Measurement

Team Saptasense has been nominated for the Best Measurement!


Sedimentation Assay to Measure Autoaggregation of Bacteria


What We Did


A key goal of our project was to develop a novel, whole-cell bacterial biosensor capable of detecting and measuring a diverse array of compounds, including the free asparagine found in “buddy” maple sap. The assay that we designed produces read-out based on the concept of autoaggregation, or “bacterial clumping”. The more antigen that is found in a test sample, the less bacterial clumping is present. Therefore our project requires that we observe the differences in autoaggregation of bacteria.
In previous research, autoaggregation/agglutination has been qualitatively observed by eye, after plating bacterial cultures in a 96-well plate1,2. We sought to apply a more quantitative approach towards determining the degree of autoaggregation of bacteria.
To achieve this, we took advantage of a common laboratory technique of measuring the optical density at 600 nm (O.D.600) to probe for bacteria. When performing the assay, we first made sure that the bacterial samples were well resuspended, and then took time points of optical density from 0 to 30 minutes. Bacterial samples exhibiting more autoaggregation have larger “clumps” of bacteria that sink to the bottom of the cuvette faster than a bacterial sample with no autoaggregation. This difference in rates of sedimentation produces a quantifiable curve that can be compared across samples. (Figure 1)
Figure 1. Animated schematic of sedimentation/autoaggregation assay. Sample 1 exhibits autoaggregation, and thus the particles inside the cuvette are large and sink to the bottom quickly. Sample 2 exhibits no autoaggregation, and thus the particles inside the cuvette are small and sink to the bottom slowly. A graph can be produced showing differential rates of sedimentation.

This sedimentation/autoaggregation assay was modeled after the work of Leo et. al3. Our protocol can be found in the section below.

Detailed Protocol


The following protocol includes instructions on sample preparation and treatment of an EibD-expressing E. coli strain including preparation of the whole-cell bacterial biosensor. This protocol can easily be adapted for any strain exhibiting autoaggregation behaviors, and “tips for adaptation” are provided.

Download Protocol on Preparation of Modular Whole Cell Bacterial Biosensor

Repeatability and Accessibility


A major advantage of this measurement approach is the accessibility to the scientific community. The only required materials of the measurement method itself include a cuvette, spectrophotometer, and your sample. Spectrophotometers are devices that include a light source and detector capable of measuring the transmission of light through a sample. They are ubiquitous instruments in biological, chemical, and physical labs, often employed in undergraduate or high school laboratory courses. Easily applied for monitoring bacterial growth, there is ample literature on the use of spectrophotometers for bacterial specimens 5.
Based on accessibility of this measurement technique, we are confident in the repeatability of measurements by other iGEM teams. As long as an iGEM team has basic bacterial culturing supplies, our measurements can be repeated.

Applicability and Usage


Measurements of autoaggregation are extremely important to the synthetic biology and iGEM community at large. Autoaggregation is a widespread phenomenon among bacterial strains and is the first step in biofilm formation, a commonly studied process. 4 Searches of the iGEM Parts Registry return multiple biobricks where autoaggregation has been observed in literature publications or cited by iGEM teams (Table 1). As can be seen, documentation and measurements are limited and are qualitative. Our measurement protocol can be applied to these biobricks for further characterization and documentation. Our measurement protocol can also be applied to biobricks involving flocculation, or aggregation of any kind.

Table 1.
BioBrick Autoaggregate Method of Documentation
BBa_K1971013 Filamentous hemagglutinin (FHA Not documented by iGEM team
BBa_K283007 Adhesin Involved in Diffuse Adherence (AIDA) Not documented by iGEM team
BBa_K1761000 Outer Membrane Protein X (OmpX) Not documented by iGEM team
BBa_K644001 Hyphal Wall Protein 1 (HWP1) Pictures of bacterial cultures exhibiting clumping
BBa_M10026 UpaG Not documented by iGEM team

Our Results


Ultimately, our sedimentation/autoaggregation assay proved to be useful in distinguishing between different experimental conditions. Below are some results that are representative of a larger collection of results characterizing EibD in the context of our project. The entire collection of results can be accessed here.
Experimental Question: How does induction concentration affect autoaggregation?
To understand how our BioBrick EibD (BBa_K4130000) is expressed, we needed to characterize the amount of autoaggregation as a function of inducer concentration. Our biobrick contains a tuneable rhamnose promoter (BBa_K914003). Therefore, we could easily control expression levels of EibD protein by varying the L-rhamnose concentration between 0% and 0.01%. The results can be seen below in Figure 2.

Effect of Induction Concentration on Autoaggregation. After brief mixing, the O.D.600 was measured for 30 minutes. Each O.D.600 value was normalized to the initial value at time zero and plotted against time in seconds. The uninduced sample exhibited constant O.D.600 over the entirety of the 30 minutes, while induced samples exhibited strong autoaggregation. Higher induction concentration of 0.01% rhamnose showed stronger autoaggregation compared to 0.001% rhamnose induction concentration.
Figure 2. Effect of Induction Concentration on Autoaggregation. After brief mixing, the O.D.600 was measured for 30 minutes. Each O.D.600 value was normalized to the initial value at time zero and plotted against time in seconds. The uninduced sample exhibited constant O.D.600 over the entirety of the 30 minutes, while induced samples exhibited strong autoaggregation. Higher induction concentration of 0.01% rhamnose showed stronger autoaggregation compared to 0.001% rhamnose induction concentration.

