skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Attention:

The NSF Public Access Repository (PAR) system and access will be unavailable from 11:00 PM ET on Friday, July 11 until 2:00 AM ET on Saturday, July 12 due to maintenance. We apologize for the inconvenience.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: Network-based modeling of drug effects on disease module in systemic sclerosis
Abstract The network-based proximity between drug targets and disease genes can provide novel insights regarding the repercussions, interplay, and repositioning of drugs in the context of disease. Current understanding and treatment for reversing of the fibrotic process is limited in systemic sclerosis (SSc). We have developed a network-based analysis for drug effects that takes into account the human interactome network, proximity measures between drug targets and disease-associated genes, genome-wide gene expression and disease modules that emerge through pertinent analysis. Currently used and potential drugs showed a wide variation in proximity to SSc-associated genes and distinctive proximity to the SSc-relevant pathways, depending on their class and targets. Tyrosine kinase inhibitors (TyKIs) approach disease gene through multiple pathways, including both inflammatory and fibrosing processes. The SSc disease module includes the emerging molecular targets and is in better accord with the current knowledge of the pathophysiology of the disease. In the disease-module network, the greatest perturbing activity was shown by nintedanib, followed by imatinib, dasatinib, and acetylcysteine. Suppression of the SSc-relevant pathways and alleviation of the skin fibrosis was remarkable in the inflammatory subsets of the SSc patients receiving TyKI therapy. Our results show that network-based drug-disease proximity offers a novel perspective into a drug’s therapeutic effect in the SSc disease module. This could be applied to drug combinations or drug repositioning, and be helpful guiding clinical trial design and subgroup analysis.  more » « less
Award ID(s):
1934568
PAR ID:
10284264
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
Scientific Reports
Volume:
10
Issue:
1
ISSN:
2045-2322
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ; (, G3 Genes|Genomes|Genetics)
    Arbeitman, M (Ed.)
    Abstract High-risk human papillomaviruses (HPVs) 16 and 18 are responsible for more than 70% of cervical cancers and majority of other HPV-associated cancers world-wide. Current treatments for these cancers have limited efficacy, which in turn has resulted in disease recurrence and poor survival rates in advanced disease stages. Hence, there is a significant need for development of novel molecularly-targeted therapeutics. This can only be achieved through improved understanding of disease mechanism. Recently, we developed a Drosophila model of HPV18E6 plus human E3 ubiquitin ligase (hUBE3A) and demonstrated that the E6-induced cellular abnormalities are conserved between humans and flies. Subsequently, we demonstrated that reduced level and activity of IKKβ, a regulator of NF-κB, suppresses the cellular abnormalities induced by E6 oncoprotein and that the interaction of IKKβ and E6 is conserved in human cells. In this study, we performed transcriptomic analysis to identify differentially expressed genes that play a role in IKKβ-mediated suppression of E6-induced defects. Transcriptome analysis identified 215 genes whose expression was altered due to reduced levels of IKKβ. Of these 215 genes, 151 genes showed annotations. These analyses were followed by functional genetic interaction screen using RNAi, overexpression, and mutant fly strains for identified genes. The screen identified several genes including genes involved in Hippo and Toll pathways as well as junctional complexes whose downregulation or upregulation resulted in alterations of E6-induced defects. Subsequently, RT-PCR analysis was performed for validation of altered gene expression level for a few representative genes. Our results indicate an involvement for Hippo and Toll pathways in IKKβ-mediated suppression of E6 + hUBE3A-induced cellular abnormalities. Therefore, this study enhances our understanding of the mechanisms underlying HPV-induced cancer and can potentially lead to identification of novel drug targets for cancers associated with HPV. 
    more » « less
  2. ; (, Artificial intelligence in medicine)
    Drug repositioning has drawn significant attention for drug development in pharmaceutical research and industry, because of its advantages in cost and time compared with the de novo drug development. The availability of biomedical databases and online health-related information, as well as the high-performance computing, empowers the development of computational drug repositioning methods. In this work, we developed a systematic approach that identifies repositioning drugs based on heterogeneous network mining using both pharmaceutical databases (PharmGKB and SIDER) and online health community (MedHelp). By utilizing adverse drug reactions (ADRs) as the intermediate, we constructed a heterogeneous health network containing drugs, diseases, and ADRs, and developed path-based heterogeneous network mining approaches for drug repositioning. Additionally, we investigated on how the data sources affect the performance on drug repositioning. Experiment results showed that combining both PharmKGB and MedHelp identified 479 repositioning drugs, which are more than the repositioning drugs discovered by other alternatives. In addition, 31% of the 479 of the discovered repositioning drugs were supported by evidence from PubMed. 
    more » « less
  3. ; ; (, Nature Communications)
    null (Ed.)
