Biology subjects

Wang, N.

Publications and source records attributed to Wang, N..

At least 73 records · Page 4Linked to original sources

bioRxiv2025

Mitophagy promotes metabolic reprogramming to enhance keratinocyte migration via ANGPTL4 during wound healing

Mitochondrial function and quality control is emerging as a key regulator of keratinocyte migration in both wounding and non-wound healing contexts, yet the cellular mechanisms that support this process are incompletely understood. In this study, using single-cell RNA sequencing data of human wounded tissue we identified a distinct population of migrating keratinocytes marked by the high expression of the mitophagy regulator BNIP3 during the proliferative stage of wound healing. Pharmacological induction of mitophagy with Urolithin A accelerated keratinocyte migration in vitro as well as keratinocyte function and regeneration in aged zebrafish, whilst RNA sequencing of primary human keratinocytes revealed the transcriptional upregulation of ANGPTL4 in Urolithin-A treated cells. Mechanistically, Urolithin A increased metabolic switching to a more glycolytic phenotype, leading to AKTGSK3 pathway activity and FOSL1-mediated ANGPTL4 transcription, ultimately promoting keratinocyte migration through enhanced laminin-332 production. Overall, our findings uncover a novel role for mitophagy in promoting keratinocyte migration during wound repair, and demonstrate that pharmacologically enhancing mitophagy promotes regenerative epithelial responses by enhancing FOSL1-mediated ANGPTL4 signalling through the modulation of metabolic switching. These insights significantly expand the understanding of the role of mitophagy on keratinocyte function during wound healing, linking mitophagy to metabolic adaption in keratinocytes, and provide a mechanistic basis for targeting mitophagy or downstream genes in wound healing therapies.

developmental biology↗

bioRxiv2025

PPARgene 2.0: leveraging large language models and multi-omics data for enhanced identification and prediction of PPAR target genes

Peroxisome proliferator-activated receptors (PPARs) are ligand-activated transcription factors of the nuclear receptor superfamily. Upon ligand binding, PPARs activate target gene transcription and regulate a variety of important physiological processes such as lipid metabolism, inflammation, wound healing and immune responses. PPARgene is a database that integrates literature-curated and computationally predicted PPAR target genes. It provides gene-level annotations including tissue specificity, species, and supporting PubMed IDs. Computational predictions are generated using a machine learning method that combines PPRE motif analysis with microarray expression data. Here, we introduce PPARgene 2.0, a 10-year update to the original PPARgene database. This update adds 35 newly reported PPAR target genes, 20 PPAR{beta}/{delta} target genes, and 72 PPAR{gamma} target genes, bringing the total number of curated target genes in the database to 337. To retrieve newly reported PPAR target genes from the literature, we used two language models to screen publications after 2016. Candidate papers were then manually reviewed, and verified target genes were added to the updated database. This update also improved the predictive method by expanding the volume of high-throughput gene expression data and incorporating PPAR-related ChIP-seq datasets alongside in silico PPRE analysis. Fivefold cross-validation demonstrated that the new predictive method outperforms the original one. The updated prediction tool is available as part of the PPARgene 2.0 platform. The database is openly accessible at https://www.ppargene.org.

bioinformatics↗

bioRxiv2025

Robust brain complexity measurement using T2* informed multivariate sample entropy from multi-echo BOLD fMRI

Sample entropy is a useful tool to analyze resting-state fMRI data and evaluate the complexity of neural activity. However, sample entropy computed from conventional BOLD signals is not highly resistant to artifacts. In contrast, T2* may be more sensitive to certain neural-related changes, and directly incorporating T2* information enables the detection of features that are often lost in BOLD-only sample entropy due to noise influences. To further improve the robustness and reliability of complexity estimation, we introduce a multivariate sample entropy approach that incorporates T2* signals derived from multi-echo BOLD signal. We applied this method to compare a cohort of 23 elder adults and 12 younger adults, and observed significant group differences in several brain regions. Notably, the proposed method yielded more pronounced group differences than conventional sample entropy computed without T2* information. Moreover, in regions with reduced neural activity due to ischemia, as well as in ventricular areas, our approach demonstrated superior performance. These results indicate that the proposed method enhances the ability to characterize the complexity of neural activity.

biophysics↗

bioRxiv2025

LorMe: a streamlined and interoperable R framework for end-to-end microbiome analysis

