CUE: CpG impUtation Ensemble for DNA Methylation Levels Across the Human Methylation450 (HM450) and EPIC (HM850) BeadChip Platforms

AbstractDNA methylation at CpG dinucleotides is one of the most extensively studied epigenetic marks. With technological advancements, geneticists can profile DNA methylation with multiple reliable approaches. However, profiling platforms can differ substantially in the CpGs they assess, consequently hindering integrated analysis across platforms. Here, we present CpG impUtation Ensemble (CUE), which leverages multiple classical statistical and modern machine learning methods, to impute from the Illumina HumanMethylation450 (HM450) BeadChip to the Illumina HumanMethylationEPIC (HM850) BeadChip. Data were analyzed from two population cohorts with methylation measured both by HM450 and HM850: the Extremely Low Gestational Age Newborns (ELGAN) study (n=127, placenta) and the VA Boston Posttraumatic Stress Disorder (PTSD) genetics repository (n=144, whole blood). Cross-validation results show that CUE achieves the lowest predicted root mean square error (RMSE) (0.026 in PTSD) and the highest accuracy (99.97% in PTSD) compared with five individual methods tested, including k-nearest-neighbors, logistic regression, penalized functional regression, random forest and XGBoost. Finally, among all 339,033 HM850-only CpG sites shared between ELGAN and PTSD, CUE successfully imputed 289,604 (85.4%) sites, where success was defined as RMSE < 0.05 and accuracy >95% in PTSD. In summary, CUE is a valuable tool for imputing CpG methylation from the HM450 to HM850 platform.

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Integrated analysis of DNA methylation profile in HLA-G gene and imaging in coronary heart disease

European Heart Journal ◽

10.1093/ehjci/ehaa946.1255 ◽

2020 ◽

Vol 41 (Supplement_2) ◽

Author(s):

G Benincasa ◽

C Schiano ◽

T Infante ◽

M Franzese ◽

R Casale ◽

...

Keyword(s):

Dna Methylation ◽

Coronary Heart Disease ◽

Heart Disease ◽

Cpg Island ◽

Methylation Profile ◽

Integrated Analysis ◽

Funding Source ◽

Relevant Parameter ◽

Cardiac Computed Tomography Angiography ◽

Dna Methylation Profile

Abstract Aims Immune endothelial inflammation, underlie coronary heart disease (CHD) related phenotypes, could provide new insight into the pathobiology of the disease. We investigated DNA methylation level of the unique CpG island of HLA-G gene in CHD patients and evaluated the correlation with cardiac computed tomography angiography (CCTA) features. Methods Thirty-two patients that underwent CCTA for suspected CHD were enrolled for this study. Obstructive CHD group included fourteen patients, in which there was a stenosis greater than or equal to 50% in one or more of the major coronary arteries detected; whereas subjects with Calcium (Ca) Score=0, uninjured coronaries and with no obstructive CHD were considered as control subjects (Ctrls) (n=18). For both groups, DNA methylation profile of the whole 5'UTR-CpG island of HLA-G was measured. The plasma soluble HLA-G (sHLA-G) levels were detected in all subjects by specific ELISA assay. Statistical analysis was performed using R software. Results For the first time, our study reported that 1) a significant hypomethylation characterized three specific fragments (B, C and F) of the 5'UTR-CpG island (p=0.05) of HLA-G gene in CHD patients compared to Ctrl group; 2) hypomethylation level of one specific fragment positively correlated with coronary Ca score, a relevant parameter of CCTA (p<0.05) between two groups. Conclusions Our results showed that reduced levels of circulating HLA-G molecules could derive from epigenetic marks inducing hypomethylation of specific regions into 5'UTR-CpG island of HLA-G gene in CHD patients with obstructive coronary stenosis vs non critical stenosis group. Funding Acknowledgement Type of funding source: Public grant(s) – National budget only. Main funding source(s): Italian Minister of Health

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Comparison of DNA Methylation Profiles of Hemostatic Genes between Liver Tissue and Peripheral Blood within Individuals

Thrombosis and Haemostasis ◽

10.1055/s-0040-1720980 ◽

2020 ◽

Author(s):

Annelie Angerfors ◽

Martina Olsson Lindvall ◽

Björn Andersson ◽

Staffan Nilsson ◽

Marcela Davila Lopez ◽

...

