1
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Duan N, Hand E, Pheko M, Sharma S, Emiola A. Structure-guided discovery of anti-CRISPR and anti-phage defense proteins. Nat Commun 2024; 15:649. [PMID: 38245560 PMCID: PMC10799925 DOI: 10.1038/s41467-024-45068-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Accepted: 01/12/2024] [Indexed: 01/22/2024] Open
Abstract
Bacteria use a variety of defense systems to protect themselves from phage infection. In turn, phages have evolved diverse counter-defense measures to overcome host defenses. Here, we use protein structural similarity and gene co-occurrence analyses to screen >66 million viral protein sequences and >330,000 metagenome-assembled genomes for the identification of anti-phage and counter-defense systems. We predict structures for ~300,000 proteins and perform large-scale, pairwise comparison to known anti-CRISPR (Acr) and anti-phage proteins to identify structural homologs that otherwise may not be uncovered using primary sequence search. This way, we identify a Bacteroidota phage Acr protein that inhibits Cas12a, and an Akkermansia muciniphila anti-phage defense protein, termed BxaP. Gene bxaP is found in loci encoding Bacteriophage Exclusion (BREX) and restriction-modification defense systems, but confers immunity independently. Our work highlights the advantage of combining protein structural features and gene co-localization information in studying host-phage interactions.
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Affiliation(s)
- Ning Duan
- Microbial Therapeutics Unit, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, USA
| | - Emily Hand
- Microbial Therapeutics Unit, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, USA
| | - Mannuku Pheko
- Microbial Therapeutics Unit, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, USA
| | - Shikha Sharma
- Microbial Therapeutics Unit, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, USA
| | - Akintunde Emiola
- Microbial Therapeutics Unit, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, USA.
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2
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Silva AMA, Luz ACO, Xavier KVM, Barros MPS, Alves HB, Batista MVA, Leal-Balbino TC. Analysis of CRISPR/Cas Genetic Structure, Spacer Content and Molecular Epidemiology in Brazilian Acinetobacter baumannii Clinical Isolates. Pathogens 2023; 12:764. [PMID: 37375454 DOI: 10.3390/pathogens12060764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 05/08/2023] [Accepted: 05/11/2023] [Indexed: 06/29/2023] Open
Abstract
CRISPR/Cas is a molecular mechanism to prevent predatory viruses from invading bacteria via the insertion of small viral sequences (spacers) in its repetitive locus. The nature of spacer incorporation and the viral origins of spacers provide an overview of the genetic evolution of bacteria, their natural viral predators, and the mechanisms that prokaryotes may use to protect themselves, or to acquire mobile genetic elements such as plasmids. Here, we report on the CRISPR/Cas genetic structure, its spacer content, and strain epidemiology through MLST and CRISPR typing in Acinetobacter baumannii, an opportunistic pathogen intimately related to hospital infections and antimicrobial resistance. Results show distinct genetic characteristics, such as polymorphisms specific to ancestor direct repeats, a well-defined degenerate repeat, and a conserved leader sequence, as well as showing most spacers as targeting bacteriophages, and several self-targeting spacers, directed at prophages. There was a particular relationship between CRISPR/Cas and CC113 in the study of Brazilian isolates, and CRISPR-related typing techniques are interesting for subtyping strains with the same MLST profile. We want to emphasize the significance of descriptive genetic research on CRISPR loci, and we argue that spacer or CRISPR typing are helpful for small-scale investigations, preferably in conjunction with other molecular typing techniques such as MLST.
