The work, published in BMC Biology by Mariachiara Grieco, Tommaso Becchi, Gabriele Boscagli and colleagues under the supervision of Matteo Cereda, spans the University of Milan, the Italian Institute for Genomic Medicine, IFOM ETS, the University of Turin and the IRCCS E. Medea institute. The central difficulty the team set out to address is that RNA-binding proteins rarely act through a single, cleanly defined binding site. Instead, they often recognize multivalent RNA motifs, combinations of short sequence elements arranged in specific positions relative to the splice sites that mark exon boundaries. Connecting such a motif to its candidate trans-acting protein from sequence information alone has remained stubbornly difficult, because many proteins recognize similar sequence patterns and because the functional consequence of binding depends heavily on where in the transcript the motif sits.

RNAmotifs 2.0 approaches the problem by coupling motif discovery with two independent streams of experimental evidence: in vivo binding data and measured splicing responses. The heart of the new framework is a module called MaRs, short for Motifs and Regulators of splicing, which integrates these data types into a single association score linking each multivalent RNA motif to a candidate RBP. The method does not treat all binding evidence equally. It learns position-specific binding and regulatory expectations, meaning it develops a statistical model of where a given protein tends to bind relative to exons and what effect that binding typically has on splicing outcomes. It also weights binding quality, so that stronger, more confident cross-linking signals contribute more to the association than weak or ambiguous ones.

Technically, the framework divides the region around each cassette exon, the type of alternative exon that is either included or skipped in the mature message, into three splice-proximal regulatory regions: the upstream intron, the exon body itself, and the downstream intron. Enrichment of a motif in these regions is captured through a combined regional enrichment measure, and the positional information is critical because a motif that enhances exon inclusion when placed downstream may repress inclusion when placed upstream. The association score that MaRs computes blends several components, including an enrichment score reflecting how strongly a motif is overrepresented near regulated exons, a binding score derived from enhanced cross-linking and immunoprecipitation, or eCLIP, experiments that map where proteins physically touch RNA inside living cells, and a measure of similarity between the predicted splicing response and the observed changes in percent spliced-in values following protein depletion.

A major methodological advance in version 2.0 is the optimization of the framework’s parameters on a per-protein basis. Rather than applying a single fixed set of thresholds and weights to every RBP, the authors use a guided hyperparameter search to tune the analysis for each protein individually. They compared a traditional grid search with Bayesian optimization, a strategy that intelligently samples the parameter space instead of exhaustively testing every combination, and found the Bayesian approach efficient for navigating the large parameter grid. To ensure that these optimized parameters generalize rather than simply overfit the training data, the team employed leave-one-RBP-out cross-validation, in which the framework is tuned without access to the protein being tested and then asked to identify that held-out protein from scratch.

The benchmark for this validation came from the ENCODE project, which has systematically depleted hundreds of RNA-binding proteins in cultured human cells using siRNA and measured the resulting changes in gene expression and splicing through RNA sequencing, alongside eCLIP maps of where each protein binds. Across this large collection of knockdown experiments, RNAmotifs 2.0 consistently prioritized the perturbed regulator as the top candidate for the motifs it discovered, with performance particularly strong when the splicing effects were large, as quantified by the absolute change in mean percent spliced-in upon protein depletion. The framework’s ability to recover the true regulator was evaluated using the area under the receiver operating characteristic curve, a standard measure of ranking quality, and the results were summarized across dozens of proteins in both HepG2 and K562 cell lines.

One practical concern with any method that integrates RNA-seq and eCLIP data is that these datasets are often generated in different cell types, and the authors explicitly tested the robustness of their approach to such mismatches. In a cross-cell-line transfer analysis, they paired knockdown exon sets from one cell line with binding maps from another, for example HepG2 knockdown data analyzed against K562 eCLIP profiles and vice versa. The framework retained substantial predictive power under these mismatched conditions, indicating that the learned positional and regulatory expectations capture biology that is at least partly shared across cellular contexts rather than being an artifact of perfectly matched datasets. An ablation analysis of the association-score components further revealed which ingredients of the score contribute most to performance, providing a transparent account of why the method works rather than presenting it as an opaque black box.

