Biology subjects

Zhao, R.

Publications and source records attributed to Zhao, R..

At least 37 records · Page 2Linked to original sources

bioRxiv2026

An Isoform-Centric, Structure-Aware Framework for Protein Function Prediction and Evaluation, Instantiated in 3DisoDeepPF

Understanding and accurately predicting function across protein isoforms has been a long-standing challenge with profound implications for both biological and translational research. However, most functional annotations and benchmarks remain tied to genes or reference protein sequences, leaving limited support for distinguishing protein isoforms from the same gene whose sequence, structure and domain composition may lead to different functions. To address this gap, we developed an isoform-centric framework for protein function and domain annotation that integrates a dense graph of sequence and structure similarity with protein features. We implemented this framework as 3DisoDeepPF, a graph-based multimodal model, and applied it to a breast cancer isoform atlas. Across canonical benchmarks and evaluations at isoform resolution, 3DisoDeepPF showed strong performance in predicting GO terms and Pfam domains and remained robust in tests with homology control. It further captured changes in Pfam domain composition among isoforms from the same gene, including reference-relative domain gain and loss. An evidence tracing module links predicted labels to supporting proteins in the graph, as illustrated by the calcium and integrin binding protein 1 (CIB1). Together, this study provides a framework informed by protein structure for function and domain annotation at isoform resolution, converting human protein isoform diversity into traceable functional evidence for cancer atlas interpretation.

cancer biology↗

bioRxiv2026

A Deep Learning Framework for Predicting Gut Microbe-Host Receptor Interactions

MotivationGut microbiota regulates host health through complex protein-protein interactions. However, deciphering this specific interactions between microbiota and human receptors remains a significant challenge due to the lack of specialized computational tools. ResultsLeveraging the hypothesis of cell communication and relevant data, HMI-Pred initially builds an ensemble classifier to screen for potential ligand sequences within microbial genomes. It then jointly evaluates sequence semantics and molecular docking to predict potential microbe-host receptor interactions.HMI-Pred achieved robust performance with F1-scores of 0.901 for microbial ligand identification and 0.883 for interaction prediction. Application to 332,381 microbial proteins revealed distinct interaction patterns: histone deacetylases (HDACs) served as broad-spectrum targets (mean score > 0.80), while G protein-coupled receptors (GPCRs) exhibited high specificity (scores 0.42-0.61). Furthermore, literature mining validated over 47% of the functional predictions, and specific immunomodulatory interactions were confirmed in Akkermansia muciniphila.HMI-Pred provides a valuable computational tool for decoding host-microbe signaling networks and facilitating the discovery of microbiome-based therapeutic targets. AvailabilityThe source code and documentation are available at https://github.com/YangLab-BUPT/HMI-Pred. Contactlihm@bupt.edu.cn

microbiology↗

bioRxiv2026

Mechanotherapeutic Potential of Survivin in Glioblastoma

Glioblastoma Multiforme (GBM) is a highly aggressive brain cancer characterized by rapid proliferation and extensive remodeling of the extracellular matrix (ECM), leading to progressive tissue stiffening. Although ECM stiffness is known to promote GBM progression, the molecular mechanisms linking mechanical cues to tumor growth remain insufficiently defined. In this study, transcriptomic comparison of GBM tumors and non-neoplastic brain tissue revealed coordinated upregulation of cell cycle regulators and matrisome-associated genes, with survivin (BIRC5) identified as a central node linking proliferative signaling and ECM remodeling networks. Analysis of GBM patient specimens further showed strong nuclear survivin expression in regions with elevated collagen deposition. To directly evaluate stiffness-dependent regulation of survivin, GBM cells were cultured on fibronectin-infused hydrogels with tunable stiffness. Stiff matrices increased survivin expression along with cyclin D1 and cyclin A, consistent with increased cell cycle progression. Pharmacologic inhibition or siRNA-mediated suppression of survivin reduced stiffness-induced proliferation and attenuated expression of matrisome components, including collagens and lysyl oxidase. These findings indicate that survivin functions as a mechanosensitive regulator that coordinates cell cycle progression with ECM production in stiff tumor microenvironments. Collectively, this study identifies survivin as a key mediator linking ECM stiffness to GBM growth and matrisome remodeling. Targeting survivin and its effectors may offer a mechanosensitive strategy to limit GBM growth.

