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

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.