Biology subjects

Fish, L.

Publications and source records attributed to Fish, L..

14 recordsLinked to original sources

bioRxiv2026

A high-resolution protein-RNA binding atlas for structure-aware interaction prediction and post-transcriptional regulatory modeling

RNA-binding proteins (RBPs) control post-transcriptional gene expression by regulating RNA processing, localization, stability, and translation. However, our understanding of RNA regulation remains limited by the resolution, scale, and uniformity of available RBP binding maps. Here we introduce eclair CLIP (ecCLIP), a high-throughput CLIP-seq platform that combines endogenous protein tagging for uniform affinity capture with ligation-free library preparation to generate high-resolution RNA-protein binding profiles. We applied ecCLIP to 67 endogenously tagged RBPs in HEK293T cells, generating a high-quality transcriptome-scale atlas that recovered known binding preferences and captured post-transcriptional regulons linked to RNA half-life. To test the utility of the atlas, we incorporated ecCLIP into Borzuki, a Borzoi-derived sequence-to-function model trained on complementary RNA degradation, expression, CLIP, and Perturb-seq data. Inclusion of ecCLIP data improved RNA half-life prediction and assessment of 3' UTR variant effects on RNA stability, demonstrating that our atlas helps capture RNA dynamics. To directly model the cis-regulatory grammar underlying these RBP-RNA interactions, we developed Pythia, a structure-aware neural network architecture that incorporates context-free grammar-inspired fixed convolutions to explicitly encode RNA base-pairing features. Pythia predicted RBP binding from sequence and structural context and decomposed predictions into sequence- and structure-dependent components. We tested this decomposition for five RBPs using massively parallel reporter CLIP (MPRC) binding assays in cells, using synthetic RNA libraries containing mutations designed to preserve or disrupt predicted RNA structures across nominated binding sites. We used the same MPRC strategy to confirm allele-specific binding effects for variants prioritized by Pythia. Together, ecCLIP and Pythia provide a resource for structure-aware decoding of RNA-protein interaction grammar and interpretation of post-transcriptional regulatory variation.

genomics↗

bioRxiv2025

Integrative analysis of mRNA stability regulation uncovers a metastasis-suppressive program in breast cancer

Heterogeneity in cancer gene expression is typically linked to genetic and epigenetic alterations, yet post-transcriptional regulation likely influences these patterns as well. However, the quantitative contribution of post-transcriptional mechanisms to cancer transcriptome dynamics remains unclear. Here, we systematically measured mRNA dynamics across diverse breast cancer models, revealing that mRNA stability significantly shapes gene expression variability. To decipher the regulatory grammar underlying these dynamics, we developed GreyHound, an interpretable multimodal deep-learning framework integrating RNA sequence features and RNA-binding protein (RBP) expression. GreyHound identified an extensive network of RBPs and their regulons underlying variations in mRNA stability. Among these, we uncovered a metastasis-suppressive regulatory axis centered on the RNA-binding protein RBMS3 and its post-transcriptional control of the redox regulator TXNIP. Functional and molecular analyses revealed that RBMS3 depletion resulted in targeted transcript destabilization, which was associated with poor clinical outcomes and enhanced metastatic potential in xenograft models. Using in vivo epistasis studies, we confirmed that RBMS3 regulation of TXNIP mRNA stability drives this metastasis-suppressive program. These findings position the RBMS3-TXNIP regulatory axis as a key post-transcriptional mechanism in breast cancer and illustrate how interpretable models of RNA dynamics can uncover hidden regulatory programs in disease.

cancer biology↗

bioRxiv2025

A multi-modal cell-free RNA language model for liquid biopsy applications

Cell-free RNA (cfRNA) profiling has emerged as a powerful tool for non-invasive disease detection, but its application is limited by data sparsity and complexity, especially in settings with constrained sample availability. We introduce Exai-1, a multi-modal, transformer-based generative foundation model that integrates RNA sequence embeddings with cfRNA abundance data to capture biologically meaningful representations of circulating RNAs. By leveraging both sequence and expression modalities, Exai-1 captures a biologically meaningful latent structure of cfRNA profiles. Pre-trained on over 306 billion tokens from 8,339 samples, Exai-1 enhances signal fidelity, reduces technical noise, and improves disease detection by generating synthetic cfRNA profiles. We show that self-attention and variational inference are particularly important for preservation of biological signals and contextual relationships. Additionally, Exai-1 facilitates cross-biofluid translation and assay compatibility through disentangling biological signals from confounders. By uniting sequence-informed embeddings with cfRNA expression patterns, Exai-1 establishes a transfer learning foundation for liquid biopsy, offering a scalable and adaptable framework for next-generation cfRNA-based diagnostics.

bioinformatics↗

bioRxiv2025

Epigenome-wide analysis reveals novel DNA methylation signatures significantly associated with the infant pupillary light reflex, a candidate intermediate phenotype for autism.