Microbiome research is rapidly evolving accompanied by increasingly diverse analytical strategies and visualization formats. However, existing R-based tools remain fragmented, differ in data structures, and often require extensive package-specific pre-processing. This increases command-line complexity and limits both interoperability and reproducibility across packages. Here, we present LorMe (Lightweight One-line Resolving Microbial Ecology), an R package that provides a unified, interoperable, and user-friendly framework for microbiome data analysis. LorMe incorporates a standardized S4 object system with fully bidirectional compatibility with phyloseq and microeco, which enables seamless data exchange across major microbial analysis packages. A global configuration system ensures consistent parameter control and visualization standards, and a modular architecture supports both complete end-to-end workflows and flexible execution of individual analytical components. Based on these, LorMe further offers an one-command pipeline that conducts the full spectrum of microbial community analyses, including alpha and beta diversity, differential abundance testing, co-occurrence network inference, and meta-network construction, while archiving all intermediate objects and source data to ensure complete reproducibility. Demonstrations using sample datasets show that LorMe performs comprehensive analyses within minute, produces publication-ready outputs, and maintains methodological transparency. Application to a real rhizosphere dataset revealed biologically coherent patterns across diversity metrics, differential taxa, and network modules, highlighting the capacity of LorMe to support robust ecological interpretation. Collectively, LorMe provides a lightweight, extensible, and flexible solution that reduces technical barriers and enhances reproducibility in microbiome research. LorMe package is freely accessible from both CRAN (https://cran.r-project.org/web/packages/LorMe) and github (https://github.com/wangnq111/LorMe)

bioinformatics↗

bioRxiv2025

High-resolution MRI Guided Whole Mouse Brain Cell Type Atlas using Deep Learning

Cell types represent groupings of cells defined by shared anatomical and functional properties. Traditional mouse brain cell atlases rely heavily on single-cell sequencing, which provides valuable molecular detail but lacks whole-brain, isotropic resolution. Diffusion MRI (dMRI) offers a complementary approach for probing cytoarchitecture and myeloarchitecture, with quantitative metrics increasingly used as biomarkers of brain development and neurodegenerative disorders. Although prior work has linked dMRI metrics with gene expression, the capacity of dMRI to directly predict cell types remains unclear. Here, we present a deep learning framework that integrates high-resolution dMRI with three-dimensional light-sheet microscopy (LSM) registered to the Allen Mouse Brain Common Coordinate Framework (CCFv3). We investigated correlations between dMRI and spatial transcriptomics-derived cell types and generated a whole-brain cell type atlas at 10 {micro}m isotropic resolution. Together, these results establish an efficient, high-resolution strategy for brain cell atlas generation and underscore the potential of advanced imaging to illuminate cellular mechanisms of the brain.

neuroscience↗

bioRxiv2025

Engineering the citrus phyllosphere microbiome for enhanced disease resistance to bacterial canker

Engineering phyllosphere microbiomes toward plant protection against pathogens on crops has shown promising effects. Here we tested the effect of application of beneficial bacteria individually and as a synthetic community (SynCom) on the phyllosphere microbiome and management of bacterial canker caused by Xanthomonas citri subsp. citri (Xcc). Foliar spray of a SynCom consisting of three citrus endophytic bacterial strains (Paenibacillus sp. ATY16, Bacillus megaterium PT6, and Bacillus subtilis PT26A, SynCom1) with mechanistically complemented plant-beneficial properties exerted a more profound effect on the leaf microbiota and suppression of citrus canker than single strains. Cultivation-dependent and bacterial 16S rRNA gene-sequence profiling analyses revealed that leaf endophytic Bradyrhizobium, Brevibacillus, Cellvibrio, Flavobacterium, and Pseudomonas were significantly enriched in the SynCom treated plants, in addition to Bacillus and Paenibacillus. The enriched Brevibacillus and Pseudomonas spp. were isolated, whole-genome sequenced, and confirmed to possess multiple beneficial traits including antimicrobial activity against Xcc, Moreover, the expression of citrus defense genes was induced by the SynCom1 inoculation. Foliar application of SynCom1 and a newly assembled SynCom (SynCom2; SynCom1 with representative Brevibacillus and Pseudomonas isolates) prior to Xcc inoculation significantly reduced the citrus canker disease severity in greenhouse assays, and SynCom2 had a better efficacy, comparable to a copper bactericide. Three-year field tests showed that both SynCom1 and SynCom2 effectively controlled citrus canker, with a 50% reduction of foliar and fruit canker incidence. Collectively, these findings provide novel insights and valuable clues for microbiome engineering to serve as a sustainable strategy for the control of phyllosphere pathogens on crops.

