scholarly journals Discrimination of DNA Methylation Signal from Background Variation for Clinical Diagnostics

2019 ◽  
Vol 20 (21) ◽  
pp. 5343 ◽  
Author(s):  
Robersy Sanchez ◽  
Xiaodong Yang ◽  
Thomas Maher ◽  
Sally A. Mackenzie
Keyword(s):  
Dna Methylation ◽  
Classification Performance ◽  
Clinical Diagnostics ◽  
Clinical Test ◽  
Learning Approaches ◽  
Methylome Analysis ◽  
High Discriminatory Power ◽  
Genomic Regions ◽  
Epigenetic Research ◽  
Methylation Signal

Advances in the study of human DNA methylation variation offer a new avenue for the translation of epigenetic research results to clinical applications. Although current approaches to methylome analysis have been helpful in revealing an epigenetic influence in major human diseases, this type of analysis has proven inadequate for the translation of these advances to clinical diagnostics. As in any clinical test, the use of a methylation signal for diagnostic purposes requires the estimation of an optimal cutoff value for the signal, which is necessary to discriminate a signal induced by a disease state from natural background variation. To address this issue, we propose the application of a fundamental signal detection theory and machine learning approaches. Simulation studies and tests of two available methylome datasets from autism and leukemia patients demonstrate the feasibility of this approach in clinical diagnostics, providing high discriminatory power for the methylation signal induced by disease, as well as high classification performance. Specifically, the analysis of whole biomarker genomic regions could suffice for a diagnostic, markedly decreasing its cost.

scholarly journals BIOM-62 SENSITIVE DETECTION AND DISCRIMINATION OF INTRACRANIAL TUMORS BY BLOOD

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii15-ii15
Author(s):  
Farshad Nassiri ◽  
Ankur Chakravarthy ◽  
Shengrui Feng ◽  
Roxana Shen ◽  
Romina Nejad ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Brain Tumors ◽  
Model Performance ◽  
High Sensitivity ◽  
Circulating Tumor Dna ◽  
Low Grade ◽  
Learning Approaches ◽  
Intracranial Tumors ◽  
Cell Of Origin ◽  
Tumor Dna

Abstract BACKGROUND The diagnosis of intracranial tumors relies on tissue specimens obtained by invasive surgery. Non-invasive diagnostic approaches, particularly for patients with brain tumours, provide an opportunity to avoid surgery and mitigate unnecessary risk to patients. We reasoned that DNA methylation profiles of circulating tumor DNA in blood can be used as a clinically useful biomarker for patients with brain tumors, given the specificity of DNA methylation profiles for cell-of-origin. METHODS We generated methylation profiles on the plasma of 608 patients with cancer (219 intracranial, 388 extracranial) and 60 healthy controls using a cell-free methylated DNA immunoprecipitation combined with deep sequencing (cfMeDIP-seq) approach. Using machine-learning approaches we generated and evaluated models to distinguish brain tumors from extracranial cancers that may metastasize to the brain, as well as additional models to discriminate common brain tumors included in the differential diagnosis of solitary extra-axial and intra-axial tumors. RESULTS We observed high sensitivity and discriminative capacity for our models to distinguish gliomas from other cancerous and healthy patients (AUC=0.99, 95%CI 0.96–1), with similar performance in IDH mutant and wildtype gliomas as well as in lower- and high-grade gliomas. Excluding non-malignant contributors to plasma methylation did not change model performance (AUC=0.982, 95%CI 0.93–1). Models generated to discriminate intracranial tumors from each other also demonstrated high accuracy for common extra-axial tumors (AUCmeningioma=0.89, 95%CI 0.80–0.97; AUChemangiopericytoma=0.95, 95%CI 0.73–1) as well as intra-axial tumors ranging from low-grade indolent glial-neuronal tumors (AUC 0.93, 95%CI 0.80 – 1) to diffuse intra-axial gliomas with distinct molecular composition (AUCIDH-mutant glioma = 0.82, 95%CI 0.66 -0.98; AUCIDH-wildtype-glioma = 0.71, 95%CI 0.53 – 0.9). Plasma cfMeDIP-seq signals originated from corresponding tumor tissue DNA methylation signals (r=0.37, p< 2.2e-16). CONCLUSIONS These results demonstrate the potential for cfMeDIP-seq profiles to not only detect circulating tumor DNA, but to accurately discriminate common intracranial tumors that share cell-of-origin lineages.

