Biology subjects

Su, J.

Publications and source records attributed to Su, J..

At least 91 records · Page 5Linked to original sources

bioRxiv2024

Recombinant manufacturing of multispecies biolubricants

Lubricin, a lubricating glycoprotein abundant in synovial fluid, forms a low-friction brush polymer interface in tissues exposed to sliding motion including joints, tendon sheaths, and the surface of the eye. Despite its therapeutic potential in diseases such as osteoarthritis and dry eye disease, there are few sources available. Through rational design, we developed a series of recombinant lubricin analogs that utilize the species-specific tissue-binding domains at the N- and C-termini to increase biocompatibility while replacing the central mucin domain with an engineered variant that retains the lubricating properties of native lubricin. In this study, we demonstrate the tissue binding capacity of our engineered lubricin product and its retention in the joint space of rats. Next, we present a new bioprocess chain that utilizes a human-derived cell line to produce O-glycosylation consistent with that of native lubricin and a purification strategy that capitalizes on the positively charged, hydrophobic N- and C-terminal domains. The bioprocess chain is demonstrated at 10 L scale in industry-standard equipment utilizing commonly available ion exchange, hydrophobic interaction and size exclusion chromatography resins. Finally, we confirmed the purity and lubricating properties of the recombinant biolubricant. The biomolecular engineering and bioprocessing strategies presented here are an effective means of lubricin production and could have broad applications to the study of mucins in general.

bioengineering↗

bioRxiv2024

xSiGra: Explainable model for single-cell spatial data elucidation

Recent advancements in spatial imaging technologies have revolutionized the acquisition of high-resolution multi-channel images, gene expressions, and spatial locations at the single-cell level. Our study introduces xSiGra, an interpretable graph-based AI model, designed to elucidate interpretable features of identified spatial cell types, by harnessing multi-modal features from spatial imaging technologies. By constructing a spatial cellular graph with immunohistology images and gene expression as node attributes, xSiGra employs hybrid graph transformer models to delineate spatial cell types. Additionally, xSiGra integrates a novel variant of Grad-CAM component to uncover interpretable features, including pivotal genes and cells for various cell types, thereby facilitating deeper biological insights from spatial data. Through rigorous benchmarking against existing methods, xSiGra demonstrates superior performance across diverse spatial imaging datasets. Application of xSiGra on a lung tumor slice unveils the importance score of cells, illustrating that cellular activity is not solely determined by itself but also impacted by neighboring cells. Moreover, leveraging the identified interpretable genes, xSiGra reveals endothelial cell subset interacting with tumor cells, indicating its heterogeneous underlying mechanisms within the complex cellular communications.

bioinformatics↗

bioRxiv2024

Cell-specific polymerization-driven biomolecular condensate formation fine-tunes root tissue morphogenesis

Formation of biomolecular condensates can be driven by weak multivalent interactions and emergent polymerization. However, the mechanism of polymerization-mediated condensate formation is less studied. We found lateral root cap cell (LRC)-specific SUPPRESSOR OF RPS4-RLD1 (SRFR1) condensates fine-tune primary root development. Polymerization of the SRFR1 N-terminal domain is required for both LRC condensate formation and optimal root growth. Surprisingly, the first intrinsically disordered region (IDR1) of SRFR1 can be functionally substituted by a specific group of intrinsically disordered proteins known as dehydrins. This finding facilitated the identification of functional segments in the IDR1 of SRFR1, a generalizable strategy to decode unknown IDRs. With this functional information we further improved root growth by modifying the SRFR1 condensation module, providing a strategy to improve plant growth and resilience.

