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Lipid Nanoparticle Induced Immunomodulatory Effects of siRNA
Published in Raj Bawa, János Szebeni, Thomas J. Webster, Gerald F. Audette, Immune Aspects of Biopharmaceuticals and Nanomedicines, 2019
Ranjita Shegokar, Prabhat Mishra
Viral delivery systems are mostly used to deliver DNA plasmids or precursor molecules [36–38]. They exert high transduction efficacy, due to the inherent ability of viruses to transport genetic material into cells [39–41]. However, the potential of mutagenicity or oncogenesis, several host immune responses, and the high cost of production limit their application. For these reasons, different kinds of nonviral siRNA delivery systems have been explored [42, 43]. However, siRNAs require more effective delivery systems that allow siRNA stabilization, specific cell recognition, internalization and subcellular localization to the cytoplasm of target tissues and cells to exert it therapeutic effect [44]. Many nonviral carriers used to deliver DNA for gene therapy have been adopted for siRNA delivery. Cationic lipids and polymers are two major classes of nonviral delivery carriers that can form complexes with negatively charged siRNA.
Polymeric Micelles for Formulation of Anti-Cancer Drugs
Published in Mansoor M. Amiji, Nanotechnology for Cancer Therapy, 2006
Helen Lee, Patrick Lim Soo, Jubo Liu, Mark Butler, Christine Allen
Shuai et al. reported delayed cell death in MCF-7 breast cancer cells when using a doxorubicin formulated PEG-b-PCL micelle system in comparison to free doxorubicin.41 The confocal microscopy images showed that the micelle-incorporated doxorubicin resides in the cytoplasm, and free doxorubicin accumulates quickly in the cell nucleus. The delayed cell death could be due to the difference in the subcellular localization of free and micellized drug because doxorubicin can only exert its cytotoxic effect after reaching the nucleus.41 Interestingly, as reported by Yoo and Park, doxorubicin-conjugated PEG-b-PLGA micelles, led to a 10-fold increase in the cytotoxicity of this drug. However, in this case, doxorubicin was found to localize in both the cytoplasm and the nucleus when delivered by micelles.77 The discrepancy between the intracellular localization of doxorubicin formulated in the two distinct systems may be explained by their different drug release profiles. The PCL core is more hydrophobic than the PLGA core and can, therefore, provide better drug retention. Although doxorubicin was conjugated to the PLGA system that should result in a much slower release, the hydrolytic linkage is susceptible to cleavage and results in a faster release of the drug when compared to the PCL system. The cytotoxicity observed in the PEG-b-PLGA micelle system can be attributed to the doxorubicin that has been cleaved from the copolymer and released from the micelles because the conjugated drug was found to be non-cytotoxic.16,32
Patient Stratification and Treatment Response Prediction
Published in Inna Kuperstein, Emmanuel Barillot, Computational Systems Biology Approaches in Cancer Research, 2019
Inna Kuperstein, Emmanuel Barillot
Examples: High Content Screening has been a trigger for the use of machine learning to microscopy data. Indeed, the first large-scale applications of cell classification were in the field of protein localization, where the objective was to investigate the subcellular localization of proteins. As individual proteins can usually not be resolved, the patterns were described by general texture features.25–27 Interestingly, similar or identical features could also be used to classify morphological phenotypes, complemented by shape features which are irrelevant for the classification of localization patterns.15,17 Morphological phenotyping by machine learning has also been applied to large-scale live cell imaging data18 to study the morphological changes of cells and cellular compartments over time. Also, phenotyping is not limited to morphologies and localization patterns: Trajectories can also be described by dedicated movement features, and with unsupervised learning techniques, we can identify the different movement types in a set of live cell imaging experiments.28 One of the most recent applications of machine learning to large-scale screening data deals with the spatial aspects of gene expression, which can be studied by single molecule in situ hybridization (smFISH). With this technique, we can visualize individual RNA molecules in cells and tissues. In contrast to protein localization assays, we can resolve individual molecules and thereby represent each cell by a point cloud, that can be described with features from spatial statistics. As for this type of data it is more difficult to obtain robust manual annotations, it is also possible to train classifiers from simulated microscopy data,23 opening up interesting perspectives to link physical and statistical models.
Identification and characterization of candidates involved in production of OMEGAs in microalgae: a gene mining and phylogenomic approach
Published in Preparative Biochemistry and Biotechnology, 2018
Vikas U. Kapase, Asha A. Nesamma, Pannaga P. Jutur
The subcellular localization defines the spatial organization of proteins with reference to their function and provides with clues in understanding their interactions with other biomolecules at cellular level.[44] Most of these tools developed has improved the accuracy of predicting subcellular localization in 11 different locations such as cytosol, endoplasmic reticulum, extracellular space, golgi, plasma membrane, mitochondria, nucleus, peroxisome, chloroplast, cytoskeleton, lysosome, and vacuole.[45] Perhaps, in silico approach provides fast and accurate prediction of subcellular localization for these uncharacterized proteins. In the present study, subcellular localization of OMEGA biosynthetic proteins has been performed by TargetP,[19] ngLOC,[20,21] and Cello[22] based up on their unique algorithms. Here, the objective of using more than one tool was to improve the prediction specificity and to rule out the occurrences of false positives and negatives.[46] Our data analysis (Figure 2) showed that maximum proteins are located in chloroplast (44%), mitochondria (16%), and nucleus (17%) while 23% proteins are distributed in other locations such as endoplasmic reticulum, cytosol, peroxisomes, vacuoles, and plasma membrane. This information regarding subcellular localization would certainly help in targeting the rate-limiting genes and/or enzymes to enhance the production of OMEGA’s among microalgae.