Biology subjects

Zhou, P.

Publications and source records attributed to Zhou, P..

At least 73 records · Page 4Linked to original sources

bioRxiv2024

Combined HDAC and eIF4A inhibition: A novel epigenetic therapy for pancreatic adenocarcinoma

Pancreatic ductal adenocarcinoma-(PDAC) needs innovative approaches due to its 12% 5-year survival despite current therapies. We show marked sensitivity of pancreatic cancer cells to the combination of a novel eIF4A inhibitor, des-methyl pateamine A (DMPatA), and a histone deacetylase inhibitor, romidepsin, inducing epigenetic reprogramming as an innovative therapeutic strategy. Exploring the mechanistic activity of this combination showed that with a short duration of romidepsin at low doses, robust acetylation persisted up to 48h with the combination, while histone acetylation rapidly faded with monotherapy. This represents an unexpected mechanism of action against PDAC cells that triggers transcriptional overload, metabolic stress, and augmented DNA damage. Structurally different class I HDAC inhibitors exhibit the same hyperacetylation patterns when co-administered with DMPatA, suggesting a class effect. We show efficacy of this combination regimen against tumor growth in a MIA PaCa-2 xenograft model of PDAC with persistent hyperacetylation confirmed in tumor samples. STATEMENT OF SIGNIFICANCEPancreatic ductal adenocarcinoma, a significant clinical challenge, could benefit from the latent potential of epigenetic therapies like HDAC inhibitors-(HDIs), typically limited to hematological malignancies. Our study shows that a synergistic low dose combination of HDIs with an eIF4A-inhibitor in pancreatic cancer models results in marked pre-clinical efficacy, offering a promising new treatment strategy.

cancer biology↗

bioRxiv2024

Inhibition of serine racemase prevents diabetic retinopathy

Retinal diabetic neuropathy (RDN) precedes retinal microvascular pathology in diabetic retinopathy (DR), but the therapy by antagonizing RDN to stall DR is rare. Emerging evidence including ours suggest that serine racemase (SRR) drives DR at least by promoting RDN. Thus, we explore the therapy by antagonizing SRR to stall and treat DR. We herein indicate that inhibition of SRR by oral gavage of l-aspartic acid {beta}-hydroxymate (L-ABH), a competitive inhibitor of SRR, mitigated photoreceptor dysfunction, loss of retinal ganglion cells (RGC) and retinal endothelial cell and pericyte in db/db mice, a type II diabetes model. To dissect the mechanism, intravitreal injection of L-ABH mitigates glutamate-induced neurotoxicity in the retina. In the whole body, gastrointestinal-based delivery of L-ABH maintained euglycemia and improved glucose tolerance either in db/db mice or in diet-induced obesity mice. In conclusion, inhibition of SRR prevented retinal neurovascular abnormalities in diabetic animals through executing neuroprotection in the retina and maintaining glucose homeostasis in the system. Our study reveals a novel strategy to prevent DR.

biochemistry↗

bioRxiv2024

stVCR: Reconstructing spatio-temporal dynamics of cell development using optimal transport

Time-series spatial transcriptome data with single-cell resolution provide an opportunity to study cell differentiation, proliferation and migration in physical space over time. Due to the destructive nature of sequencing, reconstruction of spatiotemporal dynamics from data remains challenging. Especially, the inference of migration in physical space remains a difficult task, as samples obtained at different temporal snapshots might not be in the same coordinate system due to the difference of biological replicates. Here we developed stVCR, a generative deep learning model, which integrates the dynamical optimal transport (OT) with the unbalanced setting, the density matching invariant to rigid body transformations as well as priors to model known biology and preserve spatial structure. stVCR achieves the end-to-end simultaneous reconstruction of continuous cell differentiation, proliferation, physical space migration, and spatial coordinates alignment from spatial transcriptome snapshots. In addition, stVCR allows the interpretable study of complex interactions between cell phenotype transition, spatial migration and proliferation. Through benchmarking on both simulation data and real datasets, we validated the effectiveness and robustness of stVCR and demonstrated its advantages over static OT or linear interpolation methods. We applied stVCR to dissect spatiotemporal dynamics underlying axolotl brain regeneration and 3D Drosophila embryo development.

