Journal: Computational and Structural Biotechnology Journal

Article Title: i-Modern: Integrated multi-omics network model identifies potential therapeutic targets in glioma by deep learning with interpretability

doi: 10.1016/j.csbj.2022.06.058

Figure Lengend Snippet: Ranking scheme of multi-omics signatures. (A) Copy number variation signatures ranking. (B) Gene expression signatures ranking. (C) DNA methylation signatures ranking. (D) miRNA expression signatures ranking. (E) Protein expression signatures ranking. (F) Somatic mutation signatures ranking.

Article Snippet: We obtained multi-omics glioma datasets, including RNA sequencing data (TPM normalized gene expression quantification), protein expression data (Reverse Phase Protein Array RPPA), miRNA-seq expression data (reads per million for miRNA mapping to miRbase 20), DNA methylation data (Infinium HumanMethylation450 BeadChip), copy number variation data (Affymetrix SNP Array 6.0) and somatic mutation data (DNA sequencing).

Techniques: Biomarker Discovery, Gene Expression, DNA Methylation Assay, Expressing, Mutagenesis

Journal: Molecular Oncology

Article Title: Allele‐specific proximal promoter hypomethylation of the telomerase reverse transcriptase gene ( TERT ) associates with TERT expression in multiple cancers

doi: 10.1002/1878-0261.12786

Figure Lengend Snippet: Cells with monoallelic expression of TERT have allelic methylation of the proximal TERT promoter. (A) Extent of allelic methylation across the TERT gene, which is transcribed from right to left. Relative allelic methylation was measured by calculating the difference between the mean and mode values of raw read Bis‐Seq CpG methylation data, where greater levels suggest greater allelic methylation behavior (see Fig. for examples). Positions included contained 3–6 CpGs per read and coverage of ≥ 5 reads per cell line. Error bars represent standard error of the mean. n represents the number of cancer cell lines. * P ≤ 0.05, where statistical analysis was performed using 2‐tailed Student's t ‐test with unequal variance. (B) Bisulfite conversion cloning data from genomic DNA of select CpGs flanking the TERT transcription start site (5:1295138–1295413, spanning 33 CpGs). Each row represents a different clone (or genome copy, or allele) and each circle represents a CpG, with black circles representing a methylated CpG and white circles representing an unmethylated CpG. For chromosomal positions of noted TERT features, see Table .

Article Snippet: For Fig. , raw read Bis‐Seq DNA CpG methylation data from CCLE's Bis‐Seq dataset were obtained by direct request to the Broad Institute's CCLE and analyzed for allelic patterns using stringent cutoff criteria (reads analyzed contained 3–6 CpGs per read, and coverage of ≥ 5 reads per cell line).

Techniques: Expressing, Methylation, CpG Methylation Assay, Cloning

Journal: Molecular Oncology

Article Title: Allele‐specific proximal promoter hypomethylation of the telomerase reverse transcriptase gene ( TERT ) associates with TERT expression in multiple cancers

doi: 10.1002/1878-0261.12786

Figure Lengend Snippet: Decreased TERT promoter methylation associates with histone marks of active transcription and an active exonic SNP. (A) ChIP‐Bis‐Seq of the TERT promoter using an H3ac antibody shows enrichment of unmethylated DNA in the pulled‐down samples (black) relative to the input (gray) in LN‐18 cells. The absence of any bars indicates zero percent methylation. Inclusion criteria for read positions were a greater number of reads in the pull‐down relative to the input and ≥ 10 reads in the pull‐down (mean input coverage was 9 reads; mean pull‐down coverage was 13 reads; P = 0.01 for pull‐down efficiency). (B) Confirmation of long‐range bisulfite conversion PCR enriching for unmethylated or methylated CpGs at the TERT proximal promoter (16 CpGs spanning 5:1295265–1295396; region overlaps with some of the CpGs analyzed in 3A) using unmethylated (gray)‐ or methylated (black)‐specific bisulfite conversion PCR, respectively. PCR products generated a 1448‐bp product including the proximal promoter and the exon 2 SNP analyzed in Panel C. * P ≤ 0.05 (C) Long‐range bisulfite conversion PCR (same PCRs as shown in Panel B) showing representative Sanger sequencing results (upward arrow indicates position of the exon 2 SNP) and graphs of the sequencing results ( n = 2–3 sequenced reactions). ‘Active SNP’ means that the nucleotide at the position of the SNP is the one found in the TERT mRNA transcribed in that cell line. The active SNP was either previously identified in all cell lines or was identified here (Fig. S6). Error bars represent standard error of the mean. * P ≤ 0.01, where statistical analysis was performed using 2‐tailed Student's t ‐test with unequal variance.

Article Snippet: For Fig. , raw read Bis‐Seq DNA CpG methylation data from CCLE's Bis‐Seq dataset were obtained by direct request to the Broad Institute's CCLE and analyzed for allelic patterns using stringent cutoff criteria (reads analyzed contained 3–6 CpGs per read, and coverage of ≥ 5 reads per cell line).

