Biology subjects

Wu, S.

Publications and source records attributed to Wu, S..

At least 73 records · Page 4Linked to original sources

bioRxiv2026

The tongue-brain axis mediates a hidden amino acid appetite

Selecting a diet containing all essential amino acids (EAAs) is critical for health. Following EAA deprivation, animals can select a nutritiously complete food source; however, the underlying mechanisms in vertebrates remain unclear. In mice, we show that leucine deficiency activates hypothalamic agouti-related protein (AgRP) neurons, which project to the paraventricular thalamus (PVT) via {gamma}-aminobutyric acid and are required for EAA deficiency-induced leucine appetite in mice. Furthermore, the peripheral tongue amino acid sensor general control nonderepressive-2 (GCN2) mediates acute EAA appetite via AgRP neurons. Together, these findings identify a tongue-AgRP-PVT circuit underlying EAA appetite, which is important for the rapid and accurate selection of essential nutrients.

neuroscience↗

bioRxiv2026

Label free multimodal optical imaging of metabolic heterogeneity in aging by integrating SRS, MPF, FLIM, and SHG

Cellular metabolism is governed by the coordinated organization of macromolecules, including lipids and proteins, together with redox-active cofactors such as NADH and FAD. However, resolving these biochemical features quantitatively and spatially at subcellular resolution remains challenging because no single imaging modality can capture molecular composition, redox state, and tissue architecture simultaneously without labeling. Here, we present MANIFEST (Multimodal Analysis of Nonlinear Imaging for Functional Endogenous Spatial correlaTion), a label-free imaging platform that integrates stimulated Raman scattering (SRS), second harmonic generation (SHG), multiphoton fluorescence (MPF), and fluorescence lifetime imaging microscopy (FLIM) with their spatial correlation. MANIFEST combines chemical imaging of lipids with autofluorescence- and lifetime-based quantification of NADH and FAD metabolism, enabling spatially resolved analysis of metabolic heterogeneity at organelle and tissue-compartment levels. We apply this framework to four distinct aging or disease models: amyloid-beta-treated tri-cultured brain cells, high-fat diet mouse liver, human non-ischemic cardiomyopathy tissue, and aging mouse retina. Across these systems, MANIFEST reveals disease-associated lipid remodeling, redox imbalance, disrupted metabolic zonation, collagen reorganization, and layer-specific metabolic changes. By integrating complementary nonlinear optical modalities into a single label-free platform, MANIFEST provides an extensible, proof-of-concept platform for high-resolution metabolic phenotyping in complex biological systems and offers new opportunities for studying disease mechanisms, aging biology, and metabolism-driven tissue pathology.

bioengineering↗

bioRxiv2026

Scalable genotyping in fixed transcriptomes resolves clonal heterogeneity via single-cell sequencing

Despite the promise of single-cell transcriptomics for understanding cell states in heterogeneous populations, widely used platforms have limited ability to link transcriptional states to somatic mutations within the same cells. Here, we introduce Genotyping in Fixed Transcriptomes (GIFT) for the simultaneous detection of large numbers of targeted genetic variants with whole transcriptome profiles in single cells. The core innovation of GIFT is a rationally designed gapfilling reaction between adjacent single-stranded DNA (ssDNA) probes that barcodes native transcript sequence to enable highly-specific targeted mutation detection. GIFT achieves greater than 99% genotyping accuracy and flexible capture of hundreds of mutations per cell, including in formalin-fixed, paraffin-embedded (FFPE) tissue, enabling clonal lineage tracing in heterogeneous settings. We demonstrate the unique scalability of GIFT by profiling more than 700,000 cells from 35 donors with myeloproliferative neoplasms (MPN), revealing mutation-dependent hematopoietic responses to systemic inflammation associated with the characteristic JAK2V617 mutation, including an allelic dose gradient of interferon-associated transcriptional programs and priming of hematopoietic stem cells that develop into divergent disease states. The technical advantages of GIFT enable direct resolution of genotype-to-phenotype relationships via clonal tracing with comprehensive cell-state measurements at single-cell resolution.

genomics↗

bioRxiv2026

PA-SfM: Tracker-free differentiable acoustic radiation for freehand 3D photoacoustic imaging

