Team:Oxford/Measurement

Measurement

Introduction

Reproducibility and rigorous characterisation of our parts are both key to good science. To standardise our results, the iGEM Measurement Kit was used to calibrate both fluorescence and Optical Density 600 (OD600). We used a variety of qualitative and quantitative measurements such as mass spectrometry, qualitative fluorescence microscopy and Scanning Electron Microscopy (SEM) during our project.

Calibration Protocol

In order to present reproducible data, we wanted to convert our results into a format that enables a unified comparison between different measurements and instruments. To create this conversion, we used the iGEM Measurement kit, as provided in the iGEM 2019 Distribution kit. This includes fluorescein salt and silica beads which serve as standardised conversion units for Fluorescence Intensity and OD600 respectively.

Using this kit, we obtained standard curves for fluorescein (Figure 1) and particle count (Figure 2). The R² for fluorescein is 0.9923, whereas for particle count, R²=0.9950.

Fig.s 1 & 2 Fluorescein & Particle Standard Curve

From this, we obtained the standardised parameters: Particles & MEFL as can be seen Table 1.

These parameter values can be used to convert our OD600 and Fluorescence Intensity measurements into particles and MEFL, respectively, which allows comparison of data with other measurements with different instruments.

Table 1: Standardised Parameters

	Particles/ OD600	MEFL /Arbitrary Units
Mean	$$1.95 \times 10^{8}$$	$$2.34 \times 10^9$$

OD600 vs. Fluorescence Intensity

Having followed the calibration protocol, we decided to measure the OD600 and the fluorescence of our wild type and transformed Lactobacillus reuteri simultaneously.

As shown in Figure 3 below, the growth rates of our transformed L. reuteri were slower than the corresponding wild type.

Fig. 3: Growth of L. reuteri Transformants, Calibrated

The rate of fluorescence shown in Figure 4 below was less conclusive, as at first, the wild type seemed more fluorescent than our transformed L. reuteri. However, we ultimately realised that we had not corrected for the difference in growth rates. Assuming fluorescence and OD600 were linearly correlated, we divided fluorescence by our OD600.

Fig. 4: Rate of Fluorescence for L. reuteri Transformants, Calibrated

Considering our fluorescence/OD 600 graphs in Figures 5 & 6, everything falls into place.

Fig. 5: Fluorescence/OD600 for L. reuteri Transformants, Calibrated

Our wild type L. reuteri readings were much lower compared to both GFP and CD27L transformants. For the first several hours where OD600 is less than 0.2, the fluorometer was relatively imprecise with the OD600 fluctuating significantly compared to previous measurements. This is why Figures 5 & 6 below start around the 4hr mark, where the imprecision of the fluorometer would lead to a smaller uncertainty in its readings.

Fig. 6: Fluorescence/OD600 for L. reuteri Transformants, Calibrated & Scaled

The lack of precision of the fluorometer cast a seed of doubt. Were our cells being truly more fluorescent per particle, or was this simply an inverse of our growth curve with our original assumption of linear correlation being false?

Thus, we also plotted our fluorescence of our different L. reuteri species against their OD600 with the same calibration measurements as used previously. For GFP vs. wild type, we were pleased to see that our transformed L. reuteri were more fluorescent even for a similar OD 600 as shown in Figure 7 below.

Fig. 7: Fluorescence vs. OD600 for Wild-Type & GFP-TransformedL. reuteri Transformants

And yet, when we then looked at our CD27L-transformed L. reuteri, there seemed to be no stark difference between its fluorescence and that of the wild type at the same OD. In fact, it seemed that the wild type was even more fluorescent as seen in Figures 8 & 9.

Fig. 8: Fluorescence vs. OD600 for CD27L-Transformed L. reuteri Transformants

Fig. 9: Fluorescence vs. OD600 for CD27L-Transformed L. reuteri Transformants with Erythromycin

Thus, although we had shown via colony PCR that successful transformation had inserted our constructs into L. reuteri, we had doubts on whether our CD27L transformed L. reuteri were even fluorescent.

We were faced with a problem where our fluorometry results could not confirm the fluorescence of GFP in our L. reuteri. To address this, we decided to confirm our transformations via the precise, but more qualitative method of fluorescent microscopy, as shown below in Figure 10.

