Network-Based Prediction of Novel CRISPR-Associated Genes in Metagenomes

ABSTRACT A diversity of clustered regularly interspaced short palindromic repeat (CRISPR)-Cas systems provide adaptive immunity to bacteria and archaea through recording “memories” of past viral infections. Recently, many novel CRISPR-associated proteins have been discovered via computational studie...

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Autores principales:	Jake L. Weissman, Philip L. F. Johnson
Formato:	article
Lenguaje:	EN
Publicado:	American Society for Microbiology 2020
Materias:	CRISPR metagenomics microbial ecology network Microbiology QR1-502
Acceso en línea:	https://doaj.org/article/fc1e5430a6f74a99a049cd59995f88d8
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spelling	oai:doaj.org-article:fc1e5430a6f74a99a049cd59995f88d82021-12-02T18:44:39ZNetwork-Based Prediction of Novel CRISPR-Associated Genes in Metagenomes10.1128/mSystems.00752-192379-5077https://doaj.org/article/fc1e5430a6f74a99a049cd59995f88d82020-02-01T00:00:00Zhttps://journals.asm.org/doi/10.1128/mSystems.00752-19https://doaj.org/toc/2379-5077ABSTRACT A diversity of clustered regularly interspaced short palindromic repeat (CRISPR)-Cas systems provide adaptive immunity to bacteria and archaea through recording “memories” of past viral infections. Recently, many novel CRISPR-associated proteins have been discovered via computational studies, but those studies relied on biased and incomplete databases of assembled genomes. We avoided these biases and applied a network theory approach to search for novel CRISPR-associated genes by leveraging subtle ecological cooccurrence patterns identified from environmental metagenomes. We validated our method using existing annotations and discovered 32 novel CRISPR-associated gene families. These genes span a range of putative functions, with many potentially regulating the response to infection. IMPORTANCE Every branch on the tree of life, including microbial life, faces the threat of viral pathogens. Over the course of billions of years of coevolution, prokaryotes have evolved a great diversity of strategies to defend against viral infections. One of these is the CRISPR adaptive immune system, which allows microbes to “remember” past infections in order to better fight them in the future. There has been much interest among molecular biologists in CRISPR immunity because this system can be repurposed as a tool for precise genome editing. Recently, a number of comparative genomics approaches have been used to detect novel CRISPR-associated genes in databases of genomes with great success, potentially leading to the development of new genome-editing tools. Here, we developed novel methods to search for these distinct classes of genes directly in environmental samples (“metagenomes”), thus capturing a more complete picture of the natural diversity of CRISPR-associated genes.Jake L. WeissmanPhilip L. F. JohnsonAmerican Society for MicrobiologyarticleCRISPRmetagenomicsmicrobial ecologynetworkMicrobiologyQR1-502ENmSystems, Vol 5, Iss 1 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	CRISPR metagenomics microbial ecology network Microbiology QR1-502
spellingShingle	CRISPR metagenomics microbial ecology network Microbiology QR1-502 Jake L. Weissman Philip L. F. Johnson Network-Based Prediction of Novel CRISPR-Associated Genes in Metagenomes
description	ABSTRACT A diversity of clustered regularly interspaced short palindromic repeat (CRISPR)-Cas systems provide adaptive immunity to bacteria and archaea through recording “memories” of past viral infections. Recently, many novel CRISPR-associated proteins have been discovered via computational studies, but those studies relied on biased and incomplete databases of assembled genomes. We avoided these biases and applied a network theory approach to search for novel CRISPR-associated genes by leveraging subtle ecological cooccurrence patterns identified from environmental metagenomes. We validated our method using existing annotations and discovered 32 novel CRISPR-associated gene families. These genes span a range of putative functions, with many potentially regulating the response to infection. IMPORTANCE Every branch on the tree of life, including microbial life, faces the threat of viral pathogens. Over the course of billions of years of coevolution, prokaryotes have evolved a great diversity of strategies to defend against viral infections. One of these is the CRISPR adaptive immune system, which allows microbes to “remember” past infections in order to better fight them in the future. There has been much interest among molecular biologists in CRISPR immunity because this system can be repurposed as a tool for precise genome editing. Recently, a number of comparative genomics approaches have been used to detect novel CRISPR-associated genes in databases of genomes with great success, potentially leading to the development of new genome-editing tools. Here, we developed novel methods to search for these distinct classes of genes directly in environmental samples (“metagenomes”), thus capturing a more complete picture of the natural diversity of CRISPR-associated genes.
format	article
author	Jake L. Weissman Philip L. F. Johnson
author_facet	Jake L. Weissman Philip L. F. Johnson
author_sort	Jake L. Weissman
title	Network-Based Prediction of Novel CRISPR-Associated Genes in Metagenomes
title_short	Network-Based Prediction of Novel CRISPR-Associated Genes in Metagenomes
title_full	Network-Based Prediction of Novel CRISPR-Associated Genes in Metagenomes
title_fullStr	Network-Based Prediction of Novel CRISPR-Associated Genes in Metagenomes
title_full_unstemmed	Network-Based Prediction of Novel CRISPR-Associated Genes in Metagenomes
title_sort	network-based prediction of novel crispr-associated genes in metagenomes
publisher	American Society for Microbiology
publishDate	2020
url	https://doaj.org/article/fc1e5430a6f74a99a049cd59995f88d8
work_keys_str_mv	AT jakelweissman networkbasedpredictionofnovelcrisprassociatedgenesinmetagenomes AT philiplfjohnson networkbasedpredictionofnovelcrisprassociatedgenesinmetagenomes
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Network-Based Prediction of Novel CRISPR-Associated Genes in Metagenomes

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