Measuring TF-DNA interactions

Measuring TF-DNA interactions

How is Biological Complexity Achieved? Mediated by Transcription Factors (TFs) 2

Regulation of Gene Expression by Transcription Factors TF trans-acting factors TF TF TF TF activation TF TF 1 2 3 cis-regulatory elements Gene repression 3

The big point is: how these TFs orchestrate the expression of thousands of genes in a genome to create such a spectrum of biological diversity remains a mystery Several methods have been developed in the last several years to study TF-DNA interactions and to understand the function of TFs. 4

HTP Methods for studying TF-DNA interactions Chromatin Immunoprecipitation followed by chip (ChIP- Chip) or followed by Sequencing (ChIP-Seq) Protein Binding Microarrays Yeast-1-Hybrid (Y1H) Bacterial-1-Hybrid (B1H) Systematic Evolution of Ligands by Exponential Enrichment - SELEX (obsolete) 5

Y1H and B1H 1 2 - Both are modifications of the Yeast-2-hybrid system. - Here, the bait is a library of random DNA sequences. - Surviving colonies are grown, the DNAs are sequenced (NGS) using and TFspecific DNA sequences retrieved for further analysis. 6

Protein Binding Microarrays 1 3 2 7

SELEX 8

Chromatin immunoprecipitation (ChIP) 9

What do you get from the IP? 10

Protein-DNA interactions by ChIP-chip 11

Profiled 204 transcription factors in normal conditions Another 148 TFs in 13 additional growth perturbing conditions 12

ChIP-chip and DNA binding site conservation 13 Harbison C, Gordon B, et al. Nature 2004

Predicting the DNA binding sites 14 Harbison C, Gordon B, et al. Nature 2004

Different promoter architectures 15 Harbison C, Gordon B, et al. Nature 2004

Different conditions activate TFs 16 Harbison C, Gordon B, et al. Nature 2004

Example genome annotations based on chipchip 17 Harbison C, Gordon B, et al. Nature 2004

Pros and cons of ChIP-chip PROS: In vivo method è real biological binding events Condition specific è can determine differential gene regulatory networks Easy mapping of hits to genome chip part (or seq for ChIP-seq) is real high-throughput CONS: Condition specific è loss of potential binding sites highly probable Indirect binding è you might pull down TF1 but what is bound to DNA at a certain location is TF2, onto which TF1 is piggy-backing. TF complexes è will only know that TF1 is bound at a location. Might lose fact that TF1 might be part of a TF complex ChIP is not really high-throughput ChIP is not trivial Noisy, noisy, noisy!!! 18

ChIP-Seq 19

Next Generation Sequencing Let s see this in action 20

ChIP-Seq 21

ChIP-Seq 22

ChIP-Seq Mouse Chromosome X: 2TFs, 6 Histone modifications 23

Pros and cons of ChIP-Seq PROS: As per ChIP-Chip PLUS: Resolution and data quality are way way better Way less noisy than ChIP-Chip Easier to analyse CONS: EXPEN$IVE!!!! 24

Dynamic regulatory network models Week 5

Autoregulation 26

What is Negative Autoregulation 27 Figure 1. Synthetic transcription circuits. (a) Simple transcription unit (open loop, Dh5a þ pzs12-tetr þ pze21-gfp). Cells expressing TetR can be induced, by adding atc to the medium, to produce GFP. (b) Negative autoregulation (Dh5a þ pzs p 21tetR-egfp 4 ): the tet promoter controls the production of its repressor, TetR fused to GFP. The TetR GFP fusion protein represses its own promoter. 4 Rosenfeld N, J. Mol. Biol. 2002

Autoregulation is the simplest network motif In an random Erdos-Renyi (ER) network with N nodes and E edges : p self = 1/ N # E $ k E k P( k) = % & Pself (1 pself ) ~ binomial( E, pself ) ' k ( < N > = Ep = E / N σ ER self ER self = E / N 28

Observation of autoregulation motifs in a real network Transcription interaction network from E.coli Method for identification of repetitive patterns Negative autoregulation Benefits Consider a random ER network where N=424, E=519 <N self > ER ~1.2 Actual number of autoregulated transcription factors in the example E.coli regulatory network, N self,e.coli =40 29

Negative autoregulation (n.a.r.) As long as X < K, transcription (production) rate is maximal initially degradation of X can be neglected (linear increase in X) As soon as X > K, transcription stops A K X This is a property inherent to the step function The steady-state level is K 30

Faster response of negative autoregulation 31

Effect of negative autoregulation Faster response time T / T = ( β / β ) / 2log(2) ( n. a. r) simple 1/ 2 1/ 2 simple Robustness to fluctuations in production rate 32

Negative autoregulation: robustness There exist different production rates beta between different genes Simple regulation (no feedback) strongly depends on production rate: X st =β/α Steady-state level of autorepression: X st =K K depends on more hardwired factors such as chemical bonds between X and its binding site less prone to fluctuations 33

Dynamics of Negative and Positive Autoregulation r r r e t ) - f n o - o - a d X/X st e 1 0.8 0.6 0.4 0.2 Y X b X 0 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 Cell generations c - h e e s n n e n e r d d X Negative autoregulation Simple regulation - n f s n r n. d r s. d X/X st Positive autoregulation Figure 1 Simple regulation and autoregulation. a In simple regulation, transcription factor Y is activated by a signal S y. When active, it binds the promoter of gene X to enhance or inhibit its transcription rate. b In negative autoregulation (NAR), X is a transcription factor that represses its own promoter. c In positive autoregulation (PAR), X activates its own promoter. d NAR speeds the response time (the time needed to reach halfway to the steady-state concentration) relative to a simple-regulation system that reaches the same steady-state expression. PAR slows the response time. e An experimental study of NAR, using a synthetic gene e f 1 0.8 0.6 0.5 0.4 0.2 0 0 0.21 0.5 1 1.5 2 2.5 3 Cell generations Alon U, Nat. Rev. Genet. 2007 34

Negative Autoregulation 789 Figure 3. Comparison of the experimentally observed kinetics of a negative autoregulatory circuit and a simple transcription unit. Fluorescence per cell, normalized by its maximal value, is plotted versus time in cell-cycles. The rise-time is the time to reach half of the maximal product concentration (thin dashed lines). Bold black line, induction of a simple transcription unit (open loop). Fine black line, theory (equation (3)). Red, cyan, and purple lines, kinetics of negative autoregulatory circuit. Blue, analytical solution of the mathematical model of negative autoregulation in the limit of strong autorepression, b 2/a q k (equation (7)). Broken black line, kinetics of the autoregulatory circuit prior to atc depletion, where tetr is fully inactivated and the feedback is cut. The simple transcription unit has a rise-time of one cell-cycle, while the negative autoregulatory circuit has a rise-time of 0.2 cell-cycles. Rosenfeld N, J. Mol. Biol. 2002 35

Summary: Negative Autoregulation A very simple but significantly enriched network motif It enables fast responses Provides robustness to fluctuations 36