Unsupervised Bayesian Generation of Synthetic CT from CBCT Using Patient-Specific Score-Based Prior.

BACKGROUND: Cone-beam computed tomography (CBCT) scans, performed fractionally (e.g., daily or weekly), are widely utilized for patient alignment in the image-guided radiotherapy (IGRT) process, thereby making it a potential imaging modality for the implementation of adaptive radiotherapy (ART) protocols. Nonetheless, significant artifacts and incorrect Hounsfield unit (HU) values hinder their application in quantitative tasks such as target and organ segmentations and dose calculation. Therefore, acquiring CT-quality images from the CBCT scans is essential to implement online ART in clinical settings. PURPOSE: This work aims to develop an unsupervised learning method using the patient-specific diffusion model for CBCT-based synthetic CT (sCT) generation to improve the image quality of CBCT. METHODS: The proposed method is in an unsupervised framework that utilizes a patient-specific score-based model as the image prior alongside a customized total variation (TV) regularization to enforce coherence across different transverse slices. The score-based model is unconditionally trained using the same patient's planning CT (pCT) images to characterize the manifold of CT-quality images and capture the unique anatomical information of the specific patient. The efficacy of the proposed method was assessed on images from anatomical sites including head and neck (H&N) cancer, pancreatic cancer, and lung cancer. The performance of the proposed CBCT correction method was evaluated using quantitative metrics including mean absolute error (MAE), peak signal-to-noise ratio (PSNR), and normalized cross-correlation (NCC). Additionally, the proposed algorithm was benchmarked against two other unsupervised diffusion model-based CBCT correction algorithms.

Image-domain material decomposition for dual-energy CT using unsupervised learning with data-fidelity loss.

BACKGROUND: Dual-energy computed tomography (DECT) and material decomposition play vital roles in quantitative medical imaging. However, the decomposition process may suffer from significant noise amplification, leading to severely degraded image signal-to-noise ratios (SNRs). While existing iterative algorithms perform noise suppression using different image priors, these heuristic image priors cannot accurately represent the features of the target image manifold. Although deep learning-based decomposition methods have been reported, these methods are in the supervised-learning framework requiring paired data for training, which is not readily available in clinical settings. PURPOSE: This work aims to develop an unsupervised-learning framework with data-measurement consistency for image-domain material decomposition in DECT. METHODS: The proposed framework combines iterative decomposition and deep learning-based image prior in a generative adversarial network (GAN) architecture. In the generator module, a data-fidelity loss is introduced to enforce the measurement consistency in material decomposition. In the discriminator module, the discriminator is trained to differentiate the low-noise material-specific images from the high-noise images. In this scheme, paired images of DECT and ground-truth material-specific images are not required for the model training. Once trained, the generator can perform image-domain material decomposition with noise suppression in a single step. RESULTS: In the simulation studies of head and lung digital phantoms, the proposed method reduced the standard deviation (SD) in decomposed images by 97% and 91% from the values in direct inversion results. It also generated decomposed images with structural similarity index measures (SSIMs) greater than 0.95 against the ground truth. In the clinical head and lung patient studies, the proposed method suppressed the SD by 95% and 93% compared to the decomposed images of matrix inversion. CONCLUSIONS: Since the invention of DECT, noise amplification during material decomposition has been one of the biggest challenges, impeding its quantitative use in clinical practice. The proposed method performs accurate material decomposition with efficient noise suppression. Furthermore, the proposed method is within an unsupervised-learning framework, which does not require paired data for model training and resolves the issue of lack of ground-truth data in clinical scenarios.

CT-based synthetic iodine map generation using conditional denoising diffusion probabilistic model.

