Introduction
In our project, we aimed to produce L-lactate with modified E. coli for further application in bed bugs detection. Throughout our engineering cycle, we have done several experiments to prove our concept. We have done three major experiments, including L-lactate assay, RNAi gene silencing of GFP and the investigation on diffusion properties of L-lactate in different volumes.
- L-lactate assay: Quantify the concentration of produced L-lactate
- Test RNAi gene silencing of GFP by measuring fluorescence level
- Measuring and characterizing the relationship between equilibrium time and volume in L-lactate diffusion
Measurement Part 1: L-lactate assay
Background
In our project, we aim to produce L-lactate in our modified E. coli. While pH measurement can indicate general acid production, it is a non-specific method that cannot distinguish L-lactate from D-lactate or other acidic byproducts.
To specifically quantify L-lactate, we used an enzymatic L-lactate assay kit (Solarbio, cat. BC2230). The high stereospecificity of the enzyme in this kit ensures that only the L-isomer of lactate is detected, providing an accurate measurement of its concentration in the bacterial cell lysate.
Principle
A significant challenge in the direct turbidimetric measurement of L-lactate is its inherent absorption in the near-infrared spectrum. To circumvent this issue, an enzymatic assay was employed. Following extraction, L-lactate is oxidized to pyruvate by the enzyme L-lactate dehydrogenase (L-LDH), generating NADH as a byproduct.
(fig. L-lactate to pyruvate reaction)
The enzymatic oxidation of L-lactate to pyruvate by L-LDH is an equilibrium-limited reaction that strongly favors L-lactate, which could lead to incomplete conversion. To drive the reaction to completion, the generated NADH is continuously consumed in a secondary reaction. In this process, NADH reduces the chromogenic substrate MTT to formazan in the presence of the electron mediator 1-mPMS. The resulting formazan is then quantified by its distinct absorption peak at 570 nm, ensuring a reliable and complete measurement of the initial L-lactate concentration.
(fig. NADH to Formazan reaction)
Detailed protocol are indicated in “experiment” page
Result
Standard curve of L-lactate concentration
Although comparing absorbance values is enough to investigate the yield of our modified E. coli, we wanted more than just a comparison. Therefore, we created a standard curve to define the relationship between L-lactate concentration and absorbance.
A standard curve was generated by measuring the absorbance of L-lactate at concentrations ranging from 1 to 7 μmol/mL, with measurements taken at 1 μmol/mL intervals. The resulting linear regression plot yielded a coefficient of determination (R2) of 0.981, indicating a very strong and highly reliable linear relationship between L-lactate concentration and absorbance.
With this standard curve, we can measure the accurate value of L-lactate concentration from our modified E. coli.
(fig. calibration curve of L-lactate concentration)
Measuring L-lactate yield in E. coli in different nutrient
To produce L-lactate, the raw material for respiration was also added, and L-arabinose was supplied to induce the pBAD promoter. Normally, glucose can be used to make L-lactate, but according to research in 2014 (Simcikova et al., 2014,), glucose may repress the downstream expression of pBAD promoter(p. 1). Therefore, we found another sugar—fructose as another carbon source, as suggested in the paper (Mulok et al., 2009) for L-lactate production.
This is our set-up used for testing the production of L-lactate with glucose, fructose, and arabinose:
| Plasmid transformed in E. coli | Added sugar |
|---|---|
| pBAD-ldh | 1% Arabinose + 1% Fructose |
| 1% Arabinose + 1% Glucose | |
| 1% Fructose | |
| 1% Glucose | |
| Empty pET plasmid (Neg. control) | 1% Arabinose + 1% Fructose |
| 1% Arabinose + 1% Glucose |
This is absorbance per OD 6oo measured with the L-lactate assay:
A: Arabinose G: Glucose F: Fructose
This graph illustrates the concentration of produced L-lactate divided by OD 600 of the nutrient broth. Overall, the broth with 1% glucose shows a significantly higher conc./OD600 compared to the negative control empty pET plasmid). In contrast, the addition of fructose had a negligible effect on the lactate yield. This suggests that fructose might not be a suitable carbon source in this metabolic pathway. The result indicates an unexpected discovery — the existence of arabinose does not seem to have a significant inducing effect on producing L-lactate even though the inserted promoter, pBAD, is said to be induced by L-arabinose. We suspected that the leaky expression of ldh synthesizes enough L-LDH so that it was no longer a limiting factor on producing L-lactate. We suspected that the concentration of glucose is the limiting factor. To verify our assumption, we tried these sets to compare the yield of L-lactate from broth with different glucose concentration.
