Tandem metabolic reaction-based sensors

Abstract

Mimicking metabolic pathways on electrodes enables in-vivo metabolite monitoring for decoding metabolism. The present embodiments extend the capabilities of cofactor-integrated biosensor platforms by enabling cascaded enzymatic reactions linked to oxidoreductase-based electrochemical analysis. This advancement, implemented in the present tandem metabolic pathway -like reaction (TMR) sensors, significantly broadens the range of metabolites that can be detected in vivo. This architecture ensures the cofactor-integrated biosensors' sensing performance, while additionally facilitating metabolite intermediation through enzymatic cascaded reactions. Accordingly, developed were sensors targeting a plurality metabolites incorporating cascaded reactions involving multiple cofactors, and demonstrated was an internal sensor calibration strategy via a secondary TMR to mitigate intermediary metabolites fluctuations.

Description

TANDEM METABOLIC REACTION-BASED SENSORSCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] The present application claims priority to United States Provisional Patent Appln. No. 63 / 762572 filed February 24, 2025, the contents of which are incorporated by reference herein in their entirety.STATEMENT OF GOVERNMENT SPONSORED RESEARCH

[0002] This invention was made with government support under R21DK128711, awarded by the National Institutes of Health. The government has certain rights in the invention.TECHNICAL FIELD

[0003] The present embodiments relate generally to health and more particularly to methods and apparatuses for robust in-vivo monitoring of a myriad of metabolites.BACKGROUND

[0004] Metabolites are small molecules that facilitate biochemical reactions necessary for sustaining life in every organism. They serve as substrates, products, or intermediates in metabolic pathways and participate in various biological processes, including energy management (production and storage), signaling, biomolecule synthesis / breakdown, and cellular regulation (Fig. 1A) (C. H. Johnson, J. Ivanisevic, G. Siuzdak, Metabolomics: beyond biomarkers and towards mechanisms. Nat. Rev. Mol. Cell Biol. 17, 451-459 (2016); S. A. Baker, J. Rutter, Metabolites as signalling molecules. Nat. Rev. Mol. Cell Biol. 24, 355-374 (2023)). These molecules can originate intrinsically from the host organism or extrinsically from various sources such as the microbiome, diet, and environmental xenobiotics (A. Lavelle, H. Sokol, Gut microbiota-derived metabolites as key actors in inflammatory bowel disease. Nat. Rev. Gastroenterol. Hepatol. 17, 223-237 (2020); X. Shen, R. Kellogg, D. J. Panyard, N. Bararpour, K. E. Castillo, B. Lee-McMullen, A. Delfarah, J. Ubellacker, S. Ahadi, Y. Rosenberg-Hasson, A. Ganz, K. Contrepois, B. Michael, I. Simms, C. Wang, D. Hornburg, M. P. Snyder, Multi-omics microsampling for the profiling of lifestyle-associated changes in health. Nat. Biomed. Eng. 8, 11-29 (2024); C. H. Johnson, A.12025-233-PCT S. Emaminejad et al. Atty. Dkt. 102352-1177D. Patterson, J. R. Idle, F. J. Gonzalez, Xenobiotic metabolomics: Major impact on the metabolome. Annu. Rev. Pharmacol. Toxicol. 52, 37-56 (2012)).

[0005] Quantifying metabolites within relevant biological contexts is essential for decoding the body's metabolism, particularly for understanding intricate physiological systems and developing diagnostics and therapeutics. However, current metabolomic technologies, such as mass spectrometry, while capable of quantifying numerous metabolites, are limited to ex-vivo analysis (S. Alseekh, A. Aharoni, Y. Brotman, K. Contrepois, J.D’Auria, J. Ewald, J. C. Ewald, P. D. Fraser, P. Giavalisco, R. D. Hall, M. Heinemann, H. Link, J. Luo, S. Neumann, J. Nielsen, L. Perez de Souza, K. Saito, U. Sauer, F. C. Schroeder, S. Schuster, G. Siuzdak, A. Skirycz, L. W. Sumner, M. P. Snyder, H. Tang, T. Tohge, Y. Wang, W. Wen, S. Wu, G. Xu, N. Zamboni, A. R. Fernie, Mass spectrometry-based metabolomics: a guide for annotation, quantification and best reporting practices. Nat.Methods 18, 747-756 (2021)). This restriction results in capturing only a snapshot of the metabolome at a specific collection moment, fundamentally hindering insights into metabolites' direct physiological roles, dynamic interconnectivity, and responses to stimuli within living organisms (Id.). The capital and resource intensiveness of these technologies, along with their dependance on complex sample collection and pre-processing procedures involving multiple instruments, further compromise both the sampling rate and the quality of metabolic data (Id.). All these limitations have led to persistent knowledge gaps and missed diagnostic / therapeutic opportunities in the emerging fields such as microbiome studies and personalized metabolomics (J. K. Nicholson, E. Holmes, J. Kinross, R. Burcelin, G. Gibson, W. Jia, S. Pettersson, Host-gut microbiota metabolic interactions. Science 336, 1262-1267 (2012); X. Zhou, X. Shen, J. S. Johnson, D. J. Spakowicz, M. Agnello, W. Zhou, M. Avina, A. Honkala, F. Chleilat, S. J. Chen, K. Cha, S. Leopold, C. Zhu, L. Chen, L. Lyu, D.Hornburg, S. Wu, X. Zhang, C. Jiang, L. Jiang, L. Jiang, R. Jian, A. W. Brooks, M. Wang, K. Contrepois, P. Gao, S. M. S.-F. Rose, T. D. B. Tran, H. Nguyen, A. Celli, B.-Y. Hong, E. J. Bautista, Y. Dorsett, P. B. Kavathas, Y. Zhou, E. Sodergren, G. M. Weinstock, M. P. Snyder, Longitudinal profiling of the microbiome at four body sites reveals core stability and individualized dynamics during health and disease. Cell Host Microbe 32, 506-526. e9 (2024); D. J. Panyard, B. Yu, M. P. Snyder, The metabolomics of human aging: Advances, challenges, and opportunities. Sci. Adv. 8, eadd6155 (2022); C. B. Clish, Metabolomics: an emerging but powerful tool for precision medicine. Mol. Case Stud. 1, a000588 (2015)).22025-233-PCT S. Emaminejad et al. Aty. Dkt. 102352-1177

[0006] In-vivo metabolite monitoring can transcend these limitations and drive the next frontier in metabolomic discoveries and healthcare. Nonetheless, current sensors offering potential for real-time and continuous measurements have limited metabolite detection coverage and encounter reliability challenges for in-vivo operation — whether employing enzymatic or synthetic probes such as aptamers and molecularly imprinted polymers. Enzymatic sensors predominantly exploit single-step oxidase reactions for metabolite detection, addressing only a small subset of metabolites due to restricted reaction diversity (J. Kim, I. Jeerapan, J. R. Sempionatto, A. Barfidokht, R. K. Mishra, A. S.Campbell, L. J. Hubble, J. Wang, Wearable bioelectronics: Enzyme-based body-worn electronic devices. Acc. Chem. Res. 51, 2820-2828 (2018); A. Chang, L. Jeske, S. Ulbrich, J. Hofmann, J. Koblitz, I. Schomburg, M. Neumann-Schaal, D. Jahn, D. Schomburg, BRENDA, the ELIXIR core data resource in 2021 : new developments and updates. Nucleic Acids Res. 49, D498-D508 (2021)). The few demonstrated enzymatic metabolite sensors employing nicotinamide adenine dinucleotide (NAD, a cofactor) and dehydrogenase reactions suffer from unstable cofactor incorporation, poor reaction rates, and electroactive interference (C.-C. Wang, J. W. Hennek, A. Ainla, A. A. Kumar, W.-J. Lan, J. Im, B. S. Smith, M. Zhao, G. M. Whitesides, A paper-based “pop-up” electrochemical device for analysis of beta-hydroxybutyrate. Anal. Chem. 88, 6326-6333 (2016); J. Pilas, T. Selmer, M. Keusgen, M. J. Schoning, Screen-printed carbon electrodes modified with graphene oxide for the design of a reagent-free nad+-dependent biosensor array. Anal. Chem. 91, 15293-15299 (2019); H. Teymourian, C. Moonla, F. Tehrani, E. Vargas, R. Aghavali, A. Barfidokht, T. Tangkuaram, P. P. Mercier, E. Dassau, J. Wang, Microneedle-based detection of ketone bodies along with glucose and lactate: Toward real-time continuous interstitial fluid monitoring of diabetic ketosis and ketoacidosis. Anal. Chem. 92, 2291-2300 (2020); C.Moonla, R. Del Cano, K. Sakdaphetsiri, T. Saha, E. De la Paz, A. Dusterloh, J. Wang, Disposable screen-printed electrochemical sensing strips for rapid decentralized measurements of salivary ketone bodies: Towards therapeutic and wellness applications. Biosens. Bioelectron. 220, 114891 (2023); S. Alva, K. Castorino, H. Cho, J. Ou, Feasibility of continuous ketone monitoring in subcutaneous tissue using a ketone sensor. J. Diabetes Sci. Technol. 15, 768-774 (2021)).

