Skip to main content
NCBI home page
Chemical & Biomedical Imaging logoLink to Chemical & Biomedical Imaging
. 2026 Mar 16;4(8):1765–1774. doi: 10.1021/cbmi.5c00291

18F‑CC-Omi‑X PET in Characterizing Lipid Metabolism via ACE2 Mapping

Cuicui Li †,*, Ni Wang ‡, Jie Li ‡, Xiaozheng Zhang ‡, Jinming Tian §,∥, Xiaoyi Zheng †, Changjing Zuo §, Tao Wang §, Danni Li §, Xiao Li ‡,§, Rou Li §,*, Jinming Yu ⊥,*
PMCID: PMC13508474  PMID: 42656956

Abstract

Dysregulated lipid metabolism, as seen in obesity, is closely linked to altered angiotensin-converting enzyme 2 (ACE2) expression in adipose tissue. However, real-time noninvasive monitoring of ACE2 dynamics in vivo remains a significant challenge. Therefore, we developed 18F-CC-Omi-X (Omi-X: HQPYRVVVLSFELLH), an ACE2-targeted PET tracer specifically designed for mapping ACE2 in lipid metabolic contexts. l-Propargylglycine (l-Pra) was modified to the C-terminus of Omi-X to suit 18F-labeling via the click-chemistry protocol, which minimized steric hindrance and preserved superior binding specificity compared to conventional N-terminus modification, as evidenced by a favorable IC50 (421 nM) in competitive binding assays. In diet-induced obese humanized ACE2 (hACE2) mice, 18F-CC-Omi-X PET showed high and specific uptake in adipose depots, with SUVmax strongly correlating with ex vivo ACE2 levels (r = 0.961, P < 0.05). Crucially, intervention with the ACE2 modulator ursodeoxycholic acid (UDCA) led to a significant decrease in tracer uptake in both subcutaneous and mesenteric fat, thereby enabling visualization of pharmacologically induced ACE2 downregulation in vivo. This work established 18F-CC-Omi-X as a vital molecular imaging tracer, enabling the noninvasive quantification of ACE2 dynamics directly within key lipid metabolic tissues, which is essential for elucidating its role in metabolic diseases and evaluating targeted therapies.

Keywords: ACE2, PET, molecular imaging, lipid metabolism, renin–angiotensin–aldosterone system

graphic file with name im5c00291_0007.webp

graphic file with name im5c00291_0005.webp

1. Introduction

As a key regulatory protein and therapeutic target in various diseases, including cardiovascular disorders, diabetes, hypertension, and cancer, − angiotensin-converting enzyme 2 (ACE2) plays a crucial role in counter-regulating the renin–angiotensin–aldosterone system (RAAS) and has been identified as a target for molecular imaging. , ACE2-targeted PET imaging using radiolabeled agents has shown promise in visualizing and quantifying ACE2 fluctuation in animal models and humans. , Generally, the RAAS serves as a pivotal regulator of metabolic stability. − As the key protective molecule of the RAAS axis, ACE2 plays an indispensable role in maintaining metabolic health by converting the pro-inflammatory and pro-fibrotic angiotensin II (Ang II) into Ang-(1-7), which improves insulin resistance, alleviates adipose tissue inflammation, and suppresses hepatic lipid deposition. , Beyond its established application in cancer, viral infection, and cardiovascular disease, − disorders of lipid metabolism represent the central pathophysiological characteristic underlying major public health concerns such as obesity, diabetes, liver diseases, and atherosclerosis. Therefore, early, immediate, real-time, and comprehensive monitoring and management of lipid metabolism is crucial. Lipid metabolism is a dynamically evolving chronic process; however, current understanding of ACE2’s metabolic function relies mainly on in vitro cellular and histological studies.

Existing ACE2-targeted tracers, which are primarily derived from ACE2-specific inhibitory peptide DX600, face challenges including high binding to blood ACE2 and slow clearance from circulation, which limit their clinical translation. ,, Therefore, there is a need for improved ACE2-targeted imaging agents with better specificity and target-to-background ratios. All along, virus–host interactions have always served as an important basis and condition for exploring pathogenic mechanisms, designing drugs, identifying potential targets, and developing therapeutic strategies. − Recently, we introduced a novel strategy that harnesses the receptor-binding domain (RBD) of Omicron variants as a natural resource for developing a superior peptide (HQPYRVVVLSFELLH, named Omi-X) used to construct a PET tracer (68Ga-DOTA-Omi-X), which demonstrated high ACE2-specificity and favorable retention in ACE2-expressing organs. In K18-hACE2 mice (humanized ACE2 expression mice driven by the human keratin K18 promoter) with cardiac hypertrophy, 68Ga-DOTA-Omi-X PET revealed increased ACE2 expression in hypertrophied myocardium, underscoring its potential for tracking disease progression and informing therapeutic decisions.

In this study, we further developed tracers on the basis of the Omi-X peptide by switching the conjugating site of the imaging group from the N-terminus to the C-terminus to reduce the steric hindrance. Fluorine-18 (18F) was then labeled to an alkynyl-modified Omi-X precursor (Omi-X-l-Pra) via click chemistry, named as 18F-CC-Omi-X. In vitro cell uptake and competitive binding assays using 125I-labeled Omi-X, Omi-X-l-Pra, and DOTA-Omi-X demonstrated that the Omi-X-l-Pra peptide possessed a superior ACE2-specific binding affinity. To extend the application of ACE2-targeted imaging in monitoring lipid metabolism, 18F-CC-Omi-X was subsequently evaluated in an obesity mouse model, including a cohort treated with the ACE2 modulator ursodeoxycholic acid (UDCA).

2. Materials and Methods

2.1. Chemicals and Consumables

Sodium l-ascorbate and acetonitrile (CH3CN) were procured from AcroSeal. Sodium azide (NaN3) was obtained from Jingyan Chemicals (Shanghai) Co., Ltd. 2-Azidoethyl-4-toluenesulfonate, copper­(II) sulfate (CuSO4), and saline were bought from Sinopharm Chemical Reagent Co., Ltd. The [18F]­NaF solution was purchased from Shanghai Atom Kexing Pharmaceuticals Co., Ltd.

Reverse-phase extraction Sep-Pak C18 cartridges were acquired from Waters Corporation and preconditioned with acetonitrile and water. Instant thin-layer chromatography silica gel (iTLC-SG) paper was purchased from Agilent Technologies. The tubes precoated with 100 μg of 1,3,4,6-tetrachloro-3α,6α-diphenylglycouril (Iodogen) were obtained from Nice-labeling Co., Ltd.

