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Vora DS, Bhandari SM, Sundar D. DNA shape features improve prediction of CRISPR/Cas9 activity. Methods 2024; 226:120-126. [PMID: 38641083 DOI: 10.1016/j.ymeth.2024.04.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 03/27/2024] [Accepted: 04/10/2024] [Indexed: 04/21/2024] Open
Abstract
The CRISPR/Cas9 genome editing technology has transformed basic and translational research in biology and medicine. However, the advances are hindered by off-target effects and a paucity in the knowledge of the mechanism of the Cas9 protein. Machine learning models have been proposed for the prediction of Cas9 activity at unintended sites, yet feature engineering plays a major role in the outcome of the predictors. This study evaluates the improvement in the performance of similar predictors upon inclusion of epigenetic and DNA shape feature groups in the conventionally used sequence-based Cas9 target and off-target datasets. The approach involved the utilization of neural networks trained on a diverse range of parameters, allowing us to systematically assess the performance increase for the meticulously designed datasets- (i) sequence only, (ii) sequence and epigenetic features, and (iii) sequence, epigenetic and DNA shape feature datasets. The addition of DNA shape information significantly improved predictive performance, evaluated by Akaike and Bayesian information criteria. The evaluation of individual feature importance by permutation and LIME-based methods also indicates that not only sequence features like mismatches and nucleotide composition, but also base pairing parameters like opening and stretch, that are indicative of distortion in the DNA-RNA hybrid in the presence of mismatches, influence model outcomes.
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Affiliation(s)
- Dhvani Sandip Vora
- Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, New Delhi 110016, India.
| | - Sakshi Manoj Bhandari
- Department of Mathematics, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India.
| | - Durai Sundar
- Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, New Delhi 110016, India; School of Artificial Intelligence, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India.
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Lim SR, Lee SJ. Multiplex CRISPR-Cas Genome Editing: Next-Generation Microbial Strain Engineering. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2024; 72:11871-11884. [PMID: 38744727 PMCID: PMC11141556 DOI: 10.1021/acs.jafc.4c01650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 05/02/2024] [Accepted: 05/08/2024] [Indexed: 05/16/2024]
Abstract
Genome editing is a crucial technology for obtaining desired phenotypes in a variety of species, ranging from microbes to plants, animals, and humans. With the advent of CRISPR-Cas technology, it has become possible to edit the intended sequence by modifying the target recognition sequence in guide RNA (gRNA). By expressing multiple gRNAs simultaneously, it is possible to edit multiple targets at the same time, allowing for the simultaneous introduction of various functions into the cell. This can significantly reduce the time and cost of obtaining engineered microbial strains for specific traits. In this review, we investigate the resolution of multiplex genome editing and its application in engineering microorganisms, including bacteria and yeast. Furthermore, we examine how recent advancements in artificial intelligence technology could assist in microbial genome editing and engineering. Based on these insights, we present our perspectives on the future evolution and potential impact of multiplex genome editing technologies in the agriculture and food industry.
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Affiliation(s)
- Se Ra Lim
- Department of Systems Biotechnology
and Institute of Microbiomics, Chung-Ang
University, Anseong 17546, Republic
of Korea
| | - Sang Jun Lee
- Department of Systems Biotechnology
and Institute of Microbiomics, Chung-Ang
University, Anseong 17546, Republic
of Korea
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3
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Petersen SD, Levassor L, Pedersen CM, Madsen J, Hansen LG, Zhang J, Haidar AK, Frandsen RJN, Keasling JD, Weber T, Sonnenschein N, K. Jensen M. teemi: An open-source literate programming approach for iterative design-build-test-learn cycles in bioengineering. PLoS Comput Biol 2024; 20:e1011929. [PMID: 38457467 PMCID: PMC10954146 DOI: 10.1371/journal.pcbi.1011929] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 03/20/2024] [Accepted: 02/17/2024] [Indexed: 03/10/2024] Open
Abstract
Synthetic biology dictates the data-driven engineering of biocatalysis, cellular functions, and organism behavior. Integral to synthetic biology is the aspiration to efficiently find, access, interoperate, and reuse high-quality data on genotype-phenotype relationships of native and engineered biosystems under FAIR principles, and from this facilitate forward-engineering strategies. However, biology is complex at the regulatory level, and noisy at the operational level, thus necessitating systematic and diligent data handling at all levels of the design, build, and test phases in order to maximize learning in the iterative design-build-test-learn engineering cycle. To enable user-friendly simulation, organization, and guidance for the engineering of biosystems, we have developed an open-source python-based computer-aided design and analysis platform operating under a literate programming user-interface hosted on Github. The platform is called teemi and is fully compliant with FAIR principles. In this study we apply teemi for i) designing and simulating bioengineering, ii) integrating and analyzing multivariate datasets, and iii) machine-learning for predictive engineering of metabolic pathway designs for production of a key precursor to medicinal alkaloids in yeast. The teemi platform is publicly available at PyPi and GitHub.
