Volume 10, Issue 3 (2019)                   JMBS 2019, 10(3): 511-518 | Back to browse issues page

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Panahi A, Vaseghi A. Horizontal transfer analysis of type III secretion system (T3SS) genes in several types of Pseudomonas bacteria using Seqword software. JMBS 2019; 10 (3) :511-518
URL: http://biot.modares.ac.ir/article-22-28601-en.html
1- Biology Department, Science Faculty, University of Mohaghegh Ardabili, Ardabil, Iran, Biology Department, Science Faculty, University of Mohaghegh Ardabili, Daneshgah Street, Ardabil, Iran. , arpanahi@uma.ac.ir
2- Nanobiotechnology Department, Biological Sciences Faculty, Tarbiat Modares University, Tehran, Iran
Abstract:   (3634 Views)
All bacteria have many different secretion systems to transfer of their macromolecules to out. Currently, seven secretion systems have been identified. Transfer, tracing, and horizontal transmission of this gene groups, are many important in our understanding about these gene's application in bacteria and other substances. In this study, we examined Type III secretion system (T3SS) genes in Pseudomonas with using the bioinformatics software such as SeqWord Genome Browser in some databases for instants, ACLAME, Mobil Elements Genetic (MGEs), and PAthogenicity Islands Data Base (PAIDB). The results indicated that the T3SS genes transfers are observed with the percentage between 30% and 100%. Our findings also show the P. fluorescens bacterium has the most species with 15 transmitted genes. Bioinformatics predictors showed P. fluorescens F113 subtype with 11 genes had the highest transferability of T3SS cluster genes. The bacteria species such P. Fluorescens Pf-5, P. syringae pv. Glycinea, P. syringae pv. Aptata, P. syringae pv. Japonica, P. syringae pv. Pisi, P. aeruginosa UCBPP-PA14 show up about 100% of horizontal transfer from T3SS. Our results also indicated that T3SS, which are important in the bacteria disease, have the highest transmission rates. This study indicates can be showing the systematic transmission of disease from host and pathogen during the evolution.
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Article Type: Original Research | Subject: Bioinformatics
Received: 2018/12/25 | Accepted: 2019/03/13 | Published: 2019/09/1

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