2.2. Preparation and Characterization of the Omi-X-Derived Precursor

In contrast to the 1,4,7,10-tetraazacyclododecane-N,N′,N,N′-tetraacetic acid (DOTA) conjugated to the N-terminus for 68Ga-labeling, an alkynyl-modified Omi-X precursor was custom-synthesized by conjugating l-Propargylglycine (l-Pra) to its C-terminus to enable 18F-labeling via the click-chemistry protocol (Omi-X-l-Pra). The purity of Omi-X-l-Pra was assessed by high-performance liquid chromatography (HPLC) and electrospray ionization mass spectrometry (ESMS). HPLC analysis was performed on a Waters ZQ 2000 equipped with a Kromasil 100-5C18 column (4.6 × 250 mm, 5 μm). The detection conditions were as follows: mobile phase A, acetonitrile with 0.1% trifluoroacetic acid (TFA); mobile phase B, water with 0.1% TFA; gradient, 65-40-0% B over 0-20-20.1 min; flow rate: 1 mL/min; column temperature, 50 °C. The ESMS conditions were as follows: capillary voltage, ± (2500–3000) V; dissolvent flow rate, 800 L/h; desolvation temperature, 450 °C; cone voltage, 15–30 V; acquisition time, 1 min.

To evaluate the chemical stability, Omi-X-l-Pra was dissolved in diluted hydrochloric acid (HCl, pH = 2) at 1 mg/mL, and then the resolution was stored at 37 °C for up to 24 h to mimic an in vivo acid environment. Chemical purity was assessed using analytical HPLC (IdealChrom 910, Elay-tech) equipped with a Kromasil 100-5C18 column under the parameters as above.

2.3. In Vitro Cell Uptake and Competitive Binding Studies

The tyrosine residue of the Omi-X peptide made the radioiodination process simple. Briefly, 37 MBq of [125I]­NaI was added into the tube precoated with 100 μg of Iodogen, followed by the addition of 100 μL of the Omi-X, Omi-X-l-Pra, and DOTA-Omi-X solution (1 mg/mL, 0.01 M PBS). After reacting for 15 min at room temperature, the upper liquid was collected as the target products (125I-Omi-X, 125I-Omi-X-l-Pra, and 125I-DOTA-Omi-X) with a radiochemical purity over 95%.

For the cell uptake experiment, HEK293-hACE2 cells were seeded in 24-well plates with a density of 1 × 105 cells/well in an incubator containing 5% CO2 at 37 °C overnight. The culture solution was replaced with fresh DMEM containing 1.11 MBq of 125I-Omi-X, 125I-Omi-X-l-Pra, or 125I-DOTA-Omi-X. Then, the medium was aspirated at 0.25, 0.5, 1, 2, 3, and 4 h, respectively. After being washed twice with ice-cold saline to remove the unbound radioactivity, the cells were lysed with a 0.1 M NaOH solution to measure the bound radioactivity of the cells using a γ-counter. The cell uptake fraction of 125I-Omi-X, 125I-Omi-X-l-Pra, and 125I-DOTA-Omi-X in each time point was calculated as the ratio of the cell-bound activity/total added activity.

For the competition binding assay, HEK293-hACE2 cells in 24-well plates were incubated in the presence of 1.11 MBq of 125I-Omi-X, 125I-Omi-X-l-Pra, or 125I-DOTA-Omi-X, either alone or coincubated with increasing concentrations of the corresponding nonradioactive ligand (1, 10, 100, 1000, 10,000, 100,000 ng/mL) at 37 °C for 120 min, respectively. After incubation, the medium was removed, and the cells were washed twice with ice-cold saline and subsequently lysed with 0.1 M NaOH solution. The radioactivity of cell lysates in each well was measured using a γ-counter. The cell-bound radioactivity without the competing ligands was defined as 100% (maximum binding). Data from inhibition groups were normalized to this maximum and plotted as a function of competitor concentration (log scale) to generate inhibition curves. The 50% inhibitory concentration (IC50) was determined by the nonlinear regression analysis.

2.4. Synthesis and Characterization of 18F-CC-Omi-X

2-[18F] Fluoroethylazide ([18F]-N3) was synthesized by reacting 2-azidoethyl-4-toluenesulfonate (5 mg, 0.02 mmol) in 0.4 mL of acetonitrile with the no-carrier-added dry K­[18F]­F-K2.2.2 complex for 10 min at 95 °C. [18F]-N3 was distilled at 80 °C with a flow of nitrogen (60 mL/min) into a trapping vial containing 0.1 mL of acetonitrile.

To the reactor vial with copper­(II) sulfate (25 μL, 0.45 M), sodium l-ascorbate (25 μL, 1.5 M) and 100 μL of acetonitrile were added. After the mixture was mixed with a vortex, the precursor Omi-X-l-Pra (2.0 mg, 1.96 μmol) in saline was introduced. The reaction mixture was vortexed for 1 min, and then the [18F]-N3 solution in 200 μL of acetonitrile was added and allowed to vibrate for 10 min at 50 °C. The crude 18F-CC-Omi-X reaction mixture was loaded onto a Sep-Pak C18 cartridge (preconditioned with 5 mL of 100% acetonitrile and 10 mL of H2O) and then washed with 5 mL of H2O and eluted with 0.5 mL of 20% (v/v) ethanol subsequently. Finally, the product was passed through a syringe filter (0.22 μm) and then formulated in saline for subsequent studies. 18F-CC-Omi-X was used immediately after the confirmation of the quality control, and the injected mass for each mouse was controlled at approximately 0.1 μg.

2.5. Construction of the Diet-Induced Obese hACE2 Mice

Obesity is often accompanied by chronic low-grade inflammation in adipose tissue and localized overactivation of the RAAS system. In this process, ACE2as a negative regulator of RAASmay undergo compensatory upregulation. To verify the feasibility of using 18F-CC-Omi-X as a novel PET imaging probe for ACE2-mediated lipid metabolism, animal models of obesity were established. Male humanized ACE2 mice (hACE2 mice) (C57BL/6J-Tgtn­(CAG-human ACE2-IRES-Luciferase-WPRE-polyA)­Smoc, 10 months old, 40–45 g, n = 3) were obtained from Shanghai Model Organisms Center, Inc. The mice were fed a high-fat diet (60 kcal % fat) and housed at a constant room temperature of 25 °C for 8 weeks to induce obesity (pre-UDCA). Subsequently, the obese hACE2 mice were orally administered UDCA (416 mg per kg body weight daily) on the original diet for 1 week (post-UDCA). UDCA has been reported to down-regulate ACE2 expression by inhibiting the transcription factor farnesoid X receptor (FXR). , This UDCA intervention model was then used in conjunction with 18F-CC-Omi-X PET imaging to visualize the dynamic changes in ACE2 expression.

2.6. 18F-CC-Omi-X PET Imaging of Obese Mice

18F-CC-Omi-X PET imaging was performed on mice pre- and post-UDCA administration. High-resolution images were acquired using a Siemens Inveon Micro PET/CT system at 1.5 and 3 h postinjection (p.i.) of 18F-CC-Omi-X. The image acquisition parameters were as follows. CT: tube voltage, 80 kV; tube current, 500 μA; exposure time, 1100 ms. PET: acquisition of whole-body static scans for 5 min per bed position, covering two bed positions in total.

Images were reconstructed using a three-dimensional ordered subsets expectation maximum (OSEM3D) algorithm, followed by maximum a posteriori (MAP). Three-dimensional regions of interest (ROI) were drawn over the target organ guided by CT images, and the maximum standardized uptake value (SUVmax) was subsequently quantified using the software of Inveon Research Workplace (IRW) 3.0.

