Biology subjects

Yurieva, M.

Publications and source records attributed to Yurieva, M..

7 recordsLinked to original sources

bioRxiv2026

Splicing of ultraconserved poison exons controls mitotic fidelity and stem cell viability

SR proteins are essential splicing regulators whose expression is controlled in part through poison exons (PEs) -- ultraconserved non-coding exons that trigger nonsense-mediated decay -- yet the biological functions of these elements remain undefined. Here, we show that homozygous deletion of SRSF3-PE or TRA2{beta}-PE is selected against in mouse embryos and human induced pluripotent stem cells (iPSCs), and that conditional PE deletion causes apoptotic death in iPSCs but is tolerated in post-mitotic neurons, revealing a proliferative-state-specific requirement. Mechanistically, PE deletion elevates SR protein levels, triggers widespread splicing dysregulation, and disrupts the correct splicing of a mitotic gene network associated with spindle defects and mitotic errors. These findings establish ultraconserved poison exons as essential regulators of mitotic splicing fidelity and stem cell viability.

molecular biology↗

bioRxiv2025

SpliceDecoder: A High-Throughput Tool for Guiding the Functional Interpretation of Differential Splicing Events

Alternative splicing generates different mRNA isoforms from single genes, generating protein diversity essential for normal development and tissue function. Dysregulation of splicing is implicated in numerous diseases, including cancer, neurodegeneration, diabetes, and rare genetic disorders. Although thousands of spliced isoforms have been identified in disease-relevant contexts, the functional significance of most remains unknown, hindering our understanding of splicing-driven disease mechanisms and limiting therapeutic discovery. A major challenge lies in prioritizing biologically meaningful splicing events identified through short-read RNA sequencing. Existing splicing analysis tools typically rank target events based on splicing change magnitude or gene-level annotation, often without evaluating how resulting isoforms impact protein structure or function. This creates a critical bottleneck in translating splicing data into biological and therapeutic insights. To address this, we developed SpliceDecoder, a computational workflow that predicts how each splicing event or isoform impacts transcript productivity, protein sequence, and functional domains. Each event is assigned a functional effect score to guide evidence-based prioritization. SpliceDecoder facilitates a more informed interpretation of splicing data, reduces reliance on prior knowledge, and enables identification of events with potential biological and clinical relevance. We demonstrate its utility by validating known splicing alterations and identifying novel disease-associated isoform switches across public datasets.

genomics↗

bioRxiv2025

Automating Candidate Gene Prioritization with Large Language Models: From Naive Scoring to Literature-Grounded Validation

BackgroundIdentifying promising therapeutic targets from thousands of genes in transcriptomic studies remains a major bottleneck in biomedical research. While large language models (LLMs) show potential for gene prioritization, they suffer from hallucination and lack systematic validation against expert knowledge. MethodsWe developed a two-stage computational framework that combines LLM-based screening with literature validation for systematic gene prioritization. Starting with 10,824 genes from the BloodGen3 repertoire, we applied multi-criteria evaluation for sepsis relevance, followed by retrieval-augmented generation (RAG) using 6,346 curated sepsis publications. A novel faithfulness evaluation system verified that LLM predictions aligned with retrieved literature evidence. ResultsThe framework identified 609 sepsis-relevant genes with >94% filtering efficiency, demonstrating strong enrichment for inflammatory pathways including TNF- signaling, complement activation, and interferon responses. Literature validation yielded 30 ultra-high confidence therapeutic candidates, including both established sepsis genes (IL10, TREM1, S100A9, NLRP3) and novel targets warranting investigation. Benchmark validation against expert-curated databases achieved 71.2% recall, with systematic correlation between computational confidence and evidence quality. The final candidate set balanced discovery (11 novel genes) with validation (19 known genes), maintaining biological coherence throughout the filtering process. ConclusionsThis framework demonstrates that rigorous methodology can transform unreliable LLM outputs into systematically validated biological insights. By combining computational efficiency with literature grounding, the approach provides a practical tool for prioritizing experimental validation efforts. The modular design enables adaptation to other diseases through knowledge base substitution, offering a systematic approach to literature-guided biomarker discovery. AvailabilitySource code and implementation details are available at https://github.com/taushifkhan/llm-geneprioritization-framework, vector database at https://doi.org/10.5281/zenodo.15802241 and Interactive demonstration at https://llm-geneprioritization.streamlit.app/

bioinformatics↗

bioRxiv2024

Deep functional profiling of gene sets using Large Language Models: A blueprint for tailored, context-aware functional annotation

