Integrative Alignments of DNA Elements for Transcriptional Regulation in Swine Epigenome
Jinahua Cao, Ph.D
Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction (Huazhong Agricultural University), Ministry of Education, China Outlines
• Introduction
• Data presentation
• Results(data quality; main output; examples)
• Conclusion Human ENCODE project
chr16:28820000-28886000 chr2:70270000-70430000 50 RNA-Seq ATXN2L 400 100 RNA-Seq PCBP1 400 90 RNAPII 100 E2F4 H3K4me3 200 100 GATA2 H3K4me1 30 100 70 STAT1 H3K9ac 100 30 Brg1 H3K14ac 100 20 Ini1 FAIRE 100 7 Rad21 H3K9me3 100 H3K27me3 7 CTCF TF binding Histone modification
“An integrated encyclopedia of DNA elements in the human genome”, Nature, 2012 ENCODE/ Roadmap
Roadmap Epigenomics Consortium, Nature, 2015
Structure v Massively packaged v non-randomly organized v Coiled/looped in high level orders 3D Genome v Fluid and dynamic
BMC Biophysics, 2011 Function v Gene transcription regulation v DNA replication v Genome translocation v DNA repair ChIP/RNA-Seq data of porcine cells/tissue
Ø Over 15,000 peaks for narrow peak
Ø ~10,000 enriched region for broad peak
Ø PAMs have better performance on H3K27me3. Data quality assessments: RNA-Seq Reproducibility and ChIP-Seq Correlations
3d4.h3k27me3 3d4.h3k27me3 3d4.h3k9me3 3d4.h3k9me3 pam.h3k9me3 pam.h3k9me3 pam.pol2 pam.pol2 pam.h3k36me3 pam.h3k36me3 pam.h3k27ac pam.h3k27ac pam.h3k4me1 pam.h3k4me1 pam.h3k4me3 pam.h3k4me3 pam.h3k27me3 pam.h3k27me3 pam.ctcf pam.ctcf 3d4.h3k4me3 3d4.h3k4me3 3d4.h3k36me3 3d4.h3k36me3 3d4.h3k27ac 3d4.h3k27ac 3d4.ctcf 3d4.ctcf 3d4.pol2 3d4.pol2 3d4.h3k4me1 3d4.h3k4me1 pk.h3k4me3 pk.h3k4me3 pk.h3k27ac pk.h3k27ac pk.h3k36me3 pk.h3k36me3 pk.pol2 pk.pol2 pk.ctcf pk.ctcf pk.h3k4me1 pk.h3k4me1 pk.h3k9me3 pk.h3k9me3 pk.h3k27me3 pk.h3k27me3
−1 −0.9 −0.8 −0.7 −0.6 −0.5 −0.4 −0.3 −0.2 −0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 type specific specific type investigated investigated assembly: (Genome annotation: ssc11.1, 90) release Ensembl normalized Reads TPM using sample per rep. 2 #COM: 13,778 #SP: PK Seqcharacterizationdata typeson 3 pk.2 - pk.1 3D4.2 3D4.1 pam.2 pam.1 RNA Gene networks and GO functional annotations Ø Over 75% genes are common transcriptional. Ø COM genes are responsible for cell structure, metabolism,… Ø SP genes are more dependents on cell types chromHMM: chromatin states assignments Ø Assigned 15 chromatin states based on 6 histone markers. Ø Markers within a state have more correlation. Ø Primary PAM cells have lower correlations between markers. pk.rna 3d4.rna pam.rna pk.rna 1 3d4.rna 1 pam.rna 1 pk.ctcf 3d4.ctcf pam.ctcf pk.ctcf 0.09 1 3d4.ctcf 0.15 1 pam.ctcf 0.16 1 pk.h3k27ac 3d4.h3k27ac pam.h3k27ac pk.h3k27ac 0.18 0.63 1 3d4.h3k27ac 0.17 0.52 1 pam.h3k27ac 0.08 0.36 1 pk.h3k27me3 3d4.h3k27me3 pam.h3k27me3 pk.h3k27me3 3d4.h3k27me3 −0.1 0.15 −0.02 1 pam.h3k27me3 −0.01 0.27 0.08 1 −0.13 0.36 0.16 1 pk.h3k36me3 3d4.h3k36me3 pam.h3k36me3 pk.h3k36me3 3d4.h3k36me3 0.36 0.42 0.28 −0.04 1 pam.h3k36me3 0.19 0.28 0.46 0.05 1 0.28 0.52 0.49 −0.03 1 pk.h3k4me1 3d4.h3k4me1 pam.h3k4me1 3d4.h3k4me1 0.11 0.59 0.78 0.24 0.33 1 pam.h3k4me1 0.12 0.48 0.64 0.26 0.55 1 pk.h3k4me1 0.07 0.77 0.83 0.42 0.51 1 3d4.h3k4me3 pam.h3k4me3 pk.h3k4me3 3d4.h3k4me3 0.19 0.44 0.67 0.12 0.25 0.49 1 pam.h3k4me3 0.13 0.45 0.38 0.32 0.41 