title: Causal modeling using network ensemble simulations of genetic and gene expression data predicts genes involved in rheumatoid arthritis.
abstract: 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.
authors.affiliation: Gene Network Sciences, Cambridge, Massachusetts, United States of America.
authors.forename: Heming
authors.initials: H
authors.lastname: Xing
authors.forename: Paul D
authors.initials: PD
authors.lastname: McDonagh
authors.forename: Jadwiga
authors.initials: J
authors.lastname: Bienkowska
authors.forename: Tanya
authors.initials: T
authors.lastname: Cashorali
authors.forename: Karl
authors.initials: K
authors.lastname: Runge
authors.forename: Robert E
authors.initials: RE
authors.lastname: Miller
authors.forename: Dave
authors.initials: D
authors.lastname: Decaprio
authors.forename: Bruce
authors.initials: B
authors.lastname: Church
authors.forename: Ronenn
authors.initials: R
authors.lastname: Roubenoff
authors.forename: Iya G
authors.initials: IG
authors.lastname: Khalil
authors.forename: John
authors.initials: J
authors.lastname: Carulli
mesh_terms: D000069594:Abatacept; D018501:Antirheumatic Agents / Q000627:therapeutic use; D001172:Arthritis, Rheumatoid / Q000235:genetics*; D003198:Computer Simulation; D015870:Gene Expression; D020869:Gene Expression Profiling; D006801:Humans; D018796:Immunoconjugates / Q000627:therapeutic use; D007378:Interleukins / Q000235:genetics / Q000378:metabolism; D051049:Sphingosine N-Acyltransferase / Q000235:genetics / Q000378:metabolism; D014409:Tumor Necrosis Factor-alpha / Q000627:therapeutic use
chemical_list: D018501:Antirheumatic Agents; C496971:IL32 protein, human; D018796:Immunoconjugates; D007378:Interleukins; D014409:Tumor Necrosis Factor-alpha; D000069594:Abatacept; C505722:CERS5 protein, human; D051049:Sphingosine N-Acyltransferase