Gene regulatory network

1. Gene regulatory network Jin Chen CSE891-001 2012 Fall

2. Outline • Transcriptional regulation • Co-expression & co-regulation • Bio-techniques • ChIP-seq • Bacterial one-hybrid system • Computational models for gene regulation network construction • Binding + expressions with TF knock out • Binding + time serial gene expressions

3. David J.C. MacKay, Information Theory, Inference & Learning Algorithms, 2003

4. Transcriptional regulation • Regulation of transcription controls when transcription occurs and how much RNA is created • Transcription factors are often needed to be bound to a regulatory binding site to switch a gene on (activator) or to shut off a gene (repressor) • Generally, as the organism grows more sophisticated, their cellular protein regulation becomes more complicated

5. Transcription control • Transcription control is directed primarily by two elements • Transcription factors (TF) • DNA sequences that facilitate the binding of these TFs (cis-regulatory elements)

6. Transcription control • First, there needs to be an initiating signal • This signal gives rise to the activation of a TF, and recruits other members of the "transcription machine." TFs generally simultaneously bind DNA. TFs and their cofactors, can be regulated through reversible structural alterations • Transcription is initiated at the promoter site, as an increase in the amount of an active TF binds a target DNA sequence. Other proteins, known as "scaffolding proteins" bind other cofactors and hold them in place • Frequently, extracellular signals induce the expression of immediate early genes. These are in and of themselves TFs or components thereof, and can further influence gene expression

7. Gene regulation network • Node • TF or target gene • Edge • Regulation relation • Directed • Activation “--->” • Inhibition “---|” Yeast Mata et al. Genome Biol. 2007;8(10):R217

8. Co-expression & co-regulation • Genes belonging to the same cluster are often called co-expressed • Genes with similar expression patterns might share transcription factors and functional regulatory binding sites

9. Background Topic 1. GRN Reconstruction Algorithms for GRN reconstruction based on gene expression & motif data (2002-2008) Algorithms focusing on integrating binding data with existingdata (2007-now) • 2 input data • Time serial microarray • TF binding motifs • 3 models • Time shift matching • Mutual information • Granger causality & DBN • 2 limitations • High false positive rate • Small scale (# genes~100) • 3 input data • Time serial microarray • TF binding motifs • Binding data • 2 models • Time shift matching and binding • Protein expression approximation from binding data • 2 limitations • Lack of binding data at systems level • Combinatorial TF studies 2002 2007 2008 2010 Time

10. ChIP-seq • ChIP-seq is the sequencing of the genomic DNA fragments that co-precipitate with a DNA-binding protein that is under study • DNA-binding proteins most frequently investigated in this way are transcription factors, etc • ChIP-seq can identify all DNA segments in the genome physically associated with a specific DNA-binding protein • It does not rely on prior knowledge of precise DNA binding sites Liu et al, BMC Biology 2010, 8:56

11. Flow scheme of the central steps in the ChIP-seq procedure Liu et al.BMC Biology 2010 8:56  

12. Example Algorithmic analysis for mapping and peak-calling A ChIP DNA sample from a stem cell population and the corresponding input DNA sample were both processed without amplification http://www.helicosbio.com/Applications/ChIPSeq/tabid/69/Default.aspx

13. Binding network • First action of a TF is to find and to bind DNA segments and ChIP-seq allows the binding sites of TFs to be identified across entire genomes • Protein-DNA binding network • Direct downstream targets of any transcription factor can be determined • DNA sequence motif that is recognized by the binding protein can be computed

14. Example ChIP-seq profiling of 13 TFs in embryonic stem (ES) cell development revealed the organization of regulatory elements. This provided insights in the integration of TF-mediated signaling pathways in ES cell differentiation Chen et al Cell 2008 , 133:1106-1117

15. What ChIP-seq cannot do • Many observed binding events are neutral and do not regulate transcription • Regulatory binding events often occur at enhancers that are not proximal to the target gene that they control •  The task of identifying transcriptional targets requires the integration of ChIP-seq with evidence from expression data to help associate binding events with target gene regulation Honkela et al. PNAS 2010 vol. 107 no. 17 pp 7793–7798

16. Bacterial one-hybrid system wikipedia

17. Gene expression data • TF knock-out gene expression • TF over-expression gene expression • Differential expression of genes between wild type and mutant/over-expression is indicative of a potential regulatory interaction, e.g. Yeast GRN Reimand et al, Nucleic Acids Research, 2010, Vol. 38, No. 14 pp 4768–4777

