Name
Title | ||
|---|---|---|
Causal Modeling Using Network Ensemble Simulations Of Genetic And Gene Expression Data Predicts Genes Involved In Rheumatoid Arthritis |
Abstract | ||
|---|---|---|
Tumor necrosis factor alpha (TNF-alpha) is a key regulator of inflammation and rheumatoid arthritis (RA). TNF-alpha blocker therapies can be very effective for a substantial number of patients, but fail to work in one third of patients who show no or minimal response. It is therefore necessary to discover new molecular intervention points involved in TNF-alpha blocker treatment of rheumatoid arthritis patients. We describe a data analysis strategy for predicting gene expression measures that are critical for rheumatoid arthritis using a combination of comprehensive genotyping, whole blood gene expression profiles and the component clinical measures of the arthritis Disease Activity Score 28 (DAS28) score. Two separate network ensembles, each comprised of 1024 networks, were built from molecular measures from subjects before and 14 weeks after treatment with TNF-alpha blocker. The network ensemble built from pre-treated data captures TNF-alpha dependent mechanistic information, while the ensemble built from data collected under TNF-alpha blocker treatment captures TNF-alpha independent mechanisms. In silico simulations of targeted, personalized perturbations of gene expression measures from both network ensembles identify transcripts in three broad categories. Firstly, 22 transcripts are identified to have new roles in modulating the DAS28 score; secondly, there are 6 transcripts that could be alternative targets to TNF-alpha blocker therapies, including CD86 - a component of the signaling axis targeted by Abatacept (CTLA4-Ig), and finally, 59 transcripts that are predicted to modulate the count of tender or swollen joints but not sufficiently enough to have a significant impact on DAS28. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1371/journal.pcbi.1001105 | PLOS COMPUTATIONAL BIOLOGY |
Keywords | Field | DocType |
causal models,tumor necrosis factor,genetics,gene expression,gene expression profiling,data capture,data analysis,data collection,tumor necrosis factor alpha,interleukins,computer simulation,whole blood | Arthritis,Regulator,Disease,Tumor necrosis factor alpha,Abatacept,Biology,Rheumatoid arthritis,Bioinformatics,Gene expression profiling,In silico | Journal |
Volume | Issue | ISSN |
7 | 3 | 1553-734X |
Citations | PageRank | References |
1 | 0.35 | 10 |
Authors | ||
11 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Heming Xing | 1 | 1 | 0.35 |
| Paul D McDonagh | 2 | 11 | 1.05 |
| Jadwiga Bienkowska | 3 | 12 | 1.94 |
| Tanya Cashorali | 4 | 5 | 1.76 |
| Karl Runge | 5 | 5 | 2.11 |
| Robert E. Miller | 6 | 23 | 3.34 |
| Dave Decaprio | 7 | 1 | 0.35 |
| Bruce Church | 8 | 1 | 0.35 |
| Ronenn Roubenoff | 9 | 1 | 0.35 |
| Iya G Khalil | 10 | 1 | 0.69 |
| John Carulli | 11 | 20 | 5.36 |