Biology subjects

Ye, W.

Publications and source records attributed to Ye, W..

At least 19 recordsLinked to original sources

bioRxiv2026

PlantOmicsGWAS: An end-to-end, reproducible framework for plant genome-wide association and genomic prediction using linear and pan-genome references

Genome-wide association studies (GWAS) play a crucial role in unraveling the genetic foundations of complex traits in plants but are also hampered by the application of heterogeneous tools, incompatible file formats and disparate computational environments. Existing GWAS frameworks are often restricted to a single linear reference genome, limiting the capacity for the analysis of structural variations and presence/absence variations (PAV) within plant populations. These issues pose obstacles to reproducibility, scalability, and comprehensive investigations. Here, we present PlantOmicsGWAS, an open-source Python framework for reproducible plant genome-wide association analysis and genomic prediction. It integrates reference indexing, FASTQ quality control, alignment, variant calling, VCF normalization, PLINK conversion, linkage disequilibrium analysis, population-structure estimation, association testing, marker scoring, genomic prediction, and visualization within a unified Linux and HPC workflow. The framework supports conventional linear-reference analyses and includes an optional pangenome-oriented module for working with multiple assemblies and graph-derived variation. Using a Vitis benchmark dataset containing 120 accessions and 118,247 graph-derived variants, PlantOmicsGWAS reduced manual workflow fragmentation and generated standardized association outputs. This tool provides a modular and extensible platform for plant GWAS and pan-GWAS workflows while retaining compatibility with established command-line tools and common genotype formats. The GWAS workflow described herein is adaptable to a range of sequencing methods and plant genomes, bridging research on crop related issues across various biological levels, from the individual organism to entire populations. PlantOmicsGWAS implements Bayesian sparse linear mixed modeling (BSLMM) through GEMMA for multi-trait association discovery, while also supporting FaST-LMM, regression-based approaches, and machine-learning algorithms (Random Forest, XGBoost) as benchmarking alternatives. The PlantOmicsGWAS, a versatile toolkit is available at GitHub https://github.com/plantomicsgwas1-boop/PlantOmicsGwas_V1 and on Linux and HPC platform (https://pypi.org/project/PlantOmicsGwas/1.0.2/).

bioinformatics↗

bioRxiv2026

Double Machine Learning with Multi-Gene Shared Backgroundfor Causal Inference in Single-Cell Data: Grouping Deviation Follows a Random Walk and the Accuracy-Compute Trade-Off

In high-throughput single-cell transcriptomics (p {approx} 20,000 genes), performing double machine learning (DML) causal inference on q {approx} 5,000 target genes requires nuisance function fits that grow linearly with the number of targets (Kf cross-fitting folds, Kf = 5 or 10), far exceeding feasible computational budgets, especially with deep learning. We propose a Randomized Partition Strategy (RPS): randomly divide target genes into groups, share one background compression per group, reducing deep learning model training to q/m runs (m = group size) --- a factor of m savings. The cost of grouping is accuracy loss --- we prove that the cumulative deviation of the estimator follows a one-dimensional drift-free symmetric random walk, with diffusion variance growing linearly with group size and mean squared displacement equaling the mean squared error, so accuracy loss is predictable: m = 1 is always optimal, accuracy cost is monotonically increasing, and a small accuracy sacrifice yields m-fold compute savings. On GSE189050 SLE single-cell data (Memory B cells, n = 2120), both PCA and DL methods converge to the same conclusion, confirming the random walk mechanism is method-independent; an unexpected finding is that DL diffusion growth is only 16%, far slower than PCAs 7.4 times. This work provides a quantifiable theoretical foundation for compute strategy selection in single-cell high-dimensional causal inference.

systems biology↗

bioRxiv2026

Comparison of nuisance function construction strategies for double machine learning causal inference in single-cell transcriptomics: shared unsupervised deep learning does not require cross-fitting