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Affiliation(s)
- Adrianne M A Silva
- Departamento de Microbiologia, Instituto Aggeu Magalhães, Fundação Oswaldo Cruz, Recife CEP 50740-465, Pernambuco, Brazil
| | - Ana C O Luz
- Departamento de Microbiologia, Instituto Aggeu Magalhães, Fundação Oswaldo Cruz, Recife CEP 50740-465, Pernambuco, Brazil
| | - Keyla V M Xavier
- Departamento de Microbiologia, Instituto Aggeu Magalhães, Fundação Oswaldo Cruz, Recife CEP 50740-465, Pernambuco, Brazil
| | - Maria P S Barros
- Laboratório de Bioprocessos, Centro de Tecnologias Estratégicas do Nordeste, Recife CEP 50740-545, Pernambuco, Brazil
| | - Hirisleide B Alves
- Departamento de Microbiologia, Instituto Aggeu Magalhães, Fundação Oswaldo Cruz, Recife CEP 50740-465, Pernambuco, Brazil
| | - Marcus V A Batista
- Laboratório de Genética Molecular e Biotecnologia, Centro de Ciências Biológicas e da Saúde-CCBS, Universidade Federal de Sergipe, Aracaju CEP 49060-108, Sergipe, Brazil
| | - Tereza C Leal-Balbino
- Departamento de Microbiologia, Instituto Aggeu Magalhães, Fundação Oswaldo Cruz, Recife CEP 50740-465, Pernambuco, Brazil
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3
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Zhou F, Yu X, Gan R, Ren K, Chen C, Ren C, Cui M, Liu Y, Gao Y, Wang S, Yin M, Huang T, Huang Z, Zhang F. CRISPRimmunity: an interactive web server for CRISPR-associated Important Molecular events and Modulators Used in geNome edIting Tool identifYing. Nucleic Acids Res 2023:7175359. [PMID: 37216595 DOI: 10.1093/nar/gkad425] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Revised: 04/26/2023] [Accepted: 05/10/2023] [Indexed: 05/24/2023] Open
Abstract
The CRISPR-Cas system is a highly adaptive and RNA-guided immune system found in bacteria and archaea, which has applications as a genome editing tool and is a valuable system for studying the co-evolutionary dynamics of bacteriophage interactions. Here introduces CRISPRimmunity, a new web server designed for Acr prediction, identification of novel class 2 CRISPR-Cas loci, and dissection of key CRISPR-associated molecular events. CRISPRimmunity is built on a suite of CRISPR-oriented databases providing a comprehensive co-evolutionary perspective of the CRISPR-Cas and anti-CRISPR systems. The platform achieved a high prediction accuracy of 0.997 for Acr prediction when tested on a dataset of 99 experimentally validated Acrs and 676 non-Acrs, outperforming other existing prediction tools. Some of the newly identified class 2 CRISPR-Cas loci using CRISPRimmunity have been experimentally validated for cleavage activity in vitro. CRISPRimmunity offers the catalogues of pre-identified CRISPR systems to browse and query, the collected resources or databases to download, a well-designed graphical interface, a detailed tutorial, multi-faceted information, and exportable results in machine-readable formats, making it easy to use and facilitating future experimental design and further data mining. The platform is available at http://www.microbiome-bigdata.com/CRISPRimmunity. Moreover, the source code for batch analysis are published on Github (https://github.com/HIT-ImmunologyLab/CRISPRimmunity).
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Affiliation(s)
- Fengxia Zhou
- HIT Center for Life Sciences, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150080, China
| | - Xiaorong Yu
- HIT Center for Life Sciences, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150080, China
| | - Rui Gan
- Changping Laboratory, Yard 28, Science Park Road, Changping District, Beijing 102200, China
| | - Kuan Ren
- HIT Center for Life Sciences, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150080, China
| | - Chuangeng Chen
- HIT Center for Life Sciences, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150080, China
| | - Chunyan Ren
- Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Meng Cui
- HIT Center for Life Sciences, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150080, China
| | - Yuchen Liu
- HIT Center for Life Sciences, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150080, China
| | - Yiyang Gao
- HIT Center for Life Sciences, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150080, China
| | - Shouyu Wang
- HIT Center for Life Sciences, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150080, China
| | - Mingyu Yin
- HIT Center for Life Sciences, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150080, China
| | - Tengjin Huang
- HIT Center for Life Sciences, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150080, China
| | - Zhiwei Huang
- HIT Center for Life Sciences, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150080, China
- Westlake Center for Genome Editing, Westlake Laboratory of Life Sciences and Biomedicine, School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China
- New Cornerstone Science Laboratory, Shenzhen 518054, China
| | - Fan Zhang
- HIT Center for Life Sciences, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150080, China
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
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4
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Makarova KS, Wolf YI, Koonin EV. In Silico Approaches for Prediction of Anti-CRISPR Proteins. J Mol Biol 2023; 435:168036. [PMID: 36868398 PMCID: PMC10073340 DOI: 10.1016/j.jmb.2023.168036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 02/18/2023] [Accepted: 02/23/2023] [Indexed: 03/05/2023]
Abstract
Numerous viruses infecting bacteria and archaea encode CRISPR-Cas system inhibitors, known as anti-CRISPR proteins (Acr). The Acrs typically are highly specific for particular CRISPR variants, resulting in remarkable sequence and structural diversity and complicating accurate prediction and identification of Acrs. In addition to their intrinsic interest for understanding the coevolution of defense and counter-defense systems in prokaryotes, Acrs could be natural, potent on-off switches for CRISPR-based biotechnological tools, so their discovery, characterization and application are of major importance. Here we discuss the computational approaches for Acr prediction. Due to the enormous diversity and likely multiple origins of the Acrs, sequence similarity searches are of limited use. However, multiple features of protein and gene organization have been successfully harnessed to this end including small protein size and distinct amino acid compositions of the Acrs, association of acr genes in virus genomes with genes encoding helix-turn-helix proteins that regulate Acr expression (Acr-associated proteins, Aca), and presence of self-targeting CRISPR spacers in bacterial and archaeal genomes containing Acr-encoding proviruses. Productive approaches for Acr prediction also involve genome comparison of closely related viruses, of which one is resistant and the other one is sensitive to a particular CRISPR variant, and "guilt by association" whereby genes adjacent to a homolog of a known Aca are identified as candidate Acrs. The distinctive features of Acrs are employed for Acr prediction both by developing dedicated search algorithms and through machine learning. New approaches will be needed to identify novel types of Acrs that are likely to exist.
