1
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Liu R, Liu T, Liu W, Luo B, Li Y, Fan X, Zhang X, Cui W, Teng Y. SemiSynBio: A new era for neuromorphic computing. Synth Syst Biotechnol 2024; 9:594-599. [PMID: 38711551 PMCID: PMC11070324 DOI: 10.1016/j.synbio.2024.04.013] [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: 02/02/2024] [Revised: 04/08/2024] [Accepted: 04/15/2024] [Indexed: 05/08/2024] Open
Abstract
Neuromorphic computing has the potential to achieve the requirements of the next-generation artificial intelligence (AI) systems, due to its advantages of adaptive learning and parallel computing. Meanwhile, biocomputing has seen ongoing development with the rise of synthetic biology, becoming the driving force for new generation semiconductor synthetic biology (SemiSynBio) technologies. DNA-based biomolecules could potentially perform the functions of Boolean operators as logic gates and be used to construct artificial neural networks (ANNs), providing the possibility of executing neuromorphic computing at the molecular level. Herein, we briefly outline the principles of neuromorphic computing, describe the advances in DNA computing with a focus on synthetic neuromorphic computing, and summarize the major challenges and prospects for synthetic neuromorphic computing. We believe that constructing such synthetic neuromorphic circuits will be an important step toward realizing neuromorphic computing, which would be of widespread use in biocomputing, DNA storage, information security, and national defense.
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Affiliation(s)
- Ruicun Liu
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Academy of Military Medical Sciences, Beijing, 100071, China
| | - Tuoyu Liu
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Academy of Military Medical Sciences, Beijing, 100071, China
| | - Wuge Liu
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Academy of Military Medical Sciences, Beijing, 100071, China
| | - Boyu Luo
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Academy of Military Medical Sciences, Beijing, 100071, China
| | - Yuchen Li
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Academy of Military Medical Sciences, Beijing, 100071, China
| | - Xinyue Fan
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Academy of Military Medical Sciences, Beijing, 100071, China
| | - Xianchao Zhang
- Institute of Information Network and Artificial Intelligence, Jiaxing University, Jiaxing, 314001, China
| | - Wei Cui
- South China University of Technology, Guangzhou, 510641, China
| | - Yue Teng
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Academy of Military Medical Sciences, Beijing, 100071, China
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2
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Yang L, Tang Q, Zhang M, Tian Y, Chen X, Xu R, Ma Q, Guo P, Zhang C, Han D. A spatially localized DNA linear classifier for cancer diagnosis. Nat Commun 2024; 15:4583. [PMID: 38811607 PMCID: PMC11136972 DOI: 10.1038/s41467-024-48869-y] [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: 09/09/2023] [Accepted: 05/14/2024] [Indexed: 05/31/2024] Open
Abstract
Molecular computing is an emerging paradigm that plays an essential role in data storage, bio-computation, and clinical diagnosis with the future trends of more efficient computing scheme, higher modularity with scaled-up circuity and stronger tolerance of corrupted inputs in a complex environment. Towards these goals, we construct a spatially localized, DNA integrated circuits-based classifier (DNA IC-CLA) that can perform neuromorphic architecture-based computation at a molecular level for medical diagnosis. The DNA-based classifier employs a two-dimensional DNA origami as the framework and localized processing modules as the in-frame computing core to execute arithmetic operations (e.g. multiplication, addition, subtraction) for efficient linear classification of complex patterns of miRNA inputs. We demonstrate that the DNA IC-CLA enables accurate cancer diagnosis in a faster (about 3 h) and more effective manner in synthetic and clinical samples compared to those of the traditional freely diffusible DNA circuits. We believe that this all-in-one DNA-based classifier can exhibit more applications in biocomputing in cells and medical diagnostics.
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Affiliation(s)
- Linlin Yang
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, 310022, Hangzhou, Zhejiang, China
- Institute of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 200127, Shanghai, China
- School of Pharmacy, Shandong Technology Innovation Center of Molecular Targeting and Intelligent Diagnosis and Treatment, Binzhou Medical University, 264003, Yantai, China
| | - Qian Tang
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, 310022, Hangzhou, Zhejiang, China
| | - Mingzhi Zhang
- Institute of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 200127, Shanghai, China
| | - Yuan Tian
- Institute of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 200127, Shanghai, China
| | - Xiaoxing Chen
- Institute of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 200127, Shanghai, China
| | - Rui Xu
- Intellinosis Biotech Co.Ltd., 201112, Shanghai, China
| | - Qian Ma
- Intellinosis Biotech Co.Ltd., 201112, Shanghai, China
| | - Pei Guo
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, 310022, Hangzhou, Zhejiang, China.
| | - Chao Zhang
- Institute of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 200127, Shanghai, China.
