ReviewFrontiers in microbiology2026
Plant microbiome engineering: from inoculation to genome editing.
Review in Frontiers in microbiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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.
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.
Who cites it
2 citing papers in PubMed.
- Rhizosphere Diazotrophs in Acidic Agroecosystems: An Evidence-Chain and Microbiome-Compatibility Framework for Stabilizing Crop Growth Promotion.Life (Basel, Switzerland) · 2026Review
- Engineering Plant-Associated Soil Microbiomes for Sustainable and Climate-Resilient Agriculture: Mechanisms, Technologies, and Applications.Microorganisms · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Plant-associated microbiomes are central to crop productivity, nutrient efficiency, and stress resilience, yet conventional microbiome manipulation strategies, largely based on microbial inoculation and agronomic management, often suffer from inconsistent field performance and limited persistence. Although several recent reviews have discussed CRISPR-mediated plant-microbe engineering and synthetic microbial community (SynCom) design separately, few reviews integrate genome editing, ecological stability of microbiomes, and climate-resilient agricultural applications within a unified conceptual framework. Recent advances in molecular biotechnology are transforming this landscape by enabling precision engineering of plant-microbe interactions at genetic, metabolic, and community levels. In particular, synthetic biology tools including CRISPR/Cas genome editing, RNA interference, and synthetic microbial communities (SynComs), now allow targeted modification of plant traits governing microbial recruitment, microbial pathways underpinning nutrient cycling and stress tolerance, and community-level functional complementarity. This review integrates molecular genetics, microbial ecology, and systems-level microbiome design to frame the plant and its microbiome as an engineerable holobiont. We integrate insights from genome editing in plants and microbes, omics-guided SynCom design, climate-resilience mechanisms, and emerging AI-assisted decision frameworks, including machine learning and ecological modeling approaches used to analyze multi-omics datasets, and predict plant-microbiome interactions across experimental and field-based studies. Importantly, we critically assess limitations related to ecological stability, trait trade-offs, biosafety, and regulatory challenges that constrain large-scale deployment. By bridging genome-enabled microbiome manipulation with ecological design principles, this review proposes an integrative framework for climate-smart microbiome engineering and identifies key research priorities required to transition from empirical inoculation toward predictive, sustainable, and socially responsible agricultural biotechnology.
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What OpenQuestion holds
Registered trials
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.