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Affiliation(s)
- Kira S Makarova
- National Center for Biotechnology Information, National Library of Medicine, NIH, Bethesda, USA.
| | - Yuri I Wolf
- National Center for Biotechnology Information, National Library of Medicine, NIH, Bethesda, USA
| | - Eugene V Koonin
- National Center for Biotechnology Information, National Library of Medicine, NIH, Bethesda, USA
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5
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Dao FY, Liu ML, Su W, Lv H, Zhang ZY, Lin H, Liu L. AcrPred: A hybrid optimization with enumerated machine learning algorithm to predict Anti-CRISPR proteins. Int J Biol Macromol 2023; 228:706-714. [PMID: 36584777 DOI: 10.1016/j.ijbiomac.2022.12.250] [Citation(s) in RCA: 27] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 12/12/2022] [Accepted: 12/22/2022] [Indexed: 12/29/2022]
Abstract
CRISPR-Cas, as a tool for gene editing, has received extensive attention in recent years. Anti-CRISPR (Acr) proteins can inactivate the CRISPR-Cas defense system during interference phase, and can be used as a potential tool for the regulation of gene editing. In-depth study of Anti-CRISPR proteins is of great significance for the implementation of gene editing. In this study, we developed a high-accuracy prediction model based on two-step model fusion strategy, called AcrPred, which could produce an AUC of 0.952 with independent dataset validation. To further validate the proposed model, we compared with published tools and correctly identified 9 of 10 new Acr proteins, indicating the strong generalization ability of our model. Finally, for the convenience of related wet-experimental researchers, a user-friendly web-server AcrPred (Anti-CRISPR proteins Prediction) was established at http://lin-group.cn/server/AcrPred, by which users can easily identify potential Anti-CRISPR proteins.
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Affiliation(s)
- Fu-Ying Dao
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China; School of Biological Sciences, Nanyang Technological University, Singapore 639798, Singapore
| | - Meng-Lu Liu
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Wei Su
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hao Lv
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China; Department of Molecular Life Sciences, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland; SIB Swiss Institute of Bioinformatics, Zurich, Switzerland
| | - Zhao-Yue Zhang
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hao Lin
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China.
| | - Li Liu
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324003, China.
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6
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Nidhi S, Tripathi P, Tripathi V. Phylogenetic Analysis of Anti-CRISPR and Member Addition in the Families. Mol Biotechnol 2023; 65:273-281. [PMID: 36109427 DOI: 10.1007/s12033-022-00558-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 09/05/2022] [Indexed: 01/18/2023]
Abstract
CRISPR-Cas is a widespread anti-viral adaptive immune system in the microorganisms. Viruses living in bacteria or some phages carry anti-CRISPR proteins to evade immunity by CRISPR-Cas. The anti-CRISPR proteins are prevalent in phages capable of lying dormant in a CRISPR-carrying host, while their orthologs frequently found in virulent phages. Here, we propose a probabilistic strategy of ancestral sequence reconstruction (ASR) and Hidden Markov Model (HMM) profile search to fish out sequences of anti-CRISPR proteins from environmental metagenomic, human microbiome metagenomic, human microbiome reference genome, and NCBI's non-redundant databases. Our results revealed that the metagenome database dark matter might contain anti-CRISPR encoding genes.