scholarly journals A machine learning case–control classifier for schizophrenia based on DNA methylation in blood

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chathura J. Gunasekara ◽  
Eilis Hannon ◽  
Harry MacKay ◽  
Cristian Coarfa ◽  
Andrew McQuillin ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Population Based ◽  
Case Control ◽  
Training Data ◽  
Polygenic Risk Score ◽  
Reverse Causality ◽  
Cell Type Specificity ◽  
Data Set ◽  
Human Genomic ◽  
Genomic Regions

AbstractEpigenetic dysregulation is thought to contribute to the etiology of schizophrenia (SZ), but the cell type-specificity of DNA methylation makes population-based epigenetic studies of SZ challenging. To train an SZ case–control classifier based on DNA methylation in blood, therefore, we focused on human genomic regions of systemic interindividual epigenetic variation (CoRSIVs), a subset of which are represented on the Illumina Human Methylation 450K (HM450) array. HM450 DNA methylation data on whole blood of 414 SZ cases and 433 non-psychiatric controls were used as training data for a classification algorithm with built-in feature selection, sparse partial least squares discriminate analysis (SPLS-DA); application of SPLS-DA to HM450 data has not been previously reported. Using the first two SPLS-DA dimensions we calculated a “risk distance” to identify individuals with the highest probability of SZ. The model was then evaluated on an independent HM450 data set on 353 SZ cases and 322 non-psychiatric controls. Our CoRSIV-based model classified 303 individuals as cases with a positive predictive value (PPV) of 80%, far surpassing the performance of a model based on polygenic risk score (PRS). Importantly, risk distance (based on CoRSIV methylation) was not associated with medication use, arguing against reverse causality. Risk distance and PRS were positively correlated (Pearson r = 0.28, P = 1.28 × 10−12), and mediational analysis suggested that genetic effects on SZ are partially mediated by altered methylation at CoRSIVs. Our results indicate two innate dimensions of SZ risk: one based on genetic, and the other on systemic epigenetic variants.

scholarly journals Role of CxxC-finger protein 1 in establishing mouse oocyte epigenetic landscapes

2021 ◽  
Author(s):  
Qian-Qian Sha ◽  
Ye-Zhang Zhu ◽  
Yunlong Xiang ◽  
Jia-Li Yu ◽  
Xiao-Ying Fan ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Histone H3 ◽  
Finger Protein ◽  
Maternal Genome ◽  
Wide Range ◽  
Transcriptional Changes ◽  
Nuclear Events ◽  
Genomic Regions ◽  
Cxxc Finger Protein 1

Abstract During oogenesis, oocytes gain competence and subsequently undergo meiotic maturation and prepare for embryonic development; trimethylated histone H3 on lysine-4 (H3K4me3) mediates a wide range of nuclear events during these processes. Oocyte-specific knockout of CxxC-finger protein 1 (CXXC1, also known as CFP1) impairs H3K4me3 accumulation and causes changes in chromatin configurations. This study investigated the changes in genomic H3K4me3 landscapes in oocytes with Cxxc1 knockout and the effects on other epigenetic factors such as the DNA methylation, H3K27me3, H2AK119ub1 and H3K36me3. H3K4me3 is overall decreased after knocking out Cxxc1, including both the promoter region and the gene body. CXXC1 and MLL2, which is another histone H3 methyltransferase, have nonoverlapping roles in mediating H3K4 trimethylation during oogenesis. Cxxc1 deletion caused a decrease in DNA methylation levels and affected H3K27me3 and H2AK119ub1 distributions, particularly at regions with high DNA methylation levels. The changes in epigenetic networks implicated by Cxxc1 deletion were correlated with the transcriptional changes in genes in the corresponding genomic regions. This study elucidates the epigenetic changes underlying the phenotypes and molecular defects in oocytes with deleted Cxxc1 and highlights the role of CXXC1 in orchestrating multiple factors that are involved in establishing the appropriate epigenetic states of maternal genome.

