Cargando…

Identification of Growth Inhibitors of the Fish Pathogen Saprolegnia parasitica Using in silico Subtractive Proteomics, Computational Modeling, and Biochemical Validation

Many Stramenopile species belonging to oomycetes from the genus Saprolegnia infect fish, amphibians, and crustaceans in aquaculture farms and natural ecosystems. Saprolegnia parasitica is one of the most severe fish pathogens, responsible for high losses in the aquaculture industry worldwide. Most o...

Descripción completa

Detalles Bibliográficos
Autores principales: Kumar, Sanjiv, Mandal, Rahul Shubhra, Bulone, Vincent, Srivastava, Vaibhav
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7596660/
https://www.ncbi.nlm.nih.gov/pubmed/33178154
http://dx.doi.org/10.3389/fmicb.2020.571093
_version_ 1783602163155468288
author Kumar, Sanjiv
Mandal, Rahul Shubhra
Bulone, Vincent
Srivastava, Vaibhav
author_facet Kumar, Sanjiv
Mandal, Rahul Shubhra
Bulone, Vincent
Srivastava, Vaibhav
author_sort Kumar, Sanjiv
collection PubMed
description Many Stramenopile species belonging to oomycetes from the genus Saprolegnia infect fish, amphibians, and crustaceans in aquaculture farms and natural ecosystems. Saprolegnia parasitica is one of the most severe fish pathogens, responsible for high losses in the aquaculture industry worldwide. Most of the molecules reported to date for the control of Saprolegnia infections either are inefficient or have negative impacts on the health of the fish hosts or the environment resulting in substantial economic losses. Until now, the whole proteome of S. parasitica has not been explored for a systematic screening of novel inhibitors against the pathogen. The present study was designed to develop a consensus computational framework for the identification of potential target proteins and their inhibitors and subsequent experimental validation of selected compounds. Comparative analysis between the proteomes of Saprolegnia, humans and fish species identified proteins that are specific and essential for the survival of the pathogen. The DrugBank database was exploited to select food and drug administration (FDA)-approved inhibitors whose high binding affinity to their respective protein targets was confirmed by computational modeling. At least six of the identified compounds significantly inhibited the growth of S. parasitica in vitro. Triclosan was found to be most effective with a minimum inhibitory concentration (MIC(100)) of 4 μg/ml. Optical microscopy showed that the inhibitors affect the morphology of hyphal cells, with hyper-branching being commonly observed. The inhibitory effects of the compounds identified in this study on Saprolegnia’s mycelial growth indicate that they are potentially usable for disease control against this class of oomycete pathogens. Similar approaches can be easily adopted for the identification of potential inhibitors against other plant and animal pathogenic oomycete infections.
format Online
Article
Text
id pubmed-7596660
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-75966602020-11-10 Identification of Growth Inhibitors of the Fish Pathogen Saprolegnia parasitica Using in silico Subtractive Proteomics, Computational Modeling, and Biochemical Validation Kumar, Sanjiv Mandal, Rahul Shubhra Bulone, Vincent Srivastava, Vaibhav Front Microbiol Microbiology Many Stramenopile species belonging to oomycetes from the genus Saprolegnia infect fish, amphibians, and crustaceans in aquaculture farms and natural ecosystems. Saprolegnia parasitica is one of the most severe fish pathogens, responsible for high losses in the aquaculture industry worldwide. Most of the molecules reported to date for the control of Saprolegnia infections either are inefficient or have negative impacts on the health of the fish hosts or the environment resulting in substantial economic losses. Until now, the whole proteome of S. parasitica has not been explored for a systematic screening of novel inhibitors against the pathogen. The present study was designed to develop a consensus computational framework for the identification of potential target proteins and their inhibitors and subsequent experimental validation of selected compounds. Comparative analysis between the proteomes of Saprolegnia, humans and fish species identified proteins that are specific and essential for the survival of the pathogen. The DrugBank database was exploited to select food and drug administration (FDA)-approved inhibitors whose high binding affinity to their respective protein targets was confirmed by computational modeling. At least six of the identified compounds significantly inhibited the growth of S. parasitica in vitro. Triclosan was found to be most effective with a minimum inhibitory concentration (MIC(100)) of 4 μg/ml. Optical microscopy showed that the inhibitors affect the morphology of hyphal cells, with hyper-branching being commonly observed. The inhibitory effects of the compounds identified in this study on Saprolegnia’s mycelial growth indicate that they are potentially usable for disease control against this class of oomycete pathogens. Similar approaches can be easily adopted for the identification of potential inhibitors against other plant and animal pathogenic oomycete infections. Frontiers Media S.A. 2020-10-16 /pmc/articles/PMC7596660/ /pubmed/33178154 http://dx.doi.org/10.3389/fmicb.2020.571093 Text en Copyright © 2020 Kumar, Mandal, Bulone and Srivastava. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Microbiology
Kumar, Sanjiv
Mandal, Rahul Shubhra
Bulone, Vincent
Srivastava, Vaibhav
Identification of Growth Inhibitors of the Fish Pathogen Saprolegnia parasitica Using in silico Subtractive Proteomics, Computational Modeling, and Biochemical Validation
title Identification of Growth Inhibitors of the Fish Pathogen Saprolegnia parasitica Using in silico Subtractive Proteomics, Computational Modeling, and Biochemical Validation
title_full Identification of Growth Inhibitors of the Fish Pathogen Saprolegnia parasitica Using in silico Subtractive Proteomics, Computational Modeling, and Biochemical Validation
title_fullStr Identification of Growth Inhibitors of the Fish Pathogen Saprolegnia parasitica Using in silico Subtractive Proteomics, Computational Modeling, and Biochemical Validation
title_full_unstemmed Identification of Growth Inhibitors of the Fish Pathogen Saprolegnia parasitica Using in silico Subtractive Proteomics, Computational Modeling, and Biochemical Validation
title_short Identification of Growth Inhibitors of the Fish Pathogen Saprolegnia parasitica Using in silico Subtractive Proteomics, Computational Modeling, and Biochemical Validation
title_sort identification of growth inhibitors of the fish pathogen saprolegnia parasitica using in silico subtractive proteomics, computational modeling, and biochemical validation
topic Microbiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7596660/
https://www.ncbi.nlm.nih.gov/pubmed/33178154
http://dx.doi.org/10.3389/fmicb.2020.571093
work_keys_str_mv AT kumarsanjiv identificationofgrowthinhibitorsofthefishpathogensaprolegniaparasiticausinginsilicosubtractiveproteomicscomputationalmodelingandbiochemicalvalidation
AT mandalrahulshubhra identificationofgrowthinhibitorsofthefishpathogensaprolegniaparasiticausinginsilicosubtractiveproteomicscomputationalmodelingandbiochemicalvalidation
AT bulonevincent identificationofgrowthinhibitorsofthefishpathogensaprolegniaparasiticausinginsilicosubtractiveproteomicscomputationalmodelingandbiochemicalvalidation
AT srivastavavaibhav identificationofgrowthinhibitorsofthefishpathogensaprolegniaparasiticausinginsilicosubtractiveproteomicscomputationalmodelingandbiochemicalvalidation