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A Predictive Multiparameter Screening Model Identifies Prebiotics That Enhance the Plant-Growth-Promoting Performance

Jiajie He¹, Haihua Ruan¹, Hongyang Zhang¹

¹Tianjin Key Laboratory of Food Biotechnology, School of Biotechnology and Food Science, Tianjin University of Commerce, Tianjin 300134, China.

Journal of Agricultural and Food Chemistry

|June 6, 2026

Summary

Researchers developed a new screening model to identify effective prebiotics for synthetic microbial communities (SynComs). Palmitic acid, proline, and glucose significantly improved SynCom colonization and plant growth in degraded soils.

Keywords:

plant growth promotion prebiotic saline soil remediation screening model synbiotics synthetic microbial community

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Area of Science:

Agricultural Science
Microbiology
Soil Science

Background:

Soil degradation impacts global agriculture.
Synthetic microbial communities (SynComs) show promise for soil remediation.
Limited rhizosphere colonization hinders SynCom field efficacy.

Purpose of the Study:

Develop a predictive framework for selecting effective prebiotics.
Enhance SynCom performance through targeted prebiotic supplementation.
Improve SynComs for degraded soil remediation.

Main Methods:

Created a quantitative, scalable screening model using microbial phenotypic indicators (motility, chemotaxis, growth, generation time).
Employed a continuous weighted scoring system for model optimization.
Screened 12 candidate prebiotics across four chemical categories.

Main Results:

Identified palmitic acid, proline, and glucose as the most effective prebiotics.
Validated that these prebiotics significantly enhanced SynCom colonization on maize roots.
Demonstrated improved seedling growth and salt stress alleviation.

Conclusions:

Established a robust prebiotic screening framework linking in vitro phenotyping to plant and soil benefits.
Provided a strategy for rational synbiotic design to boost crop resilience.
Offered a practical approach for enhancing agricultural productivity in degraded soils.