Drug delivery into the central nervous system (CNS) is challenging due to the blood-brain barrier (BBB) and drug delivery into the brain overcoming the BBB can be achieved using nanoparticles such as dendrimers. The conventional cationic dendrimers used are highly toxic. Therefore, the present study investigates the role of novel mixed surface dendrimers, which have potentially less toxicity and can cross the BBB when administered through the carotid artery in mice. In vitro experiments investigated the uptake of amine dendrimers (G1-NH 2 and G4-NH 2 ) and novel dendrimers (G1-90/10 and G4-90/10) by primary cortical cultures. In vivo experiments involved transplantation of G4-90/10 into mice through (1) invasive intracranial injections into the striatum; and (2) less invasive carotid injections. The animals were sacrificed 24-h and 1-week post-transplantations and their brains were analyzed. In vivo experiments proved that the G4-90/10 can cross the BBB when injected through the carotid artery and localize within neurons and glial cells. The dendrimers were found to migrate through the corpus callosum 1-week post intracranial injection. Immunohistochemistry showed that the migrating cells are the dendrimer-infected glial cells. Overall, our results suggest that poly-amidoamine (PAMAM) dendrimers may be used as a minimally invasive means to deliver biomolecules for treating neurological diseases or disorders Keywords: PAMAM dendrimer nanoparticle; blood-brain barrier; non-invasive delivery; bio-distribution and uptake; neurodegenerative diseases Int.
Background Assigning every human gene to specific functions, diseases and traits is a grand challenge in modern genetics. Key to addressing this challenge are computational methods, such as supervised learning and label propagation, that can leverage molecular interaction networks to predict gene attributes. In spite of being a popular machine-learning technique across fields, supervised learning has been applied only in a few network-based studies for predicting pathway-, phenotype- or disease-associated genes. It is unknown how supervised learning broadly performs across different networks and diverse gene classification tasks, and how it compares to label propagation, the widely benchmarked canonical approach for this problem. Results In this study, we present a comprehensive benchmarking of supervised learning for network-based gene classification, evaluating this approach and a classic label propagation technique on hundreds of diverse prediction tasks and multiple networks using stringent evaluation schemes. We demonstrate that supervised learning on a gene’s full network connectivity outperforms label propagaton and achieves high prediction accuracy by efficiently capturing local network properties, rivaling label propagation’s appeal for naturally using network topology. We further show that supervised learning on the full network is also superior to learning on node embeddings (derived using node2vec), an increasingly popular approach for concisely representing network connectivity. These results show that supervised learning is an accurate approach for prioritizing genes associated with diverse functions, diseases and traits and should be considered a staple of network-based gene classification workflows. Availability and implementation The datasets and the code used to reproduce the results and add new gene classification methods have been made freely available. Contact arjun@msu.edu Supplementary information Supplementary data are available at Bioinformatics online.
Background Constructing gene coexpression networks is a powerful approach for analyzing high-throughput gene expression data towards module identification, gene function prediction, and disease-gene prioritization. While optimal workflows for constructing coexpression networks, including good choices for data pre-processing, normalization, and network transformation, have been developed for microarray-based expression data, such well-tested choices do not exist for RNA-seq data. Almost all studies that compare data processing and normalization methods for RNA-seq focus on the end goal of determining differential gene expression. Results Here, we present a comprehensive benchmarking and analysis of 36 different workflows, each with a unique set of normalization and network transformation methods, for constructing coexpression networks from RNA-seq datasets. We test these workflows on both large, homogenous datasets and small, heterogeneous datasets from various labs. We analyze the workflows in terms of aggregate performance, individual method choices, and the impact of multiple dataset experimental factors. Our results demonstrate that between-sample normalization has the biggest impact, with counts adjusted by size factors producing networks that most accurately recapitulate known tissue-naive and tissue-aware gene functional relationships. Conclusions Based on this work, we provide concrete recommendations on robust procedures for building an accurate coexpression network from an RNA-seq dataset. In addition, researchers can examine all the results in great detail at https://krishnanlab.github.io/RNAseq_coexpression to make appropriate choices for coexpression analysis based on the experimental factors of their RNA-seq dataset.
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