Transcription factor omics Comparative transcriptomics and DNA-protein interactomics disclose novel insights into transcription factor omics. Factor I C A G A G T A G T A G Factor II C A G A G T A G C TFx TFx A A A T T TFx TBP T A A Pol II TFx cis-element cis-element TATA-Box Dierk Wanke dierk.wanke@zmbp.uni-tuebingen.de 1
Principle of functional genomics Central Dogma of Molecular Biology DNA RNA Protein Metabolism Central Dogma of functional Genomics Physiology Genome Transcriptome Metabolomics Proteome DNA RNA Protein 2 (modified from Pevsner 2003)
Principle of functional genomics Central Dogma of Molecular Biology DNA RNA Protein Metabolism Central Dogma of functional Genomics Physiology Genome Transcriptome Metabolomics Proteome Besides the four classical omes, a number of derived omes exist: with Interactome being probably the most important one! There is high interdependency between the different omes 3
Individual history Developmental stage Previous stress Diurnal changes and circadian rhythm Tissue / organ specificity Output interpretation Omics analysis Condition laboratory / environmental setting quantitative OMICS Organism responses Documentation Network analysis Species level responses Genetic engineering Kilian et al. (2011)
Quantitative vs qualitative analyses Qualitative Analysis empirical data Yes / No / Maybe Quantitative Analysis mathematical data significance 5
Quantitative vs qualitative analyses Acquiring of quantitative data of subcellular processes with high spatiotemporal resolution in living (plant) cells in their native tissue environment Quantitative data Dynamics same direction, but different speed! 6
Principle of functional genomics Central Dogma of Molecular Biology DNA RNA Protein Metabolism Central Dogma of functional Genomics Physiology Genome Transcriptome Proteome Metabolomics qualitative data only! invariable given population (basic population) e.g. number of genes, number of bp easy to define a background model relatively easy to analyze f(x) = observed P expected P significance can be analyzed by Hypergeometric p 7
Principle of functional genomics Central Dogma of Molecular Biology DNA RNA Protein Metabolism Central Dogma of functional Genomics Physiology Genome Transcriptome Proteome Metabolomics first real omics mostly qualitative information (e.g. different from control? Yes/No) data is highly variable depending on type experiment / researcher / method of detection a background model can not be defined significance can be inferred but not measured by values 8
Principle of functional genomics Central Dogma of Molecular Biology DNA RNA Protein Metabolism Central Dogma of functional Genomics Physiology Genome Transcriptome Proteome Metabolomics qualitative and quantitative readout data is highly variable depending on type experiment / researcher / method of extraction analysis is driven by researcher s objectives due to high variability and data close to detection limit, metabolomes are very hard to analyze by statistics 9
Principle of functional genomics Central Dogma of Molecular Biology DNA RNA Protein Metabolism Central Dogma of functional Genomics Physiology Genome Transcriptome Metabolomics Proteome qualitative and quantitative readout data is highly variable depending on type experiment / researcher / method of detection various background models can be defined, which all end up with different results! - Analysis is driven by researcher s objectives various values can be computed to support significance of results 10