A targeted-replacement system for identification of signals for de novo methylation in Neurospora crassa

1994 ◽  
Vol 14 (11) ◽  
pp. 7059-7067
Author(s):  
V P Miao ◽  
M J Singer ◽  
M R Rountree ◽  
E U Selker
Keyword(s):  
Dna Methylation ◽  
Neurospora Crassa ◽  
De Novo ◽  
Gene Replacement ◽  
Position Effects ◽  
Efficient Gene ◽  
Bona Fide ◽  
Replacement System ◽  
Methylation Signal ◽  
De Novo Methylation

Transformation of eukaryotic cells can be used to test potential signals for DNA methylation. This approach is not always reliable, however, because of chromosomal position effects and because integration of multiple and/or rearranged copies of transforming DNA can influence DNA methylation. We developed a robust system to evaluate the potential of DNA fragments to function as signals for de novo methylation in Neurospora crassa. The requirements of the system were (i) a location in the N. crassa genome that becomes methylated only in the presence of a bona fide methylation signal and (ii) an efficient gene replacement protocol. We report here that the am locus fulfills these requirements, and we demonstrate its utility with the identification of a 2.7-kb fragment from the psi 63 locus as a new portable signal for de novo methylation.

scholarly journals ChemTok: A New Rule Based Tokenizer for Chemical Named Entity Recognition

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Abbas Akkasi ◽  
Ekrem Varoğlu ◽  
Nazife Dimililer
Keyword(s):  
Conditional Random Fields ◽  
Named Entity Recognition ◽  
Classification Performance ◽  
Entity Recognition ◽  
Support Vector ◽  
Learning Approaches ◽  
Data Set ◽  
Rule Based ◽  
Named Entity ◽  
Vector Machines

Named Entity Recognition (NER) from text constitutes the first step in many text mining applications. The most important preliminary step for NER systems using machine learning approaches is tokenization where raw text is segmented into tokens. This study proposes an enhanced rule based tokenizer, ChemTok, which utilizes rules extracted mainly from the train data set. The main novelty of ChemTok is the use of the extracted rules in order to merge the tokens split in the previous steps, thus producing longer and more discriminative tokens. ChemTok is compared to the tokenization methods utilized by ChemSpot and tmChem. Support Vector Machines and Conditional Random Fields are employed as the learning algorithms. The experimental results show that the classifiers trained on the output of ChemTok outperforms all classifiers trained on the output of the other two tokenizers in terms of classification performance, and the number of incorrectly segmented entities.

scholarly journals Post-transcriptional gene silencing triggers dispensable DNA methylation in gene body in Arabidopsis

2019 ◽  
Vol 47 (17) ◽  
pp. 9104-9114 ◽  
Author(s):  
Christelle Taochy ◽  
Agnès Yu ◽  
Nicolas Bouché ◽  
Nathalie Bouteiller ◽  
Taline Elmayan ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Gene Silencing ◽  
De Novo ◽  
Transcriptional Silencing ◽  
Transcriptional Gene Silencing ◽  
Gene Body ◽  
Coding Sequence ◽  
Post Transcriptional Gene Silencing ◽  
Methylation Signal ◽  
Rule Out