pathology↗

bioRxiv2026

Spatio-temporal mapping of immune cell dynamics during human sequential lymph node metastasis

Regional lymph node (LN) metastasis critically influences distant metastatic progression, anti-tumour immunity, and patient prognosis. While tumour-induced immune modulation in tumour-draining LNs (TDLNs) has been extensively studied using murine models, the systematic reconstruction of the immune system from primary tumours through TDLNs and subsequent lymph nodes in human cancer progression remains understudied. Here, we utilised integrated multi-omics approaches, including imaging mass cytometry, single-cell RNA sequencing, Visium and Xenium spatial transcriptomics, and multi-colour immunofluorescence to systematically characterise immune cell dynamics across 147 paired primary tumours, sentinel TDLNs (S-TDLNs), and secondary axillary LNs (ALNs) obtained from 50 treatment-naive triple-negative breast cancer patients with different progression statuses. Our comprehensive profiling revealed critical immune alterations, such as decreased type-2 conventional dendritic cells (cDC2), naive T cells, and B cells, along with an increase in immunosuppressive macrophages. Developing a novel single-cell transformer model, we identified substantial alterations in various immune cell populations, notably MARCO+ macrophages, which strongly correlated with breast cancer patient survival outcomes. Spatial analysis combined with our newly integrated cell-cell interaction platform revealed diminished immune cell communication and impaired priming interactions among dendritic cells, B cells, and T cells within metastatic lymph nodes and primary tumour sites. In an independent neoadjuvant immunotherapy cohort of 36 TNBC patients with 52 lymph node samples, we found preservation of CD1c cDC2 in lymph nodes predicted pathological complete response and longer event-free survival, highlighting cDC2 as a potential biomarker and therapeutic target. Collectively, this systematic mapping of immune landscape alterations during human sequential LN metastasis provides essential insights for understanding cancer metastasis mechanisms and paves the way for innovative immunotherapeutic strategies.

immunology↗

bioRxiv2026

SpaMOAL: A spatial multi-omics graph contrastive learning method for spatial domains identification

Recent advances in spatial multi-omics technologies have opened new avenues for characterizing tissue architecture and function in situ, by simultaneously providing multimodal and complementary information--such as spatially resolved transcriptomic, epigenomic, and proteomic features. Current computational approaches face substantial challenges such as effective integration of multi-omics molecular information with spatial information and corresponding high-resolution histology images. To address this challenge, we proposed SpaMOAL (Spatially Multi-Omics graph contrAstive Learning), a graph-based contrastive learning approach for spatial domain identification. SpaMOAL learns clustering-friendly representations from spatial multi-omics data by integrating spatial coordinates, histological image features and molecular profiles, enabling accurate delineation of spatial tissue domains. Benchmarking across multiple recent paired spatial multi-omics datasets demonstrated that SpaMOAL consistently outperforms existing methods. By enabling accurate spatial domain delineation, SpaMOAL provides a powerful framework for interpreting tissue organization and cellular microenvironments.

bioinformatics↗

bioRxiv2026

KRT17 stabilizes EPN1 via inhibiting SMURF1-mediated ubiquitination to modulate Wnt/β-catenin signaling output and stem-like traits in ovarian cancer

