From Multi-Omics to Predictive Microbiome Engineering: Decoding Plant–Microbe Interactions for Crop Improvement
Aparna Sinha *
Department of Botany, Dr. Shyama Prasad Mukherjee University, Ranchi, India.
*Author to whom correspondence should be addressed.
Abstract
Plant-associated microorganisms influence nutrient acquisition, pathogen susceptibility and stress responses, yet reliable field manipulation of crop microbiomes remains difficult. The expansion from taxonomic surveys to host and microbial genomics, transcriptomics, proteomics, metabolomics and computational modelling has intensified the search for mechanisms that can be forecast and deliberately modified. This critical narrative review evaluates whether those molecular advances provide sufficient causal and predictive information for crop improvement. Peer-reviewed evidence was selected from plant microbiome ecology, crop genetics, synthetic-community experiments, predictive modelling and translational agronomy, with attention to experimental design, measurement validity and external transportability. The literature indicates that individual microbial functions and host pathways can causally influence root growth, nutrient acquisition and disease suppression under defined conditions. Genotype-dependent recruitment, root-exudate chemistry, microbial antagonism and cross-feeding generate testable intervention hypotheses, while reconstruction experiments and metabolic models help identify interacting strains and molecular mediators. Nevertheless, detecting a functional gene is not equivalent to observing its activity, compositional biomarkers can reflect non-causal environmental gradients, and accurate within-study models may fail across soils, genotypes and seasons. Synthetic communities allow mechanistic resolution but rarely recreate the competitive and variable conditions of intact field microbiomes. Evidence of agronomic benefit is consequently narrower than the expanding range of molecular and computational tools might imply. An evidence hierarchy is proposed that separates community description, conditional prediction, causal mechanism and deployable engineering. Progress will require coordinated host–microbe perturbations, strain-resolved colonisation measurements, temporally aligned molecular assays, prospective out-of-distribution tests and independent multi-site crop trials. Microbiome engineering is most defensible as a context-specific, hypothesis-driven component of crop management and breeding, not as a universal microbial recipe.
Keywords: Rhizosphere ecology, synthetic microbial communities, root exudates, host genetic recruitment, cross-omic integration, causal inference, external validation, crop resilience