BACKGROUND: Iodine maps, derived from image-processing of contrast-enhanced dual-energy computed tomography (DECT) scans, highlight the differences in tissue iodine intake. It finds multiple applications in radiology, including vascular imaging, pulmonary evaluation, kidney assessment, and cancer diagnosis. In radiation oncology, it can contribute to designing more accurate and personalized treatment plans. However, DECT scanners are not commonly available in radiation therapy centers. Additionally, the use of iodine contrast agents is not suitable for all patients, especially those allergic to iodine agents, posing further limitations to the accessibility of this technology. PURPOSE: The purpose of this work is to generate synthetic iodine map images from non-contrast single-energy CT (SECT) images using conditional denoising diffusion probabilistic model (DDPM). METHODS: One-hundered twenty-six head-and-neck patients' images were retrospectively investigated in this work. Each patient underwent non-contrast SECT and contrast DECT scans. Ground truth iodine maps were generated from contrast DECT scans using commercial software syngo.via installed in the clinic. A conditional DDPM was implemented in this work to synthesize iodine maps. Three-fold cross-validation was conducted, with each iteration selecting the data from 42 patients as the test dataset and the remainder as the training dataset. Pixel-to-pixel generative adversarial network (GAN) and CycleGAN served as reference methods for evaluating the proposed DDPM method. RESULTS: The accuracy of the proposed DDPM was evaluated using three quantitative metrics: mean absolute error (MAE) (1.039 ± 0.345 mg/mL), structural similarity index measure (SSIM) (0.89 ± 0.10) and peak signal-to-noise ratio (PSNR) (25.4 ± 3.5 db) respectively. Compared to the reference methods, the proposed technique showcased superior performance across the evaluated metrics, further validated by the paired two-tailed t-tests. CONCLUSION: The proposed conditional DDPM framework has demonstrated the feasibility of generating synthetic iodine map images from non-contrast SECT images. This method presents a potential clinical application, which is providing accurate iodine contrast map in instances where only non-contrast SECT is accessible.

Emerging anticancer potential and mechanisms of snake venom toxins: A review.

Animal-derived venom, like snake venom, has been proven to be valuable natural resources for the drug development. Previously, snake venom was mainly investigated in its pharmacological activities in regulating coagulation, vasodilation, and cardiovascular function, and several marketed cardiovascular drugs were successfully developed from snake venom. In recent years, snake venom fractions have been demonstrated with anticancer properties of inducing apoptotic and autophagic cell death, restraining proliferation, suppressing angiogenesis, inhibiting cell adhesion and migration, improving immunity, and so on. A number of active anticancer enzymes and peptides have been identified from snake venom toxins, such as L-amino acid oxidases (LAAOs), phospholipase A2 (PLA2), metalloproteinases (MPs), three-finger toxins (3FTxs), serine proteinases (SPs), disintegrins, C-type lectin-like proteins (CTLPs), cell-penetrating peptides, cysteine-rich secretory proteins (CRISPs). In this review, we focus on summarizing these snake venom-derived anticancer components on their anticancer activities and underlying mechanisms. We will also discuss their potential to be developed as anticancer drugs in the future.

Full-dose whole-body PET synthesis from low-dose PET using high-efficiency denoising diffusion probabilistic model: PET consistency model.

PURPOSE: Positron Emission Tomography (PET) has been a commonly used imaging modality in broad clinical applications. One of the most important tradeoffs in PET imaging is between image quality and radiation dose: high image quality comes with high radiation exposure. Improving image quality is desirable for all clinical applications while minimizing radiation exposure is needed to reduce risk to patients. METHODS: We introduce PET Consistency Model (PET-CM), an efficient diffusion-based method for generating high-quality full-dose PET images from low-dose PET images. It employs a two-step process, adding Gaussian noise to full-dose PET images in the forward diffusion, and then denoising them using a PET Shifted-window Vision Transformer (PET-VIT) network in the reverse diffusion. The PET-VIT network learns a consistency function that enables direct denoising of Gaussian noise into clean full-dose PET images. PET-CM achieves state-of-the-art image quality while requiring significantly less computation time than other methods. Evaluation with normalized mean absolute error (NMAE), peak signal-to-noise ratio (PSNR), multi-scale structure similarity index (SSIM), normalized cross-correlation (NCC), and clinical evaluation including Human Ranking Score (HRS) and Standardized Uptake Value (SUV) Error analysis shows its superiority in synthesizing full-dose PET images from low-dose inputs. RESULTS: In experiments comparing eighth-dose to full-dose images, PET-CM demonstrated impressive performance with NMAE of 1.278 ± 0.122%, PSNR of 33.783 ± 0.824 dB, SSIM of 0.964 ± 0.009, NCC of 0.968 ± 0.011, HRS of 4.543, and SUV Error of 0.255 ± 0.318%, with an average generation time of 62 s per patient. This is a significant improvement compared to the state-of-the-art diffusion-based model with PET-CM reaching this result 12× faster. Similarly, in the quarter-dose to full-dose image experiments, PET-CM delivered competitive outcomes, achieving an NMAE of 0.973 ± 0.066%, PSNR of 36.172 ± 0.801 dB, SSIM of 0.984 ± 0.004, NCC of 0.990 ± 0.005, HRS of 4.428, and SUV Error of 0.151 ± 0.192% using the same generation process, which underlining its high quantitative and clinical precision in both denoising scenario. CONCLUSIONS: We propose PET-CM, the first efficient diffusion-model-based method, for estimating full-dose PET images from low-dose images. PET-CM provides comparable quality to the state-of-the-art diffusion model with higher efficiency. By utilizing this approach, it becomes possible to maintain high-quality PET images suitable for clinical use while mitigating the risks associated with radiation. The code is availble at https://github.com/shaoyanpan/Full-dose-Whole-body-PET-Synthesis-from-Low-dose-PET-Using-Consistency-Model.