BackgroundOvarian cancer (OC) progression and chemoresistance are closely linked to dysregulated oncogenic signaling, including Wnt/{beta}-catenin pathways that contribute to cancer stem-like traits. However, the upstream mechanisms connecting cytoskeletal regulation to Wn/{beta}-catenin signaling in OC remain incompletely understood. Keratin 17 (KRT17), a type I intermediate filament protein, has been implicated in tumor progression, but its mechanistic role in OC requires clarification. MethodsGene Expression Omnibus (GEO) datasets and clinical specimens were analyzed to assess KRT17 and EPN1 expression and prognostic significance. Functional assays, xenograft models, co-immunoprecipitation, ubiquitination analyses, and rescue experiments with wild-type and ubiquitination-resistant EPN1 mutants were performed to investigate molecular mechanisms. ResultsKRT17 expression was elevated in OC tissues and correlated with poor patient survival. KRT17 depletion suppressed proliferation, migration, stem-like properties, tumor growth, and cisplatin resistance. Mechanistically, KRT17 interacted with EPN1 and weakened its association with the E3 ligase SMURF1, reducing SMURF1-mediated ubiquitination at lysine 107 and preventing proteasomal degradation. Stabilized EPN1 was associated with increased {beta}-catenin abundance and stemness-associated markers, and enhanced self-renewal capacity. ConclusionsThese findings identify a KRT17-EPN1 axis that links intermediate filament dynamics to ubiquitin-dependent regulation of EPN1 stability and Wnt/{beta}-catenin signaling outputs in ovarian cancer.

cancer biology↗

bioRxiv2026

TRU-PE: A Universal, Trackable Prime Editor Toolkit for Robust Single- and Multi-Locus Genome Engineering

Prime editing (PE) holds immense promise for precise genome editing, but its wider adoption is constrained by large payloads and inefficient delivery in recalcitrant cell types. Here, we present TRU-PE (Trackable, Robust, and Universal Prime Editor), a toolkit that overcomes these barriers via a split-vector architecture coupled with fluorescence-guided enrichment. This design ensures stoichiometric component expression, yielding marked improvements in editing efficiency and accelerated workflows compatible with both plasmid and viral delivery. By integrating a polycistronic hCtRNA-sgRNA/pegRNA array, the derivative TRU-MPE system enables simultaneous editing of up to ten genomic loci. Furthermore, it enhances the performance of evolved PE editors, allows flexible vector stoichiometry and inspires novel editing reporters. Leveraging these capabilities, we successfully generated a combinatorial human iPSC-based platform for GATA2 deficiency, which accurately recapitulates genotype-specific hematopoietic defects. TRU-PE thus provides a user-friendly, scalable framework for robust single- and multi-locus genome engineering, accelerating functional genomics and therapeutic development.

genetics↗

bioRxiv2026

Improving Growth Predictions in Aquaculture through an Improved Bioenergetics Model Incorporating Feed Composition and Nutrient Digestibility for Largemouth Bass (Micropterus salmoides)

Bioenergetics models serve as mechanistic tools to predict growth by linking energy intake, metabolic expenditure, and nutrient partitioning. However, traditional models rely primarily on gross energy (GE) intake, thereby oversimplifying the effects of feed composition and nutrient availability on fish growth. This work therefore proposed a refined bioenergetics model incorporating nutrient-specific digestibility coefficients (ADCs) and feed composition and tested using a compiled dataset (n=235; 165 for calibration and 70 for independent validation) and a field experimental dataset of largemouth bass (Micropterus salmoides). We first optimized parameters of a gross energy intake-based bioenergetics model, increasing R2 from 0.62 to 0.96 and thereby providing a calibrated foundation for subsequent refined model. The refined model demonstrated superior predictive performance on the compiled dataset (R2 = 0.97) with RMSE = 19.86 g and MAE = 10.31 g), representing reductions of 4.13% in RMSE and 19.98% in MAE and a 1.03% increase in R2 compared with the optimized GE-based model. In the field experiment, the refined model achieved high predictive accuracy (R2 = 0.98 and 0.97), whereas the optimized GE-based model showed poor performance (R2 = 0.33 and 0.06 respectively). This study is, to our knowledge, the first bioenergetics framework for largemouth bass that decomposes feed composition and nutrient-specific ADCs to compute macronutrient-resolved digestible energy, enabling formulation-aware growth prediction and nutrient-oriented optimization.

bioengineering↗

bioRxiv2026

The novel variant IBDV induces inflammatory responses via VP3-mediated pyroptosis by directly targeting Caspase-9