plant biology↗

bioRxiv2025

Spatial patterning of transcriptional and regulatory programs in the primate subcortex

Mammalian brain cell identity is shaped by intrinsic factors and external context. We present a spatially resolved transcriptomic and gene regulatory atlas of cell types across all subcortical regions in a primate, the common marmoset. Dense sampling and cross-species integration revealed spatially precise neuronal assemblies, including in complex midbrain and diencephalic structures. Chromatin accessibility and transcriptional identity are spatially tuned within and across subcortical structures; spatial gradients within hippocampal subfields are orchestrated by graded transcription factors acting through graded enhancers. The primate-expanded thalamic GABAergic population shares transcriptional and regulatory syntax with conserved midbrain populations, reflecting an evolutionary adaptation compared with rodents. Similar regional expression across cell types can arise by distinct regulatory architectures, as for telencephalic astrocytes and neurons. Conversely, distant cell types can share regulatory programs despite divergent identities: striatal GABAergic medium spiny neurons and telencephalic glutamatergic neurons share a postsynaptic regulatory program despite divergent lineage, region, and neurotransmitter identity.

neuroscience↗

bioRxiv2025

Targeting peptide-MHC complexes with designed T cell receptors and antibodies

Class I major histocompatibility complexes (MHCs), expressed on the surface of all nucleated cells, present peptides derived from intracellular proteins for surveillance by T cells. The precise recognition of foreign or mutated peptide-MHC (pMHC) complexes by T cell receptors (TCRs) is central to immune defense against pathogens and tumors. Although patient-derived TCRs specific for cancer-associated antigens have been used to engineer tumor-targeting therapies, their reactivity toward self- or near-self antigens may be constrained by negative selection in the thymus. Here, we introduce a structure-based deep learning framework, ADAPT (Antigen-receptor Design Against Peptide-MHC Targets), for the design of TCRs and antibodies that bind to pMHC targets of interest. We evaluate the ADAPT pipeline by designing and characterizing TCRs and antibodies against a diverse panel of pMHCs. Cryogenic electron microscopy structures of two designed antibodies bound to their respective pMHC targets demonstrate atomic-level accuracy at the recognition interface, supporting the robustness of our structure-based approach. Computationally designed TCRs and antibodies targeting pMHC complexes could enable a broad range of therapeutic applications, from cancer immunotherapy to autoimmune disease treatment, and insights gained from TCR-pMHC design should advance predictive understanding of TCR specificity with implications for basic immunology and clinical diagnostics.

immunology↗

bioRxiv2025

Fast Optimization of Robust Transcriptomics Embeddings using Probabilistic Inference Autoencoder Networks for multi-Omics

Advances in single-cell genomics technologies enable the routine acquisition of atlases with millions of cells. These datasets often include multiple covariates, such as donors, sequencing platforms, developmental timepoints, and species, which provide new opportunities for discovery and new challenges. To mitigate unwanted sources of variation, dataset integration is the starting point for most analyses. However, existing methods struggle with integrating large complex datasets. To address these limitations, we developed PIANO, a variational autoencoder framework that uses a negative binomial generalized linear model for stronger batch correction, and code compilation for ten times faster training than existing tools. We first demonstrate performant integration compared to commonly used methods on single-species datasets. We then show PIANO enables superior analyses of multiple atlases, solving challenging integration tasks across sequencing platforms, development, and species, while simultaneously preserving desired biological signals. Our contributions include a novel, high-performance integration method and recommendations for integration applications.

neuroscience↗

bioRxiv2025

Siglec-engaging immunosuppressive sialoglycans are upregulated in prostate cancer and are targetable to suppress bone metastasis