Abstract Spontaneous post-transcriptional silencing of sense transgenes (S-PTGS) is established in each generation and is accompanied by DNA methylation, but the pathway of PTGS-dependent DNA methylation is unknown and so is its role. Here we show that CHH and CHG methylation coincides spatially and temporally with RDR6-dependent products derived from the central and 3′ regions of the coding sequence, and requires the components of the RNA-directed DNA methylation (RdDM) pathway NRPE1, DRD1 and DRM2, but not CLSY1, NRPD1, RDR2 or DCL3, suggesting that RDR6-dependent products, namely long dsRNAs and/or siRNAs, trigger PTGS-dependent DNA methylation. Nevertheless, none of these RdDM components are required to establish S-PTGS or produce a systemic silencing signal. Moreover, preventing de novo DNA methylation in non-silenced transgenic tissues grafted onto homologous silenced tissues does not inhibit the triggering of PTGS. Overall, these data indicate that gene body DNA methylation is a consequence, not a cause, of PTGS, and rule out the hypothesis that a PTGS-associated DNA methylation signal is transmitted independent of a PTGS signal.

scholarly journals DNA methylation associated with postpartum depressive symptoms overlaps findings from a genome-wide association meta-analysis of depression

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Dana M. Lapato ◽  
Roxann Roberson-Nay ◽  
Robert M. Kirkpatrick ◽  
Bradley T. Webb ◽  
Timothy P. York ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Depressive Symptoms ◽  
Health Outcomes ◽  
Meta Analysis ◽  
Previous History ◽  
Depression Scale ◽  
Postpartum Depressive Symptoms ◽  
Genome Wide ◽  
A Genome ◽  
Genomic Regions

Abstract Background Perinatal depressive symptoms have been linked to adverse maternal and infant health outcomes. The etiology associated with perinatal depressive psychopathology is poorly understood, but accumulating evidence suggests that understanding inter-individual differences in DNA methylation (DNAm) patterning may provide insight regarding the genomic regions salient to the risk liability of perinatal depressive psychopathology. Results Genome-wide DNAm was measured in maternal peripheral blood using the Infinium MethylationEPIC microarray. Ninety-two participants (46% African-American) had DNAm samples that passed all quality control metrics, and all participants were within 7 months of delivery. Linear models were constructed to identify differentially methylated sites and regions, and permutation testing was utilized to assess significance. Differentially methylated regions (DMRs) were defined as genomic regions of consistent DNAm change with at least two probes within 1 kb of each other. Maternal age, current smoking status, estimated cell-type proportions, ancestry-relevant principal components, days since delivery, and chip position served as covariates to adjust for technical and biological factors. Current postpartum depressive symptoms were measured using the Edinburgh Postnatal Depression Scale. Ninety-eight DMRs were significant (false discovery rate < 5%) and overlapped 92 genes. Three of the regions overlap loci from the latest Psychiatric Genomics Consortium meta-analysis of depression. Conclusions Many of the genes identified in this analysis corroborate previous allelic, transcriptomic, and DNAm association results related to depressive phenotypes. Future work should integrate data from multi-omic platforms to understand the functional relevance of these DMRs and refine DNAm association results by limiting phenotypic heterogeneity and clarifying if DNAm differences relate to the timing of onset, severity, duration of perinatal mental health outcomes of the current pregnancy or to previous history of depressive psychopathology.

scholarly journals DNA methylome analysis of acute lymphoblastic leukemia cells reveals stochastic de novo DNA methylation in CpG islands

Epigenomics ◽  
2016 ◽  
Vol 8 (10) ◽  
pp. 1367-1387 ◽  
Author(s):  
Per Wahlberg ◽  
Anders Lundmark ◽  
Jessica Nordlund ◽  
Stephan Busche ◽  
Amanda Raine ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Acute Lymphoblastic Leukemia ◽  
De Novo ◽  
Lymphoblastic Leukemia ◽  
Cpg Islands ◽  
Leukemia Cells ◽  
Dna Methylome ◽  
Methylome Analysis

scholarly journals Epigenome-Wide Association Study Using Prediagnostic Bloods Identifies New Genomic Regions Associated With Pancreatic Cancer Risk

