2025/07/25 by Charles Copeland, Paul Schulze‐Lefert, Ka‐Wai Ma · 2 voices · 49 citations
Agricultural and Biological Sciences · Biochemistry, Genetics and Molecular Biology · #Bioinformatics #Biology #Biotechnology #Coevolution #Computational biology #Ecology #Microbiome #Nematode management and characterization studies #Plant Pathogens and Fungal Diseases #Plant-Microbe Interactions and Immunity
paper · pdf · doi:10.1093/plcell/koaf185
published in The Plant Cell 37(8) (Oxford University Press)
openalex publication_date 2025/07/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04
The plant microbiome can promote plant health and productivity through a multitude of mechanisms. Our understanding of plant-microbiome interaction relies on descriptive natural surveys and experiments performed under simplified laboratory environments. While reductionist approaches are essential to understand mechanisms of plant-microbiome interactions, they risk missing emergent community properties seen in nature. To bridge the gap between basic research and real-world deployment of the microbiome for translational application, one has to consider functional association as well as ecologic principles governing interspecies and interkingdom interactions. In this review, we discuss the beneficial potential of plant microbiomes to enhance plant growth, nutrition, stress tolerance, pathogen protection, and commercial value through the modulation of taste and flavors, using examples from model plants and agriculturally important crops. We then discuss how microbial invasion and persistence in standing communities, trade-offs under multiple stressors, and community instability under host- and environment-imposed modulation should be considered in the rational design of microbial inocula, followed by a scrutiny of the method of microbial delivery. We synthesize ideas on how multiomic data, including genomics, transcriptomes, and metabolomics, can be leveraged to identify strains or target genes of interest for functional studies and how machine learning algorithms can be incorporated to enable prediction of plant-microbiome interactions. Microbiome-based strategies hold promise for improvements in agriculture. Despite the intrinsic complexity of the underlying interactions, interdisciplinary approaches are constantly providing insight into microbiome functioning and assembly principles, which is key toward knowledge-based engineering of the microbiome for increased and sustainable crop performance.