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

## TL;DR

A new Python tool called MCC automates mass and charge balancing in genome-scale metabolic models, improving the efficiency of building models like one for Corynebacterium tuberculostearicum.

## Contribution

MCC introduces an automated algorithm for mass and charge balancing in metabolic models, streamlining GEM reconstruction.

## Key findings

- MCC was used to create a high-quality metabolic model of C. tuberculostearicum (iCTUB2024RM).
- The model accurately simulates growth in a synthetic nasal medium, reflecting real-world behavior.
- MCC improves the efficiency and reliability of genome-scale metabolic model curation.

## 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
flux balance constraints models in the SBML format.

The rise of antibiotic resistance has made it essential to explore
alternative treatments for bacterial infections, particularly those
caused by respiratory tract colonizers like Corynebacterium
tuberculostearicum. Understanding the metabolic behavior of
these bacteria and their interactions with the human host or microbiota
is crucial. Genome-scale metabolic models (GEMs) are powerful tools for
investigating these interactions, but they are time-consuming to build.
Our new Python package, Mass and Charge Curation, automates a crucial
step in the GEM reconstruction process—mass and charge
balancing—making it more efficient and reliable. By applying this
tool, we developed a high-quality, functional metabolic model for
C. tuberculostearicum
(iCTUB2024RM), which provides deeper insights into the
organism’s growth in a simulated human nasal environment. This
work offers a foundation for future research into microbial communities
and their role in human health.