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Pichon M, Hollenstein M. Controlled enzymatic synthesis of oligonucleotides. Commun Chem 2024; 7:138. [PMID: 38890393 PMCID: PMC11189433 DOI: 10.1038/s42004-024-01216-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Accepted: 05/24/2024] [Indexed: 06/20/2024] Open
Abstract
Oligonucleotides are advancing as essential materials for the development of new therapeutics, artificial genes, or in storage of information applications. Hitherto, our capacity to write (i.e., synthesize) oligonucleotides is not as efficient as that to read (i.e., sequencing) DNA/RNA. Alternative, biocatalytic methods for the de novo synthesis of natural or modified oligonucleotides are in dire need to circumvent the limitations of traditional synthetic approaches. This Perspective article summarizes recent progress made in controlled enzymatic synthesis, where temporary blocked nucleotides are incorporated into immobilized primers by polymerases. While robust protocols have been established for DNA, RNA or XNA synthesis is more challenging. Nevertheless, using a suitable combination of protected nucleotides and polymerase has shown promises to produce RNA oligonucleotides even though the production of long DNA/RNA/XNA sequences (>1000 nt) remains challenging. We surmise that merging ligase- and polymerase-based synthesis would help to circumvent the current shortcomings of controlled enzymatic synthesis.
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Affiliation(s)
- Maëva Pichon
- Institut Pasteur, Université Paris Cité, CNRS UMR3523, Department of Structural Biology and Chemistry, Laboratory for Bioorganic Chemistry of Nucleic Acids, 28, Rue du Docteur Roux, 75724, Paris Cedex 15, France
| | - Marcel Hollenstein
- Institut Pasteur, Université Paris Cité, CNRS UMR3523, Department of Structural Biology and Chemistry, Laboratory for Bioorganic Chemistry of Nucleic Acids, 28, Rue du Docteur Roux, 75724, Paris Cedex 15, France.
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2
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Kieffer C, Rondelez Y, Gines G. Coupling Exponential to Linear Amplification for Endpoint Quantitative Analysis. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2309386. [PMID: 38593401 DOI: 10.1002/advs.202309386] [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: 12/03/2023] [Revised: 03/26/2024] [Indexed: 04/11/2024]
Abstract
Exponential DNA amplification techniques are fundamental in ultrasensitive molecular diagnostics. These systems offer a wide dynamic range, but the quantification requires real-time monitoring of the amplification reaction. Linear amplification schemes, despite their limited sensitivity, can achieve quantitative measurement from a single end-point readout, suitable for low-cost, point-of-care, or massive testing. Reconciling the sensitivity of exponential amplification with the simplicity of end-point readout would thus break through a major design dilemma and open a route to a new generation of massively scalable quantitative bioassays. Here a hybrid nucleic acid-based circuit design is introduced to compute a logarithmic function, therefore providing a wide dynamic range based on a single end-point measurement. CELIA (Coupling Exponential amplification reaction to LInear Amplification) exploits a versatile biochemical circuit architecture to couple a tunable linear amplification stage - optionally embedding an inverter function - downstream of an exponential module in a one-pot format. Applied to the detection of microRNAs, CELIA provides a limit of detection in the femtomolar range and a dynamic range of six decades. This isothermal approach bypasses thermocyclers without compromising sensitivity, thereby opening the way to applications in various diagnostic assays, and providing a simplified, cost-efficient, and high throughput solution for quantitative nucleic acid analysis.