[0007] Sensors based on synthetic probes are challenged by poor probe affinity and specificity towards metabolites, particularly when dealing with small molecules with similar physical properties (H. Yu, O. Alkhamis, J. Canoura, Y. Liu, Y. Xiao, Advances and32025-233-PCT S. Emaminejad et al. Aty. Dkt. 102352-1177Challenges in Small-Molecule DNA Aptamer Isolation, Characterization, and Sensor Development. Angew. Chem. Int. Ed. 60, 16800-16823 (2021); N. Nakatsuka, K.-A. Yang, J. M. Abendroth, K. M. Cheung, X. Xu, H. Yang, C. Zhao, B. Zhu, Y. S. Rim, Y. Yang, P. S. Weiss, M. N. Stojanovic, A. M. Andrews, Aptamer-field-effect transistors overcome Debye length limitations for small-molecule sensing. Science 362, 319-324 (2018); M. Wang, Y. Yang, J. Min, Y. Song, J. Tu, D. Mukasa, C. Ye, C. Xu, N. Heflin, J. S. McCune, T. K. Hsiai, Z. Li, W. Gao, A wearable electrochemical biosensor for the monitoring of metabolites and nutrients. Nat. Biomed. Eng. 6, 1225-1235 (2022)). They also face stability issues due to factors like probe degradation (e.g., detachment or digestion) and susceptibility to changes in surrounding ionic strength (V. Clark, M. A. Pellitero, N. Arroyo-Curras, Explaining the decay of nucleic acid-based sensors under continuous voltammetric interrogation. Anal.Chem. 95, 4974-4983 (2023); Y. Liu, J. Canoura, O. Alkhamis, Y. Xiao, Immobilization Strategies for Enhancing Sensitivity of Electrochemical Aptamer-Based Sensors. ACS Appl. Mater. Interfaces 13, 9491-9499 (2021)). Additionally, extensive and uncertain discovery and engineering campaigns are required to establish synthetic probes with basic recognition properties for each metabolite (M. Kohlberger, G. Gadermaier, SELEX: Critical factors and optimization strategies for successful aptamer selection. Biotechnol. Appl. Biochem. 69, 1771-1792 (2022); K.-A. Yang, R. Pei, M. N. Stojanovic, In vitro selection and amplification protocols for isolation of aptameric sensors for small molecules. Methods 106, 58-65 (2016)).

[0008] It is against this technological backdrop that the present Applicant sought a technological solution to these and other problems rooted in this technology.SUMMARY

[0009] The present embodiments relate generally to health and more particularly to methods and apparatuses for robust in-vivo monitoring of a plurality of metabolites.According to certain aspects, the present embodiments extend the capabilities of a cofactor-integrated biosensor platform by enabling cascaded enzymatic reactions linked to oxidoreductase-based electrochemical analysis. This advancement, implemented in tandem metabolic pathway-like reaction (TMR) sensors, significantly broadens the range of metabolites that can be detected in vivo. This architecture ensures the cofactor-integrated biosensors’ sensing performance, while additionally facilitating metabolite intermediation through enzymatic cascaded reactions. Embodiments include sensors targeting multiple 42025-233-PCT S. Emaminejad et al. Aty. Dkt. 102352-1177example metabolites incorporating cascaded reactions involving multiple cofactors. These and other embodiments include an internal sensor calibration strategy via a secondary TMR to mitigate intermediary metabolites fluctuations.BRIEF DESCRIPTION OF THE DRAWINGS

[0010] These and other aspects and features of the present embodiments will become apparent to those ordinarily skilled in the art upon review of the following description of specific embodiments in conjunction with the accompanying figures, wherein:

[0011] FIGs. 1 A to IE illustrate example aspects of a tandem metabolic pathway-like reactions architecture for biosensing according to embodiments.

[0012] FIGs. 2A(i), 2 A(ii), 2 A(iii) and 2C to 21 illustrate various aspects of electrochemical characterization of TMR for cofactor-assisted enzymatic reactions according to embodiments.

[0013] FIGs. 3 A to 3L illustrate various aspects of characterization of TMR metabolite sensors according to embodiments.

[0014] FIGs. 4A to 4H illustrate various aspects of example metabolic acidosis studies with TMRs according to embodiments.DETAILED DESCRIPTION

[0015] The present embodiments will now be described in detail with reference to the drawings, which are provided as illustrative examples of the embodiments so as to enable those skilled in the art to practice the embodiments and alternatives apparent to those skilled in the art. Notably, the figures and examples below are not meant to limit the scope of the present embodiments to a single embodiment, but other embodiments are possible by way of interchange of some or all of the described or illustrated elements. Moreover, where certain elements of the present embodiments can be partially or fully implemented using known components, only those portions of such known components that are necessary for an understanding of the present embodiments will be described, and detailed descriptions of other portions of such known components will be omitted so as not to obscure the present embodiments. Embodiments described as being implemented in software should not be limited thereto, but can include embodiments implemented in hardware, or combinations of software and hardware, and vice-versa, as will be apparent to those skilled in the art, unless otherwise specified herein. In the present specification, an embodiment showing a singular 52025-233-PCT S. Emaminejad et al. Aty. Dkt. 102352-1177component should not be considered limiting; rather, the present disclosure is intended to encompass other embodiments including a plurality of the same component, and vice-versa, unless explicitly stated otherwise herein. Moreover, applicants do not intend for any term in the specification or claims to be ascribed an uncommon or special meaning unless explicitly set forth as such. Further, the present embodiments encompass present and future known equivalents to the known components referred to herein by way of illustration.

[0016] Introduction

[0017] The present embodiments mimic naturally refined metabolic pathways on electrodes for robust in-vivo monitoring of a myriad of metabolites. For example, as shown in FIG. 1A, natural metabolic functions 100-A are mimicked by a metabolic pathway 100-B according to embodiments.

[0018] To realize this strategy, an electrode framework (e.g. comprising single-wall carbon nanotubes (SWCNT)) integrates multifunctional enzymes and cofactors to deliver tandem metabolic pathway-like reactions (TMR). More particularly, as shown in this example framework in FIG. IB, a sensor architecture 102 includes a first cofactor (SWCNT) layer 104, a detection enzymatic layer 106, a second cofactor (SWCNT) layer 108, an intermediation enzymatic layer 110, and inactivation enzymatic layer 112 and an anti-fouling encapsulation layer 114.

[0019] This example design 102 provides versatility for tailoring a cascade of reactions toward end-point oxidoreductase catalysis — effectively transforming the target metabolite 132 (the initial substrate in the cascade, for example via intermediation enzymatic layer 110) into an electrochemically detectable product 134. In parallel, in a manner akin to detoxification in metabolism, the design can also employ an enzymatic inactivation strategy (e.g. via inactivation enzymatic layer 112) to neutralize dominant interferences 136 approaching the sensing interface, thereby enhancing the signal-to-noise ratio (SNR).

[0020] The TMR electrode retains the additional advantages of cofactor-integrated sensors (e.g. first and second SWCNT layers 104, 108) for high-performance in-vivo sensing 116. It reaches the theoretical limit of overall reaction rates set by the enzyme's redox rate through the direct adsorption of cofactor molecules and by utilizing SWCNT's high aspect ratio and superior electrocatalytic capabilities. The electrode's high specific area increases the loading of enzymes and cofactors, further enhancing the SNR. Moreover, the electrode possesses self-mediating capabilities for driving cofactor oxidation at 0 V (e.g. vs. Ag / AgCl,62025-233-PCT S. Emaminejad et al. Aty. Dkt. 102352-1177silver / silver chloride), dramatically minimizing electrode fouling and electroactive interference. This self-mediation, in turn, obviates the need for mediators and their associated challenges (e.g., irreversible responses caused by mediator leakage) and simplifies wireless circuit requirements, aligning with envisioned in-vivo operations.

