Evidence map›Paper›PMID 41749104›Full record

ArticleBMC genomics2026

Functional potential of archaeal KEGG enzymes in the Moringa oleifera rhizosphere revealed by metagenomic analysis.

Lina Baz

Abstract read
In one paragraph

Article in BMC genomics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

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

1 author.

Lina BazDepartment of Biochemistry, Faculty of Science, King Abdulaziz University, Jeddah, Kingdom of Saudi Arabia. Lbaz@kau.edu.sa.ORCID http://orcid.org/0000-0003-4128-4196

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArchaea are a major domain of life that inhabit diverse and often extreme environments, contributing to biogeochemical cycles and participating in nutrient cycling within plant rhizospheric soils. This study applies metagenomic whole-genome shotgun sequencing to characterize the archaeal component of the rhizospheric microbiome associated with the wild plant species Moringa oleifera in Saudi Arabia.

resultsBased on KEGG-annotated enzymes, Thaumarchaeota and Euryarchaeota emerged as the predominant archaeal phyla in the rhizosphere, with higher abundance than in bulk soil. The most abundant archaeal enzymes were assigned to metabolic pathways related to nitrogen and sulfur metabolism, carbon transformations, and responses to oxidative stress, indicating a putative contribution to nutrient turnover and stress-related functions. Network analysis further identified archaeal chemotaxis-related regulators and two-component sensor kinases linked to the root–soil interface. Key enzymes detected included urease, glutamine synthetase, thiosulfate sulfurtransferase, and catalase-peroxidase.

conclusionsThese findings suggest that archaeal communities form a distinct functional module within the M. oleifera rhizosphere, potentially influencing soil nutrient dynamics and plant performance. The chief limitation is reliance on DNA-based metagenomic data from a single site and time point, without multi-omics or detailed soil characterization, restricting temporal and ecological generalization. Nonetheless, the dataset provides a genome-scale view of archaeal functional potential and offers testable directions for future experimental and process-oriented studies.

Indexed as

ArchaeaMetagenomicsMoringa oleiferaRhizosphereMetabolic Networks and PathwaysPhylogenySoil MicrobiologyMetagenomic shotgun sequencingNutrient cyclingPlant–microbe crosstalkStress resilience

Identifiers

PMID41749104
PMCPMC13041309

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LicenceCC BY-NC-ND
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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.