For adipose tissue quantification, volumes of interest (VOIs) were manually drawn on the coregistered CT images to encompass the entire subcutaneous and mesenteric fat depots. The SUVmax within each VOI was automatically extracted using the IRW software. For nonadipose organs, spherical ROIs were placed on the apparent region of highest uptake on the PET images.

2.7. Quantification of ACE2 Expression in Adipose Tissue

ACE2 expression in adipose tissue was quantified using an ELISA kit (Abcam, ab235649) according to the manufacturer’s instructions. Briefly, subcutaneous and mesenteric adipose tissue were collected and immediately placed in ice-cold saline. The tissues were minced and homogenized in RIPA lysis buffer supplemented with protease inhibitors. The homogenates were centrifuged at 5000 g for 15 min at 4 °C, and the supernatants were collected for analysis. The standards were diluted by gradient at concentrations of 10, 5, 2.5, 1.25, and 0.625 ng/mL. Both standards and diluted adipose tissue lysates were added to the precoated plate in duplicate (100 μL/well) and incubated at 37 °C for 1 h. After the cells were washed with the provided buffer, a biotinylated detection antibody was added (100 μL/well) and incubated at 37 °C for 1 h. Following another wash, horseradish peroxidase (HRP)-conjugated streptavidin was added (100 μL/well), and the mixture was incubated at 37 °C for 30 min. After a final wash, the reaction was developed by adding a TMB substrate solution (100 μL/well) and incubating at room temperature for 30 min in the dark. The reaction was stopped by adding 2 M H2SO4 (50 μL/well), and the absorbance was measured at 450 nm using a microplate reader. The concentration of ACE2 in each sample was determined by interpolating from the standard curve.

2.8. Statistical Analysis

The correlation between SUVmax from PET imaging and ACE2 expression in adipose tissue was assessed by using Pearson correlation analysis. Data stability and agreement between measurements at two time points were assessed using the Bland–Altman method. The mean difference (bias) and 95% limits of agreement (mean difference ± 1.96 SD) were calculated. Bland–Altman analysis was performed to quantify agreement against the averages of the two measurements. Agreement was defined by the bias and the interval within which 95% of the differences between the two methods lie (95% limits of agreement).

GraphPad Prism version 9.3.0 and Origin 2024b were used for the statistical analysis of all data and curve fitting. All analyses were quantitatively performed at least three samples per group or per test. All experiments were conducted at least in triplicate and are presented as the mean ± SD unless otherwise stated. Data sets with only two independent groups were analyzed for statistical significance using an unpaired, two-tailed Student’s t-test. For the comparison of statistical differences between means of multiple samples, a standard two-way analysis of variance (ANOVA) was used. P values of less than 0.05 were considered as statistically significant.

3. Results and Discussion

3.1. Conjugating Site of Imaging Group Determines the ACE2 Specificity of Omi-X-Derivates

Given that the Omi-X peptide exhibits superior specificity and stability in binding to ACE2, two terminals were usually designed to conjugate with the radioactive isotope (Figure A). To enable 18F-labeling via a click-chemistry protocol, l-Pra was conjugated to the C-terminus, generating the precursor designated Omi-X-l-Pra (Figure S1). Molecular docking simulation of precursor Omi-X-l-Pra binding to the ACE2 protein is shown in Figure B, with several hydrogen bonds formed (binding energy: −14.4 kcal/mol) and no Pi–Pi stacking interactions present.

1.

1

Open in a new tab

The conjugating site determined the binding efficiency to ACE2. (A) Chemical structures (peptide sequences) affinity to ACE2, and radio-labeling protocols of Omi-X and derivatives. (B) Molecular docking simulation supported the binding tendency in precursor Omi-X-l-Pra with the ACE2 protein. (C) In vitro time-dependent cellular uptake of 125I-labeled Omi-X and the derived precursors in HEK293-hACE2 cells, and (D) the competitive cell-binding assay of these 125I-labeled precursors in HEK293T-hACE2 cells.

In vitro cellular uptake experiments were performed in HEK293T-hACE2 cells using 125I-Omi-X, 125I-Omi-X-l-Pra, and 125I-DOTA-Omi-X (Figure C), and the cell-bound radioactivity showed a time-dependent increase during the first 3 h of incubation, with little change observed in the subsequent hour. Crucially, the cellular uptake profile of Omi-X-l-Pra was similar to that of the unmodified Omi-X, reaching peak values of 19.54% ± 1.32% and 20.15% ± 1.62%, respectively. This suggests that C-terminus conjugation had a minimal impact on the core structure critical for ACE2 binding. In contrast, direct conjugation of the DOTA chelator to the N-terminus histidine residue resulted in a significantly lower cellular uptake of DOTA-Omi-X, which peaked at only 10.52% ± 0.81%. Similarly, assessment of binding affinity by a competitive cell-binding assay in HEK293T-hACE2 cells showed that both Omi-X-l-Pra (421 nM) and Omi-X (412 nM) had significantly lower IC50 values than DOTA-Omi-X (2589 nM), confirming that C-terminus modification impairs ACE2-binding affinity far less than N-terminus modification (Figure D).

To the best of our knowledge, the exploration and design of targeting molecules play a pivotal role in molecular imaging, determining the specificity, affinity, and stability in the biological process of target binding. These labeled molecules or materials can represent the functions of their target molecules in vivo or in vitro and can generate image signals for acquisition. Building upon our prior development of an ACE2-specific PET tracer (68Ga-DOTA-Omi-X), we designed a novel precursor Omi-X-l-Pra for 18F-labeling. The well-preserved target affinity of the resulting construct, featuring l-Pra conjugated at the C-terminus of Omi-X, makes it ideal for conversion into an ACE2-targeted imaging agent.

Currently, peptide-derived radiopharmaceuticals are an important branch of the integration of radionuclide targeted theranostics, especially the introduction of paired radionuclides such as 68Ga/177Lu, 203Pb/212Pb, 111In/90Y, etc., which significantly enhanced their clinical translation potential. − A rapid validation scheme to achieve this translation is to introduce chelating agents at the N-terminus of peptides by using the highly reactive terminal amino group. This approach has successfully facilitated the clinical translation of peptide derivatives like DOTATATE and FAP-2286. − However, there are also trapped cases in which the peptides are limited by steric hindrance or the N-terminus of the peptides is the key site that forms the hydrogen bond with the target molecule. In this study, the C-terminus conjugation of l-Pra to introduce the azido group smartly solved the potential difficulties. This strategy successfully preserved the peptide’s inherent binding ability to the cellular target site.

3.2. Development of 18F-CC-Omi-X

The radiolabeling scheme for 18F-CC-Omi-X is shown in Figure A. Introducing the radionuclide at the C-terminus (Figure B), compared to using an N-terminus conjugated chelator, as opposed to via a chelator conjugated to the N-terminus, yielded a peptide derivative with enhanced chemical stability. This conclusion is supported by its stability profile (Figure S2).

2.

