (n = 82) and non-responders (n = 95) were identified, and technically validated using a targeted sequencing approach. Three DMPs located in the genes CHN2 (cg23687322, p = 0.00043 and cg06926818, p = 0.0014) and JAK2 (cg08339825, p = 0.00021) were the most significantly associated with mRNA expression changes and subsequently validated. Replication was then conducted with non-responders (n = 76) and responders (n = 71) in an external cohort that underwent a similar antidepressant trial. One CHN2 site (cg06926818; p = 0.03) was successfully replicated. Our findings indicate that differential methylation at CpG sites upstream of the CHN2 and JAK2 TSS regions are possible peripheral predictors of antidepressant treatment response. Future studies can provide further insight on robustness of our candidate biomarkers, and greater characterization of functional components.

Epigenetic analysis in rheumatoid arthritis synoviocytes

Rheumatoid arthritis (RA) is a complex chronic systematic disease with progressive destruction of the joints by invasive synoviocytes. To characterize the key regulators involved in the development of RA, we obtained multilayer epigenomics data including DNA methylation by whole-genome bisulfite sequencing, miRNA profiles, genetic variations by whole-exome sequencing, and mRNA profiles from synoviocytes of RA and osteoarthritis (OA) patients. The overall DNA methylation patterns were not much different between RA and OA, but 523 low-methylated regions (LMRs) were specific to RA. The LMRs were preferentially localized at the 5′ introns and overlapped with transcription factor binding motifs for GLI1, RUNX2, and TFAP2A/C. Single base-scale differentially methylated CpGs were linked with several networks related to wound response, tissue development, collagen fibril organization, and the TGF-β receptor signaling pathway. Further, the DNA methylation of 201 CpGs was significantly correlated with 27 expressed miRNA genes. Our interpretation of epigenomic data of the synoviocytes from RA and OA patients is an informative resource to further investigate regulatory elements and biomarkers responsible for the pathophysiology of RA and OA.

Whole genome analysis of synoviocytes, specialized cells in the joint-lubricating synovial fluid, sheds light on the pathogenic mechanisms of rheumatoid arthritis (RA). Around 350 million people worldwide suffer joint pain and stiffness due to RA, but the inheritance pattern of the disease remains unclear. A study led by Tae-Young Roh at Pohang University of Science and Technology, South Korea, reveals a distinct pattern of chemical tags on the DNA of synoviocytes from RA patients. Differences in methyl group tags in over 500 regions of the genome influenced the expression of RA-associated genes and of microRNAs, small RNA molecules that are also involved in the regulation of gene expression. These differentially methylated sites may not only represent potential disease biomarkers, but also offer new insights into the regulation of RA-relevant genes.

Intra-Monozygotic Twin Pair Discordance and Longitudinal Variation of Whole-Genome Scale DNA Methylation in Adults

Monozygotic twins share identical genomic DNA and are indistinguishable using conventional genetic markers. Increasing evidence indicates that monozygotic twins are epigenetically distinct, suggesting that a comparison between DNA methylation patterns might be useful to approach this forensic problem. However, the extent of epigenetic discordance between healthy adult monozygotic twins and the stability of CpG loci within the same individual over a short time span at the whole-genome scale are not well understood. Here, we used Infinium HumanMethylation450 Beadchips to compare DNA methylation profiles using blood collected from 10 pairs of monozygotic twins and 8 individuals sampled at 0, 3, 6, and 9 months. Using an effective and unbiased method for calling differentially methylated (DM) CpG sites, we showed that 0.087%–1.530% of the CpG sites exhibit differential methylation in monozygotic twin pairs. We further demonstrated that, on whole-genome level, there has been no significant epigenetic drift within the same individuals for up to 9 months, including one monozygotic twin pair. However, we did identify a subset of CpG sites that vary in DNA methylation over the 9-month period. The magnitude of the intra-pair or longitudinal methylation discordance of the CpG sites inside the CpG islands is greater than those outside the CpG islands. The CpG sites located on shores appear to be more suitable for distinguishing between MZ twins.

DNA methylation as a potential diagnosis indicator for rapid discrimination of rare cancer cells and normal cells

The global DNA methylation degree may be a ubiquitous and early biomarker to distinguish cancer cells from benign cells. However, its usefulness in clinical diagnosis was scarcely demonstrated, because the cancer cells isolated from patients were usually very rare. Even if 10 mL of peripheral blood was sampled from a patient, only tens of cancer cells could be isolated. So a method to quantify DNA methylation from small number of cells was needed to apply DNA methylation in clinical environment. In this study, we found that normal breast cell line MCF10A and breast cancer cell line MCF7 cells present significantly different percentage of genomic 5-methylcytosine (p < 0.02, n = 8), it could be a potential indicator for rapid discrimination of rare cancer cells from normal cells. However, conventional mass spectrometry needs usually ~10⁶ cells to quantify DNA methylation degree, which was too large to be applied in clinical diagnosis. Here we developed a fast mass spectrometry-based method capable of analyzing the DNA methylation degree from only ~100 human cells. Our method could reveal the different DNA methylation degree between MCF10A and MCF7 cells in less than two hours, having the potential to provide reliable information for clinical application.

Assisted reproduction causes placental maldevelopment and dysfunction linked to reduced fetal weight in mice

Compelling evidence indicates that stress in utero, as manifested by low birth weight (LBW), increases the risk of metabolic syndrome in adulthood. Singletons conceived by assisted reproductive technology (ART) display a significant increase in LBW risk and ART offspring have a different metabolic profile starting at birth. Here, used mouse as a model, we found that ART resulted in reduced fetal weight and placental overgrowth at embryonic day 18.5 (E18.5). The ART placentae exhibited histomorphological alterations with defects in placental layer segregation and glycogen cells migration at E18.5. Further, ART treatments resulted in downregulation of a majority of placental nutrient transporters and reduction in placental efficiency. Moreover, the ART placentae were associated with increased methylation levels at imprinting control regions of H19, KvDMR1 and disrupted expression of a majority of imprinted genes important for placental development and function at E18.5. Our results from the mouse model show the first piece of evidence that ART treatment could affect fetal growth by disrupting placental development and function, suggests that perturbation of genomic imprinting resulted from embryo manipulation may contribute to these problems.