scholarly journals Corrigendum to “From phenotype to gene: Detecting disease-specific gene functional modules via a text-based human disease phenotype network construction” [FEBS Lett. 584 (2010) 3635-3643]

FEBS Letters ◽  
2012 ◽  
Vol 587 (4) ◽  
pp. 386-386
Author(s):  
Shihua Zhang ◽  
Chao Wu ◽  
Xia Li ◽  
Xi Chen ◽  
Wei Jiang ◽  
...  
2020 ◽  
Author(s):  
Vivek Sriram ◽  
Manu Shivakumar ◽  
Sang-Hyuk Jung ◽  
Lisa Bang ◽  
Anurag Verma ◽  
...  

AbstractSummaryGiven genetic associations from a PheWAS, a disease-disease network can be constructed where nodes represent phenotypes and edges represent shared genetic associations between phenotypes. To improve the accessibility of the visualization of shared genetic components across phenotypes, we developed the humaN-disEase phenoType MAp GEnerator (NETMAGE), a web-based tool that produces interactive phenotype network visualizations from summarized PheWAS results. Users can search the map by a variety of attributes, and they can select nodes to view information such as related phenotypes, associated SNPs, and other network statistics. As a test case, we constructed a network using UK BioBank PheWAS summary data. By examining the associations between phenotypes in our map, we can potentially identify novel instances of pleiotropy, where loci influence multiple phenotypic traits. Thus, our tool provides researchers with a means to identify prospective genetic targets for drug design, contributing to the exploration of personalized medicine.Availability and implementationOur service runs at https://hdpm.biomedinfolab.com. Source code can be downloaded at https://github.com/dokyoonkimlab/[email protected] informationSupplementary data and user guide are available at Bioinformatics online.


2021 ◽  
Vol 11 (8) ◽  
pp. 973
Author(s):  
Maria Cristina Petralia ◽  
Rosella Ciurleo ◽  
Alessia Bramanti ◽  
Placido Bramanti ◽  
Andrea Saraceno ◽  
...  

Schizophrenia (SCZ) is a severe psychiatric disorder with several clinical manifestations that include cognitive dysfunction, decline in motivation, and psychosis. Current standards of care treatment with antipsychotic agents are often ineffective in controlling the disease, as only one-third of SCZ patients respond to medications. The mechanisms underlying the pathogenesis of SCZ remain elusive. It is believed that inflammatory processes may play a role as contributing factors to the etiology of SCZ. Galectins are a family of β-galactoside-binding lectins that contribute to the regulation of immune and inflammatory responses, and previous reports have shown their role in the maintenance of central nervous system (CNS) homeostasis and neuroinflammation. In the current study, we evaluated the expression levels of the galectin gene family in post-mortem samples of the hippocampus, associative striatum, and dorsolateral prefrontal cortex from SCZ patients. We found a significant downregulation of LGALS8 (Galectin-8) in the hippocampus of SCZ patients as compared to otherwise healthy donors. Interestingly, the reduction of LGALS8 was disease-specific, as no modulation was observed in the hippocampus from bipolar nor major depressive disorder (MDD) patients. Prediction analysis identified TBL1XR1, BRF2, and TAF7 as potential transcription factors controlling LGALS8 expression. In addition, MIR3681HG and MIR4296 were negatively correlated with LGALS8 expression, suggesting a role for epigenetics in the regulation of LGALS8 levels. On the other hand, no differences in the methylation levels of LGALS8 were observed between SCZ and matched control hippocampus. Finally, ontology analysis of the genes negatively correlated with LGALS8 expression identified an enrichment of the NGF-stimulated transcription pathway and of the oligodendrocyte differentiation pathway. Our study identified LGALS8 as a disease-specific gene, characterizing SCZ patients, that may in the future be exploited as a potential therapeutic target.


2020 ◽  
Author(s):  
Zihu Guo ◽  
Yingxue Fu ◽  
Chao Huang ◽  
Chunli Zheng ◽  
Ziyin Wu ◽  
...  

AbstractRapid development of high-throughput technologies has permitted the identification of an increasing number of disease-associated genes (DAGs), which are important for understanding disease initiation and developing precision therapeutics. However, DAGs often contain large amounts of redundant or false positive information, leading to difficulties in quantifying and prioritizing potential relationships between these DAGs and human diseases. In this study, a network-oriented gene entropy approach (NOGEA) is proposed for accurately inferring master genes that contribute to specific diseases by quantitatively calculating their perturbation abilities on directed disease-specific gene networks. In addition, we confirmed that the master genes identified by NOGEA have a high reliability for predicting disease-specific initiation events and progression risk. Master genes may also be used to extract the underlying information of different diseases, thus revealing mechanisms of disease comorbidity. More importantly, approved therapeutic targets are topologically localized in a small neighborhood of master genes on the interactome network, which provides a new way for predicting new drug-disease associations. Through this method, 11 old drugs were newly identified and predicted to be effective for treating pancreatic cancer and then validated by in vitro experiments. Collectively, the NOGEA was useful for identifying master genes that control disease initiation and co-occurrence, thus providing a valuable strategy for drug efficacy screening and repositioning. NOGEA codes are publicly available at https://github.com/guozihuaa/NOGEA.


2019 ◽  
Vol 20 (3) ◽  
pp. 374-374
Author(s):  
Leona Gabryšová ◽  
Marisol Alvarez-Martinez ◽  
Raphaëlle Luisier ◽  
Luke S. Cox ◽  
Jan Sodenkamp ◽  
...  

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Katarzyna I. Szczerkowska ◽  
Silvia Petrezselyova ◽  
Jiri Lindovsky ◽  
Marcela Palkova ◽  
Jan Dvorak ◽  
...  

1997 ◽  
Vol 98 (2) ◽  
pp. 150
Author(s):  
M.L. Bittner ◽  
J. DeRisi ◽  
P.S. Meltzer ◽  
Y. Chen ◽  
L. Penland ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document