Cryo electron micrographs of disease resistance protein NRC2 from Nicotiana benthamiana
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ABSTRACT:
SUBMITTER: Michael Webster
PROVIDER: EMPIAR-12318 | biostudies-other |
REPOSITORIES: biostudies-other
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Project description:Phosphatidic acid plays an important role in Nicotiana benthamiana immune responses against phytopathogenic bacteria. We analyzed the contributions of endoplasmic reticulum-derived chloroplast phospholipids, including phosphatidic acid, to the resistance of N. benthamiana against Ralstonia solanacearum. Here, we focused on trigalactosyldiacylglycerol 3 (TGD3) protein as a candidate required for phosphatidic acid signaling. On the basis of Arabidopsis thaliana TGD3 sequences, we identified two putative TGD3 orthologs in the N. benthamiana genome, NbTGD3-1 and NbTGD3-2. To address the role of TGD3s in plant defense responses, we created double NbTGD3-silenced plants using virus-induced gene silencing. The NbTGD3-silenced plants showed a moderately reduced growth phenotype. Bacterial growth and the appearance of bacterial wilt disease were accelerated in NbTGD3-silenced plants, compared with control plants, challenged with R. solanacearum. The NbTGD3-silenced plants showed reduced both expression of allene oxide synthase that encoded jasmonic acid biosynthetic enzyme and NbPR-4, a marker gene for jasmonic acid signaling, after inoculation with R. solanacearum. Thus, NbTGD3-mediated endoplasmic reticulum-chloroplast lipid transport might be required for jasmonic acid signaling-mediated basal disease resistance in N. benthamiana.
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Project description:BackgroundIdentification and selection of protein particles in cryo-electron micrographs is an important step in single particle analysis. In this study, we developed a deep learning-based particle picking network to automatically detect particle centers from cryoEM micrographs. This is a challenging task due to the nature of cryoEM data, having low signal-to-noise ratios with variable particle sizes, shapes, distributions, grayscale variations as well as other undesirable artifacts.ResultsWe propose a double convolutional neural network (CNN) cascade for automated detection of particles in cryo-electron micrographs. This approach, entitled Deep Regression Picker Network or "DRPnet", is simple but very effective in recognizing different particle sizes, shapes, distributions and grayscale patterns corresponding to 2D views of 3D particles. Particles are detected by the first network, a fully convolutional regression network (FCRN), which maps the particle image to a continuous distance map that acts like a probability density function of particle centers. Particles identified by FCRN are further refined to reduce false particle detections by the second classification CNN. DRPnet's first CNN pretrained with only a single cryoEM dataset can be used to detect particles from different datasets without retraining. Compared to RELION template-based autopicking, DRPnet results in better particle picking performance with drastically reduced user interactions and processing time. DRPnet also outperforms the state-of-the-art particle picking networks in terms of the supervised detection evaluation metrics recall, precision, and F-measure. To further highlight quality of the picked particle sets, we compute and present additional performance metrics assessing the resulting 3D reconstructions such as number of 2D class averages, efficiency/angular coverage, Rosenthal-Henderson plots and local/global 3D reconstruction resolution.ConclusionDRPnet shows greatly improved time-savings to generate an initial particle dataset compared to manual picking, followed by template-based autopicking. Compared to other networks, DRPnet has equivalent or better performance. DRPnet excels on cryoEM datasets that have low contrast or clumped particles. Evaluating other performance metrics, DRPnet is useful for higher resolution 3D reconstructions with decreased particle numbers or unknown symmetry, detecting particles with better angular orientation coverage.
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Project description:Cryo-electron microscopy is a popular method for the determination of protein structures; however, identifying a sufficient number of particles for analysis can take months of manual effort. Current computational approaches find many false positives and require ad hoc postprocessing, especially for unusually shaped particles. To address these shortcomings, we develop Topaz, an efficient and accurate particle-picking pipeline using neural networks trained with a general-purpose positive-unlabeled learning method. This framework enables particle detection models to be trained with few sparsely labeled particles and no labeled negatives. Topaz retrieves many more real particles than conventional picking methods while maintaining low false-positive rates, is capable of picking challenging unusually shaped proteins (for example, small, non-globular and asymmetric particles), produces more representative particle sets and does not require post hoc curation. We demonstrate the performance of Topaz on two difficult datasets and three conventional datasets. Topaz is modular, standalone, free and open source ( http://topaz.csail.mit.edu ).
