scholarly journals Machine Learning Predicts New Anti-CRISPR Proteins

2019 ◽  
Author(s):  
Simon Eitzinger ◽  
Amina Asif ◽  
Kyle E. Watters ◽  
Anthony T. Iavarone ◽  
Gavin J. Knott ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gene Editing ◽  
External Validation ◽  
Adaptive Immune System ◽  
Streptococcus Iniae ◽  
Sequence Information ◽  
Bacterial Genomes ◽  
Training Set ◽  
Adaptive Immune ◽  
Shared Set

ABSTRACTThe increasing use of CRISPR-Cas9 in medicine, agriculture and synthetic biology has accelerated the drive to discover new CRISPR-Cas inhibitors as potential mechanisms of control for gene editing applications. Many such anti-CRISPRs have been found in mobile genetic elements that disable the CRISPR-Cas adaptive immune system. However, comparing all currently known anti-CRISPRs does not reveal a shared set of properties that can be used for facile bioinformatic identification of new anti-CRISPR families. Here, we describe AcRanker, a machine learning based method for identifying new potential anti-CRISPRs directly from proteomes using protein sequence information only. Using a training set of known anti-CRISPRs, we built a model based on XGBoost ranking and extensively benchmarked it through non-redundant cross-validation and external validation. We then applied AcRanker to predict candidate anti-CRISPRs from self-targeting bacterial genomes and discovered two previously unknown anti-CRISPRs: AcrllA16 (ML1) and AcrIIA17 (ML8). We show that AcrIIA16 strongly inhibits Streptococcus iniae Cas9 (SinCas9) and weakly inhibits Streptococcus pyogenes Cas9 (SpyCas9). We also show that AcrIIA17 inhibits both SpyCas9 and SauCas9 with low potency. The addition of AcRanker to the anti-CRISPR discovery toolkit allows researchers to directly rank potential anti-CRISPR candidate genes for increased speed in testing and validation of new anti-CRISPRs. A web server implementation for AcRanker is available online at http://acranker.pythonanywhere.com/.

scholarly journals Machine learning predicts new anti-CRISPR proteins

2020 ◽  
Vol 48 (9) ◽  
pp. 4698-4708 ◽  
Author(s):  
Simon Eitzinger ◽  
Amina Asif ◽  
Kyle E Watters ◽  
Anthony T Iavarone ◽  
Gavin J Knott ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gene Editing ◽  
Adaptive Immune System ◽  
Streptococcus Iniae ◽  
Sequence Information ◽  
Bacterial Genomes ◽  
Training Set ◽  
Direct Identification ◽  
Adaptive Immune ◽  
Shared Set

Abstract The increasing use of CRISPR–Cas9 in medicine, agriculture, and synthetic biology has accelerated the drive to discover new CRISPR–Cas inhibitors as potential mechanisms of control for gene editing applications. Many anti-CRISPRs have been found that inhibit the CRISPR–Cas adaptive immune system. However, comparing all currently known anti-CRISPRs does not reveal a shared set of properties for facile bioinformatic identification of new anti-CRISPR families. Here, we describe AcRanker, a machine learning based method to aid direct identification of new potential anti-CRISPRs using only protein sequence information. Using a training set of known anti-CRISPRs, we built a model based on XGBoost ranking. We then applied AcRanker to predict candidate anti-CRISPRs from predicted prophage regions within self-targeting bacterial genomes and discovered two previously unknown anti-CRISPRs: AcrllA20 (ML1) and AcrIIA21 (ML8). We show that AcrIIA20 strongly inhibits Streptococcus iniae Cas9 (SinCas9) and weakly inhibits Streptococcus pyogenes Cas9 (SpyCas9). We also show that AcrIIA21 inhibits SpyCas9, Streptococcus aureus Cas9 (SauCas9) and SinCas9 with low potency. The addition of AcRanker to the anti-CRISPR discovery toolkit allows researchers to directly rank potential anti-CRISPR candidate genes for increased speed in testing and validation of new anti-CRISPRs. A web server implementation for AcRanker is available online at http://acranker.pythonanywhere.com/.

