Biology subjects

Su, Q.

Publications and source records attributed to Su, Q..

At least 19 recordsLinked to original sources

bioRxiv2026

FeatureMAP enables network-guided interpretation of untargeted metabolomics

Untargeted liquid chromatography-mass spectrometry (LC-MS) metabolomics generates thousands of features, but incomplete metabolite annotation and reliance on predefined pathways limit their biological interpretation. Here we present FeatureMAP, an LC-MS feature-centric computational framework that integrates multi-evidence metabolite annotation, network-guided refinement, metabolic module discovery, and structured large language model-based functional interpretation. Across ten public datasets, FeatureMAP improved MS1-based metabolite candidate prioritization while reducing annotation redundancy. In semi-synthetic benchmarks, FeatureMAP recovered coordinated metabolic perturbations, achieving 92% recovery of a simulated perturbation, and distinguished concurrent metabolic processes. Controlled benchmarks further evaluated functional interpretation under competing metabolite-level signals. Based on FeatureMAP, we additionally developed PhenoMAP, a phenotype-aware knowledge graph comprising 12,929 dysregulated metabolic modules from 501 human metabolomics studies spanning 326 phenotypes. Module-level comparisons revealed reproducible metabolic similarities across independent studies and biologically supported relationships among phenotypes. Application to Alzheimer's disease cerebrospinal fluid and cellular datasets identified convergent membrane-lipid alterations and implicated galactose-linked glycosphingolipid remodeling. Together, FeatureMAP and PhenoMAP enable network-grounded interpretation of incompletely annotated LC-MS metabolomics data and systematic investigation of metabolic dysregulation across biological conditions.

bioinformatics↗

bioRxiv2026

ARCADE disentangles cell abundance from continuous transcriptional state in spatial transcriptomics

Spatial transcriptomics is widely used to infer both cell-type abundance and within-type transcriptional variation, but these signals are difficult to separate in mixed spot-level measurements. Here, we present ARCADE, a computational framework that addresses two major sources of uncertainty in spatial analysis: unstable single-cell references and entanglement between abundance and cell state. ARCADE first builds a robust reference through hyperparameter optimization and consensus filtering, then performs spatial inference in two stages by estimating cell-type composition before modeling continuous intratype transcriptional programs around this compositional anchor. Across simulated and semi-synthetic benchmarks, ARCADE improves deconvolution accuracy and yields more stable state inference than existing approaches. In matched human dorsolateral prefrontal cortex datasets with spatial proteogenomic readouts, ARCADE distinguishes localized cell enrichment from astrocyte and microglial activation programs. These results show that decoupled modeling can improve the interpretability of spatial transcriptomic signals in complex tissues.

bioinformatics↗

bioRxiv2026

Refining RNA-Seq Transcriptome Profiles with Transcript-Specific Minimal-Shared Regions

Accurate transcript quantification remains a critical challenge in RNA sequencing (RNA-seq) due to ambiguities in read assignment caused by shared regions among transcripts. Beyond shared exons among homologous isoforms, we identified the widespread presence of extensive undefined shared regions-overlapping sequences common to exons or transcripts of heterologous genes-across diverse species, including animals and plants. To overcome this issue, we developed a minimal-sharing strategy that partitions transcripts into unique sets of k-mers and systematically evaluates overlap across all transcripts. By isolating minimal-shared regions specific to each isoform, our approach reduces mapping uncertainty, thereby minimizing false positives and false negatives in transcript detection. Comprehensive evaluations using both in silico simulations and empirical sequencing data demonstrated that our method outperforms traditional probabilistic model-based quantification methods. This strategy improves isoform abundance estimates, enhances gene ontology annotations, and identifies distinct transcript isoforms critical for understanding biological processes, as exemplified in a Huntington's disease mouse model.

bioinformatics↗

bioRxiv2026

GaussF: An Unbiased Gaussian Framework for High-Precision Transcript Quantification with a Programmable Self-Benchmark Model

