Biology subjects

Tan, J.

Publications and source records attributed to Tan, J..

At least 55 records · Page 3Linked to original sources

bioRxiv2025

Non-segmented unsupervised learning of multispectral whole slide images for robust analysis of tissue repair and regeneration

Analyzing whole tissue architecture remains challenging due to the inherent complexity of multicellular organization, variable morphology, and the limitations of conventional segmentation-based image analysis. Traditional approaches often rely on partial sampling or nuclear/cytoplasmic boundaries, which risk introducing bias and fail to capture the contextual interplay of diverse tissue compartments. To overcome these barriers, we developed a segmentation free framework for analyzing multispectral whole slide images (WSIs). By tiling WSIs into fixed sized regions and extracting quantitative tile features, we applied unsupervised machine learning to systematically reveal patterns of tissue organization at scale. This approach preserved spatial context without the need for cell-level delineation, recapitulating expected compartments such as epidermis, adipose, and scab, while also revealing subtle but coherent substructures within stromal and granulation regions. Applied to murine wound healing, the method distinguished wild type from diabetic repair dynamics without prior labels, uncovering both gross and nuanced differences in tissue composition. Together, this work establishes a robust, unbiased strategy for whole-tissue analysis that circumvents the limitations of segmentation, leverages unsupervised learning for discovery, and advances the study of tissue repair and regenerative pathology.

cell biology↗

bioRxiv2025

Multimodal learning decodes the global binding landscape of chromatin-associated proteins

The identity of a cell is governed by regulatory proteins binding to the genome to control gene expression. Mapping these genome-wide binding events across thousands of proteins and cell types is essential for understanding development and disease at scale, yet has remained a major experimental and computational barrier. Here we present Chromnitron, a multimodal foundation model that learns the rules of protein-DNA binding from protein sequence, DNA sequence, and context-specific chromatin states. Unlike prior single-task and multi-task learning approaches, Chromnitron implements a multimodal learning framework that accurately predicts the binding landscape for proteins and cell types not seen during training. Using Chromnitron, we discovered and experimentally validated new protein regulators of T cell exhaustion. Chromnitron also uncovered previously uncharacterized dynamic shifts in the binding landscape of regulatory proteins during neurogenesis. This marks a critical step toward a predictive model of interpretable gene regulatory programs across cell types, enabling rapid discovery of regulatory circuits and identification of new therapeutic targets.

systems biology↗

bioRxiv2025

Shipped and shifted: modeling collection-induced bias in microbiome multi-omics using a tractable fermentation system

Large-scale, decentralized microbiome sampling surveys and citizen science initiatives often require periods of storage at ambient temperature, potentially altering sample composition during collection and transport. We developed a generalizable framework to quantify and model these biases using sourdough as a tractable fermentation system, with samples subjected to controlled storage conditions (4 {degrees}C, 17 {degrees}C, 30 {degrees}C, regularly sampled up to 28 days). Machine-learning models paired with multi-omics profiling -- including microbiome, targeted and untargeted metabolome profiling, and cultivation -- revealed temperature-dependent shifts in bacterial community structure and metabolic profiles, while fungal communities remained stable. Storage induced ecological restructuring, marked by reduced network modularity and increased centrality of dominant taxa at higher temperatures. Notably, storage duration and temperature were strongly encoded in the multi-omics data, with temperature exerting a more pronounced influence than time. 24 of the top 25 predictors of storage condition were metabolites, underscoring functional layers as both sensitive to and informative of environmental exposure. These findings demonstrate that even short-term ambient storage (< 2 days) can substantially reshape microbiome, metabolome, and biochemical profiles, posing risks to data comparability in decentralized studies and emphasizing the need to recognize and address such biases. Critically, the high predictability of storage history offers a path toward bias detection and correction -- particularly when standardized collection protocols are infeasible, as is common in decentralized sampling contexts. Our approach enables robust quantification and modeling of such storage effects across multi-omics datasets, unlocking more accurate interpretation of large-scale microbiome surveys.

