Electrochemical Detection of Endotoxins from Gram-Negative Bacteria Using Immobilized TLR4 Immunoproteins
Notice bibliographique
Résumé
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).
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».