Sequencing the transcriptomereveals complex levels of regulation           Carsten O. Daub     Copenhagenomics 2012-06-15
RIKEN – Omics Science Center (OSC)
Karolinska InstituteScience for Life Laboratory – SciLifeLab   Department of Biosciences and Nutrition              Techno...
Outline• Characterize cells on the molecular level• Finding regulators of gene expression  – We observe gene expression  –...
Cap Analysis of Gene Expression method                   RNA extraction                                                   ...
Traditional CAGE method                 RNA extraction           CAGE library preparation                                 ...
Traditional CAGE method                  RNA extraction                                                    Genome         ...
CAGE – Promoter Types               Nature reviews Genetics, 8(6), 424–436. doi:10.1038/nrg2026
FANTOM4 – A Systems ApproachMonoblast-like THP-1 cells were stimulated by PMA to differentiate them into monocyte-like cel...
Motif Activity Response Analysis (MARA)                                  Promoter Analysis & Motif Activity               ...
Transcription regulation network consisting of 30 core motifs (F4)            55 out of 86 edges were supported by experim...
FANTOM5              Static networks        Time-courses                   iPS      Stem          ES                      ...
Defining a cell (F5)                                     •Surface markers                                     •Morphology ...
Defining a cell (F5)            Chr1 Chr2                                    Chr22   ChrX   ChrY                          ...
High reproducibility of Helicos-CAGE (FANTOM5)         Heliscope doesnt use PCR, then avoids amplification bias.r=0.99    ...
Promoters at unexpected area(1)PUM2            In adipocyte, this is the only one promoter in this geneNCOA1        In adi...
Expression profile (F5)NKX2-5            This profile is useful   for biomarker development                          Tissu...
Selfish DNA• Transposable elements (TEs)  – the DNA sequence spreads by forming additional    copies of itself within the ...
Expression of Repeat Elements    Mouse         Human
Nat Genet. 2009 May;41(5):563-71.Enrichment of TE near TSS mouse              human              mouse               human...
FANTOM Collaborators,                                               Thanks!Australia          Western Australian Institute...
Transcriptional regulation ofadipogenesis is perturbed in obesity
Obesity – Molecular basis• Medical condition with excess of body fat• Increased fat mass is associated with a low-grade  i...
Social aspects• Increases the likelihood of various diseases   – heart disease, type 2 diabetes, obstructive sleep     apn...
Goals• Find regulators of key obesity genes in man  – Substantial differences in adipose tissue gene    expression between...
Study design                                Cohort 1                                                                      ...
Cohort 1
miRNA expression• 20 differentially expressed candidates identified  in WAT (from cohort 1)      • Some of them previously...
miRNA – functional study                                                     CCL2 secretion• CCL2 (MCP1) secretion• Over-e...
Gene expression• Substantial expression differences between obese  and non-obese human individuals in WAT (cohort  1)  –  ...
Obesity regulation• Motif Activity Response Analysis (MARA)  a) Transcription factor binding site (TFBS) motifs    with si...
Principal Component Analysis                                                                       on Motif Activities    ...
Regulatory network• TF regulation from MARA   – Overlap obesity network and adipogenesis network• miRNAs into network   – ...
Sub-network validation                     Knock/over-expression                     → mRNA, protein  Luciferace assay
Physiological evaluation                           1.4                                                                    ...
Conclusions• Regulation of adipose tissue inflammation is  multilayered   – Mediated by miRNAs directly and indirectly thr...
Acknowledgments• Lipid Lab @ Karolinska Institutet   –   Niklas Mejhert   –   Peter Arner*   –   Ingrid Dahlman   –   Agné...
Solving challenges in data analysis –               Conclusion• Bring together technology and clinical research  –   Under...
Sequencing the transcriptome reveals complex layers of regulation, Department of Biosciences and Nutrition, Karolinska Ins...