microbiology↗

bioRxiv2025

Nanopore-Based Glycan Sequencing via Fragmentation-Reassembly Strategy

Glycan sequencing remains a major analytical challenge due to the high structural complexity, extensive branching, and isomeric diversity of glycans. Nanopore sensing has emerged as a promising single-molecule platform for glycan analysis, but existing strategies--such as hydrolysis and strand sequencing, face significant limitations when applied to highly branched or non-linear glycans. Here we report the first experimental realization of glycan assembly sequencing, a nanopore-based strategy that reconstructs full glycan structures from individually analyzed fragments. Using a biantennary complex-type N-glycan as a model, we performed controlled enzymatic digestion to generate structurally defined oligosaccharide fragments. An engineered -HL nanopore was developed for enhanced glycan recognition, enabling the construction of a multidimensional electrical fingerprinting library. Fragment signals from unknown samples were matched to this reference dataset, and full-sequence reconstruction was achieved via fragment reassembly based on set-theoretic integration of structural candidates. This study establishes glycan assembly sequencing as a viable and modular approach to resolve complex glycoforms using nanopore technologies. The method expands the accessible sequence space beyond what is achievable with conventional strategies and provides a foundation for scalable, high-resolution glycomic analysis.

bioengineering↗

bioRxiv2025

Preparation of Scutellariae Radix and Corn-Soy Peptides Complex and Its Hepatoprotective Mechanisms

Amidst the growing global burden of alcohol-associated liver disease (ALD) and the limited clinical interventions, this study innovatively developed a complex of Scutellariae Radix and corn-soy composite peptides (SR-CP). SR-CP exhibited significantly enhanced bioaccessible antioxidant capacity, demonstrating a 0.84-2.89-fold increase in DPPH, ABTS, {middle dot}OH, O2-{middle dot}, and FRAP value versus individual components post-gastrointestinal digestion. In ethanol-injured BRL-3A hepatocytes, SR-CP (60 g/mL) significantly ameliorated cellular viability by 38.28% (vs. ethanol model), normalized ALT/AST leakage by 19.92%/87.35%, restored SOD/GSH antioxidant reserves to 24.74 {+/-} 1.71 U/mg prot and 55.35 {+/-} 1.64 mol/g prot respectively, while suppressing lipid peroxidation through 65.93% reduction in MDA production (vs. ethanol model). Mechanistic profiling revealed dual regulatory effects: 1) Inhibiting CYP2E1/NADPH-OX ({downarrow} 51.97%/80.52%) to prevent excessive ROS production and activating the AMPK/SIRT1 axis ({uparrow} 30.70%/6.99%); 2) Potentiation of Nrf2 nuclear translocation ({uparrow} 0.48-fold) and HO-1 induction ({uparrow} 1.63-fold) through AMPK-mediated antioxidant signaling. The superior hepatoprotection of SR-CP over single constituents establishes a novel paradigm for functional food development, bridging traditional herbal wisdom with molecular-targeted ALD intervention via the AMPK/Nrf2/HO-1 axis.

pharmacology and toxicology↗

bioRxiv2025

IL-17A-driven Macrophage Disorder Promotes Plaque Instability in Psoriatic Atherosclerosis

Psoriasis is a common chronic inflammatory skin disease that predisposes individuals to cardiovascular diseases such as atherosclerosis. However, the underlying mechanisms linking these conditions have not yet been fully elucidated. Recent studies have indicated that the inflammatory cytokine IL-17A plays a significant role in both diseases. The aim of this study was to investigate how psoriasis affects the stability of atherosclerotic plaques via the IL-17A/IL-17RA pathway. We performed single-cell sequencing on patients with both psoriasis and atherosclerosis to analyze changes in the immune microenvironment within their carotid plaques and psoriatic lesions. We found that IL-17A significantly upregulated the expression of pro-inflammatory factors (e.g., CXCL8) in macrophages of psoriatic atherosclerosis patients, leading to weakening of the fibrous cap structure of plaques. Additionally, IL-17A further exacerbated plaque instability by interfering with macrophage lipid metabolism, particularly sphingolipid metabolism. Finally, using in vitro experiments and animal models, we demonstrated that IL-17A enhances pro-inflammatory and metabolic abnormalities in macrophages by downregulating PPAR{gamma}. Therapeutic strategies targeting IL-17A may contribute to the simultaneous mitigation of psoriasis and atherosclerosis-associated cardiovascular risk, providing new directions for clinical intervention.

cell biology↗

bioRxiv2025

Cracking the egg: Isolation and discovery of distinct nonvesicular extracellular particles from Schistosoma mansoni eggs

