Modification and analysis of context-specific genome-scale metabolic models: methane-utilizing microbial chassis as a...

Area of Science:

• Systems Biology
• Metabolic Engineering
• Computational Biology

Background:

• Context-specific genome-scale model (CS-GSM) reconstruction integrates multi-scale data for genotype-phenotype exploration.
• CS modeling is challenging for non-conventional microbes, hindering fundamental and applied research.
• Methylotuvimicrobium alcaliphilum 20Z^R is a key microbial chassis for methane capture and valorization.

Purpose of the Study:

• To present a user-friendly computational workflow for reconstructing and interrogating CS-GSMs.
• To streamline CS-GSM development for non-model organisms using integrated Python tools.
• To validate the workflow using multi-omics data from M. alcaliphilum 20Z^R.

Main Methods:

• Developed a graphical user interface integrating COBRApy, EscherPy, and RIPTiDe within the BioUML platform.
• Utilized Jupyter Notebook for automated CS-GSM reconstruction and interrogation.
• Optimized a previously reconstructed whole-genome metabolic network using gene expression data.

Main Results:

• The automatically reconstructed CS-GSM for M. alcaliphilum 20Z^R showed comparable results to manually curated models.
• The model identified potential issues with phosphoketolase pathway reversibility and highlighted carbon partitioning at the formaldehyde-formate node.
• Mutagenesis experiments for formate dehydrogenase (fdhAB) and formaldehyde oxidation enzyme (fae1-2) homologs validated model predictions.

Conclusions:

• The developed computational workflow effectively supports the reconstruction and validation of CS-GSMs for non-model microbes.
• The study advances fundamental knowledge of M. alcaliphilum 20Z^R's C1 metabolism.
• The workflow facilitates the development of microbial platforms for biotechnological and environmental applications.