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Affiliation(s)
- Coline Kieffer
- Laboratoire Gulliver, UMR7083 CNRS/ESPCI Paris-PSL Research University, 10 rue Vauquelin, Paris, 75005, France
| | - Yannick Rondelez
- Laboratoire Gulliver, UMR7083 CNRS/ESPCI Paris-PSL Research University, 10 rue Vauquelin, Paris, 75005, France
| | - Guillaume Gines
- Laboratoire Gulliver, UMR7083 CNRS/ESPCI Paris-PSL Research University, 10 rue Vauquelin, Paris, 75005, France
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3
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Bardales AC, Smirnov V, Taylor K, Kolpashchikov DM. DNA Logic Gates Integrated on DNA Substrates in Molecular Computing. Chembiochem 2024; 25:e202400080. [PMID: 38385968 DOI: 10.1002/cbic.202400080] [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: 02/09/2024] [Revised: 02/21/2024] [Accepted: 02/22/2024] [Indexed: 02/23/2024]
Abstract
Due to nucleic acid's programmability, it is possible to realize DNA structures with computing functions, and thus a new generation of molecular computers is evolving to solve biological and medical problems. Pioneered by Milan Stojanovic, Boolean DNA logic gates created the foundation for the development of DNA computers. Similar to electronic computers, the field is evolving towards integrating DNA logic gates and circuits by positioning them on substrates to increase circuit density and minimize gate distance and undesired crosstalk. In this minireview, we summarize recent developments in the integration of DNA logic gates into circuits localized on DNA substrates. This approach of all-DNA integrated circuits (DNA ICs) offers the advantages of biocompatibility, increased circuit response, increased circuit density, reduced unit concentration, facilitated circuit isolation, and facilitated cell uptake. DNA ICs can face similar challenges as their equivalent circuits operating in bulk solution (bulk circuits), and new physical challenges inherent in spatial localization. We discuss possible avenues to overcome these obstacles.
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Affiliation(s)
- Andrea C Bardales
- Chemistry Department, University of Central Florida, 4111 Libra Drive, Physical Sciences Bld. Rm. 255, Orlando, FL 32816-2366, Florida
| | - Viktor Smirnov
- Laboratory of Molecular Robotics and Biosensor Materials, SCAMT Institute, ITMO University, 9 Lomonosova Str., St. Petersburg, Russian Federation
| | - Katherine Taylor
- Chemistry Department, University of Central Florida, 4111 Libra Drive, Physical Sciences Bld. Rm. 255, Orlando, FL 32816-2366, Florida
| | - Dmitry M Kolpashchikov
- Chemistry Department, University of Central Florida, 4111 Libra Drive, Physical Sciences Bld. Rm. 255, Orlando, FL 32816-2366, Florida
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4
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Berleant JD, Banal JL, Rao DK, Bathe M. Scalable search of massively pooled nucleic acid samples enabled by a molecular database query language. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.12.24305660. [PMID: 38699348 PMCID: PMC11064994 DOI: 10.1101/2024.04.12.24305660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
The surge in nucleic acid analytics requires scalable storage and retrieval systems akin to electronic databases used to organize digital data. Such a system could transform disease diagnosis, ecological preservation, and molecular surveillance of biothreats. Current storage systems use individual containers for nucleic acid samples, requiring single-sample retrieval that falls short compared with digital databases that allow complex and combinatorial data retrieval on aggregated data. Here, we leverage protective microcapsules with combinatorial DNA labeling that enables arbitrary retrieval on pooled biosamples analogous to Structured Query Languages. Ninety-six encapsulated pooled mock SARS-CoV-2 genomic samples barcoded with patient metadata are used to demonstrate queries with simultaneous matches to sample collection date ranges, locations, and patient health statuses, illustrating how such flexible queries can be used to yield immunological or epidemiological insights. The approach applies to any biosample database labeled with orthogonal barcodes, enabling complex post-hoc analysis, for example, to study global biothreat epidemiology.
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Affiliation(s)
- Joseph D. Berleant
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - James L. Banal
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Present address: Cache DNA, Inc. 733 Industrial Rd., San Carlos, CA 94070 USA
| | | | - Mark Bathe
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02139 USA
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5
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Yang S, Bögels BWA, Wang F, Xu C, Dou H, Mann S, Fan C, de Greef TFA. DNA as a universal chemical substrate for computing and data storage. Nat Rev Chem 2024; 8:179-194. [PMID: 38337008 DOI: 10.1038/s41570-024-00576-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/10/2024] [Indexed: 02/12/2024]
Abstract
DNA computing and DNA data storage are emerging fields that are unlocking new possibilities in information technology and diagnostics. These approaches use DNA molecules as a computing substrate or a storage medium, offering nanoscale compactness and operation in unconventional media (including aqueous solutions, water-in-oil microemulsions and self-assembled membranized compartments) for applications beyond traditional silicon-based computing systems. To build a functional DNA computer that can process and store molecular information necessitates the continued development of strategies for computing and data storage, as well as bridging the gap between these fields. In this Review, we explore how DNA can be leveraged in the context of DNA computing with a focus on neural networks and compartmentalized DNA circuits. We also discuss emerging approaches to the storage of data in DNA and associated topics such as the writing, reading, retrieval and post-synthesis editing of DNA-encoded data. Finally, we provide insights into how DNA computing can be integrated with DNA data storage and explore the use of DNA for near-memory computing for future information technology and health analysis applications.