Principle of functional genomics Central Dogma of Molecular Biology DNA RNA Protein Metabolism Central Dogma of functional Genomics Physiology Genome Transkriptome Proteome Metabolomics Interestingly, due to the quantitative nature of the data types, both methods cope with similar problems e.g.: accession with low abundance, are sometimes below detection limit and have high variability large standard deviation / error bars 11 accession with high abundance, are so frequent that they compete with others or they saturate detection small error bars, due to signal saturation
Transcriptome Transcriptome Protein mrna Transcript transcription-start DNA ATG Introns * cis cis TATA cis cis distal proximal Exons Promoter gene sequence Transcriptome: all transcripts at a given time 12 2 current methods in the field: - microarrays - Next Generation Sequencing (NGS)
Microarray Transcriptome 13 more than 50 Mio. probe sets on 2 cm x 2 cm
Microarray Transcriptome Oligonucleotide-Array (Genechip-Arrays): PerfectMatch (PM) AGATGATAGACAGAGCAGATGCTTG MisMatch (MM) AGATGATAGACAGACCAGATGCTTG PM MM Signal Ratio
Microarray Transcriptome Oligonucleotide-Array (Genechip-Arrays):
Microarray At2g19200 FRK1 (At2g19190) At2g19180 GeneChip Array Exon Array Tiling Array qualitative and quantitative readout BUT only the GeneChip Arrays have internal controls (mismatch probes) Exon- and Tiling Array are not assigned to ONE specific target (similar sequences in the genomes will cross-hybridize and add background) Data between different types of arrays is of low comparability
Transcriptome Transcriptome Protein mrna Transcript transcription-start DNA ATG Introns * cis cis TATA cis cis distal proximal Exons Promoter gene sequence Transcriptome: all transcripts at a given time and tissue sample 2 current methods dominate the field: - microarrays - Next Generation Sequencing (NGS) 17
Next Generation Sequencing (NGS) Transcriptome
Next Generation Sequencing (NGS) At2g19200 FRK1 (At2g19190) At2g19180 Genome-Seq no sequence low coverage no informative data talk by Prof. Cole very high sequence no sequence coverage low coverage over-sampling, no informative data satuartion
Next Generation Sequencing (NGS) At2g19200 FRK1 (At2g19190) At2g19180 Genome-Seq RNA-Seq expression signal, but there is no annotated gene badly annotated, microrna-gene, differential splicing
Next Generation Sequencing (NGS) At2g19200 FRK1 (At2g19190) At2g19180 Genome-Seq low expression signal, (low coverage?) badly annotated, RNA-Seq cryptic intron in gene model, differential splicing
Transcriptome: What does ANALYSIS mean? Transcriptome Challenge: Find differentially expressed genes! Mine for meaningful gene sets! - fold difference from control (2x; 3x, 4x...) [simple data with control-treatments only] - LIMMA (Linear Models for Microarray Data) [all data, but linear modeling only] - probabilistic dynamical model - machine-learning approaches
Transcriptome: What does ANALYSIS mean? Transcriptome Challenge: Find differentially expressed genes! Mine for meaningful gene sets! Cluster analysis - Pearson correlation - k-means clustering - hierarchical clustering Problem: house-keeping genes!!!
Why searching for co-expression Transcriptome Co-expressed genes encode for proteins that act in the same signaling cascades, are enrolled in the same enzymatic pathways or represent interacting partners that form higher order complexes. Additionally, genes with similar expression profiles in microarray experiments might be regulated by the same cis-regulatory elements (CREs).
Related to photosynthesis Grundlagen der funktionellen Genomanalyse Related to aging
Transcriptome Co-expression reveals logic connections Co expression network reveals protein interaction network Transcriptome vs. Interactome Tanz et al. 2012
Transcriptome vs. metabolome Transcriptome Metabolome Combined analysis of transcriptomics and metabolomics in barley research simple Pearson correlation Mangelsen et al. 2010, Plant Phys.