bioinformatics↗

bioRxiv2024

IL-2 enhances effector function but suppresses follicular localization of CD8+ T cells in chronic infection

Cytotoxic CD8+ T cells, essential in combating viral infections and cancer, become dysfunctional from prolonged antigen exposure. Precursors of exhausted T (TPEX) cells are pivotal in sustaining immune responses in chronic diseases and mediating immunotherapy efficacy. They also control viral infection within B-cell follicles, facilitated by CXCR5 expression. How cytokines regulate TPEX cell fate and follicular entry is not well understood. We reveal that IL-2 treatment enhances CD8+ T cell effector functions in chronic LCMV infection but hinders CXCR5+ TPEX cell formation and infection control within B-cell follicles. Mechanistically, IL-2 suppresses TPEX cell differentiation in a STAT5 and BLIMP1-dependent manner. Using an IL-2 fusion protein targeting CD122, we shifted the differentiation towards CX3CR1+ T cells with increased effector function. Clinical observations with low-dose IL-2 in autoimmune disease confirmed IL-2s inhibitory effect on CXCR5+ TPEX cells, underscoring IL-2s crucial regulatory role and therapeutic potential in modulating TPEX and effector T cell generation.

immunology↗

bioRxiv2024

Jumbo phages possess independent synthesis and utilization systems of NAD+

Jumbo phages, phages with genomes >200 kbp, contain some unique genes for successful reproduction in their bacterial hosts. Due to complex and massive genomes analogous to those of small-celled bacteria, how do jumbo phages complete their life cycle remain largely undefined. In this study, we assembled 668 high-quality jumbo phage genomes from over 15 TB of intestinal metagenomic data from 955 samples of five animal species (cow, sheep, pig, horse, and deer). Within them, we obtained a complete genome of 716 kbp in length, which is the largest phage genome so far reported in the gut environments. Interestingly, 174 out of the 668 jumbo phages were found to encode all genes required for synthesis of NAD+ by the salvage pathway or Preiss-Handler pathway, referred as NAD-jumbo phage. Besides synthesis genes of NAD+, these NAD-jumbo phages also encode at least 15 types of NAD+-consuming enzyme genes involved in DNA replication, DNA repair, and counterdefense, suggesting that these phages not only have the capacity to synthesize NAD+ but also redirect NAD+ metabolism towards phage propagation need in hosts. Phylogenetic analysis and environmental survey indicated NAD-jumbo phages are widely present in the Earths ecosystems, including the human gut, lakes, salt ponds, mine tailings, and seawater. In summary, this study expands our understanding of the diversity and survival strategies of phages, and in-depth study of the NAD-jumbo phages is crucial for understanding their role in ecological regulation.

microbiology↗

bioRxiv2024

Rbpj deletion in hepatic progenitor cells attenuates endothelial responses in a mouse model of cholestatic liver disease

BackgroundAs the role of hepatic progenitor cells (HPCs) constituting ductular reactions in pathogenesis remains ambiguous, we aimed to establish the in vivo cause-and-effect relationship between HPCs and chronic liver disease progression. We previously demonstrated that peritumoral ductules are associated with angiogenesis in liver tumors and forkhead box L1 (Foxl1)-expressing murine HPCs secrete angiogenic factors in vitro. Therefore, we hypothesized that HPCs are capable of remodeling the portal vascular microenvironment and regulating overall liver disease progression, and this function of HPCs is dependent on recombination signal binding protein for immunoglobulin kappa J region (RBPJ), a key effector of the Notch signaling pathway. Methods and ResultsWe generated HPC-specific Rbpj conditional knockout mice (CKO) using Foxl1-Cre and treated them with the DDC diet to induce chronic liver disease. CKO mice exhibited a significant reduction in serum levels of liver injury markers, ductular reactions, vascular and fibrotic areas, and hepatic expression of fibrosis and inflammation markers compared to control mice (WT). Single-nucleus RNA sequencing comparing CKO and WT livers detected transcriptome changes across multiple cell types, including endothelial cells, hepatic stellate cells, and cholangiocytes. Expression of several reactive cholangiocyte markers, including vascular cell adhesion molecule 1 (VCAM1), in HPCs was significantly downregulated in response to anti-Rbpj shRNAs in vitro. Immunofluorescence analysis indicated that the percentage of VCAM1+ cells was reduced in both HPC and cholangiocyte populations in CKO compared to WT in vivo. ConclusionsOur findings reveal Rbpj-dependent expression of reactive cholangiocyte markers in HPCs and demonstrate that Rbpj deletion in HPCs attenuates not only endothelial responses but also liver injury, fibrosis, and VCAM1 expression in cholangiocytes, highlighting the crucial role of HPCs in pathogenic progression.

