Literature DB >> 32653926

Predicting mechanism of action of cellular perturbations with pathway activity signatures.

Yan Ren1, Siva Sivaganesan2, Nicholas A Clark1, Lixia Zhang1, Jacek Biesiada1, Wen Niu1, David R Plas3, Mario Medvedovic1.   

Abstract

MOTIVATION: Misregulation of signaling pathway activity is etiologic for many human diseases, and modulating activity of signaling pathways is often the preferred therapeutic strategy. Understanding the mechanism of action (MOA) of bioactive chemicals in terms of targeted signaling pathways is the essential first step in evaluating their therapeutic potential. Changes in signaling pathway activity are often not reflected in changes in expression of pathway genes which makes MOA inferences from transcriptional signatures (TSeses) a difficult problem.
RESULTS: We developed a new computational method for implicating pathway targets of bioactive chemicals and other cellular perturbations by integrated analysis of pathway network topology, the Library of Integrated Network-based Cellular Signature TSes of genetic perturbations of pathway genes and the TS of the perturbation. Our methodology accurately predicts signaling pathways targeted by the perturbation when current pathway analysis approaches utilizing only the TS of the perturbation fail.
AVAILABILITY AND IMPLEMENTATION: Open source R package paslincs is available at https://github.com/uc-bd2k/paslincs. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Entities:  

Mesh:

Year:  2020        PMID: 32653926      PMCID: PMC7751003          DOI: 10.1093/bioinformatics/btaa590

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  44 in total

Review 1.  Regulation and function of ribosomal protein S6 kinase (S6K) within mTOR signalling networks.

Authors:  Brian Magnuson; Bilgen Ekim; Diane C Fingar
Journal:  Biochem J       Date:  2012-01-01       Impact factor: 3.857

Review 2.  Adverse outcome pathways: a conceptual framework to support ecotoxicology research and risk assessment.

Authors:  Gerald T Ankley; Richard S Bennett; Russell J Erickson; Dale J Hoff; Michael W Hornung; Rodney D Johnson; David R Mount; John W Nichols; Christine L Russom; Patricia K Schmieder; Jose A Serrrano; Joseph E Tietge; Daniel L Villeneuve
Journal:  Environ Toxicol Chem       Date:  2010-03       Impact factor: 3.742

Review 3.  Systems toxicology: applications of toxicogenomics, transcriptomics, proteomics and metabolomics in toxicology.

Authors:  Wilbert H M Heijne; Anne S Kienhuis; Ben van Ommen; Rob H Stierum; John P Groten
Journal:  Expert Rev Proteomics       Date:  2005-10       Impact factor: 3.940

Review 4.  Network propagation: a universal amplifier of genetic associations.

Authors:  Lenore Cowen; Trey Ideker; Benjamin J Raphael; Roded Sharan
Journal:  Nat Rev Genet       Date:  2017-06-12       Impact factor: 53.242

5.  CePa: an R package for finding significant pathways weighted by multiple network centralities.

Authors:  Zuguang Gu; Jin Wang
Journal:  Bioinformatics       Date:  2013-01-10       Impact factor: 6.937

6.  Phenotypic screening of the ToxCast chemical library to classify toxic and therapeutic mechanisms.

Authors:  Nicole C Kleinstreuer; Jian Yang; Ellen L Berg; Thomas B Knudsen; Ann M Richard; Matthew T Martin; David M Reif; Richard S Judson; Mark Polokoff; David J Dix; Robert J Kavlock; Keith A Houck
Journal:  Nat Biotechnol       Date:  2014-05-18       Impact factor: 54.908

7.  Targeted inhibition of mammalian target of rapamycin signaling inhibits tumorigenesis of colorectal cancer.

Authors:  Pat Gulhati; Qingsong Cai; Jing Li; Jianyu Liu; Piotr G Rychahou; Suimin Qiu; Eun Y Lee; Scott R Silva; Kanika A Bowen; Tianyan Gao; B Mark Evers
Journal:  Clin Cancer Res       Date:  2009-11-24       Impact factor: 12.531

8.  Perturbation-response genes reveal signaling footprints in cancer gene expression.

Authors:  Michael Schubert; Bertram Klinger; Martina Klünemann; Anja Sieber; Florian Uhlitz; Sascha Sauer; Mathew J Garnett; Nils Blüthgen; Julio Saez-Rodriguez
Journal:  Nat Commun       Date:  2018-01-02       Impact factor: 14.919

9.  A Bayesian approach to accurate and robust signature detection on LINCS L1000 data.

Authors:  Yue Qiu; Tianhuan Lu; Hansaim Lim; Lei Xie
Journal:  Bioinformatics       Date:  2020-05-01       Impact factor: 6.937

10.  A comparison of gene set analysis methods in terms of sensitivity, prioritization and specificity.

Authors:  Adi L Tarca; Gaurav Bhatti; Roberto Romero
Journal:  PLoS One       Date:  2013-11-15       Impact factor: 3.240

View more
  3 in total

1.  Predicting mechanism of action of novel compounds using compound structure and transcriptomic signature coembedding.

Authors:  Gwanghoon Jang; Sungjoon Park; Sanghoon Lee; Sunkyu Kim; Sejeong Park; Jaewoo Kang
Journal:  Bioinformatics       Date:  2021-07-12       Impact factor: 6.937

2.  Chemical-induced gene expression ranking and its application to pancreatic cancer drug repurposing.

Authors:  Thai-Hoang Pham; Yue Qiu; Jiahui Liu; Steven Zimmer; Eric O'Neill; Lei Xie; Ping Zhang
Journal:  Patterns (N Y)       Date:  2022-02-04

3.  Connecting omics signatures and revealing biological mechanisms with iLINCS.

Authors:  Marcin Pilarczyk; Mehdi Fazel-Najafabadi; Michal Kouril; Behrouz Shamsaei; Juozas Vasiliauskas; Wen Niu; Naim Mahi; Lixia Zhang; Nicholas A Clark; Yan Ren; Shana White; Rashid Karim; Huan Xu; Jacek Biesiada; Mark F Bennett; Sarah E Davidson; John F Reichard; Kurt Roberts; Vasileios Stathias; Amar Koleti; Dusica Vidovic; Daniel J B Clarke; Stephan C Schürer; Avi Ma'ayan; Jarek Meller; Mario Medvedovic
Journal:  Nat Commun       Date:  2022-08-09       Impact factor: 17.694

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.