Dataset Information

Pronounced peptide selectivity for melanoma through tryptophan end-tagging.

ABSTRACT: Effects of oligotryptophan end-tagging on the uptake of arginine-rich peptides into melanoma cells was investigated under various conditions and compared to that into non-malignant keratinocytes, fibroblasts, and erythrocytes, also monitoring resulting cell toxicity. In parallel, biophysical studies on peptide binding to, and destabilization of, model lipid membranes provided mechanistic insight into the origin of the selectivity between melanoma and non-malignant cells. Collectively, the results demonstrate that W-tagging represents a powerful way to increase selective peptide internalization in melanoma cells, resulting in toxicity against these, but not against the non-malignant cells. These effects were shown to be due to increased peptide adsorption to the outer membrane in melanoma cells, caused by the presence of anionic lipids such as phosphatidylserine and ganglioside GM1, and to peptide effects on mitochondria membranes and resulting apoptosis. In addition, the possibility of using W-tagged peptides for targeted uptake of nanoparticles/drug carriers in melanoma was demonstrated, as was the possibility to open up the outer membrane of melanoma cells in order to facilitate uptake of low Mw anticancer drugs, here demonstrated for doxorubicin.

SUBMITTER: Duong DT

PROVIDER: S-EPMC4847013 | biostudies-literature | 2016 Apr

REPOSITORIES: biostudies-literature

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Pronounced peptide selectivity for melanoma through tryptophan end-tagging.

Duong Dinh Thuy DT Singh Shalini S Bagheri Mojtaba M Verma Navin Kumar NK Schmidtchen Artur A Malmsten Martin M

Scientific reports 20160427

Effects of oligotryptophan end-tagging on the uptake of arginine-rich peptides into melanoma cells was investigated under various conditions and compared to that into non-malignant keratinocytes, fibroblasts, and erythrocytes, also monitoring resulting cell toxicity. In parallel, biophysical studies on peptide binding to, and destabilization of, model lipid membranes provided mechanistic insight into the origin of the selectivity between melanoma and non-malignant cells. Collectively, the result ...[more]

PMID: 27117225

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Project description:Regional soft tissue mechanical strain offers crucial insights into tissue's mechanical function and vital indicators for different related disorders. Tagging magnetic resonance imaging (tMRI) has been the standard method for assessing the mechanical characteristics of organs such as the heart, the liver, and the brain. However, constructing accurate artifact-free pixelwise strain maps at the native resolution of the tagged images has for decades been a challenging unsolved task. In this work, we developed an end-to-end deep-learning framework for pixel-to-pixel mapping of the two-dimensional Eulerian principal strains [Formula: see text] and [Formula: see text] directly from 1-1 spatial modulation of magnetization (SPAMM) tMRI at native image resolution using convolutional neural network (CNN). Four different deep learning conditional generative adversarial network (cGAN) approaches were examined. Validations were performed using Monte Carlo computational model simulations, and in-vivo datasets, and compared to the harmonic phase (HARP) method, a conventional and validated method for tMRI analysis, with six different filter settings. Principal strain maps of Monte Carlo tMRI simulations with various anatomical, functional, and imaging parameters demonstrate artifact-free solid agreements with the corresponding ground-truth maps. Correlations with the ground-truth strain maps were R = 0.90 and 0.92 for the best-proposed cGAN approach compared to R = 0.12 and 0.73 for the best HARP method for [Formula: see text] and [Formula: see text], respectively. The proposed cGAN approach's error was substantially lower than the error in the best HARP method at all strain ranges. In-vivo results are presented for both healthy subjects and patients with cardiac conditions (Pulmonary Hypertension). Strain maps, obtained directly from their corresponding tagged MR images, depict for the first time anatomical, functional, and temporal details at pixelwise native high resolution with unprecedented clarity. This work demonstrates the feasibility of using the deep learning cGAN for direct myocardial and liver Eulerian strain mapping from tMRI at native image resolution with minimal artifacts.

Dataset Information

Pronounced peptide selectivity for melanoma through tryptophan end-tagging.

Publications

Pronounced peptide selectivity for melanoma through tryptophan end-tagging.

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