Journal: Theranostics

Article Title: Genome-wide discovery and validation of diagnostic DNA methylation-based biomarkers for hepatocellular cancer detection in circulating cell free DNA

doi: 10.7150/thno.35573

Figure Lengend Snippet: Approach to identifying DNA methylation-based biomarkers for detection of early HCC in liver cirrhosis patients. We utilized two strategies to identify DNA methylation-based biomarkers. First, starting with primary tissue to discover markers and then examining their performance in cfDNA, and the converse, starting with cfDNA to discover markers then validating these markers in two independent primary tissue datasets. We then validated a 5-marker panel in 30 independent cfDNA samples by bisulfite pyrosequencing. A total of 586 patient-derived Infinium 450k profiles are assessed for this study.

Article Snippet: Demographic and clinical information on the cfDNA- and tissue-derived DNA samples used for Infinium 450k array-based DNA methylation analysis are summarized in Figure and Table .

Techniques: DNA Methylation Assay, Marker, Derivative Assay

Journal: Theranostics

Article Title: Genome-wide discovery and validation of diagnostic DNA methylation-based biomarkers for hepatocellular cancer detection in circulating cell free DNA

doi: 10.7150/thno.35573

Figure Lengend Snippet: Characterization of primary tissue- and cfDNA-derived DNA methylation landscapes. A ) Principal component analysis of differentially methylated CpGs in cfDNA (top, n=44; 22 cirrhosis in pink, 22 HCC in blue) and primary tissues (bottom, n=191; 82 cirrhosis in pink, 109 HCC in blue) using all CpGs on the array after QC filtering (including removal of X, Y, and SNP associated probes). B ) Overall methylation level bar charts (as beta values) for individual cirrhotic (green), average of all cirrhotic (dark green), individual HCC (red), and average of all HCC (dark red) patients derived from cfDNA (top) or primary liver tissues (bottom). C ) Volcano plots of 5mC changes plotted against -log P values between cirrhotic only and cirrhotic with concurrent HCC patients. Stepwise coloring of changes is based on delta beta values (0.05 increments) with black being below 0.05, dark red between 0.05-0.1, red between 0.1-0.15, orange between 0.15-0.20, and yellow greater than 0.20 in cfDNA (top) and primary tissue (bottom). D ) Bar charts representing the relative distribution of DNA methylation changes (Δβ > 0.1 tumor vs non-tumor) across the indicated features based on 450k data derived from cfDNA (top) and from primary tissue (bottom). Blue and orange bars represent hypermethylation and hypomethylation events, respectively.

Article Snippet: Demographic and clinical information on the cfDNA- and tissue-derived DNA samples used for Infinium 450k array-based DNA methylation analysis are summarized in Figure and Table .

Techniques: Derivative Assay, DNA Methylation Assay, Methylation

Journal: Theranostics

Article Title: Genome-wide discovery and validation of diagnostic DNA methylation-based biomarkers for hepatocellular cancer detection in circulating cell free DNA

doi: 10.7150/thno.35573

Figure Lengend Snippet: Discovery of DNA methylation biomarkers in primary liver tissues. A) Binned scatterplot of de novo DNA methylation events that significantly differ between cirrhotic only controls and HCC primary tissues (Δβ > 0.15, P < 0.05). A color bar representing density of CpGs is shown at the right. B) Area under the receiver operating characteristic curves for CpGs identified in (A). C) Heatmap of the 2,000 most differentially methylated CpGs between cirrhosis (green) and HCC (red) tissue. A color bar is shown with low methylation in red and high methylation in yellow. Red/green bar indicates tissue disease state. D) Binned scatterplot of AUROCs for CpGs identified in (A) in primary tissue set 1 analyzed using methylation values at the same CpG sites measured in cfDNA. E) Lasso regression analysis of CpGs identified in (A) presented as a dumbbell chart demarcated by the lower and upper 95% confidence interval of the coefficient (blue bar), with the original coefficient value shown as a black dot for 24 CpGs that reached the coefficient threshold of lower 95% CI > 0. F) A scatterplot of AUROCs from the 24 CpGs selected in (E) in cfDNA and primary tissue. The y-axis is the AUROC in cfDNA, the x-axis is the AUROC in primary tissue set 1.

Article Snippet: Demographic and clinical information on the cfDNA- and tissue-derived DNA samples used for Infinium 450k array-based DNA methylation analysis are summarized in Figure and Table .