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Affiliation(s)
- Sweta Nidhi
- Department of Genomics and Bioinformatics, Aix-Marseille University, 13007, Marseille, France
| | - Pooja Tripathi
- Department of Computational Biology and Bioinformatics, Jacob Institute of Biotechnology and Bioengineering, Sam Higginbottom University of Agriculture, Technology and Sciences, Prayagraj, Uttar Pradesh, 211007, India
| | - Vijay Tripathi
- Department of Molecular and Cellular Engineering, Jacob Institute of Biotechnology and Bioengineering, Sam Higginbottom University of Agriculture, Technology and Sciences, Prayagraj, Uttar Pradesh, 211007, India.
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7
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Zakrzewska M, Burmistrz M. Mechanisms regulating the CRISPR-Cas systems. Front Microbiol 2023; 14:1060337. [PMID: 36925473 PMCID: PMC10013973 DOI: 10.3389/fmicb.2023.1060337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Accepted: 02/10/2023] [Indexed: 03/08/2023] Open
Abstract
The CRISPR-Cas (Clustered Regularly Interspaced Short Palindromic Repeats- CRISPR associated proteins) is a prokaryotic system that enables sequence specific recognition and cleavage of nucleic acids. This is possible due to cooperation between CRISPR array which contains short fragments of DNA called spacers that are complimentary to the targeted nucleic acid and Cas proteins, which take part in processes of: acquisition of new spacers, processing them into their functional form as well as recognition and cleavage of targeted nucleic acids. The primary role of CRISPR-Cas systems is to provide their host with an adaptive and hereditary immunity against exogenous nucleic acids. This system is present in many variants in both Bacteria and Archea. Due to its modular structure, and programmability CRISPR-Cas system become attractive tool for modern molecular biology. Since their discovery and implementation, the CRISPR-Cas systems revolutionized areas of gene editing and regulation of gene expression. Although our knowledge on how CRISPR-Cas systems work has increased rapidly in recent years, there is still little information on how these systems are controlled and how they interact with other cellular mechanisms. Such regulation can be the result of both auto-regulatory mechanisms as well as exogenous proteins of phage origin. Better understanding of these interaction networks would be beneficial for optimization of current and development of new CRISPR-Cas-based tools. In this review we summarize current knowledge on the various molecular mechanisms that affect activity of CRISPR-Cas systems.
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Affiliation(s)
- Marta Zakrzewska
- Department of Environmental Microbiology and Biotechnology, Faculty of Biology, Institute of Microbiology, University of Warsaw, Warsaw, Poland.,Department of Molecular Microbiology, Biological and Chemical Research Centre, Faculty of Biology, University of Warsaw, Warsaw, Poland
| | - Michal Burmistrz
- Department of Molecular Microbiology, Biological and Chemical Research Centre, Faculty of Biology, University of Warsaw, Warsaw, Poland.,Centre of New Technologies, University of Warsaw, Warsaw, Poland
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8
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AcaFinder: Genome Mining for Anti-CRISPR-Associated Genes. mSystems 2022; 7:e0081722. [PMID: 36413017 PMCID: PMC9765179 DOI: 10.1128/msystems.00817-22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
Anti-CRISPR (Acr) proteins are encoded by (pro)viruses to inhibit their host's CRISPR-Cas systems. Genes encoding Acr and Aca (Acr associated) proteins often colocalize to form acr-aca operons. Here, we present AcaFinder as the first Aca genome mining tool. AcaFinder can (i) predict Acas and their associated acr-aca operons using guilt-by-association (GBA); (ii) identify homologs of known Acas using an HMM (Hidden Markov model) database; (iii) take input genomes for potential prophages, CRISPR-Cas systems, and self-targeting spacers (STSs); and (iv) provide a standalone program (https://github.com/boweny920/AcaFinder) and a web server (http://aca.unl.edu/Aca). AcaFinder was applied to mining over 16,000 prokaryotic and 142,000 gut phage genomes. After a multistep filtering, 36 high-confident new Aca families were identified, which is three times that of the 12 known Aca families. Seven new Aca families were from major human gut bacteria (Bacteroidota, Actinobacteria, and Fusobacteria) and their phages, while most known Aca families were from Proteobacteria and Firmicutes. A complex association network between Acrs and Acas was revealed by analyzing their operonic colocalizations. It appears very common in evolution that the same aca genes can recombine with different acr genes and vice versa to form diverse acr-aca operon combinations. IMPORTANCE At least four bioinformatics programs have been published for genome mining of Acrs since 2020. In contrast, no bioinformatics tools are available for automated Aca discovery. As the self-transcriptional repressor of acr-aca operons, Aca can be viewed as anti-anti-CRISPRs, with great potential in the improvement of CRISPR-Cas technology. Although all the 12 known Aca proteins contain a conserved helix-turn-helix (HTH) domain, not all HTH-containing proteins are Acas. However, HTH-containing proteins with adjacent Acr homologs encoded in the same genetic operon are likely Aca proteins. AcaFinder implements this guilt-by-association idea and the idea of using HMMs of known Acas for homologs into one software package. Applying AcaFinder in screening prokaryotic and gut phage genomes reveals a complex acr-aca operonic colocalization network between different families of Acrs and Acas.