Prostate cancer is a leading cause of male cancer-related deaths over the age of 50. New treatment options for prostate cancer are urgently needed, especially for tumours that have spread to bone. Aberrant sialylation holds substantial potential for the discovery of new therapeutic targets but has remained relatively unexplored in the context of prostate cancer, primarily due to the lack of reliable reagents for detecting tumour sialoglycans in clinical tissue. Here, we address this knowledge gap using high-affinity Siglec-based sialoglycan-binding reagents (HYDRAs) to quantify tumour sialoglycans in tissues representing the full clinical heterogeneity of prostate tumours. Using HYDRA immunohistochemistry, we show that sialoglycans that can engage Siglec-3, -7, and -9 are upregulated in primary prostate cancer tissue and sialoglycan ligands for Siglec-7 correlate with prostate cancer bone metastasis and poorer patient prognosis. Analysis of prostate-derived tumours growing in bone reveals Siglec receptors are expressed by immune cells in the bone metastatic tumour microenvironment, suggesting that this axis may play a role in immune cell functions in bone metastatic prostate cancer. Indicating this is clinically actionable, an engineered bisialidase (E-612) can effectively strip Siglec ligands from prostate cancer cells and prolong survival times of mice with bone metastasis. Our findings identify a novel mechanism involving Siglec-engaging sialoglycans in driving the growth of prostate cancer bone metastasis and demonstrate how this axis can be targeted to impede lethal prostate cancer progression.

cancer biology↗

bioRxiv2025

Computational Discovery of Core Protein - Targeted Therapeutics Against Ginger Wilt Pathogen Ralstonia solanacearum

Bacterial wilt caused by Ralstonia solanacearum poses a severe threat to ginger production worldwide, as no consistently effective control measures exist. Here, we present a comprehensive strategy integrating pangenome analysis, functional enrichment, molecular docking, and state-of-the-art protein structure prediction to identify antibacterial agents targeting this phytopathogen. By prioritizing core, evolutionarily conserved proteins in R. solanacearum, we utilized AutoDock Vina and the recently developed AlphaFold3 to assess the binding affinity and validate protein-ligand interactions of key candidates from commercially available antibacterial pesticides. In vitro antibacterial assays confirmed strong inhibitory effects for several compounds, notably streptomycin sulfate, phellodendrine chloride, chlorogenic acid, and allicin. Our results highlight the power of combining large-scale computational screening with experimental validation for accelerating the discovery of novel antibacterial agents against highly divergent, recalcitrant plant pathogens. ImportanceThe indiscriminate use of broad-spectrum agrochemicals causes pollution and antimicrobial resistance. Our study proposes a targeted strategy against Ralstonia solanacearum by focusing on essential core proteins. This approach aims to develop specific agents that minimize harm to soil microbiota and reduce resistance selection. Our in silico framework aligns with green chemistry, offering a sustainable path for crop protection with a lower environmental footprint.

bioinformatics↗

bioRxiv2025

Optimizing the Characterization and Quantification of Retinal Ganglion Cell Somas in Healthy and Injured Retinas Using Cellpose

Quantification of retinal ganglion cell (RGC) soma number and characterization of somal features are commonly used output metrics for investigation of optic neuropathies. Many investigators still perform these quantifications by hand, which is time consuming and prone to bias. Cellpose is an open-source Python package that can perform cellular segmentation and holds promise for automating somal analyses. Here, we designed a custom script incorporating the Cellpose package and custom-trained Cellpose models that are capable of automatic characterization and quantification of RGC somas. Our script, using our models, is capable of automatically counting RGC somas, along with characterizing RGC somal size. Further, we show that Cellpose can quantify RGCs using multiple cell-type specific markers and has the potential to quantify RGCs across the entire retina. Together, our custom Cellpose models and script which generates and analyzes Cellpose outputs provides a powerful tool for all-in-one RGC somal analysis.

neuroscience↗

bioRxiv2025

In-Cell Synthesis of Nε-acetyl-L-lysine for Facile Protein Acetylation

N{varepsilon}-acetylation of proteins is a crucial post-translational modification (PTM) that occurs across all kingdoms of life and plays key roles in nearly every cellular process. Due to its broad significance, there is a strong demand for chemical biology tools that enable efficient, site-specific acetylation of target proteins in live cells. Genetic code expansion (GCE) has emerged as a powerful tool for introducing N{varepsilon}-acetylation at specific lysine residues in proteins. However, achieving adequate expression levels typically requires adding 2-10 mM of chemically synthesized N{varepsilon}-Acetyl-L-lysine (AcK) to the culture medium, which can be cumbersome and costly. To overcome this limitation, we present the first proof-of-concept for a one-pot acetylation platform that simplifies protein acetylation in both E. coli and mammalian cells. Our approach begins with the discovery of 10 novel lysine acetyltransferases (KATs) capable of biosynthesizing AcK from basic carbon sources. When these enzymes are co-expressed with the genetic incorporation machinery for AcK, they facilitate streamlined, site-specific acetylation of any target protein without compromising E. coli viability. This innovative platform not only broadens the range of unnatural amino acids (UAAs) that can be biosynthesized and incorporated but also provides a powerful tool for probing the histone and non-histone acetylation events in live cells. In addition, this technique offers an eco-friendly and scalable method to produce synthetic acetylated proteins, which will provide practical value in acetylation/deacetylation-related research such as chemical biology, biotechnology, and drug development.