Repurposing CRISPR-Cas Systems as Genetic Tools for the Enterobacteriales

EcoSal Plus ◽  
2021 ◽  
Author(s):  
Nicholas Backes ◽  
Gregory J. Phillips
Keyword(s):  
Gene Regulation ◽  
Immune System ◽  
Gene Editing ◽  
Defense Mechanism ◽  
Adaptive Immune System ◽  
Plasmid Curing ◽  
Genetic Tools ◽  
Adaptive Immune ◽  
Domains Of Life ◽  
Eukaryotic Genomes

Over the last decade, the study of CRISPR-Cas systems has progressed from a newly discovered bacterial defense mechanism to a diverse suite of genetic tools that have been applied across all domains of life. While the initial applications of CRISPR-Cas technology fulfilled a need to more precisely edit eukaryotic genomes, creative “repurposing” of this adaptive immune system has led to new approaches for genetic analysis of microorganisms, including improved gene editing, conditional gene regulation, plasmid curing and manipulation, and other novel uses.

scholarly journals CRISPR-Cas9 in agriculture: Approaches, applications, future perspectives, and associated challenges

2020 ◽  
Vol 3 (1) ◽  
pp. 6-16
Author(s):  
Prabin Adhikari ◽  
Mousami Poudel
Keyword(s):  
Dna Cleavage ◽  
Gene Editing ◽  
Adaptive Immune System ◽  
Genetic Manipulations ◽  
Adaptive Immune ◽  
Cargo Delivery ◽  
Genome Wide ◽  
Rna Fragments ◽  
Sgrna Design ◽  
Living Organisms

AbstractThe discovery of an adaptive immune system especially in archae and bacteria, CRISPR/Cas has revolutionized the field of agriculture and served as a potential gene editing tool, producing great excitement to the molecular scientists for the improved genetic manipulations. CRISPR/Cas9 is a RNA guided endonuclease which is popular among its predecessors ZFN and TALEN’s. The utilities of CRISPR from its predecessors is the use of short RNA fragments to locate target and breaking the double strands which avoids the need of protein engineering, thus allowing time efficiency measure for gene editing. It is a simple, flexible and highly efficient programmable DNA cleavage system that can be modified for widespread applications like knocking out the genes, controlling transcription, modifying epigenomes, controlling genome-wide screens, modifying genes for disease and stress tolerance and imaging chromosomes. However, gene cargo delivery system, off target cutting and issues on the safety of living organisms imposes major challenge to this system. Several attempts have been done to rectify these challenges; using sgRNA design software, cas9 nickases and other mutants. Thus, further addressing these challenges may open the avenue for CRISPR/cas9 for addressing the agriculture related problems.

scholarly journals CRISPR systems: what’s new, where next?

2021 ◽  
Author(s):  
Ashley Parkes ◽  
Fiona Kemm ◽  
Liu He ◽  
Tom Killelea
Keyword(s):  
Dna Repair ◽  
Immune System ◽  
Genetic Engineering ◽  
Gene Editing ◽  
Adaptive Immune System ◽  
Feature Article ◽  
Adaptive Immune ◽  
The Past ◽  
Domains Of Life ◽  
Made In

The genetic signature of natural CRISPR-Cas systems were first noted in a 1989 publication and were characterized in detail from 2002 to 2007, culminating in the first report of a prokaryotic adaptive immune system. Since then, CRISPR-Cas enzymes have been adapted into molecular biology tools that have transformed genetic engineering across domains of life. In this feature article, we describe origins, uses and futures of CRISPR-Cas enzymes in genetic engineering: we highlight advances made in the past 10 years. Central to these advances is appreciation of interplay between CRISPR engineering and DNA repair. We highlight how this relationship has been manipulated to create further advances in the development of gene editing.

CRISPR/Cas-Systems: characteristics and possibilities of use for editing bacterial genomes

Bacteriology ◽  
2020 ◽  
Vol 5 (2) ◽  
pp. 38-48
Author(s):  
I.A. Blatov ◽  
◽  
A.S. Shchurova ◽  
D.Yu. Guschin ◽  
S.D. Zvereva ◽  
...  
Keyword(s):  
Immune System ◽  
Genome Editing ◽  
Bacterial Species ◽  
Adaptive Immune System ◽  
Bacterial Genomes ◽  
Modern Biology ◽  
Adaptive Immune ◽  
History Of ◽  
Genomic Editing ◽  
Classification Structure

CRISPR-Cas is the adaptive immune system of bacteria and archaea. Since 2012, when the first opportunity to use the CRISPR/Cas system for genome editing was realized, the number of studies in this area has been growing rapidly. Today, genomic editing to modify specific regions of the genomes of various organisms is considered one of the key methodologies of modern biology. This review is devoted to the history of discovery, classification, structure, operational mechanisms of CRISPRCas systems and strategies for editing the genomes of various bacterial species using this technology. Key words: genome editing, genome, CRISPR-Cas system, bacteria

