JOURNAL ARTICLE
Endophytic diazotrophic communities from rice roots are diverse and weakly associated with soil diazotrophic community composition and soil properties.
Published In: Journal of Applied Microbiology, 2024, v. 135, n. 7. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Ferrando, Lucía; Rariz, Gastón; Martínez-Pereyra, Andrea; Fernández-Scavino, Ana 3 of 3
Abstract
This article focuses on the contribution of soil diazotrophic bacteria—microorganisms capable of nitrogen fixation identified by the nifH gene—to the endophytic communities in rice (Oryza sativa L.) roots grown in representative soils from Uruguay. Using molecular techniques including qPCR, terminal restriction fragment length polymorphism (T-RFLP), and 454-pyrosequencing of the nifH gene, the study found that while soil diazotroph communities were relatively similar and dominated by Alphaproteobacteria and Planctomycetota, the root endophytic diazotrophs were more taxonomically diverse, including Alpha-, Beta-, Gammaproteobacteria, and Bacillota, many of which were undetectable in the soils. The structure of root diazotrophic communities varied independently of soil physicochemical properties or agronomic history, suggesting that plant-root traits and local conditions influence bacterial colonization. These findings highlight the complexity of root-associated diazotrophs and inform strategies for developing effective bacterial inoculants to promote sustainable rice cultivation.
Additional Information
- Source:Journal of Applied Microbiology. 2024/07, Vol. 135, Issue 7, p1
- Document Type:Article
- Subject Area:Health and Medicine
- Publication Date:2024
- ISSN:1364-5072
- DOI:10.1093/jambio/lxae157
- Accession Number:178955471
- Copyright Statement:Copyright of Journal of Applied Microbiology 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.)
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