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Affiliation(s)
- Søren D. Petersen
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Lucas Levassor
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kgs. Lyngby, Denmark
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Christine M. Pedersen
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Jan Madsen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Lea G. Hansen
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Jie Zhang
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Ahmad K. Haidar
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Rasmus J. N. Frandsen
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Jay D. Keasling
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kgs. Lyngby, Denmark
- Joint BioEnergy Institute, Emeryville, California, United States of America
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
- Department of Chemical and Biomolecular Engineering, Department of Bioengineering, University of California, Berkeley, California, United States of America
- Center for Synthetic Biochemistry, Institute for Synthetic Biology, Shenzhen Institutes of Advanced Technologies, Shenzhen, China
| | - Tilmann Weber
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Nikolaus Sonnenschein
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Michael K. Jensen
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kgs. Lyngby, Denmark
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Dixit S, Kumar A, Srinivasan K, Vincent PMDR, Ramu Krishnan N. Advancing genome editing with artificial intelligence: opportunities, challenges, and future directions. Front Bioeng Biotechnol 2024; 11:1335901. [PMID: 38260726 PMCID: PMC10800897 DOI: 10.3389/fbioe.2023.1335901] [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] [Received: 11/09/2023] [Accepted: 12/19/2023] [Indexed: 01/24/2024] Open
Abstract
Clustered regularly interspaced short palindromic repeat (CRISPR)-based genome editing (GED) technologies have unlocked exciting possibilities for understanding genes and improving medical treatments. On the other hand, Artificial intelligence (AI) helps genome editing achieve more precision, efficiency, and affordability in tackling various diseases, like Sickle cell anemia or Thalassemia. AI models have been in use for designing guide RNAs (gRNAs) for CRISPR-Cas systems. Tools like DeepCRISPR, CRISTA, and DeepHF have the capability to predict optimal guide RNAs (gRNAs) for a specified target sequence. These predictions take into account multiple factors, including genomic context, Cas protein type, desired mutation type, on-target/off-target scores, potential off-target sites, and the potential impacts of genome editing on gene function and cell phenotype. These models aid in optimizing different genome editing technologies, such as base, prime, and epigenome editing, which are advanced techniques to introduce precise and programmable changes to DNA sequences without relying on the homology-directed repair pathway or donor DNA templates. Furthermore, AI, in collaboration with genome editing and precision medicine, enables personalized treatments based on genetic profiles. AI analyzes patients' genomic data to identify mutations, variations, and biomarkers associated with different diseases like Cancer, Diabetes, Alzheimer's, etc. However, several challenges persist, including high costs, off-target editing, suitable delivery methods for CRISPR cargoes, improving editing efficiency, and ensuring safety in clinical applications. This review explores AI's contribution to improving CRISPR-based genome editing technologies and addresses existing challenges. It also discusses potential areas for future research in AI-driven CRISPR-based genome editing technologies. The integration of AI and genome editing opens up new possibilities for genetics, biomedicine, and healthcare, with significant implications for human health.
