A phytophthora effector manipulates host histone acetylation and reprograms defense gene expression to promote infection

Immune response during pathogen infection requires extensive transcription reprogramming. A fundamental mechanism of transcriptional regulation is histone acetylation. However, how pathogens interfere with this process to promote disease remains largely unknown. Here we demonstrate that the cytoplasmic effector PsAvh23 produced by the soybean pathogen Phytophthora sojae acts as a modulator of histone acetyltransferase (HAT) in plants. PsAvh23 binds to the ADA2 subunit of the HAT complex SAGA and disrupts its assembly by interfering with the association of ADA2 with the catalytic subunit GCN5. As such, PsAvh23 suppresses H3K9 acetylation mediated by the ADA2/GCN5 module and increases plant susceptibility. Expression of PsAvh23 or silencing of GmADA2/GmGCN5 resulted in misregulation of defense-related genes, most likely due to decreased H3K9 acetylation levels at the corresponding loci. This study highlights an effective counter-defense mechanism by which a pathogen effector suppresses the activation of defense genes by interfering with the function of the HAT complex during infection

Phytophthora sojae Avirulence Effector Avr3b is a Secreted NADH and ADP-ribose Pyrophosphorylase that Modulates Plant Immunity

Plants have evolved pathogen-associated molecular pattern (PAMP)-triggered immunity (PTI) and effector-triggered immunity (ETI) to protect themselves from infection by diverse pathogens. Avirulence (Avr) effectors that trigger plant ETI as a result of recognition by plant resistance (R) gene products have been identified in many plant pathogenic oomycetes and fungi. However, the virulence functions of oomycete and fungal Avr effectors remain largely unknown. Here, we combined bioinformatics and genetics to identify Avr3b, a new Avr gene from Phytophthora sojae, an oomycete pathogen that causes soybean root rot. Avr3b encodes a secreted protein with the RXLR host-targeting motif and C-terminal W and Nudix hydrolase motifs. Some isolates of P. sojae evade perception by the soybean R gene Rps3b through sequence mutation in Avr3b and lowered transcript accumulation. Transient expression of Avr3b in Nicotiana benthamiana increased susceptibility to P. capsici and P. parasitica, with significantly reduced accumulation of reactive oxygen species (ROS) around invasion sites. Biochemical assays confirmed that Avr3b is an ADP-ribose/NADH pyrophosphorylase, as predicted from the Nudix motif. Deletion of the Nudix motif of Avr3b abolished enzyme activity. Mutation of key residues in Nudix motif significantly impaired Avr3b virulence function but not the avirulence activity. Some Nudix hydrolases act as negative regulators of plant immunity, and thus Avr3b might be delivered into host cells as a Nudix hydrolase to impair host immunity. Avr3b homologues are present in several sequenced Phytophthora genomes, suggesting that Phytophthora pathogens might share similar strategies to suppress plant immunity

A Phytophthora sojae effector suppresses endoplasmic reticulum stress-mediated immunity by stabilizing plant Binding immunoglobulin Proteins

Phytophthora pathogens secrete an array of specific effector proteins to manipulate host innate immunity to promote pathogen colonization. However, little is known about the host targets of effectors and the specific mechanisms by which effectors increase susceptibility. Here we report that the soybean pathogen Phytophthora sojae uses an essential effector PsAvh262 to stabilize endoplasmic reticulum (ER)-luminal binding immunoglobulin proteins (BiPs), which act as negative regulators of plant resistance to Phytophthora. By stabilizing BiPs, PsAvh262 suppresses ER stress-triggered cell death and facilitates Phytophthora infection. The direct targeting of ER stress regulators may represent a common mechanism of host manipulation by microbes.This is the publisher’s final pdf. The published article is copyrighted by the author(s) and published by Nature Publishing Group. The published article can be found at: http://www.nature.com/ncomms/index.htm

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Leukocyte recognition algorithm in leucorrhea microscopic images based on ResNet-34 neural network

Automatic leucocyte recognition for leukorrhea microscopic images is a digital image processing technology in the field of machine learning. The existence and quantity of leukocytes in leucorrhea microscopic image is an important sign and basis to judge the inflammation of vagina or cervix. Therefore, the recognition and count of leucocyte is an effective means to evaluate the condition of the disease. To solve the problem of low efficiency of leucocyte recognition in traditional artificial microscopy, this paper proposes an automatic recognition algorithm based on ResNet-34 neural network. Firstly, Canny edge detection algorithm based on genetic algorithm is used to extract the foreground target in the leucorrhea microscopic image. Secondly, the leucocyte target is selected according to the connected region and boundary rectangle parameters of the foreground target. Finally, ResNet-34 neural network is applied for the classification of leukocytes. Experiments show that the recognition accuracy of leukocytes in leucorrhea microscopic image is 92.8%, and the recall is 97.1%, which is higher and better than other methods

Automatic segmentation and recognition of red and white cells in stool microscopic images of human

Aiming to solve the problem of low efficiency in manually recognizing the red and white cells in stool microscopic images, we propose an automatic segmentation method based on iterative corrosion with marker-controlled watershed segmentation and an automatic recognition method based on support vector machine (SVM) classification. The method first obtains saliency map of the images in HSI and Lab color spaces through saliency detection algorithm, then fuses the salient images to complete the initial segmentation. Next, we segment the red and white cells completely based on the initial segmentation images using marker-controlled watershed algorithm and other complementary methods. According to the differences in geometrical and texture features of red and white cells such as area, perimeter, circularity, energy, entropy, correlation and contrast, we extract them as feature vectors to train SVM and finally complete the classification and recognition of red and white cells. The experimental results indicate that our proposed marker-controlled watershed method can help increase the segmentation and recognition accuracy. Moreover, since it is also less susceptible to the heteromorphic red and white cells, our method is effective and robust