2

Open in a new tab

Synthesis and characterization of 18F-CC-Omi-X. (A) Scheme for the radiolabeling of Omi-X-l-Pra with 18F via click chemistry. (B) Structure of 18F-CC-Omi-X. (C) Radio- and UV-spectrum of 18F-CC-Omi-X after Sep-Pak C18 cartridge purification.

The probe 18F-CC-Omi-X was obtained in an overall radiochemical yield of 35% from aqueous [18F]­NaF (nondecay-corrected) and >99% radiochemical purity following Sep-Pak C18 cartridge purification (Figure C). Its specific activity at the end of the synthesis was 54 GBq/μmol. The key radiochemical parameters of 18F-CC-Omi-X, 125I-Omi-X, and the previously reported 68Ga-DOTA-Omi-X are summarized in Table for comparative analysis.

1. Chemical and Radiochemical Properties of Omi-X-Derived Tracers.

  125I-Omi-X 18F-CC-Omi-X 68Ga-DOTA-Omi-X
molecular weight of precursors 1837.15 1932.20 2223.15
labeling rates of nuclides (%) 97.55 ± 0.33 45.60 ± 2.55 98.55 ± 0.50
radiochemical yield (%) >95 ≈35 ≈65
lipid–water partition coefficient / –2.020 –1.985
radiochemical stability (4 h) in 0.01 M PBS (%) / 97.15 ± 0.52 96.10 ± 0.75
radiochemical stability (4 h) in 10% FBS (%) / 92.36 ± 0.59 94.10 ± 1.55
Open in a new tab

3.3. 18F-CC-Omi-X PET Imaging Reflects ACE2 Expression Levels

Figure A shows the biodistribution profile of 18F-CC-Omi-X at 1.5 and 3 h p.i. in diet-induced obese hACE2 mice. The tracer exhibited markedly higher uptake in metabolically active adipose tissues, with SUVmax values of 1.89 ± 0.21 (1.5 h p.i.) and 1.71 ± 0.18 (3 h p.i.) in subcutaneous fat and 1.68 ± 0.19 (1.5 h p.i.) and 1.55 ± 0.16 (3 h p.i.) in mesenteric fat. In contrast, significantly lower uptake was observed in nonadipose organs such as the heart, liver, and spleen across both time points. This distinct biodistribution pattern, characterized by high and sustained retention in adipose depots, demonstrated the excellent target specificity of 18F-CC-Omi-X for tissues with up-regulated ACE2 expression. Importantly, the pronounced tracer accumulation in both subcutaneous and mesenteric fat directly visualized the pathologically up-regulated ACE2 expression within the expanded and inflamed adipose tissue of obese mice, highlighting the potential of this probe for noninvasive mapping of ACE2 dynamics in metabolic disorders.

3.

3

Open in a new tab

(A) The time-dependent distribution of 18F-CC-Omi-X in diet-induced obese hACE2 mice. (B) The relationship between quantification of 18F-CC-Omi-X PET and ELISA and (C) the repeatability of multiple time point scans.

Quantification of ACE2 expression in ex vivo adipose tissues from diet-induced obese hACE2 mice by ELISA revealed a strong positive correlation with in vivo PET measurements. SUVmax of two scans acquired at 1.5 and 3 h both showed a significant positive relation with ELISA-derived ACE2 levels, r = 0.961 (P < 0.05) for 1.5 h, and r = 0.947 (P < 0.05) for 3 h. The strong correlation validated the ability of 18F-CC-Omi-X PET to characterize in vivo ACE2 expression (Figure B). Besides, 18F-CC-Omi-X exhibited excellent retention stability in adipose tissue between the 1.5 and 3 h windows. Bland–Altman analysis demonstrated that all data points fell within the 95% limits of agreement and that the mean difference (bias) was very close to zero (Figure C). This remarkable pharmacokinetic stability provides strong support for the potential of 18F-CC-Omi-X as a reliable adipose-targeting agent for diagnostic applications.

In the context of ACE2-specific molecular imaging, early-stage targeting ligands were primarily based on small-molecule inhibitors or their analogs, such as DX600 and MLN4760. − Our newly developed tracer, 18F-CC-Omi-X, differs from these precursors in two key aspects. First, its targeting sequence is derived from a novel source (Omicron RBD) rather than from DX600. Second, its 18F-labeling is achieved via a click chemistry approach, which provides a milder and more efficient alternative to the direct labeling strategy used for analogs such as 18F-MLN4760. Compared to our previously reported tracer 68Ga-DOTA-Omi-X, 18F-CC-Omi-X demonstrates distinct practical advantages for ACE2 mapping. The improved in vivo stability of 18F-CC-Omi-X allows for an extended scanning window, which is crucial for achieving higher target-to-background contrast. This enhanced contrast is particularly valuable for translational research aimed at probing dynamic physiological processes.

3.4. 18F-CC-Omi-X PET in Diagnosing Obesity with UDCA Regulation

The preclinical study focused on the applicability of 18F-CC-Omi-X PET imaging for the assessment of RAAS-mediated lipid metabolism. We exploited the reported mechanism of UDCA-mediated ACE2 downregulation by performing 18F-CC-Omi-X PET on diet-induced obese hACE2 mice pre- or post-UDCA treatment. The 1.5 h p.i. imaging results provided a visual representation of ACE2 variations within adipose tissue (Figure A). For obese mice post-UDCA on their original diet for 1 week, 18F-CC-Omi-X PET/CT recorded the ACE2 expression in adipose tissue as a significant decrease in tracer uptake of both subcutaneous fat (1.61 ± 0.45 vs 0.83 ± 0.23, P < 0.05) and mesenteric fat (1.48 ± 0.30 vs 0.78 ± 0.15, P < 0.05) (Figure B).

4.

4

Open in a new tab

Performance of 18F-CC-Omi-X PET/CT in assessing RAAS-related obesity. (A) Timeline of UDCA intervention and PET imaging protocol in diet-induced obese ACE2 mice. (B) SUVmax quantification of 18F-CC-Omi-X PET/CT images in adipose tissue ROIs pre- and post-UDCA intervention. (C) Representative PET/CT images (1.5 h p.i.; long arrow: subcutaneous fat; triangular arrow: mesenteric fat). ****: P < 0.0001; ns: not statistically significant.

As displayed in 18F-CC-Omi-X PET/CT (Figure C), a strong positive uptake could be observed in the subcutaneous fat and mesenteric fat of obese mice, while uptake in other nonadipose organs such as the heart, liver, and spleen remained at baseline levels. In the high-fat-diet-induced obese state, ACE2 is highly expressed in brown adipose tissue, where it serves an essential role in the regulation of adipocyte differentiation, adipogenesis, and lipid metabolism. The alterations in ACE2 levels at these adipose sites were the most pronounced and were sensitively modulated by external inhibitors, underscoring the sensitivity of 18F-CC-Omi-X PET/CT to mapping dynamic changes in ACE2 expression. The representative 18F-CC-Omi-X PET/CT images of obese hACE2 mice at 3 h p.i. are displayed in Figure S3.