Keyword(s):

Dna Methylation ◽

Peripheral Blood ◽

Liver Tissue ◽

Association Studies ◽

Epidemiological Studies ◽

Methylation Pattern ◽

Cpg Dinucleotides ◽

Phenotypic Differences ◽

Targeted Bisulfite Sequencing ◽

Hemostatic Genes

AbstractDNA methylation has become increasingly recognized in the etiology of complex diseases, including thrombotic disorders. Blood is often collected in epidemiological studies for genotyping and has recently also been used to examine DNA methylation in epigenome-wide association studies. DNA methylation patterns are often tissue-specific, thus, peripheral blood may not accurately reflect the methylation pattern in the tissue of relevance. Here, we collected paired liver and blood samples concurrently from 27 individuals undergoing liver surgery. We performed targeted bisulfite sequencing for a set of 35 hemostatic genes primarily expressed in liver to analyze DNA methylation levels of >10,000 cytosine-phosphate-guanine (CpG) dinucleotides. We evaluated whether DNA methylation in blood could serve as a proxy for DNA methylation in liver at individual CpGs. Approximately 30% of CpGs were nonvariable and were predominantly hypo- (<25%) or hypermethylated (>70%) in both tissues. While blood can serve as a proxy for liver at these CpGs, the low variability renders these unlikely to explain phenotypic differences. We therefore focused on CpG sites with variable methylation levels in liver. The level of blood–liver tissue correlation varied widely across these variable CpGs; moderate correlations (0.5 ≤ r < 0.75) were detected for 6% and strong correlations (r ≥ 0.75) for a further 4%. Our findings indicate that it is essential to study the concordance of DNA methylation between blood and liver at individual CpGs. This paired blood–liver dataset is intended as a resource to aid interpretation of blood-based DNA methylation results.

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DNA Methylation in Solid Tumors: Functions and Methods of Detection

International Journal of Molecular Sciences ◽

10.3390/ijms22084247 ◽

2021 ◽

Vol 22 (8) ◽

pp. 4247

Author(s):

Andrea Martisova ◽

Jitka Holcakova ◽

Nasim Izadi ◽

Ravery Sebuyoya ◽

Roman Hrstka ◽

...

Keyword(s):

Dna Methylation ◽

Solid Tumors ◽

Epigenetic Modification ◽

Single Gene ◽

Restriction Enzymes ◽

Methylation Analysis ◽

Cpg Dinucleotides ◽

Sequencing Technologies ◽

Genome Level ◽

Dna Methylation Analysis

DNA methylation, i.e., addition of methyl group to 5′-carbon of cytosine residues in CpG dinucleotides, is an important epigenetic modification regulating gene expression, and thus implied in many cellular processes. Deregulation of DNA methylation is strongly associated with onset of various diseases, including cancer. Here, we review how DNA methylation affects carcinogenesis process and give examples of solid tumors where aberrant DNA methylation is often present. We explain principles of methods developed for DNA methylation analysis at both single gene and whole genome level, based on (i) sodium bisulfite conversion, (ii) methylation-sensitive restriction enzymes, and (iii) interactions of 5-methylcytosine (5mC) with methyl-binding proteins or antibodies against 5mC. In addition to standard methods, we describe recent advances in next generation sequencing technologies applied to DNA methylation analysis, as well as in development of biosensors that represent their cheaper and faster alternatives. Most importantly, we highlight not only advantages, but also disadvantages and challenges of each method.