High-resolution MRI synthesis using a data-driven framework with denoising diffusion probabilistic modeling.

Objective. High-resolution magnetic resonance imaging (MRI) can enhance lesion diagnosis, prognosis, and delineation. However, gradient power and hardware limitations prohibit recording thin slices or sub-1 mm resolution. Furthermore, long scan time is not clinically acceptable. Conventional high-resolution images generated using statistical or analytical methods include the limitation of capturing complex, high-dimensional image data with intricate patterns and structures. This study aims to harness cutting-edge diffusion probabilistic deep learning techniques to create a framework for generating high-resolution MRI from low-resolution counterparts, improving the uncertainty of denoising diffusion probabilistic models (DDPM).Approach. DDPM includes two processes. The forward process employs a Markov chain to systematically introduce Gaussian noise to low-resolution MRI images. In the reverse process, a U-Net model is trained to denoise the forward process images and produce high-resolution images conditioned on the features of their low-resolution counterparts. The proposed framework was demonstrated using T2-weighted MRI images from institutional prostate patients and brain patients collected in the Brain Tumor Segmentation Challenge 2020 (BraTS2020).Main results. For the prostate dataset, the bicubic interpolation model (Bicubic), conditional generative-adversarial network (CGAN), and our proposed DDPM framework improved the noise quality measure from low-resolution images by 4.4%, 5.7%, and 12.8%, respectively. Our method enhanced the signal-to-noise ratios by 11.7%, surpassing Bicubic (9.8%) and CGAN (8.1%). In the BraTS2020 dataset, the proposed framework and Bicubic enhanced peak signal-to-noise ratio from resolution-degraded images by 9.1% and 5.8%. The multi-scale structural similarity indexes were 0.970 ± 0.019, 0.968 ± 0.022, and 0.967 ± 0.023 for the proposed method, CGAN, and Bicubic, respectively.Significance. This study explores a deep learning-based diffusion probabilistic framework for improving MR image resolution. Such a framework can be used to improve clinical workflow by obtaining high-resolution images without penalty of the long scan time. Future investigation will likely focus on prospectively testing the efficacy of this framework with different clinical indications.

CBCT-Based synthetic CT image generation using conditional denoising diffusion probabilistic model.