Extracellular particles (EPs) derived from helminths are emerging as promising immunomodulatory agents, yet their heterogeneity challenges reproducible purification and characterisation. This study focused on purifying and characterising extracellular particles (EPs) from Schistosoma mansoni eggs using tangential flow filtration (TFF) and differential ultracentrifugation (dUC). Contrary to expectations, cryogenic transmission electron microscopy (cryoTEM) revealed that egg-derived EPs are nonvesicular (5-30 nm) nanoparticles (eggNPs) rather than membrane-bound extracellular vesicles. TFF demonstrated superior performance over dUC, yielding higher recovery (76.19% vs. 38.90%), greater purity, and lower endotoxin contamination. Despite limitations in standard protein quantification methods, enriched protein profiles and detection of S. mansoni tetraspanin-2 in TFF-purified eggNPs highlights their distinct molecular signatures. The study also identified contaminants in cryopreservation buffers, underscoring the need for optimised storage conditions. Compared to dUC, TFF preserved particle integrity, with cryoTEM images showing fewer aggregates and distinct EggNP morphology. This work proposes TFF as a scalable and reproducible method for isolating S. mansoni-derived eggNPs, providing a foundation for their exploration as immunomodulatory agents. These findings not only expand the understanding of helminth EPs but also emphasise their therapeutic potential, particularly in inflammatory and immune-dysregulation disorders.

molecular biology↗

bioRxiv2025

Spatially resolved integrative analysis of transcriptomic and metabolomic changes in tissue injury studies

Recent developments in spatially resolved -omics have enabled studies linking gene expression and metabolite levels to tissue morphology, offering new insights into biological pathways. By capturing multiple modalities on matched tissue sections, one can better probe how different biological entities interact in a spatially coordinated manner. However, such cross-modality integration presents experimental and computational challenges. To align multimodal datasets into a shared coordinate system and facilitate enhanced integration and analysis, we propose MAGPIE (Multi-modal Alignment of Genes and Peaks for Integrated Exploration), a framework for co-registering spatially resolved transcriptomics, metabolomics, and tissue morphology from the same or consecutive sections. We illustrate the generalisability and scalability of MAGPIE on spatial multi-omics data from multiple tissues, combining Visium with both MALDI and DESI mass spectrometry imaging. MAGPIE was also applied to newly generated multimodal datasets created using specialised experimental sampling strategy to characterise the metabolic and transcriptomic landscape in an in vivo model of drug-induced pulmonary fibrosis, to showcase the linking of small-molecule co-detection with endogenous responses in lung tissue. MAGPIE highlights the refined resolution and increased interpretability of spatial multimodal analyses in studying tissue injury, particularly in pharmacological contexts, and offers a modular, accessible computational workflow for data integration.

bioinformatics↗

bioRxiv2025

A multi-scale segmentation-free self-supervised AI model to characterize the heterogeneity of the brain tumor microenvironment

Brain tumors affect about 1 million people in the U.S., with aggressive types like glioblastoma having very low survival rates due to complex tumor biology and the protective blood-brain barrier. Current treatments are limited in effectiveness, and our understanding of brain tumor biology remains incomplete. High dimensional multiplexed imaging has enabled us to better understand the tumor microenvironment (TME); however, analyses typically rely on cell segmentation, which is error-prone, may discard useful context outside the cell boundary, and neglects complex tissue-wide features. To address this limitation, we developed a segmentation-free, self-supervised representation learning framework that enables us to train directly on multiplexed images using masked image modeling. We used this approach to analyze 389 imaging mass cytometry images from 185 brain tumor patients. To study tissue-wide features, we first trained our model on 64x64 micron tiles capturing neighborhoods of 10-20 cells, which we termed local tumor microenvironments (LTMEs). To further characterize these LTMEs, we trained our model on 16x16 micron tiles centered on individual cells in our dataset, so that each tile captures a single cell and its surrounding area, which we termed single-cell microenvironments (SCMEs). This multi-scale, self-supervised approach enables a detailed analysis of the heterogeneity within the brain TME, examining single cells in their spatial context. In addition to validating known findings, we identified a novel LTME in GBM patients, composed primarily of tumor cells and a few B and T cells, which strongly correlated with increased survival. By analyzing these B cells with our SCME model, we found they were distinct from other GBM B cells, and higher concentrations of these B cells were linked to improved survival. In conclusion, our study introduces a multi-scale, segmentation-free, self-supervised machine learning model that provides unprecedented insights into brain TMEs, enabling discovery of previously unrecognized cell interactions and spatial features that are predictive of patient survival.