molecular biology↗

bioRxiv2024

DeepSS2GO: protein function prediction from secondary structure

Predicting protein function is crucial for understanding biological life processes, preventing diseases, and developing new drug targets. In recent years, methods based on sequence, structure, and biological networks for protein function annotation have been extensively researched. Although obtaining a protein in three-dimensional structure through experimental or computational methods enhances the accuracy of function prediction, the sheer volume of proteins sequenced by high-throughput technologies presents a significant challenge. To address this issue, we introduce a deep neural network model DeepSS2GO (Secondary Structure to Gene Ontology). It is a predictor incorporating secondary structure features along with primary sequence and homology information. The algorithm expertly combines the speed of sequence-based information with the accuracy of structure-based features, while streamlining the redundant data in primary sequences and bypassing the timeconsuming challenges of tertiary structure analysis. The results show that the prediction performance surpasses state-ofthe-art algorithms. It has the ability to predict key functions by effectively utilizing secondary structure information, rather than broadly predicting general Gene Ontology terms. Additionally, DeepSS2GO predicts five times faster than advanced algorithms, making it highly applicable to massive sequencing data. The source code and trained models are available at https://github.com/orca233/DeepSS2GO.

bioinformatics↗

bioRxiv2024

Unraveling the Role of MIXL1 Activation in Endoderm Differentiation of Isogenic Human Induced Pluripotent Stem Cells

Human induced pluripotent stem cells (hiPSC) possess the ability to differentiate into a multitude of cell and tissue types but display heterogeneous propensity of differentiation into specific lineage. Characterization of the transcriptome of eleven hiPSC lines showed that activation of MIXL1 at the early stage of stem cell differentiation correlated with higher efficacy in generating definitive endoderm and advancing differentiation and maturation of endoderm derivatives. Enforced expression of MIXL1 in the endoderm-inefficient hiPSCs enhanced the propensity of endoderm differentiation, suggesting that modulation of key drivers of lineage differentiation can re-wire hiPSC to the desired lineage propensity to generate the requisite stem cell products.

developmental biology↗

bioRxiv2024

Novel transcriptional activator TAC3 regulates age-dependent floral transition in Chinese fir

Plant undergo juvenile-to-adult transition to become competent for age-dependent floral induction and reproductive transition, which is of great significance for improving the seed quality and maintaining desirable genetic traits of Chinese fir, but the underlying molecular mechanize still remains unknown. Here, we investigated the function of our newly identified spermatophyte specific transcriptional co-activator TAC3 (Transcriptional Activator in Chinese fir 3) and its homologues (TAL1) in the model plant Arabidopsis. Both TAC3 and TAL1 can negatively regulate flowering, and activate miR156 expression to delay the phase transition. Moreover, we found that HDA9 and its its homologues in Chinese fir, ClHDA9, can directly binding to the promoter region of MIR156A and ClMIR156A, respectively. Directly interaction with ClHDA9 and HDA9 are necessary for the transcriptional activation of TAC3 and TAL1 on miR156, respectively. TAC3 and TAL1 also involve in the chromatin remodeling, shown as up-regulated H3K27ac level within the promoter region of ClMIR156A and MIR156A. Together, this work shows that TAC3 and its homologues are a new group of transcriptional co-activator that involving in aging-dependent flowering signal pathway of both angiosperms and gymnosperms.

plant biology↗

bioRxiv2024

Exposure assessment suggests some cytotoxic Bacillus cereus group genotypes can grow over 3 logs in HTST milk throughout the shelf life at temperature abuse conditions