BACKGROUND: Daily or weekly cone-beam computed tomography (CBCT) scans are commonly used for accurate patient positioning during the image-guided radiotherapy (IGRT) process, making it an ideal option for adaptive radiotherapy (ART) replanning. However, the presence of severe artifacts and inaccurate Hounsfield unit (HU) values prevent its use for quantitative applications such as organ segmentation and dose calculation. To enable the clinical practice of online ART, it is crucial to obtain CBCT scans with a quality comparable to that of a CT scan. PURPOSE: This work aims to develop a conditional diffusion model to perform image translation from the CBCT to the CT distribution for the image quality improvement of CBCT. METHODS: The proposed method is a conditional denoising diffusion probabilistic model (DDPM) that utilizes a time-embedded U-net architecture with residual and attention blocks to gradually transform the white Gaussian noise sample to the target CT distribution conditioned on the CBCT. The model was trained on deformed planning CT (dpCT) and CBCT image pairs, and its feasibility was verified in brain patient study and head-and-neck (H&N) patient study. The performance of the proposed algorithm was evaluated using mean absolute error (MAE), peak signal-to-noise ratio (PSNR) and normalized cross-correlation (NCC) metrics on generated synthetic CT (sCT) samples. The proposed method was also compared to four other diffusion model-based sCT generation methods. RESULTS: In the brain patient study, the MAE, PSNR, and NCC of the generated sCT were 25.99 HU, 30.49 dB, and 0.99, respectively, compared to 40.63 HU, 27.87 dB, and 0.98 of the CBCT images. In the H&N patient study, the metrics were 32.56 HU, 27.65 dB, 0.98 and 38.99 HU, 27.00, 0.98 for sCT and CBCT, respectively. Compared to the other four diffusion models and one Cycle generative adversarial network (Cycle GAN), the proposed method showed superior results in both visual quality and quantitative analysis. CONCLUSIONS: The proposed conditional DDPM method can generate sCT from CBCT with accurate HU numbers and reduced artifacts, enabling accurate CBCT-based organ segmentation and dose calculation for online ART.

Advances in understanding grapevine downy mildew: From pathogen infection to disease management.

Plasmopara viticola is geographically widespread in grapevine-growing regions. Grapevine downy mildew disease, caused by this biotrophic pathogen, leads to considerable yield losses in viticulture annually. Because of the great significance of grapevine production and wine quality, research on this disease has been widely performed since its emergence in the 19th century. Here, we review and discuss recent understanding of this pathogen from multiple aspects, including its infection cycle, disease symptoms, genome decoding, effector biology, and management and control strategies. We highlight the identification and characterization of effector proteins with their biological roles in host-pathogen interaction, with a focus on sustainable control methods against P. viticola, especially the use of biocontrol agents and environmentally friendly compounds.

Deep learning-based synthetic dose-weighted LET map generation for intensity modulated proton therapy.

The advantage of proton therapy as compared to photon therapy stems from the Bragg peak effect, which allows protons to deposit most of their energy directly at the tumor while sparing healthy tissue. However, even with such benefits, proton therapy does present certain challenges. The biological effectiveness differences between protons and photons are not fully incorporated into clinical treatment planning processes. In current clinical practice, the relative biological effectiveness (RBE) between protons and photons is set as constant 1.1. Numerous studies have suggested that the RBE of protons can exhibit significant variability. Given these findings, there is a substantial interest in refining proton therapy treatment planning to better account for the variable RBE. Dose-average linear energy transfer (LETd) is a key physical parameter for evaluating the RBE of proton therapy and aids in optimizing proton treatment plans. Calculating precise LETddistributions necessitates the use of intricate physical models and the execution of specialized Monte-Carlo simulation software, which is a computationally intensive and time-consuming progress. In response to these challenges, we propose a deep learning based framework designed to predict the LETddistribution map using the dose distribution map. This approach aims to simplify the process and increase the speed of LETdmap generation in clinical settings. The proposed CycleGAN model has demonstrated superior performance over other GAN-based models. The mean absolute error (MAE), peak signal-to-noise ratio and normalized cross correlation of the LETdmaps generated by the proposed method are 0.096 ± 0.019 keVµm-1, 24.203 ± 2.683 dB, and 0.997 ± 0.002, respectively. The MAE of the proposed method in the clinical target volume, bladder, and rectum are 0.193 ± 0.103, 0.277 ± 0.112, and 0.211 ± 0.086 keVµm-1, respectively. The proposed framework has demonstrated the feasibility of generating synthetic LETdmaps from dose maps and has the potential to improve proton therapy planning by providing accurate LETdinformation.

Synthetic CT generation from MRI using 3D transformer-based denoising diffusion model.