[0021] Given that the vast majority of metabolites are linkable to oxidoreductase reactions via known metabolic pathways, the present embodiments enable real-time and continuous sensing of metabolites with extremely broad coverage (Schomburg, G. Michal, Biochemical pathways : an atlas of biochemistry and molecular biology, John Wiley & Sons, Hoboken, N.J., ed. 2nd, 2012), pp. xi, 398 p). Just accounting for those linkable to oxidoreductase-based detection, either directly, or via one intermediation step, the present embodiments cover more than two thirds of metabolites (see FIG. 1 C).

[0022] As shown in FIG. ID, the TMR platform can easily extend to an array format for multiplexed metabolite detection by integrating the corresponding enzymes or cofactors (dictated by the target pathway) onto different TMR electrodes 122 (each comprising an architecture 102 as shown in FIG. IB) and employing a single shared reference electrode 124 in a multiplexed sensor 120. The TMR platform can serve as a viable in-vivo metabolomic tool to address the missing context, real-time interaction, and high-resolution temporal dimension in metabolomic-driven research and medical applications, such as gut-brain axis studies and the diagnosis / treatment of metabolic disorders (see FIG. IE).

[0023] Results: TMR integrates cofactors to facilitate diverse enzymatic reactions

[0024] The electrode's support for cofactor-assisted enzymatic reactions and electrochemical analysis of modified cofactor end-products (e.g. via layers 104 / 108) enables versatile metabolite intermediation and detection. To put it in perspective, considering cofactor nicotinamide adenine dinucleotide (NAD) alone: it facilitates roughly ten times more reactions than cofactor-less oxidase reactions, leading to a proportional increase in directly detectable metabolites (-800, dataset present in previous cofactor-integrated biosensors). However, the involvement of cofactor molecules, which expands reaction versatility, also increases reaction complexity, posing two key challenges. Firstly, the essential cofactor molecules required to drive reactions are scarce in vivo, necessitating integration into the sensor for in-vivo operations (A. Nikiforov, V. Kulikova, M. Ziegler, The human NAD metabolome: Functions, metabolism and compartmentalization. Crit. Rev. Biochem. Mol. Biol. 50, 284-297 (2015)). This contrasts with oxidase-based reactions, which benefit from 72025-233-PCT S. Emaminejad et al. Aty. Dkt. 102352-1177naturally abundant oxygen (X. Cheng, B. Wang, Y. Zhao, H. Hojaiji, S. Lin, R. Shih, H. Lin, S. Tamayosa, B. Ham, P. Stout, K. Salahi, Z. Wang, C. Zhao, J. Tan, S. Emaminejad, A mediator-free electroenzymatic sensing methodology to mitigate ionic and electroactive interferents’ effects for reliable wearable metabolite and nutrient monitoring. Adv. Funct. Mater. 30, 1908507 (2020)). Secondly, the larger end-product size in cofactor-based sensing hinders the application of size-exclusion methods commonly used in oxidase sensing for interference mitigation. Coupled with high overpotential requirements, this increases the sensor's susceptibility to interfering molecules in electrochemical analysis (W. J. Blaedel, R. A. Jenkins, Electrochemical oxidation of reduced nicotinamide adenine dinucleotide. Anal. Chem. 47, 1337-1343 (1975); J. Moiroux, P. J. Elving, Mechanistic aspects of the electrochemical oxidation of dihydronicotinamide adenine dinucleotide (NADH). J. Am. Chem. Soc. 102, 6533-6538 (1980); C. O. Schmakel, K. S. V. Santhanam, P. J. Elving, Nicotinamide adenine dinucleotide (NAD+) and related compounds. Electrochemical redox pattern and allied chemical behavior. J. Am. Chem. Soc. 97, 5083-5092 (1975)). The TMR electrode's unique features overcome these challenges from multiple aspects, as described below.

[0025] Cofactors such as NAD+ (NAD in oxidized form) and ATP (adenosine triphosphate) can be directly adsorbed onto the electrode’s SWCNT framework through 71-71 stacking interactions. Utilizing scanning transmission electron microscopy (S / TEM) in conjunction with energy dispersive X-ray spectroscopy (EDS), this integration can be visualized. FIG. 2A(i) provides a high-angle annular dark-field (HAADF) imaging view, and the EDS images in FIGs. 2 A(ii) and 2 A(iii) indicate a consistent and concentrated cofactor distribution across the SWCNT framework for NAD+ and NADH, respectively, where P represents phosphorus, the signature element for NAD+, and C represents carbon. The successful immobilization of NAD+ can be further verified via cyclic voltammetry (CV), which captures the distinctive redox signatures of this electroactive cofactor, as shown in FIG. 2B (R. D. Nagarajan, P. Murugan, A. K. Sundramoorthy, Selective Electrochemical Sensing of NADH and NAD+ Using Graphene / Tungstate Nanocomposite Modified Electrode. Chemistry Select 5, 14643-14651 (2020)).

[0026] The TMR electrode can electrochemically analyze the electroactive endproduct of cofactor-assisted enzymatic reactions with exceptionally high efficiency. Studied was this performance in the context of NAD redox reactions, chosen as a model due to their broad applicability to dehydrogenase-based systems within the oxidoreductase library. FIG.82025-233-PCT S. Emaminejad et al. Aty. Dkt. 102352-11772C provides a schematic view of NADH oxidation and decomposition reactions. In this context, the electrochemical oxidation of NADH theoretically involves a 2-electron transfer process, represented by the electron transfer number, ne, of 2. (I. Katakis, E. Dominguez, Catalytic electrooxidation of NADH for dehydrogenase amperometric biosensors.Microchim. Acta 126, 11-32 (1997); P. J. Elving, C. O. Schmakel, K. S. V. Santhanam, P. Zuman, Nicotinamide-NAD sequence: Redox processes and related behavior: Behavior and properties of intermediate and final products. C R C Crit. Rev. Anal. Chem. 6, 1-67 (1976)). Nonetheless, empirically, due to NADH’s inherent instability resulting in irreversible decomposition and electrode fouling, the effective electron transfer number is reduced (R. D. Braun, K. S. V. Santhanam, P. J. Elving, Electrochemical oxidation in aqueous and nonaqueous media of dihydropyridine nucleotides NMNH, NADH, and NADPH. J. Am. Chem. Soc. 97, 2591-2598 (1975)).

[0027] For conventional electrodes such as glassy carbon (GC) and carbon paste incorporating l,10-phenanthroline-5, 6, -di one-based mediators (PD / CP), ne is close to 1 (S. Immanuel, R. Sivasubramanian, Electrochemical studies of NADH oxidation on chemically reduced graphene oxide nanosheets modified glassy carbon electrode. Mater. Chem. Phys.249, 123015 (2020); G. Hilt, T. Jarbawi, W. R. Heineman, E. Steckhan, An analytical study of the redox behavior of l,10-phenanthroline-5, 6-dione, its transition-metal complexes, and its N-monom ethylated derivative with regard to their efficiency as mediators of NAD(P)+ regeneration. Chem. - Eur. J. 3, 79-88 (1997)). A TMR electrode of embodiments, based on acid-treated SWCNT, demonstrates significantly higher NADH oxidation efficiency with an ne as high as 1.90 ± 0.07 (obtained via rotating disk electrode, RDE, analysis), while also exhibiting high NADH sensitivity at 0 V (vs. Ag / AgCl) oxidation potential, for example selfmediating reactions as illustrated in FIG. 2D). More particularly, FIG. 2D provides a NADH calibration curve obtained from TMR (N = 3) with the inset showing the 150 pM NADH oxidation signal stability comparison with other commonly used electrodes operated at their intended applied voltages (GC at 0.6 V vs. Ag / AgCl, PD / CP at 0 V vs. Ag / AgCl). This enhanced performance can be attributed to the large specific area of the SWCNT substrate, the presence of quinone-based groups on the acid-treated SWCNT, and the catalytic property of the SWCNT's edge plane (L. Feng, H.-P. Li, K. Galatsis, H. G. Monbouquette, Effective NADH sensing by electrooxidation on carbon-nanotube-coated platinum electrodes. J.Electroanal. Chem. 773, 7-12 (2016); C. E. Banks, R. G. Compton, Exploring the electrocatalytic sites of carbon nanotubes for NADH detection: an edge plane pyrolytic 92025-233-PCT S. Emaminejad et al. Aty. Dkt. 102352-1177graphite electrode study. Analyst 130, 1232-1239 (2005)). In this regard, FIG. 2E provides a specific capacitance comparison among GC, PD / CP, and TMR (N = 3).

