Prediction and Analysis in silico of Genomic Islands in Aeromonas hydrophila

Aeromonas are Gram-negative rods widely distributed in the environment. They can cause severe infections in fish related to financial losses in the fish industry, and are considered opportunistic pathogens of humans causing infections ranging from diarrhea to septicemia. The objective of this study...

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Autores principales:	Antonio Camilo da Silva Filho, Jeroniza Nunes Marchaukoski, Roberto Tadeu Raittz, Camilla Reginatto De Pierri, Diogo de Jesus Soares Machado, Cyntia Maria Telles Fadel-Picheth, Geraldo Picheth
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	Aeromonas hydrophila genomic island virulence metabolism antibiotic resistance Microbiology QR1-502
Acceso en línea:	https://doaj.org/article/fe8790c5873343baafc5451761506716
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spelling	oai:doaj.org-article:fe8790c5873343baafc54517615067162021-12-01T13:47:42ZPrediction and Analysis in silico of Genomic Islands in Aeromonas hydrophila1664-302X10.3389/fmicb.2021.769380https://doaj.org/article/fe8790c5873343baafc54517615067162021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fmicb.2021.769380/fullhttps://doaj.org/toc/1664-302XAeromonas are Gram-negative rods widely distributed in the environment. They can cause severe infections in fish related to financial losses in the fish industry, and are considered opportunistic pathogens of humans causing infections ranging from diarrhea to septicemia. The objective of this study was to determine in silico the contribution of genomic islands to A. hydrophila. The complete genomes of 17 A. hydrophila isolates, which were separated into two phylogenetic groups, were analyzed using a genomic island (GI) predictor. The number of predicted GIs and their characteristics varied among strains. Strains from group 1, which contains mainly fish pathogens, generally have a higher number of predicted GIs, and with larger size, than strains from group 2 constituted by strains recovered from distinct sources. Only a few predicted GIs were shared among them and contained mostly genes from the core genome. Features related to virulence, metabolism, and resistance were found in the predicted GIs, but strains varied in relation to their gene content. In strains from group 1, O Ag biosynthesis clusters OX1 and OX6 were identified, while strains from group 2 each had unique clusters. Metabolic pathways for myo-inositol, L-fucose, sialic acid, and a cluster encoding QueDEC, tgtA5, and proteins related to DNA metabolism were identified in strains of group 1, which share a high number of predicted GIs. No distinctive features of group 2 strains were identified in their predicted GIs, which are more diverse and possibly better represent GIs in this species. However, some strains have several resistance attributes encoded by their predicted GIs. Several predicted GIs encode hypothetical proteins and phage proteins whose functions have not been identified but may contribute to Aeromonas fitness. In summary, features with functions identified on predicted GIs may confer advantages to host colonization and competitiveness in the environment.Antonio Camilo da Silva FilhoJeroniza Nunes MarchaukoskiRoberto Tadeu RaittzCamilla Reginatto De PierriDiogo de Jesus Soares MachadoCyntia Maria Telles Fadel-PichethGeraldo PichethFrontiers Media S.A.articleAeromonas hydrophilagenomic islandvirulencemetabolismantibiotic resistanceMicrobiologyQR1-502ENFrontiers in Microbiology, Vol 12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Aeromonas hydrophila genomic island virulence metabolism antibiotic resistance Microbiology QR1-502
spellingShingle	Aeromonas hydrophila genomic island virulence metabolism antibiotic resistance Microbiology QR1-502 Antonio Camilo da Silva Filho Jeroniza Nunes Marchaukoski Roberto Tadeu Raittz Camilla Reginatto De Pierri Diogo de Jesus Soares Machado Cyntia Maria Telles Fadel-Picheth Geraldo Picheth Prediction and Analysis in silico of Genomic Islands in Aeromonas hydrophila
description	Aeromonas are Gram-negative rods widely distributed in the environment. They can cause severe infections in fish related to financial losses in the fish industry, and are considered opportunistic pathogens of humans causing infections ranging from diarrhea to septicemia. The objective of this study was to determine in silico the contribution of genomic islands to A. hydrophila. The complete genomes of 17 A. hydrophila isolates, which were separated into two phylogenetic groups, were analyzed using a genomic island (GI) predictor. The number of predicted GIs and their characteristics varied among strains. Strains from group 1, which contains mainly fish pathogens, generally have a higher number of predicted GIs, and with larger size, than strains from group 2 constituted by strains recovered from distinct sources. Only a few predicted GIs were shared among them and contained mostly genes from the core genome. Features related to virulence, metabolism, and resistance were found in the predicted GIs, but strains varied in relation to their gene content. In strains from group 1, O Ag biosynthesis clusters OX1 and OX6 were identified, while strains from group 2 each had unique clusters. Metabolic pathways for myo-inositol, L-fucose, sialic acid, and a cluster encoding QueDEC, tgtA5, and proteins related to DNA metabolism were identified in strains of group 1, which share a high number of predicted GIs. No distinctive features of group 2 strains were identified in their predicted GIs, which are more diverse and possibly better represent GIs in this species. However, some strains have several resistance attributes encoded by their predicted GIs. Several predicted GIs encode hypothetical proteins and phage proteins whose functions have not been identified but may contribute to Aeromonas fitness. In summary, features with functions identified on predicted GIs may confer advantages to host colonization and competitiveness in the environment.
format	article
author	Antonio Camilo da Silva Filho Jeroniza Nunes Marchaukoski Roberto Tadeu Raittz Camilla Reginatto De Pierri Diogo de Jesus Soares Machado Cyntia Maria Telles Fadel-Picheth Geraldo Picheth
author_facet	Antonio Camilo da Silva Filho Jeroniza Nunes Marchaukoski Roberto Tadeu Raittz Camilla Reginatto De Pierri Diogo de Jesus Soares Machado Cyntia Maria Telles Fadel-Picheth Geraldo Picheth
author_sort	Antonio Camilo da Silva Filho
title	Prediction and Analysis in silico of Genomic Islands in Aeromonas hydrophila
title_short	Prediction and Analysis in silico of Genomic Islands in Aeromonas hydrophila
title_full	Prediction and Analysis in silico of Genomic Islands in Aeromonas hydrophila
title_fullStr	Prediction and Analysis in silico of Genomic Islands in Aeromonas hydrophila
title_full_unstemmed	Prediction and Analysis in silico of Genomic Islands in Aeromonas hydrophila
title_sort	prediction and analysis in silico of genomic islands in aeromonas hydrophila
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/fe8790c5873343baafc5451761506716
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Prediction and Analysis in silico of Genomic Islands in Aeromonas hydrophila

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