Electrochemical Detection of Endotoxins from Gram-Negative Bacteria Using Immobilized TLR4 Immunoproteins
Bibliographic record
Abstract
Diagnostic platforms utilizing biorecognition elements, such as antibodies and DNA molecules, have been demonstrated as useful tools for the rapid detection of pathogens. However, these bioelements are extremely specific to a particular microorganism, which for the purpose of detection, requires prior knowledge of the infection to utilize the proper receptor. In order to circumvent this challenge, our group has been developing biosensors using a family of immunoproteins belonging to the non-specific innate immune system, the Toll-Like Receptor (TLR) immunoproteins, which are programmed to recognize chemical markers from a broad source of microorganisms. This makes them ideal for the first line of detection against an unknown pathogen. For example, TLR4 proteins are selective against endotoxins found in the cell walls of Gram-negative bacteria but not in Gram-positive bacteria. Here we show the results obtained from a Gram-negative bacteria sensor based on TLR4 tethered on the surface of a self-assembled monolayer (SAM) of alkyl thiols on a Au electrode. The TLR4 biosensor assembly involves a multi-step process, starting with the co-adsorption of three alkanethiols onto Au. The resulting SAM possesses distinct characteristics derived from each alkanethiol molecule. A hydroxyl-terminated alkanethiol serves as an anti-fouling agent and a spacer, a ferrocene- (Fc-) terminated alkanethiol is the redox active molecule assisting in charge transfer mediation, and an alkanethiol with a nitrilotriacetic acid- (NTA-) motif permits the chelation of Ni2+ cations, which then immobilizes the TLR4 immunoprotein via its His-tagged recombined C-terminus. Additionally, the desired surface orientation of the TLR4 ligand-recognition sites is also achieved using this anchoring method.1 In nature, TLR4 is a transmembrane monomer that undergoes dimerization once bound to lipopolysaccharide (LPS) endotoxin.2 It is believed that the recombinant TLR4 in our sensors behaves similarly and that dimerization results in an increase in charge transfer resistance (RCT ) with the addition of endotoxin. The sensor assembly process was monitored using cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) techniques. CVs were recorded in 200 mM phosphate buffer (PB) pH 7 while tracking the Fc redox chemistry. Similarly, EIS was performed in an electrolyte containing 5 mM ferrocyanide (ferroCN) dissolved in the same buffer. The addition of ferroCN as a second redox probe allows us to monitor the charge transfer mediation between the SAM-bound Fc and the solution-phase ferroCN ions.3 During sensor assembly, CVs showed a peak shift to higher potentials, while EIS showed higher RCT values with the transition from unmodified SAM to TLR4 modification. Both techniques indicated successful assembly. Overall, the sensors show a stable RCT signal prior to LPS exposure. During sensor testing, the amount of LPS added to the sensor was monitored using EIS, tracking the RCT value, as described above. The addition of LPS resulted in a linear RCT change with logarithmic changes in the LPS concentration in the range of 0.01-100 ug/mL, while at higher concentrations, the response deviated from linearity. Early results estimated a limit of detection of 1 ug/mL LPS. Finally, the TLR4-endotoxin interaction has been characterized using the fluorescence-based microscale thermophoresis (MST) technique. In an MST experiment, a fluorophore-labeled molecule is subjected to a pulse of IR laser to heat a focused point in a solution inside a glass capillary, thus inducing microscopic temperature gradients and thermophoresis-induced migration away from that point.4 In our MST experiments, LPS concentrations from 1-2000 ug/mL were combined with a fixed 20 nM fluorescently-labelled TLR4 protein in order to determine their binding affinity. The MST binding curves of TLR4 with endotoxin allowed the determination of the dissociation constant, Kd, which was found to be ca. 11 uM. References: 1. Mayall, R. M. et al. Enhanced Signal Amplification in a Toll-like Receptor-4 Biosensor Utilizing Ferrocene-Terminated Mixed Monolayers. ACS Sensors 4, 143–151 (2019). 2. Kruger, C. L., Zeuner, M. T., Cottrell, G. S., Widera, D. & Heilemann, M. Quantitative single-molecule imaging of TLR4 reveals ligand-specific receptor dimerization. Sci. Signal. 10, (2017). 3. Alleman, K. S., Weber, K. & Creager, S. E. Electrochemical rectification at a monolayer-modified electrode. J. Phys. Chem. 100, 17050–17058 (1996). 4. Jerabek-Willemsen, M. et al. MicroScale Thermophoresis: Interaction analysis and beyond. J. Mol. Struct. 1077, 101–113 (2014).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".