[0028] The high oxidation efficiency of the TMR electrode also contributes to its superior anti-fouling performance. The results of an anti -fouling study (1-hour oxidation at 150 pM NADH) illustrate that the degradation in the TMR electrode’s NADH response is less than one percent, which is over 10-fold smaller than that observed with alternative electrodes all operated at their intended voltages for NADH oxidation (see FIG. 2D, inset). To further assess its long-term stability, the TMR sensor was subjected to continuous NADH oxidation for 10 hours — 10 times the duration of the initial test — where it still exhibited minimal signal degradation (e.g. around 1.2%).

[0029] The TMR's self-mediation not only enhances the signal generated by electroactive end-product redox reactions but also minimizes noise from electroactive interference, thus addressing a fundamental challenge in enzymatic sensing. To illustrate this benefit, characterized was the TMR’s sensitivity to NADH and a panel of electroactive molecules commonly found in biofluids (S. Lin, B. Wang, W. Yu, K. Castillo, C. Hoffman, X. Cheng, Y. Zhao, Y. Gao, Z. Wang, H. Lin, H. Hojaiji, J. Tan, S. Emaminejad, Design framework and sensing system for noninvasive wearable electroactive drug monitoring. ACS Sens. 5, 265-273 (2020). For comparison, the same procedure was conducted using GC and PD / CP electrodes and defined was the ratio of the electrodes’ reaction sensitivity to NADH vs. interfering analytes as a measure of SNR. FIG. 2F is a chart of sensor selectivity definitions compared between traditional relative response of analyte to interference (INF) and the present SNR based on sensitivity ratio of analyte to interference. This definition of SNR offers comprehensive coverage across a spectrum of target and interference concentrations, ensuring applicability to diverse sensing scenarios. It is distinct from traditional single-point interference characterization methods, which often use large concentration differences between the target and interfering molecules (Id.). FIG. 2G demonstrates that for all interference cases, the TMR exhibited significantly higher SNR compared to the alternatives. More particularly, FIG. 2G provides a normalized SNR comparison among GC, PD / CP, and TMR against a panel of electroactive interferences in biofluids (N = 3 for each electrode, and error bars indicate standard deviations). Statistical significance and P values were determined by two-tailed unpaired student’s t test. In FIG.2G, **** represents P < 0.0001, *** represents P < 0.001, ** represents P < 0.01, * represents P < 0.05, and “ns” denotes statistical non-significance.102025-233-PCT S. Emaminejad et al. Aty. Dkt. 102352-1177

[0030] The TMR architecture of embodiments also enhances both the mass transport of the enzymatic reaction product and the associated electrochemical reaction kinetics, ultimately achieving optimal overall reaction rates. In TMR, cofactor molecules (NAD+) are directly immobilized onto the porous electrode substrate (e.g. layers 104, 108) with a large specific area (AO.TMR ~ 108 / m). This design reduces the diffusion distance of the enzyme product (NADH) to the substrate electrode, down to a few nanometers. In contrast, conventional cofactor-based enzymatic sensors superficially immobilize cofactors atop the electrode substrate, such as GC and PD / CP, within the enzyme layer of approximately a few micrometers thickness (X. Chen, N. Matsumoto, Y. Hu, G. S. Wilson, Electrochemically mediated electrodeposition / electropolymerization to yield a glucose microbiosensor with improved characteristics. Anal. Chem. 74, 368-372 (2002)). Consequently, their diffusion length scales are on the order of micrometer-scale. Furthermore, the TMR of embodiments drives NADH oxidation at a substantially higher intrinsic rate than conventional electrodes, as evidenced by its large exchange current density (io.TMR ~ 17.4 ± 0.3 A / m2, as compared to io.Gc ~ 0.06 A / m2and io, PD / CP ~ 0.35 A / m2) (Q. Wu, M. Maskus, F. Pariente, F. Tobalina, V. M. Fernandez, E. Lorenzo, H. D. Abruna, Electrocatalytic oxidation of NADH at glassy carbon electrodes modified with transition metal complexes containing 1,10-phenanthroline-5, 6-dione ligands. Anal. Chem. 68, 3688-3696 (1996)). This together with TMR’s relatively large specific area as shown in FIG. 2E), allows TMR to drive overall catalytic reactions (within a fixed footprint) at a dramatically higher capacity than alternative electrodes.

[0031] To study the TMR’s enhanced enzymatic / electrochemical reaction kinetics, utilized was a finite element analysis-based simulation model. Within this model, key parameters such as cofactor arrangement, enzyme activity, and electrode properties / design (e.g., io, Ao), can be explored. First validated was the fidelity of the model by verifying its alignment with empirical measurements. Then, to study the effect of mass transport limitation, simulated was the transduced NADH oxidation current resulting from different NADH diffusion distances across three types of electrodes: SWCNT (TMR’s framework), GC, and PD / CP (operated at their intended oxidation voltages) (Id.). FIG. 2H provides example simulation results for 200 pM NADH oxidation currents with different diffusion distances for GC, PD / CP, and TMR. As shown in FIG. 2H, the results indicate that TMR transduces over 100-fold larger current. Next, studied were the reaction kinetics of the electrodes in terms of their specific area and exchange current density. To conduct this study in isolation from mass transport limitations, the model was reconfigured to simulate NADH 112025-233-PCT S. Emaminejad et al. Aty. Dkt. 102352-1177oxidation (generated by a model dehydrogenase reaction) directly taking place at the electrodes’ surfaces. The corresponding simulation results for a range of hypothetical exchange current densities and specific areas (e.g. simulation results for 200 pM BHB signal currents with different exchange current densities under different electrode specific areas) are shown in FIG. 21. They specifically indicate that not only does the TMR substantially outperform alternative GC and PD / CP electrodes, but it also facilitates catalytic reaction at a rate only limited by the enzyme activity (as evident from plateaued current response).Reaching this limit can be achieved across a wide range of enzyme activities, as shown in FIG. 2J, which illustrates example simulation results for 200 pM BHB signal currents with different exchange current densities under different enzyme activities.

[0032] TMR integrates multifunctional enzymatic reactions for versatile metabolite monitoring

[0033] Besides its distinguishing support for cofactor-assisted enzymatic / electrochemical reactions, the TMR platform of embodiments can facilitate multiple enzymatic reactions in tandem, further setting it apart from conventional in-vivo enzymatic sensors designed for single reactions. These special properties are harnessed for direct / intermediated metabolite detection and supplemental interference inactivation, further enhancing SNR.

[0034] To demonstrate direct metabolite detection, employed were dehydrogenase enzymes along with the cofactor NAD+ to catalyze metabolic reactions, resulting in NADH as the electroactive end-product. FIG. 3 A is a schematic of TMR-based direct metabolite detection according to embodiments. In this setting, metabolite substrates, serving as targets, are quantified through the electrochemical analysis of NADH (self-mediated by SWCNT, such as in layers 104,108 in FIG. IB). Introduced were ten different dehydrogenases into the TMR design, each targeting a distinct metabolite: P-hydroxybutyrate (BHB), D-glucose, L-glutamate, glucose 6-phosphate (G6P), ethanol, D-lactate, L-lactate, L-leucine, cholesterol, and glycerol. Calibration plots for each enzymatic TMR were obtained through amperometric measurements conducted within the physiological concentration ranges of the analytes. FIG.3B shows that all TMR sensors exhibited consistent, monotonic responses to target metabolite concentrations with reproducible sensitivities and minimal inter-device variations.

[0035] To target metabolites lacking specific oxidoreductases, utilized were existing metabolic pathways and harnessed was the TMR’s versatility to build an oxidoreductase 122025-233-PCT S. Emaminejad et al. Aty. Dkt. 102352-1177linkage. FIG. 3C is a schematic diagram illustrating aspects of TMR-based intermediated metabolite detection according to embodiments. As shown, the TMR can be configured to facilitate cascaded enzymatic reactions, intermediating the target metabolite into a form catalyzable by a corresponding oxidoreductase enzyme for electrochemical detection.Demonstrated were both cofactor-less and cofactor-assisted enzymatic intermediation by integrating appropriate enzymes and, when necessary, cofactors within the TMR architecture (e.g. 102 in FIG. IB). The dehydrogenase-based electrochemical sensing interfaces developed for direct detection were leveraged as the TMR's base to facilitate the final stage of the cascade (e.g. 104 / 106 in FIG. IB). This integrated design implements the entire intermediation and detection stages within a single sensor construct.

