Seconds-Resolved, In Situ Measurements of Plasma Phenylalanine Disposition Kinetics in Living Rats

Abstract

Current knowledge of the disposition kinetics of endogenous metabolites is founded almost entirely on poorly time-resolved experiments in which samples are removed from the body for later, benchtop analysis. Here, in contrast, we describe real-time, seconds-resolved measurements of plasma phenylalanine collected in situ in the body via electrochemical aptamer-based (EAB) sensors, a platform technology that is independent of the reactivity of its targets and thus is generalizable to many. Specifically, using indwelling EAB sensors, we have monitored plasma phenylalanine in live rats with a few micromolar precision and a 12 s temporal resolution, identifying a large-amplitude, few-seconds phase in the animals' metabolic response that had not previously been reported. Using the hundreds of individual measurements that the approach provides from each animal, we also identify inter-subject variability, including statistically significant differences associated with the feeding status. These results highlight the power of in vivo EAB measurements, an advancement that could dramatically impact our understanding of physiology and provide a valuable new tool for the monitoring and treatment of metabolic disorders.

Figure 1.

(A) Electrochemical aptamer-based (EAB) sensors exploit the binding-induced folding of a redox-reporter-modified aptamer that is covalently attached to an interrogating electrode via a self-assembled monolayer. (B) Target binding alters the efficiency with which the redox reporter (here methylene blue) approaches the electrode, producing an electrochemical signal easily measured using square wave voltammetry. (C) We fabricate indwelling EAB sensors using a 75 μm-diameter, 3 mm-long goldwire working electrode bundled with same-diameter platinum counter and silver/silver-chloride reference electrodes. (D) The completed sensor is small enough to be emplaced in the external jugular vein of a rat via a 22-gauge guide catheter.

Figure 2.

(A) The EAB sensor response to increasing concentrations of phenylalanine produces the expected Langmuir binding curve. As is common for sensors in this class, the device’s response is signal on at higher square wave frequencies and signal-off at lower frequencies. The physiologically relevant range of plasma phenylalanine levels in healthy individuals and individuals suffering from PKU is shown in blue.^– (B) To show that the sensor achieves clinically relevant specificity we challenged it with tryptophan (trp), tyrosine (tyr), a mixture of the amino acids glutamine, histidine, proline, arginine, and alanine (AA mix), and the phenylalanine metabolites phenylpyruvate (Pyr) and phenylacetate (Ace). Using the same frequencies as in (A), the sensor does not measurably respond to any of these at concentrations below 300 μM, a level far above those seen physiologically. (C) Finally, the sensor is rapid, responding in just a few seconds to the addition of physiologically relevant target concentrations.

Figure 3.

The adaptation of EAB sensors to the measurement of an endogenous component of blood required the development of a new calibration approach suitable for use with endogenous targets (i.e., for which target-free blood is not available). (A) For this we employed a calibration buffer comprised of Ringer’s solution and bovine serum albumin (35 mg/mL) that mimics the pH, ionic strength, protein, and sugar content of whole blood. We determined the lower portion of the calibration curve by titrating whole bovine blood of known phenylalanine concentration (80 μM) into this buffer to a final concentration of 70% blood (56 μM). To determine the upper portion of the curve we then moved the sensor into undiluted whole blood (at 80 μM phenylalanine) and adding exogenous phenylalanine until we reached a final concentration of 1 mM. Merging the data sets we can obtain the entire Langmuir isotherm, which produces dissociation constants and signal gains similar those seen in phosphate buffered saline (Figures 3A; see also Figure S3). (B) Using this approach to calibrate EAB sensors prior to deployment in vivo we observe good reproducibility between individually fabricated devices. The mean and standard deviation of the concentration estimates associated with a signal increase of 30% are 45.1 μM and 7.8 μM, respectively.

Figure 4.

When placed in situ in the body EAB sensors drift.^,, To correct this, we employed kinetic differential measurements (KDM).^, (A) Normalized signals collected at 300 Hz (black) and 10 Hz (red) square-wave frequencies drift in concert, such that taking their difference via KDM eliminates the drift. (B) Using KDM we can easily see the results of serial increasing intravenous injections of phenylalanine in an anesthetized rat. The 12 s time resolution achieved in these measurements is sufficient to monitor both the injection itself and the subsequent distribution of the metabolite within the body. This reflects a 30-fold improvement over the time resolution of the best prior studies of phenylalanine metabolism kinetics in either rats or humans.^,,– (C) As expected, the in-vivo sensor does not respond to intravenous injections of either a saline “blank” or the aromatic amino acids tyrosine or tryptophan at the indicated dosages.

Figure 5.

EAB sensors provide an unprecedentedly high-precision view into metabolism and inter-subject metabolic variability. Shown, for example, are plasma phenylalanine levels after a single, 100 mg/kg intravenous injections into four live rats, two of which were fasted for 12 h (A, B) and two of which had free access to food (C, D). These high-precision measurements reveal similar peak concentrations, distribution rates, and elimination rates for all four animals. For the two fasted animals, plasma phenylalanine levels returned to within 20% of the pre-dosing baseline over the course of approximately 20 min. In contrast to the situation with fasted animals, however, the plasma phenylalanine levels in these animals remain elevated by ~50% above pre-challenge baseline levels over the course of our experiments. The black lines represent the fit of each injection dataset to a two-compartment pharmacokinetic model (Eq. 1).