Post-transcriptional Regulatory Networks

转录后调控网络

• 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
• 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' 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

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，核导出，翻译，周转和周转和 亚细胞定位。反映其重要性，RBP及其顺式调节元素（CRE）具有 人类健康的广泛影响：RBP或CRE的突变在癌症中具有完善的作用， 发育缺陷，特别是在神经发育和神经退行性疾病中。 结合使用高通量的基于体外选择的RNA结合测定，RNACOMPETE和 机器学习（ML）模型训练从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序列和结构上下文结合偏好 RBP，所有脊椎动物RBP和绝大多数后生RBP。这些规格将用于 检测并分配了人类基因组中的RBP和顺式调节元件（CRE）以及那些 其他模型生物。规格，机器学习模型和预测的CRE将分发 通过出版物，开源软件和用户友好的Web工具（如CISBP-RNA）广泛广泛。 该项目有可能改变癌症和人类遗传学研究，以支持估计 种系或体突变对转录后调节（PTR）的功能影响。通过改进 PTR网络的重建，该项目将加速在这个新兴领域的研究 了解这个关键过程。该项目还将通过开发PTR的发展研究 仅基于基因组和转录组数据重建其他生物体中PTR网络的工具。 RNACOMPETE将用于评估511仍在纯化的RNA序列结合偏好 人类和D. rerio（斑马鱼）的RBP，从而为所有人建立了完整的绑定偏好目录 这两个物种可能是序列特异性的RBP。这些数据将与> 500的绑定数据结合 来自各种来源的其他RBP，用于训练重建RNA结合的ML模型 偏好给定RBP蛋白序列。这些模型还将利用从头开始的最新进展 从序列预测蛋白质结构。 RBP将根据（i）位置和 保护，在人类预测的目标cr中，（ii）其表达的相关性 其推定目标成绩单的PTR命运，以及（iii）其他更强大的回归方法（例如 地狱。使用体内数据将不断改进CRE预测以重新校准体外基序模型 并改善转录物RNA二级结构的硅预测。我们预测的CRE和 重建的PTR网络将通过与我们的团队收集的体内数据进行比较来验证 其他的。