CpG Markers Research Articles

Artificial intelligence and placental DNA methylation: newborn prediction and molecular mechanisms of autism in preterm children

Background Autism Spectrum Disorder (ASD) represents a heterogeneous group of disorders with a complex genetic and epigenomic etiology. DNA methylation is the most extensively studied epigenomic mechanism and correlates with altered gene expression. Artificial intelligence (AI) is a powerful tool for group segregation and for handling the large volume of data generated in omics experiments. Methods We performed genome-wide methylation analysis for differential methylation of cytosine nucleotide (CpG) was performed in 20 postpartum placental tissue samples from preterm births. Ten newborns went on to develop autism (Autistic Disorder subtype) and there were 10 unaffected controls. AI including Deep Learning (AI-DL) platforms were used to identify and rank cytosine methylation markers for ASD detection. Ingenuity Pathway Analysis (IPA) to identify genes and molecular pathways that were dysregulated in autism. Results We identified 4870 CpG loci comprising 2868 genes that were significantly differentially methylated in ASD compared to controls. Of these 431 CpGs met the stringent EWAS threshold (p-value <5 × 10−8) along with ≥10% methylation difference between CpGs in cases and controls. DL accurately predicted autism with an AUC (95% CI) of 1.00 (1–1) and sensitivity and specificity of 100% using a combination of 5 CpGs [cg13858611 (NRN1), cg09228833 (ZNF217), cg06179765 (GPNMB), cg08814105 (NKX2-5), cg27092191 (ZNF267)] CpG markers. IPA identified five prenatally dysregulated molecular pathways linked to ASD. Conclusions The present study provides substantial evidence that epigenetic differences in placental tissue are associated with autism development and raises the prospect of early and accurate detection of the disorder.

New-onset postpartum preeclampsia: epigenetic mechanism and prediction

Objective Placental cytosine (CpG) methylation was measured to predict new-onset postpartum preeclampsia (NOPP) and interrogate its molecular pathogenesis. Methods NOPP was defined as patients with a new diagnosis of postpartum preeclampsia developing ≥48 h to ≤6 weeks after delivery with no prior hypertensive disorders. Placental tissue was obtained from 12 NOPP cases and 12 normotensive controls. Genome-wide individual cytosine (CpG) methylation level was measured with the Infinium MethylationEPIC BeadChip array. Significant differential methylation (NOPP vs. controls) for individual CpG loci was defined as false discovery rate (FDR) p value <.05. Gene functional enrichment using Qiagen’s ingenuity pathway analysis (IPA) was performed to help elucidate the molecular pathogenesis of NOPP. A logistic regression model for NOPP prediction based on the methylation level in a combination of CpG loci was generated. The area under the receiver operating characteristic curves (AUC [95% CI]) sensitivity, and specificity for NOPP prediction based on the CpG methylation level was calculated for each locus. Results There were 537 (in 540 separate genes) significantly (FDR p<.05 with a ≥ 2.0-fold methylation difference) differentially methylated CpG loci between the groups. A total of 143 individual CpG markers had excellent individual predictive accuracy for NOPP prediction (AUC ≥0.80), of which 14 markers had outstanding accuracy (AUC ≥0.90). A logistic regression model based on five CpG markers yielded an AUC (95% CI)=0.99 (0.95–0.99) with sensitivity 95% and specificity 93% for NOPP prediction. IPA revealed dysregulation of critical pathways (e.g., angiogenesis, chronic inflammation, and epithelial–mesenchymal transition) known to be linked to classic preeclampsia, in addition to other previously undescribed genes/pathways. Conclusions There was significant placental epigenetic dysregulation in NOPP. NOPP shared both common and unique molecular pathways with classic preeclampsia. Finally, we have identified novel potential biomarkers for the early post-partum prediction of NOPP.