sugars and genes exhibit diurnal fluctuations Transcriptome Correlation coefficients Metabolome 10 Fructose +0.8-0.8 8 2 10 Kestose +0.8-0.8 103 138 Median normalized (log scale) 1 0.1 10 1 0.1 Glucose 10 27 1 0.1 10 Sucrose 352 434 1 0.1 several genes might act as potential sugar signaling targets
Transcriptome: What does ANALYSIS mean? meaningful Transcriptome data microarray next generation sequencing (RNA seq) quantitative data + relatively high comparability + identification of DE genes + co-expression analysis + gene regulatory network analysis - low comparability of data - difficult to identify DE genes - co-expression analysis impossible - no network analysis mainly qualitative data - requires known genome - weak on differential splicing events - weak on ChIP data + applicable to any organism + good on differential splicing events + very good on ChIP data
Interactome Chromatin-ImmunoPrecipitation-NGS (ChIP-seq) Identification of in vivo binding sites for DNA binding proteins Immunoprecipitation of specific DNA-protein-complexes with antibodies De-cross-link DNA-protein complexes Ligate adapters to DNA, use as template for sequencing (ChIP-seq)
Histone modification regulates expression Interactome transcriptional active [Euchromatin] ON Ac Ac Ac Ac Ac mono-acetylation TrxG PcGs H3K27me3 Me Me Me Ac H3K4me3 tri-methylation Me Me Me Me Me Me Me Me Me Me Me Me Me Me Me transcriptional inactive [Heterochromatin] OFF Histone-modifications are important for gene activation or repression Position of Histones on the DNA can be identified by Chromatin-Immunoprecipitation (ChIP) and subsequent NGS
Transcriptome (mrna-seq) + Epigenome (ChIP-Seq) At2g19200 FRK1 (At2g19190) At2g19180 low OFFsignal, high ONsignal high expression, good detection of mrna RNA-Seq (transcriptome) ChIP-Seq (H3K27me 3 ) OFF ChIP-Seq (H3K4me 3 ) ON
Transcriptome (mrna-seq) + Epigenome (ChIP-Seq) At2g19200 FRK1 (At2g19190) At2g19180 RNA-Seq (transcriptome) OFFsignal, but still mrna ChIP-Seq (H3K27me 3 ) OFF ChIP-Seq (H3K4me 3 ) ON
Research interest: Gene expression control
Research interest: Gene expression control mrna TF cis cis TATA distal proximal transcription-start ATG cis Introns * Exons cis Promoter Rapid gene expression response in plant - How are rapid (stress) responses controlled? - How is specificity of this response mediated? laboratory experiments bioinformatics synthetic promoters TF Motif - Interaction - How do TFs find their cis-element? - What is the specificity of this interaction? biotechnology spectro-microscopy 35
Transcription factors (TFs) act as molecular switches bzip http://www.pinkfloyd-forum.de BAM ARR WRKY BPC GATA 36
Research interest: Gene expression control Central Dogma of Molecular Biology DNA RNA Protein Metabolism Central Dogma of functional Genomics Physiology Genome Transkriptome Metabolome Proteome promoter analysis microarray analysis Interactome Protein DNAinteractome 37
Genome Is there a cryptic code in non-coding regulatory sequence? transcription-start cis cis TATA ATG cis Introns * cis distal proximal Exons Promoter 400 No. of hits 300 200 100 frequency position ATG 38 Berendzen et al., 2006
Identification of putative cis-elements by genomic enrichment Genome transcription-start cis cis TATA ATG cis Introns * cis 1600 distal proximal Exons 1400 1200 Promoter No. of hits 1000 800 600 400 200 frequency distribution curves for the 0 500 TATATA motif 450 400 350 No. of hits 300 250 200 150 100 50 0 39 Berendzen et al., 2006
Genome Only ~10% of known DNA-binding proteins have a known DNA-binding motif We don t have knowledge on ~90 % of TF-function? What if these behave differently? 2340 ~2000 18 % 415 <200 <10 % 40 AGRIS TAIR TRANSFAC Arabidopsis average eukaryote Schröder et al., Plos One 2010
Binding specificity of TFs to their DNA motif Interactome DNA-protein interaction ELISA 2 pmol bio dsdna per well epitope tagged protein of interest in crude E. coli extract colorimetric detection DNA-Protein Interaction-ELISA provides quantitative data! 41 Brand et al., 2010
Development of a DPI-ELISA screen Interactome Binding site analysis by screening of all 6-mer binding motifs. 42 Brand et al., 2013a Brand et al., 2013b
A DPI-ELISA screen with stress responsive WRKY-TFs Interactome WRKY11 DBD Compare different WRKY binding affinities Characterization / identification of binding consensi 43 Brand et al., 2013a Brand et al., 2013b
