scholarly journals Integrative Network-Based Analysis Reveals Gene Networks and Novel Drug Repositioning Candidates for Alzheimer Disease

2021 ◽  
Vol 7 (5) ◽  
pp. e622
Author(s):  
Zachary F. Gerring ◽  
Eric R. Gamazon ◽  
Anthony White ◽  
Eske M. Derks

Background and ObjectivesTo integrate genome-wide association study data with tissue-specific gene expression information to identify coexpression networks, biological pathways, and drug repositioning candidates for Alzheimer disease.MethodsWe integrated genome-wide association summary statistics for Alzheimer disease with tissue-specific gene coexpression networks from brain tissue samples in the Genotype-Tissue Expression study. We identified gene coexpression networks enriched with genetic signals for Alzheimer disease and characterized the associated networks using biological pathway analysis. The disease-implicated modules were subsequently used as a molecular substrate for a computational drug repositioning analysis, in which we (1) imputed genetically regulated gene expression within Alzheimer disease implicated modules; (2) integrated the imputed gene expression levels with drug-gene signatures from the connectivity map to identify compounds that normalize dysregulated gene expression underlying Alzheimer disease; and (3) prioritized drug compounds and mechanisms of action based on the extent to which they normalize dysregulated expression signatures.ResultsGenetic factors for Alzheimer disease are enriched in brain gene coexpression networks involved in the immune response. Computational drug repositioning analyses of expression changes within the disease-associated networks retrieved known Alzheimer disease drugs (e.g., memantine) as well as biologically meaningful drug categories (e.g., glutamate receptor antagonists).DiscussionOur results improve the biological interpretation of genetic data for Alzheimer disease and provide a list of potential antidementia drug repositioning candidates for which the efficacy should be investigated in functional validation studies.

1997 ◽  
Vol 107 (1) ◽  
pp. 1-10 ◽  
Author(s):  
D. Doenecke ◽  
W. Albig ◽  
C. Bode ◽  
B. Drabent ◽  
K. Franke ◽  
...  

2015 ◽  
Vol 17 (6) ◽  
pp. 753-767 ◽  
Author(s):  
Jason Abernathy ◽  
Stéphane Panserat ◽  
Thomas Welker ◽  
Elisabeth Plagne-Juan ◽  
Dionne Sakhrani ◽  
...  

2001 ◽  
Vol 21 (1) ◽  
pp. 61-68 ◽  
Author(s):  
Jian Yi Li ◽  
Ruben J. Boado ◽  
William M. Pardridge

The blood–brain barrier (BBB) is formed by the brain microvascular endothelium, and the unique transport properties of the BBB are derived from tissue-specific gene expression within this cell. The current studies developed a gene microarray approach specific for the BBB by purifying the initial mRNA from isolated rat brain capillaries to generate tester cDNA. A polymerase chain reaction–based subtraction cloning method, suppression subtractive hybridization (SSH), was used, and the BBB cDNA was subtracted with driver cDNA produced from mRNA isolated from rat liver and kidney. Screening 5% of the subtracted tester cDNA resulted in identification of 50 gene products and more than 80% of those were selectively expressed at the BBB; these included novel gene sequences not found in existing databases, ESTs, and known genes that were not known to be selectively expressed at the BBB. Genes in the latter category include tissue plasminogen activator, insulin-like growth factor-2, PC-3 gene product, myelin basic protein, regulator of G protein signaling 5, utrophin, IκB, connexin-45, the class I major histocompatibility complex, the rat homologue of the transcription factors hbrm or EZH1, and organic anion transporting polypeptide type 2. Knowledge of tissue-specific gene expression at the BBB could lead to new targets for brain drug delivery and could elucidate mechanisms of brain pathology at the microvascular level.


2019 ◽  
Author(s):  
Tom G Richardson ◽  
Gibran Hemani ◽  
Tom R Gaunt ◽  
Caroline L Relton ◽  
George Davey Smith

AbstractBackgroundDeveloping insight into tissue-specific transcriptional mechanisms can help improve our understanding of how genetic variants exert their effects on complex traits and disease. By applying the principles of Mendelian randomization, we have undertaken a systematic analysis to evaluate transcriptome-wide associations between gene expression across 48 different tissue types and 395 complex traits.ResultsOverall, we identified 100,025 gene-trait associations based on conventional genome-wide corrections (P < 5 × 10−08) that also provided evidence of genetic colocalization. These results indicated that genetic variants which influence gene expression levels in multiple tissues are more likely to influence multiple complex traits. We identified many examples of tissue-specific effects, such as genetically-predicted TPO, NR3C2 and SPATA13 expression only associating with thyroid disease in thyroid tissue. Additionally, FBN2 expression was associated with both cardiovascular and lung function traits, but only when analysed in heart and lung tissue respectively.We also demonstrate that conducting phenome-wide evaluations of our results can help flag adverse on-target side effects for therapeutic intervention, as well as propose drug repositioning opportunities. Moreover, we find that exploring the tissue-dependency of associations identified by genome-wide association studies (GWAS) can help elucidate the causal genes and tissues responsible for effects, as well as uncover putative novel associations.ConclusionsThe atlas of tissue-dependent associations we have constructed should prove extremely valuable to future studies investigating the genetic determinants of complex disease. The follow-up analyses we have performed in this study are merely a guide for future research. Conducting similar evaluations can be undertaken systematically at http://mrcieu.mrsoftware.org/Tissue_MR_atlas/.


2010 ◽  
Vol 24 (S1) ◽  
Author(s):  
Raghunath Chatterjee ◽  
Vikas Rishi ◽  
Julian Rozenberg ◽  
Paramita Bhattacharya ◽  
Kimberly Glass ◽  
...  

2015 ◽  
Vol 65 (5) ◽  
pp. 485-493 ◽  
Author(s):  
Tamás Csont ◽  
Zsolt Murlasits ◽  
Dalma Ménesi ◽  
János Z. Kelemen ◽  
Péter Bencsik ◽  
...  

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