BACKGROUND AND PURPOSE: Magnetic resonance imaging (MRI)-based synthetic computed tomography (sCT) simplifies radiation therapy treatment planning by eliminating the need for CT simulation and error-prone image registration, ultimately reducing patient radiation dose and setup uncertainty. In this work, we propose a MRI-to-CT transformer-based improved denoising diffusion probabilistic model (MC-IDDPM) to translate MRI into high-quality sCT to facilitate radiation treatment planning. METHODS: MC-IDDPM implements diffusion processes with a shifted-window transformer network to generate sCT from MRI. The proposed model consists of two processes: a forward process, which involves adding Gaussian noise to real CT scans to create noisy images, and a reverse process, in which a shifted-window transformer V-net (Swin-Vnet) denoises the noisy CT scans conditioned on the MRI from the same patient to produce noise-free CT scans. With an optimally trained Swin-Vnet, the reverse diffusion process was used to generate noise-free sCT scans matching MRI anatomy. We evaluated the proposed method by generating sCT from MRI on an institutional brain dataset and an institutional prostate dataset. Quantitative evaluations were conducted using several metrics, including Mean Absolute Error (MAE), Peak Signal-to-Noise Ratio (PSNR), Multi-scale Structure Similarity Index (SSIM), and Normalized Cross Correlation (NCC). Dosimetry analyses were also performed, including comparisons of mean dose and target dose coverages for 95% and 99%. RESULTS: MC-IDDPM generated brain sCTs with state-of-the-art quantitative results with MAE 48.825 ± 21.491 HU, PSNR 26.491 ± 2.814 dB, SSIM 0.947 ± 0.032, and NCC 0.976 ± 0.019. For the prostate dataset: MAE 55.124 ± 9.414 HU, PSNR 28.708 ± 2.112 dB, SSIM 0.878 ± 0.040, and NCC 0.940 ± 0.039. MC-IDDPM demonstrates a statistically significant improvement (with p < 0.05) in most metrics when compared to competing networks, for both brain and prostate synthetic CT. Dosimetry analyses indicated that the target dose coverage differences by using CT and sCT were within ± 0.34%. CONCLUSIONS: We have developed and validated a novel approach for generating CT images from routine MRIs using a transformer-based improved DDPM. This model effectively captures the complex relationship between CT and MRI images, allowing for robust and high-quality synthetic CT images to be generated in a matter of minutes. This approach has the potential to greatly simplify the treatment planning process for radiation therapy by eliminating the need for additional CT scans, reducing the amount of time patients spend in treatment planning, and enhancing the accuracy of treatment delivery.

Dual RNA-Seq Reveals Temperature-Mediated Gene Reprogramming and Molecular Crosstalk between Grapevine and Lasiodiplodia theobromae.

High temperatures associated with a fluctuating climate profoundly accelerate the occurrence of a myriad of plant diseases around the world. A comprehensive insight into how plants respond to pathogenic microorganisms under high-temperature stress is required for plant disease management, whereas the underlying mechanisms behind temperature-mediated plant immunity and pathogen pathogenicity are still unclear. Here, we evaluated the effect of high temperature on the development of grapevine canker disease and quantified the contribution of temperature variation to the gene transcription reprogramming of grapevine and its pathogenic agent Lasiodiplodia theobromae using a dual RNA-seq approach. The results showed that both grapevine and the pathogen displayed altered transcriptomes under different temperatures, and even the transcription of a plethora of genes from the two organisms responded in different directions and magnitudes. The transcription variability that arose due to temperature oscillation allowed us to identify a total of 26 grapevine gene modules and 17 fungal gene modules that were correlated with more than one gene module of the partner organism, which revealed an extensive web of plant-pathogen gene reprogramming during infection. More importantly, we identified a set of temperature-responsive genes that were transcriptionally orchestrated within the given gene modules. These genes are predicted to be involved in multiple cellular processes including protein folding, stress response regulation, and carbohydrate and peptide metabolisms in grapevine and porphyrin- and pteridine-containing compound metabolisms in L. theobromae, implying that in response to temperature oscillation, a complex web of signaling pathways in two organism cells is activated during infection. This study describes a co-transcription network of grapevine and L. theobromae in the context of considering temperature variation, which provides novel insights into deciphering the molecular mechanisms underlying temperature-modulated disease development.

Image-Domain Material Decomposition for Dual-energy CT using Unsupervised Learning with Data-fidelity Loss.

BACKGROUND: Dual-energy CT (DECT) and material decomposition play vital roles in quantitative medical imaging. However, the decomposition process may suffer from significant noise amplification, leading to severely degraded image signal-to-noise ratios (SNRs). While existing iterative algorithms perform noise suppression using different image priors, these heuristic image priors cannot accurately represent the features of the target image manifold. Although deep learning-based decomposition methods have been reported, these methods are in the supervised-learning framework requiring paired data for training, which is not readily available in clinical settings. PURPOSE: This work aims to develop an unsupervised-learning framework with data-measurement consistency for image-domain material decomposition in DECT.

2D medical image synthesis using transformer-based denoising diffusion probabilistic model.

Objective. Artificial intelligence (AI) methods have gained popularity in medical imaging research. The size and scope of the training image datasets needed for successful AI model deployment does not always have the desired scale. In this paper, we introduce a medical image synthesis framework aimed at addressing the challenge of limited training datasets for AI models.Approach. The proposed 2D image synthesis framework is based on a diffusion model using a Swin-transformer-based network. This model consists of a forward Gaussian noise process and a reverse process using the transformer-based diffusion model for denoising. Training data includes four image datasets: chest x-rays, heart MRI, pelvic CT, and abdomen CT. We evaluated the authenticity, quality, and diversity of the synthetic images using visual Turing assessments conducted by three medical physicists, and four quantitative evaluations: the Inception score (IS), Fréchet Inception Distance score (FID), feature similarity and diversity score (DS, indicating diversity similarity) between the synthetic and true images. To leverage the framework value for training AI models, we conducted COVID-19 classification tasks using real images, synthetic images, and mixtures of both images.Main results. Visual Turing assessments showed an average accuracy of 0.64 (accuracy converging to50%indicates a better realistic visual appearance of the synthetic images), sensitivity of 0.79, and specificity of 0.50. Average quantitative accuracy obtained from all datasets were IS = 2.28, FID = 37.27, FDS = 0.20, and DS = 0.86. For the COVID-19 classification task, the baseline network obtained an accuracy of 0.88 using a pure real dataset, 0.89 using a pure synthetic dataset, and 0.93 using a dataset mixed of real and synthetic data.Significance. A image synthesis framework was demonstrated for medical image synthesis, which can generate high-quality medical images of different imaging modalities with the purpose of supplementing existing training sets for AI model deployment. This method has potential applications in many data-driven medical imaging research.

Belowground microbiota analysis indicates that Fusarium spp. exacerbate grapevine trunk disease.

BACKGROUND: Grapevine trunk diseases (GTDs) are disease complexes that are major threats to viticulture in most grapevine growing regions. The microbiomes colonizing plant belowground components form complex associations with plants, play important roles in promoting plant productivity and health in natural environments, and may be related to GTD development. To investigate associations between belowground fungal communities and GTD symptomatic or asymptomatic grapevines, fungal communities associated with three soil-plant compartments (bulk soils, rhizospheres, and roots) were characterized by ITS high-throughput amplicon sequencing across two years. RESULTS: The fungal community diversity and composition differs according to the soil-plant compartment type (PERMANOVA, p < 0.001, 12.04% of variation explained) and sampling year (PERMANOVA, p < 0.001, 8.83%), whereas GTD symptomatology exhibited a weaker, but still significant association (PERMANOVA, p < 0.001, 1.29%). The effects of the latter were particularly prominent in root and rhizosphere community comparisons. Many GTD-associated pathogens were detected, but their relative abundances were not correlated (or were negatively correlated) to symptomatology. Fusarium spp., were enriched in symptomatic roots and rhizospheres compared to asymptomatic counterparts, suggesting that their abundances were positively correlated with symptomatic vines. Inoculation tests revealed that Fusarium isolates, similar to Dactylonectria macrodidyma, a pathogen associated with black foot disease, caused dark brown necrotic spots on stems in addition to root rot, which blackened lateral roots. Disease indices were higher with co-inoculation than single inoculation with a Fusarium isolate or D. macrodidyma, suggesting that Fusarium spp. can exacerbate disease severity when inoculated with other known GTD-associated pathogens. CONCLUSIONS: The belowground fungal microbiota of grapevines varied from soil-plant compartments, the years and whether showed GTD symptoms. The GTDs symptoms were related to the enrichment of Fusarium spp. rather than the relative abundances of GTD pathogens. These results demonstrate the effects of fungal microbiota of roots and rhizospheres on GTDs, while providing new insights into opportunistic pathogenesis of GTDs and potential control practices.

The woody plant-degrading pathogen Lasiodiplodia theobromae effector LtCre1 targets the grapevine sugar-signaling protein VvRHIP1 to suppress host immunity.

