Understanding Synthetic Biology using the Q Exactive GC Orbitrap GC-MS and a High Resolution Accurate Mass Metabolomics Library for Untargeted Metabolomics

Importance of the Topic

Untargeted metabolomics offers a comprehensive approach to map global metabolic changes in engineered microorganisms. It plays a critical role in synthetic biology by revealing how inducible genetic systems impact bacterial metabolism, guiding the development of sustainable bioproduction routes.

Objectives and Study Overview

This study aimed to characterize metabolic fingerprints of Escherichia coli expressing a red fluorescent protein under IPTG-inducible control. The experiments compared exometabolome profiles across different media (LB, TB) and IPTG concentrations (0, 25, 50, 100 µM) to uncover induction-related metabolic shifts.

Methodology and Instrumentation

A two-step derivatization (methoximation followed by silylation) prepared extracellular samples for high-resolution GC-MS analysis. Key instrumentation included:

• Thermo Scientific TRACE 1310 GC with TraceGOLD TG-5SilMS column
• TriPlus RSH autosampler
• Q Exactive GC Orbitrap mass spectrometer operated in full-scan mode (50-550 Da, 60,000 FWHM, EI 70 eV)

Data processing employed Thermo Scientific Compound Discoverer for alignment, normalization, PCA, ANOVA and volcano plot analyses, with TraceFinder software and both NIST2017 and Orbitrap GC-MS HRAM Metabolomics Library for compound annotation.

Main Results and Discussion

Principal component analysis achieved clear separation by medium type and IPTG induction, explaining 54% of variance in the first two components. Statistical analyses identified 39 significant metabolites, including amino acids, organic acids, sugars and biogenic amines, that exhibited dose-dependent regulation upon IPTG treatment. This demonstrates the perturbation of central carbon and nitrogen metabolism linked to recombinant protein expression.

Benefits and Practical Applications

• High resolution accurate mass and retention index data ensured confident metabolite identification.
• An untargeted workflow streamlines qualitative and quantitative analysis in one platform.
• Insights into metabolic bottlenecks facilitate rational engineering of microbial cell factories.
• Applicable to quality control and optimization in pharmaceutical, agricultural and industrial biotechnology.

Future Trends and Potential Uses

Expanding high-resolution metabolomics libraries and integrating multi-omics data will enhance biological interpretation. Real-time monitoring, larger scale validation experiments and machine learning–driven biomarker discovery are anticipated to advance synthetic biology applications and bioprocess development.

Conclusion

This work illustrates the power of Orbitrap GC-MS–based untargeted metabolomics to dissect metabolic impacts of inducible gene expression. The approach provides a robust platform for guiding metabolic engineering strategies and optimizing microbial production systems.

References

1. Carbonell P., et al. SYNBIOCHEM–a SynBio foundry for biosynthesis and sustainable production of fine and specialty chemicals. Biochem Soc Trans. 2016;44(3):675–677.
2. Toogood H.S., et al. Enzymatic menthol production: one-pot approach using engineered Escherichia coli. ACS Synth Biol. 2015;4(10):1112–1123.
3. Muhamadali H., et al. Metabolomics investigation of recombinant mTNFα production in Streptomyces lividans. Microb Cell Fact. 2015;14(1):157.
4. Sumner L.W., et al. Proposed minimum reporting standards for chemical analysis. Metabolomics. 2007;3(3):211–221.
5. Thermo Fisher Scientific Application Note 10532. Application of GC Orbitrap mass spectrometry for untargeted metabolomics of pathogenic microorganisms. 2016.
6. Carneiro S., et al. Metabolic footprint analysis of recombinant Escherichia coli strains during fed-batch fermentations. Mol BioSyst. 2011;7(3):899–910.