cell biology↗

bioRxiv2024

Discovery of orally bioavailable SARS-CoV-2 papain-like protease inhibitor as a potential treatment for COVID-19

The RNA-dependent RNA polymerase (RdRp), 3C-like protease (3CLpro), and papain-like protease (PLpro) are pivotal components in the viral life cycle of SARS-CoV-2, presenting as promising therapeutic targets. Currently, all FDA-approved antiviral drugs against SARS-CoV-2 are RdRp or 3CLpro inhibitors. However, the mutations causing drug resistance have been observed in RdRp and 3CLpro from SARS-CoV-2, which makes it necessary to develop antivirals with novel mechanisms. Through the application of a structure-based drug design (SBDD) approach, we discovered a series of novel potent non-covalent PLpro inhibitors with remarkable in vitro potency and in vivo PK properties. The co-crystal structures of PLpro with leads revealed that the residues D164 and Q269 around the S2 site are critical for improving the inhibitors potency. The lead compound GZNL-P36 not only inhibited SARS-CoV-2 and its variants at the cellular level with EC50 ranging from 58.2 nM to 306.2 nM, but also inhibited HCoV-NL63 and HCoV-229E with EC50 of 81.6 nM and 2.66 M, respectively. Oral administration of the compound resulted in significantly improved survival and notable reductions in lung viral loads and lesions in SARS- CoV-2 infection mouse model, consistent with RNA-seq data analysis. Our results indicate that PLpro inhibitor is a promising SARS-CoV-2 therapy.

microbiology↗

bioRxiv2024

Prioritizing Metabolic Gene Regulators through Multi-Omic Network Integration in Maize

Gene regulatory networks (GRNs) link transcription factors (TFs) to the biological processes they control. Assembling them remains difficult because the relevant data types (gene expression, protein-DNA interactions, and genetic variation) are large, heterogeneous, and rarely combined. Here, we developed and benchmarked a framework that integrates these data into TF-function predictions in maize. We assembled four complementary TF-target gene network layers, based on expression, protein-DNA interaction, trans-expression quantitative trait loci (eQTL), and cis-eQTL-supported interaction, from 46 Random Forest (RF)-inferred regulatory networks, 283 protein-DNA interaction assays, and eQTLs derived from 16 million SNPs across 304 inbred lines. Together these layers comprised ~4.6 million interactions. We then compared three strategies for integrating them, benchmarking each against published TF knockout data. A network-based approach, which represents every gene as a low-dimensional vector (embedding) learned from the combined network, outperformed the two overlap-based strategies, annotating over eight times more TFs (~3,000), agreeing most closely with gene knockout responses where predictions existed, and remaining robust when individual layers lacked data. The predictions recovered TF functions and predicted new regulators of hormone, developmental, and metabolic processes, which we prioritized per process and mapped to specific conditions. Using similarity on the low-dimensional vector representation (embedding), we further identified candidate functionally redundant or diverged TF paralogs. Because it relies only on data types now common across species, the framework provides a generalizable template for prioritizing regulatory genes in maize and other plants.

plant biology↗

bioRxiv2024

Unconventional IFNω-like genes dominate the type I IFN locus and the constitutive antiviral responses in bats

