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A competitive precision CRISPR method to identify the fitness effects of transcription factor binding sites.

ABSTRACT: Here we describe a competitive genome editing method that measures the effect of mutations on molecular functions, based on precision CRISPR editing using template libraries with either the original or altered sequence, and a sequence tag, enabling direct comparison between original and mutated cells. Using the example of the MYC oncogene, we identify important transcriptional targets and show that E-box mutations at MYC target gene promoters reduce cellular fitness.

SUBMITTER: Pihlajamaa P

PROVIDER: S-EPMC9931575 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature

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A competitive precision CRISPR method to identify the fitness effects of transcription factor binding sites.

Pihlajamaa Päivi P Kauko Otto O Sahu Biswajyoti B Kivioja Teemu T Taipale Jussi J

Nature biotechnology 20220926 2

Here we describe a competitive genome editing method that measures the effect of mutations on molecular functions, based on precision CRISPR editing using template libraries with either the original or altered sequence, and a sequence tag, enabling direct comparison between original and mutated cells. Using the example of the MYC oncogene, we identify important transcriptional targets and show that E-box mutations at MYC target gene promoters reduce cellular fitness. ...[more]

PMID: 36163549

Similar Datasets

A competitive precision CRISPR method to identify the fitness effects of transcription factor binding sites

Project description:Many biological processes are controlled by master regulators that activate complex downstream transcriptional and/or posttranslational networks. These networks have been hard to dissect using reverse genetics because of the difficulty of mutating large numbers of binding motifs or phosphorylation sites at different chromosomal loci. Here, we use a novel competitive genome editing (CGE) assay for dissecting the transcriptional network downstream of the master regulatory oncogene MYC. The CGE method is based on precision genome editing using template libraries that either reconstitute the original feature or introduce an altered sequence. Both types of libraries also introduce sequence tags, generating a large number of replicate lineages and enabling direct comparison between cells that carry original and mutant features. By using precision editing and by comparing two populations of edited cells, the CGE method overcomes the two main limitations of CRISPR/Cas9-technology in analyzing the effect of genotype on phenotype: the difficulty of cutting DNA exactly at the intended site, and the decreased cell proliferation caused by the DNA cuts themselves. Importantly, it provides a powerful method for studying subtle effects elicited by mutation of individual transcription factor binding sites. We show here that E-box mutations at several MYC target gene promoters resulted in reduced cellular fitness, demonstrating a direct correlation between MYC-regulated cellular processes and MYC binding, and identifying important transcriptional targets responsible for its functions.

2022-07-11 | GSE206080 | GEO

A competitive precision CRISPR method to identify the fitness effects of transcription factor binding sites

Project description:A competitive precision CRISPR method to identify the fitness effects of transcription factor binding sites

| PRJNA849144 | ENA

Biophysical fitness landscapes for transcription factor binding sites.

Project description:Phenotypic states and evolutionary trajectories available to cell populations are ultimately dictated by complex interactions among DNA, RNA, proteins, and other molecular species. Here we study how evolution of gene regulation in a single-cell eukaryote S. cerevisiae is affected by interactions between transcription factors (TFs) and their cognate DNA sites. Our study is informed by a comprehensive collection of genomic binding sites and high-throughput in vitro measurements of TF-DNA binding interactions. Using an evolutionary model for monomorphic populations evolving on a fitness landscape, we infer fitness as a function of TF-DNA binding to show that the shape of the inferred fitness functions is in broad agreement with a simple functional form inspired by a thermodynamic model of two-state TF-DNA binding. However, the effective parameters of the model are not always consistent with physical values, indicating selection pressures beyond the biophysical constraints imposed by TF-DNA interactions. We find little statistical support for the fitness landscape in which each position in the binding site evolves independently, indicating that epistasis is common in the evolution of gene regulation. Finally, by correlating TF-DNA binding energies with biological properties of the sites or the genes they regulate, we are able to rule out several scenarios of site-specific selection, under which binding sites of the same TF would experience different selection pressures depending on their position in the genome. These findings support the existence of universal fitness landscapes which shape evolution of all sites for a given TF, and whose properties are determined in part by the physics of protein-DNA interactions.

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Using DNase digestion data to accurately identify transcription factor binding sites.

Project description:Identifying binding sites of transcription factors (TFs) is a key task in deciphering transcriptional regulation. ChIP-based methods are used to survey the genomic locations of a single TF in each experiment. But methods combining DNase digestion data with TF binding specificity information could potentially be used to survey the locations of many TFs in the same experiment, provided such methods permit reasonable levels of sensitivity and specificity. Here, we present a simple such method that outperforms a leading recent method, centipede, marginally in human but dramatically in yeast (average auROC across 20 TFs increases from 74% to 94%). Our method is based on logistic regression and thus benefits from supervision, but we show that partially and completely unsupervised variants perform nearly as well. Because the number of parameters in our method is at least an order of magnitude smaller than CENTIPEDE, we dub it MILLIPEDE.

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A protein-protein interaction guided method for competitive transcription factor binding improves target predictions.

Project description:An important milestone in revealing cells' functions is to build a comprehensive understanding of transcriptional regulation processes. These processes are largely regulated by transcription factors (TFs) binding to DNA sites. Several TF binding site (TFBS) prediction methods have been developed, but they usually model binding of a single TF at a time albeit few methods for predicting binding of multiple TFs also exist. In this article, we propose a probabilistic model that predicts binding of several TFs simultaneously. Our method explicitly models the competitive binding between TFs and uses the prior knowledge of existing protein-protein interactions (PPIs), which mimics the situation in the nucleus. Modeling DNA binding for multiple TFs improves the accuracy of binding site prediction remarkably when compared with other programs and the cases where individual binding prediction results of separate TFs have been combined. The traditional TFBS prediction methods usually predict overwhelming number of false positives. This lack of specificity is overcome remarkably with our competitive binding prediction method. In addition, previously unpredictable binding sites can be detected with the help of PPIs. Source codes are available at http://www.cs.tut.fi/ approximately harrila/.