Autism is a highly heterogeneous neurodevelopmental condition, currently diagnosed based on behavioural characteristics. Candidate early intermediate phenotypes, such as the Pupillary Light Reflex (PLR), a reflexive constriction of the pupil in response to increased optical luminance, may provide insights into etiological factors and potential biomarkers, such as DNA methylation (DNAm), involved in the emergence of autism. We conducted epigenome-wide DNAm association analyses of 9-, 14-, 24-month PLR onset latency and constriction amplitude in a sample of 51 infants enriched for autism family history, using buccal DNA collected at 9-months. Our epigenome-wide analysis (EWAS) identified four stringently significant differentially methylated probes (p < 2.4 x 10-7) associated with cross-section PLR latency measurements at 14- and 24-months, and with 14-to 24-month PLR latency developmental change. Differentially methylated probes associated with PLR amplitude were identified, but at a less stringent discovery threshold (p < 5 x 10-5). Our region analyses identified several significant differentially methylated regions associated with both PLR latency and amplitude Downstream exploratory pathway analysis identified enrichment for multiple developmental biological processes, as well as several susceptibility genes to autism and related neurodevelopmental conditions including NR4A2, HNRNPU and NAV2. Our findings provide novel insight into the role of DNAm in PLR development and illuminate biological mechanisms underpinning altered PLR in infancy in emerging autism.

neuroscience↗

bioRxiv2024

Systematic annotation of orphan RNAs reveals blood-accessible molecular barcodes of cancer identity and cancer-emergent oncogenic drivers

From extrachromosomal DNA to neo-peptides, the broad reprogramming of the cancer genome leads to the emergence of molecules that are specific to the cancer state. We recently described orphan non-coding RNAs (oncRNAs) as a class of cancer-specific small RNAs with the potential to play functional roles in breast cancer progression1. Here, we report a systematic and comprehensive search to identify, annotate, and characterize cancer-emergent oncRNAs across 32 tumor types. We also leverage large-scale in vivo genetic screens in xenografted mice to functionally identify driver oncRNAs in multiple tumor types. We have not only discovered a large repertoire of oncRNAs, but also found that their presence and absence represent a digital molecular barcode that faithfully captures the types and subtypes of cancer. Importantly, we discovered that this molecular barcode is partially accessible from the cell-free space as some oncRNAs are secreted by cancer cells. In a large retrospective study across 192 breast cancer patients, we showed that oncRNAs can be reliably detected in the blood and that changes in the cell-free oncRNA burden captures both short-term and long-term clinical outcomes upon completion of a neoadjuvant chemotherapy regimen. Together, our findings establish oncRNAs as an emergent class of cancer-specific non-coding RNAs with potential roles in tumor progression and clinical utility in liquid biopsies and disease monitoring.

cancer biology↗

bioRxiv2023

RPL22 is a tumor suppressor in MSI-high cancers and a key splicing regulator of MDM4

Microsatellite instability high (MSI-H) tumors are malignant tumors that, despite harboring a high mutational burden, often have intact TP53. One of the most frequent mutations in MSI-H tumors is a frameshift mutation in RPL22, a ribosomal protein. Here, we identified RPL22 as a modulator of MDM4 splicing through an alternative splicing switch in exon 6. RPL22 loss increases MDM4 exon 6 inclusion, cell proliferation, and augments resistance to the MDM inhibitor Nutlin-3a. RPL22 represses expression of its paralog, RPL22L1, by mediating the splicing of a cryptic exon corresponding to a truncated transcript. Therefore, damaging mutations in RPL22 drive oncogenic MDM4 induction and reveal a common splicing circuit in MSI-H tumors that may inform therapeutic targeting of the MDM4-p53 axis and oncogenic RPL22L1 induction.

cancer biology↗

bioRxiv2023

Revealing the Grammar of Small RNA Secretion Using Interpretable Machine Learning

Small non-coding RNAs can be secreted through a variety of mechanisms, including exosomal sorting, in small extracellular vesicles, and within lipoprotein complexes 1,2. However, the mechanisms that govern their sorting and secretion are still not well understood. In this study, we present ExoGRU, a machine learning model that predicts small RNA secretion probabilities from primary RNA sequence. We experimentally validated the performance of this model through ExoGRU-guided mutagenesis and synthetic RNA sequence analysis, and confirmed that primary RNA sequence is a major determinant in small RNA secretion. Additionally, we used ExoGRU to reveal cis and trans factors that underlie small RNA secretion, including known and novel RNA-binding proteins, e.g., YBX1, HNRNPA2B1, and RBM24. We also developed a novel technique called exoCLIP, which reveals the RNA interactome of RBPs within the cell-free space. We used exoCLIP to reveal the RNA interactome of HNRNPA2B1 and RBM24 in extracellular vesicles. Together, our results demonstrate the power of machine learning in revealing novel biological mechanisms. In addition to providing deeper insight into complex processes such as small RNA secretion, this knowledge can be leveraged in therapeutic and synthetic biology applications.