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Genome-Wide DNA Methylation Pattern of Cancer Stem Cells in Esophageal Cancer

Technology in Cancer Research & Treatment ◽

10.1177/1533033820983793 ◽

2020 ◽

Vol 19 ◽

pp. 153303382098379

Author(s):

Xiying Yu ◽

Ying Teng ◽

Xingran Jiang ◽

Hui Yuan ◽

Wei Jiang

Keyword(s):

Dna Methylation ◽

Stem Cells ◽

Esophageal Cancer ◽

Cancer Stem Cells ◽

Side Population ◽

Methylation Status ◽

Methylation Profile ◽

Cpg Dinucleotides ◽

Genome Wide ◽

Differentially Methylated Genes

Background: Cancer stem cells (CSCs) are considered the main cause of cancer recurrence and metastasis, and DNA methylation is involved in the maintenance of CSCs. However, the methylation profile of esophageal CSCs remains unknown. Methods: Side population (SP) cells were isolated from esophageal squamous cell carcinoma (ESCC) cell lines KYSE150 and EC109. Sphere-forming cells were collected from human primary esophageal cancer cells. SP cells and sphere-forming cells were used as substitutes for cancer stem-like cells. We investigated the genome-wide DNA methylation profile in esophageal cancer stem-like cells using reduced representation bisulfite sequencing (RRBS). Results: Methylated cytosine (mC) was found mostly in CpG dinucleotides, located mostly in the intronic, intergenic, and exonic regions. Forty intersected differentially methylated regions (DMRs) were identified in these 3 groups of samples. Thirteen differentially methylated genes with the same alteration trend were detected; these included OTX1, SPACA1, CD163L1, ST8SIA2, TECR, CADM3, GRM1, LRRK1, CHSY1, PROKR2, LINC00658, LOC100506688, and NKD2. DMRs covering ST8SIA2 and GRM1 were located in exons. These differentially methylated genes were involved in 10 categories of biological processes and 3 cell signaling pathways. Conclusions: When compared to non-CSCs, cancer stem-like cells have a differential methylation status, which provides an important biological base for understanding esophageal CSCs and developing therapeutic targets for esophageal cancer.

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Intergenerational effects of maternal post-traumatic stress disorder on offspring epigenetic patterns and cortisol levels

Epigenomics ◽

10.2217/epi-2021-0015 ◽

2021 ◽

Author(s):

Line Hjort ◽

Feride Rushiti ◽

Shr-Jie Wang ◽

Peter Fransquet ◽

Sebahate P Krasniqi ◽

...

Keyword(s):

Dna Methylation ◽

Post Traumatic Stress Disorder ◽

Traumatic Stress ◽

Stress Disorder ◽

Post Traumatic Stress ◽

Negative Effects ◽

Significance Levels ◽

Post Traumatic ◽

Cortisol Levels ◽

Epigenetic Patterns

Aim: To investigate the association between maternal post-traumatic stress disorder (PTSD) during pregnancy and offspring DNA methylation and cortisol levels. Materials & methods: Blood genome-wide DNA methylation and cortisol was measured in the youngest child of 117 women who experienced sexual violence/torture during the Kosovo war. Results: Seventy-two percent of women had PTSD symptoms during pregnancy. Their children had higher cortisol levels and differential methylation at candidate genes ( NR3C1, HTR3A and BNDF) . No methylation differences reached epigenome-wide corrected significance levels. Conclusion: Identifying the biological processes whereby the negative effects of trauma are passed across generations and defining groups at high risk is a key step to breaking the intergenerational transmission of the effects of mental disorders.

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Transgenerational effects of the genocide against the Tutsi in Rwanda: A post-traumatic stress disorder symptom domain analysis

AAS Open Research ◽

10.12688/aasopenres.12848.2 ◽

2020 ◽

Vol 1 ◽

pp. 10

Author(s):

Susan Rudahindwa ◽

Leon Mutesa ◽

Eugene Rutembesa ◽

Jean Mutabaruka ◽

Annie Qu ◽

...

