A high‐throughput method to identify trans‐activation domains within transcription factor sequences

Abstract

Even though transcription factors (TFs) are central players of gene regulation and have been extensively studied, their regulatory trans‐activation domains (tADs) often remain unknown and a systematic functional characterization of tADs is lacking. Here, we present a novel high‐throughput approach tAD‐seq to functionally test thousands of candidate tADs from different TFs in parallel. The tADs we identify by pooled screening validate in individual luciferase assays, whereas neutral regions do not. Interestingly, the tADs are found at arbitrary positions within the TF sequences and can contain amino acid (e.g., glutamine) repeat regions or overlap structured domains, including helix–loop–helix domains that are typically annotated as DNA‐binding. We also identified tADs in the non‐native reading frames, confirming that random sequences can function as tADs, albeit weakly. The identification of tADs as short protein sequences sufficient for transcription activation will enable the systematic study of TF function, which—particularly for TFs of different transcription activating functionalities—is still poorly understood.

Mediator complex subunit Med19 binds directly GATA DNA-binding zinc finger and functions with Med1 in GATA-driven gene regulationin vivo

“…These data also corroborate results from a recent highthroughput approach, looking for transactivation domains of Drosophila transcription factors. This work shows that trans-activation domains of several zinc-finger-(ZF-) and basic Helix-Loop-Helix-(bHLH-) TFs overlap structured DNA-binding domains (47). Altogether, these results identify a novel class of TF characterized by overlapping activation and DNA-binding domains and suggest an emerging Med19 property as a dedicated cofactor directly connecting these TFs DNAbinding domains to the general PolII transcriptional machinery.…”

Unexpected Specificity within Dynamic Transcriptional Protein-Protein Complexes

“…We next sought to confirm whether there are structural differences between these complexes using NMR spectroscopy. Side-chain methyl 1 H, 13 C-HSQC experiments were used as a primary method to enable direct detection of effects on both surface and buried residues of Med25. Comparative analysis of Med25 1 H, 13 C-HSQC spectra bound to different ETV/PEA3 family members was consistent with the expected engagement differences between the ETV/PEA3 TADs: spectra of ETV1-and ETV4-bound Med25 exhibited several large differences in chemical shift perturbation (CSP) patterns, whereas the spectra of Med25 bound to ETV1 and ETV5 were essentially indistinguishable ( Fig.…”

“…(1)(2)(3)(4)(5)(6)(7)(8) A prevailing view of activator-coactivator PPIs is that they are largely non-specific and, further, that the selectivity necessary for appropriate gene expression comes from other sources such as activator-DNA interactions and/or co-localization. (3,(9)(10)(11)(12)(13)(14) Indeed, there is considerable data suggesting activator•coactivator complexes form via almost entirely nonspecific intermolecular interactions, from early experiments demonstrating that a wide range of natural and non-natural amphipathic molecules interact with coactivators to more recent structural studies indicating no fixed activator•coactivator binding mode. (9)(10)(11)(15)(16)(17)(18) Nonspecific recognition models, while attractive in their simplicity, are inconsistent with the critical functional role that individual activator•coactivator PPIs play in gene expression.…”

A high-throughput screen for transcription activation domains reveals their sequence characteristics and permits reliable prediction by deep learning

“…We next used ADpred to identify ADs in transcription factors where in vivo AD function has been approximately mapped (Fig 5C) (Arnold et al, 2018;Kuras and Thomas, 1995;Leuther et al, 1993;Ma and Ptashne, 1987b;Pacheco et al, 2018;Rothermel et al, 1997;Schwank et al, 1995;Stebbins and Triezenberg, 2004). For this analysis, we used an ADpred probability of ≥ 0.8 as a high confidence threshold for AD prediction.…”

“…To test the performance of our model on activators from other eukaryotes, we measured the AD function of several sequences analyzed in Drosophila using a medium-throughput ADscreening approach (Arnold et al, 2018). We examined sequences from factors MTF-1, Bteb2, and C14451 that were shown to have AD function in Drosophila and were predicted to have AD function using ADpred (Fig S4.…”