Cytotoxic Bacillus cereus group strains are common causes of foodborne illness including diarrhea. However, our ability to assess food safety risks associated with the exposure to cytotoxic B. cereus group strains via contaminated food is limited due to the lack of predictive tools. In this study, we experimentally quantified the growth of 17 cytotoxic B. cereus group strains, representing six phylogenetic groups, in skim milk broth and used the growth data to develop an exposure assessment model. While none of the tested strains showed detectable growth in HTST milk at 4 or 6{degrees}C, 15 of the 17 strains showed growth at 10{degrees}C, 1 of the 17 strains showed growth at 8{degrees}C, and all strains grew at [≥]14{degrees}C. Growth data for 16 strains allowed us to generate linear secondary growth models, which were then used to develop the exposure assessment model. We simulated a five-stage supply chain with up to 35 consumer storage days, as that was the timing when distinguishable variations in percent milk containers over 105 CFU/m with different B. cereus genotypes were observed. When the initial contamination level of the HTST milk is set at an average of 100 CFU/mL, the model predicts that, on consumer home storage day 21 and 35, 2.81{+/-}0.66 and 4.13{+/-}2.53 % (mean {+/-} standard deviation) of the milk containers would exceed B. cereus group concentrations of 105 CFU/mL; these data represent the average across all strains. Sensitivity analysis showed that variation in the input parameter Q0, the initial physiological state of cells, has the largest effect on models prediction for 1 of 4 group II isolates, 1 of 6 group IV isolates and both group V isolates, suggesting the need to better characterize the growth parameters of these isolates. What-if scenario analysis showed that increased mean and variability in storage temperature at the consumers home both have substantial influence on final predicted B. cereus group concentration in milk containers. This model introduces an initial tool designed to facilitate risk-based food safety decision making for products that are contaminated with low B. cereus group levels.

microbiology↗

bioRxiv2024

Neural ensembles that encode affective mechanical and heat pain in mouse spinal cord

Acute pain is an unpleasant experience caused by noxious stimuli. How the spinal neural circuits attribute differences in quality of noxious information remains unknown. By means of genetic capturing, activity manipulation and single cell RNA sequencing, we identified distinct neural ensembles in mouse spinal cord encoding mechanical and heat pain. Re-activation or silencing of these ensembles potentiated or stopped, respectively, affective but not reflex behaviour without altering pain behaviour to cross stimuli modality. Within ensembles, polymodal Gal+ inhibitory neurons with monosynaptic contacts to A-fiber sensory neurons gated affective pain independent of modality. Peripheral nerve injury led to microglia driven inflammation and an ensemble transition with decreased recruitment of Gal+ inhibitory neurons and increased excitatory drive. However, activating Gal+ neurons reversed hypersensitivity associated with neuropathy. Our results reveal the existence of a spinal representation which forms the neural basis of the discriminative and affective qualities of acute pain and that these neurons are under the control of a shared feed-forward inhibition.

neuroscience↗

bioRxiv2024

Retinal input is required for the maintenance of neuronal laminae in the ventral lateral geniculate nucleus

Retinal ganglion cell (RGC) axons provide direct input into several nuclei of the mouse visual thalamus, including the dorsal lateral geniculate nucleus (dLGN), which is important for classical image-forming vision, and the ventral lateral geniculate nucleus (vLGN), which is associated with non-image-forming vision. Through both activity- and morphogen-dependent mechanisms, retinal inputs play important roles in the development of dLGN, including the refinement of retinal projections, morphological development of thalamocortical relay cells (TRCs), the timing of corticogeniculate innervation, and the recruitment of inhibitory interneurons from progenitor zones. In contrast, little is known about the role of retinal inputs in the development of vLGN. Grossly, vLGN is divided into two domains, the retinorecipient external vLGN (vLGNe) and the non-retinorecipient internal vLGN (vLGNi). We previously found that vLGNe consists of transcriptionally distinct GABAergic subtypes that are distributed into at least four adjacent laminae. At present, it remains unclear whether retinal inputs influence the development of these cell-specific neuronal laminae in vLGNe. Here, we elucidated the developmental timeline for the formation and maintenance of these laminae in the mouse vLGNe and results indicate that these laminae are specified at or before birth, well before eye-opening and the emergence of experience-dependent visual activity. We observed that mutant mice without retinal inputs have a normal laminar distribution of GABAergic cells at birth; however, after the first week of postnatal development, these mutants exhibited a dramatic disruption in the laminar organization of inhibitory neurons and clear boundaries between vLGNe and vLGNi. Overall, our results show that while the formation of cell type-specific layers in vLGNe does not depend on RGC inputs, retinal signals are critical for their maintenance.