Three-dimensional photoacoustic imaging (3D PAI) has shown great potential in preclinical studies and clinical implementations, while it commonly has sparse sensor arrays, limiting both spatial sampling and the field of view (FOV). Freehand scanning could overcome these limitations through anatomy-adaptive scanning that extends spatial coverage and provides complementary views beyond the fixed array aperture, but its flexibility is conventionally constrained by the need for motor feedback or external pose tracking. Here we introduce PA-SfM, a tracker-free structure-from-motion (SfM) framework that recovers multiple poses directly from PA measurements with superior precision, enabling freehand 3D PAI without external pose measurements. PA-SfM integrates a differentiable acoustic radiation model with hierarchical optimization and rigid array constraints, it jointly estimates inter-view transformations and reconstructs 3D volumes. The performance of PA-SfM was demonstrated in numerical simulations, animal studies and human imaging experiments. Compared with motor encoder-based fusion of the mechanically scanned system, PA-SfM produced sharper and more continuous vascular reconstructions with an expanded FOV. In controlled quantitative validations, PA-SfM achieved high reconstruction fidelity, with PSNR values over 40 dB and SSIM values above 0.98 relative to ground truth or known pose reference reconstructions. These results demonstrate that PA-SfM enables robust freehand 3D PAI, with particular potential for clinical implementation of large-scale freehand imaging. The source code is publicly available at https://github.com/JaegerCQ/PA-SfM.

bioengineering↗

bioRxiv2026

Efficient Generation of Functional TCRαβ+ Cytotoxic T Cells from hiPSCs via Small-Molecule Modulation

Genetically engineered human induced pluripotent stem cells (hiPSCs) represent a promising platform for regenerative medicine and next-generation immunotherapies. While recent advances enable stroma-free differentiation of hiPSCs into mature CD3TCR{beta} cytotoxic T lymphocytes (CTLs), overall efficiency remains limited. Here, we identify small-molecule modulators that enhance T cell output, particularly at the ProT cell stage. Targeted and stage-specific inhibition of AHR, DOT1L, or GSK3 drives robust maturation from ProT to CD4 immature single-positive (ISP) cells, markedly increasing CD4CD8 populations and augmenting CTL production of up to 2000 fold. hiPSC-derived T (iT) cells matured under these conditions display superior activity in cytotoxicity assays using AMG-701 (BCMAxCD3) or Blinatumomab (CD19xCD3). These effects were reproducible across independent hiPSC lines, diverse hematopoietic progenitor generation methods, and multiple stroma-free differentiation platforms, and were further validated in cord blood CD34 cells. Notably, AHR inhibition enhanced T cell development and promoted B lymphopoiesis, revealing shared regulatory pathways in lymphoid lineage specification. We also demonstrate that the Oct4-activating compound OAC1 functions as a weak AHR inhibitor, partially recapitulating the effects of canonical AHR blockers in both cellular and zebrafish AHR reporter systems. Collectively, our findings define key molecular circuits governing human lymphoid differentiation and establish practical strategies to optimize the yield and function of hiPSC-derived cytotoxic T cells. This work advances the development of both universal and autologous hiPSC-derived T cell therapies, offering a path forward even for patient-specific hiPSC lines with suboptimal T cell differentiation potential.

cell biology↗

bioRxiv2026

KuafuPrimer: Machine learning empowers the design of 16S amplicon sequencing primers toward minimal bias for bacterial communities

Amplicon sequencing protocol targeting the 16S rRNA gene is a widely used and cost-effective method for exploring bacterial communities. However, its performance is often limited by primer bias arising from the arbitrary use of universal primers across diverse microbial communities and habitats. We propose KuafuPrimer to design the optimal 16S rRNA gene primers toward minimal bias for targeted bacterial communities, using few-shot machine learning to guide the primer design procedure based on a small number of samples. Simulations on 809 samples across 26 representative environments and habitats showed that KuafuPrimer-designed primers outperformed the universal primers in taxonomic accuracy, achieving an averaged 16.31% relative reduction in primer bias, with reductions up to 46.08% in plant samples. Notably, KuafuPrimer detected 29 rare and key taxa undetectable by the universal primers. Validation with 317 longitudinal gut microbiota samples demonstrated that KuafuPrimer-designed primers consistently outperformed the universal primers across temporal, individual, and cohort levels, with relative bias reductions of 5.03%, 3.53%, and 3.10%, respectively. Finally, in real PCR experiments on human gut samples from Clostridioides difficile-infected and healthy groups showed that polymerase chain reaction products using KuafuPrimer-designed primers correlated better with metagenomic data compared to the universal primers. More importantly, KuafuPrimer successfully detected Clostridioides difficile, the key pathogen missed by the universal primers, highlighting its potential for improving clinical diagnostics. In summary, KuafuPrimer provides a machine learning-based primer design strategy for targeted bacterial communities, with demonstrated utility in large-scale microbiome initiatives, longitudinal surveys and clinical diagnostics.

