Evidence map›Paper›PMID 41940567›Full record

ReviewMicrobial biotechnology2026

Can We Breed Microbiomes to Sustain Plant Productivity?

Noémie De Zutter, Kris Audenaert

Abstract readReview
In one paragraph

Review in Microbial biotechnology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

2 authors.

Noémie De ZutterLaboratory of Applied Mycology and Phenomics, Department of Plants and Crops, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium.ORCID https://orcid.org/0000-0002-5854-9931
Kris AudenaertLaboratory of Applied Mycology and Phenomics, Department of Plants and Crops, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium.ORCID https://orcid.org/0000-0002-8791-1282

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Global food systems face challenges from population growth, shifting diets and climate change. While decades of plant-centric breeding and high-input agriculture have generated high yielding crops, this strategy has unintentionally reshaped the plant associated microbiomes, often coinciding with a depletion of their functional diversity. We revisit these breeding strategies and propose extending breeding targets beyond the plant genome to include the plant microbiome. In this regard, resistance breeding shows, albeit unintended, that plant genetics shape the microbiome: by narrowing the genetic base, we have selected for highly specialised pathogenic microbiomes. This raises a key question: can we intentionally apply the same principle to select for beneficial microbiomes? To answer this question, a thorough insight into microbial community architecture, hubs and functional redundancy is key. We outline two complementary avenues: (i) rewilding to restore ancestral microbial partners and (ii) microbiome breeding guided by QTL/GWAS mapped host loci that gate microbial recruitment, immune filtering and exudate composition. This approach comprises the integration of trait-based phenotyping, multi-omics, network-informed SynCom design and field testing across environments to capture G × E × M (genotype × environment × microbiome) interactions. Treating the microbiome as a selectable, designable and heritable trait can convert small gains into durable long-lasting crop resilience.

Indexed as

Crops, AgriculturalMicrobiotaPlant BreedingPlantsBreedingQuantitative Trait LociG × E × M interactionsmicrobiome‐centric breedingplant‐microbiome interactionsQTL‐guided recruitmentrewilding

Identifiers

PMID41940567
PMCPMC13052011

What OpenQuestion holds

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Registered trials

None linked

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.