- Intellinosis Biotech Co.Ltd., 201112, Shanghai, China.
| | - Da Han
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, 310022, Hangzhou, Zhejiang, China.
- Institute of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 200127, Shanghai, China.
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3
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Yang J, Li G, Chen S, Su X, Xu D, Zhai Y, Liu Y, Hu G, Guo C, Yang HB, Occhipinti LG, Hu FX. Machine Learning-Assistant Colorimetric Sensor Arrays for Intelligent and Rapid Diagnosis of Urinary Tract Infection. ACS Sens 2024; 9:1945-1956. [PMID: 38530950 DOI: 10.1021/acssensors.3c02687] [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] [Indexed: 03/28/2024]
Abstract
Urinary tract infections (UTIs), which can lead to pyelonephritis, urosepsis, and even death, are among the most prevalent infectious diseases worldwide, with a notable increase in treatment costs due to the emergence of drug-resistant pathogens. Current diagnostic strategies for UTIs, such as urine culture and flow cytometry, require time-consuming protocols and expensive equipment. We present here a machine learning-assisted colorimetric sensor array based on recognition of ligand-functionalized Fe single-atom nanozymes (SANs) for the identification of microorganisms at the order, genus, and species levels. Colorimetric sensor arrays are built from the SAN Fe1-NC functionalized with four types of recognition ligands, generating unique microbial identification fingerprints. By integrating the colorimetric sensor arrays with a trained computational classification model, the platform can identify more than 10 microorganisms in UTI urine samples within 1 h. Diagnostic accuracy of up to 97% was achieved in 60 UTI clinical samples, holding great potential for translation into clinical practice applications.
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Affiliation(s)
- Jianyu Yang
- School of Materials Science and Engineering, Suzhou University of Science and Technology, Suzhou 215009, China
| | - Ge Li
- School of Materials Science and Engineering, Suzhou University of Science and Technology, Suzhou 215009, China
| | - Shihong Chen
- School of Chemistry and Chemical Engineering, Southwest University, Chongqing 400715, China
| | - Xiaozhi Su
- Shanghai Synchrotron Radiation Facility, Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201204, China
| | - Dong Xu
- Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine, Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China
- Wenling Big Data and Artificial Intelligence Institute in Medicine, Taizhou, Zhejiang 317502, China
- Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, Hangzhou, Zhejiang 310022, China
- Taizhou Key Laboratory of Minimally Invasive Interventional Therapy & Artificial Intelligence, Taizhou Campus of Zhejiang Cancer Hospital, Taizhou, Zhejiang 317502, China
| | - Yueming Zhai
- The Institute for Advanced Studies, Wuhan University, Wuhan, Hubei 430072, China
| | - Yuhang Liu
- School of Materials Science and Engineering, Suzhou University of Science and Technology, Suzhou 215009, China
| | - Guangxuan Hu
- School of Materials Science and Engineering, Suzhou University of Science and Technology, Suzhou 215009, China
| | - Chunxian Guo
- School of Materials Science and Engineering, Suzhou University of Science and Technology, Suzhou 215009, China
| | - Hong Bin Yang
- School of Materials Science and Engineering, Suzhou University of Science and Technology, Suzhou 215009, China
| | - Luigi G Occhipinti
- Department of Engineering, University of Cambridge, 9 J J Thomson Avenue, Cambridge CB3 0FA, U.K
| | - Fang Xin Hu
- School of Materials Science and Engineering, Suzhou University of Science and Technology, Suzhou 215009, China
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4
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Zhang L, Liu Q, Guo Y, Tian L, Chen K, Bai D, Yu H, Han X, Luo W, Feng T, Deng S, Xie G. DNA-based molecular classifiers for the profiling of gene expression signatures. J Nanobiotechnology 2024; 22:189. [PMID: 38632615 PMCID: PMC11025223 DOI: 10.1186/s12951-024-02445-0] [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: 09/28/2023] [Accepted: 03/28/2024] [Indexed: 04/19/2024] Open
Abstract
Although gene expression signatures offer tremendous potential in diseases diagnostic and prognostic, but massive gene expression signatures caused challenges for experimental detection and computational analysis in clinical setting. Here, we introduce a universal DNA-based molecular classifier for profiling gene expression signatures and generating immediate diagnostic outcomes. The molecular classifier begins with feature transformation, a modular and programmable strategy was used to capture relative relationships of low-concentration RNAs and convert them to general coding inputs. Then, competitive inhibition of the DNA catalytic reaction enables strict weight assignment for different inputs according to their importance, followed by summation, annihilation and reporting to accurately implement the mathematical model of the classifier. We validated the entire workflow by utilizing miRNA expression levels for the diagnosis of hepatocellular carcinoma (HCC) in clinical samples with an accuracy 85.7%. The results demonstrate the molecular classifier provides a universal solution to explore the correlation between gene expression patterns and disease diagnostics, monitoring, and prognosis, and supports personalized healthcare in primary care.