Techniques: DNA Methylation Assay, Methylation

Journal: Theranostics

Article Title: Genome-wide discovery and validation of diagnostic DNA methylation-based biomarkers for hepatocellular cancer detection in circulating cell free DNA

doi: 10.7150/thno.35573

Figure Lengend Snippet: Discovery and validation of DNA methylation biomarkers derived from cfDNA. A) Schematic representation of the analytic process used to identify biomarkers directly from genome-wide cfDNA methylation data. B ) Heatmap of 443 CpGs showing differential methylation between cirrhotic only control and HCC patient-derived cfDNA (Δβ > 0.1, P < 0.05). A color bar is shown to indicate 5mC level. C ) Resultant 95% confidence intervals for positively-weighted coefficients identified by Lasso regression of CpGs from part (B). D ) Boxplot of the additive sum of coefficients multiplied by β values for the 13 CpGs identified in (C) in cirrhotic- and HCC-derived cfDNA samples. E ) List of the high performing CpGs identified from cfDNA along with their associated gene(s), genic features, and link to liver-specific enhancers. F ) Receiver operating characteristic curves for a 5 CpG panel in cfDNA (blue), and in two independent primary tissue sets (orange, set 1; black, set 2). CpGs used: cg04645914, cg06215569, cg23663760, cg13781744, and cg07610777.

Article Snippet: Demographic and clinical information on the cfDNA- and tissue-derived DNA samples used for Infinium 450k array-based DNA methylation analysis are summarized in Figure and Table .

Techniques: Biomarker Discovery, DNA Methylation Assay, Derivative Assay, Genome Wide, Methylation, Control

Journal: Theranostics

Article Title: Genome-wide discovery and validation of diagnostic DNA methylation-based biomarkers for hepatocellular cancer detection in circulating cell free DNA

doi: 10.7150/thno.35573

Figure Lengend Snippet: DNA hypomethylation based markers that distinguish between non-tumor and tumor disease states. A) Schematic representation of the analytic process used to identify hypomethylation biomarkers directly from genome-wide cfDNA 5mC datasets. B ) Heatmap of 63 CpGs showing differential methylation between cirrhotic only control and HCC patient-derived cfDNA (Δβ <- 0.15, P < 0.05). A color bar is shown to indicate 5mC level. C ) Resultant 95% confidence intervals for positively-weighted coefficients identified by Lasso regression of CpGs from part (B). D ) Boxplot of the additive sum of coefficients multiplied by β values for the 10 CpGs identified in (C) in cirrhotic- and HCC-derived cfDNA samples. E ) List of the high performing CpGs identified from cfDNA along with their associated gene(s), genic features, and link to liver-specific enhancers. F ) Receiver operating characteristic curves for a 4 CpG panel in cfDNA (blue), and in two independent primary tissue sets (orange, set 1; black, set 2). CpGs used: cg25026480, cg14774440, cg18054281, cg00638020.

Article Snippet: Demographic and clinical information on the cfDNA- and tissue-derived DNA samples used for Infinium 450k array-based DNA methylation analysis are summarized in Figure and Table .

Techniques: Genome Wide, Methylation, Control, Derivative Assay

Journal: Journal of immunology (Baltimore, Md. : 1950)

Article Title: Activation of murine CD4+ and CD8+ T lymphocytes leads to dramatic remodeling of N-linked glycans.

doi: 10.4049/jimmunol.177.4.2431

Figure Lengend Snippet: FIGURE 1. Purification of B cells and T cells from mixed splenocytes before and after activation. CD8 T cells, CD4 T cells, and B cells were purified from mixed splenocytes before or after activation using the MACS system with anti-CD8-, anti-CD4-, or anti-CD19-coated magnetic beads, respectively. Phenotypic analysis of purified fresh and activated cell populations was performed by flow cytometry and showed 90% purity of all cell preparations, as indicated. Purified cells were used either for RNA preparation for microarray analysis, or were processed to extract glycans for MALDI profiling and methylation analysis.

Article Snippet: 6 The online version of this article contains supplemental material. at B row n U niversity on M ay 30, 2014 http://w w w .jim m unol.org/ D ow nloaded from Microarray analysis of the expression of cytokine and glycan transferase genes For gene expression analysis, mRNAs were extracted from purified cell populations of fresh and activated lymphocytes and subjected to analysis on a custom Affymetrix-based DNA microarray containing murine and human glycosyltransferase, sulfotransferase, and cytokine genes, made available by the Consortium for Functional Glycomics.

