Identification of Virulence Markers and Phylogenetic Groups' Association, and Antimicrobial Susceptibility of Uropathogenic Escherichia coli Isolates.

BACKGROUND: Urinary tract infections represent a world public health problem, which is caused mainly by Uropathogenic Escherichia coli. Although they are originally found in the intestinal microbiota in the majority of the cases, urinary tract infections can also be caused by intra-intestinal pathogenic E. coli. OBJECTIVE: The main objective of our research is to identify the virulence factors generally associated with different pathotypes across phylogenetic groups. METHODS: E. coli were isolated from patients with urinary tract infections. Antimicrobial susceptibility tests, virulence genes and phylogroups were prospected. The data analysis were performed using the chi-square and Fisher exact test. RESULTS: In total, 72.2% of isolates showed multidrug resistant. We have also depicted an important association between E. coli from inpatients with UTIs and pap and hlyA genes (p-0.041 and p-0.019 respectively). The predominant phylogenetic group in our isolates is B2 (45.4%) followed by D (12.4%). Our results showed that 9.3% of isolates have an unknown phylogroup which shows a significant association with astA gene (p-0.008). We have as well found a significant association between B2 and three virulence genes namely pap, hlyA and invE (p-0.002, p-0.001, p-0.025 respectively); B1 and pap, hlyA genes (p-0.049 and p-0.021 respectively); E and afa gene (p-0.024). CONCLUSION: Certain virulence factors have been shown to be potential targets for drug design and therapeutic pathways in order to deal with the antimicrobial resistance problem enhanced by antibiotic therapy.

A natural language processing approach based on embedding deep learning from heterogeneous compounds for quantitative structure-activity relationship modeling.

Over the past decade, rapid development in biological and chemical technologies such as high-throughput screening, parallel synthesis, has been significantly increased the amount of data, which requires the creation and the integration of new analytical methods, especially deep learning models. Recently, there is an increasing interest in deep learning utilization in computer-aided drug discovery due to its exceptional successful application in many fields. The present work proposed a natural language processing approach, based on embedding deep neural networks. Our method aims to transform the Simplified Molecular Input Line Entry System format into word embedding vectors to represent the semantics of compounds. These vectors are fed into supervised machine learning algorithms such as convolutional long short-term memory neural network, support vector machine, and random forest to build up quantitative structure-activity relationship models on toxicity data sets. The obtained results on toxicity data to the ciliate Tetrahymena pyriformis (IGC50 ), and acute toxicity rat data expressed as median lethal dose of treated rats (LD50 ) show that our approach can eventually be used to predict the activities of chemical compounds efficiently. All material used in this study is available online through the GitHub portal (https://github.com/BoukeliaAbdelbasset/NLPDeepQSAR.git).

IpiRId: Integrative approach for piRNA prediction using genomic and epigenomic data.

Many computational tools have been proposed during the two last decades for predicting piRNAs, which are molecules with important role in post-transcriptional gene regulation. However, these tools are mostly based on only one feature that is generally related to the sequence. Discoveries in the domain of piRNAs are still in their beginning stages, and recent publications have shown many new properties. Here, we propose an integrative approach for piRNA prediction in which several types of genomic and epigenomic properties that can be used to characterize these molecules are examined. We reviewed and extracted a large number of piRNA features from the literature that have been observed experimentally in several species. These features are represented by different kernels, in a Multiple Kernel Learning based approach, implemented within an object-oriented framework. The obtained tool, called IpiRId, shows prediction results that attain more than 90% of accuracy on different tested species (human, mouse and fly), outperforming all existing tools. Besides, our method makes it possible to study the validity of each given feature in a given species. Finally, the developed tool is modular and easily extensible, and can be adapted for predicting other types of ncRNAs. The IpiRId software and the user-friendly web-based server of our tool are now freely available to academic users at: https://evryrna.ibisc.univ-evry.fr/evryrna/.

In Silico Prediction of RNA Secondary Structure.

The secondary structure of an RNA molecule represents the base-pairing interactions within the molecule and fundamentally determines its overall structure. In this chapter, we overview the main approaches and existing tools for predicting RNA secondary structures, as well as methods for identifying noncoding RNAs from genomic sequences or RNA sequencing data. We then focus on the identification of a well-known class of small noncoding RNAs, namely microRNAs, which play very important roles in many biological processes through regulating post-transcriptionally the expression of genes and which dysregulation has been shown to be involved in several human diseases.