Biology subjects

Lowe, V. J.

Publications and source records attributed to Lowe, V. J..

3 recordsLinked to original sources

bioRxiv2022

Amyloid Beta Peptides Inhibit Glucose Transport at the Blood-brain Barrier by Disrupting Insulin-Akt Pathway in Alzheimer's Disease

Disruptions in glucose uptake and metabolism in the brain are implicated in metabolic disorders and Alzheimers disease (AD). Toxic soluble amyloid-beta (sA{beta}) peptides accumulating in the brain and plasma of AD patients were suggested to promote blood-brain barrier (BBB) dysfunction, brain hypometabolism, and cognitive decline. Exposure to sA{beta} peptides is reported to interfere with glucose metabolism in the brain parenchyma, although their effects on the BBB have not been fully characterized. Our data showed that the brain uptake of glucose surrogate, [18F]-fluorodeoxyglucose (18FDG), was reduced significantly in APP/PS1 transgenic mice (overproduce A{beta}) compared to wild-type (WT) mice. In addition, the influx rate of 18FDG was also decreased in both A{beta}40 and A{beta}42 pre-infused mice compared to control mice. Glucose is primarily delivered from blood into the brain via glucose transporter 1 (GLUT1). The confocal microscopy experiment showed that A{beta}40 and A{beta}42 peptides significantly decreased GLUT1 expression in polarized human cerebral microvascular endothelial cell (hCMEC/D3) monolayers. Insulin-AKT pathway has been observed to induce glucose uptake via regulating the expression of TXNIP, the only -arrestin protein known to bind to thioredoxin. We found that A{beta}40 and A{beta}42 peptides decreased p-AKT and increased TXNIP expression in the hCMEC/D3 cell monolayers. MK2206, a kinase inhibitor of AKT, was used to confirm that inhibition of insulin/AKT pathway reduced GLUT1 expression in an insulin-independent manner in the hCMEC/D3 cell monolayers. These results suggest that inhibitory effects of sA{beta} on GLUT1 expression are mediated by inhibition of the insulin/AKT pathway. The role of TXNIP on endothelial GLUT1 expression was investigated using resveratrol, which has been reported to downregulate TXNIP overexpression. Consistently, resveratrol treatment led to a significant increase in GLUT1 expression in the hCMEC/D3 cell monolayers. Furthermore, by co-incubation of resveratrol and sA{beta} peptides in hCMEC/D3 cell monolayers, we found that resveratrol rectified the aberrant TXNIP expression caused by sA{beta} peptides. Together, these findings provide novel evidence that toxic sA{beta} peptide exposure inhibits glucose transport at the BBB by decreasing GLUT1 expression via the insulin/Akt/TXNIP axis.

pharmacology and toxicology↗

bioRxiv2022

Deconvolution of plasma pharmacokinetics from dynamic heart imaging data obtained by SPECT/CT imaging

Plasma pharmacokinetic (PK) data is required as an input function for graphical analysis (e.g., Patlak plot) of single positron emission computed tomography/computed tomography (SPECT/CT) and positron emission tomography/CT (PET/CT) data to evaluate tissue influx rate of radiotracers. Dynamic heart imaging data is often used as a surrogate of plasma PK. However, accumulation of radiolabel (representing both intact and degraded tracer) in the heart tissue may interfere with accurate prediction of plasma PK from the heart data. Therefore, we developed a compartmental model, which involves forcing functions to describe intact and degraded radiolabeled proteins in plasma and their accumulation in heart tissue, to deconvolve plasma PK of 125I-amyloid beta 40 (125I-A{beta}40) and 125I-insulin from their dynamic heart imaging data. The three-compartment model was shown to adequately describe the plasma concentration-time profile of intact/degraded proteins and the heart radioactivity time data obtained from SPECT/CT imaging for both tracers. The model was successfully applied to deconvolve the plasma PK of both tracers from their naive datasets of dynamic heart imaging. In agreement with our previous observations made by conventional serial plasma sampling, the deconvolved plasma PK of 125I-A{beta}40 and 125I-insulin in young mice exhibited lower area under the curve (AUC) than the aged mice. Further, Patlak plot parameters (Ki) extracted using deconvolved plasma PK as input function successfully recapitulated age-dependent blood-to-brain influx kinetics changes for both 125I-A{beta}40 and 125I-insulin. Therefore, the compartment model developed in this study provides a novel approach to deconvolve plasma PK of radiotracers from their noninvasive dynamic heart imaging. This method facilitates the application of preclinical SPECT or PET imaging data to characterize distribution kinetics of tracers where simultaneous plasma sampling is not feasible.

pharmacology and toxicology↗

bioRxiv2022

Synthesizing Images of Tau Pathology from Cross-modal Neuroimaging using Deep Learning

Given the prevalence of dementia and the development of pathology-specific disease modifying therapies, high-value biomarker strategies to inform medical decision making are critical. In-vivo tau positron emission tomography (PET) is an ideal target as a biomarker for Alzheimers disease diagnosis and treatment outcome measure. However, tau PET is not currently widely accessible to patients compared to other neuroimaging methods. In this study, we present a convolutional neural network (CNN) model that impute tau PET images from more widely-available cross-modality imaging inputs. Participants (n=1,192) with brain MRI, fluorodeoxyglucose (FDG) PET, amyloid PET, and tau PET were included. We found that a CNN model can impute tau PET images with high accuracy, the highest being for the FDG-based model followed by amyloid PET and MRI. In testing implications of AI-imputed tau PET, only the FDG-based model showed a significant improvement of performance in classifying tau positivity and diagnostic groups compared to the original input data, suggesting that application of the model could enhance the utility of the metabolic images. The interpretability experiment revealed that the FDG- and MRI-based models utilized the non-local input from physically remote ROIs to estimate the tau PET, but this was not the case for the PiB-based model. This implies that the model can learn the distinct biological relationship between FDG PET, MRI, and tau PET from the relationship between amyloid PET and tau PET. Our study suggests that extending neuroimagings use with artificial intelligence to predict protein specific pathologies has great potential to inform emerging care models.

bioengineering↗