neuroscience↗

bioRxiv2023

Phosphorylated transcription factor PuHB40 is involved in ROS-dependent anthocyanin biosynthesis in pear exposed to high-light stress

As sessile organisms, plants are increasingly vulnerable to environmental stresses because of global warming and climate change. Stress-induced reactive oxygen species (ROS) accumulation results in plant cell damages and even cell death. Anthocyanins are important antioxidants that scavenge ROS to maintain ROS homeostasis. However, the mechanism underlying ROS-induced anthocyanin accumulation is unclear. In this study, we determined that in pear the HD-Zip I family member PuHB40 mediates ROS-dependent anthocyanin biosynthesis under high-light stress. Specifically, PuHB40 is a transcription factor that induces PuMYB123-like/PubHLH3 complex for anthocyanin biosynthesis. The transcriptional activation by PuHB40 depends on its phosphorylation level, which is regulated by protein phosphatase 2A (PP2A). High ROS contents maintain the phosphorylation of PuHB40 at a high level, while also enhancing PuHB40-induced PuMYB123-like transcription by decreasing the transcription of PuPP2AA2, ultimately leading to increased anthocyanin biosynthesis. Our study revealed the pathway regulating ROS-induced anthocyanin biosynthesis in pear, further clarifying the mechanism underlying abiotic stress-induced anthocyanin biosynthesis, which may have implications for improving plant stress tolerance. IN A NUTSHELLO_ST_ABSBackgroundC_ST_ABSVarious abiotic stresses, including high-light intensity, promote the accumulation of anthocyanins in plants, while also activating the production of reactive oxygen species (ROS). Anthocyanins can attenuate the negative effects of high-light stress by acting as antioxidants that restrict ROS accumulation. Several reports have shown that ROS can stimulate anthocyanin accumulation, but whether high-light stress-induced anthocyanin accumulation depends on ROS is undetermined. Additionally, the mechanism underlying ROS-dependent anthocyanin biosynthesis remains unclear. QuestionDoes high-light stress-induced anthocyanin biosynthesis depend on ROS? What is the molecular basis of high-light stress-induced anthocyanin biosynthesis? FindingsHigh-light stress-induced anthocyanin biosynthesis in pear seedlings is dependent on ROS accumulation. PuMYB123-like is the key MYB transcription factor for anthocyanin biosynthesis, while PuHB40 activates anthocyanin biosynthesis in response to ROS under high-light stress. Specifically, PuHB40 activates the transcription of PuMYB123-like, with the encoded protein combining with PubHLH3 to form an MBW complex that promotes anthocyanin biosynthesis. The transcriptional activation by PuHB40 depends on its phosphorylation status, which is regulated by protein phosphatase 2A (PP2A). High ROS levels inhibit the transcription of PuPP2AA2, thereby maintaining the phosphorylation of PuHB40, enhancing the transcriptional activation by PuHB40, inducing PuMYB123-like transcription, and ultimately leading to increased anthocyanin biosynthesis. Next stepsOur future research will focus on whether ROS and the MYB123-like- PuHB40-PP2A regulatory module are also involved in the anthocyanin biosynthesis induced by other abiotic and biotic stresses, which will provide insights into biotic stress-induced anthocyanin biosynthesis and form the theoretical basis for improving fruit coloration.

plant biology↗

bioRxiv2023

Scm6A: A fast and low-cost method for quantifying m6A modifications at the single-cell level