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Sequencing the transcriptome reveals complex layers of regulation, Department of Biosciences and Nutrition, Karolinska Institutet, Carsten Daub Copenhagenomics 2012

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Sequencing the transcriptome reveals complex layers of regulation, Department of Biosciences and Nutrition, Karolinska Institutet, Carsten Daub Copenhagenomics 2012

  1. 1. Sequencing the transcriptomereveals complex levels of regulation Carsten O. Daub Copenhagenomics 2012-06-15
  2. 2. RIKEN – Omics Science Center (OSC)
  3. 3. Karolinska InstituteScience for Life Laboratory – SciLifeLab Department of Biosciences and Nutrition Technology Hospital
  4. 4. Outline• Characterize cells on the molecular level• Finding regulators of gene expression – We observe gene expression – What are the regulatory elements causing the observed changes in gene expression?• Example: Inflammation of adipocytes in obesity
  5. 5. Cap Analysis of Gene Expression method RNA extraction RNA 5’ Gene CAGE library preparation 3’ 1. CAP trapper 遺伝子 Gene Gene Genome 2. Trehalose extension method 3. CAGE library RNA RNA CAP SequenceTag sequencing with the next-generation sequencer1. Genome Sequencer FLX (Roche/454)2. Genome Analyzer System (illumina/ Solexa)3. SOLiD (Applied Biosystems) Data processing Quality control Statistical variation of the obtained sequence Extraction of tag sequences Clustering Mapping Statistical variation of the mapping result Visualization with “genome browser” Statistical analysis T. Shiraki et al, PNAS, 100, 15776-15781 (2003)
  6. 6. Traditional CAGE method RNA extraction CAGE library preparation CGCATGGTCGATAGACTTG 1. CAP trapper 2. Trehalose extension method 3. CAGE library GTGCGCGTCGAATATCGAT Sequence CGAATATCGATAGACTTGTag sequencing with the next generation sequencer1. Genome Sequencer FLX (Roche/454)2. Genome Analyzer System (illumina/ Solexa)3. SOLiD (Applied Biosystems) Data processing Quality control Statistical variation of the obtained sequence Extraction of tag sequences Clustering Mapping Statistical variation of the mapping result Visualization with “genome browser” Statistical analysis T. Shiraki et al, PNAS, 100, 15776-15781 (2003)
  7. 7. Traditional CAGE method RNA extraction Genome CAGE library preparation 1. CAP trapper 2. Trehalose extension method 3. CAGE library SequenceTag sequencing with the next generation sequencer1. Genome Sequencer FLX (Roche/454)2. Genome Analyzer System (illumina/ Solexa)3. SOLiD (Applied Biosystems) Data processing GTGCGCGTCGAATATCGAT Quality control Statistical variation of the obtained sequence Extraction of tag sequences Clustering Mapping Statistical variation of the mapping result Visualization with “genome browser” Statistical analysis
  8. 8. CAGE – Promoter Types Nature reviews Genetics, 8(6), 424–436. doi:10.1038/nrg2026
  9. 9. FANTOM4 – A Systems ApproachMonoblast-like THP-1 cells were stimulated by PMA to differentiate them into monocyte-like cells.10 time point samples were collected during differentiation. Monoblast-like Monocyte-like 0 1 2 4 6 12 24 48 72 96 hour PMA Replicates Microarray check Deep CAGE RIKEN1 RIKEN3 TF qRT-PCR RIKEN5 Not good RIKEN6 Illumina (47K probes) miRNA microarray 10 time points
  10. 10. Motif Activity Response Analysis (MARA) Promoter Analysis & Motif Activity 29,857 promoters were identified. Out of these promoters 23,403 were associated with 9026 genes.Genome eps CAGE tag Promoter1 m1 m1 m1 m2 m3 PMA Promoter2 m1 m4 ・・・・ 0h 1h 2h 4h 6h 12h 48h 72h 96h Promoter 29,857 m1 m5 Number of CAGE tags that mapped on the same site e ps m R pm Ams Reaction efficiency • Number of possible binding sites Effective concentration • THP-1 cells are a Degree of conservation cell line which upon PMA treatment can differentiate into an monoblastic leukemia of the motif • adherent monocyte like cell (CD14+, CSF1R+) Chromatin status Suzuki, Forrest, van Nimwegen et al. Nature Genetics 2009, 41:5
  11. 11. Transcription regulation network consisting of 30 core motifs (F4) 55 out of 86 edges were supported by experiments/in the literature. (Novel prediction works well!!) Enriched GO: from cell growth related to cell function related Motif activity Immune Inflammatory response UpMonocyte response Cell adhesion Down Transient Size of nodes: Significance of motifs Edge support Green: siRNAMonoblast Microtubele Red: literature Mitosis Cell cycle cytoskele Blue: ChIP :enriched GO for regulated H. Suzuki et al. Nature Genetics, 41:5, 553-562 (2009) genes
  12. 12. FANTOM5 Static networks Time-courses iPS Stem ES State Transition basins Endo Meso EctoDifferentiatedstates FANTOM4 FANTOM5
