Phenazine biosynthesis protein MoPhzF regulates appressorium formation and host infection through canonical metabolic and noncanonical signaling function in Magnaporthe oryzae.

Microbe-produced secondary metabolite phenazine-1-carboxylic acid (PCA) facilitates pathogen virulence and defense mechanisms against competitors. Magnaporthe oryzae, a causal agent of the devastating rice blast disease, needs to compete with other phyllosphere microbes and overcome host immunity for successful colonization and infection. However, whether M. oryzae produces PCA or it has any other functions remains unknown. Here, we found that the MoPHZF gene encodes the phenazine biosynthesis protein MoPhzF, synthesizes PCA in M. oryzae, and regulates appressorium formation and host virulence. MoPhzF is likely acquired through an ancient horizontal gene transfer event and has a canonical function in PCA synthesis. In addition, we found that PCA has a role in suppressing the accumulation of host-derived reactive oxygen species (ROS) during infection. Further examination indicated that MoPhzF recruits both the endoplasmic reticulum membrane protein MoEmc2 and the regulator of G-protein signaling MoRgs1 to the plasma membrane (PM) for MoRgs1 phosphorylation, which is a critical regulatory mechanism in appressorium formation and pathogenicity. Collectively, our studies unveiled a canonical function of MoPhzF in PCA synthesis and a noncanonical signaling function in promoting appressorium formation and host infection.

Classifying breast cancer tissue by Raman spectroscopy with one-dimensional convolutional neural network.

As the most common cancer in women, breast cancer is becoming lethal worldwide. However, the current breast diagnosis technologies are not enough to meet the requirements in clinic due to some shortages of early-stage insensitiveness, time consumption and relying on the doctor's experience, etc. It's necessary to develop a creative method for the automatical diagnosis of breast cancer. Therefore, Raman spectroscopy and one-dimensional convolutional neural network (1D-CNN) algorithm were combined together for the first time to classify the healthy and cancerous breast tissues in this study. First, a number of Raman spectra were collected from breast samples of 20 patients for spectral analysis. Then, a 1D-CNN model was developed and trained for classification. In addition, the Fisher Discrimination Analysis (FDA) and Support Vector Machine (SVM) classifiers were trained and tested with the same spectral data for comparison. The best classification performance, namely the overall diagnostic accuracy of 92%, the sensitivity of 98% and the specificity of 86%, has been achieved by using 1D-CNN model. This study proves that 1D-CNN combined with Raman spectroscopy can classify breast tissues effectively and automatically and lay the foundation for automated cancer diagnosis in the future.

Fluorescence imaging and Raman spectroscopy applied for the accurate diagnosis of breast cancer with deep learning algorithms.

Deep learning is usually combined with a single detection technique in the field of disease diagnosis. This study focused on simultaneously combining deep learning with multiple detection technologies, fluorescence imaging and Raman spectroscopy, for breast cancer diagnosis. A number of fluorescence images and Raman spectra were collected from breast tissue sections of 14 patients. Pseudo-color enhancement algorithm and a convolutional neural network were applied to the fluorescence image processing, so that the discriminant accuracy of test sets, 88.61%, was obtained. Two different BP-neural networks were applied to the Raman spectra that mainly comprised collagen and lipid, so that the discriminant accuracy of 95.33% and 98.67% of test sets were gotten, respectively. Then the discriminant results of fluorescence images and Raman spectra were counted and arranged into a characteristic variable matrix to predict the breast tissue samples with partial least squares (PLS) algorithm. As a result, the predictions of all samples are correct, with minor error of predictive value. This study proves that deep learning algorithms can be applied into multiple diagnostic optics/spectroscopy techniques simultaneously to improve the accuracy in disease diagnosis.

Honokiol suppresses mycelial growth and reduces virulence of Botrytis cinerea by inducing autophagic activities and apoptosis.

Fungal pathogens lead to severe quality deterioration and yield loss, making it urgent to explore efficient measures to control fungal diseases at the preharvest and postharvest stages of plants. Therefore, studies on natural substances targeting alternative antimicrobial targets have become hot spots of research. Here, we show that honokiol, a polyphenolic compound obtained from Magnolia officinalis, significantly suppressed mycelial growth and reduced virulence of B. cinerea on harvested fruit by inducing autophagic activities and apoptosis. Moreover, honokiol was capable of abolishing the mitochondrial membrane potential and inducing the accumulation of reactive oxygen species. Some key genes involved in pathogenicity on fruit were also found significantly down-regulated. In summary, honokiol was effective as an alternative agent targeting autophagic and apoptotic machineries to control the incidence of gray mold, which may further enrich the toolkit of crop managers for fighting postharvest diseases caused by this and similar fungi.

Asymmetric Si-rhodamine scaffolds: rational design of pH-durable protease-activated NIR probes in vivo.

A group of asymmetric Si-rhodamine scaffolds was designed for protease-activated NIR probes. Dual pH-inertia for both spirocyclized fluorescent probes and fluorescent products of zwitterions form over a wide range of pH (4.0-11.0). Leucine aminopeptidase (LAP) and γ-glutamyl transpeptidase (GGT) were monitored by fluorescent imaging in vivo.

Efficacy of ABA-Mimicking Ligands in Controlling Water Loss and Maintaining Antioxidative Capacity of Spinacia oleracea.

Abscisic acid (ABA) is a central regulator for various developmental processes and responses to abiotic stresses in plants. However, its practical application in controlling water loss of postharvest produces is largely restrained. Herein, the present study reported that two ABA-mimicking ligands, AM1 and AMF4, markedly reduced water loss by promoting stomatal closure and effectively alleviated weight loss in spinach. AM1 and AMF4 also alleviated chlorophyll and vitamin C degradation and simultaneously reduced hydrogen peroxide and malondialdehyde (MDA) production; moreover, both enzymatic and nonenzymatic systems involved in antioxidative capacity were activated. The expression levels of SoOST1, SoSLAC1, SoRCAR3, SoPYL5, SoNCED3, and SoAREB1 were also up-regulated. These findings indicate that AM1 and AMF4 are promising as novel means for reducing water loss, maintaining visual quality, delaying senescence, and extending shelf life in leafy vegetables.