Biotechnology Journal International

Rhizobacteria occupy the chemically and physically dynamic interface between roots and soil, where bacterial traits can influence nutrient acquisition, root development, disease outcomes and stress responses. Genome sequencing and community metagenomics have transformed this field by moving inference beyond cultivation-dependent phenotypes and taxonomic inventories towards strain-resolved gene repertoires, biosynthetic capacity and community-level functional potential. This critical narrative review evaluates what genomics and metagenomics have established about rhizobacterial diversity and function, where the evidence remains conditional, and which methodological developments are most likely to improve causal understanding. Literature was selected through transparent searching of open scholarly databases and indexes, complemented by citation tracking, with emphasis on verified peer-reviewed studies spanning isolate genomics, comparative genomics, shotgun metagenomics, genome-resolved metagenomics, high-throughput cultivation and integrated multi-omics. The evidence consistently supports strong environmental filtering from bulk soil to the rhizosphere and root, while host genotype and developmental state impose additional but context-dependent effects. Comparative genomes reveal recurrent capacities for chemotaxis, transport, resource acquisition, secretion and secondary metabolism, yet large bacterial pan-genomes and extensive strain-level variation weaken attempts to predict phenotype from taxonomy alone. Shotgun metagenomics identifies functional enrichment in nutrient transformations, host-associated metabolism and antagonistic potential, but gene presence remains an imperfect surrogate for expression, metabolite production or benefit to plants. The strongest mechanistic studies therefore couple community profiling with isolates, synthetic communities, host or bacterial genetics, transcriptomics, metabolomics and phenotypic validation. Recent crop-scale genome catalogues and multi-omic field studies substantially improve reference coverage and connect host genetics with microbial functions, although geographic, soil and crop representation remain uneven. The field is consequently shifting from descriptive microbiome inventories towards testable, genome-informed ecological mechanisms. Progress in microbiome-assisted agriculture will depend on strain-level resolution, longitudinal field validation, explicit measurement of function, and predictive models that incorporate host genotype, microbial interactions and environmental context.