Inferring "whether a change in the expression of a given gene causally affects the disease state" from observational single-cell transcriptomic data is one of the central problems in single-cell biology. The difficulty lies in confounding: cell state, batch, cell cycle, and the co-expression of other genes may all simultaneously influence the target gene (treatment variable T) and the disease label (outcome variable Y), so that naive correlation analysis cannot distinguish causation from covariation. Double machine learning (DML), via orthogonal scores and cross-fitting, allows machine learning to estimate high-dimensional nuisance functions, thereby addressing the causal inference problem in high-dimensional data. Constrained by computational resources, this study takes a small-sample dataset with p{approx}n (2,120 cells, 1,999 background genes) as the experimental testbed and systematically compares three nuisance function construction strategies under this critical condition; strategies for the n>>p regime are then addressed by theoretical argument. Using systemic lupus erythematosus (SLE) peripheral blood memory B cells (GSE189050, 2,120 cells), we construct a three nuisance function construction strategies x (in-sample / cross-fitting) 2x3 factorial experiment and compare them in terms of resolution, biological plausibility, stability, and deconfounding ability in causal effect estimation. The three strategies are: (S1) direct linear nuisance regression on the high-dimensional background genes without dimensionality reduction; (S2) learning a shared low-dimensional representation with an unsupervised autoencoder, with the treatment and outcome residuals sharing that representation; (S3) fitting the treatment and outcome with two independent deep networks. The results yield a clear three-part pattern with mechanistic meaning: (1) both modes of S1 fail; (2) S2 achieves the optimum in-sample and requires no cross-fitting; (3) S3 is "rescued" under cross-fitting and becomes effective. We explain this pattern starting from the convergence rate condition of DML (Sec.5 Theoretical Foundations) and give the applicability boundaries of the two strategies: S2 (shared unsupervised deep learning) achieves estimation quality comparable to standard cross-fitted DML at a tiny fraction of the computational cost, making it a feasible scheme for large-scale screening; S3 (dual networks + cross-fitting), as the standard DML recipe, can serve as a broad-spectrum reference control for S2 results.

systems biology↗

bioRxiv2026

Water as a thermal contrast agent for artificial-intelligence-enhanced in vivo mid-infrared thermography

In vivo infrared thermography is limited by the inherently poor spatial resolution at long wavelengths, low contrast, and the lack of biocompatible contrast agents. Here, we present 3-5 m mid-wave infrared (MWIR) thermography enhanced by an artificial intelligence (AI) network and cold phosphate-buffered saline (PBS) as a thermal contrast agent for noninvasive in vivo imaging with high contrast and resolution. MWIR imaging enabled high thermal sensitivity with microscale spatial resolution, strong relative thermal contrast, and facilitated visualization of the subcutaneous vasculature in the human arm, hand, ankle, the femoral artery and vein in rats, and the femoral vessels in mice, with image contrast further enhanced by AI networks. In a 4T1 tumor-bearing mouse model, AI-enhanced MWIR resolved early-stage tumors of [~]2.3 mm and metastases as small as [~]1.7 mm. Using cold PBS as a MWIR thermal contrast agent, we achieved precise tumor boundary visualization and real-time imaging-guided tumor resection. AI-enhanced MWIR offers a promising solution for early diagnosis and improved surgical precision.

bioengineering↗

bioRxiv2026

Trichome acylsugar defenses are equally effective against leaf-mining and free-living insects

Trichomes are epidermal projections, crucial components of plant defense systems. Whether these epidermal defenses are effective against leafminers, which infest the internal mesophyll layers, remains unclear. Using the devasting invasive tomato pest, Phthorimaea absoluta (tomato leafminer), and the trichome-rich wild tomato, Solanum pennellii, we demonstrate that trichome produced acylsugars effectively inhibit the growth of endophagous P. absoluta larvae feeding inside leaves. High spatial resolution leaf- and frass-metabolite analysis by MALDI-MSI and Stimulated Raman Scattering microscopy revealed that trichome-synthesized acylsugars are translocated into the mesophyll tissues when larvae infest leaves. We identified an ATP-binding cassette (ABC) transporter, SpABCB5, as the mediator of this translocation and as an essential component of defense against leafminers. These data reveal that epidermal trichomes not only defend against free-living (ectophagous) herbivores through direct contact or rapid deposition of chemical defenses onto leaf surfaces, but also defend against endophagous leafminers by rapidly depositing chemical defenses into internal tissues. By delivering mines against leaf miners, trichomes offer new avenues for breeding crops with resistance to leafminers.

