Nucleic acid and multi-omics approaches for understanding plant-microbiome interactions in grassland ecosystems.

Abstract

Grasslands are among the largest terrestrial biomes and play essential roles in livestock production, carbon sequestration and global food security. The productivity and resilience of these ecosystems are driven by complex molecular interactions between plants and their associated microbiomes. Although recent advances in nucleic acid research and multi-omics approaches have provided new insights into these interactions, the molecular mechanisms underpinning plant-microbiome interactions in these ecosystems remain insufficiently explored. This review synthesizes the latest progress in nucleic-acid and multi-omics approaches to better understand plant-microbiome interactions. It integrates nucleic acid-based technologies with multi-omics frameworks to explain plant-microbiome interactions across molecular, ecological, and management scales. By linking microbial community structure, functional genes, gene expression, metabolite profiles, ecosystem multifunctionality and sustainable grassland management, this review provides a broader framework for translating molecular insights into practical strategies for grassland resilience, productivity, and food security. Advances in amplicon sequencing, shotgun and long-read metagenomics, environmental DNA (eDNA) monitoring, plant and microbiome genome-wide association studies (GWAS) and transcriptomics have provided valuable insights into plant-microbiome interaction. This review highlights how these techniques enable functional and mechanistic understanding by linking microbial diversity with gene expression, nutrient cycling and plant performance. Additionally, long-read sequencing technologies provide genome-resolved analysis, improving the detection of structural and epigenetic variations, which are essential for understanding these interactions. These approaches reveal the role of beneficial microbes in enhancing grassland fertility, ultimately improving grassland productivity. Integrating these findings with metabolomics and phenomics offers a novel approach for predictive modeling in sustainable grassland management. The review concludes by emphasizing the need for standardized protocols, longitudinal field studies and experimental validation through synthetic communities and genome editing to harness plant-microbiome interactions for enhanced productivity and food security.