Difference between revisions of "Part:BBa K4339000"

Latest revision as of 08:31, 12 October 2022

Non-polar amino acid transporter (CycA)

CycA is an inner membrane permease [1], which can facilitate the uptake of non-polar amino acids (including D-alanine, D-serine, glycine and L-alanine) via a proton symport mechanism [2]. CycA was first identified in the K12 strain of E. coli.

The DNA sequence of CycA originates from E.coli MG1655 K12 and was modified to remove forbidden restriction sites. The coding sequence was cloned with: the constitutive promoter J23100; the strong RBS B0034; an N-terminal His-tag and the double terminator B0015; into a medium copy, ampicillin plasmid. This was used to transform E. coli BL21 (DE3) pLysS. CycA was expressed in this strain with an aim to increase uptake of L-alanine from an alanine-doped LB growth medium, to facilitate synthesis of alanine-rich proteins such as MaSps (Major Ampullate Silk proteins).

Usage and Biology

Sequence and Features

The sequence originates from E. coli MG1655 K-12 [1]

Characterisation

Alanine tolerance

To determine the tolerance of BL21 DE3 pLysS to extracellular alanine, we produced growth curves for E. coli engineered with either an empty pX1900 plasmid backbone (control strain), or the cycA gene present in this backbone (CycA strain), incubated in a range of alanine concentrations from 5.8 (blank LB) up to 100 mM. As is indicated below in figure 1, there is no obvious difference in the growth across the range of extracellular alanine concentrations studied, for both strains examined, suggesting both can tolerate extracellular alanine concentrations up to 100 mM.

Growth curves for CycA and control strains in a range of alanine concentrations, plotted using OD₆₀₀ measurements

Growth of control vs CycA strains

To characterise the effect of CycA inclusion, we produced growth curves for both strains in blank and alanine-doped LB, measuring cells/ml for each sample every hour via image flow cytometry using an Imagestream. Figure 2 below shows the variation in cells/ml for each condition with time. It is apparent that the form of growth is essentially the same for the control strain at both alanine concentrations, having similar final cells/ml values and entering the log and stationary phases of growth at similar points in time (t = 3 and 6 hours after inoculation respectively).

The growth for the CycA strain seems to vary with alanine concentration. In undoped LB, the CycA strain does not appear to reach stationary phase within the time range measured over, suggesting expression of the protein has delayed the onset of this phase of growth. Whereas, in doped LB, the CycA strain appears to reach stationary phase at an earlier time point than the other conditions (t = 5 hours after inoculation) and may in fact enter death phase, with the final cells/ml dropping from t = 6 to t = 7 hours after inoculation. It is possible that the combination of expression of (as we will later see) likely non-functional and misfolded CycA proteins as inclusion bodies, and the associated metabolic strain is sufficient to trigger cell death by this time point.

Growth curves for CycA and control strains in blank and alanine-doped LB plotted using image flow cytometry measurements

Alanine uptake per cell

To determine alanine uptake per cell, we harvested pellets from samples of all 4 conditions and determined the alanine content of the cell lysate using the Merck distributed alanine detection kit [3]. We then normalised total alanine uptake by the no. cells harvested in each pellet, determined from cells/ml flow cytometry data. As seen in the figure below, alanine uptake for each strain is higher for the [Ala] = 100 mM condition, as is to be expected. However, total alanine uptake is greatest for the control strain at both alanine concentrations.

The most logical explanation for this is that CycA, whilst being expressed, is non-functional, resulting in investment of metabolic resources into the transcription and translation of the protein, without a coupled increase in alanine uptake. This would divert resources (ATP, transcriptional and translational machinery etc) away from synthesis of other porins responsible for alanine uptake. Moreover, CycA translation itself uses significant amounts of intracellular alanine, with alanine accounting for 10% of the AA sequence of the protein (47/470 residues). The simplest explanation for the non-functionality of CycA is that the protein is misfolding, most likely due to the lack of chaperonin proteins to guide protein folding, resulting in the formation of inclusion bodies. Therefore, in the form engineered into the BL21 DE3 pLysS strain, CycA is likely non-functional and its expression results in a net decrease in overall alanine uptake. Consequently, the coding sequence given is not appropriate for use in elevating net uptake of the L-alanine, and likely the other non-polar amino acids CycA, when functional, is known to shuttle, in this strain of E. coli.

Alanine uptake per cell for the control and CycA strains, cultured in both blank and alanine-doped LB

k_cat

When attempting to model the effect of CycA expression on synthesis of MaSps, it was found that no experimental data exists for k_cat (the rate constant for the rate-limiting step for enzyme action i.e. turnover rate) values for amino acid pumps in E. coli. Therefore, an estimated value was obtained using the ML (machine learning) tool DLKcat [4] which models k_cat values based on the structure of the substrate (alanine) and the given amino acid sequence for the enzyme which binds it (alanine). This yielded a value of k_cat = 0.9953 s^-1.

References

[1] - Hook C et al. The Escherichia coli Amino Acid Uptake Protein CycA: Regulation of Its Synthesis and Practical Application in l-Isoleucine Production. Microorganisms. 2022;10(3):647. doi: https://doi.org/10.3390/microorganisms10030647

[2] - CycA. ECOCYC version 26.0. Available at: https://biocyc.org/gene?orgid=ECOLI&id=EG12504 [Accessed 14/9/22]

[3] - Merck. Alanine Assay Kit. https://www.sigmaaldrich.com/GB/en/product/sigma/mak001 [Accessed 14/9/22]

[4] - Li, F., Yuan, L., Lu, H. et al. Deep learning-based k_cat prediction enables improved enzyme-constrained model reconstruction. Nat Catal. 2022;5: 662-672. doi: https://doi.org/10.1038/s41929-022-00798-z