systems biology↗

bioRxiv2023

A systematic search for RNA structural switches across the human transcriptome

RNA structural switches are key regulators of gene expression in bacteria, yet their characterization in Metazoa remains limited. Here we present SwitchSeeker, a comprehensive computational and experimental approach for systematic identification of functional RNA structural switches. We applied SwitchSeeker to the human transcriptome and identified 245 putative RNA switches. To validate our approach, we characterized a previously unknown RNA switch in the 3UTR of the RORC transcript. In vivo DMS-MaPseq, coupled with cryogenic electron microscopy, confirmed its existence as two alternative structural conformations. Furthermore, we used genome-scale CRISPR screens to identify trans factors that regulate gene expression through this RNA structural switch. We found that nonsense-mediated mRNA decay acts on this element in a conformation-specific manner. SwitchSeeker provides an unbiased, experimentally-driven method for discovering RNA structural switches that shape the eukaryotic gene expression landscape.

systems biology↗

bioRxiv2022

A multiomics approach reveals RNA dynamics promote cellular sensitivity to DNA hypomethylation

The search for new approaches in cancer therapy requires a mechanistic understanding of cancer vulnerabilities and anti-cancer drug mechanisms of action. Problematically, some effective therapeutics target cancer vulnerabilities that have poorly defined mechanisms of anti-cancer activity. One such drug is decitabine, a frontline therapeutic approved for the treatment of high-risk acute myeloid leukemia (AML). Decitabine is thought to kill cancer cells selectively via inhibition of DNA methyltransferase enzymes, but the genes and mechanisms involved remain unclear. Here, we apply an integrated multiomics and CRISPR functional genomics approach to identify genes and processes associated with response to decitabine in AML cells. Our integrated multiomics approach reveals RNA dynamics are key regulators of DNA hypomethylation induced cell death. Specifically, regulation of RNA decapping, splicing and RNA methylation emerge as important regulators of cellular response to decitabine.

cancer biology↗

bioRxiv2021

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

Antisense RNAs are ubiquitous in human cells, yet the role that they play in healthy and diseased states remains largely unexplored. Here, we developed a computational framework to catalog and profile antisense RNAs and applied it to poorly and highly metastatic breast cancer cell lines. We identified one antisense RNA that plays a functional role in driving breast cancer progression by upregulating the redox enzyme NQO1, and hence named NQO1-antisense RNA or NQO1-AS. This upregulation occurs via a stabilizing interaction between NQO1-AS and its complementary region in the 3UTR of NQO1 mRNA. By increasing expression of NQO1 protein, breast cancer cells are able to tolerate higher levels of oxidative stress, enabling them to colonize the lung. During this process the cancer cells become dependent on NQO1 to protect them from ferroptosis. We have shown that this dependence can be exploited therapeutically in xenograft models of metastasis. Together, our findings establish a previously unknown role for NQO1-AS in the progression of breast cancer by serving as a post-transcriptional regulator of RNA processing and decay for its sense mRNA.

cancer biology↗

bioRxiv2021

An mRNA processing pathway suppresses metastasis by governing translational control from the nucleus

Cancer cells often co-opt post-transcriptional regulatory mechanisms to achieve pathologic expression of gene networks that drive metastasis. Translational control is a major regulatory hub in oncogenesis, however its effects on cancer progression remain poorly understood. To address this, we used ribosome profiling to compare genome-wide translation efficiencies of poorly and highly metastatic breast cancer cells and patient-derived xenografts. We developed novel regression-based methods to analyze ribosome profiling and alternative polyadenylation data, and identified HNRNPC as a translational controller of a specific mRNA regulon. Mechanistically, HNRNPC, in concert with PABPC4, binds near to poly(A) signals, thereby governing the alternative polyadenylation of a set of mRNAs. We found that HNRNPC and PABPC4 are downregulated in highly metastatic cells, which causes HNRNPC-bound mRNAs to undergo 3 UTR lengthening and subsequently, translational repression. We showed that modulating HNRNPC expression impacts the metastatic capacity of breast cancer cells in xenograft mouse models. We also found that a small molecule, previously shown to induce a distal-to-proximal poly(A) site switching, counteracts the HNRNPC-PABPC4 driven deregulation of alternative polyadenylation and decreases the metastatic lung colonization by breast cancer cells in vivo.