In this experimental design, other variables affecting fat metabolism, such as hunger and cold stimulation, were rigorously controlled. Under these conditions, 18F-CC-Omi-X PET imaging directly visualized the UDCA-mediated downregulation of ACE2 expression in adipose tissues. In the state of obesity, elevated ACE2 expression was clearly visualized by 18F-CC-Omi-X PET as a pronounced tracer accumulation in subcutaneous fat. Although this elevated expression was reversed by the ACE2 down-regulator UDCA, changes in visceral fat were less discernible in PET images. This suggests that short-term ACE2 regulation by UDCA may be more active in subcutaneous fat than in visceral fat. This tissue-selective response aligns with the work of Yang et al., who demonstrated that the ACE2 pathway potently enhances the browning of subcutaneous white adipose tissue upon cold stimulation, primarily via the ACE2-Ang (1-7)-MasR axis regulating mitochondrial thermogenesis.

During obesity, adipose tissue expansion, hypoxia, and inflammation can significantly modulate the local ACE2 expression and activity. ACE2 interacts with adipokines (e.g., insulin, glucagon) to influence the energy balance, metabolic state, and insulin sensitivity of adipose tissue by regulating their synthesis, secretion, or receptor signaling. − Furthermore, dysregulated lipid metabolism is a central pathophysiological link among diabetes, hypertension, and cardiovascular disease. , Consequently, monitoring adipose tissue ACE2 could provide critical insights into how local RAAS imbalance drives insulin resistance, adipose tissue dysfunction, and systemic inflammation. − However, ACE2 expression and activity undergo dynamic changes in the context of lipid metabolism, varying with metabolic status, comorbidities, and therapeutic interventions. This dynamic underscores the need for tools capable of longitudinal monitoring.

Current methods for detecting ACE2 (e.g., immunoassays and activity assays) vary significantly across studies. They often rely on serum or plasma samples, which limits the comparability of findings and fails to capture tissue-specific dynamics. Direct measurement of ACE2 expression in key tissues typically requires invasive biopsies, further restricting its clinical applicability. Our study established 18F-CC-Omi-X PET as a unique, noninvasive methodology to longitudinally quantify ACE2 expression in metabolically active tissues. This capability addressed a significant methodological gap and opened new avenues for investigating how RAAS imbalance contributes to metabolic syndrome and for evaluating therapies aimed at modulating this pathway (e.g., ACE inhibitors, ARBs). − Monitoring such alterations in ACE2 could help predict therapeutic responses (e.g., antihypertensive efficacy, renoprotection, and metabolic improvements) and guide personalized dose adjustments. Therefore, ACE2-targeted PET imaging is expected to become an innovative tool for optimizing intervention timing and evaluating treatment outcomes in ACE2-involved lipid metabolism and related complications.

From the perspective of molecular tracer design, the accessibility and diversity of molecular probes are core driving forces for the advancement of nuclear medicine molecular imaging, especially for targets with integrated diagnostic and therapeutic capabilities. A single radionuclide-labeled probe is often insufficient to meet these complex research and clinical needs, and our previous successful construction and validation of a 68Ga-labeled ACE2-targeting PET tracer has laid a solid foundation for subsequent studies. In clinical nuclear medicine applications, the 68Ga-labeled and 18F-labeled probes often exhibit complementary advantages, such as in prostate cancer, where both 68Ga-labeled PSMA and 18F-labeled PSMA are widely used. Compared with 68Ga, 18F has a longer half-life and lower positron energy, which enables a more flexible scanning time window, higher image resolution, and facilitates broader distribution networks. Especially, in this study, 18F-CC-Omi-X, synthesized via the click-chemistry protocol, minimized steric hindrance and preserved superior binding specificity compared to conventional N-terminus modification (68Ga-DOTA-Omi-X), as evidenced by a lower IC50 in competitive binding assays. Hence, the development of an 18F-labeled ACE2-targeting PET tracer represents a significant complement to and expansion upon our previously established 68Ga-labeled probe.

Beyond metabolic diseases, the ACE2 pathway is also implicated in cancer biology, influencing processes such as angiogenesis and immune microenvironment remodeling. Given the high specificity for ACE2, 18F-CC-Omi-X PET imaging also exhibits potential advantages in tumor theranostics. ACE2 inhibits tumor angiogenesis (e.g., by downregulating VEGFα) via generation of Ang-(1-7), and its high expression is associated with a favorable prognosis. Thus, 18F-CC-Omi-X PET imaging can indirectly reflect the state of angiogenesis inhibition, potentially aiding in screening patients who are more responsive to antiangiogenic therapies. Notably, in tumors such as hepatocellular carcinoma (HCC), high ACE2 expression is associated with an immunologically “hot” tumor microenvironment, offering new functional imaging-based insights for assessing tumor malignancy and patient prognosis. Consequently, 18F-CC-Omi-X PET imaging holds promise as an emerging imaging biomarker for pretherapeutic stratification, potentially distinguishing “cold” from “hot” tumors to identify patients more likely to benefit from immune checkpoint inhibitors (e.g., PD-1/PD-L1 inhibitors). This imaging-based approach could provide an objective tool for evaluating therapeutic efficacy, optimizing regimens, and advancing precision oncology in diagnosis, treatment prediction, and strategy development.

4. Conclusion

Conjugation of l-Pra to the C-terminus of the Omi-X peptide preserved binding specificity while providing an optimal site for 18F-labeling via click chemistry. Applying this optimized strategy, we developed 18F-CC-Omi-X, an ACE2-targeted PET tracer that enables noninvasive, real-time monitoring of ACE2 expression dynamics in the context of lipid metabolism. This capacity for noninvasive, quantitative, multiorgan, and systemic characterization of ACE2 expression in vivo represents a methodological breakthrough. This innovation addresses a critical bottleneck, paving the way for elucidating the precise role of ACE2 in lipid metabolism and advancing related diagnostic and therapeutic strategies.

Supplementary Material

im5c00291_si_001.pdf (583.5KB, pdf)

All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supporting Information.

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/cbmi.5c00291.

  • Additional experimental details and data including: HPLC and the ESMS spectrum of Omi-X-l-Pra (Figure S1); affinity and molecular docking of Omi-X-l-Pra to hACE2 (Figure S2); the representative tomo-graphic images (sagittal, coronal, transverse planes) of adipose tissue in 18F-CC-Omi-X PET/CT (pre- and post-UDCA intervention, 3 h p.i, Figure S3) (PDF)

#.

C.L. and N.W. contributed equally to this work. C.L. and X.L. designed the study; J.L., X.Z (Xiaozheng Zhang), and X.L. synthesized and characterized the chemicals; R.L., N.W., D.L., and T.W. contributed to methodology, investigation, and validation; J.T. and X.Z (Xiaoyi Zhang) performed data collection and analysis; R.L., C.L., and X.L. wrote the manuscript. X.L., C.Z., and J.Y. reviewed and edited the manuscript.

This research was funded by the National Natural Science Foundation of China (82202206, 82572271, and 82402423), the Medical Research Project of Shanghai Hongkou District Health Commission (2303-23), the Taishan Scholar Project of Shandong Province (tsqn202312363), and the Collaborative Academic Innovation Project of Shandong Cancer Hospital (TS003).