bioinformatics↗

bioRxiv2026

Structural insights into target detection by the S. marcescens type III CRISPR complex and its deployment inSNP identification

Type III CRISPR systems utilize a complex containing Cas10, additional Cas proteins and a crRNA to detect foreign transcripts. Upon detection, Cas10 synthesizes cyclic oligoadenylates (cOA), signaling molecules that coordinate interference by stimulating downstream enzymes with DNase, RNase, protease or other activities. Type III systems are among the most abundant CRISPR systems in prokaryotes and understanding the structure-function relationships that control transcript detection and cOA synthesis will advance the understanding of the broader physiological roles of these systems. Type III systems possess properties well-suited to their deployment as molecule diagnostics: specific detection and activation of a cascade of multi-turnover enzymatic reactions that can be harnessed for signal generation. We determined that Serratia marcescens Cas10-Csm (SmCas10-Csm) synthesizes predominantly cA3 molecules and this synthesis is sensitive to mismatches in the crRNA-target RNA duplex adjacent to Cas10. We determined the structure of SmCas10-Csm unbound and bound to target RNA identifying conformational changes associated with target binding. We demonstrate that SmCas10-Csm can distinguish between single nucleotide polymorphisms that occur in the human HBB transcript that are associated with sickle cell disease indicating an additional role for type III CRISPR systems in point-of-care diagnostics in low-resource settings.

molecular biology↗

bioRxiv2026

STING agonist-mediated endothelial cell activation drives NK cells and neutrophils-dependent pulmonary inflammation

Stimulator of interferon genes (STING) agonists and derivative molecules have been extensively developed for tumor immunotherapy. However, systemic exposure toxicity risks have constrained clinical trial progression and even threatened patient lives. Currently, systematic toxicity assessments for STING agonists remain lacking, with the mode of action for major organ injury yet to be elucidated. Here, we focused on STING agonist-induced lung injury, revealing that systemic administration of STING agonists caused pulmonary hemorrhage, inflammatory alterations, and respiratory dysfunction. Through single-cell RNA sequencing and immune deletion studies, we found that lung endothelial cells could be stimulated by STING agonists and then secreted chemokines and IL-15 to recruit and activate NK cells. NK cells could induce endothelial cell apoptosis via IFN-{gamma}. Tbx21+ NK subpopulations, which activated by endothelial cells, could produce chemokines to recruit neutrophils. Neutrophils secreted IL-1{beta} through positive feedback pathways and form neutrophil extracellular traps during lung injury. This study elucidates the critical role of the endothelial cell-NK cell-neutrophil axis in mediating STING agonist-associated pneumonia, offering insights for developing intervention strategies for STING agonist toxicity.

pharmacology and toxicology↗

bioRxiv2026

Volume regulation of cancer cells during osmotic pressure variation

Osmotic pressure is a fundamental physical determinant of cellular homeostasis, and its perturbation is recognized as a critical factor in cancer progression and therapy. However, whether cancer cells and normal cells respond to osmotic pressure changes differently remains unclear. Here, we subjected cancer and normal cells to osmotic shocks and compared their volume and traction force changes. Under hypotonic conditions, cancer cells recovered their volume and forces much more slowly than normal cells, although both cell types responded similarly to hypertonic shock. We found that actin-based cortical tension controlled the recovery speed; whereas myosin-mediated contractility tuned the volume change extent. Substrate stiffness also influenced the recovery process by altering cytoskeletal constraint. A theoretical model integrating adhesion energy, ion and water transport, and surface tension was developed. The results matched the experimental observations. Our work uncovered the distinct mechanobiological behaviors of cancer cells upon hypotonic shock, providing a mechanical framework for understanding cancer mechanisms that may inform the development of cancer therapeutics.

biophysics↗

bioRxiv2026

Towards building a World Model to simulate perturbation-induced cellular dynamics by AlphaCell

