Seasonal Variations in Sugar Accumulation Patterns of Dendrobium officinale: Integrated Analysis via Machine...

Area of Science:

• Plant Science
• Biochemistry
• Genomics

Background:

• Dendrobium officinale, a significant medicinal orchid, faces market demand challenges due to environmental influences on its sugar content.
• Understanding sugar accumulation and regulation is crucial for optimizing D. officinale production and quality.

Purpose of the Study:

• To model seasonal sugar variations in D. officinale stems and leaves.
• To identify molecular mechanisms regulating sugar metabolism under environmental stress.
• To provide insights for improving D. officinale cultivation and breeding.

Main Methods:

• Dynamic monitoring and machine learning models over two years (2318 data points).
• Polynomial regression analysis for sugar accumulation patterns.
• Transcriptomic profiling and weighted gene co-expression network analysis (WGCNA).

Main Results:

• Seasonal sugar variations (glucose, fructose, sucrose, etc.) in D. officinale followed a polynomial regression model (R²=0.742).
• Sugar accumulation in stems peaked from October to April, with rapid increases from October to February.
• Differentially expressed genes related to sugar metabolism and abiotic stress were enriched in photosynthesis and starch/sucrose metabolism pathways.
• Fifteen hub genes, potentially regulating photosynthesis under stress, were identified via WGCNA.

Conclusions:

• Established seasonal variation models for six key sugars in D. officinale.
• Elucidated molecular regulatory networks involving sugar metabolism and environmental stress response.
• Provided a scientific basis for D. officinale harvesting and molecular breeding strategies.