It is widely accepted that m6A exhibits significant intercellular specificity, which poses challenges for its detection using existing m6A quantitative methods. In this study, we introduce Scm6A, a machine learning-based approach for single-cell m6A quantification. Scm6A leverages input features derived from the expression levels of m6A trans regulators and cis sequence features, and found that Scm6A offers remarkable prediction efficiency and reliability. To further validate the robustness and precision of Scm6A, we applied a winscore-based m6A calculation method to conduct m6A-seq analysis on CD4+ and CD8+ T-cells isolated through magnetic-activated cell sorting (MACS). Subsequently, we employed Scm6A for analysis on the same samples. Notably, the m6A levels calculated by Scm6A exhibited a significant positive correlation with m6A quantified through m6A-seq in different cells isolated by MACS, providing compelling evidence for Scm6As reliability. We also used the scm6A-seq method to validate the reliability of our approach. Additionally, we performed single-cell level m6A analysis on lung cancer tissues as well as blood samples from COVID-19 patients, and demonstrated the landscape and regulatory mechanisms of m6A in different T-cell subtypes from these diseases. In summary, our work has yielded a novel, dependable, and accurate method for single-cell m6A detection. We are confident that Scm6A will have broad applications in the realm of m6A-related research.

bioinformatics↗

bioRxiv2023

ClusterV-Web: A User-Friendly Tool for Profiling HIV Quasispecies and Generating Drug Resistance Reports from Nanopore Long-Read Data

SummaryThird-generation long-read sequencing is an increasingly utilized technique for profiling HIV quasispecies and detecting drug resistance mutations due to its ability to cover the entire viral genome in individual reads. Recently, the ClusterV tool has demonstrated accurate detection of HIV quasispecies from Nanopore long-read sequencing data. However, the need for scripting skills and a computational environment may act as a barrier for many potential users. To address this issue, we have introduced ClusterV-Web, a user-friendly web-based application that enables easy configuration and execution of ClusterV, both remotely and locally. Our tool provides interactive tables and data visualizations to aid in the interpretation of results. This development is expected to democratize access to long-read sequencing data analysis, enabling a wider range of researchers and clinicians to efficiently profile HIV quasispecies and detect drug resistance mutations. Availability and implementationClusterV-Web is freely available and open source, with detailed documentation accessible at http://www.bio8.cs.hku.hk/ClusterVW/. The standalone Docker image and source code are also available at https://github.com/HKU-BAL/ClusterV-Web. Contactrbluo@cs.hku.hk, Department of Computer Science, The University of Hong Kong, Hong Kong, China; jhsu@cs.hku.hk, Department of Computer Science, The University of Hong Kong, Hong Kong, China Supplementary informationNone

bioinformatics↗

bioRxiv2023

SaProt: Protein Language Modeling with Structure-aware Vocabulary

AO_SCPLOWBSTRACTC_SCPLOWLarge-scale protein language models (PLMs), such as the ESM family, have achieved remarkable performance in various downstream tasks related to protein structure and function by undergoing unsupervised training on residue sequences. They have become essential tools for researchers and practitioners in biology. However, a limitation of vanilla PLMs is their lack of explicit consideration for protein structure information, which suggests the potential for further improvement. Motivated by this, we introduce the concept of a "structure-aware vocabulary" that integrates residue tokens with structure tokens. The structure tokens are derived by encoding the 3D structure of proteins using Foldseek. We then propose SaProt, a large-scale general-purpose PLM trained on an extensive dataset comprising approximately 40 million protein sequences and structures. Through extensive evaluation, our SaProt model surpasses well-established and renowned baselines across 10 significant downstream tasks, demonstrating its exceptional capacity and broad applicability. We have made the code1, pre-trained model, and all relevant materials available at https://github.com/westlake-repl/SaProt.

bioinformatics↗

bioRxiv2023

Label-free, real-time monitoring of membrane binding events at zeptomolar concentrations using frequency-locked optical microresonators