plant biology↗

bioRxiv2026

Origin and evolution of grapevine genomes

Grapevines (Vitis) belonging to grape family (Vitaceae) are symbolic fruit crops pivotal to human civilization. The evolutionary history of grapevines divergent from other Vitaceae plants remains mysterious, requiring a family-wide whole-genome phylogenomic analysis. Here, we conduct chromosome-level phylogenomics to investigate the origin and evolution of grapevines using 29 genome assemblies of five genera Vitis, Parthenocissus, Ampelopsis, Tetrastigma, and Cissus, 27 of which are newly released in this study. Phylogenomic and macrosynteny analysis unanimously support Ampelopsis as a sister lineage to Parthenocissus, placing both closer to Vitis, with introgression and incomplete lineage sorting contributing to these relationships. Ancestral genome reconstruction delineates the major chromosome rearrangement events in Vitaceae karyotype evolution, highlighting the conserved karyotype in Vitis and the extensive karyotypic reorganization in Tetrastigma and Cissus. Pan-3D genome analysis highlights the contributions of structural variants (SVs) to the variation of A/B compartments and topologically associated domains (TADs), revealing a strong purifying selection of SVs at TAD boundaries. We further demonstrate that Helitron transposons drive the expansion and expression regulation of NLR immune-receptor genes in Vitis. Importantly, we discovered an NLR gene VbRpv35 from wild grapevine V. bellula resistant to downy mildew (DM), whose heterologous expression in V. vinifera confers enhanced DM resistance. Taken together, we provide phylogenomic insight into the origin and evolution of grapevines and valuable resources for grapevine improvement and understanding angiosperm evolution.

genomics↗

bioRxiv2026

Exposure to herbivore-induced plant volatiles directly induces jasmonic acid and primes chemical defences in cotton plants

Plants can deploy alternative defensive strategies in response to airborne signals from damaged neighbours to prepare for incoming attack: a straightaway response up-regulating their defences (induction), or a primed state, leading to a faster/stronger defence response after herbivory. However, it is unclear which mechanisms are involved in each response. We used a monophagous and a polyphagous leafworm species to specifically dissect induction and priming effects of exposure to herbivore-induced plant volatiles (HIPVs). Exposure to HIPVs directly elevated jasmonate levels in undamaged receivers but did not induce defensive terpenoids or volatiles. However, HIPV-primed plants accumulated high levels of toxic terpenoids (e.g. gossypol) and emitted high quantities of volatile sesquiterpenes, when damaged by either species of caterpillars. This comprehensive study demonstrates that both defence induction and priming can be detected in cotton but occur as different, linked responses which are robust to herbivore identity, providing insights into a generalised plant communication strategy.

ecology↗

bioRxiv2026

Systematic detection of abnormal samples reveals widespread mislabeling in metagenomic studies

The human microbiome plays a critical role in health and disease, and its dynamic nature has made longitudinal sampling a key strategy for elucidating microbiome-disease relationships. Although the gut microbiome generally stabilizes over time, a subset of samples frequently shows marked deviations from an individuals baseline profile. We refer to these as abnormal samples. To analyze these abnormal samples, we developed a three-stage workflow to identify and classify these abnormal samples to figure out the underlying causes of these abnormal samples. Moreover, we systematically investigated abnormal samples across 16 publicly available metagenomic datasets, comprising a total of 5,171 metagenomes. Our analysis revealed that abnormal samples are often the result of mislabeling during sample collection, processing, or sequencing. Of which, fecal samples from family are more likely mislabeled. We found evidence of mislabeling in 75% of longitudinal datasets, involving up to dozens of samples per study, and in 25% of randomly selected cross-sectional datasets. Additional factors such as disease status (e.g., inflammatory bowel disease), sampling intervals, and sampling density may also contribute to sample abnormalities owing to true biological variations. These findings highlight that mislabeling is a common yet underrecognized issue in microbiome research. Our work underscores the importance of identifying and correcting abnormal samples to ensure data integrity in microbiome studies and provides a practical solution for quality control in large-scale metagenomic datasets.

microbiology↗

bioRxiv2026

Macrophage Iron Metabolism in Allografts and Tumors

Macrophages are present at high frequencies in transplanted organs and solid tumors. These infiltrating macrophages are highly tuned by tissue niche signals. However, the underlying mechanisms that orchestrate the activation of distinct macrophage populations remain unclear. Here, using heart transplantation and tumor implantation models, we found that macrophages in cardiac allografts exhibited higher intracellular iron (Fe2+) level and higher expression of the iron transporter SLC11A1 than those in the solid tumors. In mice, the myeloid-specific deletion of Slc11a1 alleviated the proinflammatory state of macrophages and cardiac allografts rejection. Together, our findings identify SLC11A1 as a new player in immune response regulation, implicating SLC11A1 as a therapeutic target for both allograft and tumor rejection. This preprint reports initial findings; additional experiments are ongoing and will be included in a future full manuscript.

