Biology subjects

Regev, O.

Publications and source records attributed to Regev, O..

8 recordsLinked to original sources

bioRxiv2025

Interpretable Distillation Reveals that Deep-learning-based Splicing Models Suffer from Pervasive Confounders and Blind Spots

Despite their growing popularity, genomic deep-learning-based models function largely as black boxes, raising concerns about their trustworthiness. Here we develop a framework to explain model prediction logic using interpretable distillation. Applying our framework, we find that RNA splicing prediction models suffer from pervasive confounders and blind spots, leading to poor performance on non-reference sequences. Our findings illuminate fundamental limitations of training models on genomic sequences and suggest ways to overcome them.

genomics↗

bioRxiv2025

Analysis of Large Peptide Collisional Cross Section Dataset Reveals Structural Origin of Bimodal Behavior

Recent advances in ion mobility spectrometry have enabled the measurement of rotationally averaged collisional cross-sectional area (CCS) for millions of peptides, as part of routine proteomic mass spectrometry workflows. One of the most striking finding in recent large ion mobility datasets is that CCS exhibits two distinct modes, most notably for charge 3+ peptides. Here, using classical machine learning approaches, we identify that basic site positioning is a key sequence feature determining if a peptide belongs to the high or low CCS mode. Molecular dynamics simulations suggest that peptides in the high CCS mode tend to adopt more extended conformations and form charge-stabilized helical structures, whereas those in the low CCS mode adopt more compact, globular conformations. Further supporting this structural hypothesis, we provide evidence for preferential protonation near the C-terminus, and uncover multiple position-dependent sequence determinants that all suggest the predominance of helix formation in the high mode. Together, these findings will enable better integration of CCS measurements into protein identification and quantification pipelines, improving the performance of ion mobility-based proteomics.

bioinformatics↗

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↗

bioRxiv2024

Dynamics of RNA localization to nuclear speckles are connected to splicing efficiency

Nuclear speckles, a type of membraneless nuclear organelle in higher eukaryotic cells, play a vital role in gene expression regulation. Using the reverse transcription-based RNA-binding protein binding sites sequencing (ARTR-seq) method, we study human transcripts associated with nuclear speckles. We identify three gene groups whose transcripts demonstrate different speckle localization properties and dynamics: stably enriched in nuclear speckles, transiently enriched in speckles at the pre-mRNA stage, and not enriched in speckles. Specifically, we find that stably-enriched transcripts contain inefficiently spliced introns. We show that nuclear speckles specifically facilitate splicing of speckle-enriched transcripts. We further reveal RNA sequence features contributing to transcript speckle localization, underscoring a tight interplay between genome organization, RNA cis-elements, and transcript speckle enrichment, and connecting transcript speckle localization with splicing efficiency. Finally, we show that speckles can act as hubs for the regulated retention of introns during cellular stress. Collectively, our data highlight a role of nuclear speckles in both co- and post-transcriptional splicing regulation. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=189 SRC="FIGDIR/small/581881v1_ufig1.gif" ALT="Figure 1"> View larger version (53K): org.highwire.dtl.DTLVardef@1a55b13org.highwire.dtl.DTLVardef@fe50baorg.highwire.dtl.DTLVardef@6610fborg.highwire.dtl.DTLVardef@e4df5b_HPS_FORMAT_FIGEXP M_FIG GRAPHICAL ABSTRACT C_FIG

cell biology↗

bioRxiv2023

A high-throughput approach reveals distinct peptide charging behaviors in electrospray ionization mass spectrometry

Electrospray ionization is a powerful and prevalent technique used to ionize analytes in mass spectrometry. The distribution of charges that an analyte receives (charge state distribution, CSD) is an important consideration for interpreting mass spectra. However, due to an incomplete understanding of the ionization mechanism, the analyte properties that influence CSDs are not fully understood. Here, we employ a machine learning-based high-throughput approach and analyze CSDs of hundreds of thousands of peptides. Interestingly, half of the peptides exhibit charges that differ from what one would naively expect (number of basic sites). We find that these peptides can be classified into two regimes--undercharging and overcharging--and that these two regimes display markedly different charging characteristics. Strikingly, peptides in the overcharging regime show minimal dependence on basic site count, and more generally, the two regimes exhibit distinct sequence determinants. These findings highlight the rich ionization behavior of peptides and the potential of CSDs for enhancing peptide identification.

systems biology↗

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↗

bioRxiv2020

Reduced nuclear lamin A/C enhances cancer cell squeezing through rigid barriers, does not facilitate endothelial crossing, and impairs experimental metastasis

The mechanisms by which the nuclear lamina of tumor cells controls their migration and survival are poorly understood. Lamin A and its variant lamin C are key nuclear lamina proteins that control nucleus stiffness and chromatin conformation. Downregulation of lamin A/C levels in two metastatic lines, B16F10 melanoma and E0771 breast carcinoma, facilitated cell squeezing through rigid pores, elevated nuclear deformability and reduced heterochromatin. Unexpectedly, the transendothelial migration of both cancer cells in vitro and in vivo, through lung capillaries, was not elevated by lamin A/C knockdown. Both cancer cells with lamin A/C knockdown grew normally in primary tumors and in vitro on rigid surfaces. Strikingly, however, both lamin A/C deficient melanoma and breast cancer cells grew poorly in 3D spheroids expanded in soft agar cultures. Experimental lung metastasis of both lamin A/C knockdown cells was also markedly reduced. Taken together, our results suggest that high content of lamin A/C in multiple cancer cells promotes cancer cell survival and ability to generate lung metastasis without compromising cancer cell emigration from lung vessels.

cancer biology↗