Composite

Part:BBa_K1897028

Designed by: Keshiniy Madivannan   Group: iGEM16_NUS_Singapore   (2016-10-11)


p70-33-sfGFP-Terminator

The construct contains one of the synthetic lldR promoters designed by team ETH-Zurich, IGEM 2015 (Part:BBa_K1847008) with a weaker RBS flanked by non-coding sequences (Part:BBa_K1897031), followed by a coding sequence of sfGFP (Part:BBa_K1897033) and a lambda t0 terminator (Part:BBa_K1897030).


Sequence and Features


Assembly Compatibility:
  • 10
    COMPATIBLE WITH RFC[10]
  • 12
    INCOMPATIBLE WITH RFC[12]
    Illegal NheI site found at 78
    Illegal NheI site found at 101
  • 21
    INCOMPATIBLE WITH RFC[21]
    Illegal XhoI site found at 589
  • 23
    COMPATIBLE WITH RFC[23]
  • 25
    COMPATIBLE WITH RFC[25]
  • 1000
    COMPATIBLE WITH RFC[1000]

Usage and Biology

Due to their ability to deregulate normal cellular energetics, cancer cells are found to undergo aerobic glycolysis followed by lactic acid fermentation in the cytosol, resulting in the elevated production and accumulation of L-lactate in the surrounding environment [1][2][3]

This is RIOT sensor 10, along with RIOT sensor 11 (Part:BBa_K1897029), which are designed to detect the increased level of L-lactate produced by tumor cells in the microenvironment. To demonstrate this functional proof of concept, we measured the level of sfGFP expression under the control of lactate sensors (Figure 3). Commercially available L-lactate solution was used to activate the sensors, hence induce the expression of sfGFP. Detailed working mechanism of RIOT sensors are shown in Figure 1.

Figure 1: Working mechanism of RIOT sensor.
(A) In the absence of lactate, lldR binds to two operators in the promoter region and inhibit the expression of a superfolded green fluorescent protein (sfGFP).
(B) In the presence of lactate, lactate binds lldR, preventing its binding to the operators. Consequently, sfGFP is expressed and the level of sfGFP can be quantified using fluorescence microscopy.
CAMr = Chloramphenicol resistant.

Construction of RIOT sensor 10: p70-33-sfGFP-Terminator

The design of RIOT sensors 10 and 11 is based on a shorter version of the promoter region for the wild-type lldPRD operon (Part:BBa_K1897037, derived from Part Part:BBa_K822000) and a modified version of this promoter, lldRO1-J23117-lldRO2 (Part:BBa_K1847008), which are hereafter referred to as “p62” and “p70”, respectively for convenient purpose. We received them from team ETH_Zurich 2015. We minimise the basal expression of our reporter by linking ribosomal binding site (RBS) of different strength with these promoters (Table 1). The efficiency of the medium RBS (Part:BBa_B0032) and the weak RBS (Part:BBa_B0033) are about 30% and 1% relative to the strong RBS (Part:BBa_B0034), respectively.

Table 1: Details of RIOT sensors containing a superfolded green fluorescent protein (sfGFP)

RIOT Sensor No. Construct Biobrick Details
10 p70-33-sfGFP-Terminator Part:BBa_K1897028 The construct contains one of the synthetic lldR promoters designed by team ETH-Zurich, IGEM 2015 (Part:BBa_K1847008) with a weak RBS (Part:BBa_B0033), followed by a CDS of sfGFP and a lambda t0 terminator.
11 p62-33-sfGFP-Terminator Part:BBa_K1897029 The construct contains a shorter version of the promoter region for the wild-type lldPRD operon (Part:BBa_K1897037) derived from Part Part:BBa_K822000, a weak RBS (Part:BBa_B0033), followed by a CDS of sfGFP and a lambda t0 terminator.
Figure 2: Construct 10: p70-33-sfGFP-Terminator was made using PCR overlap. The expected size, including Biobrick Prefix, Suffix and bases flanking restriction enzyme recognition sequences, is 1056 base pairs. Lane 1 marks the DNA ladder. Lane 4-7 are replicates of the same PCR reaction.

Characterization of RIOT sensor 10: p70-33-sfGFP-Terminator

The performance of RIOT sensor 10 and 11 was compared to other lactate sensors via the fold change in GFP expression when lactate concentration increased. The results showed that RIOT sensor 10, p70-33-sfGFP-Terminator (Part:BBa_K1897028) is more sensitive to small changes in lactate concentration (Figure 3). The p70-34-sfGFP-Terminator sensor containing lldRO1-J23117-lldRO2 promoter (Part:BBa_K1847008) with a strong RBS and the p62-34-sfGFP-Terminator sensor containing the natural promoter with a strong RBS, had relatively high basal expression, about 7 times higher than RIOT sensor 10. In addition, they did not show significant difference in the level GFP intensity when lactate concentration increased from 10-3 M to 10-2 M. Although RIOT sensor 11, p62-33-sfGFP-Terminator (Part:BBa_K1897029) had low basal expression, it did not respond well to the increase in lactate concentration.

Figure 3: Comparisons of sensitivity to changes in lactate concentration among RIOT sensors and other lactate sensors.
Overnight cultures of bacteria transformed with different sensors were diluted and incubated with various lactate concentration including 0 M, 10-3 M and 10-2 M. The best sensor is expected to have low basal expression to minimize false positive results while exhibiting sensitivity to small changes in lactate concentration.
(A)Selected images of bacteria with fluorescence taken by microscope.
(B) Fluorescent images of individual sensor at each condition were processed by ImageJ software to obtain the corrected total cell fluorescence
*p < 0.05, **p < 0.01, ***p < 0.001, n.s. = not significant. n = 40 per group and the error bars stand for SEM.