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Affiliation(s)
- Shuo Yang
- State Key Laboratory of Metal Matrix Composites, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
- Zhangjiang Institute for Advanced Study (ZIAS), Shanghai Jiao Tong University, Shanghai, China
| | - Bas W A Bögels
- Laboratory of Chemical Biology, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Institute for Complex Molecular Systems (ICMS), Eindhoven University of Technology, Eindhoven, The Netherlands
- Computational Biology Group, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Fei Wang
- School of Chemistry and Chemical Engineering, New Cornerstone Science Laboratory, Frontiers Science Center for Transformative Molecules and National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Can Xu
- State Key Laboratory of Metal Matrix Composites, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
- Zhangjiang Institute for Advanced Study (ZIAS), Shanghai Jiao Tong University, Shanghai, China
| | - Hongjing Dou
- State Key Laboratory of Metal Matrix Composites, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
- Zhangjiang Institute for Advanced Study (ZIAS), Shanghai Jiao Tong University, Shanghai, China
| | - Stephen Mann
- State Key Laboratory of Metal Matrix Composites, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, China.
- Zhangjiang Institute for Advanced Study (ZIAS), Shanghai Jiao Tong University, Shanghai, China.
- Centre for Protolife Research and Centre for Organized Matter Chemistry, School of Chemistry, University of Bristol, Bristol, UK.
- Max Planck-Bristol Centre for Minimal Biology, School of Chemistry, University of Bristol, Bristol, UK.
| | - Chunhai Fan
- School of Chemistry and Chemical Engineering, New Cornerstone Science Laboratory, Frontiers Science Center for Transformative Molecules and National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, China.
- Institute of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acids Chemistry and Nanomedicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
| | - Tom F A de Greef
- Laboratory of Chemical Biology, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
- Institute for Complex Molecular Systems (ICMS), Eindhoven University of Technology, Eindhoven, The Netherlands.
- Computational Biology Group, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
- Institute for Molecules and Materials, Radboud University, Nijmegen, The Netherlands.
- Center for Living Technologies, Eindhoven-Wageningen-Utrecht Alliance, Utrecht, The Netherlands.
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6
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Halužan Vasle A, Moškon M. Synthetic biological neural networks: From current implementations to future perspectives. Biosystems 2024; 237:105164. [PMID: 38402944 DOI: 10.1016/j.biosystems.2024.105164] [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: 06/14/2023] [Revised: 01/03/2024] [Accepted: 02/21/2024] [Indexed: 02/27/2024]
Abstract
Artificial neural networks, inspired by the biological networks of the human brain, have become game-changing computing models in modern computer science. Inspired by their wide scope of applications, synthetic biology strives to create their biological counterparts, which we denote synthetic biological neural networks (SYNBIONNs). Their use in the fields of medicine, biosensors, biotechnology, and many more shows great potential and presents exciting possibilities. So far, many different synthetic biological networks have been successfully constructed, however, SYNBIONN implementations have been sparse. The latter are mostly based on neural networks pretrained in silico and being heavily dependent on extensive human input. In this paper, we review current implementations and models of SYNBIONNs. We briefly present the biological platforms that show potential for designing and constructing perceptrons and/or multilayer SYNBIONNs. We explore their future possibilities along with the challenges that must be overcome to successfully implement a scalable in vivo biological neural network capable of online learning.
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Affiliation(s)
- Ana Halužan Vasle
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
| | - Miha Moškon
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia.
