An Alphabet No One Can Read

Over 170 post-transcriptional RNA modifications are currently known, but how these modifications interact with one another and create multi-layered, dynamic regulatory networks within cells — and how they govern organismal phenotype — remains deeply unresolved. RNA is not the passive intermediate the central dogma once described: it participates in complex feedback loops among DNA, protein, and their many isoforms and chemically modified variants. A key unsolved question is how "reader" and "eraser" proteins distinguish one modification from another. Current tools and methodologies are insufficient for studying RNA structure, interactions, and functions at genome-wide and transcriptome-wide scales under physiological conditions.

RNA-based therapeutics (mRNA vaccines, antisense oligonucleotides, siRNA drugs) are a >$50 billion market, but their development relies on trial-and-error because we do not understand the regulatory logic that governs RNA behavior in cells. Understanding RNA modification networks could enable precision control of gene expression for agriculture (crop trait engineering), energy (biofuel pathway optimization), global health (next-generation RNA therapeutics), and climate change mitigation (engineering carbon fixation pathways). The epitranscriptome — the totality of RNA modifications — adds a layer of regulation as complex as epigenetics but far less understood.

Research has focused on relatively well-studied single modifications (e.g., m6A methylation), but the NSF PPM DCL explicitly deprioritizes this approach because single-modification studies miss the combinatorial interactions that likely govern function. Current analytical methods lack the resolution to capture how RNA processing, epitranscriptomic modification, and organization in macromolecular complexes or condensates relate to cellular activities in real time. The interdisciplinary gap between biological, chemical, computational, mathematical, and physical sciences has limited creative technological approaches — RNA biology requires chemistry expertise that most biology labs lack, and chemists lack the biological context to design relevant experiments. Most existing tools were developed for specific model systems and do not generalize across organisms or cell types.

Novel tools and methodologies for studying RNA structure, interactions, and functions at molecular or genome/transcriptome-wide scales under physiological conditions. Understanding how complex combinations of post-transcriptional and post-translational modifications interact to form dynamic regulatory networks. Generalizable approaches that extend beyond specific model systems. Computational frameworks that can predict the functional consequences of specific modification patterns. Translation of fundamental discoveries into biotechnology applications.

A student team could use publicly available RNA modification databases (RMBase, MODOMICS) and transcriptome-wide modification mapping datasets to computationally identify patterns of co-occurring RNA modifications across different cell types or organisms, testing whether specific modification combinations correlate with transcript stability or translation efficiency. Alternatively, a team with wet-lab access could develop a simplified method for profiling one RNA modification type across the transcriptome using commercially available antibodies and sequencing. Relevant skills: RNA biology, bioinformatics, computational biology, chemistry, data science.