0.75 1 pk.h3k4me3 0.18 0.58 0.79 0.21 0.42 0.65 1 3d4.h3k9me3 pam.h3k9me3 pk.h3k9me3 3d4.h3k9me3 −0.04 0.11 0.04 0.54 0.23 0.17 0.11 1 pam.h3k9me3 −0.02 0.06 0.04 0.18 0.25 0.26 0.44 1 pk.h3k9me3 −0.05 0.44 0.27 0.37 0.34 0.45 0.33 1 3d4.pol2 pam.pol2 pk.pol2 3d4.pol2 0.39 0.5 0.71 −0.18 0.48 0.52 0.72 −0.07 1 pam.pol2 0.31 0.33 0.55 0.03 0.51 0.45 0.41 0.02 1 pk.pol2 0.42 0.24 0.61 −0.16 0.37 0.31 0.65 −0.01 1 −1 −0.9 −0.8 −0.7 −0.6 −0.5 −0.4 −0.3 −0.2 −0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 −1 −0.9 −0.8 −0.7 −0.6 −0.5 −0.4 −0.3 −0.2 −0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 −1 −0.9 −0.8 −0.7 −0.6 −0.5 −0.4 −0.3 −0.2 −0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Overview of ChIP-Seq and RNA-Seq data RNA Ø MALAT1 and NEAT1 sites POL2 Ø RNAPII and CTCF factor CTCF work well in swine genome H3K4me3 H3K27ac Ø 6 Histone markers are also work well. H3K36me3 H3K4me1 H3K9me3 H3K27me3 H3K9me3 3D4/21 RNA H3K27ac PAM PK - 15 H3K36me3 H3K27me3 POL2 H3K4me3 H3K4me1 CTCF log2(Fold change vs. control) Ø Ø Ø Swine Transcriptional Regulation in DNA Elements for Integrative Alignments of −2 −1 0 1 2 3 − 5000 H3K27ac/me3 profiling characters at PAM with unique genomics CTCF, insulator for 3D transcriptional factor RNAPII (POL2), Epigenome TSS Genomic Region(5' 33% 66% − > 3') TES CTCF.PK CTCF.PAM CTCF.3D4 H3K36me3.PK H3K36me3.PAM H3K36me3.3D4 H3K9me3.PK H3K9me3.PAM H3K9me3.3D4 H3K4me1.PK H3K4me1.PAM H3K4me1.3D4 H3K27me3.PK H3K27me3.PAM H3K27me3.3D4 H3K27ac.PK H3K27ac.PAM H3K27ac.3D4 H3K4me3.PK H3K4me3.PAM H3K4me3.3D4 POL2.PK POL2.PAM POL2.3D4 RNA.PK RNA.PAM RNA.3D4 5000 3 RNA.3D4 RNA.PAM RNA.PK POL2.3D4 POL2.PAM 2 POL2.PK H3K4me3.3D4 H3K4me3.PAM RNA H3K4me3.PK H3K27ac.3D4 1 H3K27ac.PAM H3K27ac.PK H3K27me3.3D4 H3K27me3.PAM H3K27me3.PK 0 H3K4me1.3D4 H3K4me1.PAM log2(Fold change vs. control) change vs. log2(Fold H3K4me1.PK H3K9me3.3D4 POL2 H3K9me3.PAM 1 H3K9me3.PK − H3K36me3.3D4 H3K36me3.PAM H3K36me3.PK CTCF.3D4 CTCF.PAM −2000 TSS 33% 66% TES CTCF.PK 2000 H3K4me3 Genomic Region (5' −> 3') H3K27ac H3K27me3 H3K4me1 H3K9me3 H3K36me3 CTCF SOX17 PK-15 RNA POL2 H3K4me3 H3K27ac H3K27me3 H3K4me1 H3K9me3 H3K36me3 CTCF TMSB4X UBA52 GAPDH ACTB Constantly high expressed genes Ø Two categories: Ø Chromatin state free Ø Chromatin state related Ø Extension discussion: Ø Conserved “transcription factory” Ø “loop” genes TANG, Cell, 2015 APOE C1QA/C1QB Primary Porcine Alveolar Macrophages Specific expressed genes Ø APOE: CTCF binding motif Ø C1QA/C1QB: long-range interactions or enhancers SCG2 3D4/21 cell line specific highly expressed CTCF binding motif CTCF TAD POL2 transcription factory formed SCG2 gene transcription Summary and perspectives • 8 epigenomic markers on 3 cell types were surveyed chromatin states to explore DNA elements modifications in swine epigenomes. • Integrative analysis was used to elucidate the relationship between gene transcription and proximal modifications by using RNA-Seq and ChIP-Seq data. • Purposefully using RNAPII and CTCF factor in this study to peep the 3D chromatin conformation organizations and dynamics in swine genome. Acknowledgements Dr. Shuhong’s Group in HZAU Jianhua Cao Xingyun Li Mengjin Zhu Changchun Li Xiaolei Liu Shen Wang Remin Ren Ying Huang Yan Sun Liangliang Fu Changzhi Zhao Funding support: Thanks!