Researchers_in_laboratory_(1)

Network Inference Of Gene Expression Data

by / 4 Comments / 21 View / September 24, 2014

Novel application of multi-stimuli network inference to synovial fibroblasts of rheumatoid arthritis patients

 

Background: Network inference of gene expression data is an important challenge in systems biology. Novel
algorithms may provide more detailed gene regulatory networks (GRN) for complex, chronic inflammatory diseases
such as rheumatoid arthritis (RA), in which activated synovial fibroblasts (SFBs) play a major role. Since the detailed
mechanisms underlying this activation are still unclear, simultaneous investigation of multi-stimuli activation of SFBs
offers the possibility to elucidate the regulatory effects of multiple mediators and to gain new insights into disease
pathogenesis.
Methods: A GRN was therefore inferred from RA-SFBs treated with 4 different stimuli (IL-1β, TNF-α, TGF-β, and
PDGF-D). Data from time series microarray experiments (0, 1, 2, 4, 12 h; Affymetrix HG-U133 Plus 2.0) were
batch-corrected applying ‘ComBat’, analyzed for differentially expressed genes over time with ‘Limma’, and used for
the inference of a robust GRN with NetGenerator V2.0, a heuristic ordinary differential equation-based method with
soft integration of prior knowledge.
Results: Using all genes differentially expressed over time in RA-SFBs for any stimulus, and selecting the genes
belonging to the most significant gene ontology (GO) term, i.e., ‘cartilage development’, a dynamic, robust,
moderately complex multi-stimuli GRN was generated with 24 genes and 57 edges in total, 31 of which were
gene-to-gene edges. Prior literature-based knowledge derived from Pathway Studio or manual searches was reflected
in the final network by 25/57 confirmed edges (44%). The model contained known network motifs crucial for dynamic
cellular behavior, e.g., cross-talk among pathways, positive feed-back loops, and positive feed-forward motifs
(including suppression of the transcriptional repressor OSR2 by all 4 stimuli.
Conclusion: A multi-stimuli GRN highly concordant with literature data was successfully generated by network
inference from the gene expression of stimulated RA-SFBs. The GRN showed high reliability, since 10 predicted edges
were independently validated by literature findings post network inference. The selected GO term ‘cartilage
development’ contained a number of differentiation markers, growth factors, and transcription factors with potential
relevance for RA. Finally, the model provided new insight into the response of RA-SFBs to multiple stimuli implicated
in the pathogenesis of RA, in particular to the ‘novel’ potent growth factor PDGF-D.

For the full version of the article click here: Novel application of multi-stimuli network inference to synovial fibroblasts of rheumatoid arthritis patients

4 Comment

  1. You’ve made some really good points there. I looked on the web to find out more about the issue and found most individuals will go along with your views on this site.

  2. bookmarked!!, I really like your site!

  3. You made some good points there. I checked on the web to find out more about the issue and found most people will go along with your views on this site.

  4. over counter sleeping pills valium en ligne what is a geriatric doctor

Your Commment

Email (will not be published)