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Record W3025311839 · doi:10.1149/ma2020-01271889mtgabs

Electrochemical Detection of Endotoxins from Gram-Negative Bacteria Using Immobilized TLR4 Immunoproteins

2020· article· en· W3025311839 on OpenAlexaff
Armando J. Marenco, Raunak Raj Singh, Margaret Renaud‐Young, Justin L. MacCallum, Viola Birss

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAntimicrobial Peptides and Activities
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsBiosensorChemistryBacteriaCombinatorial chemistryMoleculeNanotechnologyBiochemistryMaterials scienceBiologyOrganic chemistry

Abstract

fetched live from OpenAlex

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).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.016
GPT teacher head0.225
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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