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...

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Main Authors: Sanjiv Kumar, Rahul Shubhra Mandal, Vincent Bulone, Vaibhav Srivastava
Format: Article
Language:English
Published: Frontiers Media S.A. 2020-10-01
Series:Frontiers in Microbiology
Subjects:
Online Access:https://www.frontiersin.org/article/10.3389/fmicb.2020.571093/full
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spelling doaj-d26ad813206643c2bd5cdef2ff167be72020-11-25T03:55:40ZengFrontiers Media S.A.Frontiers in Microbiology1664-302X2020-10-011110.3389/fmicb.2020.571093571093Identification of Growth Inhibitors of the Fish Pathogen Saprolegnia parasitica Using in silico Subtractive Proteomics, Computational Modeling, and Biochemical ValidationSanjiv Kumar0Rahul Shubhra Mandal1Vincent Bulone2Vincent Bulone3Vaibhav Srivastava4Division of Glycoscience, Department of Chemistry, School of Engineering Sciences in Chemistry, Biotechnology and Health, Royal Institute of Technology (KTH), AlbaNova University Centre, Stockholm, SwedenDepartment of Cancer Biology, Abramson Family Cancer Research Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United StatesDivision of Glycoscience, Department of Chemistry, School of Engineering Sciences in Chemistry, Biotechnology and Health, Royal Institute of Technology (KTH), AlbaNova University Centre, Stockholm, SwedenSchool of Agriculture, Food and Wine, The University of Adelaide, Adelaide, SA, AustraliaDivision of Glycoscience, Department of Chemistry, School of Engineering Sciences in Chemistry, Biotechnology and Health, Royal Institute of Technology (KTH), AlbaNova University Centre, Stockholm, SwedenMany 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 (MIC100) 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.https://www.frontiersin.org/article/10.3389/fmicb.2020.571093/fulldisease controlfish pathogengrowth inhibitorsoomyceteSaprolegnia parasiticain silico proteomics
collection DOAJ
language English
format Article
sources DOAJ
author Sanjiv Kumar
Rahul Shubhra Mandal
Vincent Bulone
Vincent Bulone
Vaibhav Srivastava
spellingShingle Sanjiv Kumar
Rahul Shubhra Mandal
Vincent Bulone
Vincent Bulone
Vaibhav Srivastava
Identification of Growth Inhibitors of the Fish Pathogen Saprolegnia parasitica Using in silico Subtractive Proteomics, Computational Modeling, and Biochemical Validation
Frontiers in Microbiology
disease control
fish pathogen
growth inhibitors
oomycete
Saprolegnia parasitica
in silico proteomics
author_facet Sanjiv Kumar
Rahul Shubhra Mandal
Vincent Bulone
Vincent Bulone
Vaibhav Srivastava
author_sort Sanjiv Kumar
title 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_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_sort identification of growth inhibitors of the fish pathogen saprolegnia parasitica using in silico subtractive proteomics, computational modeling, and biochemical validation
publisher Frontiers Media S.A.
series Frontiers in Microbiology
issn 1664-302X
publishDate 2020-10-01
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 (MIC100) 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.
topic disease control
fish pathogen
growth inhibitors
oomycete
Saprolegnia parasitica
in silico proteomics
url https://www.frontiersin.org/article/10.3389/fmicb.2020.571093/full
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