All animal experimental protocols for this research were approved and guided by the Ethics Committee of Shanghai Changhai Hospital and Shandong Cancer Hospital and Institute (Approval No.: 2023-MS-DS-08 and SDTHEC202504108).

The authors declare no competing financial interest.

References

  1. Li Y., Zhou W., Yang L., You R.. Physiological and Pathological Regulation of ACE2, the SARS-CoV-2 Receptor. Pharmacol. Res. 2020;157:104833. doi: 10.1016/j.phrs.2020.104833. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Santos R. A. S., Sampaio W. O., Alzamora A. C., Motta-Santos D., Alenina N., Bader M., Campagnole-Santos M. J.. The ACE2/Angiotensin-(1–7)/MAS Axis of the Renin-Angiotensin System: Focus on Angiotensin-(1–7) Physiol. Rev. 2018;98(1):505–553. doi: 10.1152/physrev.00023.2016. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Úri K., Fagyas M., Kertész A., Borbély A., Jenei C., Bene O., Csanádi Z., Paulus W. J., Édes I., Papp Z., Tóth A., Lizanecz E.. Circulating ACE2 Activity Correlates with Cardiovascular Disease Development. JRAAS. 2016;17(4):1470320316668435. doi: 10.1177/1470320316668435. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Tang X., Lu L., Li X., Huang P.. Bridging Cancer and COVID-19: The Complex Interplay of ACE2 and TMPRSS2. Cancer Med. 2025;14(7):e70829. doi: 10.1002/cam4.70829. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Mourad J.-J., Levy B. I.. Interaction between RAAS Inhibitors and ACE2 in the Context of COVID-19. Nat. Rev. Cardiol. 2020;17(5):313. doi: 10.1038/s41569-020-0368-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Liao W., Wu J.. The ACE2/Ang (1–7)/MasR Axis as an Emerging Target for Antihypertensive Peptides. Crit. Rev. Food Sci. Nutr. 2021;61(15):2572–2586. doi: 10.1080/10408398.2020.1781049. [DOI] [PubMed] [Google Scholar]
  7. Parker M. F. L., Blecha J., Rosenberg O., Ohliger M., Flavell R. R., Wilson D. M.. Cyclic 68Ga-Labeled Peptides for Specific Detection of Human Angiotensin-Converting Enzyme 2. J. Nucl. Med. 2021;62(11):1631–1637. doi: 10.2967/jnumed.120.261768. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Zhu H., Zhang H., Zhou N., Ding J., Jiang J., Liu T., Liu Z., Wang F., Zhang Q., Zhang Z., Yan S., Li L., Benabdallah N., Jin H., Liu Z., Cai L., Thorek D. L. J., Yang X., Yang Z.. Molecular PET/CT Profiling of ACE2 Expression in Vivo: Implications for Infection and Outcome from SARS-CoV-2. Adv. Sci. 2021;8(16):e2100965. doi: 10.1002/advs.202100965. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Ping Y., Fan Q., Zhang Y.. Modulating Lipid Metabolism Improves Tumor Immunotherapy. J. Immunother. Cancer. 2025;13(2):e010824. doi: 10.1136/jitc-2024-010824. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Terry A. R., Hay N.. Emerging Targets in Lipid Metabolism for Cancer Therapy. Trends Pharmacol. Sci. 2024;45(6):537–551. doi: 10.1016/j.tips.2024.04.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Petrenko V., Sinturel F., Riezman H., Dibner C.. Lipid Metabolism around the Body Clocks. Prog. Lipid Res. 2023;91:101235. doi: 10.1016/j.plipres.2023.101235. [DOI] [PubMed] [Google Scholar]
  12. Li R., Li F., Yuan L.. ACE2 Regulates Glycolipid Metabolism in Multiple Tissues. Front. Biosci.-Landmark. 2024;29(1):17. doi: 10.31083/j.fbl2901017. [DOI] [PubMed] [Google Scholar]
  13. Drucker D. J.. Diabetes, Obesity, Metabolism, and SARS-CoV-2 Infection: The End of the Beginning. Cell Metab. 2021;33(3):479–498. doi: 10.1016/j.cmet.2021.01.016. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Li D., Xiong L., Pan G., Wang T., Li R., Zhu L., Tong Q., Yang Q., Peng Y., Zuo C., Wang C., Li X.. Molecular Imaging on ACE2-Dependent Transocular Infection of Coronavirus. J. Med. Virol. 2022;94(10):4878–4889. doi: 10.1002/jmv.27958. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Li R., Xu A., Cheng C., Chen J., Wang M., Luo X., Liang S., Hou W., Cui B., Feng Y., Zuo C., Li X.. ACE2 PET in Healthy and Diseased Conditions. View. 2023;4(5):20230009. doi: 10.1002/VIW.20230009. [DOI] [Google Scholar]
  16. Ren F., Jiang H., Shi L., Zhang L., Li X., Lu Q., Li Q.. 68Ga-Cyc-DX600 PET/CT in ACE2-Targeted Tumor Imaging. Eur. J. Nucl. Med. Mol. Imaging. 2023;50(7):2056–2067. doi: 10.1007/s00259-023-06159-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Zhou P., Li Z., Li D., Xue S., Li R., Zhang L., Bai Q., Li X.. [99mTc]­Tc-Labeled Cyc-DX600-HYNIC as a SPECT Probe for ACE2-Specific Pancreatic Cancer Imaging. Am. J. Nucl. Med. Mol. Imaging. 2024;14(2):122–133. doi: 10.62347/VFHT4078. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Li X., Yin W., Li A., Li D., Gao X., Wang R., Cui B., Qiu S., Li R., Jia L., Zuo C., Zhang L., Li M.. ACE2 PET to Reveal the Dynamic Patterns of ACE2 Recovery in an Infection Model with Pseudocorona Virus. J. Med. Virol. 2023;95(2):e28470. doi: 10.1002/jmv.28470. [DOI] [PubMed] [Google Scholar]
