Cell lipid metabolism modulators 2-bromopalmitate, D609, monensin, U18666A and probucol shift discoidal HDL formation to the smaller-sized particles: implications for the mechanism of HDL assembly.

ATP-binding cassette transporter A1 (ABCA1) mediates formation of disc-shaped high-density lipoprotein (HDL) from cell lipid and lipid-free apolipoprotein A-I (apo A-I). Discoidal HDL particles are heterogeneous in physicochemical characteristics for reasons that are understood incompletely. Discoidal lipoprotein particles similar in characteristics and heterogeneity to cell-formed discoidal HDL can be reconstituted from purified lipids and apo A-I by cell-free, physicochemical methods. The heterogeneity of reconstituted HDL (rHDL) is sensitive to the lipid composition of the starting lipid/apo A-I mixture. To determine whether the heterogeneity of cell-formed HDL is similarly sensitive to changes in cell lipids, we investigated four compounds that have well-established effects on cell lipid metabolism and ABCA1-mediated cell cholesterol efflux. 2-Bromopalmitate, D609, monensin and U18666A decreased formation of the larger-sized, but dramatically increased formation of the smaller-sized HDL. 2-Bromopalmitate did not appear to affect ABCA1 activity, subcellular localization or oligomerization, but induced dissolution of the cholesterol-phospholipid complexes in the plasma membrane. Arachidonic and linoleic acids shifted HDL formation to the smaller-sized species. Tangier disease mutations and inhibitors of ABCA1 activity wheat germ agglutinin and AG 490 reduced formation of both larger-sized and smaller-sized HDL. The effect of probucol was similar to the effect of 2-bromopalmitate. Taking rHDL formation as a paradigm, we propose that ABCA1 mutations and activity inhibitors reduce the amount of cell lipid available for HDL formation, and the compounds in the 2-bromopalmitate group and the polyunsaturated fatty acids change cell lipid composition from one that favors formation of the larger-sized HDL particles to one that favors formation of the smaller-sized species.

Robust passive and active efflux of cellular cholesterol to a designer functional mimic of high density lipoprotein.

The ability of HDL to support macrophage cholesterol efflux is an integral part of its atheroprotective action. Augmenting this ability, especially when HDL cholesterol efflux capacity from macrophages is poor, represents a promising therapeutic strategy. One approach to enhancing macrophage cholesterol efflux is infusing blood with HDL mimics. Previously, we reported the synthesis of a functional mimic of HDL (fmHDL) that consists of a gold nanoparticle template, a phospholipid bilayer, and apo A-I. In this work, we characterize the ability of fmHDL to support the well-established pathways of cellular cholesterol efflux from model cell lines and primary macrophages. fmHDL received cell cholesterol by unmediated (aqueous) and ABCG1- and scavenger receptor class B type I (SR-BI)-mediated diffusion. Furthermore, the fmHDL holoparticle accepted cholesterol and phospholipid by the ABCA1 pathway. These results demonstrate that fmHDL supports all the cholesterol efflux pathways available to native HDL and thus, represents a promising infusible therapeutic for enhancing macrophage cholesterol efflux. fmHDL accepts cholesterol from cells by all known pathways of cholesterol efflux: unmediated, ABCG1- and SR-BI-mediated diffusion, and through ABCA1.

Synthetic pulmonary perfusion images from 4DCT for functional avoidance using deep learning.

Purpose.To develop and evaluate the performance of a deep learning model to generate synthetic pulmonary perfusion images from clinical 4DCT images for patients undergoing radiotherapy for lung cancer.Methods. A clinical data set of 58 pre- and post-radiotherapy99mTc-labeled MAA-SPECT perfusion studies (32 patients) each with contemporaneous 4DCT studies was collected. Using the inhale and exhale phases of the 4DCT, a 3D-residual network was trained to create synthetic perfusion images utilizing the MAA-SPECT as ground truth. The training process was repeated for a 50-imaging study, five-fold validation with twenty model instances trained per fold. The highest performing model instance from each fold was selected for inference upon the eight-study test set. A manual lung segmentation was used to compute correlation metrics constrained to the voxels within the lungs. From the pre-treatment test cases (N = 5), 50th percentile contours of well-perfused lung were generated from both the clinical and synthetic perfusion images and the agreement was quantified.Results. Across the hold-out test set, our deep learning model predicted perfusion with a Spearman correlation coefficient of 0.70 (IQR: 0.61-0.76) and a Pearson correlation coefficient of 0.66 (IQR: 0.49-0.73). The agreement of the functional avoidance contour pairs was Dice of 0.803 (IQR: 0.750-0.810) and average surface distance of 5.92 mm (IQR: 5.68-7.55).Conclusion. We demonstrate that from 4DCT alone, a deep learning model can generate synthetic perfusion images with potential application in functional avoidance treatment planning.