Binding events to elements of the cell membrane act as receptors which regulate cellular function and communication and are the targets of many small molecule drug discovery efforts for agonists and antagonists. Conventional techniques to probe these interactions generally require labels and large amounts of receptor to achieve satisfactory sensitivity. Whispering gallery mode microtoroid optical resonators have demonstrated sensitivity to detect single-molecule binding events. Here, we demonstrate the use of frequency-locked optical microtoroids for characterization of membrane interactions in vitro at zeptomolar concentrations using a supported biomimetic membrane. Arrays of microtoroids were produced using photolithography and subsequently modified with a biomimetic membrane, providing high quality (Q) factors (>106) in aqueous environments. Fluorescent recovery after photobleaching (FRAP) experiments confirmed the retained fluidity of the microtoroid supported-lipid membrane with a diffusion coefficient of 3.38 {+/-} 0.26 m2 {middle dot}s-1. Utilizing this frequency-locked membrane-on-a-chip model combined with auto-balanced detection and non-linear post-processing techniques, we demonstrate zeptomolar detection levels The binding of Cholera Toxin B-monosialotetrahexosyl ganglioside (GM1) was monitored in real-time, with an apparent equilibrium dissociation constant (kd) = 1.53 nM. The measured affiny of the agonist dynorphin A 1-13 to the {kappa}-opioid receptor revealed a kd = 3.1 nM using the same approach. Radioligand binding competition with dynorphin A 1-13 revealed a Kd in agreement (1.1 nM) with the unlabeled method. The biosensing platform reported herein provides a highly sensitive real-time characterization of membrane embedded protein binding kinetics, that is rapid and label-free, for toxin screening and drug discovery, among other applications.

biophysics↗

bioRxiv2023

ClairS: a deep-learning method for long-read somatic small variant calling

Identifying somatic variants in tumor samples is a crucial task, which is often performed using statistical methods and heuristic filters applied to short-read data. However, with the increasing demand for long-read somatic variant calling, existing methods have fallen short. To address this gap, we present ClairS, the first deep-learning-based, long-read somatic small variant caller. ClairS was trained on massive synthetic somatic variants with diverse coverages and variant allele frequencies (VAF), enabling it to accurately detect a wide range of somatic variants from paired tumor and normal samples. We evaluated ClairS using the latest Nanopore Q20+ HCC1395-HCC1395BL dataset. With 50-fold/25-fold tumor/normal, ClairS achieved a 93.01%/86.86% precision/recall rate for Single Nucleotide Variation (SNVs), and 66.54%/66.89% for somatic insertions and deletions (Indels). Applying ClairS to short-read datasets from multiple sources showed comparable or better performance than Strelka2 and Mutect2. Our findings suggest that improved read phasing enabled by long-read sequencing is key to accurate long-read SNV calling, especially for variants with low VAF. Through experiments across various coverage, purity, and contamination settings, we demonstrated that ClairS is a reliable somatic variant caller. ClairS is open-source at https://github.com/HKU-BAL/ClairS.

bioinformatics↗

bioRxiv2023

SpaRx: Elucidate single-cell spatial heterogeneity of drug responses for personalized treatment

Spatial cellular heterogeneity contributes to differential drug responses in a tumor lesion and potential therapeutic resistance. Recent emerging spatial technologies such as CosMx SMI, MERSCOPE, and Xenium delineate the spatial gene expression patterns at the single cell resolution. This provides unprecedented opportunities to identify spatially localized cellular resistance and to optimize the treatment for individual patients. In this work, we present a graph-based domain adaptation model, SpaRx, to reveal the heterogeneity of spatial cellular response to drugs. SpaRx transfers the knowledge from pharmacogenomics profiles to single-cell spatial transcriptomics data, through hybrid learning with dynamic adversarial adaption. Comprehensive benchmarking demonstrates the superior and robust performance of SpaRx at different dropout rates, noise levels, and transcriptomics coverage. Further application of SpaRx to the state-of-art single-cell spatial transcriptomics data reveals that tumor cells in different locations of a tumor lesion present heterogenous sensitivity or resistance to drugs. Moreover, resistant tumor cells interact with themselves or the surrounding constituents to form an ecosystem for drug resistance. Collectively, SpaRx characterizes the spatial therapeutic variability, unveils the molecular mechanisms underpinning drug resistance, and identifies personalized drug targets and effective drug combinations. Key PointsO_LIWe have developed a novel graph-based domain adaption model named SpaRx, to reveal the heterogeneity of spatial cellular response to different types of drugs, which bridges the gap between pharmacogenomics knowledgebase and single-cell spatial transcriptomics data. C_LIO_LISpaRx is developed tailored for single-cell spatial transcriptomics data and is provided available as a ready-to-use open-source software, which demonstrates high accuracy and robust performance. C_LIO_LISpaRx uncovers that tumor cells located in different areas within tumor lesion exhibit varying levels of sensitivity or resistance to drugs. Moreover, SpaRx reveals that tumor cells interact with themselves and the surrounding microenvironment to form an ecosystem capable of drug resistance. C_LI