Homology modeling of the WRKY protein at the binding motif Interactome WRKY WRKY WRKY33: One WRKY-protein with 2 WRKY domains! 44 Brand et al., 2013a Brand et al., 2013b
Homology modeling of the WRKY protein at the binding motif Interactome linker region variable library region WRKY-protein at the binding motif Homology modeling allows us to built hypotheses about binding of proteins to DNA, as soon as the sequences of both, protein and DNA-motifs are known linker region 45 Brand et al., 2013a Brand et al., 2013b
Molecular Dynamics (MD) simulations with WRKY33-domains Interactome weak binder strong binder MD gives an impression about binding strength requires usually complex physical experiments 46 Brand et al., 2013a Brand et al., 2013b
A DPI-ELISA screen with stress responsive WRKY-TFs Interactome Very similar WRKY domains have very different binding motifs 47 Brand et al., 2013a Brand et al., 2013b
Homology modeling and interactome provide functional clues Interactome affinity high low WRKY50 DBD hngach WRKY33 cdbd nngacw WRKY11 DBD ydgacy WRKY proteins recognize (slightly) different binding consensi Different binding consensi are bound with different affinities Quantitative binding information can be translated into a probability matrix to search genomic information for possible binding sites 48 Brand et al., 2013a Brand et al., 2013b
Microarray Transcriptome Oligonucleotide-Array (Genechip-Arrays): ATH1-Genechip: 22746 probe cells (79% of all A. thaliana genes) >24000 genes (85%) can be detected due to the high redundanzcy in the genome 21685 unique probe sets about 13600 (~60%) genes hybridize with probes to generate analyzable data We made use of the 1295 AtGenExpress Expression Profiles + ~1500 additional datasets for co-expression analyses
Transcriptome Homology modeling and interactome provide functional clues Which genes are co-expressed with WRKY genes AND have the respective binding site in their promoter?
Co-expression analysis in complex Microarray datasets Transcriptome Search for WRKYs as hubs ~22 000 genes; analyzed in ~3000 microarray hybridizations Wanke et al., QBIC 2010
Genome analysis combined with transcriptome analysis Genome Transcriptome mrna transcription-start WRKY cis cis TATA distal proximal ATG cis Introns * Exons cis Promoter Identify binding sites in promoters Identify WRKY-co-expressed genes
Genome analysis combined with transcriptome analysis Genome - Transcriptome affinity high WRKY50 DBD WRKY33 cdbd WRKY11 DBD hngach nngacw ydgacy low No. co-expressed genes: ~300 ~400 ~400 No. binding sites: ~8000 ~ 5000 ~ 3000 No. put. targets ~250 ~60 ~40 We used publicly available microarray data on WRKY33 to verify our data.
Genome analysis combined with transcriptome analysis Transcriptome 100 mock stress stress normalized signal intensity (log scale) 10 1 0.1 activator repressor 0 6 12 24 6 12 24 6 12 24 control AtWRKY33 Integration of metadata from the Somssich Lab (Birkenbihl et al., 2012)
NGS coupled genomics: In vivo target gene identification by chromatin immuno-precipitation Genome Sequencing by NGS 100 Isolation of bound DNA normalized signal intensity (log scale) 10 1 0.1 activator All ~60 putative target genes were independently identified as true in vivo targets! repressor 6 12 24 AtWRKY33 Integration of metadata from the Somssich Lab (Birkenbihl et al., 2012)
What is the function of the putative target genes! Genome - Transcriptome affinity high WRKY50 DBD WRKY33 cdbd WRKY11 DBD hngach nngacw ydgacy low cell cycle chromatin remodeling pathogen response basal resistance No. put. targets ~250 ~60 ~40
What is the function of plant specific BBR/BPC factors? 0.05 changes 100 95 Os10g0114500 Os10g0115500 99 100 93 76 97 99 73 HvBBR GmGAGA-BP AtBPC2 AtBPC1 AtBPC3 AtBPC7 AtBPC4 AtBPC5 AtBPC6 Os06g0130600 Ceratopteris richardii Group I Group II Physcomitrella patens BBR/BPCs arose evolutionary late in the green plant lineage Bind to GAGA-motifs and recruit LHP1 to DNA Wanke et al., 2011; Santi et al., 2003
Transcriptome Microarray vs. NGS Mutants in transcription factor genes exhibit differences in development Control lhp1-4 bpc4 bpc6 lhp1-4 bpc4 bpc6
Microarray vs. NGS Transcriptome Strong defects in flowers and seed production! Control lhp1-4 bpc4 bpc6 lhp1-4 bpc4 bpc6 Transcriptome (microarray) analysis to understand what is wrong!
