Understanding synthetic biology using the Q Exactive GC Orbitrap GC-MS/MS system and high-resolution, accuratemass metabolomics library for untargeted metabolomics

Significance of the Topic

The integration of synthetic biology and untargeted metabolomics offers powerful insights into how engineered microbial systems respond to genetic induction. By profiling global metabolic shifts with high-resolution, accurate-mass GC-MS, researchers can optimize promoter usage, reduce inducer costs, and enhance sustainable bioproduction workflows.

Objectives and Study Overview

This study aimed to apply an untargeted metabolomics strategy to understand how varying concentrations of IPTG affect the exo-metabolic profile of Escherichia coli DH5α engineered with an IPTG-inducible red fluorescent protein plasmid. Cultures grown in rich (LB) and more defined (TB with glucose) media were compared with and without induction to reveal metabolic perturbations related to promoter activation and recombinant protein expression.

Methodology and Instrumentation

Sample Preparation and Growth Conditions:

• E. coli DH5α from glycerol stocks was plated on LB agar, then grown overnight in LB or TB+0.4% glucose at 37 °C.
• Mid-log cultures were induced with 25, 50 or 100 µM IPTG using an automated liquid handler and incubated for 24 h.
• Spent media were quenched with cold methanol (–48 °C), centrifuged, filtered and spiked with D-glucose and l-alanine-d7 as internal standards.
• Extracts were dried and derivatized first with methoxyamine in pyridine (65 °C, 40 min) then MSTFA+1% TMCS (65 °C, 40 min).

Chromatography and Mass Spectrometry:

• Autosampler: Thermo Scientific TriPlus RSH.
• GC System: Thermo TRACE 1310 with TraceGOLD TG-5SilMS column (30 m×0.25 mm×0.25 µm).
• Injection: Split mode 40:1, 280 °C; oven program 70 °C (2 min) to 325 °C at 10 °C/min, hold 6 min; total runtime 33 min.
• MS: Q Exactive GC Orbitrap in full-scan EI mode (70 eV), mass range 50–550 Da, resolution 60 000 FWHM @ m/z 200, transfer line 280 °C, ion source 250 °C.

Data Processing Workflow:

• Randomized biological injections interspersed with pooled quality controls every five samples.
• Retention time alignment, spectral deconvolution and component extraction (±5 ppm, intensity threshold 5×10^5 counts) in Compound Discoverer.
• Normalization, PCA to visualize treatment and media effects, ANOVA with Tukey HSD and volcano plotting to select significant features (p < 0.05, |log2 FC| > 1).
• Compound identification in TraceFinder using both NIST2017 and a proprietary HRAM GC-MS metabolomics library (900 entries, RI indexed), requiring total score > 80, match index > 750 and ΔRI < 100.

Main Results and Discussion

PCA grouped samples by media type (PC1, 30% variance) and induction status (PC2, 24% variance). Volcano plots revealed 2 045 significant ions corresponding to 212 putative compounds, from which 39 metabolites were confidently annotated. Key findings:

• Sugars (trehalose, sorbose, glucose, mannose) were depleted upon IPTG induction, reflecting increased energy demand.
• Amino acids (serine, arginine, proline, tyrosine) declined, indicating substrate channeling into protein biosynthesis.
• Alterations in TCA cycle intermediates: reduced succinic acid, elevated fumaric and 3-hydroxybutyric acids suggest metabolic bottlenecks and compensatory fluxes.
• Putrescine levels rose four- to fivefold, highlighting enhanced polyamine turnover and protein recycling under induction stress.
• Minimal differences between 25, 50 and 100 µM IPTG indicated that low inducer concentrations suffice to trigger metabolic shifts, guiding cost-effective design.

Benefits and Practical Applications of the Method

The untargeted HRAM GC-MS approach provides:

• Sub-ppt sensitivity and wide dynamic range to capture low-abundance metabolites.
• High mass accuracy (<2 ppm) and resolution for confident identification in complex matrices.
• Robust QC monitoring to ensure data reproducibility and instrument stability.
• Actionable metabolic insights to optimize promoter strength, inducer dosage and pathway engineering in synthetic biology.

Future Trends and Potential Applications

Advances in high-throughput sample handling, expanded HRAM spectral libraries and integration with machine learning will further enhance untargeted metabolomics. Coupling these tools with strain design algorithms and real-time monitoring can accelerate the design-build-test cycle in industrial biotechnology. Application to diverse chassis organisms and environmental samples will broaden the impact of this workflow.

Conclusion

This work demonstrates that high-resolution untargeted GC-MS metabolomics can dissect the metabolic impact of genetic induction in engineered bacteria. Even low levels of IPTG trigger pronounced shifts in central carbon and nitrogen metabolism, enabling strategies to reduce inducer use and improve bioproduction efficiency.

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