In this study, we present a proof-of-concept approach leveraging Large Language Models (LLMs) for the deep functional profiling of gene sets, addressing the limitations of traditional functional annotation tools. By employing a stepwise prompting strategy with OpenAIs GPT-4, we systematically retrieve, consolidate, and score immune functions associated with each gene in a module, generating ranked lists of functions with detailed justifications and supporting literature evidence. As a blueprint for this approach, we demonstrate how this in-depth, context-aware analysis can characterize Module M10.4 from the BloodGen3 transcriptional module repertoire, confirming its central role in neutrophil-mediated innate immunity and antimicrobial defense while uncovering a nuanced network of immune functions overlooked by conventional methods. The LLM-based workflow identifies cell types and transcriptional programs driving these functions, offering a more granular and quantitative assessment of the gene sets functional associations compared to direct LLM prompting or pathway enrichment analysis. Our results showcase the potential of LLMs in providing biologically meaningful interpretations of gene expression signatures, accounting for cellular composition changes and transcriptional regulation. This initial proof-of-concept study establishes a foundation for the automated, high-throughput functional profiling of transcriptional signatures. By integrating advanced language models with traditional literature-based validation, we present a powerful strategy for unraveling the complex biology of gene sets and transcriptional modules, ultimately facilitating the translation of systems-level molecular data into actionable knowledge.

bioinformatics↗

bioRxiv2024

Automating Candidate Gene Prioritization with Large Language Models: Development and Benchmarking of an API-Driven Workflow Leveraging GPT-4

In this exploratory study, we developed an automated workflow that leverages Large Language Models, specifically GPT-4, to prioritize candidate genes for targeted assay development. The workflow automates interaction with OpenAI models and enables prompt creation, submission. It features customizable prompts designed to evaluate candidate genes based on criteria such as association with biological processes, biomarker potential, and therapeutic implications, which can be tailored for specific diseases or processes. Benchmarking experiments comparing the performance of the Application Programming Interface (API)-based automated prompting approach with manual prompting demonstrated high consistency and reproducibility in gene prioritization results. The automated method exhibited scalability by successfully prioritizing genes relevant to sepsis from the BloodGen3 repertoire, comprising 11,465 genes, distributed among 382 modules. The workflow efficiently identified sepsis-associated genes across the repertoire, revealing distinct gene clusters and providing insights into their distribution within module aggregates and individual modules. This proof-of-concept study demonstrates how LLMs can enhance gene prioritization, streamlining the identification process for targeted assays across various biological contexts. However, it also reveals the need for further validation and highlights the exploratory nature of this work due to scoring inconsistencies and the necessity for manual fact-checking. Despite these challenges, the automated workflow holds promise for accelerating targeted assay development for disease management and paves the way for future research.

bioinformatics↗

bioRxiv2023

Protocol for primary human lung organoid-derived air-liquid interface in vitro model to study response to SARS-CoV-2

This article presents a comprehensive protocol for establishing primary human lung organoid-derived air-liquid interface (ALI) cultures from cryopreserved human lung tissue. These cultures serve as a physiologically relevant model to study human airway epithelium in vitro. The protocol encompasses lung tissue cryostorage, tissue dissociation, lung epithelial organoid generation, and ALI culture differentiation. It also demonstrates SARS-CoV-2 infection in these cultures as an example of their utility. Quality control steps, ALI characterization, and technical readouts for monitoring virus response are included in the study. For additional details on the use and execution of this protocol, please refer to Diana Cadena Castaneda et al (https://doi.org/10.1016/j.isci.2023.107374). HighlightsO_LIHuman lung tissue dissection, embedding in OCT blocks, and tissue cryopreservation. C_LIO_LIThawing & lung tissue dissociation for lung epithelium organoid generation. C_LIO_LIOrganoid-derived air-liquid-interface cultures for the study of viral infection. C_LIO_LIBulk RNA-Seq, flow cytometry, viral titer, and imaging to follow response to virus. C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/557067v1_ufig1.gif" ALT="Figure 1"> View larger version (52K): org.highwire.dtl.DTLVardef@b47fbdorg.highwire.dtl.DTLVardef@2e6e60org.highwire.dtl.DTLVardef@508ac6org.highwire.dtl.DTLVardef@1c701a4_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2023

Spatiotemporally organized immunomodulatory response to SARS-CoV-2 virus in primary human broncho-alveolar epithelia

The COVID-19 pandemic continues to be a health crisis with major unmet medical needs. The early responses from airway epithelial cells, the first target of the virus regulating the progression towards severe disease, are not fully understood. Primary human air-liquid interface cultures representing the broncho-alveolar epithelia were used to study the kinetics and dynamics of SARS-CoV-2 variants infection. The infection measured by nucleoprotein expression, was a late event appearing between day 4-6 post infection for Wuhan-like virus. Other variants demonstrated increasingly accelerated timelines of infection. All variants triggered similar transcriptional signatures, an "early" inflammatory/immune signature preceding a "late" type I/III IFN, but differences in the quality and kinetics were found, consistent with the timing of nucleoprotein expression. Response to virus was spatially organized: CSF3 expression in basal cells and CCL20 in apical cells. Thus, SARS-CoV-2 virus triggers specific responses modulated over time to engage different arms of immune response.

immunology↗