JOURNAL ARTICLE
Biolistics-mediated transformation of hornworts and its application to study pyrenoid protein localization.
Published In: Journal of Experimental Botany, 2024, v. 75, n. 16. P. 4760 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Lafferty, Declan J; Robison, Tanner A; Gunadi, Andika; Schafran, Peter W; Gunn, Laura H; Eck, Joyce Van; Li, Fay-Wei 3 of 3
Abstract
The article focuses on the development of a biolistic (particle bombardment) transformation method for hornworts, a lineage of bryophytes important for studying land plant evolution and carbon/nitrogen assimilation mechanisms. Using the model hornwort *Anthoceros agrestis*, the method enables efficient transient expression of green fluorescent protein (GFP) within 48–72 hours and recovery of stable transgenic lines after 8–10 weeks, with an average of six stable lines per bombardment. The approach was successfully applied to transiently transform nine additional hornwort species and to stably transform *Anthoceros fusiformis*, demonstrating broad applicability across the hornwort phylogeny. Furthermore, the method facilitated fluorescent protein tagging and localization of key pyrenoid proteins—Rubisco small subunit (RbcS) and Rubisco activase (RCA)—which are central to the CO₂-concentrating mechanism (CCM) unique to hornworts. This biolistic protocol provides a valuable genetic tool for functional genomics and comparative studies in hornworts, supporting future research into their unique biology and potential biotechnological applications.
Additional Information
- Source:Journal of Experimental Botany. 2024/08, Vol. 75, Issue 16, p4760
- Document Type:Article
- Subject Area:Botany
- Publication Date:2024
- ISSN:0022-0957
- DOI:10.1093/jxb/erae243
- Accession Number:179513476
- Copyright Statement:Copyright of Journal of Experimental Botany is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Looking to go deeper into this topic? Look for more articles on EBSCOhost.