Keyword(s):

Dna Methylation ◽

Post Traumatic Stress Disorder ◽

Traumatic Stress ◽

Stress Disorder ◽

Ptsd Symptom ◽

Sub Saharan Africa ◽

Post Traumatic Stress ◽

Transgenerational Effects ◽

Symptom Domains ◽

Post Traumatic

Background: A number of studies have investigated transgenerational effects of parental post-traumatic stress disorder (PTSD) and its repercussions for offspring. Few studies however, have looked at this issue in the African context. Methods: The present study addresses this gap by utilizing a Pearson correlation matrix to investigate symptom severity within the three Diagnostic and Statistical Manual of Mental Disorders IV (DSM-IV) PTSD symptom domains in mothers exposed to the genocide against the Tutsi in Rwanda (n=25) and offspring (n=25), and an ethnically matched set of controls (n=50) who were outside of Rwanda during the 1994 genocide. All mothers were pregnant with the offspring included in the study during the time of the genocide. Results: Total PTS score was significantly (p<0.01) correlated with each of the three symptom domains at various strengths in both cases and controls. No significant differences in association of total PTS score and PTSD symptom domains were observed between exposed mothers and offspring, suggesting that each symptom domain contributed equivalently to both exposed mothers and offspring distress. In contrast, the re-experiencing symptom domain showed a significant difference in correlation to overall PTS score in non-exposed mothers compared to their offspring (p<0.05), with mothers showing a significantly higher correlation. Furthermore, the correlation between avoidance/numbing symptoms to overall PTS was significantly different (p≤0.01) across exposed and non-exposed mothers. As a secondary analysis, we explored the relationship between DNA methylation in the glucocorticoid receptor (NR3C1) locus, an important stress modulating gene, and PTSD symptom domains, finding an association between DNA methylation and re-experiencing among genocide-exposed mothers that exceeded any other observed associations by approximately two-fold. Conclusions: This is the first report, to our knowledge, of a symptom-based analysis of transgenerational transmission of PTSD in sub-Saharan Africa. These findings can be leveraged to inform further mechanistic and treatment research for PTSD.

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Effects of Brain Atlases and Machine Learning Methods on the Discrimination of Schizophrenia Patients: A Multimodal MRI Study

Frontiers in Neuroscience ◽

10.3389/fnins.2021.697168 ◽

2021 ◽

Vol 15 ◽

Author(s):

Jinyu Zang ◽

Yuanyuan Huang ◽

Lingyin Kong ◽

Bingye Lei ◽

Pengfei Ke ◽

...

Keyword(s):

Machine Learning ◽

Dimensionality Reduction ◽

Cross Validation ◽

Integrated Model ◽

Machine Learning Techniques ◽

Brain Atlas ◽

First Episode ◽

Learning Methods ◽

Machine Learning Methods ◽

Brain Atlases

Recently, machine learning techniques have been widely applied in discriminative studies of schizophrenia (SZ) patients with multimodal magnetic resonance imaging (MRI); however, the effects of brain atlases and machine learning methods remain largely unknown. In this study, we collected MRI data for 61 first-episode SZ patients (FESZ), 79 chronic SZ patients (CSZ) and 205 normal controls (NC) and calculated 4 MRI measurements, including regional gray matter volume (GMV), regional homogeneity (ReHo), amplitude of low-frequency fluctuation and degree centrality. We systematically analyzed the performance of two classifications (SZ vs NC; FESZ vs CSZ) based on the combinations of three brain atlases, five classifiers, two cross validation methods and 3 dimensionality reduction algorithms. Our results showed that the groupwise whole-brain atlas with 268 ROIs outperformed the other two brain atlases. In addition, the leave-one-out cross validation was the best cross validation method to select the best hyperparameter set, but the classification performances by different classifiers and dimensionality reduction algorithms were quite similar. Importantly, the contributions of input features to both classifications were higher with the GMV and ReHo features of brain regions in the prefrontal and temporal gyri. Furthermore, an ensemble learning method was performed to establish an integrated model, in which classification performance was improved. Taken together, these findings indicated the effects of these factors in constructing effective classifiers for psychiatric diseases and showed that the integrated model has the potential to improve the clinical diagnosis and treatment evaluation of SZ.

