Evidence map›Paper›PMID 39136439›Full record

ArticleMicrobiology spectrum2024

Characterization of differences in seed endophytic microbiome in conventional and organic rice by amplicon-based sequencing and culturing methods.

Sabin Khanal, Muhammad Imran, Xin-Gen Zhou, Sanjay Antony-Babu

Abstract read
In one paragraph

Article in Microbiology spectrum, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
  2. Screening and Genomic Analysis ofMicroorganisms · 2025
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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Sabin KhanalTexas A&M AgriLife Research Center, Beaumont, Texas, USA.ORCID 0000-0003-4379-4616
Muhammad ImranDepartment of Plant Pathology, University of Faisalabad, Faisalabad, Pakistan.
Xin-Gen ZhouTexas A&M AgriLife Research Center, Beaumont, Texas, USA.ORCID 0000-0002-0883-6502
Sanjay Antony-BabuDepartment of Plant Pathology and Microbiology, Texas A&M University, College Station, Texas, USA.ORCID 0000-0001-6887-620X

Funding

Texas Rice Research Foundation M2101762, M2202432USDA NIFA OREI 2015-51300-24286
6 · The paper itself

Abstract

The seed serves as the primary source for establishing microbial populations in plants across subsequent generations, influencing plant growth and overall health. Cropping conditions, especially farming practices, can influence the composition and functionality of the seed microbiome. Very little is known about the differences in seed microbiome between organic and conventional production systems. In this study, we characterized the endophytic microbial populations in seeds of rice grown under organic and conventional management practices through culture-dependent and -independent analyses. The V4 region of 16S rRNA was used for bacterial taxa identification, and the ITS1 region was used for the identification of fungal taxa. Our results revealed significantly higher Shannon and Simpson indices for bacterial diversity in the conventional farming system, whereas the fungal diversity was higher for observed, Shannon, and Simpson indices in the organic farming system. The cultivable endophytic bacteria were isolated and identified using the full-length 16S rRNA gene. There was no difference in culturable endophytic bacterial isolates in rice seeds grown under both conventional and organic farming systems. Among 33 unique isolates tested IMPORTANCE: In this paper, we studied the differences in the endophytic microbial composition of rice seeds grown in conventional and organic farming systems. Our results demonstrate a greater bacterial diversity in conventional farming, while organic farming showcases a higher fungal diversity. Additionally, our research reveals the ability of seed bacterial endophytes to inhibit the growth of three fungal pathogens responsible for causing seedling blight in rice. This study provides valuable insights into the potential use of beneficial seed microbial endophytes for developing a novel microbiome-based strategy in the management of rice diseases. Such an approach has the potential to enhance overall plant health and improve crop productivity.

Indexed as

BacteriaEndophytesFungiMicrobiotaOrganic AgricultureOryzaRNA, Ribosomal, 16SSeedsAgricultureBacillusBurkholderiaPantoeaPlant DiseasesRhizoctoniaRNA, Ribosomal, 16Sbiocontrolconventional farmingorganic farmingrice seed microbiome

Identifiers

PMID39136439
PMCPMC11448069

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.