immunology↗

bioRxiv2026

Microglia stabilize sleep homeostasis via adenosine A3 receptor signaling

Sleep homeostasis maintains the sleep-wake balance through sleep pressure, a process orchestrated by the accumulation of extracellular adenosine (eADO). Microglial Ca{superscript 2} activity has been implicated in sleep regulation, but the mechanism whereby microglia sense sleep pressure remains unclear. Here we show that microglia regulate sleep homeostasis through brain state-dependent Ca2+ activity driven by adenosine A3 receptor (A3R) signaling. Using miniaturized two-photon microscopy (mTPM) in freely behaving mice, we demonstrate that microglial Ca{superscript 2} activity is rapidly altered by brain-state transitions. Pharmacological experiments reveal that microglial Ca2+ dynamics are predominantly mediated by A3R in response to brain state-dependent eADO oscillations. Microglia-specific deletion of A3R attenuates these state-dependent Ca2+ dynamics, impairs microglial morphological plasticity across sleep-wake cycles, and leads to sleep fragmentation by increasing transitions between wakefulness and non-rapid eye movement (NREM) sleep. Together, these findings establish that microglia regulate sleep homeostasis by stabilizing both wakefulness and NREM sleep, a process critically dependent on eADO-A3R signaling.

neuroscience↗

bioRxiv2025

Multimodal Data Fusion Reveals Morpho-Genetic Variations in Human Cortical Neurons Associated with Tumor Infiltration

We introduce LetsACT (Light-Electron-Transcriptome synergistic ACTomography), a multimodal integration platform that overcomes the limitations of single-modality data acquisition and analysis of human brain cells while synergistically leveraging the strengths of each modality. Our approach enables the rapid sample preparation, cell injection, imaging, and multimodal integration of large-scale human neuronal datasets at single-cell resolution. By generating initial laser-scanning-microscopy based optical reconstruction of neuron morphologies followed by refining them using electron-microscopy derived morphological priors, we have assembled one of the largest human cortical morphology datasets to date: 8,398 neurons from 58 donors, with high cortical coverage. This platform is then applied to studying morphological impact of tumor infiltration. Pyramidal neurons in glioma-infiltrated tissues display clear volume shrinkage in somas and branches, tapering from the soma to nearby dendritic compartments. By integrating these morphological variations with spatial and bulk transcriptomic profiles, we find that glioblastoma tissues exhibit dysregulation of 15.29% of genes, including overexpression of TERT, whereas infiltrated tissues show 7.74% gene dysregulation, characterized by overexpression of tumor suppressors such as CDKN2A and TP53. Our analysis implies that pyramidal neurons observed in these infiltrated tissues may involve an active defense instead of undergoing passive apoptosis. Our finding also indicates that LetsACT establishes a valuable resource for the large-scale, comprehensive morpho-genetic analysis of human tissues.

neuroscience↗

bioRxiv2025

Comparative evaluation of reference genomes and cell-type annotation frameworks for single-nucleus transcriptomic analysis in apple

Single-nucleus RNA sequencing (snRNA-seq) provides a powerful tool to profile cell-type-specific transcriptional programs in woody fruit trees. Yet its application in Malus domestica has been limited by heterogeneous reference genomes and the lack of validated marker genes. Here, we first established a comprehensive set of marker genes corresponding to 29 plant cell types and identified their orthologs in apple. Using these resources, we systematically evaluated six publicly available apple genome assemblies as references for snRNA-seq analysis. Our results demonstrate that reference genome selection has a major impact on mapping performance and downstream cell type annotation, with the GDDH13 assembly consistently producing the highest transcript counts per nucleus and the greatest number of identifiable transcriptional clusters (15 clusters representing 11 distinct cell types across 35,557 high-quality nuclei). To achieve robust cell type annotation, we integrated classical marker gene-based annotation with two cross-species computational frameworks -- Orthologous Marker Gene Groups (OMGs) and XSpeciesSpanner. These three approaches enabled confident identification of 11 major cell types in apple seedlings and provided cross-validation for ambiguous or previously unannotated clusters. Pseudotime analysis reconstructed the developmental trajectories of both procambial and stomatal lineages, revealing their progressive transcriptional transitions and enabling the delineation of key molecular programs underlying vascular differentiation and early guard-cell formation. Together, this work provides a reference-quality single-nucleus transcriptomic atlas for apple and underlined the critical impact of genome choice and multi-method cell-type annotation on single-cell studies in non-model crops.