The novel variant infectious bursal disease virus (nVarIBDV), which has been widely prevalent since 2017, threatens the poultry industry by inducing severe bursal atrophy and intense inflammatory responses. Understanding the inflammatory mechanism underlying nVarIBDV infection is critical for preventing virus-induced damage. Pyroptosis, an inflammatory type of programmed cell death, may contribute to the inflammation and bursa of Fabricius damage induced by nVarIBDV. Here, we found that the nVarIBDV infection induced severe inflammatory responses both in vivo and in vitro, which were associated with pyroptosis. Further research revealed that the viral protein VP3 drove pyroptosis induced by nVarIBDV infection by indirectly activating the Caspase-3-GSDME pathway, leading to GSDME cleavage. Mechanistically, VP3 directly interacted with and activated Caspase-9, thereby initiating Caspase-9-Caspase-3-GSDME pathway. More importantly, the Serine 33 of VP3 was identified as the key amino acid for interacting with and activating Caspase-9. Mutation of this residue significantly weakened the ability of VP3 to interact with Caspase-9 and activate the Caspase-9-mediated pyroptosis pathway, altered the binding mode of the Caspase-9-VP3 protein complex, and ultimately reduced the ability of VP3 to induce pyroptosis. In conclusion, our results elucidated a novel mechanism by which nVarIBDV infection induced inflammatory responses whereby viral protein VP3 triggered pyroptosis by targeting the Caspase-9-Caspase-3-GSDME pathway. IMPORTANCEnVarIBDV is globally prevalent and poses a significant threat to the poultry industry by causing inflammation and damage to the bursa of Fabricius. This study revealed that nVarIBDV infection induced inflammatory responses and bursa of Fabricius damage, primarily mediated by the viral protein VP3 targeting Caspase-9 to induce pyroptosis. To our knowledge, this is the first report on the interaction between inflammatory responses induced by IBDV infection and pyroptosis. In addition, serine 33 was identified as the critical amino acid required for maintaining the stability of the interaction between VP3 and Caspase-9 and activation of the Caspase-9-Caspase-3-GSDME cascade pyroptosis pathway and further inflammatory responses. Collectively, our findings not only elucidate a novel mechanism by which nVarIBDV induces pyroptosis and further leads to inflammation but also suggest that the identification of this serine site may represent a potential therapeutic target to block nVarIBDV-induced inflammation and bursa of Fabricius damage.

microbiology↗

bioRxiv2026

Intermittent AP-1 activation in muscles contributes to exercise-induced health benefits

Regular physical exercise extends healthspan, yet the molecular mechanisms that translate intermittent contractile stress into lasting benefit remain incompletely understood. Using global nuclear run-on (GRO-seq) in mouse skeletal muscle after treadmill running, we profiled enhancer RNA (eRNA), a sensitive marker of enhancer activity. Activation protein-1 (AP-1), a family of pioneering factors for senescence, emerged as the top transcription factor with motif enrichment in exercise-activated enhancers. Our screen in the contracting C2C12 myotubes pinpointed cFos/JunD as the primary AP-1 factor responsible for contraction-induced transcriptional changes. Muscle-specific overexpression of A-Fos, a dominant-negative mutant of cFos, disrupted transcriptomic responses to exercise and attenuated exercise-mediated improvement in muscle functions. Interestingly, intermittent but not continuous overexpression of cFos/JunD in mouse muscles mimicked exercise-induced transcriptomic changes, increased mitochondrial volume density, enhanced muscle strength and fatigue resistance, and improved glucose tolerance. These results define a transcriptional regulatory signaling pathway linking exercise intermittency to beneficial adaptations and highlight the necessary recovery cycles in training. The paradoxical anti- and pro-aging roles of AP-1 offer insights into the timing and dynamics of stressors and stress responses in shaping senescence and healthspan.

molecular biology↗

bioRxiv2026

NORAD orchestrates KLC1-mediated SFPQ transport via liquid-liquid phase separation