synthetic biology↗

bioRxiv2025

HLA-DQ2/8 genotype imprints strain-level gut microbiome and constrains pantothenate availability in healthy adults

HLA-DQ2/8 haplotypes are established genetic risk factors for autoimmune diseases and are known to influence gut microbiota assembly in early life. However, their impact on the adult microbiome and functional consequences for host physiology remain unclear. Here, we performed a genotype-stratified multi-omics analysis of 60 healthy adults, including 28 HLA-DQ2/8 carriers and 32 non-carriers. We found that host HLA-DQ2/8 genotype was significantly associated with gut microbiome composition, with an effect size exceeding that of sex and BMI. HLA-DQ2/8 carriers exhibited higher gut microbial alpha-diversity, lower virulence factor abundance, and a distinct species profile enriched in butyrate-producing taxa. We identified pervasive intra-species phylogenetic and functional divergence linked to the DQ2/8 genotype. This diversification reflects predicted HLA-restricted microbial peptide-binding specificities, suggesting a possible role for antigen presentation-mediated immune selection, a mechanism further supported by AlphaFold3 structural modeling. We found an enrichment of microbial pathways for pantothenate and coenzyme A (CoA) biosynthesis in carriers, primarily driven by functionally divergent Blautia obeum strains. This functional shift paralleled lower levels of serum pantothenate and HDL-cholesterol in the host. Our findings suggest a potential genotype-microbiome-host axis where antigen presentation-mediated immune selection may modulate microbial adaptation, with possible implications for the host availability of essential cofactor precursors and lipid metabolism.

microbiology↗

bioRxiv2025

O-GlcNAc clusters attenuate BRD4 phase separation for transcription regulation

Post-translational modifications (PTMs) regulate the liquid-liquid phase separation (LLPS) that organizes biomolecular condensates essential for transcription, yet for the monosaccharide O-GlcNAc, the general rules and their links to protein function remain undefined, with evidence largely limited to isolated cases rather than systematic analyses. Here we combine proteome-scale analyses with experiments to show that O-GlcNAc is a widespread LLPS modulator: glycosites are enriched in intrinsically disordered regions and form dense O-GlcNAc clusters. Using BRD4 as a model, we identified twelve clustered sites in its C-terminal IDR and showed that O-GlcNAc decreases condensate size while increasing fluidity in vitro and in cells. Removing O-GlcNAc strengthened BRD4 binding at active enhancers and promoted LLPS-mediated co-recruitment of transcriptional factors, including YTHDC1, leading to altered expression programs linked to the cell cycle and DNA repair. Our findings define O-GlcNAc clusters as regulators of condensate material properties and transcriptional outcomes, supporting a general paradigm in which PTMs fine-tune the molecular grammar of biomolecular condensates.