Placental DNA methylation changes and the early prediction of autism in full-term newborns

Autism spectrum disorder (ASD) is associated with abnormal brain development during fetal life. Overall, increasing evidence indicates an important role of epigenetic dysfunction in ASD. The placenta is critical to and produces neurotransmitters that regulate fetal brain development. We hypothesized that placental DNA methylation changes are a feature of the fetal development of the autistic brain and importantly could help to elucidate the early pathogenesis and prediction of these disorders. Genome-wide methylation using placental tissue from the full-term autistic disorder subtype was performed using the Illumina 450K array. The study consisted of 14 cases and 10 control subjects. Significantly epigenetically altered CpG loci (FDR p-value <0.05) in autism were identified. Ingenuity Pathway Analysis (IPA) was further used to identify molecular pathways that were over-represented (epigenetically dysregulated) in autism. Six Artificial Intelligence (AI) algorithms including Deep Learning (DL) to determine the predictive accuracy of CpG markers for autism detection. We identified 9655 CpGs differentially methylated in autism. Among them, 2802 CpGs were inter- or non-genic and 6853 intragenic. The latter involved 4129 genes. AI analysis of differentially methylated loci appeared highly accurate for autism detection. DL yielded an AUC (95% CI) of 1.00 (1.00-1.00) for autism detection using intra- or intergenic markers by themselves or combined. The biological functional enrichment showed, four significant functions that were affected in autism: quantity of synapse, microtubule dynamics, neuritogenesis, and abnormal morphology of neurons. In this preliminary study, significant placental DNA methylation changes. AI had high accuracy for the prediction of subsequent autism development in newborns. Finally, biologically functional relevant gene pathways were identified that may play a significant role in early fetal neurodevelopmental influences on later cognition and social behavior.

Epigenetic Markers Are Associated With Differences in Isocyanate Biomarker Levels in Exposed Spray-Painters.

Isocyanates are respiratory and skin sensitizers that are one of the main causes of occupational asthma globally. Genetic and epigenetic markers are associated with isocyanate-induced asthma and, before asthma develops, we have shown that genetic polymorphisms are associated with variation in plasma and urine biomarker levels in exposed workers. Inter-individual epigenetic variance may also have a significant role in the observed biomarker variability following isocyanate exposure. Therefore, we determined the percent methylation for CpG islands from DNA extracted from mononuclear blood cells of 24 male spray-painters exposed to 1,6-hexamethylene diisocyanate (HDI) monomer and HDI isocyanurate. Spray-painters’ personal inhalation and skin exposure to these compounds and the respective biomarker levels of 1,6-diaminohexane (HDA) and trisaminohexyl isocyanurate (TAHI) in their plasma and urine were measured during three repeated industrial hygiene monitoring visits. We controlled for inhalation exposure, skin exposure, age, smoking status, and ethnicity as covariates and performed an epigenome-wide association study (EWAS) using likelihood-ratio statistical modeling. We identified 38 CpG markers associated with differences in isocyanate biomarker levels (Bonferroni < 0.05). Annotations for these markers included 18 genes: ALG1, ANKRD11, C16orf89, CHD7, COL27A, FUZ, FZD9, HMGN1, KRT6A, LEPR, MAPK10, MED25, NOSIP, PKD1, SNX19, UNC13A, UROS, and ZFHX3. We explored the functions of the genes that have been published in the literature and used GeneMANIA to investigate gene ontologies and predicted protein-interaction networks. The protein functions of the predicted networks include keratinocyte migration, cell–cell adhesions, calcium transport, neurotransmitter release, nitric oxide production, and apoptosis regulation. Many of the protein pathway functions overlap with previous findings on genetic markers associated with variability both in isocyanate biomarker levels and asthma susceptibility, which suggests there are overlapping protein pathways that contribute to both isocyanate toxicokinetics and toxicodynamics. These predicted protein networks can inform future research on the mechanism of allergic airway sensitization by isocyanates and aid in the development of mitigation strategies to better protect worker health.

Development of a multiplex methylation-sensitive restriction enzyme-based SNP typing system for deconvolution of semen-containing mixtures.

The identification of mixed stains has always been a difficult problem in personal identification in the forensic field. In recent years, tissue-specific methylation sites have proven to be very stable biomarkers for distinguishing tissue origin. However, it is still challenging to perform tissue source identification and individual identification simultaneously. In this study, we developed a method that uses tissue-specific methylation markers combined with single-nucleotide polymorphism (SNP) markers to detect semen from mixed biofluids and to identify individuals simultaneously. Semen-specific CpG markers were chosen from the literature and further validated utilizing methylation-sensitive restriction endonuclease (MSRE) combined with PCR technology. The neighboring SNP markers were searched in the flanking sequence of the target CpG within 400bp, and SNP typing was then carried out through a single-base extension reaction followed by capillary electrophoresis. Eventually, a method of MSRE combined with SNaPshot that could detect 12 compound CpG-SNP markers was developed. Using this system, 10ng of total DNA and DNA mixture with semen content up to 25% could be typed successfully. Moreover, the cumulative discrimination power of the system in the northern Chinese Han population is 0.9998. This study provides a valuable strategy for forensic practice to perform tissue origin and individual identification from mixed stains simultaneously.

