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Related Experiment Video

Updated: Feb 19, 2026

Prospecting Microbial Strains for Bioremediation and Probiotics Development for Metaorganism Research and Preservation

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Published on: October 31, 2019

Forecasting Root Rot Disease through Predictive Microbial Functional Profiling.

Chuan You¹, Peng Ren¹, Ying Guan¹

¹Jiangsu provincial key lab for solid organic waste utilization, Key lab of organic-based fertilizers of China, Jiangsu Collaborative Innovation Center for Solid Organic Wastes, Educational Ministry Engineering Center of Resource-saving fertilizers, Nanjing Agricultural University, Nanjing, P. R. China.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)

|February 17, 2026

Summary

This summary is machine-generated.

Predicting soil-borne root rot disease early is now possible using microbial functional genes as biomarkers. This approach forecasts disease risk before symptoms appear, aiding sustainable agriculture.

Keywords:

disease prediction early diagnosis machine learning microbial functional genes rhizosphere microbiome

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Area of Science:

Agricultural Science
Microbiology
Plant Pathology

Background:

Early detection of soil-borne diseases like root rot remains a significant agricultural challenge.
Microbial functional genes indicate soil health but are typically used for past or current conditions.

Purpose of the Study:

To establish microbial functional genes as predictive biomarkers for soil-borne diseases.
To develop a framework for early disease prediction in agriculture.

Main Methods:

Analyzed 199 paired metagenomes from healthy and diseased medicinal plant rhizosphere soil.
Identified core functional genes related to biofilm formation, stress response, and plant-microbe mutualism.
Developed a predictive model integrating qPCR assays and machine learning.

Main Results:

Identified a conserved set of functional genes strongly associated with root rot disease.
Achieved over 80% accuracy in predicting disease onset from pre-symptomatic soil samples.
Demonstrated the potential for early risk identification long before visible symptoms.

Conclusions:

Microbial functional genes can transition from descriptive indicators to reliable predictive biomarkers.
The developed framework offers a practical tool for proactive soil-borne disease management.
This approach supports sustainable agriculture by enabling targeted interventions.