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Affiliation(s)
- Shriniket Dixit
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India
| | - Anant Kumar
- School of Bioscience and Technology, Vellore Institute of Technology, Vellore, India
| | - Kathiravan Srinivasan
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India
| | - P. M. Durai Raj Vincent
- School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, India
| | - Nadesh Ramu Krishnan
- School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, India
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Matveeva A, Ryabchenko A, Petrova V, Prokhorova D, Zhuravlev E, Zakabunin A, Tikunov A, Stepanov G. Expression and Functional Analysis of the Compact Thermophilic Anoxybacillus flavithermus Cas9 Nuclease. Int J Mol Sci 2023; 24:17121. [PMID: 38069443 PMCID: PMC10707453 DOI: 10.3390/ijms242317121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 11/30/2023] [Accepted: 12/01/2023] [Indexed: 12/18/2023] Open
Abstract
Research on Cas9 nucleases from different organisms holds great promise for advancing genome engineering and gene therapy tools, as it could provide novel structural insights into CRISPR editing mechanisms, expanding its application area in biology and medicine. The subclass of thermophilic Cas9 nucleases is actively expanding due to the advances in genome sequencing allowing for the meticulous examination of various microorganisms' genomes in search of the novel CRISPR systems. The most prominent thermophilic Cas9 effectors known to date are GeoCas9, ThermoCas9, IgnaviCas9, AceCas9, and others. These nucleases are characterized by a varying temperature range of the activity and stringent PAM preferences; thus, further diversification of the naturally occurring thermophilic Cas9 subclass presents an intriguing task. This study focuses on generating a construct to express a compact Cas9 nuclease (AnoCas9) from the thermophilic microorganism Anoxybacillus flavithermus displaying the nuclease activity in the 37-60 °C range and the PAM preference of 5'-NNNNCDAA-3' in vitro. Here, we highlight the close relation of AnoCas9 to the GeoCas9 family of compact thermophilic Cas9 effectors. AnoCas9, beyond broadening the repertoire of Cas9 nucleases, suggests application in areas requiring the presence of thermostable CRISPR/Cas systems in vitro, such as sequencing libraries' enrichment, allele-specific isothermal PCR, and others.
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Affiliation(s)
| | | | | | | | | | | | | | - Grigory Stepanov
- Institute of Chemical Biology and Fundamental Medicine, Siberian Branch of the Russian Academy of Sciences, Novosibirsk 630090, Russia; (A.M.); (V.P.); (E.Z.); (A.T.)
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Lee S, Hyun C, Lee M. Machine Learning Big Data Analysis of the Impact of Air Pollutants on Rhinitis-Related Hospital Visits. TOXICS 2023; 11:719. [PMID: 37624224 PMCID: PMC10459777 DOI: 10.3390/toxics11080719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 08/12/2023] [Accepted: 08/19/2023] [Indexed: 08/26/2023]
Abstract
This study seeks to elucidate the intricate relationship between various air pollutants and the incidence of rhinitis in Seoul, South Korea, wherein it leveraged a vast repository of data and machine learning techniques. The dataset comprised more than 93 million hospital visits (n = 93,530,064) by rhinitis patients between 2013 and 2017. Daily atmospheric measurements were captured for six major pollutants: PM10, PM2.5, O3, NO2, CO, and SO2. We employed traditional correlation analyses alongside machine learning models, including the least absolute shrinkage and selection operator (LASSO), random forest (RF), and gradient boosting machine (GBM), to dissect the effects of these pollutants and the potential time lag in their symptom manifestation. Our analyses revealed that CO showed the strongest positive correlation with hospital visits across all three categories, with a notable significance in the 4-day lag analysis. NO2 also exhibited a substantial positive association, particularly with outpatient visits and hospital admissions and especially in the 4-day lag analysis. Interestingly, O3 demonstrated mixed results. Both PM10 and PM2.5 showed significant correlations with the different types of hospital visits, thus underlining their potential to exacerbate rhinitis symptoms. This study thus underscores the deleterious impacts of air pollution on respiratory health, thereby highlighting the importance of reducing pollutant levels and developing strategies to minimize rhinitis-related hospital visits. Further research considering other environmental factors and individual patient characteristics will enhance our understanding of these intricate dynamics.
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Affiliation(s)
- Soyeon Lee
- School of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, Republic of Korea;
| | - Changwan Hyun
- Department of Urology, Korea University College of Medicine, Seoul 02841, Republic of Korea;
| | - Minhyeok Lee
- School of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, Republic of Korea;
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