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Zhu L, Wang X, Li F, Song J. PreAcrs: a machine learning framework for identifying anti-CRISPR proteins. BMC Bioinformatics 2022; 23:444. [PMID: 36284264 PMCID: PMC9597991 DOI: 10.1186/s12859-022-04986-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Accepted: 10/14/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Anti-CRISPR proteins are potent modulators that inhibit the CRISPR-Cas immunity system and have huge potential in gene editing and gene therapy as a genome-editing tool. Extensive studies have shown that anti-CRISPR proteins are essential for modifying endogenous genes, promoting the RNA-guided binding and cleavage of DNA or RNA substrates. In recent years, identifying and characterizing anti-CRISPR proteins has become a hot and significant research topic in bioinformatics. However, as most anti-CRISPR proteins fall short in sharing similarities to those currently known, traditional screening methods are time-consuming and inefficient. Machine learning methods could fill this gap with powerful predictive capability and provide a new perspective for anti-CRISPR protein identification. RESULTS Here, we present a novel machine learning ensemble predictor, called PreAcrs, to identify anti-CRISPR proteins from protein sequences directly. Three features and eight different machine learning algorithms were used to train PreAcrs. PreAcrs outperformed other existing methods and significantly improved the prediction accuracy for identifying anti-CRISPR proteins. CONCLUSIONS In summary, the PreAcrs predictor achieved a competitive performance for predicting new anti-CRISPR proteins in terms of accuracy and robustness. We anticipate PreAcrs will be a valuable tool for researchers to speed up the research process. The source code is available at: https://github.com/Lyn-666/anti_CRISPR.git .
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Affiliation(s)
- Lin Zhu
- grid.263488.30000 0001 0472 9649Institute for Advanced Study, Shenzhen University, Shenzhen, China
| | - Xiaoyu Wang
- grid.1002.30000 0004 1936 7857Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800 Australia
| | - Fuyi Li
- grid.1008.90000 0001 2179 088XDepartment of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Melbourne, VIC Australia
| | - Jiangning Song
- grid.1002.30000 0004 1936 7857Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800 Australia ,grid.1002.30000 0004 1936 7857Monash Data Futures Institute, Monash University, Melbourne, VIC 3800 Australia
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10
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Genetic Mining of Newly Isolated Salmophages for Phage Therapy. Int J Mol Sci 2022; 23:ijms23168917. [PMID: 36012174 PMCID: PMC9409062 DOI: 10.3390/ijms23168917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 07/29/2022] [Accepted: 08/07/2022] [Indexed: 11/16/2022] Open
Abstract
Salmonella enterica, a Gram-negative zoonotic bacterium, is mainly a food-borne pathogen and the main cause of diarrhea in humans worldwide. The main reservoirs are found in poultry farms, but they are also found in wild birds. The development of antibiotic resistance in S. enterica species raises concerns about the future of efficient therapies against this pathogen and revives the interest in bacteriophages as a useful therapy against bacterial infections. Here, we aimed to decipher and functionally annotate 10 new Salmonella phage genomes isolated in Spain in the light of phage therapy. We designed a bioinformatic pipeline using available building blocks to de novo assemble genomes and perform syntaxic annotation. We then used genome-wide analyses for taxonomic annotation enabled by vContact2 and VICTOR. We were also particularly interested in improving functional annotation using remote homologies detection and comparisons with the recently published phage-specific PHROG protein database. Finally, we searched for useful functions for phage therapy, such as systems encoded by the phage to circumvent cellular defenses with a particular focus on anti-CRISPR proteins. We, thus, were able to genetically characterize nine virulent phages and one temperate phage and identify putative functions relevant to the formulation of phage cocktails for Salmonella biocontrol.