Predicting cellular responses to perturbations is crucial for therapeutic discovery, yet experimental screening is severely constrained by the combinatorial vastness of biological space. While computational simulations offer a scalable alternative, current models are limited by the incomplete latent representation----mainly relying on highly variable genes in feature representation; the poor genome-wide reconstruction fidelity; and the ungeneralizable dynamic laws across diverse contexts. Consequently, they fail to mechanistically transfer learned dynamics to unseen cellular contexts. To address these systemic flaws, we introduce AlphaCell, a generative Virtual Cell World Model that unifies genome-wise representation with continuous state transition modeling. AlphaCell achieves three synergistic innovations: (1) Latent Manifold Rectification, processing the full protein-coding transcriptome to construct a differentiable Virtual Cell Space, effectively filtering noise while preserving intrinsic cellular topology; (2) Biological Reality Reconstruction, utilizing a massive, knowledge-rich decoder to translate abstract latent states back into high-fidelity, genome-wide expression profiles; and (3) Universal State Transition, applying Optimal Transport Conditional Flow Matching to model perturbations as continuous, deterministic vector fields. By abstracting perturbation mechanisms into generalized dynamic laws, AlphaCell makes robust prediction of perturbation responses in a compositional generalization scenario and enables zero-shot prediction of cellular dynamics in entirely unseen cellular contexts, providing a foundational engine for cellular-context-generalizable perturbation prediction and perturbation-induced cellular dynamics simulation.

bioinformatics↗

bioRxiv2026

Genome-wide classification of tumor-derived reads from bulk long-read sequencing

DNA extracted from tissue samples typically derives from a complex mixture of cell types. Without single cell analysis, it has been generally impossible to determine the cell type of origin for most molecules. One clear example of this is in the complex milieu of a human neoplasm. Here, we develop ROCIT (https://github.com/tobybaker/rocit), a transformer-based model to classify the tumor or non-tumor origin of individual reads from bulk tumor samples sequenced with long-read whole genome sequencing. Using somatic mutations to derive training data, ROCIT uses read-level methylation patterns to accurately classify reads from any-where in the genome without requiring the adjacent normal tissue or the explicit identification of tumor differentially methylated regions. We apply ROCIT to a cohort of prostate and ovarian tumors and demonstrate high classification accuracy across the entire genome. We then demonstrate the potential of ROCIT predictions to improve somatic variant calling. ROCIT represents a major step forward in the analysis of bulk tumors with long-reads, enabling the accurate and sensitive identification of reads with specific cell types of origin genome-wide.

bioinformatics↗

bioRxiv2026

Hierarchical Anchoring Gating Dictates Specific H4K16 Acetylation by the Human MSL Complex

Histone acetylation is a versatile post-translational modification essential for diverse biological processes. While nearly all histone acetyltransferases (HATs) act promiscuously on multiple lysine residues, the human male-specific lethal (MSL) acetyltransferase complex mediates strictly site-specific acetylation of histone H4 at lysine 16 (H4K16ac), with its underlying mechanism remaining enigmatic. Here, we leveraged chemical protein synthesis to engineer a semisynthetic nucleosome probe that traps the MSL complex in a catalytically engaged state and determined its 3.12 [A] cryo-electron microscopy (cryo-EM) structure, which reveals a hierarchical anchoring-gating mechanism responsible for stringent H4K16 selectivity. The MSL complex adopts a dual-anchoring mode to bind nucleosomal DNA at superhelix location (SHL) 1.5 and the H2A-H2B acidic patch. Three spatially coordinated gating elements (the MSL1 anchor helix, KAT8 gating helix, and KAT8 gating hairpin) then conformationally constrain the H4 N-terminal tail, funneling H4K16 side chain exclusively into the active site while sterically occluding flanking lysines. Targeted mutations disrupting these interfaces impair H4K16ac in vitro and in cells, with concomitant defects in chromatin accessibility and transcription. Beyond the monomeric 1:1 complex, cryo-EM analysis identifies a 2:2 MSL-nucleosome assembly, wherein MSL3 dimerization mediates inter-nucleosomal contacts linked to genome-wide H4K16ac patterning. Collectively, our work delineates a residue-to-domain framework in which a chromatin acetylation writer hardwires specific catalysis into nucleosome engagement and scales this precision to higher-order chromatin architecture, providing a mechanistic foothold for understanding aberrant MSL-encoded chromatin programs in diseases.

biochemistry↗

bioRxiv2026

LinkDTI: Drug-Target Interactionsprediction through a Link Predictionframework on Biomedical KnowledgeGraph