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ANALISIS SUARA PERNAPASAN PARU-PARU ASMA DENGAN TIDAK ASMA MENGGUNAKAN METODE K NEAREST NEIGHBORS

DIELEKTRIKA ◽

10.29303/dielektrika.v8i1.251 ◽

2021 ◽

Vol 8 (1) ◽

pp. 1

Author(s):

Ari Satriadi

Keyword(s):

Cross Validation ◽

Nearest Neighbors ◽

K Nearest Neighbors ◽

Fold Cross Validation

Asma adalah penyakit pada saluran napas yang menyebabkan peningkatan hiperesponsif jalan napas dan menimbulkan gejala mengi/wheeze (napas berbunyi ngik-ngik). Bunyi napas wheeze merupakan salah satu ciri yang menandakan seseorang menderita asma. Penelitian ini dilakukan untuk membuat serta menguji suatu sistem yang dapat mengidentifikasi perbedaan ciri suara pernapasan wheeze pada pasien asma dan pernapasan lainnya dengan metode k-Nearest Neighbors (k-NN). Ciri suara yang digunakan yaitu rata-rata sinyal dan standar deviasi sinyal dalam domain waktu, rata-rata spektrum, standar deviasi spektrum, magnitude tertinggi saat frekuensi 0Hz, frekuensi dengan magnitude tertinggi pertama, kedua, dan ketiga. K-NN adalah sebuah metode untuk melakukan klasifikasi terhadap objek berdasarkan data pembelajaran yang jaraknya paling dekat dengan objek tersebut. Didapatkan data suara pernapasan wheeze dan non wheeze melalui perekaman langsung kepada subjek penderita asma dan tidak asma. Dari seluruh data suara yang didapatkan kemudian dilakukan segmentasi data untuk mengambil event pernapasasn yang dibutuhkan kemudian dilakukan ekstraksi ciri untuk mendapatkan ciri matematis dari suara tersebut. 80% dari total keseluruhan data dilakukan pelatihan menggunakan metode 10 fold cross validation dan diapatkan hasil pelatihan dengan kemampuan klasifikasi maksimum pada k=3 dan k=5 dengan validitas yang sama 97,2%. Untuk pengujian kinerja k-NN pada tahap akhir diperoleh kemampuan maksimum pengklasifikasian untuk k=3 adalah 86,6% dan k=5 adalah 86,6%.

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DNA methylation marks inter-nucleosome linker regions throughout the human genome

10.7287/peerj.preprints.27 ◽

2013 ◽

Author(s):

Benjamin P. Berman ◽

Yaping Liu ◽

Theresa K. Kelly

Keyword(s):

Dna Methylation ◽

Human Genome ◽

Ctcf Binding ◽

Gene Promoters ◽

Cpg Dinucleotides ◽

Sequencing Technologies ◽

Nucleosome Organization ◽

Genome Wide ◽

A Genome ◽

Sequencing Studies

Background: Nucleosome organization and DNA methylation are two mechanisms that are important for proper control of mammalian transcription, as well as epigenetic dysregulation associated with cancer. Whole-genome DNA methylation sequencing studies have found that methylation levels in the human genome show periodicities of approximately 190 bp, suggesting a genome-wide relationship between the two marks. A recent report (Chodavarapu et al., 2010) attributed this to higher methylation levels of DNA within nucleosomes. Here, we analyzed a number of published datasets and found a more compelling alternative explanation, namely that methylation levels are highest in linker regions between nucleosomes. Results: Reanalyzing the data from (Chodavarapu et al., 2010), we found that nucleosome-associated methylation could be strongly confounded by known sequence-related biases of the next-generation sequencing technologies. By accounting for these biases and using an unrelated nucleosome profiling technology, NOMe-seq, we found that genome-wide methylation was actually highest within linker regions occurring between nucleosomes in multi-nucleosome arrays. This effect was consistent among several methylation datasets generated independently using two unrelated methylation assays. Linker-associated methylation was most prominent within long Partially Methylated Domains (PMDs) and the positioned nucleosomes that flank CTCF binding sites. CTCF adjacent nucleosomes retained the correct positioning in regions completely devoid of CpG dinucleotides, suggesting that DNA methylation is not required for proper nucleosomes positioning. Conclusions: The biological mechanisms responsible for DNA methylation patterns outside of gene promoters remain poorly understood. We identified a significant genome-wide relationship between nucleosome organization and DNA methylation, which can be used to more accurately analyze and understand the epigenetic changes that accompany cancer and other diseases.

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