Short-read RNA sequencing has revolutionized transcriptome analysis, but accurate isoform-level quantification faces substantial challenges from overlapping sequences among complex isoforms and inherent sequencing biases, leading to uneven coverage. Current alignment-based and alignment-free methods, while valuable, largely rely on empirical, data-derived factors for bias correction and isoform resolution. This dependency limits their capacity to fully capture the intricate spectrum of sequencing data. We introduce GaussF, a novel alignment-free methodology specifically designed for precise isoform quantification by directly addressing these biases and the isoform recognition problem without relying on empirical data. GaussF is predicated on the observation that k-mer counts categorized by GC content across a natural transcript exhibit an intrinsic Gaussian distribution. It establishes a transcript-specific self-benchmarking workflow utilizing a theoretical Gaussian cumulative distribution function (CDF). Crucially, the parameters defining this CDF are derived independently of any empirical sequencing data. This parameter-fixed theoretical CDF serves as an unbiased benchmark, capturing only the genuine contribution of abundance while systematically excluding all bias-related data. To improve isoform resolution despite shared regions, GaussF characterizes isoforms through identifying minimal-shared regions and restructures the k-mer reference accordingly. This approach simplifies complexity and enhances accuracy. Rigorous validation using artificial and real RNA samples, alongside comparative analysis with leading quantification tools like Kallisto, Salmon, Cufflinks, StringTie, and RSEM, demonstrates GaussF's superior performance in accurate isoform detection and quantification. GaussF is available on GitHub and PyPI, representing a significant advancement in RNA-seq analysis.

bioinformatics↗

bioRxiv2026

Spatial Transcriptomics Identifies Muscle Inflammation Susceptibility as a Distinct Periarticular Skeletal Muscle Phenotype in End-Stage Knee Osteoarthritis

Skeletal muscle dysfunction is a major contributor to disability and incomplete functional recovery in patients with end-stage knee osteoarthritis (KOA), yet the spatial components of disease-associated molecular remodeling remain poorly understood. Here, we applied spatial transcriptomics to paired skeletal muscle biopsies obtained from the surgical (Sx) and contralateral (Ct) limbs of individuals undergoing total knee arthroplasty (TKA) to define the cellular architecture of periarticular muscle and determine how muscle inflammation susceptibility (MuIS) shapes local transcriptional programs. Integrated analysis of 27,087 spots obtained from 22 muscle histological cross-sections (11 Sx-Ct pairs) revealed seven spatially resolved transcriptional domains corresponding to slow and fast myofiber states, an extracellular matrix/fibroadipogenic-enriched domain, a pericyte/smooth muscle domain, and a satellite cell/myogenic-enriched domain. Despite advanced unilateral disease, the major annotated cellular compartments were similarly represented between Sx and Ct limbs. KOA-associated remodeling was reflected primarily by within-cluster transcriptional changes, with the most informative differences observed in fibroadipogenic, pericyte/smooth muscle, and satellite/myogenic domains. Within Sx, MuIS stratification identified a coordinated transcriptional program characterized by denervation- and regeneration-associated genes and altered contractile and metabolic features. Neighborhood analysis localized denervation-associated signals primarily to fast-myofiber-rich regions, while local adjacency patterns among the examined myofiber, fibroadipogenic, and pericyte/smooth muscle domains were broadly preserved. Our findings provide the first spatial transcriptomic analysis of periarticular skeletal muscle in end-stage KOA and identify MuIS as a distinct local transcriptional phenotype in diseased muscle.

physiology↗

bioRxiv2026

An Epigenetic Signature of Vulnerable Neurons is Under Selective Pressure Associated with Longevity Across Placental Mammals.

Age is the primary risk factor for neurodegenerative diseases, which are characterized by cell-type-specific vulnerability. Yet brain-aging mechanisms remain unclear given the complex, interacting age-associated pathways across diverse neural cell types. Here, we dissect cell type- cell state-specific aging gene regulatory programs and their contribution to cellular vulnerability by leveraging epigenomics, AI methodology, and natural lifespan diversity across placental mammals. Applying the TACIT method, we associated lifespans of 240 placental mammals to the predicted open chromatin levels of over 3 million orthologous loci across 18 cortical cell types. We identified thousands of lifespan-associated open chromatin regions, enriched near genes associated with hallmarks of aging, which stratified greatly by cell type. For example, regions near mitochondrial genes showed differential selective pressure in long-lived species in energetically-demanding layer V ET neurons, while regions near inflammatory response genes were under selective pressure in glial populations. We next asked whether regions linked to vulnerable or resilient neurons in the human brain were under differential selective pressure in longer lived species. Using an adaptive representation learning approach, we decompose intrinsic aging programs from systemic effects in the prefrontal cortex and define an aging signature predictive of cell-type-specific vulnerability. In Alzheimer's disease, this intrinsic aging signature more strongly predicts vulnerability than systemic effects. Active regions in vulnerable neurons showed lower predicted activity in species with longer lifespans, suggesting selective pressure to down-regulate the vulnerability-associated networks. Overall, our findings argue against a single master regulator of aging, instead implicating different hallmarks across different cell types.

