Author(s): Chiamaka Francisca Igweonu

Abstract: Plant-microbe interactions (PMIs) play a vital role in agriculture, influencing everything from plant health to productivity and resilience in various ecological settings. Thanks to the rise of high-resolution functional genomics, significant strides have been made in understanding the intricate molecular conversations between plants and microbes, whether they’re harmful or helpful. This review offers a thorough overview of the latest advancements in PMI functional genomics, covering areas like host immune profiling, effector biology, and co-evolutionary genomic signatures. Emphasis were made on how techniques like transcriptomics, proteomics, and metabolomics have unveiled dynamic patterns of defense and symbiosis over time and space, while studies on epigenomics and small RNAs have shed light on post-transcriptional regulation and communication across different kingdoms. The increasing use of CRISPR-Cas systems was reviewed, reverse genetics, and synthetic biology to validate key regulators and create new traits. Innovations like spatial transcriptomics and single-cell omics are paving the way for precise functional mapping, allowing us to pinpoint microbe-responsive gene expression at the cellular level. At the same time, synthetic microbial communities (SynComs) and genome-guided microbiome engineering are emerging as groundbreaking methods for enhancing plant-microbe compatibility. Despite the progresses made, challenges remain, such as functional redundancy, limited mapping of genotypes to microbiomes, and issues with data integration. In response, artificial intelligence (AI) and machine learning are being harnessed to build predictive models of host-microbe networks, speeding up trait discovery and practical applications in crops. By combining insights from model systems and applying them to crop-microbe interactions, this work envisions a future where sustainable, genomically informed agriculture is propelled by a deep understanding of PMIs—from the molecular level all the way to ecosystems.

DOI: 10.22271/27893065.2023.v3.i1a.133

Pages: 58-63 | Views: 652 | Downloads: 259

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