Bats are the natural reservoir hosts of some viruses, some of which may spillover to humans and cause global-scale pandemics. Different to humans, bats may coexist with high pathogenic viruses without showing symptoms of diseases. As one of the most important first defenses, bat type I interferon (IFN-Is) were thought to play a role during this virus coexistence and thus were studied in recent years. However, there are arguments that whether bats have a contracted genome locus or constitutive expressed IFNs, mainly due to species-specific findings. We hypothesized that because of the lacking of pan-bat analysis, the common characters for bat IFN-Is have not been revealed yet. Here, we characterized the IFN-I locus for 9 Yangochiroptera bats and 3 Yinpterochiroptera based on the their high quality bat genomes. We also compared the basal expression for 6 bats and compared the antiviral, anti-proliferative activity and thermo-stability of a representative Rhinolophus bat IFNs. We found a dominance of unconventional IFN{omega}-like responses in the IFN-I system, which is unique to bats. In contrast to IFNa-dominated IFN-I loci in the majority of other mammals, bats generally have shorter IFN-I loci with more unconventional IFN{omega}-like genes (IFN{omega} or related IFNa{omega}), but with less or even no IFNa genes. In addition, bats generally have constitutively expressed IFNs, the highest expressed of which is more likely an IFN{omega}-like gene. Likewise, the highly expressed IFN{omega}-like protein also demonstrated the best antiviral activity, anti-proliferative activity or thermo-stability, as shown in a representative Rhinolophus bat species. Overall, we revealed pan-bat unique characteristics in IFN-I system, which provide insights into our understanding of the innate immunity that contribute to a special coexistence between bats and viruses.

immunology↗

bioRxiv2024

DexDesign: A new OSPREY-based algorithm for designing de novo D-peptide inhibitors

With over 270 unique occurrences in the human genome, peptide-recognizing PDZ domains play a central role in modulating polarization, signaling, and trafficking pathways. Mutations in PDZ domains lead to diseases such as cancer and cystic fibrosis, making PDZ domains attractive targets for therapeutic intervention. D-peptide inhibitors offer unique advantages as therapeutics, including increased metabolic stability and low immunogenicity. Here, we introduce DexDesign, a novel OSPREY-based algorithm for computationally designing de novo D-peptide inhibitors. DexDesign leverages three novel techniques that are broadly applicable to computational protein design: the Minimum Flexible Set, K*-based Mutational Scan, and Inverse Alanine Scan, which enable exponential reductions in the size of the peptide sequence search space. We apply these techniques and DexDesign to generate novel D-peptide inhibitors of two biomedically important PDZ domain targets: CAL and MAST2. We introduce a new framework for analyzing de novo peptides--evaluation along a replication/restitution axis--and apply it to the DexDesign-generated D-peptides. Notably, the peptides we generated are predicted to bind their targets tighter than their targets endogenous ligands, validating the peptides potential as lead therapeutic candidates. We provide an implementation of DexDesign in the free and open source computational protein design software OSPREY.

bioinformatics↗

bioRxiv2024

Decoding cell identity with multi-scale explainable deep learning

Cells are the fundamental structural and functional units of life. Studying the definition and composition of different cell types can help us understand the complex mechanisms underlying biological diversity and functionality. The increasing volume of extensive single-cell omics data makes it possible to provide detailed characterisations of cell types. Recently, there has been a rise in deep learning-based approaches that generate cell type labels solely through mapping query data to reference data. However, these approaches lack multi-scale descriptions and interpretations of identified cell types. Here, we propose Cell Decoder, a biological prior knowledge informed model to achieve multi-scale representation of cells. We implemented automated machine learning and post-hoc analysis techniques to decode cell identity. We have shown that Cell Decoder compares favourably to existing methods, offering multi-view interpretability for decoding cell identity and data integration. Furthermore, we have showcased its applicability in uncovering novel cell types and states in both human bone and mouse embryonic contexts, thereby revealing the multi-scale heterogeneity inherent in cell identities.

bioinformatics↗

bioRxiv2024

BAMBI: Integrative biostatistical and artificial-intelligence method discover coding and non-coding RNA genes as biomarkers