bioinformatics↗

bioRxiv2025

Rapid ecosystem collapse and biofilter formation following seabed methane leakage

Globally, vast amounts of methane are trapped within subseafloor gas hydrates. Recent evidence suggests that climate- and human-induced perturbations can destabilize gas hydrates and trigger methane release, yet the response of deep-sea ecosystems to these changes remains poorly understood. Here we show multi-year in situ monitoring of seabed ecosystem following methane leakage induced by hydrate exploration activities. Within just two years, benthic microbial and eukaryotic diversity in the affected areas declined significantly, while microbial and macrofaunal abundance increased. Integrated geochemical and omics analyses reveal the rapid successional shift of seabed ecosystem to a novel chemosynthetic system. Aerobic methanotrophs (Methyloprofundus) co-established with unexpectedly fast growing anaerobic methanotrophic archaea (ANME-3), accompanied by a rapid recruitment of opportunistic polychaetes that bioirrigated the seabed to >50 cm depths. The active animal-microbe interactions sustained exceptionally high rates of methane oxidation that utilized diverse electron acceptors. We demonstrate that methane hydrate destabilization can trigger rapid collapse of native seabed ecosystem while driving the formation of an effective methane biofilter consuming this potent greenhouse gas much faster than previously estimated. Understanding seabed ecosystem response and feedback is critical for predicting benthic methane cycling under ongoing global change and for developing sustainable strategies for methane hydrate resource management.

ecology↗

bioRxiv2025

Varicella-zoster virus (VZV)-human interacting proteins enable the clustering of giant cell arteritis (GCA) samples: supports the association of VZV with a subset of GCA cases

ObjectiveTo investigate the association between Varicella-Zoster Virus (VZV) and Giant Cell Arteritis (GCA). MethodsOne publicly available plasma proteomics dataset and three transcriptomics datasets were used. From the GCA samples, the corresponding values of VZV-human interacting proteins were extracted, and spectral clustering was performed on the GCA samples to calculate the silhouette coefficient. Subsequently, from the GCA samples, proteins or genes of the same quantity were randomly sampled 10,000 times; the identical spectral clustering method was applied to these randomly sampled sets, and the silhouette coefficient was calculated for each. Differences in spectral clustering performance and silhouette coefficients were compared between the VZV-human interacting proteins and the 10,000 randomly sampled protein/gene sets in the GCA samples. The same entire analytical process was repeated for the control group. ResultsIn the GCA samples, the VZV-human interacting proteins exhibited a distinct clustering effect. Their silhouette coefficient was greater than that of most randomly sampled protein or gene sets, and this difference was statistically significant. No similar phenomenon was observed in the control group. ConclusionVaricella-Zoster Virus is associated with a subset of Giant Cell Arteritis cases.

immunology↗

bioRxiv2025

Haplotype-resolved T2T gap-free genomes of the winegrape cultivar 'Cabernet Sauvignon'

Cabernet Sauvignon (CS), a cultivar of winegrape (Vitis vinifera), is among the most renowned winegrape varieties globally. In this study, we released the haplotype-resolved telomere-to-telomere (T2T) CS genome assembled using a combination of PacBio HiFi, ONT ultra-long, and Hi-C sequencing data. The two T2T gap-free haplotype-resolved assemblies CS-T2T.Hap1 and CS-T2T.Hap2 sized 491.11 Mb (contig N50 = 25.09 Mb) and 491.90 Mb (contig N50 = 25.51 Mb), respectively. Genome annotation predicted a total of 36,456 genes in CS-T2T.Hap1 and 35,471 genes in CS-T2T.Hap2. By genome comparison, we discovered and validated megabase inversion events on Chromosome 03,11,18 and 19, which are not prevalent in other haplotype-resolved V. vinifera genomes. In summary, this haplotype-resolved T2T genome represents an essential genomic resource for Cabernet Sauvignon, and lays the foundation for its genetic studies, improvement and utilization.

genomics↗

bioRxiv2025

Grapevine phyllosphere pan-metagenomics reveals pan-microbiome structure, diversity, and functional roles in downy mildew resistance

