NSF Postdoctoral Fellowship in Biology: Machine Learning and High-throughput Reverse Genetics to Identify and Functionally Characterize Peptides Encoded in Short ORFs of lncRNAs

本行动资助2023财年美国国家科学基金会植物基因组生物学博士后研究奖学金。该奖学金支持奖学金获得者在主办实验室的研究和培训计划，该奖学金获得者还提出了扩大生物学参与的计划。Alyssa Kearly的研究和培训计划的标题是“用于鉴定和功能表征lncrna短orf编码肽的机器学习和高通量反向遗传方法”。该奖学金的主办机构是博伊斯·汤普森研究所，赞助科学家是博伊斯·汤普森博士。Andrew Nelson和Aleksandra Skirycz。植物产生多种小肽，作为通讯信号和关键过程的调节剂，包括植物生长、发育和对环境变化的反应。作为天然衍生化合物，它们是开发更安全的农药、食品防腐剂和药物的有吸引力的候选者。因此，全面鉴定和功能表征小肽的努力不仅会对植物生物学的一般认识产生巨大影响，而且会对农业和医学产生巨大影响。通过该项目开发的用于小肽预测的改进资源和计算工具将对更完整地探索这些具有生物学意义的肽具有不可估量的价值。此外，提出的生成功能预测的努力将提供对它们的角色和交叉应用潜力的洞察。这项工作将提供机器学习、转录组学、蛋白质组学和比较功能基因组学方面的培训。在整个项目中，研究员将参与本科生指导机会，拓宽科学研究和研讨会的机会，提高她作为教育者的效率。小肽作为调控分子和信号分子在植物发育和逆境反应中发挥着重要作用。尽管长链非编码rna （lncrna）被定义为低蛋白质编码潜力，但最近发现含有小开放阅读框（sorf）的子集可以进行主动翻译，这使得lncrna成为一种未开发的小肽来源。功能性sorf的鉴定及其编码肽（sep）的检测一直受到预测方法偏差和实验技术限制的阻碍。这导致了对未知数量的含sorf的lncrna的持续错误分类，并忽略了其中编码的潜在生物活性sep。该项目旨在开发改进的计算工具，用于预测功能sorf，并在功能表征其肽产物方面取得进展。通过对已发表的核糖体分析数据集的再分析，该研究员将建立一个跨模型和作物植物物种的翻译lncRNA sorf数据库，并通过TAIR （arabidopsis.org）向更广泛的科学界开放。该数据库将用于开发一种功能sORF预测算法，该算法训练于实验验证的翻译sORF，并且不受未经验证的基因注释的偏倚。通过使用SEP突变体的高通量表型分析和蛋白质组学筛选来鉴定相互作用蛋白，研究员将对候选SEP进行功能预测并评估其保守性。该项目的研究结果将有助于对植物功能基因组学的基本认识，为未来sORF和SEP的研究提供资源，同时为尚未表征的lncrna的生物学功能提供见解。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。

This action funds an NSF Plant Genome Postdoctoral Research Fellowship in Biology for FY 2023. The fellowship supports a research and training plan in a host laboratory for the Fellow who also presents a plan to broaden participation in biology. The title of the research and training plan for this fellowship to Alyssa Kearly is “Machine learning and high-throughput reverse genetic approaches for the identification and functional characterization of peptides encoded in the short ORFs of lncRNAs.” The host institution for the fellowship is the Boyce Thompson Institute and the sponsoring scientists are Drs. Andrew Nelson and Aleksandra Skirycz.Plants produce a variety of small peptides that serve as communication signals and regulators of critical processes, including plant growth, development, and responses to environmental change. As naturally derived compounds, they represent attractive candidates for the development of safer pesticides, food preservatives, and medicines. Therefore, efforts to comprehensively identify and functionally characterize small peptides could have tremendous impacts not only on the general understanding of plant biology, but also on agriculture and medicine. The improved resources and computational tools for small peptide prediction to be developed through this project will be invaluable to a more complete exploration of these biologically significant peptides. Additionally, the proposed endeavors to generate functional predictions will provide insight into their roles and potential for cross-application. This work will provide training in machine learning, transcriptomics, proteomics, and comparative functional genomics. Throughout the project, the Fellow will engage in undergraduate mentorship opportunities that broaden access to scientific research and workshops that will increase her effectiveness as an educator.Small peptides play critical roles as regulatory and signaling molecules in plant development and stress responses. Although long non-coding RNAs (lncRNAs) are defined by their low protein-coding potential, a subset containing small open reading frames (sORFs) have recently been shown to undergo active translation, introducing lncRNAs as an unexplored source of small peptides. The identification of functional sORFs and detection of their encoded peptides (SEPs) has historically been hindered by biased prediction methods and limitations of experimental techniques. This has led to the persistent misclassification of an unknown number of sORF-containing lncRNAs and the overlooking of the potentially bioactive SEPs encoded therein. This project aims to develop improved computational tools for the prediction of functional sORFs and to make strides in functionally characterizing their peptide products. Through the reanalysis of published ribosomal profiling datasets, the Fellow will build a database of translated lncRNA sORFs across model and crop plant species, to be made accessible to the broader scientific community via TAIR (arabidopsis.org). This database will be used to develop a functional sORF prediction algorithm trained on experimentally validated translated sORFs and unbiased by unvalidated gene annotations. Through the use of high-throughput phenotyping of SEP mutants and proteomic screens to identify interacting proteins, the Fellow will make functional predictions for candidate SEPs and assess their conservation. The results of this project will contribute to the fundamental understanding of plant functional genomics and provide resources for future sORF and SEP research, while offering insight into the biological function of as-of-yet uncharacterized lncRNAs.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.