A random mutagenesis screen enriched for missense mutations in bacterial effector proteins

To remodel their hosts and escape immune defenses, many pathogens rely on large arsenals of proteins (effectors) that are delivered to the host cell using dedicated translocation machinery. Effectors hold significant insight into the biology of both the pathogens that encode them and the host pathways that they manipulate. One of the most powerful systems biology tools for studying effectors is the model organism, Saccharomyces cerevisiae. For many pathogens, the heterologous expression of effectors in yeast is growth inhibitory at a frequency much higher than housekeeping genes, an observation ascribed to targeting conserved eukaryotic proteins. Abrogation of yeast growth inhibition has been used to identify bacterial suppressors of effector activity, host targets, and functional residues and domains within effector proteins. We present here a yeast-based method for enriching for informative, in-frame, missense mutations in a pool of random effector mutants. We benchmark this approach against three effectors from Legionella pneumophila, an intracellular bacterial pathogen that injects a staggering &gt;330 effectors into the host cell. For each protein, we show how in silico protein modeling (AlphaFold2) and missense-directed mutagenesis can be combined to reveal important structural features within effectors. We identify known active site residues within the metalloprotease RavK, the putative active site in SdbB, and previously unidentified functional motifs within the C-terminal domain of SdbA. We show that this domain has structural similarity with glycosyltransferases and exhibits in vitro activity consistent with this predicted function.</p

Molecular Detection and Associated Risk Factors of <i>Brucella melitensis</i> in Aborted Sheep and Goats in Duhok Province, Iraq

Brucellosis in sheep and goats has a significant economic and zoonotic impact on the livestock population of Duhok province, Iraq. A total of 681 blood samples from aborted sheep and goats were collected from different flocks in seven districts of Duhok and tested using real-time polymerase chain reaction (RT-PCR). Logistic regression was used for the analysis of the potential risk factors associated with RT-PCR positivity. Results revealed an overall prevalence of 35.45% (CI = 2.57) and 23.8% 18 (CI = 0.44) in sheep and goats, respectively. A statistically significant (p = 0.004) difference in prevalence was found between the two species. RT-PCR detected more positive cases in older-aged animals (OR = 0.7164; p = 0.073). A significant difference was found in RT-PCR positivity in relation to different risk factors, including body condition, treatment taken, and abortion frequency (B. melitensis and shared a common ancestor and were genetically related to the United States of America (USA), Greece, China, and Nigeria. This study demonstrates that brucellosis is widely prevalent in the study regions. Therefore, the study suggests the implementation of preventive control measures for brucellosis

Hazards&Robots: A dataset for visual anomaly detection in robotics

We propose Hazards&Robots, a dataset for Visual Anomaly Detection in Robotics. The dataset is composed of 324,408 RGB frames, and corresponding feature vectors; it contains 145,470 normal frames and 178,938 anomalous ones categorized in 20 different anomaly classes. The dataset can be used to train and test current and novel visual anomaly detection methods such as those based on deep learning vision models. The data is recorded with a DJI Robomaster S1 front facing camera. The ground robot, controlled by a human operator, traverses university corridors. Considered anomalies include presence of humans, unexpected objects on the floor, defects to the robot. Preliminary versions of the dataset are used in [1,3]. This version is available at [12