[0036] With cofactor-less intermediation, demonstrated was the sensing of lactose and glucose 1 -phosphate (G1P), which lack corresponding oxidoreductases. By integrating P-galactosidase (P-GAL) and phosphoglucomutase (PGM) enzymes within the original D-glucose-TMR and G6P-TMR sensing interfaces, lactose and G1P were transformed into D-glucose and G6P for subsequent electrochemical detection, respectively. FIGs. 3D to 3F are graphs illustrating TMRs’ intermediated detection mechanism and calibration responses targeting lactose (FIG. 3D), G1P (FIG. 3E), and D-glucose (FIG. 3F). N = 3 for each tested TMR, and error bars indicate standard deviations. To illustrate the extensibility of this approach to cofactor-assisted intermediation, inspiration can be drawn from the glycolysis pathway and demonstrated intermediated glucose detection as proof of concept (T. TeSlaa, M. A. Teitell, Techniques to Monitor Glycolysis. Method Enzymol 542, 91-114 (2014)). Using hexokinase and ATP cofactors integrated within the TMR framework (e.g. 104 / 108 in FIG. IB), D-glucose is converted into G6P, whose concentration is measurable via the NAD+ / G6P-dehydrogenase base (FIG. 3F). As shown in FIGs. 3D to 3F, all TMR sensors implementing cascaded reactions consistently displayed monotonic responses to varying concentrations of target metabolites, exhibiting reproducible sensitivities and minimal variations. In the case of severe fluctuations in intermediary metabolite levels in vivo affecting the sensor response, the confounding effect can be mitigated by calibrating the primary TMR sensor response against a secondary TMR detecting the intermediary metabolites. Since the primary TMR incorporates the design of the secondary TMR as its subunit, accurate concentration estimation is ensured.

[0037] In some embodiments, specifically employed was enzymatic inactivation (e.g.110 in FIG. IB) to neutralize interference from ascorbic acid (AA) by integrating the132025-233-PCT S. Emaminejad et al. Aty. Dkt. 102352-1177corresponding enzyme layer (AA oxidase, AAOx). FIG. 3G is a schematic diagram illustrating example aspects of TMR-based interference inactivation according to embodiments. In most enzymatic sensing scenarios, including cofactor-based ones, AA serves as the most dominant noise source, distorting sensor responses through unwanted reactions with the electrodes' substrates (M. Wu, X. Mao, X. Li, X. Yang, L. Zhu, 1,10-phenanthroline-5, 6-dione adsorbed on carbon nanotubes: The electrochemistry and catalytic oxidation of ascorbic acid. J Electroanal Chem 682, 1-6 (2012)). The TMR’s exceptionally large specific area makes it suitable for immobilizing AAOx with high loading to effectively counter this challenge. The aforementioned procedure was followed for characterizing the SNR to study and to benchmark the performance of the AAOx-coupled TMR in minimizing interference from AA. FIG. 3H is a graph providing a normalized SNR comparison among GC, PD / CP, bare TMR, and AAOx-coupled-TMR against AA. N = 3 for each tested sensor, and error bars indicate standard deviations. FIG. 3H shows that this enzymatic inactivation strategy was extremely effective, as evidenced by the AAOx-coupled TMR’s more than 100-fold larger SNR compared to other traditionally used electrodes.

[0038] To further ensure the TMR’s selectivity, recorded were the representative BHB, D-glucose, and L-glutamate-TMRs’ responses to a panel of progressively introduced molecules, including small molecules, ionic species, and electroactive species at their physiologically -relevant concentrations, with AA (dominant interference) tested at a high concentration (100 pM, compared to 50 pM, high end of salivary AA concentration) (M. Bariya, H. Y. Y. Nyein, A. Javey, Wearable sweat sensors. Nat. Electron. 1, 160-171 (2018)). FIG. 31 provides a real-time amperometric selectivity study with a representative BHB-TMR. As shown in FIG. 31, the TMRs of embodiments exhibited negligible response against the interference group or the enzymatic inactivation product (here, hydrogen peroxide generated by the AAOx-catalyzed reaction) due to their self-mediating capability for driving cofactor oxidation at 0 V. The latter findings particularly illustrate there is no reaction crosstalk between the two enzymatic layers (i.e. interference inactivation and detection enzymes).

[0039] To demonstrate multiplexed metabolite monitoring, fabricated was an array of 7 TMRs onto a soft substrate (styrene-ethylene-butylene-styrene block copolymer, SEBS), sharing a single reference electrode (Fig. ID). Each TMR targeted a specific metabolite: BHB, D-glucose, L-glutamate, G6P, ethanol, D-lactate, and L-leucine. Tested was this array’s response in serum by concurrently recording the amperometric measurements of all 7142025-233-PCT S. Emaminejad et al. Aty. Dkt. 102352-1177channels and intermittently introducing individual analyte targets. FIG. 3 J provides multiplexed real-time amperometric measurements in undiluted porcine serum with a panel of TMRs targeting BHB, D-glucose, L-glutamate, G6P, ethanol, D-lactate, and L-leucine. As shown in FIG. 3 J, the sensors stably responded to their corresponding analytes with no detectable crosstalk.

[0040] In some embodiments, the incorporation of an encapsulation layer (here, polyvinyl chloride, PVC), within the TMR design (e.g. 114 in FIG. IB), combined with the robust immobilization of cofactor molecules on the TMR’s framework (e.g. layers 104 / 108), ensures the stability and reversibility of the enzymatic TMR’s response. The reversibility of TMR was assessed by repeatedly immersing representative BHB, D-glucose, and L-glutamate-TMRs in solutions with increasing or decreasing target concentrations and continuously recording their responses at each concentration level. FIG. 3K provides realtime BHB amperometric responses with a representative BHB-TMR. In all cases, the TMRs consistently adjusted to the expected response levels, with changes of less than 7.5% for each introduced concentration. The TMRs’ anti-fouling capability was assessed through continuous measurements in a protein-rich environment (phosphate-buffered saline, PBS, buffer with 20 mg / mL bovine serum albumin) (S. Lin, X. Cheng, J. Zhu, B. Wang, D.Jelinek, Y. Zhao, T.-Y. Wu, A. Horrillo, J. Tan, J. Yeung, W. Yan, S. Forman, H. A. Coller, C. Milla, S. Emaminejad, Wearable microneedle-based electrochemical aptamer biosensing for precision dosing of drugs with narrow therapeutic windows. Sci. Adv. 8, eabq4539 (2022)). During 1000-minute studies involving varying target concentrations, the enzymatic TMR responses’ declines were within 2% at each level, demonstrating the TMR’s ability to facilitate small molecule (i.e., metabolite) diffusion to the sensing substrate while effectively blocking larger protein molecules (fouling agents). Also conducted was a prolonged characterization study, continuously recording the TMR’s response in a PBS buffer. FIG. 3L provides results of a 3 -day long real-time amperometric measurement performed with BHB-TMRs in PBS supplemented with a 2 mM BHB increase from baseline (0 mM). Normalized response = (I - IBaseline) / (IMax - IBaseline), error band indicates standard deviations (N = 3). As shown in FIG. 3L, the TMR of embodiments exhibited minimal response deviation, remaining within a few percentages, even after 3 days of continuous operation, indicating negligible leakage of sensing molecules.152025-233-PCT S. Emaminejad et al. Aty. Dkt. 102352-1177

[0041] TMR tracks metabolite dynamics in vivo for metabolic disorders and gut-brain axis

[0042] Collectively, the ex-vivo characterization results support the high level of adaptability, sensitivity, selectivity, stability, and reversibility of the TMR architecture for in-vivo biomonitoring. After validating the TMR’s biocompatibility through cellular viability studies, TMR sensors were adapted and deployed for two metabolic acidosis scenarios: ketoacidosis and D-lactate acidosis. For both scenarios, initially established was the significance of the target metabolites in relevant biomatrices and evaluated was the accuracy of the TMR sensors for their analysis. Then, applied were the TMR sensors for real-time and continuous in-vivo monitoring, demonstrating their potential for tracking the metabolite dynamics underlying metabolic states and the gut-brain axis.

