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<p>In the context of intensifying climate stress, soil degradation, and diminishing returns from synthetic fertilizers, there is an urgent need for sustainable and efficient nutrient mobilization strategies. Although microbial potassium-solubilizers (KSMs) have been studied extensively, their inconsistent field efficacy and lack of system level predictive validation hinder practical deployment. This study addresses this critical gap by exploring the gut microbiome of <i>Eisenia fetida</i>, an ecologically rich yet underexplored niche for potent KSMs with multifunctional plant growth-promoting (PGP) traits. Importantly, the work integrates microbiome mining with AI driven trait modeling to offer a precision-oriented, climate-resilient alternative to chemical fertilizers. Four bacterial isolates <i>Raoultella sp.</i> K1, <i>Pseudomonas sp.</i> K2, <i>Brucella sp.</i> K3, and <i>Glutamicibacter sp.</i> K4 were identified via 16&#xa0;S rRNA sequencing. Their K-solubilization potential and PGP traits (IAA, GA, siderophores, ammonia, HCN) were quantified. Bioassays in <i>Phaseolus vulgaris L</i>., nutrient uptake studies, and PCA were conducted. Sobol sensitivity analysis, a machine learning-based global modeling tool, was applied for the first time in this context to determine strain-specific contributions to K uptake and agronomic performance. K4 significantly enhanced K translocation to pods and improved chlorophyll content and yield over chemical controls. <i>Raoultella sp.</i> was newly reported as a KSM and <i>Glutamicibacter sp.</i> emerged as a potent novel gut inhabitant. This integrative, AI-enabled framework offers a data-driven route for sustainable biofertilizer design in climate-resilient agriculture.</p> Graphical Abstract <p></p>

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Data-Driven Discovery of Earthworm Gut-Derived K-Solubilizers: Bridging Microbiome Mining and Machine Learning for Next-Gen Agro-innovation

  • 期刊论文

  • 发表日期：

    2026年2月4日

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  • Inrikynti Mary Kharmawphlang,
  • Sagarika Doloi,
  • Saibal Ghosh,
  • Nazneen Hussain

摘要

In the context of intensifying climate stress, soil degradation, and diminishing returns from synthetic fertilizers, there is an urgent need for sustainable and efficient nutrient mobilization strategies. Although microbial potassium-solubilizers (KSMs) have been studied extensively, their inconsistent field efficacy and lack of system level predictive validation hinder practical deployment. This study addresses this critical gap by exploring the gut microbiome of Eisenia fetida, an ecologically rich yet underexplored niche for potent KSMs with multifunctional plant growth-promoting (PGP) traits. Importantly, the work integrates microbiome mining with AI driven trait modeling to offer a precision-oriented, climate-resilient alternative to chemical fertilizers. Four bacterial isolates Raoultella sp. K1, Pseudomonas sp. K2, Brucella sp. K3, and Glutamicibacter sp. K4 were identified via 16 S rRNA sequencing. Their K-solubilization potential and PGP traits (IAA, GA, siderophores, ammonia, HCN) were quantified. Bioassays in Phaseolus vulgaris L., nutrient uptake studies, and PCA were conducted. Sobol sensitivity analysis, a machine learning-based global modeling tool, was applied for the first time in this context to determine strain-specific contributions to K uptake and agronomic performance. K4 significantly enhanced K translocation to pods and improved chlorophyll content and yield over chemical controls. Raoultella sp. was newly reported as a KSM and Glutamicibacter sp. emerged as a potent novel gut inhabitant. This integrative, AI-enabled framework offers a data-driven route for sustainable biofertilizer design in climate-resilient agriculture.

Graphical Abstract