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Bacteriophage Genetic Edition Using LSTM

Bacteriophages are gaining increasing interest as antimicrobial tools, largely due to the emergence of multi-antibiotic–resistant bacteria. Although their huge diversity and virulence make them particularly attractive for targeting a wide range of bacterial pathogens, it is difficult to select suita...

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Autores principales: Ataee, Shabnam, Brochet, Xavier, Peña-Reyes, Carlos Andrés
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9639385/
https://www.ncbi.nlm.nih.gov/pubmed/36353213
http://dx.doi.org/10.3389/fbinf.2022.932319
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author Ataee, Shabnam
Brochet, Xavier
Peña-Reyes, Carlos Andrés
author_facet Ataee, Shabnam
Brochet, Xavier
Peña-Reyes, Carlos Andrés
author_sort Ataee, Shabnam
collection PubMed
description Bacteriophages are gaining increasing interest as antimicrobial tools, largely due to the emergence of multi-antibiotic–resistant bacteria. Although their huge diversity and virulence make them particularly attractive for targeting a wide range of bacterial pathogens, it is difficult to select suitable phages due to their high specificity which limits their host range. In addition, other challenges remain such as structural fragility under certain environmental conditions, immunogenicity of phage therapy, or development of bacterial resistance. The use of genetically engineered phages may reduce characteristics that hinder prophylactic and therapeutic applications of phages. Nowadays, there is no systematic method to modify a given phage genome conferring its sought characteristics. We explore the use of artificial intelligence for this purpose as it has the potential to both guide and accelerate genome modification to generate phage variants with unique properties that overcome the limitations of natural phages. We propose an original architecture composed of two deep learning–driven components: a phage–bacterium interaction predictor and a phage genome-sequence generator. The former is a multi-branch 1-D convolutional neural network (1D-CNN) that analyses phage and bacterial genomes to predict interactions. The latter is a recurrent neural network, more particularly a long short-term memory (LSTM), that performs genomic modifications to a phage to offer substantial host range improvement. For this component, we developed two different architectures composed of one or two stacked LSTM layers with 256 neurons each. These generators are used to modify, more precisely to rewrite, the genome sequence of 42 selected phages, while the predictor is used to estimate the host range of the modified bacteriophages across 46 strains of Pseudomonas aeruginosa. The proposed generators, trained with an average accuracy of 96.1%, are able to improve the host range for an average of 18 phages among the 42 under study, increasing both their average host range, by 73.0 and 103.7%, and the maximum host ranges from 21 to 24 and 29, respectively. These promising results showed that the use of deep learning methodologies allows genetic modification of phages to extend, for instance, their host range, confirming the potential of these approaches to guide bacteriophage engineering.
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spelling pubmed-96393852022-11-08 Bacteriophage Genetic Edition Using LSTM Ataee, Shabnam Brochet, Xavier Peña-Reyes, Carlos Andrés Front Bioinform Bioinformatics Bacteriophages are gaining increasing interest as antimicrobial tools, largely due to the emergence of multi-antibiotic–resistant bacteria. Although their huge diversity and virulence make them particularly attractive for targeting a wide range of bacterial pathogens, it is difficult to select suitable phages due to their high specificity which limits their host range. In addition, other challenges remain such as structural fragility under certain environmental conditions, immunogenicity of phage therapy, or development of bacterial resistance. The use of genetically engineered phages may reduce characteristics that hinder prophylactic and therapeutic applications of phages. Nowadays, there is no systematic method to modify a given phage genome conferring its sought characteristics. We explore the use of artificial intelligence for this purpose as it has the potential to both guide and accelerate genome modification to generate phage variants with unique properties that overcome the limitations of natural phages. We propose an original architecture composed of two deep learning–driven components: a phage–bacterium interaction predictor and a phage genome-sequence generator. The former is a multi-branch 1-D convolutional neural network (1D-CNN) that analyses phage and bacterial genomes to predict interactions. The latter is a recurrent neural network, more particularly a long short-term memory (LSTM), that performs genomic modifications to a phage to offer substantial host range improvement. For this component, we developed two different architectures composed of one or two stacked LSTM layers with 256 neurons each. These generators are used to modify, more precisely to rewrite, the genome sequence of 42 selected phages, while the predictor is used to estimate the host range of the modified bacteriophages across 46 strains of Pseudomonas aeruginosa. The proposed generators, trained with an average accuracy of 96.1%, are able to improve the host range for an average of 18 phages among the 42 under study, increasing both their average host range, by 73.0 and 103.7%, and the maximum host ranges from 21 to 24 and 29, respectively. These promising results showed that the use of deep learning methodologies allows genetic modification of phages to extend, for instance, their host range, confirming the potential of these approaches to guide bacteriophage engineering. Frontiers Media S.A. 2022-07-13 /pmc/articles/PMC9639385/ /pubmed/36353213 http://dx.doi.org/10.3389/fbinf.2022.932319 Text en Copyright © 2022 Ataee, Brochet and Peña-Reyes. https://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 Bioinformatics
Ataee, Shabnam
Brochet, Xavier
Peña-Reyes, Carlos Andrés
Bacteriophage Genetic Edition Using LSTM
title Bacteriophage Genetic Edition Using LSTM
title_full Bacteriophage Genetic Edition Using LSTM
title_fullStr Bacteriophage Genetic Edition Using LSTM
title_full_unstemmed Bacteriophage Genetic Edition Using LSTM
title_short Bacteriophage Genetic Edition Using LSTM
title_sort bacteriophage genetic edition using lstm
topic Bioinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9639385/
https://www.ncbi.nlm.nih.gov/pubmed/36353213
http://dx.doi.org/10.3389/fbinf.2022.932319
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