Collaborative Research: MFB: Deciphering RNA-based regulatory logic with interpretable machine learning

合作研究：MFB：通过可解释的机器学习破译基于 RNA 的调控逻辑

• 批准号: 2226633
• 负责人: SHU-BING QIAN
• 金额: $ 60.5万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2226633&HistoricalAwards=false
• 关键词: Collaborative; Research; MFB; Deciphering; RNA

RNA transcripts, single-stranded ribonucleic acid products synthesized by transcription of DNA, contain multiple, complex, and overlapping codes that dictate their biochemical processing. Two RNA processes, RNA splicing and 5’UTR regulation, play key roles in the fundamental transfer of information from DNA to functional RNA and protein products. Understanding the regulatory logic of these RNA-based codes is required for the rational design of RNA transcripts in biotechnology. Despite decades of genetics, biochemistry, and bioinformatics research, understanding RNA-based regulatory logic remains elusive. Recent applications of “off-the-shelf” machine learning methods to limited datasets have provided limited insights into the underlying regulatory logic, hindering rational RNA transcript design. In this collaborative project, “interpretable-by-design” machine learning algorithms that can explain how they arrive at their predictions will be designed, deployed, and trained on massively parallel reporter assays and interpretability will be demonstrated by experimental validation. The project will have broader impacts through the development of generalizable experimental and machine learning approaches that can be applied to other biomolecular systems, its potential impact for biotechnology, recruitment, participation, and professional development for trainees, with an emphasis on supporting students and researchers from diverse backgrounds underrepresented in the sciences, and development of curricula for undergraduate students in computer science and biology. This Molecular Foundations of Biotechnology (MFB) project is focused on two applications: (1) A comprehensive understanding of the splicing code in determining exon skipping. During RNA splicing, introns are removed, and exons are ligated to form the mature RNA transcript. It is currently unknown how exactly an exon’s sequence determines whether it would be included or not. Using a massively parallel reporter assay dataset with 350,000 constructs and analyzed using an interpretable neural network the investigators have derived multiple insights into the splicing code. In this project, experimental RNA binding protein binding data will be incorporated to capture non-additive effects among sequence features and elucidate the effect of secondary structure. (2) A comprehensive understanding of the role of the 5’UTR code in translation initiation and RNA stability. The 5’UTR sequence plays important roles in regulating translation and stability, yet our understanding of this 5’UTR code is far from complete. In this project, interpretable neural networks will be designed to decipher the 5’UTR code, based on preliminary analysis, as well as information about microRNAs, and bidirectional 5’UTR scanning. This project will demonstrate that interpretable machine learning can be used to decipher RNA-based regulatory logic, a critical step forward for basic research with direct and generalizable implications for biotechnology applications.This project is jointly supported by the Division of Molecular and Cellular Biosciences (MCB), the Division of Chemistry (CHE), and the Division of Information and Intelligent Systems (IIS).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

RNA转录物是由DNA转录合成的单链核糖核酸产物，包含多种复杂和重叠的代码，这些代码决定了它们的生化过程。RNA剪接和5'UTR调控这两个RNA过程在DNA向功能性RNA和蛋白质产物的基本信息传递中起着关键作用。了解这些基于RNA的编码的调控逻辑是生物技术中RNA转录物的合理设计所必需的。尽管经过了几十年的遗传学、生物化学和生物信息学研究，理解基于rna的调控逻辑仍然难以捉摸。最近“现成的”机器学习方法在有限数据集上的应用，对潜在的调控逻辑提供了有限的见解，阻碍了合理的RNA转录物设计。在这个合作项目中，“可解释的设计”机器学习算法将被设计、部署，并在大规模并行报告分析中进行训练，可解释性将通过实验验证来证明。该项目将通过开发可应用于其他生物分子系统的可推广的实验和机器学习方法，对生物技术、招聘、参与和学员的专业发展产生更广泛的影响，重点是支持来自不同背景的学生和研究人员，这些学生和研究人员在科学领域的代表性不足，并为计算机科学和生物学的本科生开发课程。生物技术分子基础（MFB）项目主要集中在两个方面的应用：(1)全面了解外显子跳变的剪接密码。在RNA剪接过程中，内含子被移除，外显子被连接形成成熟的RNA转录物。目前还不清楚外显子的序列是如何决定它是否被包括在内的。利用一个包含35万个结构的大规模并行报告分析数据集，并使用可解释的神经网络进行分析，研究人员获得了对剪接代码的多种见解。本项目将结合实验RNA结合蛋白结合数据，捕捉序列特征之间的非加性效应，阐明二级结构的影响。(2)全面了解5’utr编码在翻译起始和RNA稳定性中的作用。5'UTR序列在调节翻译和稳定性方面发挥着重要作用，但我们对这段5'UTR编码的了解还远远不够。在本项目中，将基于初步分析、microrna信息和双向5'UTR扫描，设计可解释的神经网络来破译5'UTR编码。该项目将证明可解释的机器学习可用于破译基于rna的调控逻辑，这是基础研究向前迈出的关键一步，对生物技术应用具有直接和广泛的影响。本项目由分子与细胞生物科学部（MCB）、化学部（CHE）和信息与智能系统部（IIS）联合支持。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Lysosomal cystine governs ferroptosis sensitivity in cancer via cysteine stress response.

溶酶体胱氨酸通过半胱氨酸应激反应控制癌症中的铁死亡敏感性。

• DOI: 10.1016/j.molcel.2023.08.004
• 发表时间: 2023
• 期刊: Molecular cell
• 影响因子: 16
• 作者: Swanda,RobertV;Ji,Quanquan;Wu,Xincheng;Yan,Jingyue;Dong,Leiming;Mao,Yuanhui;Uematsu,Saori;Dong,Yizhou;Qian,Shu-Bing
• 通讯作者: Qian,Shu-Bing