genomics↗

bioRxiv2026

New genes play a prominent role in evolution of new sperm classes of Drosophila

The widespread and regulated production of infertile parasperm assisting fertilization of eusperm provides a compelling model for elucidating the mechanisms of cellular innovation and altruism. Here we identify two specific parasperm lineages in Drosophila pseudoobscura that are segregated in development from the eusperm lineage by their single-cell transcriptomes. Most X-linked genes, except for species-specific new genes, show testis-specific repressive epigenetic modifications and become downregulated upon meiotic entry, supporting meiotic X chromosome inactivation. Between sperm classes, nearly half of the known meiotic arrest genes and their downstream targets are differentially transcribed, with over 200 genes displaying a gradient of transcription levels correlated with that of tail lengths. New genes are overrepresented throughout the hierarchical regulatory network of sperm divergence and over 60% of them exhibit a new cell-level expression compared to their parental copies. These results together indicate that new genes play a prominent role in the emergence of low-cost parasperm that shield the eusperm until fertilization.

evolutionary biology↗

bioRxiv2026

BBBP_Atlas: Unified Interpretable Modeling of Blood Brain Barrier Permeability across Small Molecules and Peptides

Accurate prediction of blood-brain barrier permeability (BBBP) is essential for central nervous system drug discovery, yet existing models are often limited by their reliance on predefined physicochemical descriptors, small-molecule-centered training sets, or conformation-dependent representations, which restricts their transferability across chemically diverse modalities especially peptides. In addition, publicly available BBBP datasets remain fragmented, inconsistently standardized, and weakly controlled for molecular redundancy, increasing the risk of data leakage and overestimated model performance. In this study, we propose BBBP-Atlas, a structure-aware BBB permeability prediction model designed for unified modeling of small molecules and peptides with the first cross-modal dataset OmniBBBP. Designed to bypass descriptor and conformation dependencies, our model represents standardized molecular structures as atom-level graphs to capture local atom-bond environments and long-range topological dependencies associated with BBB transport. This design enables direct learning of structure-permeability relationships from molecular topology. For model training and evaluation, we curated a cross-modal, redundancy-filtered database OmniBBBP that seamlessly unifies small molecules and complex peptides, containing 10,218 unique compounds with 9,316 small molecules and 902 peptides. BBBP-Atlas achieved an accuracy of 0.8914 and an MCC of 0.7678 on the independent test set. On a balanced external benchmark of 200 compounds, our model reached an AUC of 0.9108, an accuracy of 0.8500, and an MCC of 0.7000, outperforming LightBBB by an absolute MCC gain of 6%. Case studies further showed that BBBP-Atlas captured clinically meaningful BBB permeability patterns, correctly identifying lorlatinib as BBB-permeable and vancomycin as BBB-impermeable with high confidence. The OmniBBBP-backed BBBP-Atlas offers a versatile and cross-modal approach for single-compound prediction, batch screening, and dataset exploration for CNS drug discovery. BBBP-Atlas is available at https://cadd.drugflow.com/bbbp/.

bioinformatics↗

bioRxiv2026

Combination of engineered cell type-specific promoters and a high-efficiency AAV capsid restores hearing in adult DFNB1 mice model with demonstrated safety in nonhuman primate

A major challenge in gene therapy for GJB2-related hearing loss (DFNB1)--the most common form of hereditary deafness--is achieving efficient and precise connexin 26 delivery. Herein, we engineered two cell type-specific promoters (GJB2-1 and WFS1-2274) and developed an AAV capsid, AAV-MAS012, with enhanced transduction efficiency in mature cochlear cells. Our AAV-mediated gene therapy systems restored hearing of low-to-mid-frequencies in newborn Gjb2 cKO mice to wild-type levels and maintained for 45 weeks. Additionally, our therapeutic systems restored low-to-mid-frequencies hearing function to wild-type levels in adult Gjb2 cKO mice. A humanized version of the therapy, AAV-MAS012-WFS1-2274-hGJB2, rescued hearing function in two distinct Gjb2-deficient mouse models, and demonstrated a favorable safety profile in nonhuman primates. This study represents the first successful hearing restoration in adult Gjb2-deficient mice. The significant therapeutic efficacy of the humanized gene therapy system shows great potential for clinical translation in DFNB1 patients.

genetics↗

bioRxiv2025

Beyond magnetosomes: ubiquitous and diverse intracellular inclusions expand the role of magnetotactic bacteria in biogeochemical cycling