Fig. 10: Fluorescence Microscopy Images of L. reuteri Transformants

Fig. 10 Fluorescence Microscopy: top row: micrographs of normalised exposure show the relative levels of exogenous protein expression in 3 strains of Lactobacillus reuteri 100-23c: wild type, pTRKH3-erm-GFP and pTRKH3-erm-slpMod CD27L_mClover. Bottom row: the corresponding bright field imaging mode. As expected, no fluorescent protein expression is detected in the wild type strain, while significant levels are observable in the GFP transformants. However, the CD27L shows low level expression concentrated in inclusion body-like structures.

We were skeptical about the CD27L endolysin, but zooming in and adjusting the exposure, our CD27L did in fact show fluorescence with inclusion-body like structures. This indicates that we successfully transformed our constructs into L. reuteri.

Fig. 11: Adjusted Fluorescence Microscopy Images of CD27L-Transformed L. reuteri

Through our experience, we showed that fluorescence microscopy and fluorometry can act as complementary tools in determining transformation success and subcellular protein localisation, and hope that our work can be used as an example for other teams looking to perform similar experiments.

Killing Assay

To quantify the effects of the CD27L endolysin, we needed an assay which showed whether the endolysin was functional. From the literature, we found that CD27L, besides being highly specific to Clostridioides difficile, is also able to cleave the cell wall of several other bacteria with the same peptidoglycan type, A1γ. Namely, we used Bacillus subtilis, a Category 1 Gram-positive bacteria¹ due to safety concerns with C. difficile.

In order to assay whether B. subtilis is lysed by our endolysin, we measured absorbance at 600nm (OD600) throughout an 8 hour time period at every 15 minutes. This way, we were able to monitor how the B. subtilis culture is affected by CD27L.

Our kill assay differs from the one described by Mayer et al. (2008)¹. Their protocol involved washing and concentrating the cells to OD600 = 2.0 in Phosphate Buffered Saline (PBS), following by mixing with CD27L and measuring the rate of decrease in OD600.

In our case, we grew up a B. subtilis culture until they reached mid-log phase (OD600 ≈ 0.6), then diluted them in Lysogenic Broth (LB), added our solution (CD27L or Lysozyme or PBS) and then measured the growth over time by monitoring the change in OD600. The measurements were made automatically by a plate reader, BMG FLUOstar Omega and the cells were grown in a 96 well plate from Greiner (CellStar® 96 well plate F-type).

The use of a plate reader allowed us to measure the absorbance at OD600 over a long time period, which greatly reduces the possible human error. Additionally, the instrument allowed us to conduct experiments on a larger scale than otherwise possible. Notably, it is capable of incubating the cells at 37 °C and shaking at 500 rpm, which helped to carry out the experiments in a reproducible manner. Furthermore, to increase confidence in our results, our experiments were always carried out in triplicates so that we could perform statistical analysis from our results.

Having a rigorous protocol for preparing the samples and utilising an instrument allowing consistency across measurements enabled us to generate more trustworthy and reliable data.

Fig. 1: Generic Killing Assay Growth Curve

Fig. 1: Generic Killing Assay Growth Curve, Error bars represent ± 1 standard deviation.

One concern over our starting OD600 was the accuracy of the machine. According to the manufacturer’s website, the accuracy of the instrument at OD 2.0 is less than 1%, the precision at OD 1.0 is <0.5%, and <0.8% at OD 2.0 and it increases with higher ODs. Hence, we diluted our samples to 0.01 so that most of the values from our measurements are within the range of 0.1-1.5.

However, on our plate reader, we set the plate reader to normalise the measurements to 1 cm. Thus, our presented data has a higher OD600 than absorbance measured in the plate reader due to the shorter path length in the 96-well plate. Consequently, our data still falls within the range of OD 0.1-1.5, where the machine’s precision is at its highest.

This method proved us that our endolysin is able to kill and inhibit the growth of B. subtilis in growth media. This assay gives a much more powerful insight into how we need to design our product, how it will behave in a more realistic scenario and where the cells are actually growing while CD27L has to kill the target.

Densitometry

For our protein purification experiments, we wanted to use a convenient method to quantify the purity of our product. However, due to limitations in resources, we were not able to obtain a pure enough sample to perform assay-based quantification.

Instead, we used densitometric measurements in SDS-PAGE gels following Coomassie Brilliant Blue staining using BioRad’s Chemidoc XRS and Image Lab (6.0.1) software to measure Lane Percentage (%), which we refer to as lane purity.

