Jan 01 2025 Conserved RNA regulatory switches in living cells

Most transcriptome-wide RNA structure studies still summarize each RNA with a single consensus structure. This paper tackles the more difficult and more realistic problem: many RNAs populate ensembles of alternative conformations, and some of those alternative states act as regulatory switches in living cells. The challenge is to recover such ensembles at transcriptome scale and distinguish functional structural heterogeneity from background noise.

The study combines two ingredients. First, transcriptome-wide MaP-based structure probing data are deconvolved with the DRACO algorithm to infer RNA secondary structure ensembles rather than single structures. Second, the resulting conformations are filtered with an automated conservation framework, DeConStruct, which uses covariation and comparative analysis to prioritize candidate regulatory structures. This combination makes it possible to move from transcriptome-wide structural profiling to the systematic discovery of conserved RNA switches.

In bacteria, the approach identified a substantial set of regions that populate two or more conformations in vivo and recovered known regulatory elements, confirming that the method can detect genuine structural switching behavior. More importantly, it uncovered several previously uncharacterized RNA thermometers in the 5' UTRs of cspG, cspI, cpxP, and lpxP, and then followed these candidates mechanistically during cold adaptation. In this context, the work also resolved a role for the CspE chaperone in regulating lpxP, making the paper a strong example of how ensemble mapping can lead to concrete functional hypotheses.

The eukaryotic part is equally notable. By introducing a dedicated 5'UTR-MaP strategy, the paper extends ensemble-scale RNA structure mapping into human 5' UTRs and identifies structural switches connected to differential open reading frame usage in transcripts such as CKS2 and TXNL4A. This is a useful reminder that RNA structure prediction becomes more powerful when it is treated as an ensemble problem rather than a single-structure problem, especially in regulatory regions where alternative conformations can alter translation behavior.

For computational RNA biology, this is an important paper because it brings together transcriptome-scale probing, ensemble deconvolution, and evolutionary support, which are often treated as separate layers. It therefore sits squarely in the RNA structure prediction space, but in a way that moves beyond static secondary structure models and toward experimentally anchored maps of regulatory structure dynamics in living cells.