Techniques: Methylation, Generated, Sequencing

Journal: Molecular Oncology

Article Title: Allele‐specific proximal promoter hypomethylation of the telomerase reverse transcriptase gene ( TERT ) associates with TERT expression in multiple cancers

doi: 10.1002/1878-0261.12786

Figure Lengend Snippet: TERT promoter is characterized by conserved upstream hypermethylation and proximal hypomethylation across different cancer tissue types. (A) Bisulfite conversion sequencing (Bis‐Seq) DNA CpG methylation data for 95 positions across the TERT promoter for 23 different cancerous tissues, showing mean values from 833 cancer cell lines ( n represents the number of cell lines per tissue). Colored circles indicate individual CpG sites with statistically significant ( P ≤ 0.005) differences between the tissue and all other tissues. Each graph groups tissues by total mean percent methylated, from most to least methylated (top to bottom, respectively). Each chromosomal position includes data from at least two cell lines for all 23 tissues. (B) Bis‐Seq DNA CpG methylation data for 129 positions across the TERT promoter for 109 cell lines with known allelic expression and activating mutation classifications. Lines had been classified as having wild‐type (WT) monoallelic expression (MAE) of TERT (‘WT MAE’), −124 or −146 C>T activating promoter mutations (‘mutant’), biallelic expression (BAE) of TERT (‘WT BAE’), or alternative lengthening of telomeres (ALT). Each row represents a different cell line. Colors range from red to blue for more to less methylated CpGs, respectively. White represents unavailable data. Each chromosomal position includes data from at least 10 cell lines. (C) Bis‐Seq DNA CpG methylation data for 122 positions across the TERT promoter for 107 cell lines shown in 1B ( n represents the number of cell lines per tissue). Colored circles indicate statistically significant ( P ≤ 0.05) differences in the listed pairwise comparisons, where statistical analysis was performed using 2‐tailed Student's t ‐test with unequal variance. Each chromosomal position includes data from at least two cell lines for all three cell types. See Table for chromosomal positions and Table for cell line data.

Article Snippet: For Fig. , raw read Bis‐Seq DNA CpG methylation data from CCLE's Bis‐Seq dataset were obtained by direct request to the Broad Institute's CCLE and analyzed for allelic patterns using stringent cutoff criteria (reads analyzed contained 3–6 CpGs per read, and coverage of ≥ 5 reads per cell line).

Techniques: Sequencing, CpG Methylation Assay, Methylation, Expressing, Mutagenesis

Journal: Clinical Epigenetics

Article Title: DNA hypermethylation contributes to colorectal cancer metastasis by regulating the binding of CEBPB and TFCP2 to the CPEB1 promoter

doi: 10.1186/s13148-021-01071-z

Figure Lengend Snippet: TFCP2 binds to the core TF-binding region of CPEB1 when it is hypermethylated. a Silver staining of the nucleoproteins identified in DNA pull-down assay under various conditions. p-WT, the non-methylated CPEB1 promoter; p-Me, the hypermethylated CPEB1 promoter; control, magnetic beads without probes; Input, total nucleoproteins extracted from HCT116 cells; M, protein molecular mass marker. b WB detecting immunoreactive CEBPB in the nucleoprotein fraction after DNA pull-down with the anti-CEBPB antibody. The molecular mass of CEBPB is approximately 35 kDa. c EMSA revealed that CEBPB protein was unable to bind to its target sequence in the hypermethylated TF-binding region of CPEB1 ; 50 × cold probe WT, 50-fold concentration of the unlabelled wild-type CPEB1 promoter which was served as the competitor probe; Bio-Probe WT, a biotin-labelled wild-type probe of CPEB1 upstream region; Bio-Probe Mut, a biotin-labelled mutant probe of CPEB1 upstream region; Nucleoprotein, nucleoprotein extracted from HCT116 cells; Me-Bio Probe WT, a biotin-labelled hypermethylated probe of CPEB1 upstream region. d TFCP2 may be a candidate methylation reader at the upstream region of CPEB1 ; Methylation, the hypermethylated CPEB1 upstream region probe; Wild-type, the wild-type CPEB1 upstream region probe; Control, a probe with a scrambled sequence of CPEB1 upstream region; TF, the TFs capable of binding to the CPEB1 upstream as determined by ChIP-Seq. e Competitive EMSA to confirm TFCP2 as a methylation reader for CPEB1 . Bio-probe, a biotin-labelled wild-type CPEB1 upstream region probe; Me-Bio Probe, a biotin-labelled hypermethylated CPEB1 upstream region probe; 50 × Cold Probe, 50-fold concentration of the unlabelled wild-type CPEB1 upstream region probe that served as a competitor of the Bio-probe; 50 × Cold Me-Probe, 50-fold concentration the unlabelled hypermethylated CPEB1 upstream region probe that served as a competitor of the Me-Bio Probe

Article Snippet: Publicly available high-throughput DNA methylation microarray data (Illumina Human Methylation 450 K) of 387 CRC tumours and 45 samples of para-tumour tissue were obtained from the TCGA database (level-3).

Techniques: Binding Assay, Silver Staining, Pull Down Assay, Methylation, Control, Magnetic Beads, Marker, Sequencing, Concentration Assay, Mutagenesis, ChIP-sequencing