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The base pair-scale diffusion of nucleosomes modulates binding of transcription factors.

ABSTRACT: The structure of promoter chromatin determines the ability of transcription factors (TFs) to bind to DNA and therefore has a profound effect on the expression levels of genes. However, the role of spontaneous nucleosome movements in this process is not fully understood. Here, we developed a single-molecule optical tweezers assay capable of simultaneously characterizing the base pair-scale diffusion of a nucleosome on DNA and the binding of a TF, using the luteinizing hormone β subunit gene (Lhb) promoter and Egr-1 as a model system. Our results demonstrate that nucleosomes undergo confined diffusion, and that the incorporation of the histone variant H2A.Z serves to partially relieve this confinement, inducing a different type of nucleosome repositioning. The increase in diffusion leads to exposure of a TF's binding site and facilitates its association with the DNA, which, in turn, biases the subsequent movement of the nucleosome. Our findings suggest the use of mobile nucleosomes as a general transcriptional regulatory mechanism.

SUBMITTER: Rudnizky S

PROVIDER: S-EPMC7056936 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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The base pair-scale diffusion of nucleosomes modulates binding of transcription factors.

Rudnizky Sergei S Khamis Hadeel H Malik Omri O Melamed Philippa P Kaplan Ariel A

Proceedings of the National Academy of Sciences of the United States of America 20190530 25

The structure of promoter chromatin determines the ability of transcription factors (TFs) to bind to DNA and therefore has a profound effect on the expression levels of genes. However, the role of spontaneous nucleosome movements in this process is not fully understood. Here, we developed a single-molecule optical tweezers assay capable of simultaneously characterizing the base pair-scale diffusion of a nucleosome on DNA and the binding of a TF, using the luteinizing hormone β subunit gene (<i>L ...[more]

PMID: 31147470

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Project description:MotivationMachine learning models for predicting cell-type-specific transcription factor (TF) binding sites have become increasingly more accurate thanks to the increased availability of next-generation sequencing data and more standardized model evaluation criteria. However, knowledge transfer from data-rich to data-limited TFs and cell types remains crucial for improving TF binding prediction models because available binding labels are highly skewed towards a small collection of TFs and cell types. Transfer prediction of TF binding sites can potentially benefit from a multitask learning approach; however, existing methods typically use shallow single-task models to generate low-resolution predictions. Here, we propose NetTIME, a multitask learning framework for predicting cell-type-specific TF binding sites with base-pair resolution.ResultsWe show that the multitask learning strategy for TF binding prediction is more efficient than the single-task approach due to the increased data availability. NetTIME trains high-dimensional embedding vectors to distinguish TF and cell-type identities. We show that this approach is critical for the success of the multitask learning strategy and allows our model to make accurate transfer predictions within and beyond the training panels of TFs and cell types. We additionally train a linear-chain conditional random field (CRF) to classify binding predictions and show that this CRF eliminates the need for setting a probability threshold and reduces classification noise. We compare our method's predictive performance with two state-of-the-art methods, Catchitt and Leopard, and show that our method outperforms previous methods under both supervised and transfer learning settings.Availability and implementationNetTIME is freely available at https://github.com/ryi06/NetTIME and the code is also archived at https://doi.org/10.5281/zenodo.6994897.Supplementary informationSupplementary data are available at Bioinformatics online.

Project description:In computational methods, position weight matrices (PWMs) are commonly applied for transcription factor binding site (TFBS) prediction. Although these matrices are more accurate than simple consensus sequences to predict actual binding sites, they usually produce a large number of false positive (FP) predictions and so are impoverished sources of information. Several studies have employed additional sources of information such as sequence conservation or the vicinity to transcription start sites to distinguish true binding regions from random ones. Recently, the spatial distribution of modified nucleosomes has been shown to be associated with different promoter architectures. These aligned patterns can facilitate DNA accessibility for transcription factors. We hypothesize that using data from these aligned and periodic patterns can improve the performance of binding region prediction. In this study, we propose two effective features, "modified nucleosomes neighboring" and "modified nucleosomes occupancy", to decrease FP in binding site discovery. Based on these features, we designed a logistic regression classifier which estimates the probability of a region as a TFBS. Our model learned each feature based on Sp1 binding sites on Chromosome 1 and was tested on the other chromosomes in human CD4+T cells. In this work, we investigated 21 histone modifications and found that only 8 out of 21 marks are strongly correlated with transcription factor binding regions. To prove that these features are not specific to Sp1, we combined the logistic regression classifier with the PWM, and created a new model to search TFBSs on the genome. We tested the model using transcription factors MAZ, PU.1 and ELF1 and compared the results to those using only the PWM. The results show that our model can predict Transcription factor binding regions more successfully. The relative simplicity of the model and capability of integrating other features make it a superior method for TFBS prediction.

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The base pair-scale diffusion of nucleosomes modulates binding of transcription factors.

Publications

The base pair-scale diffusion of nucleosomes modulates binding of transcription factors.

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