Prediction of Genotype Positivity in Patients with Hypertrophic Cardiomyopathy Using Machine Learning

2021 ◽  
Author(s):  
Lusha W. Liang ◽  
Michael A. Fifer ◽  
Kohei Hasegawa ◽  
Mathew S. Maurer ◽  
Muredach P. Reilly ◽  
...  
Keyword(s):  
Machine Learning ◽  
Genetic Testing ◽  
Hypertrophic Cardiomyopathy ◽  
Predictive Value ◽  
External Validation ◽  
Scoring Systems ◽  
Training Set ◽  
Test Set ◽  
Net Reclassification Improvement ◽  
Mayo Score

Background - Genetic testing can determine family screening strategies and has prognostic and diagnostic value in hypertrophic cardiomyopathy (HCM). However, it can also pose a significant psychosocial burden. Conventional scoring systems offer modest ability to predict genotype positivity. The aim of our study was to develop a novel prediction model for genotype positivity in patients with HCM by applying machine learning (ML) algorithms. Methods - We constructed three ML models using readily available clinical and cardiac imaging data of 102 patients from Columbia University with HCM who had undergone genetic testing (the training set). We validated model performance on 76 patients with HCM from Massachusetts General Hospital (the test set). Within the test set, we compared the area under the receiver operating characteristic curves (AUCs) for the ML models against the AUCs generated by the Toronto HCM Genotype Score ("the Toronto score") and Mayo HCM Genotype Predictor ("the Mayo score") using the Delong test and net reclassification improvement (NRI). Results - Overall, 63 of the 178 patients (35%) were genotype positive. The random forest ML model developed in the training set demonstrated an AUC of 0.92 (95% CI 0.85-0.99) in predicting genotype positivity in the test set, significantly outperforming the Toronto score (AUC 0.77, 95% CI 0.65-0.90, p=0.004, NRI: p<0.001) and the Mayo score (AUC 0.79, 95% CI 0.67-0.92, p=0.01, NRI: p=0.001). The gradient boosted decision tree ML model also achieved significant NRI over the Toronto score (p<0.001) and the Mayo score (p=0.03), with an AUC of 0.87 (95% CI 0.75-0.99). Compared to the Toronto and Mayo scores, all three ML models had higher sensitivity, positive predictive value, and negative predictive value. Conclusions - Our ML models demonstrated a superior ability to predict genotype positivity in patients with HCM compared to conventional scoring systems in an external validation test set.

scholarly journals Machine learning identifies the immunological signature of Juvenile Idiopathic Arthritis

2018 ◽  
Author(s):  
Erika Van Nieuwenhove ◽  
Vasiliki Lagou ◽  
Lien Van Eyck ◽  
James Dooley ◽  
Ulrich Bodenhofer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Juvenile Idiopathic Arthritis ◽  
Large Scale ◽  
Inflammatory Diseases ◽  
Adaptive Immune System ◽  
Healthy Children ◽  
Adaptive Immune ◽  
Systemic Jia ◽  
Response Group ◽  
Learning Analysis

AbstractJuvenile idiopathic arthritis (JIA) is the most common childhood rheumatic disease, with a strongly debated pathophysiological origin. Both adaptive and innate immune processes have been proposed as primary drivers, which may account for the observed clinical heterogeneity, but few high-depth studies have been performed. Here we profiled the adaptive immune system of 85 JIA patients and 43 age-matched controls, identifying immunological changes unique to JIA and others common across a broad spectrum of childhood inflammatory diseases. The JIA immune signature was shared between clinically distinct subsets, but was accentuated in the systemic JIA patients and those patients with active disease. Despite the extensive overlap in the immunological spectrum exhibited by healthy children and JIA patients, machine learning analysis of the dataset proved capable of diagnosis of JIA patients with ~90% accuracy. These results pave the way for large-scale longitudinal studies of JIA, where machine learning could be used to predict immune signatures that correspond to treatment response group.

scholarly journals Expanding the catalog of cas genes with metagenomes

2013 ◽  
Vol 42 (4) ◽  
pp. 2448-2459 ◽  
Author(s):  
Quan Zhang ◽  
Thomas G. Doak ◽  
Yuzhen Ye
Keyword(s):  
Human Microbiome ◽  
Adaptive Immune System ◽  
Integrated Approach ◽  
Defense System ◽  
Bacterial Genomes ◽  
Adaptive Immune ◽  
Analysis Application ◽  
Cas Genes ◽  
Similarity Searches ◽  
Genomic Neighborhood