Firstly, to use this method, we created a standard curve to test whether densitometry scales linearly and whether purity values change with different concentrations of protein loaded. For our trial protein, we used partially purified mClover3 (via IMAC). Then, we created a series of dilutions and loaded them onto the same SDS-PAGE gel. Following staining with Coomassie Blue and destaining, we captured the image and analysed with Image Lab. The bands and lanes were detected automatically via the software.

Fig. 1: Absolute mClover3 Concentration vs. Band Volume

From this figure, and the calculated best fit line (R²=0.959), we can conclude that there is a linear relationship between band intensity and protein concentration.

In the below Table 1, wwe can also see how the Lane % (disk size 2 mm), which we refer to as the purity of the sample, changes at different protein concentrations.

Table 1: Protein Purity (Lane %) vs. Concentration (μg mL^-1)

Concentration (μg mL-1)	Protein Purity (Lane %)
1500	70.7
1000	65.8
750	59.9
500	73.4
250	78.5
125	77.5
50	69.4
25	50.8
Mean	68.3
S.D	9.3

The purity is 68.3 ± 9.3 % (mean ± 1 S.D.); however, this data shows a substantial degree of variation, especially at low protein concentrations. We hypothesise that this could be due to two factors:

1. at lower concentration, the band intensity for contamination is less or not visible, which can create inconsistent purity results
2. the software algorithm, by which the software calculates Lane %, overestimates lower intensity bands and underestimates higher intensity bands.

Given this, for our final measurements as shown in Figure 2, we loaded our samples at a higher protein concentration (1mg/ml), so that we were able to see the low intensity contaminants clearly without overexposing the band of interest.

Fig. 2: Densitometric Analysis of Spy&Go vs. Ni-NTA Purifcation - mClover3

Densitometry provides an easy way to quantitatively analyse the purity of your SDS-PAGE gels without requiring assays. We hope that this technique proves useful to other teams looking to easily analyse the purity of their protein preparations.

Autoinducer Peptide (AIP)

To test the efficacy of our own future quorum sensing mechanism in L reuteri, we had to obtain or synthesise C. difficile’s auto-inducing peptide (AIP). iGEM Nottingham very kindly sent us samples of C. difficile’s supernatant to help us characterise C. difficile’s AIP. These three samples included sterile culture media, supernatant from a 3 hour pre-log phase culture and supernatant from a 48hr culture. All three samples were purified as in the first few steps of Darkoh et al. (2014)²’s purification.

Specifically,

1. Sample was boiled for 10 minutes
2. Sample was cooled and centrifuged for 10 minutes at 10,000 x g to remove denatured toxins and other high-molecular-weight proteins
3. The resulting supernatant was precipitated in 60% ice-cold acetone and incubated overnight at -20℃.
4. The resulting precipitate is centrifuged at 1,000 x g for 20 minutes, and the pellet then resuspended in 40 mL of Milli-Q water, sterilised via a 0.2 μm surfactant-free cellulose acetate (SFCA) filter (Corning Inc., Corning, NY)

Electrospray ionisation mass spectrometry (ESI-MS) was used to determine whether molecular fragments of certain weights were present in the sample. It proved to be a sensitive and effective technique for determining the composition of our supernatants. We used the spectra that were gathered from ESI-MS to try and identify whether the AIP was present in the supernatant of any of our samples, particularly the 3-hour pre-log and the 48-hour stationary phase samples

Fig. 1 Proposed Chemical Structure for AIP (Darkoh et al. (2014))²

The generated mass spectra were then compared to each other in order to correct the data. This correction allowed us to determine which peaks changed over time. This allowed us to compare the changes in masses between the C. difficile-containing samples and the sterile media.

Unfortunately, amidst all our spectra, only one spectrum yielded a small peak around 612.36 Da. In fact, for most, there seemed to be no peak at this molecular weight as further detailed in our Results page.

Fig. 2: Mass Spectra Sample Corrected Using Sterile Media, 1000Da Range

Fig. 2.1: Mass Spectrometry 3-Hour Pre-Log Sample Corrected Using Sterile Media, 1000Da Range

Fig. 2.2: Mass Spectrometry 48-Hour Stationary Sample Corrected Using Sterile Media, 1000Da Range

We wanted to avoid overinterpreting our data under these circumstances. Given the large number of equal-intensity peaks, we felt no conclusion could be made, even if some peaks appeared in the range we were expecting.

In addition to a peak at 612Da, we were also looking for any peaks that seemed to have a noticeably larger abundance for the 48hr sample but were still present in the 3hr sample

To try to determine the presence of the AIP further, the 3hr prelog sample was also used as background against the 48hr, the rationale being that the 48hr sample would have AIP in a much greater abundance. We also decided to simultaneously use hydroxylamine to try and determine whether any of the peaks contained a thioester (Fig. 3).