Accurate disease diagnosis and prognosis are crucial for effective treatment management and improving patient outcomes. However, accurately detecting early signs of certain diseases or recurrence remains challenging. Existing machine-learning methods for identifying gene expression biomarkers have several limitations, including poor performance on independent test datasets, inability to directly process omics data, and difficulty in identifying noncoding RNA genes as biomarkers. Additionally, these methods may not provide sufficient biological interpretation of their results, and the panel biomarkers they identify may not be suitable for clinical application. To address these limitations, we have developed a new computational method called BAMBI, which integrates multiple machine-learning algorithms and statistical approaches to identify putative coding and noncoding genes as biomarkers for disease diagnosis and prognosis. We evaluated BAMBI ability to identify diagnostic and prognostic biomarkers by analyzing multiple RNA-seq datasets from cancerous and non-cancerous diseases at population levels. The results from BAMBI demonstrate significant biological interpretability and state-of-the-art prediction performance. When the singular gene identified by BAMBI is used as a diagnostic biomarker, it achieves a balance accuracy exceeding 95% in studies of both breast cancer and psoriasis. Additionally, the prognostic biomarkers that BAMBI identifies from RNA-seq data of Acute Myeloid Leukemia (AML) patients significantly correlate with the survival rates in an independent AML patient cohort. Additionally, BAMBI outperforms existing methods by delivering more robust results, identifying biomarkers with fewer genes, and simultaneously achieving superior prediction accuracy. We have implemented BAMBI into user-friendly software for the research community. In summary, BAMBI serves as a more reliable pipeline for identifying both coding and noncoding genes as biosignature markers, enhancing the accuracy of disease diagnosis and prognosis. BAMBI is available via https://github.com/CZhouLab/BAMBI.

bioinformatics↗

bioRxiv2023

Uncovering Minimal Pathways in Melanoma Initiation

Cutaneous melanomas are clinically and histologically heterogeneous. Most display activating mutations in Braf or Nras and complete loss of function of one or more tumor suppressor genes. Mouse models that replicate such mutations produce fast-growing, pigmented tumors. However, mice that combine Braf activation with only heterozygous loss of Pten also produce tumors and, as we show here, in an Albino background this occurs even with Braf activation alone. Such tumors arise rarely, grow slowly, and express low levels of pigmentation genes. The timing of their appearance was consistent with a single step stochastic event, but no evidence could be found that it required de novo mutation, suggesting instead the involvement of an epigenetic transition. Single-cell transcriptomic analysis revealed such tumors to be heterogeneous, including a minor cell type we term LNM (Low-pigment, Neural- and extracellular Matrix-signature) that displays gene expression resembling "neural crest"-like cell subsets detected in the fast-growing tumors of more heavily-mutated mice, as well as in human biopsy and xenograft samples. We provide evidence that LNM cells pre-exist in normal skin, are expanded by Braf activation, can transition into malignant cells, and persist with malignant cells through multiple rounds of transplantation. We discuss the possibility that LNM cells not only serve as a pre-malignant state in the production of some melanomas, but also as an important intermediate in the development of drug resistance.

cancer biology↗

bioRxiv2023

Evolution-guided large language model is a predictor of virus mutation trends

The increasing frequency of emerging viral infections necessitates a rapid human response, highlighting the cost-effectiveness of computational methods. However, existing computational approaches are limited by their input forms or incomplete functionalities, preventing a unified prediction of diverse viral variation drivers and hindering in-depth applications. To address this issue, we propose a unified evolution-driven framework for predicting virus variation drivers, named E2VD, which is guided by virus evolutionary traits priors. With evolution-inspired design, E2VD comprehensively and significantly outperforms state-of-the-art methods across various virus variation drivers prediction tasks. Moreover, E2VD effectively captures the fundamental patterns of virus evolution. It not only distinguishes different types of mutations but also accurately identifies rare beneficial mutations that are critical for virus to survival, while maintains generalization capabilities on different viral lineages. Importantly, with predicted biological drivers, E2VD perceives virus evolutionary trends, in which potential high-risk mutation sites are accurately recommended. Overall, E2VD represents a unified, structure-free, and interpretable approach for analyzing and predicting viral evolutionary fitness, providing an ideal alternative to costly wet-lab measurements to accelerate responses to emerging viral infections.