A forensic case study for body fluid identification using DNA methylation analysis.

Recently, a method of identifying body fluids using DNA methylation has been developed (Frumkin et al., 2011). An existing multiplex assay using 9 CpG markers could differentiate 5 body fluids: semen, blood, saliva, menstrual blood, and vaginal fluid. To validate this technique, we evaluated the previously described body fluid identification method by means of single base extension (SBE). DNA methylation was applied to 22 samples in 18 forensic cases; seven of these were semen, three were blood, eight were saliva, three were vaginal fluid, and one was menstrual blood. Total of 18 samples were tested, the DNA methylation profiles were coincident from preliminary tests (acid phosphatase (AP), leucomalachite green (LMG, Sigma Aldrich, St Louis, MO, USA) and SALIgAE®) except one sample which displayed an all-negative result. After applying the DNA methylation method to forensic samples, we determined that it could be very useful for differentiating vaginal secretions from menstrual blood, for which there is no conventional preliminary testing method.

Age estimation by DNA methylation levels in Iraqi subjects

Epigenomic studies suggest that DNA methylation profiles can indicate chronological age, which is essential in forensic investigation. Here, we analyzed DNA methylation at CpG sites in five genes to investigate their potential to predict human age. Human blood DNA samples, obtained from Iraqi subjects, were subjected to bisulfite conversion and pyrosequencing of 27 CpG sites located in the ELOVL2, TRIM59, KLF14, FHL2, and MIR29B2CHG genes. An age prediction model based on multivariate regression analysis and leave-one-out cross-validation (LOOCV) was built using the three highest age-correlated loci present in ELOVL2, TRIM59, and KLF14 genes, which provided a mean absolute deviation of 4.85 and 5.17, and root mean square error of 5.69 and 6.58 years. The results of ANOVA suggest that there is no significant difference between DNA methylation levels of males and females in the five genes. Finally, the new set of three methylated CpG markers is capable of producing an accurate age-predicted model of blood samples. Predicted age correlated well with chronological age in the 18–39 and 40–59 year age categories, but less accurately in the ≥60 year age category.

Discrimination between human populations using a small number of differentially methylated CpG sites: a preliminary study using lymphoblastoid cell lines and peripheral blood samples of European and Chinese origin

BackgroundEpigenetics is one of the factors shaping natural variability observed among human populations. A small proportion of heritable inter-population differences are observed in the context of both the genome-wide methylation level and the methylation status of individual CpG sites. It has been demonstrated that a limited number of carefully selected differentially methylated sites may allow discrimination between main human populations. However, most of the few published results have been performed exclusively on B-lymphocyte cell lines.ResultsThe goal of our study was to identify a set of CpG sites sufficient to discriminate between populations of European and Chinese ancestry based on the difference in the DNA methylation profile not only in cell lines but also in primary cell samples. The preliminary selection of CpG sites differentially methylated in these two populations (pop-CpGs) was based on the analysis of two groups of commercially available ethnically-specific B-lymphocyte cell lines, performed using Illumina Infinium Human Methylation 450 BeadChip Array. A subset of 10 pop-CpGs characterized by the best differentiating criteria (|Mdiff| > 1, q < 0.05; lack of the confounding genomic features), and 10 additional CpGs in their immediate vicinity, were further tested using pyrosequencing technology in both B-lymphocyte cell lines and in the primary samples of the peripheral blood representing two analyzed populations. To assess the population-discriminating potential of the selected set of CpGs (further referred to as “composite pop (CEU-CHB)-CpG marker”), three classification methods were applied. The predictive ability of the composite 8-site pop (CEU-CHB)-CpG marker was assessed using 10-fold cross-validation method on two independent sets of samples.ConclusionsOur results showed that less than 10 pop-CpG sites may distinguish populations of European and Chinese ancestry; importantly, this small composite pop-CpG marker performs well in both lymphoblastoid cell lines and in non-homogenous blood samples regardless of a gender.

