Post-transcriptional Regulatory Networks

转录后调控网络

基本信息

批准号：
10736019
负责人：
Timothy Hughes
金额：
$ 69.29万
依托单位：
SLOAN-KETTERING INST CAN RESEARCH
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-08-04 至 2027-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10736019
关键词：
3&apos Untranslated Regions Alternative Splicing Amino Acid Sequence Artificial Intelligence Binding Binding Sites Biochemical Biological Assay Biological Models Calibration Catalogs Cells Code Collaborations Collection Computer Models Computer software Data Data Set Defect Development Evolution Family Funding Genes Genetic Research Genomics Germ-Line Mutation Health Homology Modeling Human Human Genetics Human Genome Human Resources In Vitro Industry Standard Instruction Knowledge Learning Location Malignant Neoplasms Maps Measures Messenger RNA Methods Modeling Mutation Neurodegenerative Disorders Nuclear Export Organism Outcome Peptides Play Poly A Polyadenylation Post-Transcriptional Regulation Procedures Process Protein Binding Domain Proteins Publications RNA RNA Binding RNA Recognition Motif RNA Sequences RNA Splicing RNA Stability RNA-Binding Proteins Regulation Regulatory Element Research Research Support Role Scanning Scientist Somatic Mutation Source Specificity Speed Structure Study models Techniques Training Transcript Translations Validation Variant Vertebrates Vocabulary Yeasts Zebrafish Zinc Fingers anticancer research cancer genetics deep neural network detection method experience fly follow-up genomic data human disease improved in silico in vivo knock-down machine learning model model organism neurodevelopment open source posttranscriptional predictive modeling preference protein structure prediction reconstruction tool transcription regulatory network transcriptome sequencing transcriptomics user-friendly web portal web-based tool

项目摘要

RNA-binding proteins (RBPs) play key roles in RNA splicing, editing, nuclear export, translation, turnover, and subcellular localization. Reflecting their importance, RBPs and their cis-regulatory elements (CREs) have broad implications in human health: mutations in RBPs or CREs have well-established roles in cancer, developmental defects, particularly in neural development, and in neural degenerative diseases. Using a combination of a high-throughput, in-vitro-selection-based RNA binding assay, RNAcompete, and machine learning (ML) models trained to map from an RBP’s protein sequence to its RNA binding preferences, this project will endeavor to assign RNA sequence- and structural-context binding preferences to all human RBPs, all vertebrate RBPs, and the vast majority of metazoan RBPs. These specificities will then be used to detect and assign function to RBPs and cis-regulatory elements (CREs) in human genomes, as well as those of other model organisms. The specificities, machine learning models, and predicted CREs will be distributed widely via publication, open-source software, and user-friendly web tools like cisBP-RNA. This project has the potential to transform cancer and human genetics research supporting the estimation of the functional impact of germline or somatic mutations on post-transcriptional regulation (PTR). By improving the reconstruction of PTR networks, this project will speed research in this emerging field toward a complete understanding of this key process. This project will also permit the study of the evolution of PTR by developing tools to reconstruct PTR networks in other organisms based solely on genomic and transcriptomic data. RNAcompete will be used to assess the RNA sequence-binding preferences of the 511 still-uncharacterized RBPs in humans and D. rerio (zebrafish), thereby establishing a complete catalog of binding preferences for all likely sequence-specific RBPs in these two species. These data will be combined with binding data for >500 other RBPs from a variety of sources and used to train an ML model that reconstructs RNA-binding preferences given RBP protein sequences. These models will also leverage recent advances in de novo prediction of protein structure from sequence. RBPs will be assigned roles in PTR based on (i) the location and conservation, in human transcripts of their predicted target CREs, (ii) the correlation of their expression with the PTR fate of their putative target transcripts, and (iii) other, more powerful regression methods like the Inferelator. CRE predictions will be continuously improved using in vivo data to recalibrate in vitro motif models and to improve in silico predictions of transcript RNA secondary structure. Our predicted CREs and reconstructed PTR networks will be validated by comparisons with in vivo data collected by our team and others.

RNA 结合蛋白 (RBP) 在 RNA 剪接、编辑、核输出、翻译、周转和转录过程中发挥着关键作用。 RBP 及其顺式调控元件 (CRE) 具有亚细胞定位的重要性。对人类健康的广泛影响：RBP 或 CRE 的突变在癌症、发育缺陷，特别是神经发育和神经退行性疾病。结合使用高通量、基于体外选择的 RNA 结合测定、RNAcompete 和机器学习 (ML) 模型经过训练，可将 RBP 的蛋白质序列映射到其 RNA 结合偏好，该项目将努力为所有人类分配 RNA 序列和结构背景结合偏好 RBP、所有脊椎动物的 RBP 和绝大多数后生动物的 RBP 将被用于分析。检测人类基因组中的 RBP 和顺式调控元件 (CRE) 并为其分配功能，以及其他模型生物的特性、机器学习模型和预测的 CRE 将被分发。通过出版物、开源软件和用户友好的网络工具（如 cisBP-RNA）广泛传播。该项目有可能改变癌症和人类遗传学研究，支持估计通过改善种系或体细胞突变对转录后调控（PTR）的功能影响。 PTR 网络的重建，该项目将加速这一新兴领域的研究，迈向完整的方向了解这一关键过程也将允许通过开发来研究 PTR 的演变。仅基于基因组和转录组数据在其他生物体中重建 PTR 网络的工具。 RNAcompete 将用于评估 511 尚未表征的 RNA 序列结合偏好人类和斑马鱼中的 RBP，从而为所有人建立了完整的结合偏好目录这两个物种中可能存在序列特异性的 RBP，这些数据将与超过 500 个的结合数据相结合。来自各种来源的其他 RBP，用于训练重建 RNA 结合的 ML 模型给定 RBP 序列蛋白质的偏好，这些模型还将利用 de novo 的最新进展。根据序列预测蛋白质结构将根据 (i) 位置和在 PTR 中分配角色。保守性，在其预测的目标 CRE 的人类转录本中，（ii）它们的表达与他们假定的目标转录本的 PTR 命运，以及 (iii) 其他更强大的回归方法，例如 Inferelator。将使用体内数据重新校准体外基序模型来不断改进 CRE 预测。并改进我们预测的 CRE 和转录本 RNA 二级结构的计算机预测。重建的 PTR 网络将通过与我们团队收集的体内数据进行比较来验证其他的。