Neuronal function requires precise long-distance axonal transport mediated by molecular motors and RNA-binding proteins like SFPQ, though regulatory mechanisms remain poorly defined. We identify long non-coding RNA NORAD as a master regulator of this process through liquid-liquid phase separation (LLPS). While SFPQ and kinesin-1 mediate cargo delivery, the specific RNA coordinating their interaction was unknown. We demonstrate NORAD directly binds kinesin light chain 1 (KLC1) and promotes SFPQ condensation into dynamic LLPS droplets, enabling efficient transport. CRISPR-assisted mapping and functional assays show NORAD depletion disrupts granule dynamics, impairs neuroprotective mRNA localization, and induces axonal degeneration. In vitro reconstitution confirms NORAD-KLC1 synergy enhances SFPQ phase separation, and neuron-specific knockout mice exhibit motor deficits with reduced neuronal density. These findings establish the first evidence of lncRNA NORAD-mediated LLPS in axonal transport, revealing a new paradigm for RNA-guided neuronal maintenance.

molecular biology↗

bioRxiv2025

Improved surfactant-assisted one-pot sample preparation for robust convenient single cell proteomics

Recent advances in mass spectrometry-based single-cell proteomics (SCP) technologies have revolutionized the SCP field for comprehensive characterization of cellular heterogeneity. However, most of the current SCP approaches employ sub-{micro}L to 1 {micro}L processing volume for effective single-cell sample preparation using either ultralow-volume specialized devices or a 384-well plate by frequently adding water to the plate well to compensate water evaporation, which greatly limits their broad accessibility and analytical robustness. Here we report a robust convenient SCP method termed iSOP (improved Surfactant-assisted One-Pot processing) for processing of single cells at the low {micro}L processing volume using the 384-well plate with tight sealing to avoid sample drying loss. This iSOP SCP method was built upon our previously developed SOP method by the replacement of a PCR tube or 96-well plate with the low-volume 384-well plate and systematic optimization of the single-cell processing conditions. After optimization, 3 {micro}L was selected as the processing volume with a mixture of 2 ng trypsin and 2 ng Lys-C enzymes in terms of robustness, detection sensitivity, and operation convenience. With a commonly accessible LC-MS platform, iSOP-MS can detect and quantify [~]1,200-1,800 protein groups from single HeLa or MCF7 cells. Application of iSOP-MS to two neuroblastoma cell lines has demonstrated that iSOP-MS enabled reliable identification of an average of [~]1,700 and [~]2,050 protein groups from single BE2-C and SK-N-SH cells, respectively, and precise characterization of cellular heterogeneity between the two distinct cell types and within the same cell type. When compared to other available SCP methods, iSOP-MS is more robust and convenient for routine, cost-effective quantitative SCP analysis.

biochemistry↗

bioRxiv2025

Reciprocal Homoeologous Replacement of MYB Factors Drives the Evolutionary Trade-off between Yield and Fiber Quality in Cotton

The long-standing phenotypic trade-off between Upland cotton (Gossypium hirsutum, high yield) and Sea Island cotton (G. barbadense, high quality) represents a major bottleneck in cotton breeding and industrialization. Despite rapid advances in genomics, the genomic structural variations driving the divergence of these two domestication strategies, and their subsequent impact on downstream transcriptional networks, remain unclear. This study integrates multi-tissue transcriptomic atlases spanning the full growth period with pan-genomic analyses of both Upland and Sea Island cotton, proposing a "Reciprocal Selective Retention (RSR)" strategy adopted by the two species during evolution. Transcriptomic analysis revealed that while the transcriptional chassis of the two species is highly conserved, a drastic "phase inversion" occurs during fiber development. Sea Island cotton appears to trade off for quality by specifically activating a "delayed elongation" module (at 20 DPA), whereas Upland cotton initiates a "precocious filling" program to pursue yield. Further Weighted Gene Co-expression Network Analysis (WGCNA), combined with genomic Presence/Absence Variation (PAV) analysis, identified a set of key transcription factors exhibiting a "3-vs-3" reciprocal loss pattern. Upland cotton specifically retained CRF10, WIND1, and MYB93, constructing a genetic foundation of "robust root system and strong regeneration" to support high yield. Conversely, Sea Island cotton specifically retained MYB111 and ERF105/017 to maintain long-staple characteristics and environmental buffering. Whole-genome structural variation and microsynteny analyses confirmed that these differences stem from asymmetric physical deletions (e.g., a 5 kb deletion in MYB111) or fragment sequence collapse (e.g., a 15.9 kb sequence divergence in MYB93) on homologous chromosomes. The RSR model proposed in this study not only offers a novel insight into the genetic roots of the negative correlation between yield and quality but also provides clear genomic targets for precision improvement using gene-editing technologies such as CRISPR.

