Identification and functional characterization of transcriptional activators in human cells

Alerasool, Nader; He, Liang; Lin, Zhen‐Yuan; Gingras, Anne‐Claude; Taipale, Mikko

doi:10.1016/j.molcel.2021.12.008

Cited by 83 publications

(71 citation statements)

References 156 publications

(181 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is instead becoming a key step of any structural biology project, providing structures that must be properly analyzed. The reliability of predicted protein structures is such that they can be used as starting models for molecular replacement in X-ray crystallography, for the interpretation of cryo-electron microscopy (cryoEM) 3D reconstructions, and for the rational design of mutations and functional assays ( Kryshtafovych et al, 2021 ; Slavin et al, 2021 ; Alerasool et al, 2022 ; Ivanov et al, 2022 ; McCoy et al, 2022 ; Paul et al, 2022 ). Moreover, AlphaFold2 can go a step further and predict the structure of large and flexible machineries, thereby circumventing intrinsic limitations of experimental methods ( Goulet and Cambillau, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Present Impact of AlphaFold2 Revolution on Structural Biology, and an Illustration With the Structure Prediction of the Bacteriophage J-1 Host Adhesion Device

Goulet

Cambillau

2022

Front. Mol. Biosci.

View full text Add to dashboard Cite

In 2021, the release of AlphaFold2 - the DeepMind’s machine-learning protein structure prediction program - revolutionized structural biology. Results of the CASP14 contest were an immense surprise as AlphaFold2 successfully predicted 3D structures of nearly all submitted protein sequences. The AlphaFold2 craze has rapidly spread the life science community since structural biologists as well as untrained biologists have now the possibility to obtain high-confidence protein structures. This revolution is opening new avenues to address challenging biological questions. Moreover, AlphaFold2 is imposing itself as an essential step of any structural biology project, and requires us to revisit our structural biology workflows. On one hand, AlphaFold2 synergizes with experimental methods including X-ray crystallography and cryo-electron microscopy. On the other hand, it is, to date, the only method enabling structural analyses of large and flexible assemblies resistant to experimental approaches. We illustrate this valuable application of AlphaFold2 with the structure prediction of the whole host adhesion device from the Lactobacillus casei bacteriophage J-1. With the ongoing improvement of AlphaFold2 algorithms and notebooks, there is no doubt that AlphaFold2-driven biological stories will increasingly be reported, which questions the future directions of experimental structural biology.

show abstract

Section: Introductionmentioning

confidence: 99%

Present Impact of AlphaFold2 Revolution on Structural Biology, and an Illustration With the Structure Prediction of the Bacteriophage J-1 Host Adhesion Device

Goulet

Cambillau

2022

Front. Mol. Biosci.

View full text Add to dashboard Cite

show abstract

“…TFs fused to ABI1's cognate domain PYL1 are recruited to the locus bound by ABI1-dCas9 upon the addition of phytohormone S-(+) -abscisic acid (ABA) [23][24][25] , enabling precise spatiotemporal control. This strategy provides a straightforward control for non-specific effects of TF overexpression (comparing conditions with and without ABA), and scales easily because PYL1 is small compared to dCas9 (176 vs. 1360 amino acids 23 ), facilitating library generation.…”

Section: Resultsmentioning

confidence: 99%

“…Applying CRISPRburst, we characterized 78 human TFs known to induce transcription through CRISPR activation (CRISPRa) 25 . Those include sequence-specific TFs spanning several families, transcriptional co-activators, and a synthetic activator (VP64).…”

Section: Functional Characterization Of Tfs Using An Imaging-based Sy...mentioning

confidence: 99%

“…We created ABI1-dCas9 stably expressing lines by infection with lentivirus particles generated from the pLX303 ABI1-dCas9 25 plasmid which encodes a cassette co-expressing a blasticidin resistance gene from the hPGK promoter. We treated LTR-MS2 reporter cells placed in 10 cm dishes with viral supernatant supplemented with 8 μg/ml polybrene (Sigma TR1003G).…”

