We’ve all seen them hanging on the freezers and filling otherwise empty wall space in labs–signal transduction maps that look like abstract artwork. They’re enough to make even the most dedicated researcher wonder if there’s light at the end of the tunnel. As difficult as it is to make sense of complex transcriptional cascades, generating models of transcriptional regulatory networks on a genomic scale can be magnitudes worse.
In the past the approach has gone something like…give the model a poke and see which genes are turned on, then give it a different poke, and note the difference (expression profiling) – being careful not to infer a causal relationship from a mere sequential occurrence. Repeat.
Recently a group from the Broad Institute in Cambridge blindsided the problem from a different angle. They wanted to see how dendritic cells help the immune system deal with different threats, so they profiled the response to five different types of antigen (including the bacterial endotoxin LPS), identifying 125 candidate regulators including chromatin modifiers and RNA binding proteins. Then they used shRNA to knock these down one at a time, stimulated the cells with LPS, and again profiled the response. When all the dust settled and the data were analyzed, the team found:
- 24 big dial regulators that were involved in controlling entire regulatory arms, or response pathways
- 75 small dial, fine-tuning regulators that adjusted expression levels of their targets
- 12 of these regulators camped out in genetic loci that were in linkage disequilibrium with various SNPs associated with autoimmune diseases
Even if you’re not that into immunology, the general approach can be applied to dissect complex transcriptional networks in your model too, so ditch the wall hanging and check out all the details in Science, Sept 2009