Magnetotactic bacteria (MTB) are capable of accumulating and storing intercellular pools containing phosphorus, sulfur, nitrogen and carbon. Yet, the metabolic pathways connected with their intracellular storage of polyphosphate, elemental sulfur, nitrate, and calcium carbonate, and the environmental influence on MTB inclusions and their quantitative contribution to sedimentary chemical budgets remain underexplored. Using a combination of chemical characterization, ultrastructural and compositional analyses, and phylogenetic and genomic insights into microbial assemblages, we investigated the influence of geochemical parameters on the diversity, ecophysiology, and distribution of MTB in freshwater and brackish sediments. Special focus is given to intracellular inclusions and their metabolic pathways to uncover functional traits and ecological roles in elemental cycling. Of the 118 examined MTB cells, polyphosphate granules (75%) and nitrate-containing vacuoles (67%) were the most common inclusions followed by sulfur globules (25%) and calcium carbonate granules (8%). The intracellular phosphorus, nitrogen, and sulfur stored in MTB cells were conservatively estimated to account for 0.60%, 0.91%, and 0.23% of the total sedimentary P, N, and S in the investigated freshwater sediments, respectively. The ubiquitous nature and important ecological role of MTB can be explained by their ability to sequester chemical elements into intracellular inclusions, giving them a metabolic advantage in dynamic chemically stratified environments. The extraordinary phylogenetic diversity of MTB, coupled with their capability to hyperaccumulate and store a wide range of elements and compounds, represents a significant resource for biotechnology innovation.

microbiology↗

bioRxiv2025

A dual-metric framework for quantifying the biological fidelity of scRNA-seq pipelines

Single-cell RNA sequencing (scRNA-seq) enables high-resolution transcriptional profiling, but early-stage processing pipelines differ markedly in barcode recovery, UMI correction, and read assignment-variations that can propagate and bias downstream analyses. We present a reproducible, parameter-aware benchmarking framework to quantify the biological fidelity of four primary pipelines-STARsolo, Cell Ranger, Kallisto|Bustools, and Alevin-fry-across simulated ground-truth datasets and complex biological contexts, including a Huntingtons disease (HD) mouse model. Our approach introduces two complementary metrics: the Cluster Annotation Score (CAS), assessing concordance between direct cell-level and cluster-level consensus labels, and the Marker Concordance Score (MCS), measuring cohesion of de novo marker genes per cell type. By systematically varying highly variable gene (n_HVG) and principal component (n_PC) settings, we map how upstream quantification interacts with downstream parameter choice. Simulations show STARsolo and Kallisto|Bustools deliver high technical accuracy, but only STARsolo consistently preserves stable cell identities and coherent marker expression across parameter regimes. In empirical datasets, alignment-based pipelines (STARsolo, Cell Ranger) yield higher CAS/MCS values and more biologically faithful annotations, while alignment-free methods show reduced signal fidelity despite faster runtimes. Differences introduced during primary processing persist after batch correction and integration, altering disease-associated cell type detection. Our open-source CAS-MCS-Scoring toolkit enables transparent evaluation of pipeline performance, providing a practical guide for selecting analysis strategies that maximize reproducibility and biological interpretability in scRNA-seq studies.

bioinformatics↗

bioRxiv2025

The TEAIV model: Extending the standard TEIV model to account for viral budding ramp up

Understanding how viruses replicate and spread within a host is fundamental to predicting disease progression, timing, and dosage of effective therapeutic and prophylactic interventions. A target-cell limited approach is often used to model within-host viral kinetics to characterize disease infection dynamics. The standard target-cell limited model, the TEIV model, has been instrumental for understanding SARS-CoV-2 within-host viral kinetics, however, its core assumptions of instantaneous viral budding and exponentially distributed cell lifetimes oversimplify fundamental biological processes, potentially limiting predictive power. In this work, we consider a novel model approach, the TEAIV model: an extension to the TEIV model that considers an infected partially-productive cell state that ramps up through n stages to a fully productive state. We fit the TEAIV model to various SARS-CoV-2 viral load datasets, separated by hospitalized (severe infection) and non-hospitalized (mild infection) individual data, consider multiple different ramping functions, and compare to standard TEIV model fits. We further compare TEAIV and TEIV model fits with and without infected cell death incorporated in the eclipse (E) and partially infected (A) compartments and investigate best-fit frameworks up to 10 A states. We find that linear budding ramp-up dynamics minimizes the BIC (with {Delta}BIC > 2) across all model formulations when the total number of eclipse and budding compartments exceeds three. Furthermore, we find that the inclusion or exclusion of cell death applied to eclipse or ramping compartments does not substantially affect this result. Finally, we analytically consider the most general TEAIV model and discuss future modelling considerations. Our results demonstrate that accounting for non-instantaneous viral budding provides a better fit to SARS-CoV-2 viral load data and establishes a foundation for more mechanistically informed within-host models.