Next, we investigated the effect of glucose supplementation in a broth with a fixed arabinose concentration:
| Glucose | Arabinose |
|---|---|
| 0% | 1% |
| 1% | |
| 2% | |
| 3% | |
| 4% |
This is absorbance per OD 6oo measured with the L-lactate assay:
A: Arabinose G: Glucose
(fig. concentration of L-lactate/OD 600 with different glucose concentrations)
The results show that increasing the concentration of glucose were not able to significantly enhance the production yield, which indicates that glucose is also not a limiting factor on the yield of L-lactate.
Conclusion
Our investigation into the expression of lactate dehydrogenase ldh under the control of the pBAD promoter has revealed anomalous, constitutive-like activity, contrary to its expected arabinose-inducible nature. This behavior indicates a potential failure in the AraC-mediated repression mechanism, which is essential for silencing the promoter. Such a regulatory failure could stem from a complete absence of a functional AraC protein, which might occur if the gene was omitted from the plasmid construct used in a non-complementing araC- host strain like DH5α. Alternatively, the issue may be an insufficient AraC concentration to repress all promoter copies—a phenomenon known as repressor titration, which is common when using high-copy-number plasmids. A final possibility is the presence of a mutation within the AraC gene itself, rendering the protein non-functional and unable to form the DNA loop required for repression. Any of these scenarios would result in significant basal transcription, explaining the observed constitutive expression that is not suppressed by high glucose concentrations.
(fig. pBAD AraC mechanism)
Measurement Part 2: Effect on fluorescence by RNAi gene silencing of GFP
Background
Our objective is to enhance the production yield of L-lactate. To achieve this, we are employing RNA interference (RNAi) to repress the expression of the ldhA gene. By silencing ldhA, we aim to minimize the diversion of pyruvate, making it more available for the L-lactate synthesis pathway.
Principle
To compare how different designs of RNAi affect the repression rate, we design three different asRNA expression plasmids, respectively, to test which asRNA design gives the strongest repression on GFP intensity.
One of our main goals is to test the difference between RNAi with or without a terminal paired stem-loop (pt7), which is used to stabilize the asRNA. Another thing for us to test is the length of RNAi. In this experiment, all the RNAi designs have a length of 400 bp.
Measurement of fluorescence level to represent the strength of RNAi
We chose to test our RNAi technology on Green Fluorescent Protein (GFP) instead of the ldhA gene.
The reason is that measuring L-lactate production is complex and influenced by many factors, making it difficult to assess the direct effectiveness of the RNAi. It is also time-consuming because it requires induction, anaerobic culture etc. In contrast, GFP offers a much simpler and more direct measurement. The fluorescence level directly corresponds to the amount of protein produced by translation, allowing us to clearly and accurately determine how well the RNAi is working.
(For detailed design, refer to “engineering” page)
We chose co-transformation over cloning for faster results. However, its main disadvantage is that the plasmid copy number varies between colonies. This inconsistency means we cannot use a single colony to accurately measure the effect of the RNAi.
We co-transformed E.coli with a GFP expressing plasmid (Chl) and an asRNA plasmid (Kan):GFP-asGFP or GFP-asGFP-pt7 or GFP-asRFP and grew them in liquid culture.
(fig. X co-transformation of GFP expressing plasmid (Chl) with asRNA plasmid (Kan):GFP-asGFP or GFP-asGFP-pt7 or GFP-asRFP observed under blue light)
Measurement of multiple colonies to increase accuracy
In co-transformation, the resulting colonies can have varied copy numbers of the two plasmids, thus the GFP intensity could be affected by the plasmid number instead of reflecting only the RNAi efficiency. This inconsistency in plasmid copy number can make our experimental results inaccurate.
In order to increase the reliability of our result, we grow multiple colonies into separated broth and measure their fluorescence intensity respectively. By increasing more biological replicates, it can reduce the effect of the varied plasmid number on the final GFP intensity result.
To find the best RNAi design, we compared the fluorescence of our samples to that of a negative control (GFP-asRFP). A lower intensity indicated successful gene repression.
Result
To find the best RNAi design, we compared the fluorescence of our samples to that of a negative control (GFP-asRFP). A lower intensity indicated successful gene repression.
(fig. 15 RNAi test flu/OD 600 graph **<0.005, n.s. = non significant)
Conclusion
The experimental data reveal that the efficacy of RNAi is critically dependent on the structural stability of the asRNA transcript. Constructs lacking the pt7 stemloop sequence failed to produce a statistically significant reduction in GFP fluorescence. Conversely, the incorporation of the pt7 stem-loop, which is presumed to enhance transcript stability and prevent degradation, mediated a significant repression of reporter gene expression. These findings collectively demonstrate that a 400 bp asRNA stabilized by a terminal stem-loop structure is an effective molecular tool for achieving targeted protein knockdown in E. coli.