Grapevines are among the most economically important fruit crops, and microbiome profoundly influence their health, yield, and quality. However, mechanistic insights into microbiome-orchestrated grapevine biology remain limited. Here, we conduct large-scale pan-metagenomic and pan-metatranscriptomic analyses of the phyllosphere microbiome from 107 grapevine accessions spanning 34 Vitis species. We show that the grapevine core microbiome is dominated by phylum Bacillota and Pseudomonadota. Leveraging PacBio sequencing, we assembled 19 high-quality metagenome-assembled genomes (MAGs) from the grapevine pan-microbiome, representing the first MAG reconstruction in plant-associated microbial communities using PacBio reads. These MAGs encode genes associated with antibiotic resistance, secondary metabolism, and carbohydrate-active enzymes (CAZymes), which could potentially influence grapevine biology. During downy mildew (DM) infection, DM-resistant grapevines exhibit significantly higher microbial network complexity than susceptible counterparts. Among the key taxa contributing to this complexity, Bacillota emerged as the dominant phylum with displaying strong abundance correlations with phylum Euglenozoa and Cyanobacteriota, and an isolated Bacillota species from the grapevine leaves, Bacillus cereus, demonstrated potent biocontrol activity against DM infection. Pan-metatranscriptomic analysis further revealed significant upregulation of eukaryotic microbial genes involved in primary and secondary metabolism. This pan-metagenomic study offers unprecedented insights into the complex structure, diversity and functional roles of grapevine phyllosphere microbiome, and presents valuable genomic and microbial resources for microbiome research and engineering to enhance viticulture productivity and quality.

microbiology↗

bioRxiv2025

Design of peptide-based PAC1 antagonists combining molecular dynamics simulations and a biologically relevant cell-based assay

The PACAP receptor PAC1 is a Gs-coupled family B1 GPCR for which the highest-affinity endogenous peptide ligands are the pituitary adenylate cyclase-activating peptides PACAP38 and PACAP27, and whose most abundant endogenous ligand is PACAP38. PACAP action at PAC1 is implicated in neuropsychiatric disorders, atherosclerosis, pain chronification, and protection from neurodegeneration and ischemia. As PACAP also interacts with two related receptors, VPAC1 and VPAC2, highly selective ligands, both agonists and antagonists, for PAC1 have been sought. To date, the peptide PACAP(6-38) and polypeptide M65, which is related to maxadilan, a sandfly vasodilator peptide, have been identified as selective for PAC1. Several non-peptide small molecule compounds (SMOLs) have been reported to be specific antagonists at PAC1, albeit their specificities have not been rigorously documented. Here, we present a platform of cellular assays for the screening of biologically relevant antagonists at PAC1 and show that some currently proposed SMOL antagonists do not have activity in this cell reporter assay, while we confirm that PACAP(6-38) and M65 are competitive antagonists. We have used this assay system to explore other peptide antagonists at PAC1, guided by molecular dynamics analysis of the PACAP-PAC1 interaction based on cryo-EM structural models of PAC1 complexed with a number of biologically active ligands. The affinity-trap model for the PAC1-ligand interaction successfully predicts the engagement behavior of PACAP27 and PACAP38 peptide-based PAC1 inhibitors. In particular, C-terminal deletants of PACAP(6-38) that maintain equipotency to PACAP(6-38) allow the shorter sequence to function as a scaffold for further peptide-based antagonist exploration.

pharmacology and toxicology↗

bioRxiv2025

Layered stomatal immunity contributes to resistance of Vitis riparia against downy mildew Plasmopara viticola

Downy mildew (DM), caused by Plasmopara viticola, is one of the most serious grapevine diseases. Resistant grapevines are a well-known tool for mitigating pathogen-caused damage. We evaluated 29 global grapevine cultivars from 7 species for the sensitivity to P. viticola. Chardonnay belonging to the sensitive species V. vinifera and Qingdahean belonging to the well-known resistant species V. riparia were chosen for further investigation on the resistance mechanism against DM. Unlike Chardonnay, Qingdahean exerted an inhibitory effect on stomatal targeting, suppression of stomatal closure, stomatal penetration of P. viticola, and the development of primary hyphae and haustoria during the early phase of infection, and contained higher levels of malondialdehyde (MDA), which was significantly increased by P. viticola infection, toxic to the pathogen and had an interfering effect on the stomatal targeting. Furthermore, Qingdahean resisted pathogen invasion through the rapid induction of guard cell death and the hypersensitive responses (HR) of other cell types. These findings suggest that resistance to P. viticola consists of layered stomatal immunity in addition to the well-known HR in V. riparia, which is overcome by the pathogen in V. vinifera.

plant biology↗