Techniques: Activation Assay, Magnetic Beads, Cytometry, Microarray, Methylation

Journal: PLoS ONE

Article Title: Allele-Specific Methylation Occurs at Genetic Variants Associated with Complex Disease

doi: 10.1371/journal.pone.0098464

Figure Lengend Snippet: A) A simplified representation of the Methyl-Sensitive Restriction Enzyme (MSRE) based Allele-Specific Methylation (ASM) assay. DNA is MSRE treated (left panels) and MSRE sites with methylated CpGs protected from digestion (upper panels, Allele-A) while its homologous chromosomal region with unmethylated CpGs are not (lower panels, Allele-B). The DNA is digested with StyI and NspI to form 200–1200 bp fragments, linkers ligated and DNA amplified to create amplicons that are hybridized to the array. Only regions with protected MSRE sites (methylated CpG) are amplified and can hybridize to show signal on the array (final panel). B) Bioinformatic detection of Allele-Specific Methylation (ASM) from Affymetrix SNP 6.0 arrays signals after MSRE digestion. In the scatter plot on the left, 4 different expected states after MSRE digest at a heterozygous region are compared to the typical distribution of probe intensities observed within the HapMap samples for the same MPR (here portrayed by light grey squares): biallelic methylation (dark grey circles), monoallelic A methylation (blue circles), monoallelic B methylation (yellow circles) and finally biallelic lack of methylation (red circles). The primary calling method relies on feature extraction by way of conversion of 2-dimensional A and B probe intensity data (scatter plot) from heterozygotes to log2(A/B) values and is compared against the typical log2(A/B) distribution observed for this MPR within the HapMap samples (histogram, light grey). Simply put, MPRs diverging from this distribution after MSRE treatment are called ASM. Using this method, biallelic unmethylated states have the potential to result in false positive ASM calls as any log2(A/B) value would be based on background noise, so are filtered out by removing MPRS with low total intensities (highlighted here with a red quarter-circle, for further information on how this filter was devised, see ).

Article Snippet: All samples were genotyped once and all Affymetrix SNP 6.0 methylation array based analyses run in duplicate.

Techniques: Methylation, Amplification

Journal: International Journal of Molecular Sciences

Article Title: Epigenetic Factors Related to Low Back Pain: A Systematic Review of the Current Literature

doi: 10.3390/ijms24031854

Figure Lengend Snippet: Details of the included studies concerning LBP-related epigenetic regulation: DNA methylation in peripheral blood cells. Studies are listed in chronological order.

Article Snippet: Genome-wide association study (GWAS) (II) , Discovery cohort: 32 - Control group: 16 - Low back pain group: 16 Validation cohort: 63 - Control group: 16 - Low back pain group: 37 , Discovery cohort: - Control females: 43.8 ± 4.6 - Low back pain females: 41.3 ± 3.8 - Control males: 43.8 ± 4.0 - Low back pain males: 42.6 ± 3.6 Males: 16 (50%) Females: 16 (50%) Validation cohort: - Control females: 38.5 ± 3.5 - Low back pain females: 46.1 ± 2.7 - Control males: 43.1 ± 3.2 - Low back pain males: 48.4 ± 2.6 Males: 31 (49%) Females: 32 (51%) Caucasian , Canadian adaptation of NIH low back pain taskforce, DN4 and ODI \ NS , T cells isolated from peripheral blood Array-based methylation analyis (Illumina) after bisulfite treatment, followed by validation by pyrosequencing , 850,000 CpG sites , Of the 736,414 CpGs identified in men, 179 were hypermethylated and 240 were hypomethylated in LBP patients compared to controls. Of the 735,863 CpGs identified in women, 601 were hypermethylated and 1895 were hypomethylated ( p -value < 0.05). The generation of a polygenic methylation score for LBP in men and women with three surrogate CpG loci: cg07420274 for women; cg21149944 and cg22831726 for men. In women, the percentage of methylation at position cg07420274 was 39.5 ± 6 2.7% and 49.7 ± 6 3.2% in the control ( n = 21) and LBP groups ( n = 25), respectively ( p < 0.05). A statistically significant association between methylation at cg07420274 and LBP was observed (OR = 1.05, 95% CI: 1.01–1.11, p < 0.03). In men, a statistically significant association was found between LBP and cg21149944 methylation (OR = 0.89, 95% CI: 0.82–0.95, p < 0.0015) as well as cg22831726 methylation (OR = 0.9, 95% CI: 0.84–0.96, p < 0.0036). , S. Grègoire (2021) [ ] .

Techniques: DNA Methylation Assay, Genome Wide, Methylation, Expressing, Methylation Sequencing, Isolation, Enzyme-linked Immunosorbent Assay