Engineering agricultural soil microbiomes and predicting plant phenotypes

• Published in: Trends in microbiology (Regular ed.) Vol. 32; no. 9; pp. 858 - 873
• Main Authors: Berruto, Chiara A., Demirer, Gozde S.
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.09.2024
• Subjects: Agriculture; Bacteria - classification; Bacteria - genetics; Bacteria - metabolism; community modeling; Crops, Agricultural - microbiology; host–microbe interactions; machine learning; microbiome-associated phenotype; Microbiota - physiology; Phenotype; Plant Development; plant microbiome; Plant Roots - microbiology; Plants - microbiology; Rhizosphere; rhizosphere engineering; Soil - chemistry; Soil Microbiology; host–microbe interactions; machine learning; rhizosphere engineering; plant microbiome; community modeling; microbiome-associated phenotype
• ISSN: 0966-842X; 1878-4380; 1878-4380
• DOI: 10.1016/j.tim.2024.02.003

Engineering plant microbiomes presents a promising strategy for advancing sustainable agriculture practices.Genome-wide association studies can identify plant host genomic regions that influence microbiome structure and function, thereby informing genetic engineering and targeted breeding strategies.Co-inoculation experiments and metabolic modeling can identify critical microbial interspecies interactions.The incorporation of machine learning algorithms into microbiome research has expanded the predictive capabilities.Integration of experimental and computational tools has been successful in designing microbiomes with precise functions. Plant growth-promoting rhizobacteria (PGPR) can improve crop yields, nutrient use efficiency, plant tolerance to stressors, and confer benefits to future generations of crops grown in the same soil. Unlocking the potential of microbial communities in the rhizosphere and endosphere is therefore of great interest for sustainable agriculture advancements. Before plant microbiomes can be engineered to confer desirable phenotypic effects on their plant hosts, a deeper understanding of the interacting factors influencing rhizosphere community structure and function is needed. Dealing with this complexity is becoming more feasible using computational approaches. In this review, we discuss recent advances at the intersection of experimental and computational strategies for the investigation of plant–microbiome interactions and the engineering of desirable soil microbiomes. Plant growth-promoting rhizobacteria (PGPR) can improve crop yields, nutrient use efficiency, plant tolerance to stressors, and confer benefits to future generations of crops grown in the same soil. Unlocking the potential of microbial communities in the rhizosphere and endosphere is therefore of great interest for sustainable agriculture advancements. Before plant microbiomes can be engineered to confer desirable phenotypic effects on their plant hosts, a deeper understanding of the interacting factors influencing rhizosphere community structure and function is needed. Dealing with this complexity is becoming more feasible using computational approaches. In this review, we discuss recent advances at the intersection of experimental and computational strategies for the investigation of plant–microbiome interactions and the engineering of desirable soil microbiomes.