Biology subjects

Liao, S. E.

Publications and source records attributed to Liao, S. E..

3 recordsLinked to original sources

bioRxiv2025

RNA localization to nuclear speckles follows splicing logic

Nuclear speckles are membraneless organelles implicated in multiple RNA processing steps. In this work, we systematically characterize the sequence logic determining RNA localization to nuclear speckles. We find extensive similarities between the speckle localization code and the RNA splicing code, even for transcripts that do not undergo splicing. Specifically, speckle localization is enhanced by the presence of unspliced exon-like or intron-like sequence features. We demonstrate that interactions required for early splicesomal complex assembly contribute to speckle localization. We also show that speckle localization of isolated endogenous exons is reduced by disease-associated single nucleotide variants. Finally, we find that speckle localization strongly correlates with splicing kinetics of splicing-competent constructs and is tightly linked to the decision between exon inclusion and skipping. Together, these results suggest a model in which RNA speckle localization is associated with the formation of the early spliceosomal complex and enhances the efficiency of splicing reactions. HighlightsO_LISequences containing hallmarks of pre-mRNA dictate speckle localization C_LIO_LIRNA speckle localization is coupled to early spliceosome assembly C_LIO_LIDisease-associated single nucleotide variants reduce localization of isolated exons C_LIO_LIRNA speckle localization strongly correlates with splicing kinetics C_LI O_FIG O_LINKSMALLFIG WIDTH=191 HEIGHT=200 SRC="FIGDIR/small/656493v1_ufig1.gif" ALT="Figure 1"> View larger version (57K): org.highwire.dtl.DTLVardef@1efbb02org.highwire.dtl.DTLVardef@1deb7aaorg.highwire.dtl.DTLVardef@ec1ec2org.highwire.dtl.DTLVardef@1d78b72_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract C_FIG

biochemistry↗

bioRxiv2022

Nuclear speckle-localized RNAs exhibit preferential positioning and orientation

RNA molecules often play critical roles in assisting the formation of membraneless organelles in eukaryotic cells. Yet, little is known about the organization of RNAs within membraneless organelles. Here, using super-resolution imaging and nuclear speckles as a model system, we demonstrate that different sequence domains of RNA transcripts exhibit differential spatial distributions within speckles. Specifically, we image transcripts containing a region enriched in SR protein binding motifs and another region enriched in hnRNP binding motifs. We show that these transcripts localize to the outer shell of speckles, with the SR motif-rich region localized closer to the speckle center relative to the hnRNP motif-rich region. Further, we identify that this intra-speckle RNA organization is driven by the strength of RNA-protein interactions inside and outside speckles. Our results hint at novel functional roles of nuclear speckles and likely other membraneless organelles in organizing RNA substrates for biochemical reactions.

biophysics↗

bioRxiv2022

Machine learning for discovery: deciphering RNA splicing logic

Machine learning methods, particularly neural networks trained on large datasets, are transforming how scientists approach scientific discovery and experimental design. However, current state-of-the-art neural networks are limited by their uninterpretability: despite their excellent accuracy, they cannot describe how they arrived at their predictions. Here, using an "interpretable-by-design" approach, we present a neural network model that provides insights into RNA splicing, a fundamental process in the transfer of genomic information into functional biochemical products. Although we designed our model to emphasize interpretability, its predictive accuracy is on par with state-of-the-art models. To demonstrate the models interpretability, we introduce a visualization that, for any given exon, allows us to trace and quantify the entire decision process from input sequence to output splicing prediction. Importantly, the model revealed novel components of the splicing logic, which we experimentally validated. This study highlights how interpretable machine learning can advance scientific discovery.

molecular biology↗