biochemistry↗

bioRxiv2025

Targeted α-Synuclein mRNA Degradation by PMO-Based RNA-Degrading Chimeras

-Synucleinopathies are devastating neurodegenerative diseases characterized by pathological accumulation of a neuronal protein, -synuclein (Syn). Lowering soluble Syn levels is a promising therapeutic strategy to limit aggregation and neurotoxicity, but directly targeting this protein is hindered by its intrinsically disordered structure and other factors, such as its conformational heterogeneity and intracellular drug delivery barriers. Consequently, increasing attention has been directed toward targeting the SNCA transcript, which encodes Syn. Here, we developed phosphorodiamidate morpholino oligonucleotide (PMO)-based RNA-degrading chimeras (RDCs) that selectively bind the 5' untranslated region of SNCA mRNA and recruit RNase L for targeted RNA degradation. Through the systematic evaluation of 10 RDCs, we identified and optimized 4-D1, which effectively reduced SNCA mRNA and Syn protein expression in HEK293T cells in an RNase L-dependent manner. 4-D1 lowered SNCA transcript and Syn protein levels in both primary cortical neurons from humanized SNCA mice and in human induced pluripotent stem cell-derived cortical neurons. This reduction prevented prion-like seeding induced by patient-derived Syn fibrils and protected neurons from fibril-induced cytotoxicity. Finally, in vivo studies confirmed the efficacy of 4-D1 in reducing Syn mRNA expression in humanized SNCA mice. These findings indicate that PMO-based RDCs may represent a promising therapeutic modality for -synucleinopathies. Significance StatementAbnormal aggregation of the neuronal protein -synuclein is central to Parkinsons disease and related disorders, yet therapeutic candidates that directly target this protein have yet to demonstrate efficacy. in clinical trials. We developed a new strategy that lowers -synuclein production at the RNA level using phosphorodiamidate morpholino oligonucleotide (PMO)-based RNA-degrading chimeras (RDCs). These molecules recruit a natural RNA-degrading enzyme to selectively destroy the RNA transcript encoding -synuclein. Our lead RDC reduced -synuclein levels in cultured cells, humanized mouse and human neurons, blocked -synuclein pathological aggregation, and protected neurons from toxicity. This study establishes RDCs as a promising therapeutic platform for Parkinsons disease and other neurodegenerative diseases driven by -synuclein.

biochemistry↗

bioRxiv2025

Optic nerve regeneration requires the intracellular domain of LIFRa/CD118

Identifying cell-autonomous and non-autonomous factors that govern retinal ganglion cells (RGCs) ability to extend axons is an important step in developing therapies to achieve recovery after optic nerve injury. Here we report that the intracellular domain of the leukemia inhibitory factor receptor (LIFR/CD118) is essential for mature RGCs ability to regenerate injured axons independent of the cognate ligand (LIF) and other therapies. Overexpression of LIFR in adult RGCs induces neurite outgrowth in cultured RGCs and axon regeneration in vivo while strongly amplifying RGCs response to LIF itself and to unrelated growth factors. Conversely, in loss-of-function studies, down-regulation of LIFR eliminates the pro-regenerative effects of Pten deletion and other potent stimuli. LIFR bidirectionally regulates the constitutive activity of the MAP kinase pathway, in contrast to LIF itself, which primarily activates pSTAT3. Expression of a truncated LIFR lacking the extracellular domain is sufficient to promote axon regeneration and selectively increases the survival of particular RGC subtypes, placing the LIFR intracellular domain as an essential, cell-autonomous regulator of optic nerve regeneration.

neuroscience↗

bioRxiv2025

A secreted citrus protease cleaves an outer membrane protein of the Huanglongbing pathogen

Plants secrete a variety of proteases as a defense response during infection by microbial pathogens. However, the relationship between their catalytic activities and antimicrobial functions remains largely unknown. Particularly, few biologically relevant substrates of these proteases have been identified. Huanglongbing (HLB) has been a major threat to the citrus industry worldwide. The HLB-associated bacterium, Candidatus Liberibacter asiaticus (Las), was previously shown to deploy an inhibitor of papain-like cysteine proteases (PLCPs) to promote disease in citrus. In this study, we identified an outer membrane protein (OMP) of Las, LasOMP1, as a substrate of the citrus PLCP CsRD21a. LasOMP1 is one of the most highly expressed genes in Las. CsRD21a cleaves LasOMP1 and produces cleaved peptide products, which could be detected in vitro and in HLB-diseased citrus plants. We found that CsRD21a targets the N-terminal portion of LasOMP1, potentially at an extracellular loop region. Importantly, transgenic sweet orange overexpressing CsRD21a showed reduced Las titers and delayed disease symptoms, highlighting that engineering this protease is a promising strategy to enhance HLB resistance in citrus. Together, our work reveals a pathogen-derived substrate of plant PLCPs and highlights bacterial OMPs as direct targets of plant defense. Significance StatementHuanglongbing (HLB) is the most devastating disease of citrus with no resistance having been identified in commercial cultivars. Previous work implicated papain-like cysteine proteases (PLCPs) as an important hub of defense in citrus; however, their precise role in HLB tolerance remained unclear. Here, we identify and characterize an outer membrane protein (OMP) from the HLB-associated bacterium as a substrate of the citrus PLCP RD21a. We demonstrate a specific cleavage of the bacterial OMP by citrus RD21a, which may impair pathogen growth and/or activate plant immunity. Importantly, overexpression of RD21a enhances HLB tolerance in sweet oranges. This work identifies OMPs as substrates of plant PLCPs and provides insights into protease defense functions.

plant biology↗