Pipeline for Analyzing Activity of Metabolic Pathways in Planktonic Communities Using Metatranscriptomic Data

Area of Science:

• Microbial Ecology
• Metabolic Pathway Analysis
• Bioinformatics

Background:

• Metabolic activity is crucial for microbial community function.
• Analyzing microbial metatranscriptomes provides insights into community metabolism.
• Existing methods may lack accuracy in estimating pathway activity.

Purpose of the Study:

• To develop and validate a novel pipeline for analyzing microbial metabolic activity using metatranscriptomic data.
• To accurately estimate enzyme expression and metabolic pathway activity levels.
• To investigate the influence of environmental parameters and diurnal cycles on microbial metabolism.

Main Methods:

• Developed a novel pipeline based on the expectation-maximization (EM) algorithm.
• Calculated enzyme expression and metabolic pathway activity levels.
• Computed enzyme participation coefficients for improved metabolic pathway activity approximation.
• Applied the pipeline to a plankton community metatranscriptomic dataset from the Northern Gulf of Mexico.

Main Results:

• The EM-based pipeline accurately estimates enzyme expression and pathway activity.
• Enzyme participation coefficients significantly improved metabolic activity estimates.
• Microbial metabolic pathway activity showed statistically significant correlations with environmental parameters (salinity, temperature, brightness).
• A dependence of microbial metabolism on the day-night cycle was observed.

Conclusions:

• The developed EM-based pipeline is a statistically validated and meaningful method for analyzing microbial metabolic activity.
• Metabolic pathway activity is significantly influenced by environmental factors.
• Understanding diurnal cycles is important for microbial ecology studies.