Accurate isoform quantification by joint short- and long-read RNA-sequencing.

Accurate quantification of transcript isoforms is crucial for understanding gene regulation, functional diversity, and cellular behavior. Existing RNA sequencing methods have significant limitations: short-read (SR) sequencing provides high depth but struggles with isoform deconvolution, whereas long-read (LR) sequencing offers isoform resolution at the cost of lower depth, higher noise, and technical biases. Addressing this gap, we introduce Multi-Platform Aggregation and Quantification of Transcripts (MPAQT), a generative model that combines the complementary strengths of different sequencing platforms to achieve state-of-the-art isoform-resolved transcript quantification, as demonstrated by extensive simulations and experimental benchmarks. By applying MPAQT to an in vitro model of human embryonic stem cell differentiation into cortical neurons, followed by machine learning-based modeling of transcript abundances, we show that untranslated regions (UTRs) are major determinants of isoform proportion and exon usage; this effect is mediated through isoform-specific sequence features embedded in UTRs, which likely interact with RNA-binding proteins that modulate mRNA stability. These findings highlight MPAQT's potential to enhance our understanding of transcriptomic complexity and underline the role of splicing-independent post-transcriptional mechanisms in shaping the isoform and exon usage landscape of the cell.

Systematic identification of post-transcriptional regulatory modules.

In our cells, a limited number of RNA binding proteins (RBPs) are responsible for all aspects of RNA metabolism across the entire transcriptome. To accomplish this, RBPs form regulatory units that act on specific target regulons. However, the landscape of RBP combinatorial interactions remains poorly explored. Here, we perform a systematic annotation of RBP combinatorial interactions via multimodal data integration. We build a large-scale map of RBP protein neighborhoods by generating in vivo proximity-dependent biotinylation datasets of 50 human RBPs. In parallel, we use CRISPR interference with single-cell readout to capture transcriptomic changes upon RBP knockdowns. By combining these physical and functional interaction readouts, along with the atlas of RBP mRNA targets from eCLIP assays, we generate an integrated map of functional RBP interactions. We then use this map to match RBPs to their context-specific functions and validate the predicted functions biochemically for four RBPs. This study provides a detailed map of RBP interactions and deconvolves them into distinct regulatory modules with annotated functions and target regulons. This multimodal and integrative framework provides a principled approach for studying post-transcriptional regulatory processes and enriches our understanding of their underlying mechanisms.

A sense-antisense RNA interaction promotes breast cancer metastasis via regulation of NQO1 expression.

Antisense RNAs are ubiquitous in human cells, yet their role is largely unexplored. Here we profiled antisense RNAs in the MDA-MB-231 breast cancer cell line and its highly lung metastatic derivative. We identified one antisense RNA that drives cancer progression by upregulating the redox enzyme NADPH quinone dehydrogenase 1 (NQO1), and named it NQO1-AS. Knockdown of either NQO1 or NQO1-AS reduced lung colonization in a mouse model, and investigation into the role of NQO1 indicated that it is broadly protective against oxidative damage and ferroptosis. Breast cancer cells in the lung are dependent on this pathway, and this dependence can be exploited therapeutically by inducing ferroptosis while inhibiting NQO1. Together, our findings establish a role for NQO1-AS in the progression of breast cancer by regulating its sense mRNA post-transcriptionally. Because breast cancer predominantly affects females, the disease models used in this study are of female origin and the results are primarily applicable to females.