  13. 13. Defining a cell (F5) •Surface markers •Morphology (shape, volume, polarity) •Single or multinucleated, enucleated •Ploidy •Motility (adherent, resident, migratory) •Differentiation potential •Self renewal potential •Developmental/lineage history •Tissue of origin •Developmental age (doublings?) •Doubling time Defined outputs (eg growth factors) Response to inputsSelf reinforcing stable internal network
  14. 14. Defining a cell (F5) Chr1 Chr2 Chr22 ChrX ChrY •Surface markersHuman 1bp •Morphology (shape, volume, polarity) 3x109bpgenome •Single or multinucleated, enucleated •Ploidy All promoters on human genome will be revealed. •Motility (adherent, resident, migratory) •Differentiation potential The •Self renewal potential most objective definition of the cell !! •Developmental/lineage history •Tissue of origin •Developmental age (doublings?) •Doubling time Defined outputs (eg growth factors) Response to inputs Self reinforcing stable internal network Transcriptional regulatory NW
  15. 15. High reproducibility of Helicos-CAGE (FANTOM5) Heliscope doesnt use PCR, then avoids amplification bias.r=0.99 r=0.98 r=0.89 r=0.69 HeliScope CAGE
  16. 16. Promoters at unexpected area(1)PUM2 In adipocyte, this is the only one promoter in this geneNCOA1 In adipocyte, this is the only one promoter in this gene The 2nd example has good EST evidence Both are annotated in Gencode Some orphans are NOT orphans
  17. 17. Expression profile (F5)NKX2-5 This profile is useful for biomarker development Tissue-specific Heart-fetal
  18. 18. Selfish DNA• Transposable elements (TEs) – the DNA sequence spreads by forming additional copies of itself within the genome – makes no specific contribution to the reproductive success of its host organism. – transposition can be "copy and paste" or "cut and paste"• Nobel prize in 1983 to Barbara McClintock
  19. 19. Expression of Repeat Elements Mouse Human
  20. 20. Nat Genet. 2009 May;41(5):563-71.Enrichment of TE near TSS mouse human mouse human Mapping bias Over-expression Unrelated to expression when close to genes
  21. 21. FANTOM Collaborators, Thanks!Australia Western Australian Institute for Medical Research Peter KLINKEN, Louise WINTERINGHAMCanada McGill University Hisashi MIURA, Josee DOSTIE The University of British Columbia, Center for Molecular Medecine and Therapeutics Thomas Jonghyun HADenmark University of Copenhagen, Department of Biology Robin ANDERSSON, Albin SANDELIN, Eivind VALENFinland University of Helsinki, Department of Medical Genetics Alessandro BONETTIFrance University Pierre & Marie Curie, Laboratoire Microorganisms Genomics Hugues RICHARDGermany Charité - Universitätsmedizin Berlin, Allergy Center Magda BABINA University Hospital Regensburg Christian SCHMIDL, Michael REHLIItaly Dulbecco Telethon Institute Valerio ORLANDO, Beatrice BODEGA Fondazione Bruno Kessler (FBK) Marco CHIERICI, Cesare FURLANELLO, Marco RONCADOR International School for Advanced Studies (SISSA) Stefano GUSTINICH, Silvia ZUCCHELLI National Lab of Italian Consortium for Biotechnology (L.N.C.I.B.) Silvano PIAZZA, Claudio SCHNEIDER, Roberto VERARDO Thank you.Japan Database Center for Life Science Hidemasa BONO Keio University, School of Medicine Shigeo KOYASU, Kazuyo MORO, Jun-ichi FURUSAWA General Organizer Kyushu University Daisuke SUGIYAMA Yoshihide HAYASHIZAKI National Institute of Advanced Industrial Science and Technology (AIST), Computational Biology Research Center(CBRC)
  22. 22. Transcriptional regulation ofadipogenesis is perturbed in obesity
  23. 23. Obesity – Molecular basis• Medical condition with excess of body fat• Increased fat mass is associated with a low-grade inflammatory state in white adipose tissue• Adipocytes secrete key inflammatory proteins – TNFa, IL6, CCL2 (MCP1) – Markedly increased expression in obesity• CCL2 attracts macrophages from the blood stream• Mechanism of CCL2 expression control poorly understood
  24. 24. Social aspects• Increases the likelihood of various diseases – heart disease, type 2 diabetes, obstructive sleep apnea, certain types of cancer, and osteoarthritis• The medical care costs of obesity in the United States in 2008 totaled about US$147 billion (Health Affairs 2009; 28(5): w822-w831)
  25. 25. Goals• Find regulators of key obesity genes in man – Substantial differences in adipose tissue gene expression between obese / non-obese (JCEM, 2005) – Includes many genes involved in the functional regulation of adipose tissue • Adipogenesis, metabolism, adipokine production, inflammation• The regulatory elements that control obesity key genes are so far poorly characterized • TFs and upstream modulators (epigenetics, miRNAs)
