Dataset Information

DIRECTEUR: transcriptome-based prediction of small molecules that replace transcription factors for direct cell conversion.

ABSTRACT:

Motivation

Direct reprogramming (DR) is a process that directly converts somatic cells to target cells. Although DR via small molecules is safer than using transcription factors (TFs) in terms of avoidance of tumorigenic risk, the determination of DR-inducing small molecules is challenging.

Results

Here we present a novel in silico method, DIRECTEUR, to predict small molecules that replace TFs for DR. We extracted DR-characteristic genes using transcriptome profiles of cells in which DR was induced by TFs, and performed a variant of simulated annealing to explore small molecule combinations with similar gene expression patterns with DR-inducing TFs. We applied DIRECTEUR to predicting combinations of small molecules that convert fibroblasts into neurons or cardiomyocytes, and were able to reproduce experimentally verified and functionally related molecules inducing the corresponding conversions. The proposed method is expected to be useful for practical applications in regenerative medicine.

Availability and implementation

The code and data are available at the following link: https://github.com/HamanoLaboratory/DIRECTEUR.git.

SUBMITTER: Hamano M

PROVIDER: S-EPMC10868337 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

DIRECTEUR: transcriptome-based prediction of small molecules that replace transcription factors for direct cell conversion.

Hamano Momoko M Nakamura Toru T Ito Ryoku R Shimada Yuki Y Iwata Michio M Takeshita Jun-Ichi JI Eguchi Ryohei R Yamanishi Yoshihiro Y

Bioinformatics (Oxford, England) 20240201 2

<h4>Motivation</h4>Direct reprogramming (DR) is a process that directly converts somatic cells to target cells. Although DR via small molecules is safer than using transcription factors (TFs) in terms of avoidance of tumorigenic risk, the determination of DR-inducing small molecules is challenging.<h4>Results</h4>Here we present a novel in silico method, DIRECTEUR, to predict small molecules that replace TFs for DR. We extracted DR-characteristic genes using transcriptome profiles of cells in wh ...[more]

PMID: 38273708

Similar Datasets

Project description:Identifying transcription factors (TF) involved in producing a genome-wide transcriptional profile is an essential step in building mechanistic model that can explain observed gene expression data. We developed a statistical framework for constructing genome-wide signatures of TF activity, and for using such signatures in the analysis of gene expression data produced by complex transcriptional regulatory programs. Our framework integrates ChIP-seq data and appropriately matched gene expression profiles to identify True REGulatory (TREG) TF-gene interactions. It provides genome-wide quantification of the likelihood of regulatory TF-gene interaction that can be used to either identify regulated genes, or as genome-wide signature of TF activity. To effectively use ChIP-seq data, we introduce a novel statistical model that integrates information from all binding "peaks" within 2 Mb window around a gene's transcription start site (TSS), and provides gene-level binding scores and probabilities of regulatory interaction. In the second step we integrate these binding scores and regulatory probabilities with gene expression data to assess the likelihood of True REGulatory (TREG) TF-gene interactions. We demonstrate the advantages of TREG framework in identifying genes regulated by two TFs with widely different distribution of functional binding events (ER? and E2f1). We also show that TREG signatures of TF activity vastly improve our ability to detect involvement of ER? in producing complex diseases-related transcriptional profiles. Through a large study of disease-related transcriptional signatures and transcriptional signatures of drug activity, we demonstrate that increase in statistical power associated with the use of TREG signatures makes the crucial difference in identifying key targets for treatment, and drugs to use for treatment. All methods are implemented in an open-source R package treg. The package also contains all data used in the analysis including 494 TREG binding profiles based on ENCODE ChIP-seq data. The treg package can be downloaded at http://GenomicsPortals.org.

Dataset Information

DIRECTEUR: transcriptome-based prediction of small molecules that replace transcription factors for direct cell conversion.

Motivation

Results

Availability and implementation

Publications

DIRECTEUR: transcriptome-based prediction of small molecules that replace transcription factors for direct cell conversion.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets