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.

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.

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.

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.

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.

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.

miR-142 downregulation alleviates rat PTSD-like behaviors, reduces the level of inflammatory cytokine expression and apoptosis in hippocampus, and upregulates the expression of fragile X mental retardation protein.

BACKGROUND: FMRP is a selective mRNA-binding protein that regulates protein synthesis at synapses, and its loss may lead to the impairment of trace fear memory. Previously, we found that FMRP levels in the hippocampus of rats with post-traumatic stress disorder (PTSD) were decreased. However, the mechanism underlying these changes remains unclear. METHODS: Forty-eight male Sprague-Dawley rats were randomly divided into four groups. The experimental groups were treated with the single-prolonged stress (SPS) procedure and injected with a lentivirus-mediated inhibitor of miR-142-5p. Behavior test as well as morphology and molecular biology experiments were performed to detect the effect of miR-142 downregulation on PTSD, which was further verified by in vitro experiments. RESULTS: We found that silence of miRNA-142 (miR-142), an upstream regulator of FMRP, could alleviate PTSD-like behaviors of rats exposed to the SPS paradigm. MiR-142 silence not only decreased the levels of proinflammatory mediators, such as interleukin-1ß, interleukin-6, and tumor necrosis factor-α, but also increased the expressive levels of synaptic proteins including PSD95 and synapsin I in the hippocampus, which was one of the key brain regions associated with PTSD. We further detected that miR-142 silence also downregulated the transportation of nuclear factor kappa-B (NF-κB) into the nuclei of neurons and might further affect the morphology of neurons. CONCLUSIONS: The results revealed miR-142 downregulation could alleviate PTSD-like behaviors through attenuating neuroinflammation in the hippocampus of SPS rats by binding to FMRP.

Systemic Identification and Functional Characterization of Common in Fungal Extracellular Membrane Proteins in Lasiodiplodia theobromae.

Plant pathogenic fungi deploy secreted proteins into apoplastic space or intracellular lumen to promote successful infections during plant-pathogen interactions. In the present study, fourteen CFEM domain-containing proteins were systemically identified in Lasiodiplodia theobromae and eight of them were functionally characterized. All eight proteins were confirmed to be secreted into extracellular space by a yeast signal peptide trapping system. The transcriptional levels of most CFEM genes, except for LtCFEM2 and LtCFEM6, were significantly elevated during infection. In addition, almost all LtCFEM genes, apart from LtCFEM2, LtCFEM3, and LtCFEM6, were transcriptionally up-regulated at 35°C in contrast to that at 25°C and 30°C. As two elicitors, LtCFEM1 induced local yellowish phenotype and LtCFEM4 triggered cell death in Nicotiana benthamiana leaves. Furthermore, these proteins displayed distinct subcellular localizations when expressed transiently in N. benthamiana. Moreover, two genes, LtCFEM7 and LtCFEM8, were found to be spliced alternatively by RT-PCR and sequencing. Therefore, our data suggest that LtCFEM proteins play important roles in multiple aspects, including pathogenicity and plant immune response, which will enhance our understanding of the sophisticated pathogenic mechanisms of plant opportunistic pathogen L. theobromae.

A LysM Domain-Containing Protein LtLysM1 Is Important for Vegetative Growth and Pathogenesis in Woody Plant Pathogen Lasiodiplodia theobromae.

Lysin motif (LysM) proteins are reported to be necessary for the virulence and immune response suppression in many herbaceous plant pathogens, while far less is documented in woody plant pathogens. In this study, we preliminarily characterized the molecular function of a LysM protein LtLysM1 in woody plant pathogen Lasiodiplodia theobromae. Transcriptional profiles revealed that LtLysM1 is highly expressed at infectious stages, especially at 36 and 48 hours post inoculation. Amino acid sequence analyses revealed that LtLysM1 was a putative glycoprotein with 10 predicted N-glycosylation sites and one LysM domain. Pathogenicity tests showed that overexpressed transformants of LtLysM1 displayed increased virulence on grapevine shoots in comparison with that of wild type CSS-01s, and RNAi transformants of LtLysM1 exhibited significantly decreased lesion length when compared with that of wild type CSS-01s. Moreover, LtLysM1 was confirmed to be a secreted protein by a yeast signal peptide trap assay. Transient expression in Nicotiana benthamiana together with protein immunoblotting confirmed that LtLysM1 was an N-glycosylated protein. In contrast to previously reported LysM protein Slp1 and OsCEBiP, LtLysM1 molecule did not interact with itself based on yeast two hybrid and co-immunoprecipitation assays. These results indicate that LtLysM1 is a secreted protein and functions as a critical virulence factor during the disease symptom development in woody plants.

LtEPG1, a Secretory Endopolygalacturonase Protein, Regulates the Virulence of Lasiodiplodia theobromae in Vitis vinifera and Is Recognized as a Microbe-Associated Molecular Patterns.

The Lasiodiplodia theobromae genome encodes numerous glycoside hydrolases involved in organic matter degradation and conducive to pathogen infection, whereas their molecular mechanisms are still largely unknown. Here, we identified the glycoside hydrolase family 28 endopolygalacturonase LtEPG1 in L. theobromae and characterized its function in detail. LtEPG1 acts as a virulence factor during L. theobromae infection. Overexpression and silencing of LtEPG1 in L. theobromae led to significantly increased and decreased lesion areas, respectively. Further, the high transcript level of LtEPG1 during the infection process supported its virulence function. Polygalacturonase activity of LtEPG1 was substantiated by detecting its ability to degrade pectin. Furthermore, LtEPG1 functioned as microbe-associated molecular patterns during the infection process. Both transient expression of LtEPG1 in planta and infiltration of purified LtEPG1 triggered cell death in Nicotiana benthamiana. Site-directed mutation of LtEPG1 indicated that the enzymatic activity of LtEPG1 is independent from its elicitor activity. A protein kinase, KINß1, was shown to interact in the yeast two-hybrid system with LtEPG1. This interaction was further confirmed in vitro using a pull-down assay. Our data indicate that LtEPG1 functions as a polygalacturonase and also serves as an elicitor with two independent mechanisms. Moreover, LtEPG1 may be able to manipulate host immune responses by regulating the KINß1-mediated signal pathway and consequently promote its own successful infection and symptom development.