The unfolded protein response (UPR) is a eukaryotic signaling pathway linking protein flux through the endoplasmic reticulum to transcription and translational repression. Herein, we demonstrate UPR activation in the leukodystrophy Pelizaeus-Merzbacher disease (PMD) as well as in three mouse models of this disease and transfected fibroblasts expressing mutant protein. The CHOP protein, widely known as a proapoptotic transcription factor, modulates pathogenesis in the mouse models of PMD; however, this protein exhibits antiapoptotic activity. Together, these data show that the UPR has the potential to modulate disease severity in many cells expressing mutant secretory pathway proteins. Thus, PMD represents the first member of a novel class of disparate degenerative diseases for which UPR activation and signaling is the common pathogenic mechanism.
With the development of high-throughput experimental techniques such as microarray, mass spectrometry and large-scale mutagenesis, there is an increasing need to automatically annotate gene sets and identify the involved pathways. Although many pathway analysis tools are developed, new tools are still needed to meet the requirements for flexible or advanced analysis purpose. Here, we developed an R-based software package (SubpathwayMiner) for flexible pathway identification. SubpathwayMiner facilitates sub-pathway identification of metabolic pathways by using pathway structure information. Additionally, SubpathwayMiner also provides more flexibility in annotating gene sets and identifying the involved pathways (entire pathways and sub-pathways): (i) SubpathwayMiner is able to provide the most up-to-date pathway analysis results for users; (ii) SubpathwayMiner supports multiple species (∼100 eukaryotes, 714 bacteria and 52 Archaea) and different gene identifiers (Entrez Gene IDs, NCBI-gi IDs, UniProt IDs, PDB IDs, etc.) in the KEGG GENE database; (iii) the system is quite efficient in cooperating with other R-based tools in biology. SubpathwayMiner is freely available at http://cran.r-project.org/web/packages/SubpathwayMiner/.
Synergistic regulations among multiple microRNAs (miRNAs) are important to understand the mechanisms of complex post-transcriptional regulations in humans. Complex diseases are affected by several miRNAs rather than a single miRNA. So, it is a challenge to identify miRNA synergism and thereby further determine miRNA functions at a system-wide level and investigate disease miRNA features in the miRNA–miRNA synergistic network from a new view. Here, we constructed a miRNA–miRNA functional synergistic network (MFSN) via co-regulating functional modules that have three features: common targets of corresponding miRNA pairs, enriched in the same gene ontology category and close proximity in the protein interaction network. Predicted miRNA synergism is validated by significantly high co-expression of functional modules and significantly negative regulation to functional modules. We found that the MFSN exhibits a scale free, small world and modular architecture. Furthermore, the topological features of disease miRNAs in the MFSN are distinct from non-disease miRNAs. They have more synergism, indicating their higher complexity of functions and are the global central cores of the MFSN. In addition, miRNAs associated with the same disease are close to each other. The structure of the MFSN and the features of disease miRNAs are validated to be robust using different miRNA target data sets.
Background: Stroke is a leading cause of adult disability that can severely compromise patients' quality of life, yet no effective medication currently exists to accelerate rehabilitation. A variety of circular RNA (circRNAs) molecules are known to function in ischemic brain injury. Lentivirus-based expression systems have been widely used in basic studies of circRNAs, but safety issues with such delivery systems have limited exploration of potential therapeutic roles for circRNAs. Methods: Circular RNA SCMH1 (circSCMH1) was screened from the plasma of acute ischemic stroke (AIS) patients using circRNA microarrays. Engineered RVG-circSCMH1-extracellular vesicles (RVG-circSCMH1-EVs) were generated to selectively deliver circSCMH1 to the brain. Nissl staining was used to examine infarct size. Behavioral tasks were performed to evaluate motor functions in both rodent and nonhuman primate ischemic stroke models. Golgi staining and immunostaining were used to examine neuroplasticity and glial activation. Proteomic assays and RNA-seq data combined with transcriptional profiling were used to identify downstream targets of circSCMH1. Results: CircSCMH1 levels were significantly decreased in plasma of AIS patients, offering significant power in predicting stroke outcomes. The decreased levels of circSCMH1 were further confirmed in the plasma and peri-infarct cortex of photothrombotic (PT) stroke mice. Beyond demonstrating proof-of-concept for an RNA drug delivery technology, we observed that circSCMH1 treatment improved functional recovery post stroke in both mice and monkeys, and discovered that circSCMH1 enhanced the neuronal plasticity and also inhibited glial activation and peripheral immune cell infiltration. Mechanistically, circSCMH1 binds to the transcription factor MeCP2, thereby releasing repression of MeCP2 target gene transcription. Conclusions: RVG-circSCMH1-EVs afford protection by promoting functional recovery in the rodent and the nonhuman primate ischemic stroke models. Our study presents a potentially widely applicable nucleotide drug delivery technology and demonstrates the basic mechanism of how circRNAs can be therapeutically exploited to improve post-stroke outcomes.
BackgroundMicroRNAs (miRNAs) are important post-transcriptional regulators that have been demonstrated to play an important role in human diseases. Elucidating the associations between miRNAs and diseases at the systematic level will deepen our understanding of the molecular mechanisms of diseases. However, miRNA-disease associations identified by previous computational methods are far from completeness and more effort is needed.ResultsWe developed a computational framework to identify miRNA-disease associations by performing random walk analysis, and focused on the functional link between miRNA targets and disease genes in protein-protein interaction (PPI) networks. Furthermore, a bipartite miRNA-disease network was constructed, from which several miRNA-disease co-regulated modules were identified by hierarchical clustering analysis. Our approach achieved satisfactory performance in identifying known cancer-related miRNAs for nine human cancers with an area under the ROC curve (AUC) ranging from 71.3% to 91.3%. By systematically analyzing the global properties of the miRNA-disease network, we found that only a small number of miRNAs regulated genes involved in various diseases, genes associated with neurological diseases were preferentially regulated by miRNAs and some immunological diseases were associated with several specific miRNAs. We also observed that most diseases in the same co-regulated module tended to belong to the same disease category, indicating that these diseases might share similar miRNA regulatory mechanisms.ConclusionsIn this study, we present a computational framework to identify miRNA-disease associations, and further construct a bipartite miRNA-disease network for systematically analyzing the global properties of miRNA regulation of disease genes. Our findings provide a broad perspective on the relationships between miRNAs and diseases and could potentially aid future research efforts concerning miRNA involvement in disease pathogenesis.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.