Microarray vs. NGS microarrays provide the potential to compare the data with already existing microarrays (intercomparability)
Microarray vs. NGS more induced genes in the mutants function as repressor proteins repression coincides with already known repressive H3K27me3 marks
Microarray vs. NGS 30 6 17 10 412 221 27 identical RNA used in both experiments two replicates libraries paired end reads ~ 80 Mio analyzable reads from each library microarray NGS Verify the data by Transcriptome (NGS) analysis!
Microarray vs. NGS 30 6 27 123 12 65 10 17 221 12 28 323 412 729 microarray 17 0 28 NGS Both methods provide Transcriptome data, but which is correct?
Microarray vs. NGS Genome - Interactome two replicate libraries from BPC6-expressing plants single end reads ~ 50 Mio analyzable reads each Isolation of target DNA Sequencing by NGS Interactome (ChIP-seq) analysis to identify direct target genes! We identified 350 putative in vivo target genes!
Microarray vs. NGS 30 6 27 123 12 65 10 17 221 12 28 323 412 729 microarray 17 2 0 28 6 NGS 33 putative direct targets overlapped with microarray expression 47 putative direct targets overlapped with NGS expression only a total of 8 were picked up with both techniques
Next Generation Sequencing (NGS) Transcriptome GLOBAL vs. LOCAL NGS is a quantitative approach, but reveals mostly (high value) qualitative data Quantitative analyses on a Global scale are informative, while specific local questions provide qualitative data only or remain hidden. Unfortunately, NGS for transcriptomics is often useless (to date) local expression analysis is difficult co-expression analysis is almost impossible Chromatin-ImmunoPrecipitation-NGS (ChIP-seq) data is highly informative, but mainly of qualitative nature!
Transcription factor omics Quantitative omics data can be normalized and, thus, is comparable. Quantitative data from different sources can be used directly in a simultaneous combined analysis (e.g. transcriptomics and metabolomics) NGS data harbors great potential, but (at present) can not substitute quantitative gene expression data e.g. in co-expression analyses Novel methods, such as the DPI-ELISA, enable quantitative proteomics The combined analysis of quantitative transcriptome and interactome data provide unique insights into protein function
What does ANALYSIS mean - personally? Challenge: Mine for meaningful data Problem: Conflicting interests bioinformatics biologists significant output is not interested in a special gene or outcome (unbiased) independent of funding biologically relevant output is ONLY interested in his special gene(s) of interest (biased) important for funding
Thank you! Tübingen Andreas Hecker Sachie Kimura Rebecca Dautel Joachim Kilian Luise Brand Saarland Nathalie Sebening Christine Zehren Angelika Anna Dagmar Pogomara Jan Hirsch MPI Tübingen Markus Schmid Silvio Collani Uni Rostock Birgit Piechulla Katrin Wenke INRA Versailles Valerie Gaudin ETH Zürich Sam Zeeman Sebastian Soyk Uni Würzburg Wolfgang Dröge Laser Christoph Weiste