  19. Elste J., Saini A., Mejia-Alvarez R., Mejía A., Millán-Pacheco C., Swanson-Mungerson M., Tiwari V.. Significance of Artificial Intelligence in the Study of Virus-Host Cell Interactions. Biomolecules. 2024;14(8):911. doi: 10.3390/biom14080911. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Satta S., Meng Z., Hernandez R., Cavallero S., Zhou T., Hsiai T. K., Zhou C.. An Engineered Nano-Liposome-Human ACE2 Decoy Neutralizes SARS-CoV-2 Spike Protein-Induced Inflammation in Both Murine and Human Macrophages. Theranostics. 2022;12(6):2639–2657. doi: 10.7150/thno.66831. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Guo S., Li K., Hu B., Li C., Zhang M., Hussain A., Wang X., Cheng Q., Yang F., Ge K., Zhang J., Chang J., Liang X., Weng Y., Huang Y.. Membrane-destabilizing Ionizable Lipid Empowered Imaging-guided siRNA Delivery and Cancer Treatment. Exploration. 2021;1(1):35–49. doi: 10.1002/EXP.20210008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Li R., Li J., Xue S., Li D., Yang Z., Wang T., Hai W., Li X.. Natural Selection-Guided ACE2-Targeted Molecular Imaging: A New Paradigm for PET Tracer Development. Chem. Biomed. Imaging. 2025;4:64–78. doi: 10.1021/cbmi.5c00053. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Brevini T., Maes M., Webb G. J., John B. V., Fuchs C. D., Buescher G., Wang L., Griffiths C., Brown M. L., Scott W. E., Pereyra-Gerber P., Gelson W. T. H., Brown S., Dillon S., Muraro D., Sharp J., Neary M., Box H., Tatham L., Stewart J., Curley P., Pertinez H., Forrest S., Mlcochova P., Varankar S. S., Darvish-Damavandi M., Mulcahy V. L., Kuc R. E., Williams T. L., Heslop J. A., Rossetti D., Tysoe O. C., Galanakis V., Vila-Gonzalez M., Crozier T. W. M., Bargehr J., Sinha S., Upponi S. S., Fear C., Swift L., Saeb-Parsy K., Davies S. E., Wester A., Hagström H., Melum E., Clements D., Humphreys P., Herriott J., Kijak E., Cox H., Bramwell C., Valentijn A., Illingworth C. J. R., Dahman B., Bastaich D. R., Ferreira R. D., Marjot T., Barnes E., Moon A. M., Barritt A. S., Gupta R. K., Baker S., Davenport A. P., Corbett G., Gorgoulis V. G., Buczacki S. J. A., Lee J.-H., Matheson N. J., Trauner M., Fisher A. J., Gibbs P., Butler A. J., Watson C. J. E., Mells G. F., Dougan G., Owen A., Lohse A. W., Vallier L., Sampaziotis F.. UK-PBC; Consortium. FXR Inhibition May Protect from SARS-CoV-2 Infection by Reducing ACE2. Nature. 2023;615(7950):134–142. doi: 10.1038/s41586-022-05594-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Chen M., Zhu R., Zhang M.. UDCA, a Novel Strategy for Preventing SARS-CoV-2 Infection via FXR-Mediated ACE2 Downregulation. Acta Biochim. Biophys. Sin. 2023;55(5):885–887. doi: 10.3724/abbs.2023075. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Fu Q.. Harnessing Biomarker Activatable Probes for Early Stratification and Timely Assessment of Therapeutic Efficacy in Cancer. Exploration. 2025;5:20240037. doi: 10.1002/EXP.20240037. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Zhang S., Wang X., Gao X., Chen X., Li L., Li G., Liu C., Miao Y., Wang R., Hu K.. Radiopharmaceuticals and Their Applications in Medicine. Signal Transduction Targeted Ther. 2025;10(1):1. doi: 10.1038/s41392-024-02041-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Yamaguchi A., Yamazaki C. M., Anami Y., Ha S. Y. Y., Xiong W., Ta R. T., Zhang N., Manning H. C., An Z., Tsuchikama K.. 177Lu-Labeled Antibody-Drug Conjugate: A Dual-Mechanistic Treatment Modality in Solid Tumors. Mol. Cancer Ther. 2025;24(6):907–919. doi: 10.1158/1535-7163.MCT-24-0254. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Michler E., Kästner D., Pretze M., Hartmann H., Freudenberg R., Schultz M. K., Bundschuh R. A., Kotzerke J., Brogsitter C.. [203/212Pb]­Pb-VMT-α-NET as a Novel Theranostic Agent for Targeted Alpha Radiotherapy-First Clinical Experience. Eur. J. Nucl. Med. Mol. Imaging. 2025;52(11):4171–4183. doi: 10.1007/s00259-025-07269-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Berner K., Hernes E., Kvassheim M., Revheim M.-E., Bastiansen J., Selboe S., Bakken C. L., Grønningsæter S. R., Bruland Ø. S., Larsen R. H., Bouzelmat L., Jardine V. L., Stokke C.. First-in-Human Phase 0 Study of AB001, a Prostate-Specific Membrane Antigen-Targeted 212Pb Radioligand, in Patients with Metastatic Castration-Resistant Prostate Cancer. J. Nucl. Med. 2025;66(5):732–738. doi: 10.2967/jnumed.124.269299. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Lan D., Chen Z., Mu J., Chen H., Zhao Y.. Approaches to Radiolabeling Nanobodies for Biomedical Applications. ACS Appl. Mater. Interfaces. 2025;17(7):10265–10275. doi: 10.1021/acsami.4c21014. [DOI] [PubMed] [Google Scholar]
  31. Fan M., Guo X., Liu G., Tian C., Zhao J., Chen Y.. Exploratory Analysis of [68Ga]­Ga-FAP-2286 PET/CT Quantitative Parameters for Predicting Response to [177Lu]­Lu-FAP-2286 Therapy in Non-Small Cell Lung Cancer. Clin. Nucl. Med. 2026;51:108. doi: 10.1097/RLU.0000000000006210. [DOI] [PubMed] [Google Scholar]
  32. Wang R., Huang M., Wang W., Li M., Wang Y., Tian R.. Preclinical Evaluation of 68Ga/177Lu-Labeled FAP-Targeted Peptide for Tumor Radiopharmaceutical Imaging and Therapy. J. Nucl. Med. 2025;66(2):250–256. doi: 10.2967/jnumed.124.268689. [DOI] [PubMed] [Google Scholar]
  33. Xu J., Chen J., Song S., Song L., Wang R., Hao J., Du X., Cao D., Gao Y., Lan X., Yang A., Miao W., Xu H., Chen Y., Li L., Shi H., Yuan X., Ye F., Wang J., Xu N., Han X., Li X., Huang R., Zhang T., Li E., Wang R., Zhou Y., Chen H., Zhu X., Zhao J., Su X., Cui Y., Ren P., Wang P.. 177Lu-Dotatate versus High-Dose Long-Acting Octreotide for the Treatment of Patients with Advanced, Grade 1–2, Well-Differentiated Gastroenteropancreatic Neuroendocrine Tumours (XT-XTR008–3-01): An Open-Label, Randomised, Phase III Trial. Ann. Oncol. 2025;36(12):1458–1467. doi: 10.1016/j.annonc.2025.08.3758. [DOI] [PubMed] [Google Scholar]
  34. Wang R., Zhou P., Xu W., Li D., Xue S., Guo Z., Li J., Jin L., Zuo C., Chen H., Li R., Li X., Lou J.. An Auger Electron-Loaded Theranostic Biosensor Triggered by the ACE2-Mediated Virus/Host Endocytosis. Talanta. 2025;285:127288. doi: 10.1016/j.talanta.2024.127288. [DOI] [PubMed] [Google Scholar]
  35. Zhang Q., Liu T., Ding J., Zhou N., Yu Z., Ren Y., Qin X., Du P., Yang Z., Zhu H.. Evaluation of 68Ga- and 177Lu-Labeled HZ20 Angiotensin-Converting Enzyme 2-Targeting Peptides for Tumor-Specific Imaging. Mol. Pharmaceutics. 2022;19(11):4149–4156. doi: 10.1021/acs.molpharmaceut.2c00541. [DOI] [PubMed] [Google Scholar]