Computational drug-target interactions (DTI) prediction serves as a valuable tool for drug discovery and repurposing by cost-effectively narrowing down the potential drug-target space. This paper presents LinkDTI, a computational framework that predicts DTIs by identifying connections within a heterogeneous knowledge graph of drugs, proteins, diseases, and side effects. Unlike methods that rely on mathematical techniques like matrix completion or similarity-based scoring, LinkDTI uses an advanced graph-based approach to capture relationships between biomedical entities. Specifically, LinkDTI applies a modified version of the multilayer GraphSAGE model that learns from the heterogeneous knowledge graph and predicts potential drug-target interactions. Our model incorporates negative sampling that balances the data to address the issue of having more negative than positive interactions. Our results show that LinkDTI consistently performs better in AUROC and AUPRC than baseline methods by at least 2.5% across different sampling ratios and conditions. Subsequently, it identifies approximately 945 new potential DTIs, marking a 49.14% increase over known DTIs. Overall, LinkDTI offers a simple yet effective method for integrating diverse biomedical data to identify potential drug-target interactions. The code and data can be found at https://github.com/hub2nature/LinkDTI_heterogenous_KG.git.

bioinformatics↗

bioRxiv2026

Identification and Characterization of Metastasis-initiating cells

Metastasis, the primary cause of cancer-related mortality, is a dynamic and complex process driven by a subset of cells known as metastasis-initiating cells (MICs). Accurate identification of MICs is therefore critical for metastasis diagnosis and therapeutic decision-making. However, current approaches rely either on mouse tracing experiments, which are difficult to translate to human systems, or on indirect strategies such as stemness, trajectory, pathway, and biomarker analyses that often yield inconsistent results. To address these limitations, we propose scMIC, a computational framework designed to explicitly and reliably identify MICs from single-cell data (available at https://github.com/swu13/scMIC). scMIC integrates an embedding-based representation, unbalanced optimal transport, and a top-k selection strategy to robustly capture metastasis-initiating potential. The framework was validated and applied across multiple cancer types, species, and multi-omics datasets. Our results demonstrate the reliability of scMIC for MIC identification, its potential clinical utility in metastasis prognosis, and its effectiveness in discovering metastasis-related gene programs and molecular biomarkers. Elucidating the mechanisms of metastasis initiation not only advances our understanding of metastatic progression but also enables the development of therapeutic strategies that target the more aggressive MIC population rather than non-MICs, thereby avoiding unintended increases in metastatic risk. Collectively, scMIC provides a powerful tool for cancer metastasis research and drug discovery.

bioinformatics↗

bioRxiv2026

CD28 Co-stimulation Is Organized by Signaling Condensates and Counteracted by PD-1

T cell activation relies on the coordinated integration of TCR and co-signaling pathways, yet how these signals are physically organized remains unclear. Here, we demonstrate that CD28 co-stimulation is driven by a phase-separation mechanism in which the CD28 intracellular domain (ICD) forms signaling condensates with the Src-family kinase Lck. Ligand engagement promotes the CD28 ICD accessibility, and triggers rapid CD28/Lck condensation, enhancing CD28 phosphorylation, cytoskeletal polarization, and cytokine production. We further identify PD-1 as a graded regulator of this process: monophosphorylated PD-1 is recruited into CD28/Lck condensates, whereas di-phosphorylated PD-1 competitively binds the CD28 ICD and dissolves these assemblies. CD28 and PD-1 ICDs also undergo condensation directly, revealing a dynamic and compositionally heterogeneous signaling system. Guided by this competitive phase-separation mechanism, we engineer CD28 ICD mutants that preserve Lck condensation while resisting PD-1-mediated disruption. Incorporation of these condensation-selective mutants into CAR constructs confers resistance to inhibitory signaling and enhances antitumor efficacy in vivo. These findings establish phase separation as a central organizing principle of CD28 co-stimulation and provide a biophysical framework for engineering more resilient therapeutic T cells.