To demonstrate that the framework generalizes beyond the ENCODE benchmark, the team performed their own experimental validation in PC3 prostate cancer cells depleted of HNRNPK, a member of the heterogeneous nuclear ribonucleoprotein family with well-documented roles in RNA processing. After knocking down HNRNPK and profiling splicing changes with rMATS, a standard tool for detecting differential splicing from replicate RNA-seq data, they ran RNAmotifs 2.0 on the resulting set of regulated cassette exons. The framework identified HNRNPK as the splicing regulator, confirming that the method can recover the correct answer in a completely new biological setting with data generated independently of the training resources. This out-of-sample success is arguably the most convincing evidence that the learned motif-to-regulator associations reflect genuine molecular recognition rules.

Interpretability is a defining feature of the new framework and one that distinguishes it from purely predictive machine-learning approaches. Because the association score is built from named, quantifiable components, enrichment, binding quality, positional specificity and response similarity, researchers can inspect exactly why a particular protein was nominated as a candidate regulator of a particular set of exons. The authors also condensed the output into a compact visualization that summarizes the results for each RBP, making it feasible to survey many proteins and cell lines at a glance. Supplementary analyses addressed additional design choices, including the effect of motif width on RBP identification and the robustness of eCLIP-derived binding profiles, with the motif-width benchmark showing how this parameter influences both the identification of the correct protein and the number of enriched multivalent motifs detected.

The significance of this work extends well beyond method development. Alternative splicing is frequently misregulated in cancer and in a wide range of genetic diseases, and mutations in splice-regulatory motifs are increasingly recognized as drivers of pathology. A tool that can connect altered splicing patterns to the proteins responsible, using only sequence data, binding maps and expression measurements, gives researchers a systematic way to dissect these regulatory networks and to generate hypotheses about which factors might be therapeutically targeted. The framework is openly available, and the study’s authors, whose work was supported by AIRC and the Compagnia di San Paolo Foundation, have released extensive supplementary documentation covering every analytical decision. As eCLIP datasets continue to accumulate across tissues and disease states, frameworks like RNAmotifs 2.0 are poised to become standard instruments for translating the growing mountain of RNA data into concrete statements about which molecular players control which splicing decisions.

Subject of Research: A computational framework for linking multivalent RNA motifs to their candidate RNA-binding protein regulators of alternative splicing

Article Title: RNAmotifs 2.0: a framework for inferring multivalent RNA motifs and their candidate regulators of splicing

Article References: Grieco, M., Becchi, T., Boscagli, G., Priante, F., Caizzi, L., Pozzoli, U., & Cereda, M. (2026). RNAmotifs 2.0: a framework for inferring multivalent RNA motifs and their candidate regulators of splicing. BMC Biology. https://doi.org/10.1186/s12915-026-02745-x

Image Credits: AI Generated

DOI: 10.1186/s12915-026-02745-x

Keywords: alternative splicing, RNA-binding proteins, multivalent RNA motifs, RNAmotifs 2.0, eCLIP, RNA sequencing, splicing regulation, ENCODE, Bayesian optimization, HNRNPK, motif discovery, computational biology

Enhancers are regulatory DNA elements that can be located thousands or even hundreds of thousands of bases away from the promoters of the genes they regulate. Their activity depends in part on the formation of three-dimensional chromatin loops that allow enhancer-bound regulatory factors to contact promoter-bound transcription machinery. Although researchers have known that many enhancers produce short-lived transcripts called enhancer RNAs, or eRNAs, the precise role of these molecules has remained difficult to define. Promoter regions also generate nascent RNAs as transcription begins, creating an opportunity for RNA molecules from the two regulatory regions to interact. The new study proposes that hnRNPK recognizes and organizes these emerging transcripts, converting RNA–RNA contacts into a stable molecular bridge between enhancer and promoter.

Rather than functioning only as a conventional RNA-binding factor, hnRNPK appears to provide an architectural framework for transcriptional regulation. The researchers found that it binds preferentially to nascent RNAs produced from active enhancers and promoters. These RNAs are generated locally while the relevant chromatin regions are being transcribed, placing them close to the DNA sites that need to communicate. By associating with both classes of transcript, hnRNPK may concentrate enhancer- and promoter-derived RNAs in the same molecular environment. Such proximity could stabilize interactions between the RNAs and help fold the intervening chromatin into a loop. The model offers a mechanistic explanation for how transient RNA molecules can influence a much larger and more persistent structure in the genome.