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Affiliation(s)
- Li Zhang
- Key Laboratory of Laboratory Medical Diagnostics, Ministry of Education, Department of Laboratory Medicine, Chongqing Medical University, Chongqing, 400016, China
- Department of Forensic Medicine, Chongqing Medical University, Chongqing, 400016, China
| | - Qian Liu
- Nuclear Medicine Department, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, 400010, China
| | - Yongcan Guo
- Clinical Laboratory, Traditional Chinese Medicine Hospital Affiliated to Southwest Medical University, Luzhou, 646000, China
| | - Luyao Tian
- Key Laboratory of Laboratory Medical Diagnostics, Ministry of Education, Department of Laboratory Medicine, Chongqing Medical University, Chongqing, 400016, China
| | - Kena Chen
- Key Laboratory of Laboratory Medical Diagnostics, Ministry of Education, Department of Laboratory Medicine, Chongqing Medical University, Chongqing, 400016, China
| | - Dan Bai
- Key Laboratory of Laboratory Medical Diagnostics, Ministry of Education, Department of Laboratory Medicine, Chongqing Medical University, Chongqing, 400016, China
| | - Hongyan Yu
- Key Laboratory of Laboratory Medical Diagnostics, Ministry of Education, Department of Laboratory Medicine, Chongqing Medical University, Chongqing, 400016, China
| | - Xiaole Han
- Key Laboratory of Laboratory Medical Diagnostics, Ministry of Education, Department of Laboratory Medicine, Chongqing Medical University, Chongqing, 400016, China
| | - Wang Luo
- Key Laboratory of Laboratory Medical Diagnostics, Ministry of Education, Department of Laboratory Medicine, Chongqing Medical University, Chongqing, 400016, China
| | - Tong Feng
- Key Laboratory of Laboratory Medical Diagnostics, Ministry of Education, Department of Laboratory Medicine, Chongqing Medical University, Chongqing, 400016, China
| | - Shixiong Deng
- Department of Forensic Medicine, Chongqing Medical University, Chongqing, 400016, China.
| | - Guoming Xie
- Key Laboratory of Laboratory Medical Diagnostics, Ministry of Education, Department of Laboratory Medicine, Chongqing Medical University, Chongqing, 400016, China.
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5
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Zhang M, Yancey C, Zhang C, Wang J, Ma Q, Yang L, Schulman R, Han D, Tan W. A DNA circuit that records molecular events. SCIENCE ADVANCES 2024; 10:eadn3329. [PMID: 38578999 PMCID: PMC10997190 DOI: 10.1126/sciadv.adn3329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 03/04/2024] [Indexed: 04/07/2024]
Abstract
Characterizing the relative onset time, strength, and duration of molecular signals is critical for understanding the operation of signal transduction and genetic regulatory networks. However, detecting multiple such molecules as they are produced and then quickly consumed is challenging. A MER can encode information about transient molecular events as stable DNA sequences and are amenable to downstream sequencing or other analysis. Here, we report the development of a de novo molecular event recorder that processes information using a strand displacement reaction network and encodes the information using the primer exchange reaction, which can be decoded and quantified by DNA sequencing. The event recorder was able to classify the order at which different molecular signals appeared in time with 88% accuracy, the concentrations with 100% accuracy, and the duration with 75% accuracy. This simultaneous and highly programmable multiparameter recording could enable the large-scale deciphering of molecular events such as within dynamic reaction environments, living cells, or tissues.