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11
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Call SN, Andrews LB. CRISPR-Based Approaches for Gene Regulation in Non-Model Bacteria. Front Genome Ed 2022; 4:892304. [PMID: 35813973 PMCID: PMC9260158 DOI: 10.3389/fgeed.2022.892304] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 04/11/2022] [Indexed: 01/08/2023] Open
Abstract
CRISPR interference (CRISPRi) and CRISPR activation (CRISPRa) have become ubiquitous approaches to control gene expression in bacteria due to their simple design and effectiveness. By regulating transcription of a target gene(s), CRISPRi/a can dynamically engineer cellular metabolism, implement transcriptional regulation circuitry, or elucidate genotype-phenotype relationships from smaller targeted libraries up to whole genome-wide libraries. While CRISPRi/a has been primarily established in the model bacteria Escherichia coli and Bacillus subtilis, a growing numbering of studies have demonstrated the extension of these tools to other species of bacteria (here broadly referred to as non-model bacteria). In this mini-review, we discuss the challenges that contribute to the slower creation of CRISPRi/a tools in diverse, non-model bacteria and summarize the current state of these approaches across bacterial phyla. We find that despite the potential difficulties in establishing novel CRISPRi/a in non-model microbes, over 190 recent examples across eight bacterial phyla have been reported in the literature. Most studies have focused on tool development or used these CRISPRi/a approaches to interrogate gene function, with fewer examples applying CRISPRi/a gene regulation for metabolic engineering or high-throughput screens and selections. To date, most CRISPRi/a reports have been developed for common strains of non-model bacterial species, suggesting barriers remain to establish these genetic tools in undomesticated bacteria. More efficient and generalizable methods will help realize the immense potential of programmable CRISPR-based transcriptional control in diverse bacteria.
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Affiliation(s)
- Stephanie N. Call
- Department of Chemical Engineering, University of Massachusetts Amherst, Amherst, MA, United States
| | - Lauren B. Andrews
- Department of Chemical Engineering, University of Massachusetts Amherst, Amherst, MA, United States
- Biotechnology Training Program, University of Massachusetts Amherst, Amherst, MA, United States
- Molecular and Cellular Biology Graduate Program, University of Massachusetts Amherst, Amherst, MA, United States
- *Correspondence: Lauren B. Andrews,
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12
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Thousands of small, novel genes predicted in global phage genomes. Cell Rep 2022; 39:110984. [PMID: 35732113 DOI: 10.1016/j.celrep.2022.110984] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 02/14/2022] [Accepted: 05/27/2022] [Indexed: 11/22/2022] Open
Abstract
Small genes (<150 nucleotides) have been systematically overlooked in phage genomes. We employ a large-scale comparative genomics approach to predict >40,000 small-gene families in ∼2.3 million phage genome contigs. We find that small genes in phage genomes are approximately 3-fold more prevalent than in host prokaryotic genomes. Our approach enriches for small genes that are translated in microbiomes, suggesting the small genes identified are coding. More than 9,000 families encode potentially secreted or transmembrane proteins, more than 5,000 families encode predicted anti-CRISPR proteins, and more than 500 families encode predicted antimicrobial proteins. By combining homology and genomic-neighborhood analyses, we reveal substantial novelty and diversity within phage biology, including small phage genes found in multiple host phyla, small genes encoding proteins that play essential roles in host infection, and small genes that share genomic neighborhoods and whose encoded proteins may share related functions.
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13
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A Phage Foundry Framework to Systematically Develop Viral Countermeasures to Combat Antibiotic-Resistant Bacterial Pathogens. iScience 2022; 25:104121. [PMID: 35402883 PMCID: PMC8983348 DOI: 10.1016/j.isci.2022.104121] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
At its current rate, the rise of antimicrobial-resistant (AMR) infections is predicted to paralyze our industries and healthcare facilities while becoming the leading global cause of loss of human life. With limited new antibiotics on the horizon, we need to invest in alternative solutions. Bacteriophages (phages)—viruses targeting bacteria—offer a powerful alternative approach to tackle bacterial infections. Despite recent advances in using phages to treat recalcitrant AMR infections, the field lacks systematic development of phage therapies scalable to different applications. We propose a Phage Foundry framework to establish metrics for phage characterization and to fill the knowledge and technological gaps in phage therapeutics. Coordinated investment in AMR surveillance, sampling, characterization, and data sharing procedures will enable rational exploitation of phages for treatments. A fully realized Phage Foundry will enhance the sharing of knowledge, technology, and viral reagents in an equitable manner and will accelerate the biobased economy.
