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Microbial Nutrition01:28

Microbial Nutrition

Organisms exhibit remarkable metabolic diversity, categorized based on how they acquire energy and carbon. These strategies enable survival in various ecological niches and are essential for maintaining energy flow and nutrient cycling within ecosystems.Energy and Carbon SourcesOrganisms are classified as phototrophs or chemotrophs based on energy acquisition. Phototrophs use light as their energy source, while chemotrophs rely on oxidizing chemical compounds. Further differentiation arises...
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Chemolithotrophs are microorganisms that obtain energy by oxidizing inorganic molecules such as hydrogen gas (H₂), ammonia (NH₃), reduced sulfur compounds (H₂S, S²⁻), and ferrous iron (Fe²⁺). Unlike heterotrophic organisms that rely on organic carbon, chemolithotrophs transfer electrons from these inorganic donors to the electron transport chain (ETC), generating a proton motive force (PMF) that drives ATP synthesis through oxidative phosphorylation.
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Modification and analysis of context-specific genome-scale metabolic models: methane-utilizing microbial chassis as a

M A Kulyashov1, R Hamilton2, Y Afshin2

  • 1Department of Computational Biology, Scientific Center for Genetics and Life Sciences, Sirius University of Science and Technology, Sochi, Russia.

Msystems
|December 19, 2024
PubMed
Summary

We developed a computational workflow to reconstruct context-specific genome-scale metabolic models (CS-GSMs) for non-model microbes like Methylotuvimicrobium alcaliphilum. This tool aids in understanding genotype-phenotype relationships and optimizing microbial platforms for methane capture and valorization.

Keywords:
Methylotuvimicrobium alcaliphilum 20ZRcontext-specific genome-scale metabolic modelingmethane-utilizing bacteriamethanotrophy

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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 20ZR 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 20ZR.

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 20ZR 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 20ZR's C1 metabolism.
  • The workflow facilitates the development of microbial platforms for biotechnological and environmental applications.