bioinformatics↗

bioRxiv2023

Therapy-associated remodeling of pancreatic cancer revealed by single-cell spatial transcriptomics and optimal transport analysis

In combination with cell intrinsic properties, interactions in the tumor microenvironment modulate therapeutic response. We leveraged high-plex single-cell spatial transcriptomics to dissect the remodeling of multicellular neighborhoods and cell-cell interactions in human pancreatic cancer associated with specific malignant subtypes and neoadjuvant chemotherapy/radiotherapy. We developed Spatially Constrained Optimal Transport Interaction Analysis (SCOTIA), an optimal transport model with a cost function that includes both spatial distance and ligand-receptor gene expression. Our results uncovered a marked change in ligand-receptor interactions between cancer-associated fibroblasts and malignant cells in response to treatment, which was supported by orthogonal datasets, including an ex vivo tumoroid co-culture system. Overall, this study demonstrates that characterization of the tumor microenvironment using high-plex single-cell spatial transcriptomics allows for identification of molecular interactions that may play a role in the emergence of chemoresistance and establishes a translational spatial biology paradigm that can be broadly applied to other malignancies, diseases, and treatments.

cancer biology↗

bioRxiv2023

The novel nematicide chiricanine A suppresses Bursaphelenchus xylophilus pathogenicity in Pinus massoniana by inhibiting Aspergillus and its secondary metabolite, sterigmatocystin

BACKGROUNDPine wilt disease (PWD) is responsible for extensive economic and ecological damage to Pinus spp. forests and plantations worldwide. PWD is caused by the pine wood nematode (PWN, Bursaphelenchus xylophilus) and transmitted into pine trees by a vector insect, the Japanese pine sawyer (JPS, Monochamus alternatus). Host infection by PWN will attract JPS to spawn, which leads to the co-existence of PWN and JPS within the host tree, an essential precondition for PWD outbreaks. Through the action of their metabolites, microbes can manipulate the co-existence of PWN and JPS, but our understanding on how key microorganisms engage in this process remains limited, which severely hinders the exploration and utilization of promising microbial resources in the prevention and control of PWD. RESULTSIn this study we investigated how the PWN-associated fungus Aspergillus promotes the co-existence of PWN and JPS in the host trees (Pinus massoniana) via its secondary metabolite, sterigmatocystin (ST), by taking a multi-omics approach (phenomics, transcriptomics, microbiome, and metabolomics). We found that Aspergillus was able to promote PWN invasion and pathogenicity by increasing ST biosynthesis in the host plant, mainly by suppressing the accumulation of ROS (reactive oxygen species) in plant tissues that could counter PWN. Further, ST accumulation triggered the biosynthesis of VOC (volatile organic compounds) that attracts JPS and drives the coexistence of PWN and JPS in the host plant, thereby encouraging the local transmission of PWD. Meanwhile, we show that application of an Aspergillus inhibitor (chiricanine A treatment) results in the absence of Aspergillus and decreases the in vivo ST amount, thereby sharply restricting the PWN development in host. This further proved that Aspergillus is vital and sufficient for promoting PWD transmission. CONCLUSIONSAltogether, these results document, for the first time, how the function of Aspergillus and its metabolite ST is involved in the entire PWD transmission chain, in addition to providing a novel and long-term effective nematicide for better PWD control in the field.

plant biology↗