Untargeted metabolic profiling for bacteria using gas chromatography-mass spectrometry (GC-MS)
Understanding an organism means understanding the components and mechanisms within it. While profiling of DNA (genomic sequencing), RNA (RNA sequencing), and proteins (protein expression and characterization) are commonly performed in synthetic biology, metabolic profiling is relatively less reported. Metabolic profiling allows one to capture the state of a cell from the point of view of compounds and metabolites. It may explain why a cell exhibits slow growth and shed light on how an implemented function or pathway can affect biological function. Further, capturing the perturbed cell state of an engineered cell and comparing it to the original cell state can help researchers understand the impact of an implemented function or pathway, and thus ways to optimize for compatibility and activity. In certain cases, when an enzyme produces an exotic metabolite unproduced by the host metabolism, metabolic profiling can provide concrete evidence of enzyme expression and may even provide enzyme activity with further analysis. With all things considered, metabolic profiling is a useful and comprehensive way to understand the state of a cell and determine how synthetic biology applications affect biological functions.
In our iGEM project, we relied on metabolic profiling to screen for metabolic perturbations and thus infer gene expression. Using literature from the Plant Journal [1], we adapted a protocol for the extraction and derivatization of minimal cell metabolites. Derivatization of metabolites is necessary because many biological compounds are polar, since they are water-soluble, and will not be captured by the GC-MS in their original state. Our adapted protocol offers a comprehensive coverage of biological metabolites that has demonstrated success in the minimal cell.
References:
[1] González-Cabanelas D, Wright LP, Paetz C, Onkokesung N, Gershenzon J, Rodríguez-Concepción M, Phillips MA. The diversion of 2-C-methyl-D-erythritol-2,4-cyclodiphosphate from the 2-C-methyl-D-erythritol 4-phosphate pathway to hemiterpene glycosides mediates stress responses in Arabidopsis thaliana. Plant J. 2015 Apr;82(1):122-37. doi: 10.1111/tpj.12798. PMID: 25704332.
To test our protocol, we performed a test run with an untransformed minimal cell (JCVI-Syn3B). After analyzing it with a GC-MS, we obtained the following profile.
Figure 1.1: Overview of the minimal cell metabolic profile by GC-MS.
At a glance we see that the metabolic profile obtained through this protocol is quite clean. There is little to no noise, and even when zooming in, metabolic peaks are nicely distinguished and separated. By looking at the metabolites captured (peaks) within this profile, we discover a nice coverage of biological compounds from fast-eluting polar central metabolites to amino acids to lipids and then cholesterol.
Figure 1.2: Derivatized pyruvate and its mass-spectrometry.
One of the first things we capture is pyruvate. We can confirm that this mass-spectrometry indeed belongs to pyruvate by comparing it to the pyruvate standard showcased in the Results section. Similar controls for metabolites such as oxalate in which a standard is created with the pure compound to assist with finding and identifying the metabolite in a cell’s profile. Immediately following pyruvate we find derivatized lactic acid, a common waste product for organisms.
Figure 1.3: Derivatized lactic acid, its mass-spectrometry, and predicted structure using NIST MS database.
We can confirm this peak to be lactic acid because the mass-spectrometry yields lactic acid capped at both ends (from derivatization) when searching for its structure in the NIST MS database. Moving further down the profile (longer elution time), we find derivatized amino acids and other metabolites.
Figure 1.4: Derivatized glycine, its mass-spectrometry, and predicted structure using NIST MS database.
Figure 1.5: Derivatized aspartic acid, its mass-spectrometry, and predicted structure using NIST MS database.
Figure 1.6: Derivatized aminoadipic acid, its mass-spectrometry, and predicted structure using NIST MS database.
Even further down the profile, we find carbohydrates and cholesterol.
Figure 1.7: Derivatized carbohydrate, its mass-spectrometry, and predicted structure using NIST MS database.
Figure 1.8: Derivatized cholesterol, its mass-spectrometry, and predicted structure using NIST MS database.
This protocol affords a comprehensive coverage of biological metabolites from polar primary metabolites to unpolar carbohydrates. Further, GC-MS allows for the quantification of a metabolite by integrating the area under the elution curve. If one knows exactly which compound he or she wishes to screen, a standard with various concentrations of that compound can be made and analyzed under the GC-MS. The area under the curve, when associated with the known concentration, will yield a standard curve that allows one to convert area to concentration and vice versa. This allows for quantification of metabolic states and perturbation, which can be useful when one wishes to investigate the biological effects of an implemented function. We have more snips of the compounds captured by this profiling, as well as the GC-MS file itself in the supplemental section! We hope this extraction and derivatization protocol can be of use to future iGEM teams and synthetic biologists in need of metabolic profiling!