genomics↗

bioRxiv2025

Reverse transcribed ssDNA derepresses translation of a retron antiviral protein

Retrons are bacterial immune systems that prevent the spread of phages by initiating a toxic response within infected hosts. All previously characterized retrons produce high levels of multicopy single-stranded DNA (msDNA) in the cell by reverse transcription, which acts as an antitoxin in the absence of phage infection. However, we describe here a non-canonical mechanism for Type VI retrons, which do not produce detectable msDNA in the absence of phage, yet still provide phage defense. Focusing primarily on Retron-Vpa2, a Type VI retron from Vibrio parahaemolyticus, we show broad defense against phages and identify triggers of the system within phage recombination systems. Within the Retron-Vpa2 operon, we find a highly enriched, structured transcript that we term a hybrid RNA (hyRNA), which contains both the retrons reverse transcription template and a translationally repressed toxic effector coding sequence. We find that phage infection induces the accumulation of high levels of msDNA and that this msDNA is necessary for derepressing translation of the antiviral toxin. These findings present key biological and mechanistic insights into a distinct group of retrons while highlighting the diversity of systems that participate in bacterial immunity.

microbiology↗

bioRxiv2025

New proteomic biomarkers identified in plasma extracellular vesicles in sarcoidosis: a case-control matched study

BACKGROUNDSarcoidosis is a heterogeneous disease with unknown mechanisms, nonspecific therapies, and multiple etiologies. The role of blood extracellular vesicles (EVs) in the diagnosis and pathogenesis of sarcoidosis remains obscure. AIMS/OBJECTIVES. This study aims to test the hypothesis that the EV proteins in the blood can serve as phenotypic biomarkers of sarcoidosis. METHODS. We combined EV proteomics with machine learning algorithms to identify and prioritize biomarkers, enrich their functions, and cluster networks in case-control matched ACCESS patients. RESULTS. In total, 278 plasma EV proteins were significantly upregulated or downregulated in 40 sarcoidosis patients compared with 40 matched healthy controls. We identified 97 proteins that could serve as biomarkers with an AUC > 0.75. Of these, the AUC was > 0.90 for 13 proteins. 62 differentially expressed EV proteins strongly correlated with 20 clinical variables of severity, chest X-ray findings, and/or laboratory results. Functional annotation and network analysis suggest that these differentially expressed proteins regulate endocytosis, host responses to external stimuli, and transcription processes. Moreover, the top three ranked pathways were clathrin-mediated endocytosis, Hsp90 chaperone cycle, and spliceosome. CONCLUSIONS. This study demonstrates that plasma EV proteins can serve as biomarkers of various clinical phenotypes of the disease. At a Glance CommentaryCurrent Scientific Knowledge on the Subject: Sarcoidosis is a heterogeneous condition affecting multiple organs. The role of blood extracellular vesicles in the diagnosis and pathogenesis of this condition remains unknown. What This Study Adds to the Field: We identified differentially expressed proteins in plasma EVs by combining proteomics and machine learning algorithms. Top-ranked proteins can serve as diagnostic biomarkers and potential mechanisms for the development of sarcoidosis.

molecular biology↗

bioRxiv2025

Deep learning the dynamic regulatory sequence code of cardiac organoid differentiation