    Abstract Most diseases disrupt multiple proteins, and drugs treat such diseases by restoring the functions of the disrupted proteins. How drugs restore these functions, however, is often unknown as a drug’s therapeutic effects are not limited to the proteins that the drug directly targets. Here, we develop the multiscale interactome, a powerful approach to explain disease treatment. We integrate disease-perturbed proteins, drug targets, and biological functions into a multiscale interactome network. We then develop a random walk-based method that captures how drug effects propagate through a hierarchy of biological functions and physical protein-protein interactions. On three key pharmacological tasks, the multiscale interactome predicts drug-disease treatment, identifies proteins and biological functions related to treatment, and predicts genes that alter a treatment’s efficacy and adverse reactions. Our results indicate that physical interactions between proteins alone cannot explain treatment since many drugs treat diseases by affecting the biological functions disrupted by the disease rather than directly targeting disease proteins or their regulators. We provide a general framework for explaining treatment, even when drugs seem unrelated to the diseases they are recommended for. 
    more » « less
  4. ; ; (, Briefings in Bioinformatics)
    Abstract The escalating drug addiction crisis in the United States underscores the urgent need for innovative therapeutic strategies. This study embarked on an innovative and rigorous strategy to unearth potential drug repurposing candidates for opioid and cocaine addiction treatment, bridging the gap between transcriptomic data analysis and drug discovery. We initiated our approach by conducting differential gene expression analysis on addiction-related transcriptomic data to identify key genes. We propose a novel topological differentiation to identify key genes from a protein–protein interaction network derived from DEGs. This method utilizes persistent Laplacians to accurately single out pivotal nodes within the network, conducting this analysis in a multiscale manner to ensure high reliability. Through rigorous literature validation, pathway analysis and data-availability scrutiny, we identified three pivotal molecular targets, mTOR, mGluR5 and NMDAR, for drug repurposing from DrugBank. We crafted machine learning models employing two natural language processing (NLP)-based embeddings and a traditional 2D fingerprint, which demonstrated robust predictive ability in gauging binding affinities of DrugBank compounds to selected targets. Furthermore, we elucidated the interactions of promising drugs with the targets and evaluated their drug-likeness. This study delineates a multi-faceted and comprehensive analytical framework, amalgamating bioinformatics, topological data analysis and machine learning, for drug repurposing in addiction treatment, setting the stage for subsequent experimental validation. The versatility of the methods we developed allows for applications across a range of diseases and transcriptomic datasets. 
    more » « less
  5. ; ; ; ; ; ; ; ; ; ; et al (, Genes)
    Arachidonic acid (AA) metabolites have been associated with several diseases across various organ systems, including the cardiovascular, pulmonary, and renal systems. Lipid mediators generated from AA oxidation have been studied to control macrophages, T-cells, cytokines, and fibroblasts, and regulate inflammatory mediators that induce vascular remodeling and dysfunction. AA is metabolized by cyclooxygenase (COX), lipoxygenase (LOX), and cytochrome P450 (CYP) to generate anti-inflammatory, pro-inflammatory, and pro-resolutory oxidized lipids. As comorbid states such as diabetes, hypertension, and obesity become more prevalent in cardiovascular disease, studying the expression of AA pathway genes and their association with these diseases can provide unique pathophysiological insights. In addition, the AA pathway of oxidized lipids exhibits diverse functions across different organ systems, where a lipid can be both anti-inflammatory and pro-inflammatory depending on the location of metabolic activity. Therefore, we aimed to characterize the gene expression of these lipid enzymes and receptors throughout multi-organ diseases via a transcriptomic meta-analysis using the Gene Expression Omnibus (GEO) Database. In our study, we found that distinct AA pathways were expressed in various comorbid conditions, especially those with prominent inflammatory risk factors. Comorbidities, such as hypertension, diabetes, and obesity appeared to contribute to elevated expression of pro-inflammatory lipid mediator genes. Our results demonstrate that expression of inflammatory AA pathway genes may potentiate and attenuate disease; therefore, we suggest further exploration of these pathways as therapeutic targets to improve outcomes. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email