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Epic Methylation analysis on Glycogen Storage Disorder type 1a and Healthy Controls fibroblasts

ABSTRACT: An analysis of differences in the genomic methylation pattern between GSD1a and HC samples using the Illumina EPIC V2 array as shown in the Disease-associated programming of cell memory in glycogen storage disorder type 1a manuscript. To assess the global differences between samples from the two groups, we used the full dataset of processed probes and initially performed a regression analysis with the aim to assess the in-group variability for each of HC and GSD1a groups tested. Results from this analysis showed lower variability in the GSD1a group which implies that GSD1a fibroblasts present a methylation phenotype that is consistent between group samples as compared to the HC samples. These differences were also observed by an unsupervised clustering analysis that we performed based on K-means searching for two clusters between our 8 tested samples and by PCA. Differential methylation analysis comparing GSD1a to HC cells was performed. Thousands of significantly different probe signals between the groups were found. Differential methylation region analysis which is based on methylated probes distribution located at similar regions in the genome, obtained many differentially methylated regions (DMR) between the groups. GSEA comparing GSD1a cells to HC cells using the methylation dataset and the GO database revealed significantly enriched pathways related to lysosomal and metabolic pathways. In addition, pathways related to epigenetic modifications are also differentially enriched between GSD1a and HC cells. An integratino of this experiment with ATAC-seq and RNA-seq data is shown in the manuscript.

ORGANISM(S): Homo sapiens

PROVIDER: GSE330325 | GEO | 2026/05/12

REPOSITORIES: GEO

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Project description:Purpose: Leptomeningeal metastasis (LM) is a dismal terminal stage disease of solid cancer without definitive treatment. Both the limitation of cerebrospinal fluid (CSF) sample volume and a paucity of floating cancer cells are difficulties to study the genomic profiling of LM. As the profiling of microRNAs reflect the strategy and behavior of cancer cells to survive, and CSF is carrying micro-molecules from central nervous system (CNS), we evaluated the extracellular microRNA profiles of CSF from different CNS tumor status including LM. Materials and Methods: We prospectively collected CSF from 65 patients of five groups of cancer control (CC), healthy control (HC), LM, brain metastasis (BM); and brain tumor (BT). Extracellular RNA was extracted from 2 mL of CSF after proper cell down, and preceded to small RNA microarray with Affymetrix miRNA 4.0 microarray chips. Results: The mean RNA yield of LM patients was significantly higher compared to that of other patients groups (9.28 vs. 6.30 ㎍, p = 0.003). The small RNA (< 70 nucleotides) percentage was the highest in controls than that of other groups (53% vs. 26%, p < 0.001). Among 6,599 small RNA probes, mature microRNAs showed higher expression than that of pre-microRNAs and small nucleolar RNAs. The number of probes with the Present call in all samples is 22 mature microRNAs, 18 pre-mature microRNAs, and 27 small nucleolar RNAs. Expression value of mature microRNAs regarding their direction of 5 prime (5p) and 3 prime (3p), the expression of 3p microRNA is higher than those of 5p microRNA in both LM and BM samples (p < 0.001). Both supervised and unsupervised hierarchical clustering of 263 microRNAs, which showed more than 2 fold change at a significance level of p < 0.05, can differentiate LM from other CNS tumor patient groups. Prediction analysis of microarray (PAM) identified 13 microRNAs differentiate LM group from others including hsa-miR-335-5p and hsa-miR-466. MicroRNA profiling using significance of analysis of microarrays (SAM) analysis did not differentiate HC and CC. Whereas CSF samples from patients with LM showed the most number of 108 differentially expressed (more than 2 fold change) microRNAs compared to control group and also revealed 36 differentially expressed microRNAs between LM and BM including hsa-miR-466, hsa-miR-190a-3p, hsa-miR-98-3p, hsa-miR-34b-3p and so on. Representative discriminative microRNAs from microarray data were confirmed their expression level by digital droplet PCR in available CSF samples among the microarray samples. Gene Set Enrichment Analysis was performed using both 10 highly expressed and 6 suppressed microRNAs between LM and BM after normalization and 13 microRNAs from PAM analysis. The most targeted pathway was ‘positive-/ negative-transcription from RNA polymerase II promoter followed by ‘positive regulation of transcription, DNA-templated’ by discriminative microRNAs. When pathways were denoted by microRNAs from PAM, the most enriched pathway was inflammatory response and transcription relative to immune response. Conclusion: We performed extracellular small RNA microarray successfully with small volume of CSF. Analysis of profiles of microRNA according to patients with various CNS tumor status suggested the unique profiles might responsible for leptomeningeal metastasis.

Project description:Methods routinely used to analyze RNA sequencing data focus on statistical significance and the detection gene differential expression changes that meet a two-fold minimum change between groups. Due to the unique expression variability present in RNA sequencing data, this strategy may potentially overlook or obscure the detection of valuable information as a result of large expression variability in specific genes in certain samples. This paper develops tools and methods that apply variance and dispersion estimates to intra-group data in order to identify genes with expression values that diverge from the group envelope. STRING database analysis of the genes identified with this analysis characterize gene affiliations involved in physiological regulatory networks that are associated to biological variability. Samples or genes identified as divergent can be judiciously evaluated prior to any standard differential analysis. A three-step process is presented for evaluating biological variability within a group in RNA sequencing data in which gene counts were: (1) scaled to minimize heteroscedasticity; (2) rank-ordered to potentially divergent “trendlines” for every gene in the data set; and (3) tested with the STRING database to identify statistically significant pathway associations among the genes displaying marked trendline variability and dispersion. This approach was used to identify and portray the “trendline” profile of every gene in three test data sets. Control data from an in-house data set and two archived samples revealed that 65-70 % of the sequenced genes displayed trendlines with minimal variation and dispersion across the sample group after rank-ordering the samples; this is referred to as linear trendline. Nonlinear trendlines refer to all cases where the trendline is not linear. Smaller subsets of genes within the three data sets displayed markedly skewed trendlines, wide dispersion, and variability. STRING database analysis of these genes identified interferon-mediated response networks in 11-20 % of the individuals sampled at the time of blood collection. For example, in the three control data sets, 14 to 26 genes in the defense response to virus pathway were identified in 7 individuals at false discovery rates ≤ 1.92 E-15. Gene clusters involving leukocyte and neutrophil activation and degranulation pathways were also detected.

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Epic Methylation analysis on Glycogen Storage Disorder type 1a and Healthy Controls fibroblasts

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