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Affiliation(s)
- Mingzhi Zhang
- Institute of Molecular Medicine (IMM), Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China
- Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Colin Yancey
- Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Chao Zhang
- Institute of Molecular Medicine (IMM), Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China
- Intellinosis Biotech Co. Ltd., Shanghai, 201112, China
| | - Junyan Wang
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China
| | - Qian Ma
- Intellinosis Biotech Co. Ltd., Shanghai, 201112, China
| | - Linlin Yang
- Institute of Molecular Medicine (IMM), Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China
| | - Rebecca Schulman
- Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
- Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Da Han
- Institute of Molecular Medicine (IMM), Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China
| | - Weihong Tan
- Institute of Molecular Medicine (IMM), Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China
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6
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Liu X, Zhang X, Cui S, Xu S, Liu R, Wang B, Wei X, Zhang Q. A signal transmission strategy driven by gap-regulated exonuclease hydrolysis for hierarchical molecular networks. Commun Biol 2024; 7:335. [PMID: 38493265 PMCID: PMC10944543 DOI: 10.1038/s42003-024-06036-5] [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: 10/20/2023] [Accepted: 03/11/2024] [Indexed: 03/18/2024] Open
Abstract
Exonucleases serve as efficient tools for signal processing and play an important role in biochemical reactions. Here, we identify the mechanism of cooperative exonuclease hydrolysis, offering a method to regulate the cooperative hydrolysis driven by exonucleases through the modulation of the number of bases in gap region. A signal transmission strategy capable of producing amplified orthogonal DNA signal is proposed to resolve the polarity of signals and byproducts, which provides a solution to overcome the signal attenuation. The gap-regulated mechanism combined with DNA strand displacement (DSD) reduces the unpredictable secondary structures, allowing for the coexistence of similar structures in hierarchical molecular networks. For the application of the strategy, a molecular computing model is constructed to solve the maximum weight clique problems (MWCP). This work enhances for our knowledge of these important enzymes and promises application prospects in molecular computing, signal detection, and nanomachines.
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Affiliation(s)
- Xin Liu
- School of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, Liaoning, China
| | - Xun Zhang
- School of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, Liaoning, China
| | - Shuang Cui
- School of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, Liaoning, China
| | - Shujuan Xu
- Key Lab of Biotechnology and Bioresources Utilization of Ministry of Education, College of Life Science, Dalian Minzu University, Dalian, 116600, Liaoning, China
| | - Rongming Liu
- MOE Key Laboratory of Bio-Intelligent Manufacturing, School of Bioengineering, Dalian University of Technology, Dalian, 116024, Liaoning, China
| | - Bin Wang
- Key Laboratory of Advanced Design and Intelligent Computing, Ministry of Education, School of Software Engineering, Dalian University, Dalian, 116622, Liaoning, China
| | - Xiaopeng Wei
- School of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, Liaoning, China
| | - Qiang Zhang
- School of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, Liaoning, China.
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7
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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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8
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Abbo LM, Vasiliu-Feltes I. Disrupting the infectious disease ecosystem in the digital precision health era innovations and converging emerging technologies. Antimicrob Agents Chemother 2023; 67:e0075123. [PMID: 37724872 PMCID: PMC10583659 DOI: 10.1128/aac.00751-23] [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] [Indexed: 09/21/2023] Open
Abstract
This commentary explores the convergence of precision health and evolving technologies, including the critical role of artificial intelligence (AI) and emerging technologies in infectious diseases (ID) and microbiology. We discuss their disruptive impact on the ID ecosystem and examine the transformative potential of frontier technologies in precision health, public health, and global health when deployed with robust ethical and data governance guardrails in place.