Hydroxylamine provides a novel way of identifying the presence of a thioester. It attacks the thioester bond, and in an addition-elimination type reaction should add around 33Da to the molecular ion peak, assuming the thioester is part of the cyclic system. This is exemplified in Figure 3 below, again assuming Darkoh et al. (2014)’s proposed structure.

Fig. 3: Effect of Hydroxylamine on the Proposed Structure of the C. difficile AIP

Having followed the purification, some samples were incubated with 200 mM Hydroxylamine at room temperature for two hours at a pH of 8.5 (Fig. 4.2 shown as an example). Although no shifts were seen, these measurements prove to be a useful way for detecting the composition of a biological supernatant.

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Fig. 4: Mass Spectra Sample Corrected Using 3-Hour Pre-Log Sample, Pre/Post-Hydroxylamine Treatment, 600Da Range

Fig. 4.1: Mass Spectrometry 48-Hour Stationary Sample Corrected Using 3-Hour Pre-Log Sample, 600Da Range

Fig. 4.2: Mass Spectrometry 48-Hour Stationary Sample Corrected Using 3-Hour Pre-Log Sample, 600Da Range, Post-Hydroxylamine Treatment

In the future, we would ideally use tandem mass spectrometry, which fragments specific peaks following quadrupole mass selection, to determine the fragmentation pattern of a specific peak. Ideally, this would allow us to determine the composition of each peptide, hopefully isolating one with a fragmentation pattern expected based on the AIP’s peptide sequence.

Given the sensitivity and molecular weight specificity of ESI-MS, we anticipate that ESI-MS analysis could be useful in detection of other secreted peptides, such as CD27L. We hope that other teams are able to make use of this advanced technique for peptide detection and analysis.

Lactobacillus Protocols: The Good, The Bad and The Ugly

As noted in our Results page, transformation of the Quadram Institute L. reuteri strain 10023C using the protocol provided by Dimitris Latousakis, was successful first time. Yet, after having initially attempted many protocols unsuccessfully, we wanted to know the reasons for our initially limited success. Were our electroporations not working as a result of the strain, the protocols or a mixture of both?

We had already attempted Dimitri’s protocol with our original strain. This resulted in no growth on both the negative control and our transformed plates. For the sake of completeness, we decided to test transformation of Dimitri’s own strain (10023C) with some of the previous electroporation protocols we had used and calculate transformation efficiency.

Testing the transformation efficiency of our ldh-mClover3 plasmid, we attempted both the Aukrust et al. (1995)³’s and Dimitri’s protocol simultaneously. No colonies formed from the Aukrust et al. (1995) protocol, but Dimitris’ protocol yielded the following data across 4 plates, as shown in Table 1

Table 1: Transformation Efficiency of L. reuteri Dimitris' Protocol Transformants

Average number of colonies per 100μL = 47.7

Thus, there is a L. reuteri population density of 318 cfu mL^-1 for 500ng of plasmid (ldh-mClover3)

	D1 100 μL	D2 100 μL	D1 800μL	D2 800μL
Number of colonies	39	96	183	262
Number of colonies per 100μL	39	96	22.9	32.8

This illustrates the specificity of the electroporation conditions to the particular strain being used. Our success largely owes to matching the appropriate strain of bacteria to the right conditions. Had we not done this, our transformations may not have been successful at all.

Scanning Electron Microscopy

We used SEM to visualize the lysis of B. subtilis as a result of CD27L endolysin activity. This method allows us to visualise changes in B. subtilis cell morphology due to CD27L activity.

References

#	Reference
1	Mayer, M. J., et al. “Molecular Characterization of a Clostridium Difficile Bacteriophage and Its Cloned Biologically Active Endolysin.” Journal of Bacteriology, vol. 190, no. 20, 2008, pp. 6734–6740., doi:10.1128/jb.00686-08.
2	Darkoh, Charles, et al. “Toxin Synthesis by Clostridium Difficile Is Stringently Regulated Through Quorum Signalling.” American Society for Microbiology, Cell-Cell Communication in Bacteria, 2014, pp. 84–84.
3	Aukrust, T. W., M. B. Brurberg, and I. F. Nes. 1995. Transformation of Lactobacillus by electroporation. Methods Mol. Biol. 47:201-208