Exploring and exploiting the rice phytobiome to tackle climate change challenges

Abstract

The future of agriculture is uncertain under the current climate change scenario. Climate change directly and indirectly affects the biotic and abiotic elements that control agroecosystems, jeopardizing the safety of the world's food supply. A new area that focuses on characterizing the phytobiome is emerging. The phytobiome comprises plants and their immediate surroundings, involving numerous interdependent microscopic and macroscopic organisms that affect the health and productivity of plants. Phytobiome studies primarily focus on the microbial communities associated with plants, which are referred to as the plant microbiome. The development of high-throughput sequencing technologies over the past 10 years has dramatically advanced our understanding of the structure, functionality, and dynamics of the phytobiome; however, comprehensive methods for using this knowledge are lacking, particularly for major crops such as rice. Considering the impact of rice production on world food security, gaining fresh perspectives on the interdependent and interrelated components of the rice phytobiome could enhance rice production and crop health, sustain rice ecosystem function, and combat the effects of climate change. Our review re-conceptualizes the complex dynamics of the microscopic and macroscopic components in the rice phytobiome as influenced by human interventions and changing environmental conditions driven by climate change. We also discuss interdisciplinary and systematic approaches to decipher and reprogram the sophisticated interactions in the rice phytobiome using novel strategies and cutting-edge technology. Merging the gigantic datasets and complex information on the rice phytobiome and their application in the context of regenerative agriculture could lead to sustainable rice farming practices that are resilient to the impacts of climate change.

Keywords: artificial intelligence; climate change; microbial ecology; rhizosphere engineering; rice microbiome; rice phytobiome.

Figure 1

Complex interactions in the rice phytobiome network. The rice phytobiome network is composed of a community of microorganisms (bacteria, fungi, and viruses) that colonize rice plant compartments (seed, phyllosphere, endosphere, rhizosphere) and soil and macroorganisms (arthropods, weeds, rodents, birds, vertebrates, and other phytobiome influencers); these organisms interact via communication signals (e.g., phytohormones, phytoalexins, allelochemicals, pheromones, QS autoinducers, VOCs, Myc factors, AHL) and are influenced by climatic and edaphic factors (e.g., atmospheric gases, soil texture, soil aeration) and management practices (e.g., application of chemical pesticides and fertilizer, cultural practices).

Figure 2

Manipulatable components of the rice phytobiome for optimized stress response to climate change . Rice phytobiome signaling networks and optimization of stress response under (A) biotic and abiotic stresses triggered by climate change. The intricate (B) signaling pathways facilitate the (C) activation and optimization of defense mechanisms and adaptive responses. They are enhanced by microbiome communities, enabling rice plants to have (D) better plant fitness and improved plant health, mitigate the detrimental effects of climate-induced stressors, and achieve a sustainable rice ecosystem.

Figure 3

Schematic of synthetic biology–enabled microbiome engineering steps to achieve climate-resilient rice and their approaches. (A) Numerous microorganisms with a variety of functions are linked to plants. Certain microorganisms (first group) can benefit their host plants by PGP; the second group can strongly colonize them. It is crucial to gather both groups at the first stage. The first group supplies PGP genes and pathways, together with switches and sensors to regulate gene expression. To impart designed PGP features to host plants, the second group could offer the best framework. (B) Methods for phytobiome genetic/genome engineering. Two methods exist for genetic engineering or genome engineering of the phytobiome. Using a bottom-up methodology, plant-associated microorganisms are isolated, individual strains are manipulated to confer desired features, and plants are injected with the modified strains. The top-down method introduces features into various hosts in situ by horizontal gene transfer. Omics technologies and accompanying equipment are then used to identify the host phenotypes.

Figure 4

The interaction and interface in a general workflow to use ML approaches in encounters with rice phytobiome microbiome omics datasets.