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Dong C, Wang X, Ma C, Zeng Z, Pu DK, Liu S, Wu CS, Chen S, Deng Z, Guo FB. Anti-CRISPRdb v2.2: an online repository of anti-CRISPR proteins including information on inhibitory mechanisms, activities and neighbors of curated anti-CRISPR proteins. Database (Oxford) 2022; 2022:6555051. [PMID: 35348649 PMCID: PMC9248852 DOI: 10.1093/database/baac010] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 02/13/2022] [Accepted: 02/21/2022] [Indexed: 12/30/2022]
Abstract
We previously released the Anti-CRISPRdb database hosting anti-CRISPR proteins (Acrs) and associated information. Since then, the number of known Acr families, types, structures and inhibitory activities has accumulated over time, and Acr neighbors can be used as a candidate pool for screening Acrs in further studies. Therefore, we here updated the database to include the new available information. Our newly updated database shows several improvements: (i) it comprises more entries and families because it includes both Acrs reported in the most recent literatures and Acrs obtained via performing homologous alignment; (ii) the prediction of Acr neighbors is integrated into Anti-CRISPRdb v2.2, and users can identify novel Acrs from these candidates; and (iii) this version includes experimental information on the inhibitory strength and stage for Acr-Cas/Acr-CRISPR pairs, motivating the development of tools for predicting specific inhibitory abilities. Additionally, a parameter, the rank of codon usage bias (CUBRank), was proposed and provided in the new version, which showed a positive relationship with predicted result from AcRanker; hence, it can be used as an indicator for proteins to be Acrs. CUBRank can be used to estimate the possibility of genes occurring within genome island-a hotspot hosting potential genes encoding Acrs. Based on CUBRank and Anti-CRISPRdb, we also gave the first glimpse for the emergence of Acr genes (acrs). DATABASE URL http://guolab.whu.edu.cn/anti-CRISPRdb.
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Affiliation(s)
- Chuan Dong
- Key Laboratory of Combinatorial Biosynthesis and Drug Discovery, Ministry of Education, and School of Pharmaceutical Sciences, Wuhan University, No. 185, Donghu Road, Wuchang, Wuhan 430071, China
| | - Xin Wang
- School of Life Science and Technology, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu 611731, China
| | - Cong Ma
- School of Life Science and Technology, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu 611731, China
| | - Zhi Zeng
- School of Life Science and Technology, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu 611731, China
| | - Dong-Kai Pu
- School of Life Science and Technology, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu 611731, China
| | - Shuo Liu
- School of Life Science and Technology, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu 611731, China
| | - Candy-S Wu
- Thomas Worthington High School, 300 West Granville Road, Worthington, OH 43085, USA
| | - Shixin Chen
- Key Laboratory of Combinatorial Biosynthesis and Drug Discovery, Ministry of Education, and School of Pharmaceutical Sciences, Wuhan University, No. 185, Donghu Road, Wuchang, Wuhan 430071, China
| | - Zixin Deng
- Key Laboratory of Combinatorial Biosynthesis and Drug Discovery, Ministry of Education, and School of Pharmaceutical Sciences, Wuhan University, No. 185, Donghu Road, Wuchang, Wuhan 430071, China
| | - Feng-Biao Guo
- Key Laboratory of Combinatorial Biosynthesis and Drug Discovery, Ministry of Education, and School of Pharmaceutical Sciences, Wuhan University, No. 185, Donghu Road, Wuchang, Wuhan 430071, China
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Vyas P, Harish. Anti-CRISPR proteins as a therapeutic agent against drug-resistant bacteria. Microbiol Res 2022; 257:126963. [PMID: 35033831 DOI: 10.1016/j.micres.2022.126963] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Revised: 01/06/2022] [Accepted: 01/06/2022] [Indexed: 02/08/2023]
Abstract
The continuous deployment of various antibiotics to treat multiple serious bacterial infections leads to multidrug resistance among the bacterial population. It has failed the standard treatment strategies through different antibacterial agents and serves as a significant threat to public health worldwide at devastating levels. The discovery of anti-CRISPR proteins catches the interest of researchers around the world as a promising therapeutic agent against drug-resistant bacteria. Anti-CRISPR proteins are known to inhibit bacterial CRISPR-Cas defense systems in multiple possible ways. The CRISPR-Cas nucleoprotein assembly provides adaptive immunity in bacteria against diverse categories of phage infections. Parallelly, phages also try to break the CRISPR-Cas barrier by producing anti-CRISPR proteins, leading to growth inhibition and bacterial lysis. This review begins with a brief description of the bacterial CRISPR-Cas system, followed by a detailed portrayal of anti-CRISPR proteins, including their discovery and evolution, mechanism of action, regulation of expression, and potential applications in the healthcare sector as an alternative therapeutic strategy to combat severe bacterial infections.
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Affiliation(s)
- Pallavi Vyas
- Plant Biotechnology Laboratory, Department of Botany, Mohanlal Sukhadia University, Udaipur, 313 001, Rajasthan, India
| | - Harish
- Plant Biotechnology Laboratory, Department of Botany, Mohanlal Sukhadia University, Udaipur, 313 001, Rajasthan, India.
