A peptide-based method for 13C Metabolic Flux Analysis in microbial communities

Area of Science:

• Microbiology
• Metabolic Engineering
• Systems Biology

Background:

• Understanding microbial community behavior requires analyzing intracellular metabolic fluxes and inter-species metabolite exchange.
• 13C Metabolic Flux Analysis (13C MFA) is the standard for measuring intracellular fluxes using metabolite labeling patterns.
• Obtaining metabolite labeling patterns for individual community members is challenging.

Purpose of the Study:

• To develop a novel 13C MFA approach using peptide labeling instead of amino acid labeling.
• To enable simultaneous microbial species identification and intracellular flux inference.
• To assess the feasibility and information recovery of peptide-based 13C MFA.

Main Methods:

• Proposed a new 13C MFA method inferring fluxes from peptide labeling patterns.
• Utilized peptide sequence for microbial species identification.
• Leveraged high-throughput proteomics techniques for peptide identification and labeling pattern acquisition.
• Computationally tested the method on a two-species microbial community.

Main Results:

• Demonstrated theoretical possibility of recovering intracellular metabolic fluxes comparable to standard 13C MFA.
• Quantified information loss associated with using peptides versus amino acids.
• Showed that a small number of peptides can effectively mitigate information loss.

Conclusions:

• Peptide-based 13C MFA offers a viable alternative for studying microbial communities.
• This method integrates species-specific flux analysis with high-throughput proteomics.
• The approach holds promise for advancing microbial community research and prediction.