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| Graphical Abstract |
That story is useful. It is also incomplete.
RNA is not simply a courier moving genetic instructions from one place to another. It folds. It bends. It hides some regions and exposes others. It can adopt more than one structure, sometimes within the same population of molecules. These alternative shapes can influence whether an RNA is translated, degraded, stabilized, or ignored.
A new study in Nature Methods pushes this idea further by showing how individual RNA molecules can be read not only as sequences, but as structural objects. The authors developed a method called sm-PORE-cupine, which combines chemical RNA structure probing with nanopore direct RNA sequencing to detect RNA structure ensembles in single molecules. In simpler terms, they built a way to ask: what shapes are different copies of the same RNA molecule actually taking inside a cell?
Why RNA Structure Is Hard to See
RNA structure is usually measured as an average. Scientists treat many copies of an RNA molecule with a chemical probe, sequence the result, and infer which bases are paired or unpaired. This is powerful, but it hides variation.
Imagine taking a photograph of a crowd and averaging all the faces into one image. You would get a blurry “average person,” but you would lose the actual individuals.
RNA has the same problem.
One transcript may not exist as a single structure. Some molecules may fold one way, others another way. These different structural states are called RNA structure ensembles. The biological meaning may lie not in the average structure, but in the minority conformation that appears only under certain conditions.
That is the central challenge this study addresses.
The Core Idea: Read RNA Directly, Then Recover Its Shape
The method builds on nanopore direct RNA sequencing. Unlike many sequencing methods that first convert RNA into cDNA, direct RNA sequencing pulls native RNA molecules through a nanopore and measures current changes as the molecule passes through.
The authors combined this with SHAPE chemical probing using NAI-N3, a reagent that preferentially modifies flexible, single-stranded RNA regions. Modified bases alter the nanopore signal. By detecting those altered signals along each molecule, the researchers could infer which parts of that individual RNA molecule were structurally exposed.
This sounds straightforward, but there was a technical trap. Higher chemical modification rates improve structural information, but heavily modified RNA reads become harder to basecall and map. Many reads that contain valuable structure information are lost because standard alignment struggles with them.
The clever solution was to stop relying only on basecalled sequence alignment. The authors used direct signal alignment with dynamic time warping, allowing them to recover reads that conventional mapping would miss. In benchmark RNAs, this rescued a substantial fraction of otherwise failed reads and increased the usable data for downstream structure analysis.
That detail matters. The reads most likely to be thrown away are often the ones carrying rich modification signals. Recovering them improves the ability to distinguish structural populations.
Sorting RNA Molecules Into Structural Populations
After detecting modification patterns on individual molecules, the next problem was clustering: how do you separate one RNA shape from another?
The authors tested several clustering approaches and found that a Bernoulli mixture model performed well for separating RNA structural populations. They validated this using known riboswitches, including the adenosine riboswitch.
Riboswitches are useful test cases because they change structure when bound to specific ligands. The method could distinguish ligand-bound and unbound populations and even detect intermediate or minority conformations. Importantly, it could identify alternative structure populations even when one state represented only about 10% of the molecules.
This is the biological payoff: not merely “RNA has this structure,” but “this RNA population contains multiple structural states, and their proportions change.”
SARS-CoV-2: One Genome, Many Structural Possibilities
The authors then applied sm-PORE-cupine to SARS-CoV-2 RNA. Viral RNAs are especially interesting because structure can regulate replication, translation, packaging, and immune evasion.
The study found that the 3′ end of the SARS-CoV-2 genome is highly structurally heterogeneous. This region contains several subgenomic RNAs, and the authors showed that different subgenomic RNAs, including nucleocapsid, ORF7a, and ORF8, display different levels of structural heterogeneity. The nucleocapsid RNA was especially heterogeneous among the tested subgenomic RNAs.
This suggests that viral RNA structure is not a fixed map. It is more like a set of competing layouts, with different viral transcripts folding into distinct structural populations.
That has major implications. If RNA structure affects viral gene expression, then drugs or antisense strategies targeting viral RNA may need to account for structural diversity, not just sequence.
Candida albicans: RNA Structure During a Cellular Identity Shift
The most biologically interesting part of the study may be its work in Candida albicans, a fungal pathogen that can shift from yeast-like growth at 30 °C to hyphal growth at 37 °C.
This transition matters because the hyphal form is associated with pathogenicity. The authors asked whether RNA structural ensembles change during this temperature-dependent transition.
They performed structure probing in vivo and in vitro at both temperatures and found several important patterns.
First, RNA structures were generally more homogeneous in vitro than in vivo. That means the cellular environment introduces structural complexity that purified RNA does not fully capture.
Second, RNA structures became modestly more homogeneous at higher temperature.
Third, coding sequences were more structurally heterogeneous than 3′ untranslated regions, while highly translated transcripts tended to have more homogeneous 3′ UTR structures at 37 °C.
This points toward a regulatory role for 3′ UTR structure. The 3′ UTR is often treated as a control panel for RNA stability, localization, and translation. This study adds another layer: the structure of that control panel may shift with temperature.
RNA Thermometers Beyond Bacteria?
The authors identified 95 regions in C. albicans 3′ UTRs that changed structural heterogeneity between 30 °C and 37 °C. They focused on two transcripts, RPS19A and RPL29, and showed that their 3′ UTR structural changes were linked to changes in translation using luciferase reporter assays.
This is a striking result because it suggests that some fungal mRNAs may behave like RNA thermometers. Their structures respond to temperature, and those structural changes affect protein production.
The phrase “RNA thermometer” is familiar in bacterial gene regulation, but this study suggests a broader principle: eukaryotic mRNAs may also use temperature-sensitive structure ensembles to tune expression.
Why This Study Matters
The real advance here is not just another RNA probing method. It is a change in resolution.
Older approaches often asked:
What is the average structure of this RNA?
This study asks:
How many structural states does this RNA population contain, and how do those states change across conditions?
That distinction matters for RNA biology, virology, fungal pathogenesis, and therapeutic targeting. If an RNA exists in multiple structural states, then the biologically relevant state may not be the dominant one. A low-abundance conformation could control translation, expose a regulatory motif, recruit a protein, or create a druggable structural pocket.
The study also highlights a broader lesson for transcriptomics. RNA sequencing has become extremely good at counting molecules and identifying isoforms. But RNA molecules are not linear strings floating passively in the cell. Their folding creates another layer of information—one that may explain why two RNAs with similar abundance can behave differently.
The Bigger Picture
Biology is moving from sequence to structure, from averages to single molecules, and from static models to ensembles.
sm-PORE-cupine fits directly into that transition. It gives researchers a way to observe RNA structural diversity molecule by molecule, transcript by transcript, and condition by condition.
The work also reminds us that the cell is not a test tube. RNA folding in vivo is shaped by temperature, proteins, translation, decay machinery, molecular crowding, and local cellular context. A structure predicted on a computer or measured in purified RNA may capture only part of the story.
RNA is not just a message.
It is a molecule with memory, movement, and choice. It can fold into different futures. This study gives us a sharper way to watch those futures form.