[0043] Ketoacidosis, a serious metabolic disorder often associated with diabetes, arises when ketone bodies like BHB accumulate in the bloodstream, causing an acid-base imbalance (K. K. Dhatariya, N. S. Glaser, E. Codner, G. E. Umpierrez, Diabetic ketoacidosis. Nat. Rev. Dis. Primer 6, 1-20 (2020)). FIG. 4A is a schematic illustration of the ketosis mechanism and diffusion of metabolites from blood to non-invasively retrievable biofluids (e.g., sweat and saliva) in the human body. Here, first investigated was the utility of BHB sensing in sweat and saliva for non-invasive wearable and mobile health monitoring. This approach is beneficial for individuals with conditions such as diabetes or those on ketogenic diets for epilepsy (I. D’Andrea Meira, T. T. Romao, H. J. Pires do Prado, L. T. Kruger, M. E. P. Pires, P. O. da Conceigao, Ketogenic diet and epilepsy: What we know so far. Front.Neurosci. 13 (2019)). However, from a sensing perspective, it is challenging due to over tenfold secretion-induced dilution of BHB and high background noise from fluctuating interfering molecules such as AA (often influenced by diet), which current enzymatic electrochemical sensors fail to address (J. Heikenfeld, A. Jajack, B. Feldman, S. W. Granger, S. Gaitonde, G. Begtrup, B. A. Katchman, Accessing analytes in biofluids for peripheral biochemical monitoring. Nat. Biotechnol. 37, 407-419 (2019); M. Levine, C. Conry-Cantilena, Y. Wang, R. W. Welch, P. W. Washko, K. R. Dhariwal, J. B. Park, A. Lazarev, J. F. Graumlich, J. King, L. R. Cantilena, Vitamin C pharmacokinetics in healthy volunteers: evidence for a recommended dietary allowance. Proc. Natl. Acad. Sci. 93, 3704-3709 (1996)).

[0044] A TMR sensor of embodiments, with its intrinsically high SNR measurements and low limit of detection, can effectively overcome these challenges. FIG. 4B provides 162025-233-PCT S. Emaminejad et al. Aty. Dkt. 102352-1177example saliva-blood and sweat-blood BHB concentration correlations for healthy subjects with a ketone supplement and epileptic patients under ketogenic diets. Sampled were saliva and sweat from two cohorts: epileptic patients on a ketogenic diet and healthy subjects who consumed a ketone supplement. The results showed strong correlations between sweat and saliva BHB levels vs. blood (r = 0.84 for saliva-blood, and r = 0.92 for sweat-blood), also validating the BHB-TMR's accuracy in analyzing sweat and saliva (mean bias -2 pM with 95% confidence intervals within ± 45 pM). FIG. 4C is a Bland-Altman plot comparing the standard assay-quantified BHB concentrations with the TMR-measured BHB concentrations in human saliva and sweat samples. Confirmed was the TMR's compatibility with low-power consumer electronics for wireless operation and it was used to track changes in metabolic states. Following consumption of a carbohydrate-rich beverage, salivary BHB levels rapidly dropped from -250 pM to -100 pM within an hour, suggesting a shift from ketosis to glycolysis (corroborated by capillary blood glucose analysis) (A. Hernandez, L. Truckenbrod, Q. Federico, K. Campos, B. Moon, N. Ferekides, M. Hoppe, D. D’Agostino, S. Burke, Metabolic switching is impaired by aging and facilitated by ketosis independent of glycogen. Aging 12, 7963-7984 (2020)).

[0045] To validate TMR's in-vivo monitoring capability relevant to diabetic ketoacidosis, tracked were blood BHB and glucose levels in mice. BHB- and glucose-TMRs were integrated into an array, alongside a TMR lacking detection enzymes serving as a negative control. FIG. 4D illustrates aspects of in-vivo real-time blood BHB and glucose monitoring in a mouse with multiplexed TMRs functionalized for BHB, glucose and negative control according to embodiments. The shaded green and blue bands indicate the BHB and glucose tail-vein injection time windows respectivel (Inset shows the experimental setup. Normalized current = (I - IBaseline) / IBaseline). As shown in FIG. 4D, following intravenous administration of each metabolite, both BHB and glucose TMRs (affixed on the mouse back for subdermal blood analysis) promptly captured the dynamic changes of the corresponding analytes, while the negative control maintained its baseline response. The results highlight the TMR’s ability for in-vivo metabolic data acquisition with minute-level resolution, surpassing the sampling rates of traditional methods by two to three orders of magnitude, especially advantageous in small animals with limited sampling volume thresholds (S. Parasuraman, R. Raveendran, R. Kesavan, Blood sample collection in small laboratory animals. J. Pharmacol. Pharmacother. 1, 87-93 (2010)).172025-233-PCT S. Emaminejad et al. Aty. Dkt. 102352-1177

[0046] For D-lactate acidosis, a focus was placed on detecting the bacterial metabolite D-lactate in blood and brain for its applications in disease diagnosis and treatment (e.g., short bowel syndrome and encephalopathy) and for advancing understanding of microbiome-gut-brain axis dynamics. FIG. 4E is a schematic illustration of example aspects of D-lactate acidosis. As can be seen, D-lactate is a byproduct of gut bacterial carbohydrate fermentation. Several interacting factors, including dysbiosis of the gut microbiota, malabsorption of intestinal nutrients, and decreased intestinal barrier integrity can enhance flux of D-lactate from the intestinal lumen into systemic circulation (B. Remund, B. Yilmaz, C. Sokollik, D-Lactate: Implications for Gastrointestinal Diseases. Children 10, 945 (2023)). D-lactate typically crosses the blood-brain barrier via monocarboxylate transporters, but can exhibit increased translocation in pathological conditions. Within the brain, D-lactate accumulates at least in part due its slower metabolism compared to endogenous L-lactate, leading to neurotoxicity and various neurological complications, including confusion, disorientation, and seizures (Id.).

[0047] To study the effect of bacterial fermentation on D-lactate levels in blood vs. brain, we monocolonized mice with B. thetaiotaomicron, a prominent member of the human gut microbiome that plays a crucial role in digesting complex carbohydrates. Mice were then fed a custom diet containing the host non-digestible carbohydrate levan as the sole carbohydrate source, which B. thetaiotaomicron selectively ferments (J. Xu, M. K. Bjursell, J. Himrod, S. Deng, L. K. Carmichael, H. C. Chiang, L. V. Hooper, J. I. Gordon, A Genomic View of the Human-Bacteroides thetaiotaomicron Symbiosis. Science 299, 2074-2076 (2003)). Compared to germ-free controls, colonization with B. thetaiotaomicron modestly increased serum D-lactate levels without elevating brain D-lactate levels. This suggests that under non-pathological conditions the gut microbiome promotes D-lactate in the serum without affecting brain levels. Next modeled was high carbohydrate feeding and intestinal barrier dysfunction as key risk factors for D-lactate acidosis and co-morbid encephalopathy. To do so, first treated were conventionally colonized mice with dextran sodium sulfate (DSS), a common model of experimental colitis, to induce intestinal barrier permeability (P. Nighot, R. Al-Sadi, M. Rawat, S. Guo, D. M. Watterson, T. Ma, Matrix metalloproteinase 9-induced increase in intestinal epithelial tight junction permeability contributes to the severity of experimental DSS colitis. Am. J. Physiol. -Gastrointest. Liver Physiol. 309, G988-G997 (2015)). Following 7 days of DSS treatment, mice were fasted and orally gavaged with a182025-233-PCT S. Emaminejad et al. Aty. Dkt. 102352-1177mixture of host non-digestible carbohydrates (fructooligosaccharide, inulin, cellulose, and gum arabic) to promote rapid bacterial fermentation and D-lactate production.

[0048] FIG. 4F is a graph illustrating D-lactate concentrations in brain samples from control vs. DSS-treated mice. Statistical significance and P value were determined by two-tailed unpaired Student’s t test. * P < 0.05. FIG. 4F demonstrates that DSS-treated mice exhibited significantly elevated D-lactate levels in the brain compared to vehicle-treated controls, indicating that intestinal injury leads to increased entry of bacterial-derived D-lactate into the brain. There was no significant correlation between serum and brain D-lactate levels within individual animals, emphasizing the necessity for methods that can concurrently measure bacterial metabolites in circulation and local environments like the brain to better understand the microbiota-host interactions. FIG. 4G is a Bland-Altman plot comparing the standard assay-quantified D-lactate concentrations with the TMR-measured D-lactate concentrations in mice serum and brain samples. As shown in FIG. 4G, the TMR of embodiments proves to be a fitting solution, given its high accuracy in analyzing D-lactate in both blood and brain matrices (mean bias -3 pM with 95% confidence intervals within ±36 pM).