Lasiodiplodia theobromae is a causal agent of Botryosphaeria dieback, which seriously threatens grapevine production worldwide. Plant pathogens secrete diverse effectors to suppress host immune responses and promote the progression of infection, but the mechanisms underlying the manipulation of host immunity by L. theobromae effectors are poorly understood. In this study, we characterized LtCre1, which encodes a L. theobromae effector that suppresses BAX-triggered cell death in Nicotiana benthamiana. RNAi-silencing and overexpression of LtCre1 in L. theobromae showed impaired and increased virulence, respectively, and ectopic expression in N. benthamiana increased susceptibility. These results suggest that LtCre1 is as an essential virulence factor for L. theobromae. Protein-protein interaction studies revealed that LtCre1 interacts with grapevine RGS1-HXK1-interacting protein 1 (VvRHIP1). Ectopic overexpression of VvRHIP1 in N. benthamiana reduced infection, suggesting that VvRHIP1 enhances plant immunity against L. theobromae. LtCre1 was found to disrupt the formation of the VvRHIP1-VvRGS1 complex and to participate in regulating the plant sugar-signaling pathway. Thus, our results suggest that L. theobromae LtCre1 targets the grapevine VvRHIP1 protein to manipulate the sugar-signaling pathway by disrupting the association of the VvRHIP1-VvRGS1 complex.

LtGAPR1 Is a Novel Secreted Effector from Lasiodiplodia theobromae That Interacts with NbPsQ2 to Negatively Regulate Infection.

The effector proteins secreted by a pathogen not only promote the virulence and infection of the pathogen but also trigger plant defense response. Lasiodiplodia theobromae secretes many effectors that modulate and hijack grape processes to colonize host cells, but the underlying mechanisms remain unclear. Herein, we report LtGAPR1, which has been proven to be a secreted protein. In our study, LtGAPR1 played a negative role in virulence. By co-immunoprecipitation, 23 kDa oxygen-evolving enhancer 2 (NbPsbQ2) was identified as a host target of LtGAPR1. The overexpression of NbPsbQ2 in Nicotiana benthamiana reduced susceptibility to L. theobromae, and the silencing of NbPsbQ2 enhanced L. theobromae infection. LtGAPR1 and NbPsbQ2 were confirmed to interact with each other. Transiently, expressed LtGAPR1 activated reactive oxygen species (ROS) production in N. benthamiana leaves. However, in NbPsbQ2-silenced leaves, ROS production was impaired. Overall, our report revealed that LtGAPR1 promotes ROS accumulation by interacting with NbPsbQ2, thereby triggering plant defenses that negatively regulate infection.

Transcriptomic Analysis of Resistant and Wild-Type Botrytis cinerea Isolates Revealed Fludioxonil-Resistance Mechanisms.

Botrytis cinerea, the causal agent of gray mold, is one of the most destructive pathogens of cherry tomatoes, causing fruit decay and economic loss. Fludioxonil is an effective fungicide widely used for crop protection and is effective against tomato gray mold. The emergence of fungicide-resistant strains has made the control of B. cinerea more difficult. While the genome of B. cinerea is available, there are few reports regarding the large-scale functional annotation of the genome using expressed genes derived from transcriptomes, and the mechanism(s) underlying such fludioxonil resistance remain unclear. The present study prepared RNA-sequencing (RNA-seq) libraries for three B. cinerea strains (two highly resistant (LR and FR) versus one highly sensitive (S) to fludioxonil), with and without fludioxonil treatment, to identify fludioxonil responsive genes that associated to fungicide resistance. Functional enrichment analysis identified nine resistance related DEGs in the fludioxonil-induced LR and FR transcriptome that were simultaneously up-regulated, and seven resistance related DEGs down-regulated. These included adenosine triphosphate (ATP)-binding cassette (ABC) transporter-encoding genes, major facilitator superfamily (MFS) transporter-encoding genes, and the high-osmolarity glycerol (HOG) pathway homologues or related genes. The expression patterns of twelve out of the sixteen fludioxonil-responsive genes, obtained from the RNA-sequence data sets, were validated using quantitative real-time PCR (qRT-PCR). Based on RNA-sequence analysis, it was found that hybrid histidine kinase, fungal HHKs, such as BOS1, BcHHK2, and BcHHK17, probably involved in the fludioxonil resistance of B. cinerea, in addition, a number of ABC and MFS transporter genes that were not reported before, such as BcATRO, BMR1, BMR3, BcNMT1, BcAMF1, BcTOP1, BcVBA2, and BcYHK8, were differentially expressed in the fludioxonil-resistant strains, indicating that overexpression of these efflux transporters located in the plasma membranes may associate with the fludioxonil resistance mechanism of B. cinerea. All together, these lines of evidence allowed us to draw a general portrait of the anti-fludioxonil mechanisms for B. cinerea, and the assembled and annotated transcriptome data provide valuable genomic resources for further study of the molecular mechanisms of B. cinerea resistance to fludioxonil.