Measurement Part 3: Characterization of L-lactate diffusion properties
Background
Our bed bug trap uses lactic acid as a lure—the higher its concentration, the better it attracts them. To work effectively, the trap must be in an enclosed space so the lactic acid builds up quickly instead of escaping
However, the trap has a limited lifespan. Eventually, the lactic acid spreads out evenly, reaching a state of equilibrium. Once this happens, there is no concentration gradient to guide the bugs, and the lure stops working.
To have an estimation of the lifespan of L-lactate in different volumes, it is necessary to investigate the effect of different enclosed volumes on equilibrium time.
Difficulties in measuring equilibrium time
As we needed to investigate the lifespan of our detection kit, we must find a way to measure the time taken for L-lactate to reach equilibrium. Here are some challenges that made it difficult to measure the equilibrium time:
- Gaseous kinetics is hard to predict and calculate
- It was extremely difficult to measure the partial pressure of L-lactate
- The experiment must be done in an enclosed area with known and controllable volume.
In order to tackle this, we design a new set-up, which is a new and straightforward method to estimate the time taken for L-lactate to reach equilibrium.
Principle
L-lactic acid will undergo evaporation and gaseous diffusion through an enclosed air volume. This diffusion will be quantifiable by measuring the progressive decrease in the pH of an open-surface distilled water sample placed within the same sealed plastic box. The pH of the water will decrease as the acidic gas dissolves into the water and dissociates into hydrogen ions and lactate ions. When the pH value stabilises at its lowest value, it is considered equilibrium.
(Fig 1, the design of the diffusion set up)
(image 1, the model we built for diffusion rate as timepoint measurement)
Increasing the accuracy by controlling the background effect
To increase the accuracy, all experiments maintain strict consistency regarding the acid source and volume. Specifically, all trials will use the same concentration and volume of L-lactic acid and will be conducted within containers of the identical box volume(15000cm3). Furthermore, all measurements are to be performed under consistent temperature conditions (e.g., controlled ambient temperature) to eliminate thermal effects as a confounding variable. This standardization is critical for isolating the performance of the pH measurement tools during the subsequent diffusion rate assessment.
All our distilled water came from the same source. However, its pH value often fluctuated because it lacks ions to keep it stable. Due to this instability of initial pH, a direct graph of pH against days would not be accurate. To present reliable data, we instead calculated the change in pH (ΔpH) and plotted a graph of this change against the number of days.
Result
Volume of boxes used for testing:
| length (cm) | width (cm) | height (cm) | total volume (cm3) | |
|---|---|---|---|---|
| Small (S) | 16 | 24 | 20 | ~7500 (50%) |
| Medium (M) | 20 | 30 | 25 | ~15000 (100%) |
| Large (L) | 23 | 34 | 29 | ~22500 (150%) |
The following graphs illustrate the effect of varying volume on the equilibrium time for a 50% L-lactate solution:
Blue line: pH change of water with L-lactate in the box
Red line (Neg. control): pH change of water without L-lactate in the box
(fig. 8 pH change against day in S size box)
(fig. 9 pH change against day in M size box)
(fig. 10 pH change against time(day) in L size box)
For each dataset, a regression curve was fitted to the plotted points using computational software. Differential calculus was then employed to determine the minimum point of each curve. The equilibrium time, defined as the x-coordinate of this minimum, was subsequently indicated on the respective graph.
Subsequently, a graph of equilibrium time versus box volume was plotted:
| Size of boxes | Volume (cm3) | Equilibrium time (day) |
| S | 7680 | 2.87 |
| M | 15000 | 3.54 |
| L | 22678 | 4.35 |
(fig. 11 Equilibrium time against volume with 50% L-lactate)
The analysis reveals a strong positive linear relationship between equilibrium time (t) and box volume (v). This relationship is described by the linear regression equation: t = 9×10-5 + 2.09
The model's high reliability is substantiated by a coefficient of determination (R²) of 0.998. This value indicates that 99.8% of the variance in equilibrium time is predictable from the volume, confirming the model's excellent predictive power. With this equation, we can calculate and instruct users the usage duration of our final bed bug traps of different sizes.
Reference
[1]https://www.solarbio.com/goodsInfo?id=6262
[2]Mulok, T. E. T. Z., Chong, M. L., Shirai, Y., Rahim, R. A., & Hassan, M. A. (2009). Engineering of E. coli for increased production of L-lactic acid. African Journal of Biotechnology Vol. 8, 8(18), 4597–4603. https://doi.org/10.5897/ajb09.614
[3]Simcikova, M., Prather, K. L., Prazeres, D. M., & Monteiro, G. A. (2014b). On the dual effect of glucose during production of pBAD/AraC-based minicircles. Vaccine, 32(24), 2843–2846. https://doi.org/10.1016/j.vaccine.2014.02.035