[0049] Deployed were TMR sensors for in-vivo monitoring of local and circulating D-lactate in a rat model. Affixed were D-lactate-TMRs to the brain and back for subdural and subdermal analysis, respectively. An accompanying L-lactate-TMR sensor analyzing blood served as a negative control. Continuously recorded were the TMR sensor responses before and after intravenous D-lactate injection. The control device exhibited minor transient disturbances post-injection, attributable to the momentarily increased osmotic load from the lactate buffer injection, consistent with prior reports (J. Lund, A. W. Breum, C. Gil, S. Falk, F. Sass, M. S. Isidor, O. Dmytriyeva, P. Ranea-Robles, C. V. Mathiesen, A. L. Basse, O. S. Johansen, N. Fadahunsi, C. Lund, T. S. Nicolaisen, A. B. Klein, T. Ma, B. Emanuelli, M. Kleinert, C. M. Sorensen, Z. Gerhart-Hines, C. Clemmensen, The anorectic and thermogenic effects of pharmacological lactate in male mice are confounded by treatment osmolarity and co-administered counterions. Nat. Metab. 5, 677-698 (2023)). FIG. 4H illustrates example in-vivo real-time blood and brain D-lactate monitoring with TMRs in a rat model. The shaded bands indicate the D-lactate tail-vein injection time windows. Inset shows the experimental setup. As shown in FIG. 4H, the D-lactate sensors tracked acute increases in D-lactate following the injection, revealing a slower rate of concentration increase in the brain compared to the blood, suggesting limited transport rates of D-lactate into the brain (E. M.192025-233-PCT S. Emaminejad et al. Aty. Dkt. 102352-1177Nemato, J. W. Severinghaus, Stereospecific Permeability of Rat Blood-Brain Barrier to Lactic Acid. Stroke 5, 81-84 (1974)).

[0050] Discussion

[0051] A strategy of embodiments harnesses naturally proven metabolic pathways that are linkable to oxidoreductase-based electrochemical analysis as a blueprint for bioelectronic design. Implemented through the TMR electrode with exceptional electrochemical properties, this design makes multifunctional use of evolutionarily robust molecular toolkits (enzymes and cofactors) to support underlying reactions. This approach enables reliable monitoring of a plethora of metabolites, with NAD-assisted enzymatic sensing alone capable of directly detecting over 800 metabolites.

[0052] To support broader in-vivo applications, TMR sensors could benefit from enhancing their antifouling properties (e.g., exploring the use of surfactants) (S. M. Reddy, P. M. Vagama, Surfactant-modified poly (vinyl chloride) membranes as biocompatible interfaces for amperometric enzyme electrodes. Anal. Chim. Acta, 350(1-2), 77-89 (1997)), further miniaturization, and integration with soft or microneedle bioelectronic substrates (R. Whitaker, B. Hernaez -Estrada, R. M. Hernandez, E. Santos- Vizcaino, K. L. Spiller, Immunomodulatory biomaterials for tissue repair. Chem. Rev., 121(18), 11305-11335 (2021)) or lateral flow devices for analyzing various biomatrices in diverse clinical settings. Additionally, the TMR’s solution-based fabrication is compatible with industrial manufacturing processes, enabling flexible and streamlined large-scale production.

[0053] TMR’s versatility in monitoring endogenous and bacterial metabolites in vivo across various biomatrices makes it a powerful metabolomic tool for propelling biomedical research and healthcare. In microbiome research, it can help decipher the temporal dynamics of microbiota-host metabolic communication, recognized as one of the ‘greatest challenges’ in the field (Id.). TMR’s adaptation into wearable and implantable formats can advance sparse metabolite-based point-of-care testing to continuous point-of-person monitoring for chronic disease prevention / management, fitness optimization, and infectious disease detection (Q. Yu, L. Xue, J. Hiblot, R. Griss, S. Fabritz, C. Roux, P.-A. Binz, D. Haas, J. G. Okun, K. Johnsson, Semisynthetic sensor proteins enable metabolic assays at the point of care. Science 361, 1122-1126 (2018)). Moreover, TMR’s focus on metabolic pathways aligns seamlessly with tracking bacterial and tumor metabolism within their microenvironments (C. R. Bartman, B. Faubert, J. D. Rabinowitz, R. J. DeBerardinis, Metabolic pathway analysis 202025-233-PCT S. Emaminejad et al. Aty. Dkt. 102352-1177using stable isotopes in patients with cancer. Nat. Rev. Cancer 23, 863-878 (2023)). This capability can empower the design and monitoring of the efficacy of antibiotics and chemotherapeutics targeting key metabolic processes within pathogens or cancer cells to minimize drug resistance (J. H. Yang, P. Bhargava, D. McCloskey, N. Mao, B. O. Palsson, J. J. Collins, Antibiotic-Induced Changes to the Host Metabolic Environment Inhibit Drug Efficacy and Alter Immune Function. Cell Host Microbe 22, 757-765. e3 (2017)). Thus, future efforts can include large-scale clinical trials to validate TMR's clinical utility in these applications, while ensuring adherence to regulatory standards for safe and effective implementation.

[0054] Ultimately, scaling and deployment of TMR in these contexts will generate massive, multidimensional, and real-time metabolic datasets with high temporal resolutions, facilitating deeper understanding and interaction with biology and advancing personalized medicine.

[0055] Example Materials and Methods

[0056] Example Materials. Single-walled carbon nanotubes (SWCNT), reduced nicotinamide adenine dinucleotide (NADH), nicotinamide adenine dinucleotide (NAD+), D-(+)-glucose, D-glucose 6-phosphate sodium salt (G6P), a-D-glucose 1 -phosphate di sodium salt hydrate (G1P), lactose, adenosine 5’ -triphosphate disodium salt hydrate (ATP) , glutaraldehyde solution (25 wt%), polyvinyl chloride (PVC), tetrahydrofuran (THF), P-galactosidase, hexokinase, and phosphoglucomutase (PGM) were purchased from Sigma-Aldrich (MO, USA). Glucose dehydrogenase, glucose-6-phosphate dehydrogenase (G6PDH), and ascorbate oxidase were purchased from Toyobo USA, Inc. (NY, USA). Water (Optima LC-MS Grade), toluene, sulfuric acid (98%), nitric acid (70%), porcine serum, tissue protein extraction reagent, and phosphate-buffered saline (1*, Gibco PBS, pH 7.2) were purchased from Fisher Scientific (MA, USA). Sliver / silver chloride (Ag / AgCl) ink was purchased from Ercon Incorporated (MA, USA). Polyethylene terephthalate (PET, 100 pm thick) was purchased from MG Chemicals (BC, Canada).

[0057] Construction of the SWCNT -based NADH sensor and tandem metabolic pathway-like reactions-based sensors (e.g. TMR 102 in FIG. IB). The biosensors (e.g. 122 in FIG. ID) were fabricated on gold (Au) electrodes (diameter: 3 mm; 30 nm chromium, Cr / 100 nm Au), deposited and patterned on a PET substrate. The reference electrode (e.g. 124 in212025-233-PCT S. Emaminejad et al. Aty. Dkt. 102352-1177FIG. ID) was fabricated by depositing 3.5 pL of Ag / AgCl ink on the Au electrode, then dried on a hot plate at 70 °C for 20 min.

[0058] An acid-treated SWCNT solution (12 mg / mL in PBS, e.g. for 104 / 108 in FIG. IB) was prepared according to the protocol of previous demonstrated cofactor-integrated biosensors by dispersing SWCNT into a mixture of sulfuric acid and nitric acid solution (1:3) and heating at 80 °C for 4 hours while stirring followed by centrifuging and washing with PBS to remove the residual acid.

[0059] For NADH sensors, 3.5 pL of the acid-treated SWCNT solution was drop-cast onto the Au electrode and dried in the ambient environment.

[0060] For enzymatic TMRs (e.g. including layer 106 in FIG. IB), NAD+ was integrated into SWCNT solution (12 mg / mL in PBS) according to the protocol of previous demonstrated cofactor-integrated biosensors by dispersing acid-treated SWCNT into a NAD+ solution (50 mM in PBS) and stirring at 4 °C for 20 hours followed by centrifuging and washing with PBS and water. 3.5 pL of the NAD+ integrated SWCNT solution was drop-cast onto the Au electrode and dried in the ambient environment. Then, the electrode was further functionalized with a dehydrogenase layer (DH) by drop-casting an enzyme solution (prepared following the instruction provided by the vendor) and dried in the ambient environment. Then, another 1.75 pL of the NAD+ integrated SWCNT was drop-cast onto the Au / SWCNT-NAD+ / DH electrode and dried in the ambient environment. The integration of ATP into SWCNT and the fabrication of enzymatic TMRs (e.g. including layers 106 in FIG. IB) for direct metabolite detection follows the same procedure described above with the use of their corresponding enzyme solution (20000 U / mL for G6P dehydrogenase, and 8000 U / mL for glucose dehydrogenase). For sensors with intermediation functions (e.g. layer 110 in FIG. IB), 1.13 pL of intermediation enzyme solution (5000 U / mL P-galactosidase, PGM, and hexokinase) was drop-cast onto the Au / SWCNT-NAD+ / DH / SWCNT-NAD+ (ATP) electrode. To eliminate the interference from AA (e.g. including layer 112 in FIG. IB), according to the protocol of previous demonstrated cofactor-integrated biosensors, 1.13 pL of the ascorbate oxidase solution (15000 U / mL) was drop-cast onto the Au / SWCNT-NAD+ / DH / SWCNT-NAD+ electrode, followed by drop-casting 1 pL of the glutaraldehyde solution (0.4 wt% in PBS solution). An anti-fouling encapsulation layer (e.g. 114 in FIG. IB) was deposited according to the protocol of previous demonstrated cofactor-integrated biosensors by drop-casting of 1 pL of the PVC solution (0.1 wt% in THF) for 3 times.222025-233-PCT S. Emaminejad et al. Aty. Dkt. 102352-1177Sensors were allowed to dry overnight at 4 °C, while being protected from light. The sensors were stored at 4 °C in the dark when not in use.