Exploring the relation between socioeconomic position and DNA methylation in a European population

Abstract Background Previous investigations have reported that adverse socioeconomic circumstances across the life-course lead to the alteration of major biological processes, eventually resulting in a higher disease risk and premature death. In particular, a low life-course socioeconomic position (SEP) has been associated with a modified epigenetic signature of loci involved in inflammation, the physiological response to stress, and other regulatory processes. Methods In this study, we investigated the association between nine indicators of SEP across the life-course and the differential methylation of 451'000 genome-wide CpG markers, using data from 690 adults included in a Swiss population-based study. We further examined the interrelations between the SEP-related CpGs, and the biological pathways in which the identified markers are involved. Results Three SEP indicators in adulthood were associated the differential methylation of 161 genome-wide CpG markers, whereby 156 CpGs were less methylated in people with low versus high SEP. Among the identified CpGs, a substantial proportion of markers were no longer associated with SEP upon accounting for health behaviors and cardiometabolic disorders. In addition, the identified CpGs were found to be involved in immune, inflammatory, and cancer-related processes. Conclusions Our results support the hypothesis that adverse socioeconomic circumstances may lead to the dysregulation of inflammatory processes, eventually resulting in the occurrence of serious chronic conditions such as atherosclerosis, diabetes, or cancer. Key messages Socioeconomic position is a major determinant of health-related outcomes. Epigenetic modifications may constitute a biological mechanism through which socioeconomic circumstances affect health.

Abstract 3514: DNA methylation markers for risk of metastasis in a cohort of men with localized prostate cancer

Abstract Background: Most localized prostate cancers (PCa) are indolent and will not require treatment, but a small proportion can progress to metastasis. Distinguishing aggressive from indolent tumors can better inform treatment decisions. There has been interest in cancer genomics to predict PCa aggressiveness but with no consensus on methylation's role. We examined whether DNA methylation status of selected candidate genes can predict risk of PCa metastasis in the absence of curative treatment. Methods: A retrospective cohort of men diagnosed with PCa at Kaiser Permanente Southern California 1997-2007 who met the following inclusion criteria were identified: (1) diagnosed at stage I and II and (2) did not receive PCa treatment for at least 6 months after diagnosis. Men were followed for metastasis and censored at the initiation of prostatectomy or radiation, non-PCa related death, membership termination, or end of 2016. Potential metastasis was identified by an algorithm, then manually reviewed and confirmed. For each metastasis case, up to 4 controls were selected using density sampling, matched on age, race (black vs. non-black) and Gleason grade. For each case and control, FFPE blocks of diagnostic prostate biopsy cores were retrieved. The study pathologists reviewed the H/E slides and circled cancerous areas, which were macro-dissected for DNA extraction. Methylation status was obtained using Illumina's Infinium Methylation EPIC BeadChip. The candidate genes (N=118) were selected from the literature in the functional categories of cell cycle control; tumor suppressors; cell signaling; cell adhesion; angiogenesis; immune function; and stem cell markers. In total, 4,518 CpG markers from the Infinium assay covered regions of the candidate genes. Three statistical methods were used to detect the genetic associations, including univariate, principal components (PC)-based, and cluster-based analysis. To adjust for multiple comparisons within a gene and across genes, a double false discovery rate (DFDR) procedure was applied. The DFDR-adjusted p-value for each marker was calculated at 0.05 alpha level. Results: In total 163 cases and 311 matched controls were included in the analyses after excluding those with insufficient DNA or that failed the assay. For each analysis, univariate, PC-based, and cluster-based, the following number of candidate genes were found significant after DFDR procedure: 5 genes (PLAU, RASSF1, SLC5A6, KDR, CCL2), 8 genes (BCL2, KLF4, CTNND2, NRP2, RAP1GAP, LPL, EFEMP1, HSPA1A), and 7 genes (ETV4, ANGPTL4, BRMS1, LPL, BCL2, NANOG, ADM), respectively. The overall agreement of gene-level testing significances was at 84% between PC-based and cluster-based analysis. Conclusions: Among those showing consistent statistical significance, two genes, BCL2 and LPL, were identified in two analyses. Further study may provide insight to predict risk of prostate cancer metastasis. Citation Format: Kim Cannavale, Jeff Slezak, Yu-Hsiang Shu, Gary W. Chien, XuFeng Chen, Feng Shi, Kim Siegmund, Stephen Van Den Eeden, Jiaoti Huang, Chun R. Chao. DNA methylation markers for risk of metastasis in a cohort of men with localized prostate cancer [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3514.

