ArticleBMC bioinformatics2009
Integrative modeling of transcriptional regulation in response to antirheumatic therapy.
Article in BMC bioinformatics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
25 citing papers in PubMed, 39 citations in OpenAlex.
- Transcriptome profiling of peripheral blood immune cell populations in multiple sclerosis patients before and during treatment with a sphingosine-1-phosphate receptor modulator.CNS neuroscience & therapeutics · 2018Article
- LASSIM-A network inference toolbox for genome-wide mechanistic modeling.PLoS computational biology · 2017Article
- Review
- High-Density Peptide Microarray Analysis of IgG Autoantibody Reactivities in Serum and Cerebrospinal Fluid of Multiple Sclerosis Patients.Molecular & cellular proteomics : MCP · 2016Article
- How to Predict Molecular Interactions between Species?Frontiers in microbiology · 2016Review
- Master regulators of infiltrate recruitment in autoimmune disease identified through network-based molecular deconvolution.Cell systems · 2015Article
- Data- and knowledge-based modeling of gene regulatory networks: an update.EXCLI journal · 2015Review
- CyNetworkBMA: a Cytoscape app for inferring gene regulatory networks.Source code for biology and medicine · 2015Article
- NERI: network-medicine based integrative approach for disease gene prioritization by relative importance.BMC bioinformatics · 2015Article
- Inference of the Xenopus tropicalis embryonic regulatory network and spatial gene expression patterns.BMC systems biology · 2014Article
- A bayesian framework that integrates heterogeneous data for inferring gene regulatory networks.Frontiers in bioengineering and biotechnology · 2014Review
- Interferon-beta therapy in multiple sclerosis: the short-term and long-term effects on the patients' individual gene expression in peripheral blood.Molecular neurobiology · 2013Article
- Dynamic modelling of microRNA regulation during mesenchymal stem cell differentiation.BMC systems biology · 2013Article
- Glatiramer acetate treatment effects on gene expression in monocytes of multiple sclerosis patients.Journal of neuroinflammation · 2013Article
- MicroRNA expression changes during interferon-beta treatment in the peripheral blood of multiple sclerosis patients.International journal of molecular sciences · 2013Article
- Inference of dynamical gene-regulatory networks based on time-resolved multi-stimuli multi-experiment data applying NetGenerator V2.0.BMC systems biology · 2013Article
- Global transcriptome sequencing identifies chlamydospore specific markers in Candida albicans and Candida dubliniensis.PloS one · 2013Article
- The extended TILAR approach: a novel tool for dynamic modeling of the transcription factor network regulating the adaption to in vitro cultivation of murine hepatocytes.BMC systems biology · 2012Article
- Integrating external biological knowledge in the construction of regulatory networks from time-series expression data.BMC systems biology · 2012Article
- Network analysis of transcriptional regulation in response to intramuscular interferon-β-1a multiple sclerosis treatment.The pharmacogenomics journal · 2012Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe investigation of gene regulatory networks is an important issue in molecular systems biology and significant progress has been made by combining different types of biological data. The purpose of this study was to characterize the transcriptional program induced by etanercept therapy in patients with rheumatoid arthritis (RA). Etanercept is known to reduce disease symptoms and progression in RA, but the underlying molecular mechanisms have not been fully elucidated.
resultsUsing a DNA microarray dataset providing genome-wide expression profiles of 19 RA patients within the first week of therapy we identified significant transcriptional changes in 83 genes. Most of these genes are known to control the human body's immune response. A novel algorithm called TILAR was then applied to construct a linear network model of the genes' regulatory interactions. The inference method derives a model from the data based on the Least Angle Regression while incorporating DNA-binding site information. As a result we obtained a scale-free network that exhibits a self-regulating and highly parallel architecture, and reflects the pleiotropic immunological role of the therapeutic target TNF-alpha. Moreover, we could show that our integrative modeling strategy performs much better than algorithms using gene expression data alone.
conclusionWe present TILAR, a method to deduce gene regulatory interactions from gene expression data by integrating information on transcription factor binding sites. The inferred network uncovers gene regulatory effects in response to etanercept and thus provides useful hypotheses about the drug's mechanisms of action.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.