[0061] Electrochemical characterization of enzymatic TMRs. Amperometric measurements: the sensitivity of glucose, lactose, and G1P was obtained by amperometric measurements during stepwise addition of increasing concentrations of the target metabolite stock solution in the PBS buffer.

[0062] Characterization of chemical compositions. Scanning transmission electron microscope (S / TEM): the chemical composition of SWCNT after ATP integration was characterized by energy dispersive spectroscopy (EDS) (Oxford X-MaxTEM 100N TLE Windowless SDD 100 mm2) in a Titan S / TEM (FEI). The S / TEM sample was prepared by dissolving ATP integrated SWCNT in ethanol then drop-casting onto a support film grid (Ultrathin Carbon Film on Lacey Carbon Support Film, 400 mesh, Copper, TED PELLA, Inc., CA).

[0063] Surveys for metabolic pathway. The metabolic pathway surveys were summarized from Roche Biochemical Pathways (4th Edition, Part 1). All metabolites were screened for linkability to a cofactor-involved oxidoreductase reaction through an optional single intermediation step.

[0064] The herein described subject matter sometimes illustrates different components contained within, or connected with, different other components. It is to be understood that such depicted architectures are illustrative, and that in fact many other architectures can be implemented which achieve the same functionality. In a conceptual sense, any arrangement of components to achieve the same functionality is effectively "associated" such that the desired functionality is achieved. Hence, any two components herein combined to achieve a particular functionality can be seen as "associated with" each other such that the desired functionality is achieved, irrespective of architectures or intermedial components. Likewise, any two components so associated can also be viewed as being "operably connected," or "operably coupled," to each other to achieve the desired functionality, and any two components capable of being so associated can also be viewed as being "operably coupleable," to each other to achieve the desired functionality. Specific examples of operably coupleable include but are not limited to physically mateable and / or physically interacting components and / or wirelessly interactable and / or wirelessly interacting components and / or logically interacting and / or logically interactable components.232025-233-PCT S. Emaminejad et al. Aty. Dkt. 102352-1177

[0065] With respect to the use of plural and / or singular terms herein, those having skill in the art can translate from the plural to the singular and / or from the singular to the plural as is appropriate to the context and / or application. The various singular / plural permutations may be expressly set forth herein for sake of clarity.

[0066] It will be understood by those within the art that, in general, terms used herein, and especially in the appended claims (e.g., bodies of the appended claims) are generally intended as "open" terms (e.g., the term "including" should be interpreted as "including but not limited to," the term "having" should be interpreted as "having at least," the term "includes" should be interpreted as "includes but is not limited to," etc.).

[0067] Although the figures and description may illustrate a specific order of method steps, the order of such steps may differ from what is depicted and described, unless specified differently above. Also, two or more steps may be performed concurrently or with partial concurrence, unless specified differently above. Such variation may depend, for example, on the software and hardware systems chosen and on designer choice. All such variations are within the scope of the disclosure. Likewise, software implementations of the described methods could be accomplished with standard programming techniques with rule-based logic and other logic to accomplish the various connection steps, processing steps, comparison steps, and decision steps.

[0068] It will be further understood by those within the art that if a specific number of an introduced claim recitation is intended, such an intent will be explicitly recited in the claim, and in the absence of such recitation, no such intent is present. For example, as an aid to understanding, the following appended claims may contain usage of the introductory phrases "at least one" and "one or more" to introduce claim recitations. However, the use of such phrases should not be construed to imply that the introduction of a claim recitation by the indefinite articles "a" or "an" limits any particular claim containing such introduced claim recitation to inventions containing only one such recitation, even when the same claim includes the introductory phrases "one or more" or "at least one" and indefinite articles such as "a" or "an" (e.g., "a" and / or "an" should typically be interpreted to mean "at least one" or "one or more"); the same holds true for the use of definite articles used to introduce claim recitations. In addition, even if a specific number of an introduced claim recitation is explicitly recited, those skilled in the art will recognize that such recitation should typically be interpreted to mean at least the recited number (e.g., the bare recitation of "two242025-233-PCT S. Emaminejad et al. Aty. Dkt. 102352-1177recitations," without other modifiers, typically means at least two recitations, or two or more recitations).

[0069] Furthermore, in those instances where a convention analogous to "at least one of A, B, and C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B, and C" would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and / or A, B, and C together, etc.). In those instances where a convention analogous to "at least one of A, B, or C, etc." is used, in general, such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B, or C" would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and / or A, B, and C together, etc.). It will be further understood by those within the art that virtually any disjunctive word and / or phrase presenting two or more alternative terms, whether in the description, claims, or drawings, should be understood to contemplate the possibilities of including one of the terms, either of the terms, or both terms. For example, the phrase "A or B" will be understood to include the possibilities of "A" or "B" or "A and B."

[0070] Further, unless otherwise noted, the use of the words “approximate,” “about,” “around,” “substantially,” etc., mean plus or minus ten percent.

[0071] Although the present embodiments have been particularly described with reference to preferred examples thereof, it should be readily apparent to those of ordinary skill in the art that changes and modifications in the form and details may be made without departing from the spirit and scope of the present disclosure. It is intended that the appended claims encompass such changes and modifications.252025-233-PCT S. Emaminejad et al. Aty. Dkt. 102352-1177

Claims

WHAT IS CLAIMED IS:

1. A method of in-vivo metabolite monitoring for decoding metabolism by mimicking metabolic pathways on electrodes, including:preparing a biosensor platform with one or more cofactor electrode layers; and configuring the one or more cofactor electrode layers for performing cascaded enzymatic reactions linked to oxidoreductase-based electrochemical analysis.

2. The method of claim 1, wherein preparing includes implementing tandem metabolic pathway-like reaction (TMR) sensors.

3. The method of claim 1, further comprising facilitating metabolite intermediation through enzymatic cascaded reactions.

4. The method of claims 1 to 3, further comprising configuring a secondary TMR to implement a calibration strategy to mitigate intermediary metabolites fluctuations.

5. The method of claim 1, wherein preparing further includes employing an enzymatic inactivation strategy to reduce interference.

6. A device for robust in-vivo monitoring of a myriad of metabolites, comprising: an electrode framework that integrates multifunctional enzymes and cofactors to deliver tandem metabolic pathway-like reactions (TMR).

7. The device of claim 6, wherein the electrode framework includes one or more cofactor electrode layers.

8. The device of claim 6, wherein the cofactor electrode layers comprise single-wall carbon nanotubes (SWCNTs).

9. The device of claims 7 or 8, wherein the electrode framework further comprises a detection enzymatic layer disposed in or on one of the cofactor electrode layers.262025-233-PCT S. Emaminejad et al. Atty. Dkt. 102352-117710. The device of any of claims 7-9, wherein the electrode framework further comprises an intermediation enzymatic layer disposed in or on one of the cofactor electrode layers.

11. The device of any of claims 7-9, wherein the electrode framework further comprises an inactivation enzymatic layer disposed in or on one of the cofactor electrode layers.

12. The device of any of claims 7-9, wherein the electrode framework further comprises antifouling encapsulation layer.

13. The device of claim 7, wherein the one or more cofactor electrode layers comprises a plurality of cofactor electrode layers respectively provided in a plurality of separate sensor electrodes, and further comprising a reference electrode separate from the plurality of sensor electrodes.

14. The device of claim 13, wherein each of the separate sensor electrodes is configured for sensing a respective separate metabolite.272025-233-PCT S. Emaminejad et al. Aty. Dkt. 102352-1177

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