DNA Methylation-Derived Immune Cell Profiles, CpG Markers of Inflammation, and Pancreatic Cancer Risk.

Pancreatic cancer is projected to become the second most common cause of cancer-related death over the next 5 years. Because inflammation is thought to be a common trajectory for disease initiation, we sought to prospectively characterize immune profiles using DNA methylation markers and examine DNA methylation levels previously linked to inflammation biomarkers to evaluate whether these immune markers play a key role in pancreatic cancer. In a nested case-control study pooling three U.S. prospective cohort studies, DNA methylation was measured in prediagnostic leukocytes of incident pancreatic cancer cases and matched controls using the Illumina MethylationEPIC array. Differentially methylated regions were used to predict immune cell types, and CpGs previously associated with inflammatory biomarkers were selected for the analysis. DNA methylation data from a retrospective case-control study conducted in Spain (PanGenEU) was used for independent replication. Immune cell proportions and ratio of cell proportions were not associated with pancreatic cancer risk in the nested case-control study. Methylation extent of CpGs residing in or near gene MNDA was significantly associated with pancreatic cancer risk in the nested case-control study and replicated in PanGenEU. Methylation level of a promoter CpG of gene PIM-1 was associated with survival in both studies. Using a targeted approach, we identified several CpGs that may play a role in pancreatic carcinogenesis in two large, independent studies with distinct study designs. These findings could provide insight into critical pathways that may help identify new markers of early disease and survival.

Application of Multiplex Bisulfite PCR–Ligase Detection Reaction–Real-Time Quantitative PCR Assay in Interrogating Bioinformatically Identified, Blood-Based Methylation Markers for Colorectal Cancer

The analysis of CpG methylation in circulating tumor DNA fragments has emerged as a promising approach for the noninvasive early detection of solid tumors, including colorectal cancer (CRC). The most commonly employed assay involves bisulfite conversion of circulating tumor DNA, followed by targeted PCR, then real-time quantitative PCR (alias methylation-specific PCR). This report demonstrates the ability of a multiplex bisulfite PCR-ligase detection reaction-real-time quantitative PCR assay to detect seven methylated CpG markers (CRC or colon specific), in both simulated (approximately 30 copies of fragmented CRC cell line DNA mixed with approximately 3000 copies of fragmented peripheral blood DNA) and CRC patient-derived cell-free DNAs. This scalable assay is designed for multiplexing and incorporates steps for improved sensitivity and specificity, including the enrichment of methylated CpG fragments, ligase detection reaction, the incorporation of ribose bases in primers, and use of uracil DNA glycosylase. Six of the seven CpG markers (located in promoter regions of PPP1R16B, KCNA3, CLIP4, GDF6, SEPT9, and GSG1L) were identified through integrated analyses of genome-wide methylation data sets for 31 different types of cancer. These markers were mapped to CpG sites at the promoter region of VIM; VIM and SEPT9 are established epigenetic markers of CRC. Additional bioinformatics analyses show that the methylation at these CpG sites negatively correlates with the transcription of their corresponding genes.

Prediction model construction of mouse stem cell pluripotency using CpG and non-CpG DNA methylation markers

BackgroundGenome-wide studies of DNA methylation across the epigenetic landscape provide insights into the heterogeneity of pluripotent embryonic stem cells (ESCs). Differentiating into embryonic somatic and germ cells, ESCs exhibit varying degrees of pluripotency, and epigenetic changes occurring in this process have emerged as important factors explaining stem cell pluripotency.ResultsHere, using paired scBS-seq and scRNA-seq data of mice, we constructed a machine learning model that predicts degrees of pluripotency for mouse ESCs. Since the biological activities of non-CpG markers have yet to be clarified, we tested the predictive power of CpG and non-CpG markers, as well as a combination thereof, in the model. Through rigorous performance evaluation with both internal and external validation, we discovered that a model using both CpG and non-CpG markers predicted the pluripotency of ESCs with the highest prediction performance (0.956 AUC, external test). The prediction model consisted of 16 CpG and 33 non-CpG markers. The CpG and most of the non-CpG markers targeted depletions of methylation and were indicative of cell pluripotency, whereas only a few non-CpG markers reflected accumulations of methylation. Additionally, we confirmed that there exists the differing pluripotency between individual developmental stages, such as E3.5 and E6.5, as well as between induced mouse pluripotent stem cell (iPSC) and somatic cell.ConclusionsIn this study, we investigated CpG and non-CpG methylation in relation to mouse stem cell pluripotency and developed a model thereon that successfully predicts the pluripotency of mouse ESCs.

