Causal modeling using network ensemble simulations of genetic and gene expression data predicts genes involved in rheumatoid arthritis.
Tumor necrosis factor α (TNF-α) is a key regulator of inflammation and rheumatoid arthritis (RA). TNF-α 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 m...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2011-03-01
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| Series: | PLoS Computational Biology |
| Online Access: | http://europepmc.org/articles/PMC3053315?pdf=render |
| _version_ | 1818027024693854208 |
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| author | Heming Xing Paul D McDonagh Jadwiga Bienkowska Tanya Cashorali Karl Runge Robert E Miller Dave Decaprio Bruce Church Ronenn Roubenoff Iya G Khalil John Carulli |
| author_facet | Heming Xing Paul D McDonagh Jadwiga Bienkowska Tanya Cashorali Karl Runge Robert E Miller Dave Decaprio Bruce Church Ronenn Roubenoff Iya G Khalil John Carulli |
| author_sort | Heming Xing |
| collection | DOAJ |
| description | Tumor necrosis factor α (TNF-α) is a key regulator of inflammation and rheumatoid arthritis (RA). TNF-α 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-α 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-α blocker. The network ensemble built from pre-treated data captures TNF-α dependent mechanistic information, while the ensemble built from data collected under TNF-α blocker treatment captures TNF-α 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-α 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. |
| first_indexed | 2024-12-10T04:41:19Z |
| format | Article |
| id | doaj.art-d80bf7a0804f4a0aad4ebd11b36730c2 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-12-10T04:41:19Z |
| publishDate | 2011-03-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-d80bf7a0804f4a0aad4ebd11b36730c22022-12-22T02:01:52ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582011-03-0173e100110510.1371/journal.pcbi.1001105Causal modeling using network ensemble simulations of genetic and gene expression data predicts genes involved in rheumatoid arthritis.Heming XingPaul D McDonaghJadwiga BienkowskaTanya CashoraliKarl RungeRobert E MillerDave DecaprioBruce ChurchRonenn RoubenoffIya G KhalilJohn CarulliTumor necrosis factor α (TNF-α) is a key regulator of inflammation and rheumatoid arthritis (RA). TNF-α 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-α 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-α blocker. The network ensemble built from pre-treated data captures TNF-α dependent mechanistic information, while the ensemble built from data collected under TNF-α blocker treatment captures TNF-α 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-α 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.http://europepmc.org/articles/PMC3053315?pdf=render |
| spellingShingle | Heming Xing Paul D McDonagh Jadwiga Bienkowska Tanya Cashorali Karl Runge Robert E Miller Dave Decaprio Bruce Church Ronenn Roubenoff Iya G Khalil John Carulli Causal modeling using network ensemble simulations of genetic and gene expression data predicts genes involved in rheumatoid arthritis. PLoS Computational Biology |
| title | Causal modeling using network ensemble simulations of genetic and gene expression data predicts genes involved in rheumatoid arthritis. |
| title_full | Causal modeling using network ensemble simulations of genetic and gene expression data predicts genes involved in rheumatoid arthritis. |
| title_fullStr | Causal modeling using network ensemble simulations of genetic and gene expression data predicts genes involved in rheumatoid arthritis. |
| title_full_unstemmed | Causal modeling using network ensemble simulations of genetic and gene expression data predicts genes involved in rheumatoid arthritis. |
| title_short | Causal modeling using network ensemble simulations of genetic and gene expression data predicts genes involved in rheumatoid arthritis. |
| title_sort | causal modeling using network ensemble simulations of genetic and gene expression data predicts genes involved in rheumatoid arthritis |
| url | http://europepmc.org/articles/PMC3053315?pdf=render |
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