Human cells use complex system of transcription-factor combinations
Jun 19, 2006, 01:22, Reviewed by: Dr. Priya Saxena
|“A cell’s surprising ability to mix and match so many different combinations of these factors to achieve a high degree of complexity and specificity in the expression of its genes is impossible for even the most experienced cell biologists to conceptualize. That’s why we have computers.”
Scientists eager to help develop a new generation of pharmaceuticals are studying cellular proteins called transcription factors, which bind to upstream sequences of genes to turn the expression of those genes on or off. Some pharmaceutical companies are also hoping to develop drugs that selectively block the binding of transcription factors as a way to short-circuit the harmful effects of diseases, and researchers at the University of California, San Diego on June 16 reported new findings that could aid that effort.
Bioengineering researchers at UCSD and two research institutes in Germany report in the June 16 issue of PLoS Computational Biology that transcription factors act not only in isolation, but also in pairs, trios, and combinations of up to 13 to regulate distinct sets of genes. The researchers, led by UCSD bioengineering professor Trey Ideker, reported a list with 363 combinations of 91 transcription factors that regulate a large proportion of genes in the yeast genome. The team used rigorous statistical tests to discover active combinations of transcription factors, as if a cell were mixing and matching parts of its regulatory-protein wardrobe to respond to different environmental conditions.
The researchers expect that human cells use a similar system of transcription-factor combinations, but on a larger scale.
“A cell’s surprising ability to mix and match so many different combinations of these factors to achieve a high degree of complexity and specificity in the expression of its genes is impossible for even the most experienced cell biologists to conceptualize,” said Andreas Beyer, a post-doctoral fellow at the UCSD Jacobs School of Engineering’s Department of Bioengineering. “That’s why we have computers.”
The researchers combined the results of their laboratory with other large-scale measurements of transcription factor-gene binding, such as those reported earlier by MIT biology professor Richard A. Young and his collaborators.
Ideker’s team was able to identify new transcription factor binding patterns by borrowing a concept from computer science. The team considered the binding of one transcription factor to one gene as analogous to one “hop” of a data packet from one Internet router to another.
In the case of gene regulation, Ideker’s team identified “2hop” relationships by first focusing on single transcription factor-gene associations, plus other experimental evidence that indicates that that gene regulates a second gene.
To enlarge the scope of the model further, Ideker’s group also incorporated other previously discovered transcription-factor interactions and related genetic results. They relied on a total of eight types of direct and indirect evidence to create a model. That model predicts 980 as-yet-undiscovered transcription factor-gene binding interactions.
“This ‘systems biology’ approach, using so many different lines of evidence, has given us a much more revealing and detailed picture of how cells orchestrate gene regulation to cope with different environments,” said Ideker. “We’re far from understanding the full picture of gene regulation in a cell, but this new information should give scientists who are interested in blocking transcription factors a powerful new tool to narrow their search to the most promising candidates.”
- Andreas Beyer, Christopher Workman, Jens Hollunder, Dörte Radke, Ulrich Möller, Thomas Wilhelm, Trey Ideker, "Integrated Assessment and Prediction of Transcription Factor Binding" (2006). PLoS Computational Biology. 2(6): e70.
Read the research article
Other researchers involved in the project include: Christopher Workman, UCSD Jacobs School of Engineering’s Department of Bioengineering (now at The Technical University of Denmark); Jens Hollunder and Thomas Wilhelm, Leibniz Institute for Age Research, Fritz Lipmann Institute, Jena, Germany; Dörte Radke and Ulrich Möller, Leibniz Institute for Natural Products Research and Infection Biology, Hans Knöll Institute, Jena, Germany.
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