Defining temporal gene regulatory programs driving human organogenesis is essential for understanding congenital defects. We combined a time-resolved, single-cell multi-omic atlas of human induced pluripotent stem cell-derived cardiac organoids with deep learning models of chromatin accessibility, enabling systematic discovery of cis-regulatory syntax underlying heart development and disease. This framework identified cell-state-specific motif syntax, linked motif instances to candidate target genes, and resolved programs governing lineage divergence. Integrating cell-state-resolved molecular profiles with computationally predicted variant effects from congenital heart disease (CHD) cases enabled the prioritization of noncoding variants predicted to disrupt developmental transitions, supporting the paradigm that disease etiology derives from perturbations to regulatory networks governing cardiogenesis. Experimental validation demonstrated that an intronic ANGPTL2 variant altered differentiation outcomes, implicating ANGPTL2 in CHD. This study bridges developmental regulation with disease genetics, establishing a framework for discovering the genetic and molecular basis of congenital disorders.

genetics↗

bioRxiv2025

Integrating Mutation and Stop Signals for Improved RNA Structure Analysis and Insight Discovery

BackgroundUnderstanding RNA structures is essential for exploring its diverse cellular roles. Chemical modification-based RNA structure probing remains a key approach to revealing RNA structures in complex in vivo conditions. Practically, chemical modifications generate both mutation and stop signals during reverse transcription within a single experiment. However, traditional analysis pipelines often rely on only one of the two signals without fully leveraging both. ResultsTo address this, we developed a machine learning-based approach, STONE, that integrates both signals from a single experiment. STONE improved RNA structure analysis across multiple independent validation regions, notably 28S rRNA, viral RNA and regulatory RNAs. In genome-wide datasets, especially single-cell data, STONE significantly increased nucleotide coverage per transcript and improved gene detection reliability. In genome-wide datasets, especially single-cell data, STONE substantially enhanced structural information coverage at both transcript and nucleotide levels. This maximized signal utilization, yielding RNA structures in single-cell data comparable to those from bulk datasets. Furthermore, STONE-derived structural scores allow direct identification of RNA-binding protein binding sites directly from a single probing experiment, with results on RNAs such as U1 snRNA, RNase P RNA, and XIST lncRNA closely matching established techniques like CLIP-seq and RNP-MaP. ConclusionsBy integrating mutation and stop signals from one single experiment, STONE advances RNA structure analysis accuracy, extends nucleotide coverage, and facilitates complex RNA-protein interaction studies, broadening the methods applications in RNA-based research.

bioinformatics↗

bioRxiv2025

Hydration Network Drives Activation and G Protein Selectivity in GPR174

G protein-coupled receptor 174 (GPR174), a key modulator of autoimmune responses, maintains immune homeostasis through distinct G protein signaling pathways, particularly Gs and Gi. Although the structural mechanism of lysophosphatidylserine (LysoPS)-activated GPR174 in the Gs pathway has been characterized, how hydration-mediated interactions influence GPR174 activation and signaling selectivity remains unclear. Here, we determined high-resolution cryo-electron microscopy (cryo-EM) structures of LysoPS-activated GPR174 bound to Gs (2.0 [A]) and Gi (3.4 [A]), revealing a continuous hydration-mediated signaling transduction network that bridges the sodium-binding pocket, NPxxY and DRY motifs, and the G protein-binding interface. This network stabilizes the active-state conformation of GPR174 and dynamically reshapes the intracellular cavity, thereby enabling differential engagement of Gs and Gi. Molecular dynamics simulations and functional assays demonstrated that the hydration network is essential for receptor activation and selectively modulates G protein coupling. To evaluate its conservation, we performed sequence alignment and structural analysis across class A GPCRs, defining three hydration cavities: the conserved water cavity (CWC), the junction water cavity (JWC), and the extended water cavity (EWC), whose hydration is determined by residue properties at position 5.58. Together, our study reveals a hydration-driven molecular mechanism that underlies the activation of GPR174 and its dual G protein selectivity. These findings advance the understanding of hydration-mediated signaling in GPR174 and provide a framework for investigating water-mediated regulation across class A GPCRs.

biophysics↗