Molecular causes of transcriptional response: a Bayesian prior knowledge approach.
BACKGROUND
The abundance of many transcripts changes significantly in response to a variety of molecular and environmental perturbations. A key question in this setting is as follows: what intermediate molecular perturbations gave rise to the observed transcriptional changes? Regulatory programs are not exclusively governed by transcriptional changes but also by protein abundance and post-translational modifications making direct causal inference from data difficult. However, biomedical research over the last decades has uncovered a plethora of causal signaling cascades that can be used to identify good candidates explaining a specific set of transcriptional changes.


METHODS
We take a Bayesian approach to integrate gene expression profiling with a causal graph of molecular interactions constructed from prior biological knowledge. In addition, we define the biological context of a specific interaction by the corresponding Medical Subject Headings terms. The Bayesian network can be queried to suggest upstream regulators that can be causally linked to the altered expression profile.


RESULTS
Our approach will treat candidate regulators in the right biological context preferentially, enables hierarchical exploration of resulting hypotheses and takes the complete network of causal relationships into account to arrive at the best set of upstream regulators. We demonstrate the power of our method on distinct biological datasets, namely response to dexamethasone treatment, stem cell differentiation and a neuropathic pain model. In all cases relevant biological insights could be validated.


METHODS
Source code for the method is available upon request.
Computational Sciences Center of Emphasis, Pfizer Worldwide Research & Development, Cambridge, MA 02140, USA, Department of Mathematics, University of Massachusetts Boston, Boston, MA 02125, USA, Drug Safety Research & Development, Pfizer, Groton, CT 06340, USA and Neusentis, Pfizer Worldwide Research & Development, Cambridge CB21 6GS, UK.
Kourosh
K
Zarringhalam
Ahmed
A
Enayetallah
Alex
A
Gutteridge
Ben
B
Sidders
Daniel
D
Ziemek
D000818:Animals; D001499:Bayes Theorem; D002454:Cell Differentiation; D002478:Cells, Cultured; D003198:Computer Simulation; D003907:Dexamethasone / Q000494:pharmacology; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation; D006801:Humans; D050417:Insulin-Secreting Cells / Q000166:cytology / Q000378:metabolism; D015603:Keratinocytes / Q000166:cytology / Q000187:drug effects / Q000378:metabolism; D008390:Markov Chains; D051379:Mice; D008954:Models, Biological; D010146:Pain / Q000235:genetics / Q000378:metabolism / Q000473:pathology; D011499:Protein Processing, Post-Translational; D051381:Rats; D050436:Regulatory Elements, Transcriptional; D015398:Signal Transduction; D013234:Stem Cells / Q000166:cytology / Q000378:metabolism
D003907:Dexamethasone