Hemodynamic molecular imaging of tumor-associated enzyme activity in the living brain.
Ontology highlight
ABSTRACT:
SUBMITTER: Desai M
PROVIDER: S-EPMC8691830 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:For over a century, enzymatic activity has been studied in vitro, assuming similar activity in the crowded cellular milieu. Here, we determined in real time the catalytic activity of TEM1-?-lactamase inside living cells and compared the values to those obtained in vitro We found the apparent in vivo catalytic efficiency, kcat/Km , to be lower than in vitro, with significant cell-to-cell variability. Surprisingly, the results show that inside the cell the apparent catalytic efficiency decreases, and Km increases with increasing enzyme concentration. To rationalize these findings, we measured enzyme and substrate diffusion rates in the cell and found the latter to be slower than expected. Simulations showed that for attenuated diffusion the substrate flux becomes rate-limiting, explaining why reaction rates in vivo can be independent on enzyme concentrations. The octanol/water partition of the substrate is 4.5, which is in the range of Food and Drug Administration-approved drugs. This suggests substrate-limited reaction rates to be common. These findings indicate that in vitro data cannot be simply extrapolated to the crowded in vivo environment.
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Project description:BACKGROUND:Depression in patients with brain tumors is associated with impaired quality of life and shorter survival. Altered metabolism of tryptophan to serotonin and kynurenine metabolites may play a role in tumor-associated depression. Our recent studies with alpha[(11)C]methyl-L-tryptophan (AMT)-PET in brain tumor patients indicated abnormal tryptophan metabolism not only in the tumor mass but also in normal-appearing contralateral brain. In the present study, we explored if tryptophan metabolism in such brain regions is associated with depression. METHODS:Twenty-one patients (mean age: 57 years) with a brain tumor (10 meningiomas, 8 gliomas, and 3 brain metastases) underwent AMT-PET scanning. MRI and AMT-PET images were co-registered, and AMT kinetic parameters, including volume of distribution (VD', an estimate of net tryptophan transport) and K (unidirectional uptake, related to tryptophan metabolism), were measured in the tumor mass and in unaffected cortical and subcortical regions contralateral to the tumor. Depression scores (based on the Beck Depression Inventory-II [BDI-II]) were correlated with tumor size, grade, type, and AMT-PET variables. RESULTS:The mean BDI-II score was 12?±?10 (range: 2-33); clinical levels of depression were identified in seven patients (33 %). High BDI-II scores were most strongly associated with high thalamic AMT K values both in the whole group (Spearman's rho?=?0.63, p?=?0.004) and in the subgroup of 18 primary brain tumors (r?=?0.68, p?=?0.004). Frontal and striatal VD' values were higher in the depressed subgroup than in non-depressed patients (p?<?0.05); the group difference was even more robust when moderately/severely depressed patients were compared to patients with no/mild depression (frontal: p?=?0.005; striatal: p?<?0.001). Tumor size, grade, and tumor type were not related to depression scores. CONCLUSIONS:Abnormalities of tryptophan transport and metabolism in the thalamus, striatum, and frontal cortex, measured by PET, are associated with depression in patients with brain tumor. These changes may indicate an imbalance between the serotonin and kynurenine pathways and serve as a molecular imaging marker of brain tumor-associated depression. TRIAL REGISTRATION:ClinicalTrials.gov NCT02367469.
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Project description:Herein, we report the development of an 18 F-labeled, activity-based small-molecule probe targeting the cancer-associated serine hydrolase NCEH1. We undertook a focused medicinal chemistry campaign to simultaneously preserve potent and specific NCEH1 labeling in live cells and animals, while permitting facile 18 F radionuclide incorporation required for PET imaging. The resulting molecule, [18 F]JW199, labels active NCEH1 in live cells at nanomolar concentrations and greater than 1000-fold selectivity relative to other serine hydrolases. [18 F]JW199 displays rapid, NCEH1-dependent accumulation in mouse tissues. Finally, we demonstrate that [18 F]JW199 labels aggressive cancer tumor cells in vivo, which uncovered localized NCEH1 activity at the leading edge of triple-negative breast cancer tumors, suggesting roles for NCEH1 in tumor aggressiveness and metastasis.
