Rasoul Godini, Hossein Fallahi

Slides:



Advertisements
Similar presentations
Figure S1: (A) Table shows number of genes that passed Welchs t-test at different q-values (FDR corrected p-values) and Fold Change cut-offs. (B) Immune.
Advertisements

What is neural stemness? Why is it important? What are the molecular signatures of neural stemness? What are the regulatory networks that control neural.
Supplementary Fig. 1. Transcriptome analysis of MENX-associated pituitary adenomas and and comparison with human studies. Control samples from wild-type.
Characterization of Transcriptional Regulatory Networks controlling plant cell adaptation to environmental stresses.
Computational Approaches in Epigenomics Guo-Cheng Yuan Department of Biostatistics and Computational Biology Dana-Farber Cancer Institute Harvard School.
Noninvasive Prenatal Methylomic Analysis by Genomewide Bisulfite Sequencing of Maternal Plasma DNA F.M.F. Lun, R.W.K. Chiu, K. Sun, T.Y. Leung, P. Jiang,
Epigenome 1. 2 Background: GWAS Genome-Wide Association Studies 3.
A systems biology approach to the identification and analysis of transcriptional regulatory networks in osteocytes Angela K. Dean, Stephen E. Harris, Jianhua.
An Introduction to ENCODE Mark Reimers, VIPBG (borrowing heavily from John Stamatoyannopoulos and the ENCODE papers)
DNA Methylation in Histone H3.3 Lysine to Methionine Mutants Ellie Degen with Stefan Lundgren, Siddhant Jain and Dr. Peter W. Lewis UW Department of Biomolecular.
Presented by R5 李霖昆 Supervised by VS 顏厥全 大夫 報告日期 : | nature | vol 483 | 22 March 2012.
Apostolos Zaravinos and Constantinos C Deltas Molecular Medicine Research Center and Laboratory of Molecular and Medical Genetics, Department of Biological.
Identification of co-expression networks by comparison of a multitude of different functional states of genome activity Marc Bonin 1, Stephan Flemming.
Case Study: Characterizing Diseased States from Expression/Regulation Data Tuck et al., BMC Bioinformatics, 2006.
Histone Methylation Marks : Permanent or Reversible?
Agenda  Epigenetics and microRNAs – Update –What’s epigenetics? –Preliminary results.
*** * * * Figure S1 Supplementary Figure 1. Analysis of JARID1A–D mRNA levels showed that JARID1D knockdown had insignificant or weak effects on expression.
Yuna Jo. Introduction Prostate cancer (PCa) continues to burden the Western world with its high rates of incidence and mortality despite the.
Date of download: 9/19/2016 Copyright © 2016 American Medical Association. All rights reserved. From: Germline Epigenetic Regulation of KILLIN in Cowden.
Squeezing out the histone modifications data Wieslawa Mentzen with Matteo Floris and Paolo Uva Connections between epigenetics and microRNAs during embryonic.
Epigenetics of cancer Vilja ja Mia.
miRNA-targets cross-talks: key players in glioblastoma multiforme
Yiming Kang, Hien-haw Liow, Ezekiel Maier, & Michael Brent
Bioinformatic Analysis of Altered microRNA Production in
Monica Britton, Ph.D. Sr. Bioinformatics Analyst June 2016 Workshop
DNA Methylation Regulates Gene Expression in Intracranial Aneurysms
Figure S2 A B Log2 Fold Change (+/- cAMP) Transcriptome (9hr)
A Systems Toxicology-based Approach Reveals Biological Pathways Dysregulated by Prenatal Arsenic Exposure  Jessica E. Laine, MS, Rebecca C. Fry, PhD 
Functional Genomics Analysis Reveals a MYC Signature Associated with a Poor Clinical Prognosis in Liposarcomas  Dat Tran, Kundan Verma, Kristin Ward,
Ashwani Kumar and Tiratha Raj Singh*
Skin Pharmacol Physiol 2017;30: DOI: /
A. B. Supplemental Figure S2.  A.  Cell cycle G1/S Growth Factor Regulation.  Publically available data from (20;
Volume 20, Issue 12, Pages (September 2017)
Volume 21, Issue 1, Pages e6 (July 2017)
CNA‐driven network of glioblastoma.
Characterization of microRNA transcriptome in tumor, adjacent, and normal tissues of lung squamous cell carcinoma  Jun Wang, MD, PhD, Zhi Li, MD, PhD,
Volume 2, Issue 4, Pages (April 2008)
Using Epigenetic Reprogramming to Treat Pediatric Brain Cancer
Loyola Marymount University
Epigenomics of Retinal Development in Mice and Humans
Unmasking Transcriptional Heterogeneity in Senescent Cells
Wing Y. Chang, William L. Stanford  Cell Stem Cell 
Volume 9, Issue 3, Pages (September 2017)
Volume 23, Issue 11, Pages (June 2018)
Review Warm-Up What is the Central Dogma?
Review Warm-Up What is the Central Dogma?
Figure 3. Genes differentially expressed in batch cultures during adaptation to low temperature. Genes differentially expressed in batch cultures during.
Volume 22, Issue 3, Pages (January 2018)
Volume 2, Issue 2, Pages (February 2008)
Volume 6, Issue 4, Pages (April 2010)
Volume 22, Issue 3, Pages (January 2018)
Volume 19, Issue 1, Pages (January 2014)
Volume 11, Issue 2, Pages (April 2015)
Molecular Convergence of Neurodevelopmental Disorders
Epigenomic Profiling Reveals DNA-Methylation Changes Associated with Major Psychosis  Jonathan Mill, Thomas Tang, Zachary Kaminsky, Tarang Khare, Simin.
Volume 14, Issue 6, Pages (June 2014)
Volume 132, Issue 6, Pages (March 2008)
Volume 14, Issue 6, Pages (June 2014)
Volume 20, Issue 12, Pages (September 2017)
Loyola Marymount University
Volume 6, Issue 4, Pages (April 2016)
Volume 15, Issue 12, Pages (June 2016)
Volume 26, Issue 12, Pages e5 (March 2019)
Integrative analysis of 111 reference human epigenomes
Loyola Marymount University
Loyola Marymount University
CD4+CLA+CD103+ T cells from human blood and skin share a transcriptional profile. CD4+CLA+CD103+ T cells from human blood and skin share a transcriptional.
Loyola Marymount University
Symmetrical Dose-Dependent DNA-Methylation Profiles in Children with Deletion or Duplication of 7q11.23  Emma Strong, Darci T. Butcher, Rajat Singhania,
Fig. 2 Tissue-specific transcriptomic alterations in response to acute sleep loss in healthy humans. Tissue-specific transcriptomic alterations in response.
Presentation transcript:

