IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v15y2024i1d10.1038_s41467-024-55436-y.html
   My bibliography  Save this article

The need for adaptability in detection, characterization, and attribution of biosecurity threats

Author

Listed:
  • William Mo

    (The Charles Stark Draper Laboratory, Inc.
    University of Colorado Boulder)

  • Christopher A. Vaiana

    (Inc.)

  • Chris J. Myers

    (University of Colorado Boulder)

Abstract

Modern biotechnology necessitates robust biosecurity protocols to address the risk of engineered biological threats. Current efforts focus on screening DNA and rejecting the synthesis of dangerous elements but face technical and logistical barriers. Screening should integrate into a broader strategy that addresses threats at multiple stages of development and deployment. The success of this approach hinges upon reliable detection, characterization, and attribution of engineered DNA. Recent advances notably aid the potential to both develop threats and analyze them. However, further work is needed to translate developments into biosecurity applications. This work reviews cutting-edge methods for DNA analysis and recommends avenues to improve biosecurity in an adaptable manner.

Suggested Citation

  • William Mo & Christopher A. Vaiana & Chris J. Myers, 2024. "The need for adaptability in detection, characterization, and attribution of biosecurity threats," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-55436-y
    DOI: 10.1038/s41467-024-55436-y
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-024-55436-y
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-024-55436-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Johnny H. Hu & Shannon M. Miller & Maarten H. Geurts & Weixin Tang & Liwei Chen & Ning Sun & Christina M. Zeina & Xue Gao & Holly A. Rees & Zhi Lin & David R. Liu, 2018. "Evolved Cas9 variants with broad PAM compatibility and high DNA specificity," Nature, Nature, vol. 556(7699), pages 57-63, April.
    2. Joseph L. Watson & David Juergens & Nathaniel R. Bennett & Brian L. Trippe & Jason Yim & Helen E. Eisenach & Woody Ahern & Andrew J. Borst & Robert J. Ragotte & Lukas F. Milles & Basile I. M. Wicky & , 2023. "De novo design of protein structure and function with RFdiffusion," Nature, Nature, vol. 620(7976), pages 1089-1100, August.
    3. Nicole N. Thadani & Sarah Gurev & Pascal Notin & Noor Youssef & Nathan J. Rollins & Daniel Ritter & Chris Sander & Yarin Gal & Debora S. Marks, 2023. "Learning from prepandemic data to forecast viral escape," Nature, Nature, vol. 622(7984), pages 818-825, October.
    4. Qi Wang & Bryce Kille & Tian Rui Liu & R. A. Leo Elworth & Todd J. Treangen, 2021. "PlasmidHawk improves lab of origin prediction of engineered plasmids using sequence alignment," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    5. John B. Ingraham & Max Baranov & Zak Costello & Karl W. Barber & Wujie Wang & Ahmed Ismail & Vincent Frappier & Dana M. Lord & Christopher Ng-Thow-Hing & Erik R. Van Vlack & Shan Tie & Vincent Xue & S, 2023. "Illuminating protein space with a programmable generative model," Nature, Nature, vol. 623(7989), pages 1070-1078, November.
    6. Joseph H. Collins & Kevin W. Keating & Trent R. Jones & Shravani Balaji & Celeste B. Marsan & Marina Çomo & Zachary J. Newlon & Tom Mitchell & Bryan Bartley & Aaron Adler & Nicholas Roehner & Eric M. , 2021. "Engineered yeast genomes accurately assembled from pure and mixed samples," Nature Communications, Nature, vol. 12(1), pages 1-15, December.
    7. Alec A. K. Nielsen & Christopher A. Voigt, 2018. "Deep learning to predict the lab-of-origin of engineered DNA," Nature Communications, Nature, vol. 9(1), pages 1-10, December.
    8. Vladimir Gligorijević & P. Douglas Renfrew & Tomasz Kosciolek & Julia Koehler Leman & Daniel Berenberg & Tommi Vatanen & Chris Chandler & Bryn C. Taylor & Ian M. Fisk & Hera Vlamakis & Ramnik J. Xavie, 2021. "Structure-based protein function prediction using graph convolutional networks," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
    9. Tijana Radivojević & Zak Costello & Kenneth Workman & Hector Garcia Martin, 2020. "A machine learning Automated Recommendation Tool for synthetic biology," Nature Communications, Nature, vol. 11(1), pages 1-14, December.
    10. Ethan C. Alley & Miles Turpin & Andrew Bo Liu & Taylor Kulp-McDowall & Jacob Swett & Rey Edison & Stephen E. Stetina & George M. Church & Kevin M. Esvelt, 2020. "A machine learning toolkit for genetic engineering attribution to facilitate biosecurity," Nature Communications, Nature, vol. 11(1), pages 1-12, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Oliver M. Crook & Kelsey Lane Warmbrod & Greg Lipstein & Christine Chung & Christopher W. Bakerlee & T. Greg McKelvey & Shelly R. Holland & Jacob L. Swett & Kevin M. Esvelt & Ethan C. Alley & William , 2022. "Analysis of the first genetic engineering attribution challenge," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    2. Lucien F. Krapp & Fernando A. Meireles & Luciano A. Abriata & Jean Devillard & Sarah Vacle & Maria J. Marcaida & Matteo Dal Peraro, 2024. "Context-aware geometric deep learning for protein sequence design," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    3. Chase R. Freschlin & Sarah A. Fahlberg & Pete Heinzelman & Philip A. Romero, 2024. "Neural network extrapolation to distant regions of the protein fitness landscape," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    4. Adibvafa Fallahpour & Vincent Gureghian & Guillaume J. Filion & Ariel B. Lindner & Amir Pandi, 2025. "CodonTransformer: a multispecies codon optimizer using context-aware neural networks," Nature Communications, Nature, vol. 16(1), pages 1-12, December.