bioinformatics↗

bioRxiv2023

MaUGT79 confers drought tolerance by regulating scopolin biosynthesis in plants

The coumarin scopoletin and its glycosylated form scopolin constitute a vast class of natural products that are considered to be high-value compounds, distributed widely in the plant kingdom, they help plants adapt to environmental stresses. However, the underlying molecular mechanism of how scopolin is involved in the regulation of plant drought tolerance remains largely unexplored. Here, UDP-glycosyltransferase 79 (MaUGT79) was genetically mapped as the target gene by bulk segregant analysis sequencing (BSA-seq) from two Melilotus albus near-isogenic lines (NILs). MaUGT79 exhibits glucosyltransferase activity toward scopoletin. The expression of MaUGT79 is induced by drought stress and it was found to mediate scopolin accumulation and reactive oxygen species (ROS) scavenging under drought stress. Moreover, the transcription of MaUGT79 was demonstrated to be directly and positively regulated by MaMYB4, which is a key integrator of both scopolin biosynthesis and drought tolerance. Collectively, this study reveals that MaMYB4 is a positive regulator in drought stress by targeting the MaUGT79 promoter and activating its expression to coordinately mediate scopolin biosynthesis and drought tolerance, providing insights into the regulatory mechanism for plant growth adaption to environmental changes through accumulation of scopolin.

physiology↗

bioRxiv2023

Multi-region calcium imaging in freely behaving mice with ultra-compact head-mounted fluorescence microscopes

To investigate the circuit-level neural mechanisms of behavior, simultaneous imaging of neuronal activity in multiple cortical and subcortical regions is highly desired. Miniature head-mounted microscopes offer the capability of calcium imaging in freely behaving animals. However, implanting multiple microscopes on a mouse brain remains challenging due to space constraints and the cumbersome weight of equipment. Here, we present TINIscope, a Tightly Integrated Neuronal Imaging microscope optimized for electronic and opto-mechanical design. With its compact and lightweight design of 0.43 g, TINIscope enables unprecedented simultaneous imaging of behavior-relevant activity in up to four brain regions in mice. Proof-of-concept experiments with TINIscope recorded over 1000 neurons in four hippocampal subregions and revealed concurrent activity patterns spanning across these regions. Moreover, we explored potential multi-modal experimental designs by integrating additional modules for optogenetics, electrical stimulation or local field potential recordings. Overall, TINIscope represents a timely and indispensable tool for studying the brain-wide interregional coordination that underlies unrestrained behaviors.

neuroscience↗

bioRxiv2023

maskNMF: A denoise-sparsen-detect approach for extracting neural signals from dense imaging data

A number of calcium imaging methods have been developed to monitor the activity of large populations of neurons. One particularly promising approach, Bessel imaging, captures neural activity from a volume by projecting within the imaged volume onto a single imaging plane, therefore effectively mixing signals and increasing the number of neurons imaged per pixel. These signals must then be computationally demixed to recover the desired neural activity. Unfortunately, currently-available demixing methods can perform poorly in the regime of high imaging density (i.e., many neurons per pixel). In this work we introduce a new pipeline (maskNMF) for demixing dense calcium imaging data. The main idea is to first denoise and temporally sparsen the observed video; this enhances signal strength and reduces spatial overlap significantly. Next we detect neurons in the sparsened video using a neural network trained on a library of neural shapes. These shapes are derived from segmented electron microscopy images input into a Bessel imaging model; therefore no manual selection of "good" neural shapes from the functional data is required here. After cells are detected, we use a constrained non-negative matrix factorization approach to demix the activity, using the detected cells shapes to initialize the factorization. We test the resulting pipeline on both simulated and real datasets and find that it is able to achieve accurate demixing on denser data than was previously feasible, therefore enabling faithful imaging of larger neural populations. The method also provides good results on more "standard" two-photon imaging data. Finally, because much of the pipeline operates on a significantly compressed version of the raw data and is highly parallelizable, the algorithm is fast, processing large datasets faster than real time.

neuroscience↗

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↗