cancer biology↗

bioRxiv2025

PRDM16 controls smooth muscle cell fate in atherosclerosis

Vascular smooth muscle cells (SMCs) normally exist in a contractile state but can undergo fate switching to produce various cell phenotypes in response to pathologic stimuli1-3. In atherosclerosis, these phenotypically modulated SMCs regulate plaque composition and influence the risk of major adverse cardiovascular events4,5. We found that PRDM16, a transcription factor that is genetically associated with cardiovascular disease, is highly expressed in arterial SMCs and downregulated during SMC fate switching in human and mouse atherosclerosis. Loss of Prdm16 in SMCs of mice activates a synthetic modulation program under homeostatic conditions. Single cell analyses show that loss of Prdm16 drives a synthetic program in all SMC populations. Upon exposure to atherogenic stimuli, SMC-selective Prdm16 deficient mice develop SMC-rich, fibroproliferative plaques that contain few foam cells. Acute loss of Prdm16 results in the formation of collagen-rich lesions with thick fibrous caps. Reciprocally, increasing PRDM16 expression in SMCs blocks synthetic processes, including migration, proliferation, and fibrosis. Mechanistically, PRDM16 binds to chromatin and decreases activating histone marks at synthetic genes. Altogether, these results define PRDM16 as a critical determinant of SMC identity and atherosclerotic lesion composition.

molecular biology↗

bioRxiv2025

Developing Foundation Models for Predicting Viral Animal Host Range in Intelligent Surveillance

Emerging human infectious viruses originating from animals continue to pose a persistent threat to global public health. Understanding the host range of animal viruses is crucial for identifying potential spillover pathways and mitigating the risk of future pandemics. Here, we present VirHRanger, a prediction method that integrates foundation models trained on viral genome and protein sequences, alongside genomic and protein compositional traits, viral phylogeny, and protein-protein interactions. To systematically predict the animal host range, VirHRanger incorporates host taxonomy-aware neural networks trained on a comprehensive collection of animal-virus associations spanning mammals, birds, and arthropods. Within a dataset of 4,006 virus species spanning 99 viral families, our model achieved robust performance with a micro-averaged AUROC of 0.938 across all host categories, demonstrating its effectiveness in capturing generalizable host signals from viral genetic data. On a dataset of 315 novel viruses, which are associated with key reservoir animal hosts and insect vectors, VirHRanger notably outperformed the homology-based method, exhibiting a strong generalizability to novel viruses. Furthermore, VirHRanger identified host range variations among closely related viruses within the Coronaviridae family and successfully predicted the ability of SARS-CoV-2 to infect humans and other animal hosts. These findings highlight the potential of VirHRanger to transform sequencing data into timely insights for disease control during the early stages of zoonotic outbreaks.

bioinformatics↗

bioRxiv2025

Repeated application of bifocal transcranial alternating current stimulation (tACS) improves network connectivity and driving performance: a double-blind sham control study

Mounting evidence suggests that transcranial alternating current stimulation (tACS) can enhance response inhibition, a cognitive process crucial for sustained effort and decision-making. However, most prior studies have focused on within-session effects, with limited investigation into the effects of repeated applications, which are crucial for clinical applications. We examined the effects of repeated bifocal tACS targeting the right inferior frontal gyrus (rIFG) and pre-supplementary motor area (preSMA), regions implicated in response inhibition, on inhibitory control. We also explored changes to functional connectivity and whether this stimulation improved simulated driving performance. Thirty young adults (18-35 years) were assigned to either a sham or tACS group (20 Hz, 20 minutes), undergoing five stimulation sessions over two weeks. Resting-state electroencephalography (EEG) was used to assess functional connectivity between the preSMA and rIFG during the first and fifth bifocal tACS sessions and at a 7-day follow-up. Response inhibition was measured using a stop-signal task (SST) administered throughout the sessions. Participants completed two simulated driving tasks (braking, general driving) before the first and after the final tACS intervention. Results revealed a significant improvement in functional connectivity in the tACS group across sessions, although no changes were observed in response inhibition and the braking task. Notably, general driving performance improved, with participants demonstrating closer adherence to the speed limit and greater spare attentional capacity. These findings highlight the potential of repeated bifocal tACS to enhance functional connectivity and related cognitive and motor processes, suggesting promising clinical applications for addressing issues related to cortical connectivity.