  36. Wang Z., Liu Z., Yang L., Ding J., Wang F., Liu T., Yang Z., Wang C., Zhu H., Liu Y.. Noninvasive Mapping of Angiotensin Converting Enzyme-2 in Pigeons Using Micro Positron Emission Tomography. Life (Basel, Switz.) 2022;12(6):793. doi: 10.3390/life12060793. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Wang X., He J., Hosseini-Gerami L., Thomas M., Thompson S., Ford J., Ortalli S., Chen Z., Destro G., Bender A., Aigbirhio F., Gouverneur V.. Synthesis and Inhibitory Assessment of ACE2 Inhibitors for SARS-CoV-2: An in Silico and in Vitro Study. J. Org. Chem. 2025;90(30):10941–10947. doi: 10.1021/acs.joc.5c00918. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Ding J., Zhang Q., Jiang J., Zhou N., Yu Z., Wang Z., Meng X., Daggumati L., Liu T., Wang F., Lu Z., Yang X., Yang Z., Zhang H., Thorek D. L. J., Du P., Zhu H.. Preclinical Evaluation and Pilot Clinical Study of 18F-Labeled Inhibitor Peptide for Noninvasive Positron Emission Tomography Mapping of Angiotensin Converting Enzyme 2. ACS Pharmacol. Transl. Sci. 2024;7(6):1758–1769. doi: 10.1021/acsptsci.3c00337. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Cao X., Shi T.-T., Zhang C.-H., Jin W.-Z., Song L.-N., Zhang Y.-C., Liu J.-Y., Yang F.-Y., Rotimi C. N., Xu A., Yang J.-K.. ACE2 Pathway Regulates Thermogenesis and Energy Metabolism. eLife. 2022;11:e72266. doi: 10.7554/eLife.72266. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Cao X., Song L.-N., Yang J.-K.. ACE2 and Energy Metabolism: The Connection between COVID-19 and Chronic Metabolic Disorders. Clin. Sci. 2021;135(3):535–554. doi: 10.1042/CS20200752. [DOI] [PubMed] [Google Scholar]
  41. Ma C., Shi T., Song L., Liu J., Yuan M.. Angiotensin­(1–7) Attenuates Visceral Adipose Tissue Expansion and Lipogenesis by Suppression of Endoplasmic Reticulum Stress via Mas Receptor. Nutr. Metab. 2022;19(1):82. doi: 10.1186/s12986-022-00716-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Cano L., Desquilles L., Ghukasyan G., Angenard G., Landreau C., Corlu A., Clément B., Turlin B., Le Ferrec E., Aninat C., Massart J., Musso O.. SARS-CoV-2 Receptor ACE2 Is Upregulated by Fatty Acids in Human MASH. JHEP Rep. 2024;6(1):100936. doi: 10.1016/j.jhepr.2023.100936. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Deprince A., Haas J. T., Staels B.. Dysregulated Lipid Metabolism Links NAFLD to Cardiovascular Disease. Mol. Metab. 2020;42:101092. doi: 10.1016/j.molmet.2020.101092. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Szántó M., Gupte R., Kraus W. L., Pacher P., Bai P.. PARPs in Lipid Metabolism and Related Diseases. Prog. Lipid Res. 2021;84:101117. doi: 10.1016/j.plipres.2021.101117. [DOI] [PubMed] [Google Scholar]
  45. Shin J., Toyoda S., Nishitani S., Onodera T., Fukuda S., Kita S., Fukuhara A., Shimomura I.. SARS-CoV-2 Infection Impairs the Insulin/IGF Signaling Pathway in the Lung, Liver, Adipose Tissue, and Pancreatic Cells via IRF1. Metabolism. 2022;133:155236. doi: 10.1016/j.metabol.2022.155236. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Mkhize B. C., Mosili P., Ngubane P. S., Sibiya N. H., Khathi A.. Increased Plasma Levels of ACE and Ang II in Prediabetes May Contribute to Adipose Tissue Dysfunction. Int. J. Mol. Sci. 2025;26(12):5517. doi: 10.3390/ijms26125517. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Gómez-Zorita S., Milton-Laskibar I., García-Arellano L., González M., Portillo M. P.. An Overview of Adipose Tissue ACE2Modulation by Diet and Obesity. Potential Implications in COVID-19 Infection and Severity. Int. J. Mol. Sci. 2021;22(15):7975. doi: 10.3390/ijms22157975. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Young M. J., Clyne C. D., Chapman K. E.. Endocrine Aspects of ACE2 Regulation: RAAS, Steroid Hormones and SARS-CoV-2. J. Endocrinol. 2020;247(2):R45–R62. doi: 10.1530/JOE-20-0260. [DOI] [PubMed] [Google Scholar]
  49. Cacanyiova S., Cebova M., Simko F., Baka T., Bernatova I., Kluknavsky M., Zorad S., Krskova K., Shaman E., Zemancikova A., Barta A., Aydemir B. G., Berenyiova A.. The Effect of Zofenopril on the Cardiovascular System of Spontaneously Hypertensive Rats Treated with the ACE2 Inhibitor MLN-4760. Biol. Res. 2023;56(1):55. doi: 10.1186/s40659-023-00466-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Herman-Edelstein M., Guetta T., Barnea A., Waldman M., Ben-Dor N., Barac Y. D., Kornowski R., Arad M., Hochhauser E., Aravot D.. Expression of the SARS-CoV-2 receptorACE2 in Human Heart Is Associated with Uncontrolled Diabetes, Obesity, and Activation of the Renin Angiotensin System. Cardiovasc. Diabetol. 2021;20(1):90. doi: 10.1186/s12933-021-01275-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Khanna P., Soh H. J., Chen C.-H., Saxena R., Amin S., Naughton M., Joslin P. N., Moore A., Bakouny Z., O’Callaghan C., Catalano P., Signoretti S., McKay R., Choueiri T. K., Bhasin M., Walther T., Bhatt R. S.. ACE2 Abrogates Tumor Resistance to VEGFR Inhibitors Suggesting Angiotensin-(1–7) as a Therapy for Clear Cell Renal Cell Carcinoma. Sci. Transl. Med. 2021;13(577):eabc0170. doi: 10.1126/scitranslmed.abc0170. [DOI] [PubMed] [Google Scholar]
  52. Xie P., Guo L., Yu Q., Zhao Y., Yu M., Wang H., Wu M., Xu W., Xu M., Zhu X.-D., Xu Y., Xiao Y.-S., Huang C., Zhou J., Fan J., Hung M.-C., Sun H., Ye Q.-H., Zhang B., Li H.. ACE2 Enhances Sensitivity to PD-L1 Blockade by Inhibiting Macrophage-Induced Immunosuppression and Angiogenesis. Cancer Res. 2025;85(2):299–313. doi: 10.1158/0008-5472.CAN-24-0954. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

im5c00291_si_001.pdf (583.5KB, pdf)

Data Availability Statement

All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supporting Information.

Articles from Chemical & Biomedical Imaging are provided here courtesy of American Chemical Society

RESOURCES