cancer biology↗

bioRxiv2020

RBMS1 suppresses colon cancer metastasis through targeted stabilization of its mRNA regulon

Broad dysregulation of gene expression control is a hallmark of cancer progression. Identifying the underlying master regulators that drive pathological gene expression is a key challenge in precision oncology. Here, we have developed a network analytical framework, named PRADA, that identifies oncogenic RNA-binding proteins through the systematic detection of coordinated changes in their target regulons. Application of this approach to data collected from clinical samples, patient-derived xenografts, and cell line models of colon cancer metastasis revealed the RNA-binding protein RBMS1 as a suppressor of colon cancer progression. We observed that silencing RBMS1 results in increased metastatic capacity in xenograft mouse models, and that restoring its expression blunts metastatic liver colonization. We have found that RBMS1 functions as a post-transcriptional regulator of RNA stability by directly binding and stabilizing ~80 target mRNAs. Measurements in more than 180 clinical samples as well as survival analyses in publicly available datasets, have shown that RBMS1 silencing and the subsequent downregulation of its targets are strongly associated with disease progression and poor survival in colon cancer patients. Together, our findings establish a role for RBMS1 as a previously unknown regulator of RNA stability and as a suppressor of colon cancer metastasis with clinical utility for risk stratification of patients. SignificanceBy applying a new analytical approach to transcriptomic data from clinical samples and models of colon cancer progression, we have uncovered RBMS1 as a suppressor of metastasis and as a post-transcriptional regulator of RNA stability. Notably, RBMS1 silencing and downregulation of its targets are negatively associated with patient survival.

systems biology↗

bioRxiv2018

An adaptive stress-induced tRNA depletion response mediates codon-based gene repression and growth suppression

Eukaryotic transfer RNAs can become selectively fragmented upon various stresses, generating tRNA-derived small RNA fragments. Such fragmentation has been reported to impact a small fraction of the tRNA pool and thus presumed to not directly impact translation. We report that oxidative stress can rapidly generate tyrosine tRNAGUA fragments in human cells--causing significant depletion of the precursor tRNA. Tyrosine tRNAGUA depletion impaired translation of growth and metabolic genes enriched in cognate tyrosine codons. Depletion of tyrosine tRNAGUA or its translationally regulated targets USP3 and SCD repressed proliferation--revealing a dedicated tRNA-regulated growth suppressive pathway for oxidative stress response. Tyrosine fragments are generated in a DIS3L2 exoribonuclease-dependent manner and inhibit hnRNPA1-mediated transcript destabilization. Moreover, tyrosine fragmentation is conserved in C. elegans. Thus, tRNA fragmentation can coordinately generate trans-acting small-RNAs and functionally deplete a tRNA. Our findings reveal the existence of an underlying adaptive codon-based regulatory response inherent to the genetic code.

molecular biology↗

bioRxiv2018

Nuclear TARBP2 drives oncogenic dysregulation of RNA splicing and decay

Post-transcriptional regulation of RNA stability is a key step in gene expression control. We describe a regulatory program, mediated by the double-stranded RNA binding protein TARBP2, that controls RNA stability in the nucleus. TARBP2 binding to pre-mRNAs results in increased intron retention, subsequently leading to targeted degradation of TARBP2-bound transcripts. This is mediated by TARBP2 recruitment of the m6A RNA methylation machinery to its target transcripts, where deposition of m6A marks influences the recruitment of splicing regulators, inhibiting efficient splicing. Interactions between TARBP2 and the nucleoprotein TPR then promote degradation of these TARBP2-bound transcripts by the nuclear exosome. Additionally, analysis of clinical gene expression datasets revealed a functional role for this TARBP2 pathway in lung cancer. Using xenograft mouse models, we find that TARBP2 impacts tumor growth in the lung, and that this function is dependent on TARBP2-mediated destabilization of ABCA3 and FOXN3. Finally, we establish the transcription factor ZNF143 as an upstream regulator of TARBP2 expression.\n\nRESEARCH HIGHLIGHTSO_LIThe RNA-binding protein TARBP2 controls the stability of its target transcripts in the nucleus\nC_LIO_LINuclear TARBP2 recruits the methyltransferase complex to deposit m6A marks on its target transcripts\nC_LIO_LITARBP2 and m6A-mediated interactions with splicing and nuclear RNA surveillance complexes result in target transcript intron retention and decay.\nC_LIO_LIIncreased TARBP2 expression is associated with lung cancer and promotes lung cancer growth in vivo.\nC_LIO_LIThe transcription factor ZNF143 drives oncogenic TARBP2 upregulation in lung cancer.\nC_LI

molecular biology↗