neuroscience↗

bioRxiv2025

Decreased auditory cortical activations and altered long-range alpha-band connectivity characterize passive auditory spatial attention in ASD

Autism Spectrum Disorder (ASD) is a developmental condition characterized by difficulties in social interaction, communication and sensory processing. The ability to orient towards sounds is a key component of social interactions, yet auditory spatial attention remains relatively understudied in ASD, despite prior research indicating differences in this domain. Here, we investigate the neural signatures associated with passive auditory spatial attention in children with ASD (n = 21, ages 6-17) relative to age- and IQ-matched typically developing (TD) children (n = 31), using source-localized magnetoencephalography (MEG). Participants listened passively, while watching a silenced movie, to non-social auditory stimuli designed to either remain lateralized to one hemifield (stay trials), or to change in location from one side to the contralateral hemifield (jump trials). Linear mixed effects modeling showed lower cortical activation in the auditory cortex in the ASD group in response to jump trials, relative to the TD group. Additionally, functional connectivity analyses showed higher alpha-band functional connectivity in the ASD group between left auditory cortex seeds and right prefrontal and left parietal regions known to be recruited during auditory spatial attention. Right prefrontal alpha-band connectivity estimates were associated with behaviorally assessed auditory processing scores, whereas left parietal connectivity estimates were associated with ASD symptomatology. Our results align with the hypothesis that auditory spatial attention generally, and specifically orientation to sounds even when experienced passively, differs in ASD individuals.

neuroscience↗

bioRxiv2024

CellSexID: Sex-Based Computational Tracking of Cellular Origins in Chimeric Models

Cell tracking in chimeric models is essential yet challenging, particularly in developmental biology, regenerative medicine, and transplantation research. Existing methods such as fluorescent labeling and genetic barcoding are technically demanding, costly, and often impractical for dynamic or heterogeneous tissues. Here, we introduce CellSexID, a computational framework that leverages sex as a surrogate marker for cell origin inference. Using a machine learning model trained on single-cell transcriptomic data, CellSexID accurately predicts the sex of individual cells, enabling in silico distinction between donor and recipient cells in sex-mismatched settings. The model identifies minimal sex-linked gene sets through ensemble feature selection and has been validated using both public datasets and experimental flow sorting, confirming the biological relevance of predicted populations. We further demonstrate CellSexIDs applicability beyond chimeric models, including organ transplantation and multiplexed sample demultiplexing. As a scalable and cost-effective alternative to physical labeling, CellSexID facilitates precise cell tracking and supports diverse biomedical applications involving mixed cellular origins.

bioinformatics↗

bioRxiv2024

Poxviruses induce a nuclear environment in the cytoplasm to promote viral replication

The family Orthopoxviridae comprises several members that can cause disease in humans, including Small-, Cow-, Camel-, and Monkeypoxvirus. In 2022 a global outbreak of the zoonotic monkeypoxvirus started with human- to- human transmission and more than 100 000 global cases in all continents. This outbreak emphasizes the need for more basic research to get a better understanding of poxvirus biology. We characterized the cellular response to poxvirus infection and performed bulk RNA-sequencing of primary human fibroblasts infected with Modified-Vaccinia Ankara Virus (MVA), Vaccinia Virus, Cowpoxvirus, Camelpoxvirus and 4 different Monkeypoxvirus isolates (clade 1-2b) at various time points to compare the transcriptome of infected cells. We could demonstrate that MVA infection leads to a potent activation of innate immune genes that is absent in other samples. Camelpoxvirus infection results in a strong activation of NF-kB signaling 2h post infection whereas all other viruses inhibit NF-kB activation. In addition, we observed transport of cellular proteins like histone H1, lamin A/C, emerin and topoisomerase II A/B from the nucleus into cytoplasmic poxvirus factories. This correlates with drastic structural changes inside the nucleus in electron microscopy, namely formation of nuclear filaments, indicating intensive remodeling of the nuclear architecture. Accordingly, inhibition of topoisomerase using the clinically approved topoisomerase inhibitor epirubicin reduced viral replication of several Mpox strains. We observe a reduction of viral replication by epirubicin in cells of human and mouse species, indicating that the recruitment of TOP2B into viral factories is crucial for MPXV replication.

