Francisella–Arthropod Vector Interaction and its Role in Patho-Adaptation to Infect Mammals

Francisella tularensis is a Gram-negative, intracellular, zoonotic bacterium, and is the causative agent of tularemia with a broad host range. Arthropods such as ticks, mosquitoes, and flies maintain F. tularensis in nature by transmitting the bacteria among small mammals. While the tick is largely believed to be a biological vector of F. tularensis, transmission by mosquitoes and flies is largely believed to be mechanical on the mouthpart through interrupted feedings. However, the mechanism of infection of the vectors by F. tularensis is not well understood. Since F. tularensis has not been localized in the salivary gland of the primary human biting ticks, it is thought that bacterial transmission by ticks is through mechanical inoculation of tick feces containing F. tularensis into the skin wound. Drosophila melanogaster is an established good arthropod model for arthropod vectors of tularemia, where F. tularensis infects hemocytes, and is found in hemolymph, as seen in ticks. In addition, phagosome biogenesis and robust intracellular proliferation of F. tularensis in arthropod-derived cells are similar to that in mammalian macrophages. Furthermore, bacterial factors required for infectivity of mammals are often required for infectivity of the fly by F. tularensis. Several host factors that contribute to F. tularensis intracellular pathogenesis in D. melanogaster have been identified, and F. tularensis targets some of the evolutionarily conserved eukaryotic processes to enable intracellular survival and proliferation in evolutionarily distant hosts

Automatic Arabic Text Summarization System (AATSS) Based on Semantic Feature Extraction

Recently, one of the problems arisen due to the amount of information and it’s availability on the web, is the increased need for effective and powerful tool to automatically summarize text. For English and European languages an intensive works have been done with high performance and nowadays they look forward to multi-document and multi-language summarization. However, Arabic language still suffers from the little attentions and research done in this filed. In our research we propose a model to automatically summarize Arabic text using text extraction. Various steps are involved in the approach: preprocessing text, extract set of feature from sentences, classify sentence based on scoring method, ranking sentences and finally generate an extract summary. The main difference between our proposed system and other Arabic summarization systems are the consideration of semantics, entity objects such as names and places, and similarity factors in our proposed system. The proposed system has been applied on news domain using a dataset obtained from Falesteen newspaper. Manual evaluation techniques are used to evaluate and test the system. The results obtained by the proposed method achieve 86.5% similarity between the system and human summarization. A comparative study between our proposed system and Sakhr Arabic online summarization system has been conducted. The results show that our proposed system outperforms the Shakr system

ArbDialectID at MADAR Shared Task 1: Language Modelling and Ensemble Learning for Fine Grained Arabic Dialect Identification

In this paper, we present a Dialect Identification system (ArbDialectID) that competed at Task 1 of the MADAR shared task, MADARTravel Domain Dialect Identification. We build a course and a fine-grained identification model to predict the label (corresponding to a dialect of Arabic) of a given text. We build two language models by extracting features at two levels (words and characters). We firstly build a coarse identification model to classify each sentence into one out of six dialects, then use this label as a feature for the fine-grained model that classifies the sentence among 26 dialects from different Arab cities, after that we apply ensemble voting classifier on both sub-systems. Our system ranked 1st that achieving an f-score of 67.32%. Both the models and our feature engineering tools are made available to the research community.In this paper, we present a Dialect Identification system (ArbDialectID) that competed at Task 1 of the MADAR shared task, MADARTravel Domain Dialect Identification. We build a course and a fine-grained identification model to predict the label (corresponding to a dialect of Arabic) of a given text. We build two language models by extracting features at two levels (words and characters). We firstly build a coarse identification model to classify each sentence into one out of six dialects, then use this label as a feature for the fine-grained model that classifies the sentence among 26 dialects from different Arab cities, after that we apply ensemble voting classifier on both sub-systems. Our system ranked 1st that achieving an f-score of 67.32%. Both the models and our feature engineering tools are made available to the research community

Molecular Characterization of Exploitation of the Polyubiquitination and Farnesylation Machineries of Dictyostelium Discoideum by the AnkB F-Box Effector of Legionella Pneumophila

The Dot/Icm-translocated Ankyrin B (AnkB) F-box effector of Legionella pneumophila is essential for intra-vacuolar proliferation and functions as a platform for the docking of polyubiquitinated proteins to the Legionella-containing vacuole (LCV) within macrophages and ameba. Here we show that ectopically expressed AnkB in Dictyostelium discoideum is targeted to the plasma membrane where it recruits polyubiquitinated proteins and it trans-rescues the intracellular growth defect of the ankB null mutant, which has never been demonstrated for any effector in ameba. Using co-immunoprecipitation and bimolecular fluorescence complementation we show specific interaction of Skp1 of D. discoideum with the F-box domain of AnkB, which has never been demonstrated in ameba. We show that anchoring of AnkB to the cytosolic face of the LCV membrane in D. discoideum is mediated by the host farnesylation of the C-terminal eukaryotic CaaX motif of AnkB and is independent of the F-box and the two ANK domains, which has never been demonstrated in ameba. Importantly, the three host farnesylation enzymes farnesyl transferase, RCE-1, and isoprenyl cysteine carboxyl methyl transferase of D. discoideum are recruited to the LCV in a Dot/Icm-dependent manner, which has never been demonstrated in ameba. We conclude that the polyubiquitination and farnesylation enzymatic machineries of D. discoideum are recruited to the LCV in a Dot/Icm-dependent manner and the AnkB effector exploits the two evolutionarily conserved eukaryotic machineries to proliferate within ameba, similar to mammalian cells. We propose that L. pneumophila has acquired ankB through inter-kingdom horizontal gene transfer from primitive eukaryotes, which facilitated proliferation of L. pneumophila within human cells and the emergence of Legionnaires’ disease

Oiahcr: online isolated arabic handwritten character recognition using neural network.

