Spatiotemporal temperature trajectories underpin predictive modeling of microbial succession in single-batch...

Area of Science:

• Microbiology
• Biotechnology
• Fermentation Science

Background:

• Solid-state fermentation (SSF) efficiency is influenced by microbial consortia and temperature.
• Spatial temperature variations within SSF systems complicate understanding of microbial community assembly and product formation.

Purpose of the Study:

• To investigate the impact of spatial temperature heterogeneity on microbial community structure, volatile profiles, and abundance modeling in high-temperature SSF.
• To establish a framework for analyzing microbial responses to dynamic thermal regimes in SSF.

Main Methods:

• Integrated spatial multi-omics, machine learning, and physiological characterizations across a 35-65°C gradient.
• Analyzed microbial community assembly, succession, and volatile compound profiles.
• Employed Extra Trees regression for microbial abundance modeling.

Main Results:

• Observed significant phylogenetic clustering and niche partitioning along the thermal gradient, forming temperature-sensitive and temperature-resistant groups.
• Identified a microbial community transition around 45°C, with Lactobacillaceae enriched at lower temperatures and pyrazine-related volatiles associated with higher temperatures.
• Demonstrated moderate-to-high predictive performance (R² = 0.75–0.97) for core bacterial genera using machine learning models.

Conclusions:

• Spatial thermal heterogeneity significantly structures microbial communities and influences volatile profiles in SSF.
• Machine learning models can effectively link intra-batch thermal trajectories to microbial abundance and volatile production.
• This study provides a foundational framework for optimizing SSF systems by considering spatial temperature dynamics.