IIBR Research Methods: Developing novel methods to sequence, visualize and analyze GlycoRNAs in diverse organisms

University Of Texas At Austin, Austin TX

Investigators

Abstract

An award is made to the University of Texas at Austin (UT Austin) to explore the emerging field of glycosylated RNAs (glycoRNAs) by developing novel tools to sequence, visualize, and analyze these unique molecules in various organisms, including humans, plants, yeast, and bacteria. GlycoRNAs represent a new class of biomolecules that combine the properties of RNA and glycans, potentially playing critical roles in diverse biological processes such as gene regulation, cellular communication, and immune responses. The significance of this project lies in its potential to uncover fundamental biological mechanisms and advance our understanding of RNA biology and glycobiology. By developing innovative methods and establishing a comprehensive open-access database, this research aims to provide invaluable resources that will accelerate scientific discovery in this emerging field and foster interdisciplinary collaboration among scientists. Importantly, the project will create new opportunities to integrate research with the education of the next generation of scientists. This work enhances the scientific community's ability to study glycoRNAs, ultimately benefiting society at large through advancements in biotechnology, medicine, and education. The project aims to develop advanced methods for the enrichment, sequencing, and spatial imaging of glycoRNAs across various species, addressing key technological challenges. Aim 1 involves creating an unbiased enrichment method for glycoRNAs using glycan-specific ligands and Thermostable Group II Intron Reverse Transcriptase RNA sequencing (TGIRT-seq). This method will accurately identify glycoRNA sequences, leveraging TGIRT-seq's capability to handle highly structured RNAs and pinpoint glycosylation sites at nucleotide resolution. Phenylboronic acid-based affinity ligands will be developed to enrich glycoRNAs from native RNA samples without biases from metabolic labeling. Enriched glycoRNAs will be sequenced using next-generation sequencing (NGS), creating libraries with minimal bias and high efficiency. Deep learning will predict glycosylation sites, with sequences and the analysis pipeline shared through an open-access database. Aim 2 focuses on advanced spatial imaging techniques to visualize glycoRNAs at single-cell and tissue levels. DNA aptamer-based probes for glycan binding, RNA in situ hybridization, and proximity ligation will be integrated to enable multiplex imaging of multiple glycoRNAs simultaneously. Computational image segmentation methods will infer cellular origins of glycoRNAs from tissue images, with spatial distribution data incorporated into the open-access database. Aim 3 will demonstrate the developed methods' applicability in diverse model organisms, including plants, yeast, and bacteria, establishing glycoRNA profiles and visualizing glycoRNAs. Cross-species comparative analyses will investigate conserved glycoRNA motifs and families, integrating sequencing and imaging data into the open-access database, facilitating comparative studies and expanding its utility. 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.