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Project description:Background:Serine hydrolases (SHs) are a functionally diverse family of enzymes playing pivotal roles in health and disease and have emerged as important therapeutic targets in many clinical conditions. Activity-based protein profiling (ABPP) using fluorophosphonate (FP) probes has been a powerful chemoproteomic approach in studies unveiling roles of SHs in various biological systems. ABPP utilizes cell/tissue proteomes and features the FP-warhead, linked to a fluorescent reporter for in-gel fluorescence imaging or a biotin tag for streptavidin enrichment and LC-MS/MS-based target identification. Existing ABPP approaches characterize global SH activity based on mobility in gel or MS-based target identification and cannot reveal the identity of the cell-type responsible for an individual SH activity originating from complex proteomes. Results:Here, by using an activity probe with broad reactivity towards the SH family, we advance the ABPP methodology to glioma brain cryosections, enabling for the first time high-resolution confocal fluorescence imaging of global SH activity in the tumor microenvironment. Tumor-associated cell types were identified by extensive immunohistochemistry on activity probe-labeled sections. Tissue-ABPP indicated heightened SH activity in glioma vs. normal brain and unveiled activity hotspots originating from tumor-associated neutrophils (TANs), rather than tumor-associated macrophages (TAMs). Thorough optimization and validation was provided by parallel gel-based ABPP combined with LC-MS/MS-based target verification. Conclusions:Our study advances the ABPP methodology to tissue sections, enabling high-resolution confocal fluorescence imaging of global SH activity in anatomically preserved complex native cellular environment. To achieve global portrait of SH activity throughout the section, a probe with broad reactivity towards the SH family members was employed. As ABPP requires no a priori knowledge of the identity of the target, we envisage no imaginable reason why the presently described approach would not work for sections regardless of species and tissue source.
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Project description:BackgroundMagnetic resonance imaging (MRI) is a primary non-invasive imaging modality for tumor segmentation, leveraging its exceptional soft tissue contrast and high resolution. Current segmentation methods typically focus on structural MRI, such as T1-weighted post-contrast-enhanced or fluid-attenuated inversion recovery (FLAIR) sequences. However, these methods overlook the blood perfusion and hemodynamic properties of tumors, readily derived from dynamic susceptibility contrast (DSC) enhanced MRI. This study introduces a novel hybrid method combining density-based analysis of hemodynamic properties in time-dependent perfusion imaging with deep learning spatial segmentation techniques to enhance tumor segmentation.MethodsFirst, a U-Net convolutional neural network (CNN) is employed on structural images to delineate a region of interest (ROI). Subsequently, Hierarchical Density-Based Scans (HDBScan) are employed within the ROI to augment segmentation by exploring intratumoral hemodynamic heterogeneity through the investigation of tumor time course profiles unveiled in DSC MRI.ResultsThe approach was tested and evaluated using a cohort of 513 patients from the open-source University of Pennsylvania glioblastoma database (UPENN-GBM) dataset, achieving a 74.83% Intersection over Union (IoU) score when compared to structural-only segmentation. The algorithm also exhibited increased precision and localized predictions of heightened segmentation boundary complexity, resulting in a 146.92% increase in contour complexity (ICC) compared to the reference standard provided by the UPENN-GBM dataset. Importantly, segmenting tumors with the developed new approach uncovered a negative correlation of the tumor volume with the scores in the Karnofsky Performance Scale (KPS) clinically used for assessing the functional status of patients (-0.309), which is not observed with the prevailing segmentation standard.ConclusionsThis work demonstrated that including hemodynamic properties of tissues from DSC MRI can improve existing structural or morphological feature-based tumor segmentation techniques with additional information on tumor biology and physiology. This approach can also be applied to other clinical indications that use perfusion MRI for diagnosis or treatment monitoring.
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