Rasoul Godini, Hossein Fallahi Epigenome And Transcriptome Analysis Revealed Core Regulatory Network Involved In Glioblastoma Rasoul Godini, Hossein Fallahi Dep. of Biology, School of Sciences, Razi University, Baq-e-Abrisham, Kermanshah, Iran. Objectives Glioblastoma is a main type of brain tumors that derived from astrocytes. Glioblastoma are classified as very aggressive cancers with no clear cause. Transcriptome of the cancerous cells dramatically differs from normal ones. On the other hand, role of epigenetic alterations has been shown in cancer development. Here, using bioinformatics and biostatistics, we have analyzed transcriptome and epigenome data of 4 glioblastoma specimens to reveal main transcription factors (TFs) and their epigenetic status. Additionally, we have constructed a core regulatory network for glioblastoma. Figure 5. Gene ontology of core TF targets. Methylation level Figure 2. Hierarchical clustering of all TFs in all considered samples based similarity Methods To do this study, we used dataset GSE46016 which covers several samples for transcriptome, methylome, and histone modification data for 4 brain tumor samples, neural stem cells and normal astrocytes samples. Using GEO2R tool of NCBI compared tumor samples against astrocytes and applying FDR equal or less than 0.01 and log2FC 0.6 and 1.5 DE genes were detected. We used hierarchical clustering to cluster the TFs and predict differentially expressed TFs (DE-TFs), which are controlling all other TFs using Enrichr database. Core regulatory network of main TFs were constructed and analyzed with Cytoscape 3.4.0 program to find hub TFs. Afterward, methylation status and histone modifications were considered to confirm transcriptome findings. Figure 6. Methylation level and corresponding log2 fold change of expression of the core TFs. Results Our analysis revealed that there are 119 common TFs in all tumor samples. Using data of ChEA and ENCODE databases, we predicted 22 DE-TFs controlling all other TFs. Despite of almost revers pattern of gene expression in one of the samples, we found 7 TFs, including ZNF217, TCF4, and JUN, up-regulated in glioblastoma. Almost all target genes of these TFs were up-regulated, as well. Gene ontology revealed that this genes are related to apoptosis and neuron differentiation and migration. Methylome analysis of upstream these TFs showed hypomethylation of DMRs. H3K27me3 and H3K4me3 modifications were majors for most of the TFs. Figure 3. Core regulatory network that controls all other TFs in glioblastoma, either up- or down regulated. References Conclusions In conclusion, using transcriptome and epigenome data, we have detected several hub TFs significantly involved in glioblastoma, which confirm previous findings and also predict potentially important candidates for further studies. Figure 4. Histone modification of up- and down regulated TFs in glioblastoma samples. Figure 1. Veen diagram of common TFs among glioblastoma samples Poster No. 756