Neuroprotective Properties of Quinone Reductase 2 Inhibitor M-11, a 2-Mercaptobenzimidazole Derivative

Voronin, M. V.; Kadnikov, I. A.; Зайнуллина, Л. Ф.; Logvinov, I. O.; Verbovaya, Ekaterina R; Антипова, Т. А.; Vakhitova, Yu. V.; Seredenin, S. B.

doi:10.3390/ijms222313061

Cited by 13 publications

(9 citation statements)

References 108 publications

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“…One of the predicted 3-hop MOA paths (“Entinostat” → “decreases activity of” → “HDAC1 gene” → “interacts with” → “Histone H4” → “gene associated with condition” → “Huntington’s disease”) are supported by the previous research [Shukla and Tekwani, 2020, Yu et al, 2009]. Primaquine is predicted to act on the NQ02 gene and the IKBKG gene to play a therapeutic role in neurodegenerative disease which is reported in [Voronin et al, 2021, Singh and Singh, 2020]. Isradipine is predicted to have a potential therapeutic effect for HD by mainly regulating the genes of the Calcium Voltage-Gated Channel (e.g., CACNA1S, CACNA1D, CACNA1C, CACNB2, CACNA2D2) which might be associated with the symptoms (e.g., chorea, depression and dementia) of HD [Yagami et al, 2012] while Amifampridine is predicted to regulate the genes of the Potassium Voltage-Gated Channel to associate with HD [Noh et al, 2019].…”

Section: Other Experiments Resultssupporting

confidence: 53%

KGML-xDTD: A Knowledge Graph-based Machine Learning Framework for Drug Treatment Prediction and Mechanism Description

Liu

Zhou

et al. 2022

Preprint

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Computational drug repurposing is a cost- and time-efficient method to identify new indications of approved or experimental drugs/compounds. It is especially critical for emerging and/or orphan diseases due to its cheaper investment and shorter research cycle compared with traditional wet-lab drug discovery approaches. However, the underlying mechanisms of action between repurposed drugs and their target diseases remain largely unknown, which is still an unsolved issue in existing repurposing methods. As such, computational drug repurposing has not been widely adopted in clinical settings. In this work, based on a massive biomedical knowledge graph, we propose a computational drug repurposing framework that not only predicts the treatment probabilities between drugs and diseases but also predicts the path-based, testable mechanisms of action (MOAs) as their biomedical explanations. Specifically, we utilize the GraphSAGE model in an unsupervised manner to integrate each entity's neighborhood information and employ a Random Forest model to predict the treatment probabilities between pairs of drugs and diseases. Moreover, we train an adversarial actor-critic reinforcement learning model to predict the potential MOA for explaining drug purposing. To encourage the model to find biologically reasonable paths, we utilize the curated molecular interactions of drugs and a PubMed-publication-based concept distance to extract potential drug MOA paths from the knowledge graph as "demonstration paths" to guide the model during the process of path-finding. Comprehensive experiments and case studies show that the proposed framework outperforms state-of-the-art baselines in both predictive performance of drug repurposing and explanatory performance of recapitulating human-curated DrugMechDB-based paths.

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Section: Other Experiments Resultssupporting

confidence: 53%

KGML-xDTD: A Knowledge Graph-based Machine Learning Framework for Drug Treatment Prediction and Mechanism Description

Liu

Zhou

et al. 2022

Preprint

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“…The analyses above revealed that this genetic variant may disrupt LIN28 regulations on the splicing and editing events of the two adjacent genes. One gene of them ( NQO2 ) has showed its ability in neurodegenerative pathogenesis due to its roles in the increase of free radicals production under enhanced generation of quinone derivatives of catecholamines (78). Thus, the RNA editing QTL causing the phenotype changes of this gene could be pointed out as a potential biomarker of neurodegeneration.…”

Section: Resultsmentioning

confidence: 99%

Genetic control of RNA editing in Neurodegenerative disease

Xue

Yang

et al. 2022

Preprint

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A-to-I RNA editing diversifies human transcriptome to confer its functional effects on the downstream genes or regulations, potentially involving in neurodegenerative pathogenesis. Its variabilities are attributed to multiple regulators, including the key factor of genetic variant. To comprehensively investigate the potentials of neurodegenerative disease-susceptibility variants from the view of A-to-I RNA editing, we analyzed matched genetic and transcriptomic data of 1,596 samples across nine brain tissues and whole blood from two large consortiums, Accelerating Medicines Partnership - Alzheimer's Disease (AMP-AD) and Parkinson's Progression Markers Initiative (PPMI). The large-scale and genome-wide identification of 95,637 RNA editing quantitative trait loci revealed the preferred genetic effects on adjacent editing events. Furthermore, to explore the underlying mechanisms of the genetic controls of A-to-I RNA editing, several top RNA binding proteins were pointed out, such as EIF4A3, U2AF2, NOP58, FBL, NOP56, and DHX9, since their regulations on multiple RNA editing events probably interfered by these genetic variants. Moreover, these variants may also contribute to the variability of other molecular phenotypes associated with RNA editing, including the functions of four proteins, expressions of 148 genes, and splicing of 417 events. All the analyses results shown in NeuroEdQTL (https://relab.xidian.edu.cn/NeuroEdQTL/) constituted a unique resource for the understanding of neurodegenerative pathogenesis from genotypes to phenotypes related to A-to-I RNA editing.

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“…For example, we found 8 genes whose inferred gr-expression in the putamen were associated with variation in regional homogeneity (p ≤ 0.005). This included NQO2, whose mutations have been linked to several neurodegenerative diseases [39][40][41] . Across all ten regions, we found a total of 51 unique genes associated with regional homogeneity and 42 unique genes associated with ALFF.…”

Section: Links Between Individual Variation In Gene Expression and Ne...mentioning

confidence: 99%

Integration of estimated regional gene expression with neuroimaging and clinical phenotypes at biobank scale

Hoang¹,

Sardaripour²,

Chen³

et al. 2022

Preprint

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Recent studies have revealed mappings between brain-wide gene expression profiles and a range of brain structure and activity phenotypes. Most of these studies have used group-averaged expression data. While informative, this approach cannot capture the high degree of individual variation in brain phenotypes. The investigation of this variation to date has been limited by availability of gene expression and neuroimaging data for many individuals. Here, we addressed this limitation by adopting PrediXcan, an established framework for inferring genetically regulated gene expression using localized genetic variants. This framework has allowed us to study the otherwise inaccessible expression profiles for brain regions across many individuals. We used this approach to identify genes whose inferred expression across brain regions of interest correlated with a range of phenotypes. Our analyses bridge a gap in human neuroimaging by enabling the study of associations between individual-level gene expression and brain structure and activity. Ultimately, this approach can help reveal mechanistic pathways from gene expression to healthy or diseased brain function.

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Neuroprotective Properties of Quinone Reductase 2 Inhibitor M-11, a 2-Mercaptobenzimidazole Derivative

Cited by 13 publications

References 108 publications

KGML-xDTD: A Knowledge Graph-based Machine Learning Framework for Drug Treatment Prediction and Mechanism Description

KGML-xDTD: A Knowledge Graph-based Machine Learning Framework for Drug Treatment Prediction and Mechanism Description

Genetic control of RNA editing in Neurodegenerative disease

Integration of estimated regional gene expression with neuroimaging and clinical phenotypes at biobank scale

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