Epigenetic age signatures in bones

Age prediction can help identify skeletal remains by limiting the search range for a missing person. Although age prediction methods based on odontology and anthropology are frequently used in the forensic field, DNA methylation is particularly promising age-predictive biomarker. In this study, we generated genome-wide DNA methylation profiles of bone samples from 32 identified skeletal remains with an age at death ranging from 31 to 96 years. Only 12 provided more than 800 K quality-filtered CpG methylation values using Illumina’s Infinium MethylationEPIC BeadChip array. Methylation ages of the bone samples calculated using a recently developed skin & blood clock composed of 391 CpG sites were found to be very similar to their actual ages (MAD = 6.4 years). However, the low success rate in methylation profiling of bone DNA samples may prevent researchers from applying the array to this type of samples. Therefore, we selected a set of CpG sites that would enable age prediction based on only a few CpG sites in bone DNA samples. Nineteen age-associated CpG marker candidates were selected from 720 K quality-filtered CpG values of 21 male skeletal remain samples. Because age signatures for blood, such as markers on the ELOVL2, FHL2, KLF14 and TRIM59 genes, had showed strong age associations in 12 bone samples, we further tested the age association of the 5 well-known markers in a blood-based model and the 13 out of 19 CpG markers from the array of 21 bone samples with an independent set of 30 skeletal remain samples using SNaPshot multiplex based on single nucleotide primer extension. Four CpG sites on TMEM51, TRIM59, ELOVL2, and EPHA6 genes showed moderate or weak correlations between methylation and age, which suggests further investigation of these markers to predict the age of bones.

Prediction of blood-based biomarkers and subsequent design of bisulfite PCR-LDR-qPCR assay for breast cancer detection

BackgroundInterrogation of site-specific CpG methylation in circulating tumor DNAs (ctDNAs) has been employed in a number of studies for early detection of breast cancer (BrCa). In many of these studies, the markers were identified based on known biology of BrCa progression, and interrogated using methyl-specific PCR (MSP), a technique involving bisulfite conversion, PCR, and qPCR.MethodsIn this report, we are demonstrating the development of a novel assay (Multiplex Bisulfite PCR-LDR-qPCR) which can potentially offer improvements to MSP, by integrating additional steps such as ligase detection reaction (LDR), methylated CpG target enrichment, carryover protection (use of uracil DNA glycosylase), and minimization of primer-dimer formation (use of ribose primers and RNAseH2). The assay is designed to for breast cancer-specific CpG markers identified through integrated analyses of publicly available genome-wide methylation datasets for 31 types of primary tumors (including BrCa), as well as matching normal tissues, and peripheral blood.ResultsOur results indicate that the PCR-LDR-qPCR assay is capable of detecting ~ 30 methylated copies of each of 3 BrCa-specific CpG markers, when mixed with excess amount unmethylated CpG markers (~ 3000 copies each), which is a reasonable approximation of BrCa ctDNA overwhelmed with peripheral blood cell-free DNA (cfDNA) when isolated from patient plasma. The bioinformatically-identified CpG markers are located in promoter regions of NR5A2 and PRKCB, and a non-coding region of chromosome 1 (upstream of EFNA3). Additional bioinformatic analyses would reveal that these methylation markers are independent of patient race and age, and positively associated with signaling pathways associated with BrCa progression (such as those related to retinoid nuclear receptor, PTEN, p53, pRB, and p27).ConclusionThis report demonstrates the potential utilization of bisulfite PCR-LDR-qPCR assay, along with bioinformatically-driven biomarker discovery, in blood-based BrCa detection.