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Dai W, Li J, Li Q, Cai J, Su J, Stubenrauch C, Wang J. PncsHub: a platform for annotating and analyzing non-classically secreted proteins in Gram-positive bacteria. Nucleic Acids Res 2022; 50:D848-D857. [PMID: 34551435 PMCID: PMC8728121 DOI: 10.1093/nar/gkab814] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Revised: 08/30/2021] [Accepted: 09/07/2021] [Indexed: 12/28/2022] Open
Abstract
From industry to food to health, bacteria play an important role in all facets of life. Some of the most important bacteria have been purposely engineered to produce commercial quantities of antibiotics and therapeutics, and non-classical secretion systems are at the forefront of these technologies. Unlike the classical Sec or Tat pathways, non-classically secreted proteins share few common characteristics and use much more diverse secretion pathways for protein transport. Systematically categorizing and investigating the non-classically secreted proteins will enable a deeper understanding of their associated secretion mechanisms and provide a landscape of the Gram-positive secretion pathway distribution. We therefore developed PncsHub (https://pncshub.erc.monash.edu/), the first universal platform for comprehensively annotating and analyzing Gram-positive bacterial non-classically secreted proteins. PncsHub catalogs 4,914 non-classically secreted proteins, which are delicately categorized into 8 subtypes (including the 'unknown' subtype) and annotated with data compiled from up to 26 resources and visualisation tools. It incorporates state-of-the-art predictors to identify new and homologous non-classically secreted proteins and includes three analytical modules to visualise the relationships between known and putative non-classically secreted proteins. As such, PncsHub aims to provide integrated services for investigating, predicting and identifying non-classically secreted proteins to promote hypothesis-driven laboratory-based experiments.
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Affiliation(s)
- Wei Dai
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China
- Infection and Immunity Program, Biomedicine Discovery Institute and Department of Microbiology, Monash University, VIC 3800, Australia
- Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou 325011, China
| | - Jiahui Li
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China
| | - Qi Li
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China
| | - Jiasheng Cai
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China
| | - Jianzhong Su
- Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou 325011, China
- School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
| | - Christopher Stubenrauch
- Infection and Immunity Program, Biomedicine Discovery Institute and Department of Microbiology, Monash University, VIC 3800, Australia
- Centre to Impact AMR, Monash University, VIC 3800, Australia
| | - Jiawei Wang
- Infection and Immunity Program, Biomedicine Discovery Institute and Department of Microbiology, Monash University, VIC 3800, Australia
- Centre to Impact AMR, Monash University, VIC 3800, Australia
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Rigden DJ, Fernández XM. The 2021 Nucleic Acids Research database issue and the online molecular biology database collection. Nucleic Acids Res 2021; 49:D1-D9. [PMID: 33396976 PMCID: PMC7778882 DOI: 10.1093/nar/gkaa1216] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
The 2021 Nucleic Acids Research database Issue contains 189 papers spanning a wide range of biological fields and investigation. It includes 89 papers reporting on new databases and 90 covering recent changes to resources previously published in the Issue. A further ten are updates on databases most recently published elsewhere. Seven new databases focus on COVID-19 and SARS-CoV-2 and many others offer resources for studying the virus. Major returning nucleic acid databases include NONCODE, Rfam and RNAcentral. Protein family and domain databases include COG, Pfam, SMART and Panther. Protein structures are covered by RCSB PDB and dispersed proteins by PED and MobiDB. In metabolism and signalling, STRING, KEGG and WikiPathways are featured, along with returning KLIFS and new DKK and KinaseMD, all focused on kinases. IMG/M and IMG/VR update in the microbial and viral genome resources section, while human and model organism genomics resources include Flybase, Ensembl and UCSC Genome Browser. Cancer studies are covered by updates from canSAR and PINA, as well as newcomers CNCdatabase and Oncovar for cancer drivers. Plant comparative genomics is catered for by updates from Gramene and GreenPhylDB. The entire Database Issue is freely available online on the Nucleic Acids Research website (https://academic.oup.com/nar). The NAR online Molecular Biology Database Collection has been substantially updated, revisiting nearly 1000 entries, adding 90 new resources and eliminating 86 obsolete databases, bringing the current total to 1641 databases. It is available at https://www.oxfordjournals.org/nar/database/c/.
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Affiliation(s)
- Daniel J Rigden
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Crown Street, Liverpool L69 7ZB, UK
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