RNA structure inference through chemical mapping after accidental or intentional mutations

Area of Science:

• Molecular Biology
• Genetics
• Biochemistry

Background:

• RNA structure is crucial for gene regulation.
• Existing sequencing methods for RNA base pairing lack accuracy.
• Accurate RNA structure determination is essential for understanding biological functions.

Purpose of the Study:

• To develop a novel, accurate method for experimentally determining RNA base pairs.
• To improve the resolution of RNA secondary structures using chemical mapping.
• To validate the new method across diverse RNA types and biological contexts.

Main Methods:

• Developed "mutate-and-map read out through next-generation sequencing" (M2-seq).
• Utilized sparsely mutated nucleotides to induce detectable structural perturbations.
• Employed dimethyl sulfate (DMS) probing and mutational profiling for signal detection.
• Validated M2-seq with enhanced error-prone PCR and a neural network (M2-net).

Main Results:

• M2-seq accurately resolves RNA helices, achieving 33 of 68 helices in diverse RNAs with one false positive.
• The method successfully detected RNA helices in complex biological environments, such as Xenopus egg extract.
• Previously overlooked signals from fortuitous errors were identified as M2-seq helix signatures.
• Inferences did not require energy minimization algorithms, allowing for direct inspection or algorithmic analysis.

Conclusions:

• M2-seq offers a significant advancement in experimentally determining RNA base pairs and secondary structures.
• The method is robust, accurate, and applicable to various structured RNAs in vitro and in vivo.
• M2-seq provides a powerful tool for studying RNA function and regulation in complex biological systems.