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Affiliation(s)
- Lilian M. Abbo
- Jackson Health System, Miami, Florida, USA
- Division of Infectious Diseases, Miller School of Medicine, University of Miami, Miami, Florida, USA
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9
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Lee H, Xie T, Yu X, Schaffter SW, Schulman R. Plug-and-play protein biosensors using aptamer-regulated in vitro transcription. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.10.552680. [PMID: 37645783 PMCID: PMC10461910 DOI: 10.1101/2023.08.10.552680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
Molecular biosensors that accurately measure protein concentrations without external equipment are critical for solving numerous problems in diagnostics and therapeutics. Modularly transducing the binding of protein antibodies, protein switches or aptamers into a useful output remains challenging. Here, we develop a biosensing platform based on aptamer-regulated transcription in which aptamers integrated into transcription templates serve as inputs to molecular circuits that can be programmed to a produce a variety of responses. We modularly design molecular biosensors using this platform by swapping aptamer domains for specific proteins and downstream domains that encode different RNA transcripts. By coupling aptamer-regulated transcription with diverse transduction circuits, we rapidly construct analog protein biosensors or digital protein biosensors with detection ranges that can be tuned over two orders of magnitude. Aptamer-regulated transcription is a straightforward and inexpensive approach for constructing programmable protein biosensors suitable for diverse research and diagnostic applications.
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Affiliation(s)
- Heonjoon Lee
- Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21218
| | - Tian Xie
- Biochemistry and Molecular Biology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205
| | - Xinjie Yu
- Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218
| | | | - Rebecca Schulman
- Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218
- Computer Science, Johns Hopkins University, Baltimore, MD 21218
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10
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Liu J, Zhang C, Song J, Zhang Q, Zhang R, Zhang M, Han D, Tan W. Unlocking Genetic Profiles with a Programmable DNA-Powered Decoding Circuit. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023:e2206343. [PMID: 37116171 PMCID: PMC10369254 DOI: 10.1002/advs.202206343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Revised: 04/12/2023] [Indexed: 06/19/2023]
Abstract
Human genetic architecture provides remarkable insights into disease risk prediction and personalized medication. Advances in genomics have boosted the fine-mapping of disease-associated genetic variants across human genome. In healthcare practice, interpreting intricate genetic profiles into actionable medical decisions can improve health outcomes but remains challenging. Here an intelligent genetic decoder is engineered with programmable DNA computation to automate clinical analyses and interpretations. The DNA-based decoder recognizes multiplex genetic information by one-pot ligase-dependent reactions and interprets implicit genetic profiles into explicit decision reports. It is shown that the DNA decoder implements intended computation on genetic profiles and outputs a corresponding answer within hours. Effectiveness in 30 human genomic samples is validated and it is shown that it achieves desirable performance on the interpretation of CYP2C19 genetic profiles into drug responses, with accuracy equivalent to that of Sanger sequencing. Circuit modules of the DNA decoder can also be readily reprogrammed to interpret another pharmacogenetics genes, provide drug dosing recommendations, and implement reliable molecular calculation of polygenic risk score (PRS) and PRS-informed cancer risk assessment. The DNA-powered intelligent decoder provides a general solution to the translation of complex genetic profiles into actionable healthcare decisions and will facilitate personalized healthcare in primary care.
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Affiliation(s)
- Junlan Liu
- Institute of Molecular Medicine (IMM), Renji Hospital, School of Medicine, and College of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Chao Zhang
- Institute of Molecular Medicine (IMM), Renji Hospital, School of Medicine, and College of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Jinxing Song
- Institute of Molecular Medicine (IMM), Renji Hospital, School of Medicine, and College of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Qing Zhang
- Institute of Molecular Medicine (IMM), Renji Hospital, School of Medicine, and College of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Rongjun Zhang
- Institute of Molecular Medicine (IMM), Renji Hospital, School of Medicine, and College of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Mingzhi Zhang
- Institute of Molecular Medicine (IMM), Renji Hospital, School of Medicine, and College of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Da Han
- Institute of Molecular Medicine (IMM), Renji Hospital, School of Medicine, and College of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
- The Key Laboratory of Zhejiang Province for Aptamers and Theranostics, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, 310022, China
| | - Weihong Tan
- Institute of Molecular Medicine (IMM), Renji Hospital, School of Medicine, and College of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
- The Key Laboratory of Zhejiang Province for Aptamers and Theranostics, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, 310022, China
- Molecular Science and Biomedicine Laboratory (MBL), State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, College of Biology, Aptamer Engineering Center of Hunan Province, Hunan University, Changsha, Hunan, 410082, China
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