2020 ◽  
Vol 4 (5) ◽  
Author(s):  
Dominique S Michaud ◽  
Mengyuan Ruan ◽  
Devin C Koestler ◽  
Dong Pei ◽  
Carmen J Marsit ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Dna Methylation ◽  
Study Design ◽  
Peripheral Blood ◽  
Association Studies ◽  
Gene Set Enrichment Analysis ◽  
Blood Collection ◽  
Blood Draw ◽  
Genomic Regions ◽  
Prospective Study Design

Abstract Background Epigenome-wide association studies using peripheral blood have identified specific sites of DNA methylation associated with risk of various cancers and may hold promise to identify novel biomarkers of risk; however, few studies have been performed for pancreatic cancer and none using a prospective study design. Methods Using a nested case-control study design, incident pancreatic cancer cases and matched controls were identified from participants who provided blood at baseline in 3 prospective cohort studies. DNA methylation levels were measured in DNA extracted from leukocytes using the Illumina MethylationEPIC array. Average follow-up period for this analysis was 13 years. Results Several new genomic regions were identified as being differentially methylated in cases and controls; the 5 strongest associations were observed for CpGs located in genes TMEM204/IFT140, MFSD6L, FAM134B/RETREG1, KCNQ1D, and C6orf227. For some CpGs located in chromosome 16p13.3 (near genes TMEM204 and IFT140), associations were stronger with shorter time to diagnosis (eg, odds ratio [OR] = 5.95, 95% confidence interval [CI] = 1.52 to 23.12, for top vs bottom quartile, for &lt;5 years between blood draw and cancer diagnosis), but associations remained statistically significantly higher even when cases were diagnosed over 10 years after blood collection. Statistically significant differences in DNA methylation levels were also observed in the gastric secretion pathway using Gene Set Enrichment Analysis (GSEA) analysis. Conclusions Changes in DNA methylation in peripheral blood may mark alterations in metabolic or immune pathways that play a role in pancreatic cancer. Identifying new biological pathways in carcinogenesis of pancreatic cancer using epigenome-wide association studies approach could provide new opportunities for improving treatment and prevention.

scholarly journals Histone acetylation recruits the SWR1 complex to regulate active DNA demethylation in Arabidopsis

2019 ◽  
Vol 116 (33) ◽  
pp. 16641-16650 ◽  
Author(s):  
Wen-Feng Nie ◽  
Mingguang Lei ◽  
Mingxuan Zhang ◽  
Kai Tang ◽  
Huan Huang ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Conceptual Framework ◽  
Histone Acetylation ◽  
Chromatin Remodeling ◽  
Nuclear Protein ◽  
Dna Demethylation ◽  
Specific Target ◽  
Active Dna Demethylation ◽  
Chromatin Remodeling Complex ◽  
Genomic Regions

Active DNA demethylation is critical for controlling the DNA methylomes in plants and mammals. However, little is known about how DNA demethylases are recruited to target loci, and the involvement of chromatin marks in this process. Here, we identify 2 components of the SWR1 chromatin-remodeling complex, PIE1 and ARP6, as required for ROS1-mediated DNA demethylation, and discover 2 SWR1-associated bromodomain-containing proteins, AtMBD9 and nuclear protein X1 (NPX1). AtMBD9 and NPX1 recognize histone acetylation marks established by increased DNA methylation 1 (IDM1), a known regulator of DNA demethylation, redundantly facilitating H2A.Z deposition at IDM1 target loci. We show that at some genomic regions, H2A.Z and DNA methylation marks coexist, and H2A.Z physically interacts with ROS1 to regulate DNA demethylation and antisilencing. Our results unveil a mechanism through which DNA demethylases can be recruited to specific target loci exhibiting particular histone marks, providing a conceptual framework to understand how chromatin marks regulate DNA demethylation.

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