MCC : automated mass and charge curation at the genome scale applied to C. tuberculostearicum

Abstract

For many years, antibiotics reliably protected mankind against bacterial infections, including the respiratory tract colonizer Corynebacterium tuberculostearicum. However, the spread of antimicrobial resistance necessitates the search for new treatment options, where the microbiota may play a crucial role. One way to investigate the complex nature of bacteria and their interactions with human hosts or microbiota is through genome-scale metabolic models (GEMs). Constructing GEMs is labor-intensive and time-consuming. We introduce the Python package Mass and Charge Curation (MCC), which implements a new automated algorithm to facilitate mass and charge balancing—one of the most time-consuming reconstruction steps. This package manipulates reconstructions by consolidating data from multiple resources and updating the notes field with relevant changes. It also visually compares draft and curated models, ensuring high-quality metabolic reconstructions. Using MCC, we developed a metabolic reconstruction of C. tuberculostearicum strain DSM 44922. The model was improved based on standardization policies, resulting in a functional, well-annotated, high-quality product. We also simulated the organism’s growth in synthetic nasal medium 3 (SNM3). The high-quality model iCTUB2024RM consistently resembles growth behavior under realistic conditions in an artificial human nasal environment, enhancing the understanding of C. tuberculostearicum and its potential impact on health and disease. The curation process of this model led to the development of the MCC package, which facilitates the mass and charge balancing of arbitrary fluxfluxbalance constraints models in the SBML format.