A Predictive Multiparameter Screening Model Identifies Prebiotics That Enhance the Plant-Growth-Promoting Performance of Synthetic Microbial Communities

Soil degradation seriously threatens global agricultural productivity, and synthetic microbial communities (SynComs) offer a promising remediation approach, but their field efficacy is often constrained by inadequate rhizosphere colonization. Targeted prebiotic supplementation could enhance SynCom performance, yet a predictive framework for selecting effective prebiotics is currently lacking. In this study, we developed a quantitative, standardized, and scalable screening model based on four key microbial phenotypic indicators: motility, chemotaxis, growth, and generation times. The model employs a continuous weighted scoring system optimized through comparative evaluation of equal/unequal weighting and fixed/quantile schemes. Screening of 12 candidate prebiotics across four chemical categories identified palmitic acid, proline, and glucose as the most effective. Experimental validation demonstrated that these top-ranked prebiotics significantly enhanced SynCom colonization on maize roots, promoted seedling growth, and alleviated salt stress, as evidenced by reduced leaf electrolyte leakage, increased accumulation of chlorophyll, soluble sugars, and proline, as well as improved soil enzyme activities. This work establishes a robust and transferable prebiotic screening framework linking in vitro microbial phenotyping with plant- and soil-level benefits, providing a practical strategy for the rational design of synbiotics to enhance crop resilience in degraded soils.