Variable DNA methylation of aging-related genes is associated with male COPD

BackgroundChronic obstructive pulmonary disease (COPD) is a chronic lung inflammatory disease which has a close relationship with aging. Genome-wide analysis reveals that DNA methylation markers vary obviously with age. DNA methylation variations in peripheral blood have the potential to be biomarkers for COPD. However, the specific DNA methylation of aging-related genes in the peripheral blood of COPD patients remains largely unknown.MethodsFirstly, 9 aging-related differentially expressed genes (DEGs) in COPD patients were screened out from the 25 aging-related genes profile through a comprehensive screening strategy. Secondly, qPCR and multiple targeted bisulfite enrichment sequencing (MethTarget) were used to detect the mRNA level and DNA methylation level of the 9 differentially expressed genes in the peripheral blood of 60 control subjects and 45 COPD patients. The candidate functional CpG sites were selected on the basis of the regulation ability of the target gene expression. Thirdly, the correlation was evaluated between the DNA methylation level of the key CpG sites and the clinical parameters of COPD patients, including forced expiratory volume in one second (FEV1), forced expiratory volume in one second as percentage of predicted volume (FEV1%), forced expiratory volume/ forced vital capacity (FEV/FVC), modified British medical research council (mMRC) score, acute exacerbation frequency and the situation of frequent of acute aggravation (CAT) score. Lastly, differentially methylated CpG sites unrelated to smoking were also determined in COPD patients.ResultsOf the 9 differentially expressed aging-related genes, the mRNA expression of 8 genes were detected to be significantly down-regulated in COPD group, compared with control group. Meanwhile, the methylated level of all aging-related genes was changed in COPD group containing 219 COPD-related CpG sites in total. Notably, 27 CpG sites of FOXO3 gene showed a lower False Discovery Rate (FDR) and higher methylation difference values. Also, some variable DNA methylation is associated with the severity of COPD. Additionally, of the 219 COPD-related CpG sites, 147 CpG sites were not related to smoking.ConclusionThese results identified that the mRNA expression and DNA methylation level of aging-related genes were changed in male COPD patients, which provides a molecular link between aging and COPD. The identified CpG markers are associated with the severity of COPD and provide new insights into the prediction and identification of COPD.

DNA Methylation Markers for Pan-Cancer Prediction by Deep Learning

For cancer diagnosis, many DNA methylation markers have been identified. However, few studies have tried to identify DNA methylation markers to diagnose diverse cancer types simultaneously, i.e., pan-cancers. In this study, we tried to identify DNA methylation markers to differentiate cancer samples from the respective normal samples in pan-cancers. We collected whole genome methylation data of 27 cancer types containing 10,140 cancer samples and 3386 normal samples, and divided all samples into five data sets, including one training data set, one validation data set and three test data sets. We applied machine learning to identify DNA methylation markers, and specifically, we constructed diagnostic prediction models by deep learning. We identified two categories of markers: 12 CpG markers and 13 promoter markers. Three of 12 CpG markers and four of 13 promoter markers locate at cancer-related genes. With the CpG markers, our model achieved an average sensitivity and specificity on test data sets as 92.8% and 90.1%, respectively. For promoter markers, the average sensitivity and specificity on test data sets were 89.8% and 81.1%, respectively. Furthermore, in cell-free DNA methylation data of 163 prostate cancer samples, the CpG markers achieved the sensitivity as 100%, and the promoter markers achieved 92%. For both marker types, the specificity of normal whole blood was 100%. To conclude, we identified methylation markers to diagnose pan-cancers, which might be applied to liquid biopsy of cancers.

Identifying blood-specific age-related DNA methylation markers on the Illumina MethylationEPIC® BeadChip

The past decade has seen rapid development in DNA methylation (DNAm) microarrays, including the Illumina HumanMethylation27 and HumanMethylation450 (450K) chips, which have played an essential role in identifying and evaluating age-related (AR) DNAm markers in different tissues. Recently, a new array, the Illumina MethylationEPIC (EPIC) was introduced, with nearly double the number of probes as the 450K (∼850,000 probes). In this study, we test these newly added probes for age association using a large cohort of 754 DNAm profiles from blood samples assayed on the EPIC BeadChip, for individuals aged 0–88 years old. 52 AR CpG sites (Spearman’s abs(rho) >0.6 and P-value <10−83) were identified, 21 of which were novel sites and mapped to 18 genes, nine of which (LHFPL4, SLC12A8, EGFEM1P, GPR158, TAL1, KIAA1755, LOC730668, DUSP16, and FAM65C) have never previously been reported to be associated with age. The data were subsequently split into a 527-sample training set and a 227-sample testing set to build and validate two age prediction models using elastic net regression and multivariate regression. Elastic net regression selected 425 CpG markers with a mean absolute deviation (MAD) of 2.6 years based on the testing set. To build a multivariate linear regression model, AR CpG sites with R2 > 0.5 at FDR < 0.05 were input into stepwise regression to select the best subset for age prediction. The resulting six CpG markers were linearly modelled with age and explained 81% of age-correlated variation in DNAm levels. Age estimation accuracy using bootstrap analysis was 4.5 years, with 95% confidence intervals of 4.56 to 4.57 years based on the testing set. These results suggest that EPIC BeadChip probes for age estimation fall within the range of probes found on the previous Illumina HumanMethylation platforms in terms of their age-prediction ability.