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An efficient method for statistical significance calculation of transcription factor binding sites.

Project description:Various statistical models have been developed to describe the DNA binding preference of transcription factors, by which putative transcription factor binding sites (TFBS) can be identified according to scores assigned. Statistical significance of these scores, usually known as the p-value, play a critical role in identification. We developed an efficient algorithm to provide precise calculation of the statistical significance, remarkably enhancing the calculation efficiency by reducing the time complexity from an exponent scale to a linear scale, and successfully extended the application of this algorithm to a wide range of models, from the commonly used position weight matrix models to the complicated Bayesian Network models. Further, we calculated p-values of all transcription factor DNA binding sites recorded in the database, JASPAR, and based on these, we investigated some unseen properties of p-values as a whole, such as the p-value distribution of different models and the p-value variance according to changed scoring schemes. We hope that our algorithm and the result of computational experiments would offer an improved solution to the statistical significance of transcription factor binding sites. The software to implement our method can be downloaded from http://pcal.biosino.org/pCal.html.

| S-EPMC2241927 | biostudies-literature

ConTra v3: a tool to identify transcription factor binding sites across species, update 2017.

Project description:Transcription factors are important gene regulators with distinctive roles in development, cell signaling and cell cycling, and they have been associated with many diseases. The ConTra v3 web server allows easy visualization and exploration of predicted transcription factor binding sites (TFBSs) in any genomic region surrounding coding or non-coding genes. In this updated version, with a completely re-implemented user interface using latest web technologies, users can choose from nine reference organisms ranging from human to yeast. ConTra v3 can analyze promoter regions, 5΄-UTRs, 3΄-UTRs and introns or any other genomic region of interest. Thousands of position weight matrices are available to choose from for detecting specific binding sites. Besides this visualization option, additional new exploration functionality is added to the tool that will automatically detect TFBSs having at the same time the highest regulatory potential, the highest conservation scores of the genomic regions covered by the predicted TFBSs and strongest co-localizations with genomic regions exhibiting regulatory activity. The ConTra v3 web server is freely available at http://bioit2.irc.ugent.be/contra/v3.

| S-EPMC5570180 | biostudies-literature

ConTra v2: a tool to identify transcription factor binding sites across species, update 2011.

Project description:Transcription factors are important gene regulators with distinctive roles in development, cell signaling and cell cycling, and they have been associated with many diseases. The ConTra v2 web server allows easy visualization and exploration of predicted transcription factor binding sites in any genomic region surrounding coding or non-coding genes. In this new version, users can choose from nine reference organisms ranging from human to yeast. ConTra v2 can analyze promoter regions, 5'-UTRs, 3'-UTRs and introns or any other genomic region of interest. Hundreds of position weight matrices are available to choose from, but the user can also upload any other matrices for detecting specific binding sites. A typical analysis is run in four simple steps of choosing the gene, the transcript, the region of interest and then selecting one or more transcription factor binding sites. The ConTra v2 web server is freely available at http://bioit.dmbr.ugent.be/contrav2/index.php.

| S-EPMC3125763 | biostudies-literature

Energy-dependent fitness: a quantitative model for the evolution of yeast transcription factor binding sites.

Project description:We present a genomewide cross-species analysis of regulation for broad-acting transcription factors in yeast. Our model for binding site evolution is founded on biophysics: the binding energy between transcription factor and site is a quantitative phenotype of regulatory function, and selection is given by a fitness landscape that depends on this phenotype. The model quantifies conservation, as well as loss and gain, of functional binding sites in a coherent way. Its predictions are supported by direct cross-species comparison between four yeast species. We find ubiquitous compensatory mutations within functional sites, such that the energy phenotype and the function of a site evolve in a significantly more constrained way than does its sequence. We also find evidence for substantial evolution of regulatory function involving point mutations as well as sequence insertions and deletions within binding sites. Genes lose their regulatory link to a given transcription factor at a rate similar to the neutral point mutation rate, from which we infer a moderate average fitness advantage of functional over nonfunctional sites. In a wider context, this study provides an example of inference of selection acting on a quantitative molecular trait.

| S-EPMC2527919 | biostudies-literature

Novel sequence-based method for identifying transcription factor binding sites in prokaryotic genomes.

Project description:MotivationComputational techniques for microbial genomic sequence analysis are becoming increasingly important. With next-generation sequencing technology and the human microbiome project underway, current sequencing capacity is significantly greater than the speed at which organisms of interest can be studied experimentally. Most related computational work has been focused on sequence assembly, gene annotation and metabolic network reconstruction. We have developed a method that will primarily use available sequence data in order to determine prokaryotic transcription factor (TF) binding specificities.ResultsSpecificity determining residues (critical residues) were identified from crystal structures of DNA-protein complexes and TFs with the same critical residues were grouped into specificity classes. The putative binding regions for each class were defined as the set of promoters for each TF itself (autoregulatory) and the immediately upstream and downstream operons. MEME was used to find putative motifs within each separate class. Tests on the LacI and TetR TF families, using RegulonDB annotated sites, showed the sensitivity of prediction 86% and 80%, respectively.Availabilityhttp://ural.wustl.edu/∼gsahota/HTHmotif/

| S-EPMC2981494 | biostudies-literature

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