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7
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Nakakuki T, Toyonari M, Aso K, Murayama K, Asanuma H, de Greef TFA. DNA Reaction System That Acquires Classical Conditioning. ACS Synth Biol 2024; 13:521-529. [PMID: 38279958 PMCID: PMC10877613 DOI: 10.1021/acssynbio.3c00459] [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: 07/26/2023] [Revised: 01/10/2024] [Accepted: 01/11/2024] [Indexed: 01/29/2024]
Abstract
Biochemical reaction networks can exhibit plastic adaptation to alter their functions in response to environmental changes. This capability is derived from the structure and dynamics of the reaction networks and the functionality of the biomolecule. This plastic adaptation in biochemical reaction systems is essentially related to memory and learning capabilities, which have been studied in DNA computing applications for the past decade. However, designing DNA reaction systems with memory and learning capabilities using the dynamic properties of biochemical reactions remains challenging. In this study, we propose a basic DNA reaction system design that acquires classical conditioning, a phenomenon underlying memory and learning, as a typical learning task. Our design is based on a simple mechanism of five DNA strand displacement reactions and two degradative reactions. The proposed DNA circuit can acquire or lose a new function under specific conditions, depending on the input history formed by repetitive stimuli, by exploiting the dynamic properties of biochemical reactions induced by different input timings.
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Affiliation(s)
- Takashi Nakakuki
- Department
of Intelligent and Control Systems, Faculty of Computer Science and
Systems Engineering, Kyushu Institute of
Technology 680-4 Kawazu, Iizuka, Fukuoka 8208502, Japan
| | - Masato Toyonari
- Department
of Intelligent and Control Systems, Faculty of Computer Science and
Systems Engineering, Kyushu Institute of
Technology 680-4 Kawazu, Iizuka, Fukuoka 8208502, Japan
| | - Kaori Aso
- Department
of Intelligent and Control Systems, Faculty of Computer Science and
Systems Engineering, Kyushu Institute of
Technology 680-4 Kawazu, Iizuka, Fukuoka 8208502, Japan
| | - Keiji Murayama
- Department
of Biomolecular Engineering, Graduate School of Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 4648603, Japan
| | - Hiroyuki Asanuma
- Department
of Biomolecular Engineering, Graduate School of Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 4648603, Japan
| | - Tom F. A. de Greef
- Laboratory
of Chemical Biology and Institute for Complex Molecular Systems and
Computational Biology Group, Department of Biomedical Engineering, Eindhoven University of Technology, De Zaale, Eindhoven 5600 MB, The Netherlands
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8
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Yu L, Yan H. DNA-based computation for multiple biomarkers. Nat Biomed Eng 2023; 7:1535-1536. [PMID: 38097810 DOI: 10.1038/s41551-023-01161-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2023]
Affiliation(s)
- Lu Yu
- Center for Molecular Design and Biomimetics, Biodesign Institute & School of Molecular Sciences, Arizona State University, Tempe, AZ, USA
| | - Hao Yan
- Center for Molecular Design and Biomimetics, Biodesign Institute & School of Molecular Sciences, Arizona State University, Tempe, AZ, USA.
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McDonnell KJ. Leveraging the Academic Artificial Intelligence Silecosystem to Advance the Community Oncology Enterprise. J Clin Med 2023; 12:4830. [PMID: 37510945 PMCID: PMC10381436 DOI: 10.3390/jcm12144830] [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: 06/07/2023] [Revised: 07/05/2023] [Accepted: 07/07/2023] [Indexed: 07/30/2023] Open
Abstract
Over the last 75 years, artificial intelligence has evolved from a theoretical concept and novel paradigm describing the role that computers might play in our society to a tool with which we daily engage. In this review, we describe AI in terms of its constituent elements, the synthesis of which we refer to as the AI Silecosystem. Herein, we provide an historical perspective of the evolution of the AI Silecosystem, conceptualized and summarized as a Kuhnian paradigm. This manuscript focuses on the role that the AI Silecosystem plays in oncology and its emerging importance in the care of the community oncology patient. We observe that this important role arises out of a unique alliance between the academic oncology enterprise and community oncology practices. We provide evidence of this alliance by illustrating the practical establishment of the AI Silecosystem at the City of Hope Comprehensive Cancer Center and its team utilization by community oncology providers.
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Affiliation(s)
- Kevin J McDonnell
- Center for Precision Medicine, Department of Medical Oncology & Therapeutics Research, City of Hope Comprehensive Cancer Center, Duarte, CA 91010, USA
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