immunology↗

bioRxiv2026

In Utero CFTR Modulation Alleviates Disease in G551D Cystic Fibrosis Pigs

Previous studies indicate that pigs with CFTR-null and CFTR-{Delta}F508 mutations develop multiorgan disease similar to that in people with cystic fibrosis (CF). At birth, their airways exhibit host defense defects that predispose to airway infection, inflammation, and mucus accumulation. The CFTR-G551D mutation causes CF by producing CFTR channels that localize correctly but have reduced channel activity. Ivacaftor (VX-770) is a small molecule drug developed to potentiate CFTR activity. To test the phenotype of the CFTR-G551D mutation in pigs and determine whether ivacaftor can rescue CF abnormalities, we developed CFTRG551D/G551D (CF-G551D) pigs through homologous recombination in fetal fibroblasts and somatic cell nuclear transfer. Newborn CF-G551D piglets exhibited phenotypes similar to CF-null piglets, including meconium ileus, exocrine pancreatic destruction, micro-gallbladder, vas deferens destruction, and airway structural abnormalities. Compared to wild-type pigs, CF-G551D pigs had reduced forskolin-stimulated short-circuit current in airway and intestinal tissues. Ivacaftor increased the single-channel open state probability of CFTR-G551D and increased short-circuit current to near wild-type levels. Similar to our other CF pig models, we found that 100% of CF-G551D pigs were born with meconium ileus. To test whether in utero ivacaftor treatment could prevent or alleviate meconium ileus, pregnant sows were treated with ivacaftor beginning at day 35 of gestation and continuing until delivery. This treatment rescued the pancreas, gallbladder, and vas deferens phenotype in the majority of CF-G551D pigs. Animals that were spared from meconium ileus were able to survive without ivacaftor treatment. Airway disease developed similar to other CF pig models. These findings indicate that this model may be useful for studies in which CFTR function can be reversed, for investigating in utero CFTR correction strategies, and for longitudinal studies in CF pigs.

physiology↗

bioRxiv2026

CTLA-4 inhibitors drive colitis through metabolic reprogramming-mediated Treg/Th17 imbalance

Immune checkpoint inhibitors (ICIs), especially CTLA-4 inhibitors (CTLA-4), exhibit a high incidence of colitis as an immune-related adverse event (irAE) during cancer treatment, severely limiting patient benefit. Clinically, both treatment interruption and existing intervention drugs for ICI-mediated colitis may compromise antitumor efficacy. However, there is inadequate research on the pathogenesis of ICI-mediated colitis, with findings often conflicting. Here, we first established multiple clinically relevant animal models, including an immuno-humanized ICI-mediated colitis model. Through time-series transcriptomics, we discovered that CTLA-4-induced colonic toxicity exhibits characteristics ranging from early metabolic reprogramming represented by glycolysis to later immune disorders represented by Th17 responses. By targeting colonic CTLA-4+ T cells, CTLA-4 blocked CD80/CD86-CTLA-4 interaction, thereby activating the PI3K-AKT-mTOR pathway. Subsequently, mTOR mediated metabolic reprogramming in T cells, shifting them from Treg-biased oxidative phosphorylation to Th17-biased glycolysis. The colonic toxicity of CTLA-4 has also been demonstrated to depend on the PI3K-AKT-mTOR pathway, glycolysis, and Th17 responses. Notably, metformin significantly relieved ICI-mediated colitis by inhibiting mTOR without impeding antitumor efficacy. Collectively, these findings highlighted the metabolic-immune axis in the colonic toxicity of ICI and provided a clinically superior intervention strategy.

pharmacology and toxicology↗

bioRxiv2026

Integrative analysis of papain-like cysteine proteases and cystatins reveals stress-dependent regulatory modules in Arabidopsis thaliana

Plant papain-like cysteine proteases (PLCPs) and cystatins constitute a major protease-inhibitor system that contributes to plant signaling and responses to biotic and abiotic stress. Although the Arabidopsis genome encodes 31 predicted PLCPs and 7 cystatins, their coordinated regulation has not been systematically evaluated across major stress conditions. Here, we reprocessed and integrated AtGenExpress microarray datasets to analyze the expression patterns of 28 PLCPs and seven cystatins in response to bacterial infection (virulent and avirulent Pseudomonas syringae), wounding, and drought stress. In parallel, we performed in vitro enzyme activity assays to assess inhibition specificity of seven Arabidopsis cystatins against five abundantly expressed PLCPs. Finally, we used the support vector machine (SVM) package e1071 in R to integrate co-expression and inhibition data and to generate a hypothesis-generating PLCP-cystatin interaction network. Together, these analyses suggest that Arabidopsis deploys distinct PLCP and cystatin gene subsets in response to virulent versus avirulent bacterial infection, and that transcriptional modules associated with bacterial infection and wounding are largely distinct. In contrast, several drought-associated PLCP-cystatin modules overlap with those associated with basal defense, suggesting partially shared regulatory programs across abiotic and biotic stress.

plant biology↗