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Project description:PevD1 is a fungal protein secreted by Verticillium dahliae. Our previous researches showed that this protein could induce hypersensitive responses-like necrosis and systemic acquired resistance (SAR) in cotton and tobacco. To understand immune activation mechanisms whereby PevD1 elicits defense response, the yeast two-hybrid (Y2H) assay was performed to explore interacting protein of PevD1 in Arabidopsis thaliana, and a partner AtNRP (At5g42050) was identified. Here, AtNRP homolog in Nicotiana benthamiana was identified and designated as Nbnrp1. The Nbnrp1 could interact with PevD1 via Y2H and bimolecular fluorescence complementation (BiFC) analyses. Moreover, truncated protein binding assays demonstrated that the C-terminal 132 amino acid (development and cell death, DCD domain) of Nbnrp1 is required for PevD1-Nbnrp1 interaction. To further investigate the roles of Nbnrp1 in PevD1-induced defense response, Nbnrp1-overexpressing and Nbnrp1-silence transgenic plants were generated. The overexpression of Nbnrp1 conferred enhancement of PevD1-induced necrosis activity and disease resistance against tobacco mosaic virus (TMV), bacterial pathogen Pseudomonas syringae pv. tabaci and fungal pathogen V. dahliae. By contrast, Nbnrp1-silence lines displayed attenuated defense response compared with the wild-type. It is the first report that an asparagine-rich protein Nbnrp1 positively regulated V. dahliae secretory protein PevD1-induced cell death response and disease resistance in N. benthamiana.
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Project description:Single-particle cryo-electron microscopy (cryo-EM) is a powerful imaging modality capable of visualizing proteins and macromolecular complexes at near-atomic resolution. The low electron-doses used to prevent radiation damage to the biological samples, however, result in images where the power of the noise is 100 times greater than the power of the signal. To overcome these low signal-to-noise ratios (SNRs), hundreds of thousands of particle projections are averaged to determine the three-dimensional structure of the molecule of interest. The sampling requirements of high-resolution imaging impose limitations on the pixel sizes that can be used for acquisition, limiting the size of the field of view and requiring data collection sessions of several days to accumulate sufficient numbers of particles. Meanwhile, recent image super-resolution (SR) techniques based on neural networks have shown state-of-the-art performance on natural images. Building on these advances, here, we present a multiple-image SR algorithm based on deep internal learning designed specifically to work under low-SNR conditions. Our approach leverages the internal image statistics of cryo-EM movies and does not require training on ground-truth data. When applied to single-particle datasets of apoferritin and T20S proteasome, we show that the resolution of the 3D structure obtained from SR micrographs can surpass the limits imposed by the imaging system. Our results indicate that the combination of low magnification imaging with in silico image SR has the potential to accelerate cryo-EM data collection by virtue of including more particles in each exposure and doing so without sacrificing resolution.
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Project description:Each oomycete pathogen encodes a large number of effectors. Some effectors can be used in crop disease resistance breeding, such as to accelerate R gene cloning and utilisation. Since cytoplasmic effectors may cause acute physiological changes in host cells at very low concentrations, we assume that some of these effectors can serve as functional genes for transgenic plants. Here, we generated transgenic Nicotiana benthamiana plants that express a Phytophthora sojae CRN (crinkling and necrosis) effector, PsCRN115. We showed that its expression did not significantly affect the growth and development of N. benthamiana, but significantly improved disease resistance and tolerance to salt and drought stresses. Furthermore, we found that expression of heat-shock-protein and cytochrome-P450 encoding genes were unregulated in PsCRN115-transgenic N. benthamiana based on digital gene expression profiling analyses, suggesting the increased plant defence may be achieved by upregulation of these stress-related genes in transgenic plants. Thus, PsCRN115 may be used to improve plant tolerance to biotic and abiotic stresses.
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