bioRxivBioinformaticsPreprint
Identification and comparison of somatic antigen composition for bacteria from Providencia genus
Polina D. KuchuriD, Anna I. ChurkinaiD, Anna A. RybinaiD, Aleksey S. KomissaroviD
Abstract
Providencia is a genus of Gram-negative bacteria belonging to the Morganellaceae family. This genus includes nine species (P. stuartii, P. sneebia, P. rettgeri, P. rustigianii, P. heimbachae, P. burhodogranariea, P. alcalifaciens, P. huaxiensis, and P. vermicola) with varying degrees of virulence, capable of infecting humans and insects [1, 2]. For Gram-negative bacteria, the somatic antigen (O-antigen) has become one of the key virulence factors. It is the highly immunogenic part of lipopolysaccharides due to the distal location. O-antigens are characterized by structural heterogeneity, providing varying degrees of inter- and intraspecific virulence. At the genetic level, somatic antigens have an operon structure. Operon genes responsible for the synthesis and transformation of O-polysaccharide are transcribed together. Analysis of O-antigen operon organization determines genes specific for each O-serogroup. It is beneficial for molecular typing of strains and for studies of bacterial evolution. This study focuses on identifying and comparing candidates for O-antigen operons in Providencia species with different levels of virulence. The hypothesis is the presence of an association between the O-antigen operon composition and the bacteria lifestyle. Data processing and analysis are carried out by a pipeline developed by the authors. Pipeline combines five steps of the genome analysis: genome quality evaluation, assembly annotation, operon identification with verification of operon boundaries, and visualization of O-antigen operons. The results reveal previously undescribed O-antigen genes and the changes in the O-antigen operons structure. Among the changes are a transposon insertion leading to tetracycline resistance and the presence of IS elements.
- organism: bacteria (other)
- assay: whole-genome sequencing
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 1
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