Intention of the dry lab project and why it is important for GLOW.coli
During our initial research we discovered that the endogenous SCFA-metabolism of E.coli might interfere with our measurements. We wanted to find out under which specific conditions E.coli is producing or consuming e.g. acetate. Furthermore, we got interested in how capable our bacteria will be to take up extracellular acetate. This is important since the SCFAs from our samples have to enter the bacterial cell first before they can be detected by our reporter system. Therefore we decided to set up a stoichiometric model of the central E.coli acetate metabolism to get dynamic information on which enzymes, operons and extracellular conditions are affecting it.
Methods, programms and mathematical background
We based our model on the mathematical idea of a stoichiometric matrix containing the information on which metabolites are part of certain reactions[1]. For the setup it is only necessary to know the chemical equations which make up the metabolic network that is going to be simulated[2]. We used the open source programme CopasiUI (ArtisticLicense 2.0). To receive authentic data for E.coli we used data from proteomics[3] and metabolics[4] research conducted with LC-MS. We chose to use data from cells which grew in an abundance of glucose, since we plan to use it to nurture the bacteria while performing our measurements.
Results and conclusions for the Wet Lab experiments
We used the differential equations derived from the stoichiometric matrix in Fig.1 to perform
metabolic control analysis (MCA) and compute elasticity coefficients Fig.2. These coefficients
indicate which metabolite concentrations have the biggest impact (highlighted in green) on the
reaction rates. This information is extremely useful to understand which parameters need to be
adjusted to e.g. knock out metabolic pathways.
Finally, we ran a time course simulation to observe how the concentrations of acetate and related
intracellular compounds develop over time. As visible in Fig.3 intracellular acetate which is turned
into acetyl-phosphate is turned into acetyl-CoA which feeds in to the cells energy metabolism by
entering the citrate cycle. Strikingly the extracellular acetate concentration seems to be nearly
unaffected by this turnover. This implies that little transport of acetate is happening via the
bacterial membrane under the given conditions. The reason might be, that membrane transporters for
acetate are weakly expressed in the presence of glucose because acetate is not needed as an energy
source. Though diffusion of acetate through the membrane is thought to be possible its rate is very
low.
On the one hand, this is beneficial to our purpose, because the acetate concentration of the sample
will be widely unaffected by our GLOW.coli reporters. On the other hand this result raises the question
of how much the extracellular acetate can influence intracellular processes like the expression of the
transgenic fluorescent proteins.
We conclude that as little glucose as possible should be added to the culture medium before and
while the measurement of SCFA. This should increase the expression of acetate transporters like
actP as shown in [3]. Also the fluorescent measurement should be done quickly after adding GLOW.coli
to the SCFA sample. By that, an interference of the metabolism with the extracellular acetate, which
becomes a carbon and energy source in the absence of glucose, can be kept as small as possible.
References
[1] Francisco Llaneras, Jesús Picó, Stoichiometric modelling of cell metabolism, Journal of Bioscience and Bioengineering, Volume 105, Issue 1, 2008, Pages 1-11, ISSN 1389-1723, https://doi.org/10.1263/jbb.105.1.
[2] Bernal V, Castaño-Cerezo S, Cánovas M. Acetate metabolism regulation in Escherichia coli: carbon overflow, pathogenicity, and beyond. Appl Microbiol Biotechnol. 2016 Nov;100(21):8985-9001. doi:10.1007/s00253-016-7832-x Epub 2016 Sep 20. PMID: 27645299.
[3] Schmidt, A., Kochanowski, K., Vedelaar, S. et al. The quantitative and condition-dependent Escherichia coli proteome. Nat Biotechnol 34, 104–110 (2016). https://doi.org/10.1038/nbt.3418.
[4] Bennett, B., Kimball, E., Gao, M. et al. Absolute metabolite concentrations and implied enzyme active site occupancy in Escherichia coli. Nat Chem Biol 5, 593–599 (2009). https://doi.org/10.1038/nchembio.186.