Lineage-Specific Genome Architecture Links Enhancers and Non-coding Disease Variants to Target Gene Promoters

This is the final version of the article. It first appeared from Elsevier (Cell Press) via https://doi.org/10.1016/j.cell.2016.09.03

Structural and biochemical analysis of the Tn10 transposase synaptic complex

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The positive and negative regulation of Tn10 transposition by IHF is mediated by structurally asymmetric transposon arms

The Tn10 transpososome has symmetrical components on either side: there are two transposon ends each of which has binding sites for a monomer of transposase and an IHF heterodimer. The DNA bending activity of IHF stimulates assembly of an intermediate with tightly folded transposon ends in which transposase has additional ‘subterminal’ DNA contacts, located distal to the IHF site. These subterminal contacts are required to activate later steps in the reaction. Quantitative hydroxyl radical footprinting and gel retardation unfolding experiments show that the transpososome is fundamentally asymmetric, despite having identical components on either side. Major differences between the transposon ends define α and β sides of the complex. IHF can dissociate from the transposon arm on the β side of the complex in the absence of metal ion. However, IHF is locked onto the α side of the complex, probably by the subterminal transposase contacts, until released by a metal ion-dependent conformational change. Later in the reaction, IHF inhibits target interactions. Using a very short transposon arm, target interactions are demonstrated at a saturating IHF concentration. This suggests that inhibition of target interactions is due to steric hindrance of the target binding site by a single IHF-folded transposon arm

An integrated model of transcription factor diffusion shows the importance of intersegmental transfer and quaternary protein structure for target site finding.

We present a computational model of transcription factor motion that explains both the observed rapid target finding of transcription factors, and how this motion influences protein and genome structure. Using the Smoldyn software, we modelled transcription factor motion arising from a combination of unrestricted 3D diffusion in the nucleoplasm, sliding along the DNA filament, and transferring directly between filament sections by intersegmental transfer. This presents a fine-grain picture of the way in which transcription factors find their targets two orders of magnitude faster than 3D diffusion alone allows. Eukaryotic genomes contain sections of nucleosome free regions (NFRs) around the promoters; our model shows that the presence and size of these NFRs can be explained as their acting as antennas on which transcription factors slide to reach their targets. Additionally, our model shows that intersegmental transfer may have shaped the quaternary structure of transcription factors: sequence specific DNA binding proteins are unusually enriched in dimers and tetramers, perhaps because these allow intersegmental transfer, which accelerates target site finding. Finally, our model shows that a 'hopping' motion can emerge from 3D diffusion on small scales. This explains the apparently long sliding lengths that have been observed for some DNA binding proteins observed in vitro. Together, these results suggest that transcription factor diffusion dynamics help drive the evolution of protein and genome structure

Antenna effect.

<p>A) Illustration of the ‘antenna effect’: target gene finding times are reduced when TFs can get to their targets by diffusing along the DNA. The TF (light blue circle) diffuses along the antenna DNA (grey bar) to reach the TG (orange hexagon). B) Effect of antenna length on the number of TF-TG complexes at steady-state. C) Effect of antenna length on the time for the first TF to bind to the first TG in the simulation (blue) and on the time required for half of the steady-state number of TF-TG complexes (from panel B) to form (red). D) Effect of the DNA dissociation rate (<i>k_off</i>) and antenna length on the number of steady-state TF-TG complexes. Simulation parameters: <i>D</i>_3D = 2.72 µm² s⁻¹, <i>D</i>_1D = 0.0262 µm² s⁻¹, <i>k_on</i> = 1.7 µm/s, <i>k_off</i> = 11.6 s⁻¹ unless otherwise noted, <i>σ_b</i> = 2 nm, IST rate = 0, and specific binding was reversible with dissociation rate 0.025 s⁻¹; 50 TFs were started at random 3D locations and there were 20 TGs, each at the centre of a DNA segment. Error bars represent one standard deviation, determined from 20 repeated simulations.</p

Modes of transcription factor motion.

<p>A) Schematic of the four modes of transcription factor (TF) motion (modified from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0108575#pone.0108575-Sokolov1" target="_blank">[117]</a>). B) Schematic of their implementation in the Smoldyn model. Modes: a) 3D diffusion within solution by Brownian motion, b) 1D sliding of a TF non-specifically bound to DNA, c) intersegmental transfer, where a TF binds two DNA segments and moves from one to the other, and d) hopping, in which a TF makes short excursions away from DNA (simulated as a sequence of elementary unbinding, diffusion, and binding processes).</p

Transcription factor hopping.

<p>The number of TF-TG complexes that formed for TFs that started at various distances away from their targets and that could not slide along the DNA. The distance dependence shown here is indicative of hopping motion, in which TFs repeatedly unbound from the DNA, diffused briefly in 3D space, and rebound to the DNA at a location close to the unbinding location. Simulation parameters: <i>D</i>_3D = 2.72 µm² s⁻¹, <i>D</i>_1D = 0, <i>k_on</i> = 1.7 µm/s, <i>k_off</i> = 11.6 s⁻¹, <i>σ_b</i> = 2 nm, IST rate = 0, specific binding was irreversible, and multiple TFs binding to a single TG was allowed; on each of 20 DNA segments, 6 labeled TFs were started at 60 bp distance increments away from a single TG. Bar heights represent the number of TF-TG binding events, out of 20 possible, for each TF location after 60 minutes. Error bars represent one standard deviation, determined from 20 replicate simulations.</p