Experimental Question: How does addition of anti-asparagine antibody affect autoaggregation?
In the context of our project, EibD is a cell-surface protein that is capable of binding to the constant region of immunoglobulins G and A (antibodies). We sought to utilize this binding capability to generate specificity to antigens to create a whole-cell biosensor. Therefore, we had to characterize the effect of incubation with antibodies on autoaggregation. 0.001% rhamnose-induced bacterial cultures were incubated with 1mL of 2ug/mL anti-asparagine antibody at room temperature, gently rocking, overnight. The results can be seen below in Figure 3.
Effect of Anti-Asparagine Antibody on Autoaggregation. After brief mixing, the O.D.600 was measured for 30 minutes. Each O.D.600 value was normalized to the initial value at time zero and plotted against time in seconds. The lines plotted are averages of three separate samples, treated identically. Error bars represent +/- 1 standard deviation. Addition of the antibody resulted in significantly less autoaggregation (p < 0.05).
Figure 3. Effect of Anti-Asparagine Antibody on Autoaggregation. After brief mixing, the O.D.600 was measured for 30 minutes. Each O.D.600 value was normalized to the initial value at time zero and plotted against time in seconds. The lines plotted are averages of three separate samples, treated identically. Error bars represent +/- 1 standard deviation. Addition of the antibody resulted in significantly less autoaggregation (p < 0.05).

Experimental Question: How does addition of anti-GFP antibody affect autoaggregation?
Bacteria were incubated with or without 2 mg/mL antibody in PBS for 20 hours. The bacteria were resuspended briefly and the O.D.600 was measured over 30 minutes. Experiments were performed in triplicate, and the average fraction O.D.600 was calculated for each time point. The error ribbon on the graph represents 1 standard deviation from the mean (n=3).
Effect of Anti-GFP Antibody on Autoaggregation. After brief mixing, the O.D.600 was measured for 30 minutes. Each O.D.600 value was normalized to the initial value at time zero and plotted against time in seconds. The lines plotted are averages of three separate samples, treated identically. Error bars represent +/- 1 standard deviation.
Figure 4. Effect of Anti-GFP Antibody on Autoaggregation. After brief mixing, the O.D.600 was measured for 30 minutes. Each O.D.600 value was normalized to the initial value at time zero and plotted against time in seconds. The lines plotted are averages of three separate samples, treated identically. Error bars represent +/- 1 standard deviation.

Validation and Calibration


At each stage of the measurement process, we were sure to incorporate controls into our experiments. The stages of our experiments and their controls can be seen below in Table 2.

Table 2.
Experimental Question Controls Utilized Results from Controls Interpretations of Result from Controls
How does autoaggregation depend on induction concentration? E. coli BL21 with no transformed plasmid, induced with 0.01% rhamnose Nearly constant OD600 for the duration of the 30 minutes. There is little to no autoaggregation.
E. coli BL21 transformed with EibD gene, uninduced Nearly constant OD600 for the duration of the 30 minutes. There is little to no autoaggregation. Our expression system is not leaky.
How does incubation with antibodies affect autoaggregation? E. coli BL21 with no transformed plasmid, induced with 0.01% rhamnose Nearly constant OD600 for the duration of the 30 minutes. No response to addition of the antibody. There is little to no autoaggregation. The strain is not binding to the antibody.
E. coli BL21 transformed with EibD gene, uninduced Nearly constant OD600 for the duration of the 30 minutes. No response to addition of the antibody. There is little to no autoaggregation. The strain is not binding to the antibody.
How does incubation with antibodies + antigen affect autoaggregation? E. coli BL21 with no transformed plasmid, induced with 0.01% rhamnose Nearly constant OD600 for the duration of the 30 minutes. No response to addition of the antigen. There is little to no autoaggregation. The strain is not binding to the antigen.
E. coli BL21 transformed with EibD gene, uninduced Nearly constant OD600 for the duration of the 30 minutes. No response to addition of the antigen. There is little to no autoaggregation. The strain is not binding to the antigen.
Incubation in antigen-free PBS for same amount of time Nearly constant OD600 for the duration of the 30 minutes. No response to addition of the antigen. Without addition of the antibody, the biosensor is not specific to the antigen.
Incubation with beads but without antibody Reduction in OD600, similar to the curve generated 12/16 hours earlier. The reduction in autoaggregation upon addition of the antigen is not due to a possible natural breakdown of the bacterial clumps over time.
How does incubation with BSA-Asn beads affect autoaggregation? Incubation with beads but without antibody Nearly constant OD600 for the duration of the 30 minutes. No response to the addition of the beads. Without addition of the antibody, the biosensor is not specific to bind the beads.

References

  1. Kylilis, N. et al. Whole-cell biosensor with tuneable limit of detection enables low-cost agglutination assays for medical diagnostic applications. ACS sensors, (2019) https://doi.org/10.1021/acssensors.8b01163.
  2. Riangrungroj, P., Bever, C.S., Hammock, B.D. et al. A label-free optical whole-cell Escherichia coli biosensor for the detection of pyrethroid insecticide exposure. Sci Rep 9, 12466 (2019). https://doi.org/10.1038/s41598-019-48907-6
  3. Leo, J. C., Lyskowski, A., Hattula, K., Hartmann, M. D., Schwarz, H., Butcher, S. J., Linke, D., Lupus, A. N., Goldman, A. 2011. The Structure of E. coli IgG-Binding Protein D Suggests a General Model for Bending and Binding in Trimeric Autotransporter Adhesins. Structure 19(7):1021-1030.
  4. Trunk T, Khalil HS, Leo JC. Bacterial autoaggregation. AIMS Microbiol. 2018 Mar 1;4(1):140-164. doi: 10.3934/microbiol.2018.1.140. PMID: 31294207; PMCID: PMC6605025.
  5. Myers, J. A., Curtis, B. S. & Curtis, W. R. Improving accuracy of cell and chromophore concentration measurements using optical density. BMC Biophys. 6, 4 (2013).