In order to demonstrate the potential of our RIOT sensors to detect lactate in real-world conditions, we grew bacteria transformed with our RIOT sensors 10, p70-33-sfGFP-Terminator (Part:BBa_K1897028) and 11, p62-33-sfGFP-Terminator (Part:BBa_K1897029) in diluted supernatant of HeLa and HepG2. HeLa is a human cervical cancer cell line and HepG2 is a human liver cancer cell line. Based on Warburg effect, HeLa and HepG2 are expected to produce elevated level of lactate in the supernatant which mimics the tumour microenvironment inside our body. Therefore, if RIOT sensors are activated by lactate in the cancer cell supernatant, they have the potential to function in our body.

We measured the lactate concentration in the supernatants of HeLa and HepG2 using a lactate assay kit (Sigma-Aldrich, catalog number: MAK064), as well as the GFP intensity of transformed bacteria grown in these cell supernatants using fluorescence microscopy. We also measured lactate concentration in the supernatant of bacteria grown in LB to check whether the bacteria also produce lactate. Figure 4 is the lactate standard curve with the linear regression equation used to calculate the lactate concentration in the supernatants. As shown in Table 2, HeLa and HepG2 cells produced lactate at a concentration of 0.461 x 10-2 M and 0.197 x 10-2 M. In contrast, bacteria produced much lower lactate concentration.

Figure 4. Lactate standard curve.
Lactate concentration is determined by an enzymatic assay, which results in a colorimetric (570nm) product, proportional to the lactate present. Therefore the lactate concentration can be calculated based on the standards curve. This measurement was carried out using a lactate assay kit from Sigma-Aldrich.

Table 2: Lactate concentration in supernatants of HeLa, HepG2, bacteria.

Each sample was 100x diluted. 1 μl of diluted supernatant of HeLa, HepG2 and 5 μl of diluted bacterial supernatant grown in LB and the cell media, DMEM were added into the buffer and enzyme mix separately for each well in the 96-well plate. DMEM which is free of phenol red and serum serves as a negative control.

Amount of lactate per well (nmol)= Absorbance x 0.1762

Concentration of lactate (nmol/μl) = Amount of lactate per well / Volume of sample added.

This measurement was carried out using a lactate assay kit from Sigma-Aldrich.
N = 3 ± SEM, P < 0.01

Sample Absorbance (A570) of 100x diluted sample Lactate concentration (10-2 M)
HeLa 0.262 ± 0.128 0.461 ± 0.0225
HepG2 0.112 ± 0.000839 0.197 ± 0.00148
Bacteria 0.0343 ± 0.000694 0.0121 ± 0.000265
DMEM 0.007 ± 0.000882 0.000 ± 0.000309

Figure 5 showed that RIOT sensor 10 (Part:BBa_K1897028) and 11 (Part:BBa_K1897029), as well as other sensors, were able to activate the expression of GFP under simulated conditions in the lab. Compared to the basal expression, there was a significant increase in GFP intensity, about 1.6 and 2.6 times, when RIOT sensor 10 was induced by the supernatant of HeLa and HepG2, respectively. Similarly, RIOT sensor 11 also showed a significant increase in GFP expression of about 1.9 and 1.5 times after being induced by the supernatant of HeLa and HepG2, respectively.

In conclusion, RIOT sensor 10 not only had the lowest basal expression but also showed significant fold change in GFP intensity, which is about 2 times higher when lactate concentration increased from 10-2 M to 10-3 M. Moreover, we found that HeLa and HepG2 produce elevated level of lactate and our RIOT sensors 10 and 11 were able to detect lactate in the supernatant of these cancer cell lines. These finding supports the validity of our proof of concept that our RIOT sensors have the potential to detect high concentration of lactate in the tumour microenvironment inside the body.

Response of RIOT sensors and other lactate sensors in the supernatant mammalian cells.
Overnight cultures of bacteria transformed with different sensors were diluted. Then 40 μl of HeLa and HepG2’s supernatant were added into 160 μl of each diluted bacteria culture separately, followed by 3-4 hours incubation. If our RIOT sensors are responsive, bacteria will express GFP which can be measured by fluorescence microscope.
(A)Selected images of bacteria with fluorescence taken by microscope.
(B) Fluorescent images of individual sensor at each condition were processed by ImageJ software to obtain the corrected total cell fluorescence
*p < 0.05, **p < 0.01, ***p < 0.001, n.s. = not significant. n = 40 per group and the error bars stand for SEM.

References

  1. Alfarouk, K.O., Verduzco, D., Rauch, C., Muddathir, A.K., Bashir, A.H., Elhassan, G.O., Ibrahim, M.E., Orozco, J.D.P., Cardone, R.A., Reshkin, S.J. and Harguindey, S. (2015). Glycolysis, tumor metabolism, cancer growth and dissemination. A new pH-based etiopathogenic perspective and therapeutic approach to an old cancer question. Oncoscience, 2(4), p.317.
  2. Chen, Z., Lu, W., Garcia-Prieto, C., & Huang, P. (2007). The Warburg effect and its cancer therapeutic implications. Journal of bioenergetics and biomembranes, 39(3), 267-274.
  3. Vander Heiden, M. G., Cantley, L. C., & Thompson, C. B. (2009). Understanding the Warburg effect: the metabolic requirements of cell proliferation. Science, 324(5930), 1029-1033.
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