Artificial intelligence analysis of newborn leucocyte epigenomic markers for the prediction of autism

Epigenetic modification appears to be important in the pathogenesis of Autism and Autism spectrum disorder (ASD). Early detection and intervention is known to correlate with improved long-term outcomes. There is thus intense scientific interest in the disease pathogenesis and early prediction. Our goal was to investigate the epigenetic basis of classic autism and identify early biomarkers. Cytosine (‘CpG’) methylation was measured genome-wide in 14 autism cases and 10 controlsusing the Infinium HumanMethylation450 BeadChip assay. Six Machine Learning/Artificial Intelligence (AI) platforms including Deep-Learning (DL) were then used for autism prediction. Ingenuity Pathway Analysis (IPA) was further used to interrogate autism pathogenesis by identifying over-represented biological pathways. Highly significant CpG methylation changes were found in 230 loci (249 genes). Using only individual markers meeting a stringent p-value threshold criterion (p < 5 x 10-8), achieved an AUC (95% CI) = 1.0 (0.80–1.00) with 97.5% sensitivity and 100.0% specificity. Conventional regression analysis using 3 CpG markers yielded an AUC (95% CI) = 1.00 (1.00–1.00) with 100.0% sensitivity and specificity each. Epigenetic dysregulation was identified in several important candidate genes including some previously linked to autism development e.g.: EIF4E, FYN, SHANK1 and VIM. We found epigenetic dysregulation of pathways involved in neuroinflammation signaling, synaptic long term potentiation, serotonin degradation, mTOR signaling and signaling by Rho-Family GTPases. Blood epigenomics and AI techniques achieved accurate newborn prediction of autism. Our results provide further evidence in support of a significant role of epigenetic alteration in autism pathogenesis.

Blood DNA Methylation and Breast Cancer: A Prospective Case-Cohort Analysis in the Sister Study.

Peripheral blood DNA methylation may be associated with breast cancer, but studies of candidate genes and global and genome-wide DNA methylation have been inconsistent. We performed an epigenome-wide study using Infinium HumanMethylation450 BeadChips with prospectively collected blood DNA samples from the Sister Study (1552 cases, 1224 subcohort). Differentially methylated cytosine-phosphate-guanine sites (dmCpGs) were identified using case-cohort proportional hazard models and replicated using deposited data from European Prospective Investigation into Cancer and Nutrition in Italy (EPIC-Italy) (n = 329). The correlation between methylation and time to diagnosis was examined using robust linear regression. Causal or consequential relationships of methylation to breast cancer were examined by Mendelian randomization using OncoArray 500 K single-nucleotide polymorphism data. All statistical tests were two-sided. We identified 9601 CpG markers associated with invasive breast cancer (false discovery rate = q < 0.01), with 510 meeting a strict Bonferroni correction threshold (10-7). A total of 2095 of these CpGs replicated in the independent EPIC-Italy dataset, including 144 meeting the Bonferroni threshold. Sister Study women who developed ductal carcinoma in situ had methylation similar to noncases. Most (1501, 71.6%) dmCpGs showed lower methylation in invasive cases. In case-only analysis, methylation was statistically significantly associated (false discovery rate = q < 0.05) with time to diagnosis for 892 (42.6%) of the dmCpGs. Analyses based on genetic association suggest that methylation differences are likely a consequence rather than a cause of breast cancer. Pathway analysis shows enrichment of breast cancer-related gene pathways, and dmCpGs are overrepresented in known breast cancer susceptibility genes. Our findings suggest that the DNA methylation profile of blood starts to change in response to invasive breast cancer years before the tumor is clinically detected.