Lasiodiplodia theobromae protein LtScp1 contributes to fungal virulence and protects fungal mycelia against hydrolysis by grapevine chitinase.

The LysM proteins have been reported to be important for the virulence and host immunity suppression in herbaceous plant pathogens, whereas far less information is documented in the woody plant pathogen Lasiodiplodia theobromae. To investigate the functional mechanism of LysM protein in L. theobromae, one gene LtScp1 was cloned and characterized detailedly in the current study. Transcription profiling revealed that LtScp1 was highly expressed at the infectious stages. Compared to wild type, overexpression and silencing of LtScp1 in L. theobromae led to significantly increased and decreased lesion areas, respectively. Moreover, LtScp1 was determined to be a secreted protein via a yeast signal peptide trapping system. Interestingly, LtScp1 was confirmed to be modified by the N-glycosylation, which is necessary for the homodimerization of LtScp1 molecules. Furthermore, it was found that LtScp1 interacted with the grapevine chitinase VvChi4 and interfered the ability of VvChi4 to bind chitin. Collectively, these results suggest that LtScp1 functions as a virulence factor to protect the fungus from degradation during the infection.

An NmrA-Like Protein, Lws1, Is Important for Pathogenesis in the Woody Plant Pathogen Lasiodiplodia theobromae.

The NmrA-like proteins have been reported to be important nitrogen metabolism regulators and virulence factors in herbaceous plant pathogens. However, their role in the woody plant pathogen Lasiodiplodia theobromae is less clear. In the current study, we identified a putative NmrA-like protein, Lws1, in L. theobromae and investigated its pathogenic role via gene silencing and overexpression experiments. We also evaluated the effects of external carbon and nitrogen sources on Lws1 gene expression via qRT-PCR assays. Moreover, we analyzed the molecular interaction between Lws1 and its target protein via the yeast two-hybrid system. The results show that Lws1 contained a canonical glycine-rich motif shared by the short-chain dehydrogenase/reductase (SDR) superfamily proteins and functioned as a negative regulator during disease development. Transcription profiling revealed that the transcription of Lws1 was affected by external nitrogen and carbon sources. Interaction analyses demonstrated that Lws1 interacted with a putative GATA family transcription factor, LtAreA. In conclusion, these results suggest that Lws1 serves as a critical regulator in nutrition metabolism and disease development during infection.

A Putative Effector LtCSEP1 from Lasiodiplodia theobromae Inhibits BAX-Triggered Cell Death and Suppresses Immunity Responses in Nicotiana benthamiana.

Lasiodiplodia theobromae is a causal agent of grapevine trunk disease, and it poses a significant threat to the grape industry worldwide. Fungal effectors play an essential role in the interaction between plants and pathogens. However, few studies have been conducted to understand the functions of individual effectors in L. theobromae. In this study, we identified and characterized a candidate secreted effector protein, LtCSEP1, in L. theobromae. Gene expression analysis suggested that transcription of LtCSEP1 in L. theobromae was induced at the early infection stages in the grapevine. Yeast secretion assay revealed that LtCSEP1 contains a functional signal peptide. Transient expression of LtCSEP1 in Nicotiana benthamiana suppresses BAX-trigged cell death and significantly inhibits the flg22-induced PTI-associated gene expression. Furthermore, the ectopic expression of LtCSEP1 in N. benthamiana enhanced disease susceptibility to L. theobromae by downregulating the defense-related genes. These results demonstrated that LtCSEP1 is a potential effector of L. theobromae, which contributes to suppressing the plant's defenses.