    5. Gi Bae Kim & Ha Rim Kim & Sang Yup Lee, 2025. "Comprehensive evaluation of the capacities of microbial cell factories," Nature Communications, Nature, vol. 16(1), pages 1-15, December.
    6. Shunshi Kohyama & Béla P. Frohn & Leon Babl & Petra Schwille, 2024. "Machine learning-aided design and screening of an emergent protein function in synthetic cells," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    7. Fang Liang & Yu Zhang & Lin Li & Yexin Yang & Ji-Feng Fei & Yanmei Liu & Wei Qin, 2022. "SpG and SpRY variants expand the CRISPR toolbox for genome editing in zebrafish," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    8. Karel Weg & Erinc Merdivan & Marie Piraud & Holger Gohlke, 2025. "TopEC: prediction of Enzyme Commission classes by 3D graph neural networks and localized 3D protein descriptor," Nature Communications, Nature, vol. 16(1), pages 1-16, December.
    9. Zhaohui Zhong & Guanqing Liu & Zhongjie Tang & Shuyue Xiang & Liang Yang & Lan Huang & Yao He & Tingting Fan & Shishi Liu & Xuelian Zheng & Tao Zhang & Yiping Qi & Jian Huang & Yong Zhang, 2023. "Efficient plant genome engineering using a probiotic sourced CRISPR-Cas9 system," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    10. Jian Wang & Yuxi Teng & Ruihua Zhang & Yifei Wu & Lei Lou & Yusong Zou & Michelle Li & Zhong-Ru Xie & Yajun Yan, 2021. "Engineering a PAM-flexible SpdCas9 variant as a universal gene repressor," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
    11. Simeon D. Castle & Michiel Stock & Thomas E. Gorochowski, 2024. "Engineering is evolution: a perspective on design processes to engineer biology," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    12. Marion Rosello & Malo Serafini & Luca Mignani & Dario Finazzi & Carine Giovannangeli & Marina C. Mione & Jean-Paul Concordet & Filippo Del Bene, 2022. "Disease modeling by efficient genome editing using a near PAM-less base editor in vivo," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    13. Péter István Kulcsár & András Tálas & Zoltán Ligeti & Eszter Tóth & Zsófia Rakvács & Zsuzsa Bartos & Sarah Laura Krausz & Ágnes Welker & Vanessza Laura Végi & Krisztina Huszár & Ervin Welker, 2023. "A cleavage rule for selection of increased-fidelity SpCas9 variants with high efficiency and no detectable off-targets," Nature Communications, Nature, vol. 14(1), pages 1-20, December.
    14. Aika Iwama & Ryoji Kise & Hiroaki Akasaka & Fumiya K. Sano & Hidetaka S. Oshima & Asuka Inoue & Wataru Shihoya & Osamu Nureki, 2024. "Structure and dynamics of the pyroglutamylated RF-amide peptide QRFP receptor GPR103," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    15. Giulia I. Corsi & Kunli Qu & Ferhat Alkan & Xiaoguang Pan & Yonglun Luo & Jan Gorodkin, 2022. "CRISPR/Cas9 gRNA activity depends on free energy changes and on the target PAM context," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    16. Angelo Miskalis & Shraddha Shirguppe & Jackson Winter & Gianna Elias & Devyani Swami & Ananthan Nambiar & Michelle Stilger & Wendy S. Woods & Nicholas Gosstola & Michael Gapinske & Alejandra Zeballos , 2024. "SPLICER: a highly efficient base editing toolbox that enables in vivo therapeutic exon skipping," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    17. Zeyu Lu & Lingtian Zhang & Qing Mu & Junyang Liu & Yu Chen & Haoyuan Wang & Yanjun Zhang & Rui Su & Ruijun Wang & Zhiying Wang & Qi Lv & Zhihong Liu & Jiasen Liu & Yunhua Li & Yanhong Zhao, 2024. "Progress in Research and Prospects for Application of Precision Gene-Editing Technology Based on CRISPR–Cas9 in the Genetic Improvement of Sheep and Goats," Agriculture, MDPI, vol. 14(3), pages 1-17, March.
    18. Tomasz Śmiałkowski & Andrzej Czyżewski, 2022. "Detection of Anomalies in the Operation of a Road Lighting System Based on Data from Smart Electricity Meters," Energies, MDPI, vol. 15(24), pages 1-23, December.
    19. Kerr Ding & Jiaqi Luo & Yunan Luo, 2024. "Leveraging conformal prediction to annotate enzyme function space with limited false positives," PLOS Computational Biology, Public Library of Science, vol. 20(5), pages 1-21, May.
    20. Yaan J. Jang & Qi-Qi Qin & Si-Yu Huang & Arun T. John Peter & Xue-Ming Ding & Benoît Kornmann, 2024. "Accurate prediction of protein function using statistics-informed graph networks," Nature Communications, Nature, vol. 15(1), pages 1-12, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-55436-y. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.