IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v13y2022i1d10.1038_s41467-022-28074-5.html
   My bibliography  Save this article

Massively parallel characterization of engineered transcript isoforms using direct RNA sequencing

Author

Listed:
  • Matthew J. Tarnowski

    (University of Bristol)

  • Thomas E. Gorochowski

    (University of Bristol
    University of Bristol)

Abstract

Transcriptional terminators signal where transcribing RNA polymerases (RNAPs) should halt and disassociate from DNA. However, because termination is stochastic, two different forms of transcript could be produced: one ending at the terminator and the other reading through. An ability to control the abundance of these transcript isoforms would offer bioengineers a mechanism to regulate multi-gene constructs at the level of transcription. Here, we explore this possibility by repurposing terminators as ‘transcriptional valves’ that can tune the proportion of RNAP read-through. Using one-pot combinatorial DNA assembly, we iteratively construct 1780 transcriptional valves for T7 RNAP and show how nanopore-based direct RNA sequencing (dRNA-seq) can be used to characterize entire libraries of valves simultaneously at a nucleotide resolution in vitro and unravel genetic design principles to tune and insulate termination. Finally, we engineer valves for multiplexed regulation of CRISPR guide RNAs. This work provides new avenues for controlling transcription and demonstrates the benefits of long-read sequencing for exploring complex sequence-function landscapes.

Suggested Citation

  • Matthew J. Tarnowski & Thomas E. Gorochowski, 2022. "Massively parallel characterization of engineered transcript isoforms using direct RNA sequencing," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-28074-5
    DOI: 10.1038/s41467-022-28074-5
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-022-28074-5
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-022-28074-5?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
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Evangelos-Marios Nikolados & Arin Wongprommoon & Oisin Mac Aodha & Guillaume Cambray & Diego A. Oyarzún, 2022. "Accuracy and data efficiency in deep learning models of protein expression," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    2. 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.
    3. Noor Radde & Genevieve A. Mortensen & Diya Bhat & Shireen Shah & Joseph J. Clements & Sean P. Leonard & Matthew J. McGuffie & Dennis M. Mishler & Jeffrey E. Barrick, 2024. "Measuring the burden of hundreds of BioBricks defines an evolutionary limit on constructability in synthetic biology," Nature Communications, Nature, vol. 15(1), pages 1-17, 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:13:y:2022:i:1:d:10.1038_s41467-022-28074-5. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.