Abstract The CRISPR (clusters of regularly interspaced short palindromic repeats)–Cas adaptive immune system is an important defense system in bacteria, providing targeted defense against invasions of foreign nucleic acids. CRISPR–Cas systems consist of CRISPR loci and cas (CRISPR-associated) genes: sequence segments of invaders are incorporated into host genomes at CRISPR loci to generate specificity, while adjacent cas genes encode proteins that mediate the defense process. We pursued an integrated approach to identifying putative cas genes from genomes and metagenomes, combining similarity searches with genomic neighborhood analysis. Application of our approach to bacterial genomes and human microbiome datasets allowed us to significantly expand the collection of cas genes: the sequence space of the Cas9 family, the key player in the recently engineered RNA-guided platforms for genome editing in eukaryotes, is expanded by at least two-fold with metagenomic datasets. We found genes in cas loci encoding other functions, for example, toxins and antitoxins, confirming the recently discovered potential of coupling between adaptive immunity and the dormancy/suicide systems. We further identified 24 novel Cas families; one novel family contains 20 proteins, all identified from the human microbiome datasets, illustrating the importance of metagenomics projects in expanding the diversity of cas genes.

scholarly journals Various Aspects of a Gene Editing System—CRISPR–Cas9

2020 ◽  
Vol 21 (24) ◽  
pp. 9604
Author(s):  
Edyta Janik ◽  
Marcin Niemcewicz ◽  
Michal Ceremuga ◽  
Lukasz Krzowski ◽  
Joanna Saluk-Bijak ◽  
...  
Keyword(s):  
Genome Editing ◽  
Gene Editing ◽  
Genome Engineering ◽  
High Efficiency ◽  
Adaptive Immune System ◽  
Zinc Finger Nucleases ◽  
Transcription Activator ◽  
Guide Rna ◽  
Adaptive Immune ◽  
Cas9 Protein

The discovery of clustered, regularly interspaced short palindromic repeats (CRISPR) and their cooperation with CRISPR-associated (Cas) genes is one of the greatest advances of the century and has marked their application as a powerful genome engineering tool. The CRISPR–Cas system was discovered as a part of the adaptive immune system in bacteria and archaea to defend from plasmids and phages. CRISPR has been found to be an advanced alternative to zinc-finger nucleases (ZFN) and transcription activator-like effector nucleases (TALEN) for gene editing and regulation, as the CRISPR–Cas9 protein remains the same for various gene targets and just a short guide RNA sequence needs to be altered to redirect the site-specific cleavage. Due to its high efficiency and precision, the Cas9 protein derived from the type II CRISPR system has been found to have applications in many fields of science. Although CRISPR–Cas9 allows easy genome editing and has a number of benefits, we should not ignore the important ethical and biosafety issues. Moreover, any tool that has great potential and offers significant capabilities carries a level of risk of being used for non-legal purposes. In this review, we present a brief history and mechanism of the CRISPR–Cas9 system. We also describe on the applications of this technology in gene regulation and genome editing; the treatment of cancer and other diseases; and limitations and concerns of the use of CRISPR–Cas9.

scholarly journals Harnessing the CRISPR-Cas Systems to Combat Antimicrobial Resistance

2021 ◽  
Vol 12 ◽  
Author(s):  
Cheng Duan ◽  
Huiluo Cao ◽  
Lian-Hui Zhang ◽  
Zeling Xu
Keyword(s):  
Immune System ◽  
Antimicrobial Resistance ◽  
Global Health ◽  
Adaptive Immune System ◽  
Type I ◽  
Type Ii ◽  
Bacterial Genomes ◽  
Adaptive Immune ◽  
The Past ◽  
Attractive Option

The emergence of antimicrobial-resistant (AMR) bacteria has become one of the most serious threats to global health, necessitating the development of novel antimicrobial strategies. CRISPR (clustered regularly interspaced short palindromic repeats)-Cas (CRISPR-associated) system, known as a bacterial adaptive immune system, can be repurposed to selectively target and destruct bacterial genomes other than invasive genetic elements. Thus, the CRISPR-Cas system offers an attractive option for the development of the next-generation antimicrobials to combat infectious diseases especially those caused by AMR pathogens. However, the application of CRISPR-Cas antimicrobials remains at a very preliminary stage and numerous obstacles await to be solved. In this mini-review, we summarize the development of using type I, type II, and type VI CRISPR-Cas antimicrobials to eradicate AMR pathogens and plasmids in the past a few years. We also discuss the most common challenges in applying CRISPR-Cas antimicrobials and potential solutions to overcome them.

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