Section: Stable Cell Line Generationmentioning

confidence: 99%

See 1 more Smart Citation

The kinetic landscape of human transcription factors

Mamrak

Alerasool

Griffith

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Cell-to-cell variability is shaped by transcription dynamics because genes are transcribed in bursts interspersed with inactive periods. The stochasticity of bursting means that genes transcribed in rare bursts exhibit more heterogeneity at the single cell level than genes that burst often1,2. Transcription starts with the binding of Transcription Factors (TFs) to specific sequence motifs where they recruit the transcription machinery3. In some systems, individual TF binding events temporally correlate with the firing of transcriptional bursts, defining the target gene's frequency and duration4-6. However, in the absence of methods that assess the impact of different TFs on transcription dynamics at the same genetic loci, it remains unclear whether DNA binding kinetics are the sole determinant of bursting. Here we develop an imaging-based synthetic recruitment assay, CRISPRburst, and measure how 92 human TFs impact bursting kinetics. We show that TFs recruited to chromatin under identical conditions generate diverse bursting signatures, some TFs increasing the probability of the gene turning on while others increase the number of mRNA molecules transcribed per burst. We find that the association of TFs with specific protein partners determines their bursting output, and train a model to predict the kinetic signatures of all human TFs. These kinetic signatures can be used as a TF classification system complementary to existing families based on DNA binding domains. Additionally, kinetic signatures provide a rational framework to design synthetic activators, model transcription regulation, and understand expression heterogeneity.

show abstract

“…Sequence-specific transcription factors (TF) regulate the expression of their target genes by recognizing short DNA sequences known as transcription factor binding sites (TFBS). The specificity of TF-DNA interactions can also be affected by genome and epigenome context or protein cofactors [1][2][3][4][5][6] . In particular, most TFs work within a complex or interact with other proteins to regulate gene expression in vivo 7 .…”

Section: Introductionmentioning

confidence: 99%

Uncovering the “ZIP code” for bZIP dimers reveals novel motifs, regulatory rules and one billion years of cis-element evolution

Lin

Yao

et al. 2022

Preprint

View full text Add to dashboard Cite

Many eukaryotic transcription factors (TF) form homodimer or heterodimer complexes to regulate gene expression. For example, dimerization properties of the basic leucine zipper (bZIP) family play a critical role in regulating the unique biological functions in all eukaryotes. However, the molecular mechanism underlying the binding sequence and functional specificity of homo- versus heterodimers remains elusive. To fill this gap, we developed a double DNA Affinity Purification sequencing (dDAP-seq) technique that maps heterodimer DNA binding sites in an endogenous genome context. Our genome-wide binding profiles of twenty pairs of C/S1 bZIP heterodimers and S1 homodimers in Arabidopsis revealed that heterodimerization significantly expands the DNA binding preferences of bZIP TFs. Analysis of the heterodimer target genes in stress response and development suggest heterodimerization gives rise to regulatory responses that are distinct from the homodimers. In addition to the classic ACGT elements recognized by plant bZIPs, we found that the C/S1 heterodimers bound to motifs that might share an origin with the GCN4 cis-elements in yeast that diverged from plants more than one billion years ago. Importantly, heterodimer binding specificities can be distinguished by their relative preference for ACGT motifs versus GCN4-related motifs. More broadly, our study demonstrates the potential of dDAP-seq in deciphering the DNA binding specificities of interacting TFs that are key for combinatorial gene regulation.

show abstract

Identification and functional characterization of transcriptional activators in human cells

Cited by 83 publications

References 156 publications

Present Impact of AlphaFold2 Revolution on Structural Biology, and an Illustration With the Structure Prediction of the Bacteriophage J-1 Host Adhesion Device

Present Impact of AlphaFold2 Revolution on Structural Biology, and an Illustration With the Structure Prediction of the Bacteriophage J-1 Host Adhesion Device

The kinetic landscape of human transcription factors

Uncovering the “ZIP code” for bZIP dimers reveals novel motifs, regulatory rules and one billion years of cis-element evolution

Contact Info

Product

Resources

About