18. Comprehensive analysis of TF knockout expression data in Yeast • 269 TF knockout microarrays, covering almost all yeast TFs • DNA–protein interactions derived from ChIP-chip experiments • Predicted TF binding sites with position weight matrices Hu et al. Nat. Genet., 39, 683–687, 2007; Harbison et al. Nature, 431, 99–104. 2004

19. Comprehensive analysis of TF knockout expression data in Yeast • Checked the expression levels of the TFs • Intuitively one expects the TF under consideration to have lower expression in the mutant strain compared with the wild type strain • confirms this for 155 TFs • 78 TFs display a negative fold change at statistically non-significant levels • 36 TFs are lethal Reimand et al, Nucleic Acids Research, 2010, Vol. 38, No. 14 pp 4768–4777

20. Reimand et al, Nucleic Acids Research, 2010, Vol. 38, No. 14 pp 4768–4777

21. Comprehensive analysis of TF knockout expression data in Yeast • Examine functional annotations of differentially expressed genes • As most TFs are considered to regulate distinct cellular processes, their target genes should be associated with a coherent set of molecular and biological functions • Used g:Profiler to identify GO, KEGG and Reactome pathway annotations Reimand et al, Nucleic Acids Research, 2010, Vol. 38, No. 14 pp 4768–4777

22. Reimand et al, Nucleic Acids Research, 2010, Vol. 38, No. 14 pp 4768–4777

23. Comprehensive analysis of TF knockout expression data in Yeast • Overlap between TF-binding and TF knockout data • Collect binding sites for 142 TFs, comprising 5,188 ChIP-chip interactions and 17,091 motif predictions • Calculate the intersection between the list of differentially expressed genes from the TF knockout and targets identified by ChIP-chip or binding-site predictions • 2,230 regulation relations Reimand et al, Nucleic Acids Research, 2010, Vol. 38, No. 14 pp 4768–4777

24. Comprehensive analysis of TF knockout expression data in Yeast • Include protein-protein interaction information as an additional perspective to the assessment of GRN • TFs that function together may show significant overlap in their target genes • Out of 115 pairs of physically interacting TFs in the dataset, 92 display such an overlap • TFs tend to regulate genes that interact with each other • Out of 110,487 differentially expressed genes, there are 3,846 pair-wise interactions between co-regulated genes, covering 2,262 genes in total • Most TFs target at least one pair of interacting genes Reimand et al, Nucleic Acids Research, 2010, Vol. 38, No. 14 pp 4768–4777

25. Temporal gene expression data • A problem with the above approach is that the creation of mutant strains is challenging or impossible for many TFs of interest • Even when available, mutants may provide very limited information because of redundancy or due to the confounding of signal from indirect regulatory feedback • Temporal dynamics: use time serial wild-type gene expression. e.g. Drosophila GRN Honkela et al. PNAS 2010 vol. 107 no. 17 pp 7793–7798

26. Other models for gene regulation network construction • Expression based study • Dynamic Bayesian Network • Granger causality • TF binding motif based study • Weader • AlignAce

27. Gene regulation network analysis • Transcriptional regulation is mediated by the combinatorial interplay between cis-regulatory DNA elements and trans-acting transcription factors, and is perhaps the most important mechanism for controlling gene expression • A transcriptional regulatory network that integrates such information can lead to a systems-level understanding of regulatory mechanisms Kim et al. Wiley Interdisciplinary Reviews: Systems Biology and Medicine. Vol. 3, Iss 1, pp 21–35 2011

28. Discovery of motifs and regulatory modules • Binding motif prediction only based on the knowledge afforded by PWMs often suffers from high false-positive rates • To reduce the false-positive rate, a number of biological insights have been used • evolutionary pressure placed on these important cis-regulatory elements • co-expression with genes that have well-documented functions and expression patterns • clustering of cis-regulatory features into cis-regulatory modules

29. Discovery of motifs and regulatory modules • Collect the binding sequences for known TFs and to identify potential binding sites in unannotated genome sequence • position frequency matrix • cluster of individual TF binding sites • regulatory relations among 4 genes Modeling of individual transcription factor binding sites and cis-regulatory modules Kim et al. Wiley Interdisciplinary Reviews: Systems Biology and Medicine. Vol. 3, Iss 1, pp 21–35 2011

30. Co-regulation • A density-based subspace clustering algorithms for coherent clustering of gene expression data • The model allows • Expression profiles of genes in a cluster to follow any shifting-and-scaling patterns in subspace • Expression value changes across any two conditions of the cluster to be significant • Experimental results show that the algorithm is able to detect a significant amount of high biological significant clusters missed by previous models