microbiology↗

bioRxiv2024

Optimizing PSC culture for generating neural organoids

Cerebral organoids generated according to unguided protocols produce neural tissue with exceptional cell diversity and fidelity to in vivo. However, with only minimal extrinsic intervention, the importance of high quality starting material becomes paramount. Better understanding of what constitutes a high quality stem cell line and how to maintain those properties throughout prolonged culture is therefore a crucial foundation for successful organoid differentiation. In this study, we investigate the proteome and phospho-proteome of human pluripotent stem cells to uncover the mechanisms that drive neural organoid competence. We identify aberrant cell-extracellular matrix interaction and increased oxidative metabolism as hallmarks of poor neural differentiators. Drawing on the proteomic data and published literature, we optimise culturing conditions by using improved coating matrix, sustained supply of the key growth factor FGF2 and reducing oxidative stress. These adjustments improve brain organoid generation across all tested cell lines, though with varying degrees of efficiency. This work highlights the importance of optimal culture conditions to best support stem cells, ultimately enhancing the quality of brain organoids produced.

cell biology↗

bioRxiv2024

Distribution of haploid chromosomes into separate nuclei in two pathogenic fungi

The presence of nuclei defines eukaryotes, enabling compartmentalization of macromolecules and cellular regulation. Inside the nucleus, chromosome numbers vary greatly across organisms, both in terms of ploidy status and haploid chromosome number. For cells harboring multiple nuclei as in many fungal mycelia and animal muscle cells, each healthy nucleus is traditionally believed to carry at least one haploid set of chromosomes. Abnormal chromosome numbers in nuclei are often associated with aging, diseases such as cancer, developmental disorders or lethality. Here we report a surprising discovery that chromosomes in haploid cells of the fungal species Sclerotinia sclerotiorum and Botrytis cinerea are segregated into separate nuclei, with each nucleus containing only a fraction of the chromosomes. This is the first report of eukaryotic cells that partition chromosome subsets into distinct nuclei, bringing new questions and opening fresh avenues for chromosome biology.

microbiology↗

bioRxiv2024

Switch of TIR signaling by a Ca2+ sensor activates ADR1 recognition of pRib-AMP-EDS1-PAD4 for stomatal immunity

Plants swiftly close stomata upon detecting pathogen entry, a crucial defense termed stomatal immunity. The process is initiated by cell-surface pattern recognition receptors (PRRs) that perceive pathogen-associated molecular patterns (PAMPs) and evoke a series of early cellular responses including calcium ions (Ca2+) influx, and is conducted by the intracellular nucleotide-binding leucine-rich-repeat receptors (NLRs) ADR1s within an EDS1-PAD4-ADR1 module. However, the underlying mechanisms linking PRR signaling to the NLRs ADR1s remain unclear. Here, we show that the Nicotiana benthamiana Toll/interleukin-1 receptor (TIR)-only protein Stomatal TIR1 (STIR1) produces the immune molecule pRib-AMP, induces formation of EDS1-PAD4-ADR1 complexes, and mediates stomatal immunity. The Inhibitor of Stomatal Immunity C2-domain protein 1 (ISIC1) interacts with and constrains STIR1 function at basal condition, whereas upon pathogen infection, ISIC1 senses Ca2+ signals and de-represses STIR1 signaling. Cryo-electron microscopy structure of pathogen infection-elicited Arabidopsis AtEDS1-AtPAD4-AtADR1-L2 complex reveals the pRib-AMP binding to AtEDS1-AtPAD4 receptor and the AtADR1-L2 recognition of pRib-AMP-AtPAD4-AtEDS1 for stomatal immunity. Collectively, this study uncovers a repression/de-repression mechanism linking PRR signaling to NLRs by a Ca2+ sensor/TIR-only node, and elucidates an NLR recognition mechanism of the pRib-AMP-EDS1-PAD4 complex in governing innate immunity. SynopsisAt basal condition, the Ca2+ sensor ISIC1 interacts with and inhibits the TIR-only protein STIR1; upon pathogen infection, ISIC1 perceives Ca2+ signal and releases STIR1 to produce pRib-AMP; the EDS1-PAD4 receptor binds pRib-AMP and is recognized by the NLR ADR1-L2, thereby activating stomatal immunity.

plant biology↗