The study further indicates that hnRNPK can assemble into phase-separated condensates. These are dynamic, concentrated compartments formed when proteins and nucleic acids cluster through numerous weak interactions rather than through a single permanent molecular bond. Phase separation is increasingly recognized as a way for cells to organize biochemical reactions without enclosing them in a membrane. In the case of hnRNPK, the condensates were described as containing cavities that encapsulate RNA polymerase II. This arrangement could create a specialized transcriptional environment in which regulatory RNAs, chromatin-associated factors and the polymerase are brought together at high local concentrations, making productive transcription more likely.

A particularly important feature of the proposed mechanism involves the RPB3 subunit of RNA polymerase II. The investigators report that hnRNPK interacts with Pol II through RPB3, providing a direct protein-based route for incorporating the transcriptional enzyme into hnRNPK condensates. Once Pol II is concentrated within these assemblies, enhancer-associated complexes may be able to influence its delivery to nearby or physically connected promoters. The researchers suggest that hnRNPK dimerization could contribute to this process: one portion of an hnRNPK assembly could engage enhancer-derived RNA and regulatory factors, while another connects with promoter-associated RNA and Pol II. In this framework, enhancer–promoter looping is not merely a passive consequence of chromatin folding but an active process coordinated by RNA-binding proteins.

This mechanism also helps explain why enhancer transcription may be functionally important even when eRNAs are rapidly degraded and do not encode proteins. Their role may depend less on their final abundance than on their production at the correct genomic location and time. As these RNAs emerge from the DNA, they can serve as temporary molecular signals or scaffolds for proteins such as hnRNPK. The resulting assemblies could integrate several signals at once, including the identity of the enhancer, the activity state of the promoter and the availability of Pol II. By linking these signals, hnRNPK may help ensure that developmental genes are activated only when the appropriate regulatory elements are engaged.

The biological significance of the findings was tested using a mutation in hnRNPK associated with Au–Kline syndrome, a rare developmental disorder. The mutation, designated c.953+1dupG, altered the physical properties of hnRNPK condensates. Instead of remaining liquid-like and dynamic, the mutant condensates became more gel-like. Liquid-like condensates can continuously exchange their molecular components with the surrounding nucleus, allowing them to assemble, reorganize and dissolve as transcriptional requirements change. A gel-like state may restrict this exchange, trapping components or preventing the rapid rearrangements required for enhancer–promoter communication. The results suggest that the material state of a condensate is not a cosmetic property but a critical determinant of gene regulation.

Knock-in mice carrying the disease-associated mutation developed developmental defects, providing evidence that the molecular changes have consequences at the level of the organism. Fibroblasts derived from these animals showed reduced enhancer–promoter looping and diminished recruitment of Pol II to the promoters of key developmental genes. These observations connect the mutation’s effects across several scales: it changes the physical behavior of hnRNPK condensates, weakens three-dimensional contacts between regulatory DNA elements, reduces transcriptional machinery at gene promoters and ultimately contributes to developmental abnormalities. The findings are consistent with the idea that many congenital disorders may result not only from loss of a protein’s biochemical activity but also from changes in the dynamics and material properties of nuclear assemblies.

The work presents hnRNPK as a general structural regulator of gene expression rather than a factor restricted to a small set of specialized genes. By mediating interactions between enhancer and promoter RNAs, organizing phase-separated condensates and engaging Pol II through RPB3, hnRNPK may provide a common platform for communication across the genome. The proposed model also broadens the view of noncoding transcription: RNAs produced from regulatory DNA may act as active architectural components, even when they do not persist as stable cellular molecules. Further research will be needed to determine how hnRNPK selects particular RNA sequences, how its condensates are regulated by signaling pathways and whether similar defects in condensate dynamics contribute to other human diseases. For now, the study identifies a molecular link between nascent RNA, chromatin looping and transcriptional activation, revealing how the genome’s distant control elements can work together with remarkable precision.

Subject of Research: hnRNPK-mediated enhancer–promoter looping, RNA–RNA interactions, phase-separated condensates and RNA polymerase II recruitment

Article Title: hnRNPK condensates facilitate enhancer–promoter looping and RNA polymerase II recruitment

Article References: Ye, R., Zhao, H., Chen, J. et al. hnRNPK condensates facilitate enhancer–promoter looping and RNA polymerase II recruitment. Nat Genet (2026). https://doi.org/10.1038/s41588-026-02710-y

Image Credits: AI Generated

DOI: https://doi.org/10.1038/s41588-026-02710-y

Keywords: hnRNPK, enhancer RNA, promoter RNA, enhancer–promoter looping, RNA polymerase II, phase separation, condensates, chromatin architecture, Au–Kline syndrome, gene regulation