systems biology↗

bioRxiv2025

Molecular Mechanisms Underlying Human Vγ9Vδ2 T Cell Activation by Butyrophilin-3 (BTN3) Targeted Antibodies

{gamma}{delta} T cells express T cell receptors (TCRs) composed of paired {gamma} and {delta} chains. The V{gamma}9V{delta}2 T cells are the main subpopulation of {gamma}{delta} T cells in human peripheral blood, which responds to phosphoantigens (pAgs) through butyrophilins (BTN) molecules, such as BTN3A1/2/3 and BTN2A1. Antibodies targeting BTN3As, such as ICT01, can activate V{gamma}9V{delta}2 T cells independently of pAgs, although the underlying mechanism remains poorly characterized. In this study, we reveal the molecular basis of ICT01-mediated V{gamma}9V{delta}2 T cell activation using structural, biochemical, and cellular analyses. ICT01 binds to a unique region in the extracellular domain of BTN3As, destabilizing the BTN2A1-BTN3As interface and facilitating V{gamma}9V{delta}2 TCR engagement, ultimately resulting in activation of V{gamma}9V{delta}2 T cells. Our findings provide insights into the mechanism by which agonist antibodies induce {gamma}{delta} T cell activation and provide guide strategies for developing BTN-targeted immunotherapies.

immunology↗

bioRxiv2025

Phosphoantigen-Driven Dissociation of Butyrophilin Oligomers Activates γδ T Cells

{gamma}{delta} T cells represent a promising avenue for cancer immunotherapy. The V{gamma}9V{delta}2 T cell receptor (TCR), expressed by the predominant subset of {gamma}{delta} T cells, responds to phosphoantigen (pAg)-engaged butyrophilins (BTNs) on various cancer cells. However, the molecular mechanism underlying pAg-mediated activation of V{gamma}9V{delta}2 TCRs remains a subject of debate. Here, we employed an integrative approach to elucidate the mechanism of pAg reactivity in V{gamma}9V{delta}2 T cells. Our results demonstrate that BTNs form higher-order oligomers in the absence of pAg. Upon pAg binding, these oligomers dissociate into tetramers, enabling V{gamma}9V{delta}2 TCR engagement. This pAg-induced dissociation of higher-order BTN complexes is critical for pAg-mediated activation of {gamma}{delta} T cells. Our findings reveal a mechanism of BTN dissociation-driven pAg sensing, providing valuable insight for future immunotherapeutic strategies.

immunology↗

bioRxiv2024

Combined aerobic and resistance exercise training alters the spatial transcriptome of skeletal muscle in young adults

Chronic exercise training substantially improves skeletal muscle function and performance. The repeated demands and stressors of each exercise bout drive coordinated molecular adaptations within multiple cell types in muscle tissue, leading to enhanced neuromuscular recruitment and contractile function, stem cell activation, myofiber hypertrophy, mitochondrial biogenesis, and angiogenesis, among others. To comprehensively profile molecular changes induced by combined resistance and endurance exercise training, we employed spatial transcriptomics coupled with immunofluorescence and computational approaches to resolve effects on myofiber and mononuclear cell populations in human muscle. By computationally identifying fast and slow myofibers using immunofluorescence data of spatially sequenced tissue sections, we identified fiber type-specific, exercise-induced gene expression changes that correlated with muscle functional improvements. Additionally, spatial transcriptome profiling and integration of human muscle single cell RNAseq data identified an exercise-induced shift in interstitial cell populations coincident with angiogenesis. Overall, these data provide a unique spatial molecular profiling resource for exploring muscle adaptations to exercise, and provide a pipeline and rationale for future studies in human muscle.

physiology↗

bioRxiv2024

Sensitive Fluorescent Biosensor Reveals Differential Subcellular Regulation of PKC

