Cofactors work with transcription factors (TFs) to enable efficient transcription of a TF's target gene. The Bussemaker Lab showed that genetic alterations in the cofactor gene (cQTLs) change the nature of this interaction, affecting the connectivity between the TF and its target gene. This, combined with other factors called aQTLs that affect the availability of the TF in the nucleus, can lead to downstream changes in gene expression.
When different people receive the same drug, they often respond to it in different ways — what is highly effective in one patient can often have no benefit or even cause dangerous side effects in another. From the perspective of systems biology, this is because variants in a person’s genetic code lead to differences in the networks of genes, RNA, transcription factors (TFs), and other proteins that implement the drug’s effects inside the cell. These multilayered networks are much too complex to observe directly, and so systems biologists have been developing computational methods to infer how subtle differences in the genome sequence produce these effects. Ultimately, the hope is that this knowledge could improve scientists’ ability to identify drugs that would be most effective in specific patients, an approach called precision medicine.
In a paper published in the Proceedings of the National Academy of Sciences, a team of Columbia University researchers led by Harmen Bussemaker proposes a novel approach for discovering some critical components of this molecular machinery. Using statistical methods to analyze biological data in a new way, the researchers identified genetic alterations they call connectivity quantitative trait loci (cQTLs), a class of variants in transcription cofactors that affect the connections between specific TFs and their gene targets.