  26. 26. Study design Cohort 1 Cohort 2 Obese (n=30) and Obese and non-obese non-obese (n=26) women (n=12) women 1. Hypothesis generation 3. Validation & mechanistical studies In vitro Intact adipose Isolated, mature 2. Relevance differentiated tissue fat cells adipocytes 4. Physiological roleGene expression Gene expressionmiRNA expression Knockdowns qRT-PCR of Over-expressions TFs & miRNAs 3’ UTR assay
  27. 27. Cohort 1
  28. 28. miRNA expression• 20 differentially expressed candidates identified in WAT (from cohort 1) • Some of them previously observed in obese mice – Most miRNAs down-regulated in obese samples • Fits well with the observation that 90% of differentially regulated genes are up-regulated – Differential expression validated with qPCR in WAT • 14 miRNAs validated in tissue and purified cells• In mature differentiated fat cells (from cohort 2) – All 20 expressed – 11 miRNAs diff. expressed and overlapping with WAT
  29. 29. miRNA – functional study CCL2 secretion• CCL2 (MCP1) secretion• Over-expressing 12 miRNAs in pre-adipocyte differentiation CCL2 IL6 3.0 *** TNF ** 2.5 2.0 ** **(microRNA/control) ***Relative secretion 1.5 1.0 *** * * **** ** ** ** ***** * *** *** *** *** *** *** *** ** *** 0.5 *** *** *** *** *** 0 Control Let-7d Let-7a miR-16 miR-126 miR-145 miR-143 miR-652 miR-193b miR-92a miR-26a miR-486-5p miR-342-3p
  30. 30. Gene expression• Substantial expression differences between obese and non-obese human individuals in WAT (cohort 1) – Many genes differentially expressed – Around 100 transcription factors – 90% of genes up-regulated – Confirms previous findings • Known up/down-regulated genes • GO terms – Up: inflammation, extracellular matrix – Down: metabolism, insulin signaling
  31. 31. Obesity regulation• Motif Activity Response Analysis (MARA) a) Transcription factor binding site (TFBS) motifs with significant difference in activity between obese and non-obese, intersected with b) targets with significant difference in expression according to SAM (5% FDR) → nodes in candidate “core” network
  32. 32. Principal Component Analysis on Motif Activities Obese 55 0.15 Non-obeseSecond principal component Body mass index (kg/m2) 0.10 45 0.05 35 0 -0.05 25 -0.10 15 -0.15 -0.15 -0.10 -0.05 0 0.05 0.10 0.15 -0.25 -0.15 -0.05 0.05 0.15 Second principal component First principal component 1400 160 80 Fat cell volume (pL) 1050 135 65 Body fat (%) Waist (cm) 700 110 50 350 85 35 0 60 20 -0.15 -0.10 -0.05 0 0.05 0.10 0.15 -0.15-0.10-0.05 0 0.05 0.10 0.15 -0.15-0.10-0.05 0 0.05 0.10 0.15 Second principal component Second principal component Second principal component
  33. 33. Regulatory network• TF regulation from MARA – Overlap obesity network and adipogenesis network• miRNAs into network – Based on target predictions (Miranda, TargetScan)• Final candidate network – Developed during adipocyte differentiation – Perturbed in obesity – Modulated by miRNAs• Sub-network: inflammation – Obesity is characterized by chronic, low-grade inflammation in adipose tissue • Coupled to obesity related conditions, i.e. insulin sensitivity • TNF, IL6, CCL2
  34. 34. Sub-network validation Knock/over-expression → mRNA, protein Luciferace assay
  35. 35. Physiological evaluation 1.4 1400 0.9 1050 Fat cell volume (pL) 0.4 700 Log10 HOMAIR -0.1 350 -0.6 0 -0.2 0.2 0.6 1.0 1.4 -0.2 0.2 0.6 1.0 1.4 Log10 miR-193b levels (A.U.) Log10 miR-193b levels (A.U.) 2.0 160 Waist circumference (cm) 1.4 135Log10 insulin-stimulatedadipocyte lipogenesis 0.8 110 0.2 85 -0.4 60 -0.2 0.2 0.6 1.0 1.4 -0.2 0.2 0.6 1.0 1.4 Log10 miR-193b levels (A.U.) Log10 miR-193b levels (A.U.)
  36. 36. Conclusions• Regulation of adipose tissue inflammation is multilayered – Mediated by miRNAs directly and indirectly through TFs – Developed during pre-adipocyte differentiation – Perturbed in obesity• Approach is viable for exposing unknown, potentially clinically relevant regulation – Hypothesis free initial step enables study of other processes such as adipogenesis, metabolism and adipokine production• Published in Diabetes on June 11, 2012
  37. 37. Acknowledgments• Lipid Lab @ Karolinska Institutet – Niklas Mejhert – Peter Arner* – Ingrid Dahlman – Agné Kulyté – Mikael Rydén• Biozentrum Basel – Erik van Nimwegen* – Mikhail Pachkov – Piotr Balwierz• OSC RIKEN – Erik Arner – Yoshihide Hayashizaki Grant from MEXT to YH.
  38. 38. Solving challenges in data analysis – Conclusion• Bring together technology and clinical research – Understand experimental system and data in detail – Understand the results of the computational analysis – Jointly & iteratively – Soft skills very helpful• Aim for concrete findings that can be functionally validated

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