The protein kinase C (PKC) family of serine/threonine kinases, which consist of three distinctly regulated subfamilies, have long been established as critical for a variety of cellular functions. However, how PKC enzymes are regulated at different subcellular locations, particularly at emerging signaling hubs such as the ER, lysosome, and Par signaling complexes, is unclear. Here, we present a sensitive Excitation Ratiometric (ExRai) C Kinase Activity Reporter (ExRai-CKAR2) that enables the detection of minute changes in subcellular PKC activity. Using ExRai-CKAR2 in conjunction with an enhanced diacylglycerol (DAG) biosensor capable of detecting intracellular DAG dynamics, we uncover the differential regulation of PKC isoforms at distinct subcellular locations. We find that G-protein coupled receptor (GPCR) stimulation triggers sustained PKC activity at the ER and lysosomes, primarily mediated by Ca2+ sensitive conventional PKC (cPKC) and novel PKC (nPKC), respectively, with nPKC showing high basal activity due to elevated basal DAG levels on lysosome membranes. The high sensitivity of ExRai-CKAR2, targeted to either the cytosol or Par-complexes, further enabled us to detect previously inaccessible endogenous atypical PKC (aPKC) activity in 3D organoids. Taken together, ExRai-CKAR2 is a powerful tool for interrogating PKC regulation in response to physiological stimuli.

bioengineering↗

bioRxiv2023

Unbiased Complete Estimation of Chloroplast Number in Plant Cells Using Deep Learning Methods

Chloroplasts are essential organelles in plants that are involved in plant development and photosynthesis. Accurate quantification of chloroplast numbers is important for understanding the status and type of plant cells, as well as assessing photosynthetic potential and efficiency. Traditional methods of counting chloroplasts using microscopy are time-consuming and face challenges such as the possibility of missing out-of-focus samples or double counting when adjusting the focal position. Here, we developed an innovative approach called Detecting- and-Counting-chloroplasts (D&Cchl) for automated detection and counting of chloroplasts. This approach utilizes a deep-learning-based object detection algorithm called You-Only-Look-Once (YOLO), along with the Intersection Over Union (IOU) strategy. The application of D&Cchl has shown excellent performance in accurately identifying and quantifying chloroplasts. This holds true when applied to both a single image and a three-dimensional (3D) structure composed of a series of images. Furthermore, by integrating Cellpose, a cell-segmentation tool, we were able to successfully perform single-cell 3D chloroplast counting. Compared to manual counting methods, this approach improved the accuracy of detection and counting to over 95%. Together, our work not only provides an efficient and reliable tool for accurately analyzing the status of chloroplasts, enhancing our understanding of plant photosynthetic cells and growth characteristics, but also makes a significant contribution to the convergence of botany and deep learning. One-sentence summaryThis deep learning-based approach enables the accurate complete detection and counting of chloroplasts in 3D single cells using microscopic image stacks, and showcases a successful example of utilizing deep learning methods to analyze subcellular spatial information in plant cells. The authors responsible for distribution of materials integral to the findings presented in this article in accordance with the policy described in the Instructions for Authors (https://academic.oup.com/plcell/) is: Zhao Dong (dongzhao@hebeu.edu.cn), Shaokai Yang, (shaokai1@ualberta.ca), Ningjing Liu (liuningjing1@yeah.net), and Qiong Zhao (qzhao@bio.ecnu.edu.cn).

cell biology↗

bioRxiv2023

Hormonal and transcriptomic analysis reveals the role of ABA and BR in breaking the barrier of inter-subgeneric hybridization in water lily (Nymphaea)

Understanding the process of signal communication between pollen and stigma is of significant importance for plant sexual reproduction. In the case of inter-subgeneric hybridization in water lily, there exists a pre-fertilization hybridization barrier, the regulatory mechanism of which remains unclear. In this study, we conducted hormone and transcriptome analyses of unpollinated stigmas (Mock), self-pollinated stigmas (SP), cross-pollinated stigmas within the same subgenus (CP), and inter-subgenus cross-pollination stigmas (ISCP) in water lily to elucidate the formation mechanism of the inter-subgeneric hybridization barrier. Our results indicated that the lack of ABA and BR in ISCP stigmas are key factors contributing to the formation of the inter-subgeneric hybridization barrier in water lily. Exogenous application of ABA and BR can help overcome the barrier between inter-subgeneric water lily crosses. Through transcriptome analysis, we identified nine candidate genes involved in regulating the inter-subgeneric hybridization barrier in water lily. In addition, we further demonstrated the importance of the NCED2-mediated ABA synthesis pathway in the hybridization process through AS-ODN technology. Our study confirms that ABA and BR are critical for breaking the inter-subgeneric hybridization barrier in water lily. The identification of the nine candidate genes provides important clues for further research on the hybridization recognition mechanism in water lily.

plant biology↗