In this paper, an online isolated Arabic handwritten character recognition system is introduced. The system can be adapted to achieve the demands of hand-held and digital tablet applications. To achieve this goal, despite of single neural networks, four neural networks are used, one for each cluster of characters. Feed forward back propagation neural networks are used in classification process. This approach is employed as classifiers due to the low computation overhead during training and recall process. The system recognizes on-line isolated Arabic character and achieves an accuracy rate 9٥. 7% from untrained writers and 99.1% for trained writers

Exploitation of Host Polyubiquitination Machinery through Molecular Mimicry by Eukaryotic-Like Bacterial F-Box Effectors

Microbial pathogens have evolved exquisite mechanisms to interfere and intercept host biological processes, often through molecular mimicry of specific host proteins. Ubiquitination is a highly conserved eukaryotic post-translational modification essential in determining protein fate, and is often hijacked by pathogenic bacteria. The conserved SKP1/CUL1/F-box (SCF) E3 ubiquitin ligase complex plays a key role in ubiquitination of proteins in eukaryotic cells. The F-box protein component of the SCF complex provides specificity to ubiquitination by binding to specific cellular proteins, targeting them to be ubiquitinated by the SCF complex. The bacterial pathogens. Legionella pneumophila, Agrobacterium tumefaciens, and Ralstonia solanacearum utilize type III or IV translocation systems to inject into the host cell eukaryotic-like F-box effectors that interact with the host SKP1 component of the SCF complex to trigger ubiquitination of specific host cells targets, which is essential to promote proliferation of these pathogens. Our bioinformatic analyses have identified at least 74 genes encoding putative F-box proteins belonging to 22 other bacterial species, including human pathogens, plant pathogens, and amebal endosymbionts. Therefore, subversion of the host ubiquitination machinery by bacterial F-box proteins may be a widespread strategy amongst pathogenic bacteria. The findings that bacterial F-box proteins harbor Ankyrin repeats as protein–protein interaction domains, which are present in F-box proteins of primitive but not higher eukaryotes, suggest acquisition of many bacterial F-box proteins from primitive eukaryotic hosts rather than the mammalian host

Identification of a Gal/GalNAc Lectin in the Protozoan \u3cem\u3eHartmannella vermiformis\u3c/em\u3e as a Potential Receptor for Attachment and Invasion by the Legionnaires\u27 Disease Bacterium

The Legionnaire\u27s disease bacterium, Legionella pneumophila, is a facultative intracellular pathogen which invades and replicates within two evolutionarily distant hosts, free-living protozoa and mammalian cells. Invasion and intracellular replication within protozoa are thought to be major factors in the transmission of Legionnaire\u27s disease. Although attachment and invasion of human macrophages by L. pneumophila is mediated in part by the complement receptors CR1 and CR3, the protozoan receptor involved in bacterial attachment and invasion has not been identified. To define the molecular events involved in invasion of protozoa by L. pneumophila, we examined the role of protein tyrosine phosphorylation of the protozoan host Hartmannella vermiformis upon attachment and invasion by L. pneumophila. Bacterial attachment and invasion were associated with a time-dependent tyrosine dephosphorylation of multiple host cell proteins. This host cell response was highly specific for live L. pneumophila, required contact with viable bacteria, and was completely reversible following washing off the bacteria from the host cell surface. Tyrosine dephosphorylation of host proteins was blocked by a tyrosine phosphatase inhibitor but not by tyrosine kinase inhibitors. One of the tyrosine dephosphorylated proteins was identified as the 170-kD galactose/N-acetylgalactosamine–inhibitable lectin (Gal/GalNAc) using immunoprecipitation and immunoblotting by antibodies generated against the Gal/GalNAc lectin of the protozoan Entamoeba histolytica. This Gal/GalNAc–inhibitable lectin has been shown previously to mediate adherence of E. histolytica to mammalian epithelial cells. Uptake of L. pneumophila by H. vermiformis was specifically inhibited by two monovalent sugars, Gal and GalNAc, and by mABs generated against the 170-kD lectin of E. histolytica. Interestingly, inhibition of invasion by Gal and GalNAc was associated with inhibition of bacterial-induced tyrosine dephosphorylation of H. vermiformis proteins. High stringency DNA hybridization confirmed the presence of the 170-kD lectin gene in H. vermiformis. We conclude that attachment of L. pneumophila to the H. vermiformis 170-kD lectin is required for invasion and is associated with tyrosine dephosphorylation of the Gal lectin and other host proteins. This is the first demonstration of a potential receptor used by L. pneumophila to invade protozoa