I’ve been to a few conferences recently and I’ve witnessed a divide opening up between the scientists that use high-throughput methods and everybody else. This I think is partly because although large datasets look impressive, we’re just not sure what it all means yet. Some researchers have even said to me that the interest of some of the top journals in publishing large datasets is simply because they lead to good citations, and help the impact factor. A recent paper on the ‘PML interactome’, which I describe below, is a nice example of how assembling the data in one place gives a very good overview of the situation and provides some functional clues too.
On the left is a picture of a slice through a human cell with a protein called PML coloured in red. The notable thing is that PML clusters in dots, known as PML nuclear bodies. We’ve known about these nuclear bodies for 50 years but, to be honest, we’re still not totally sure what the point of them is, although we’ve got some good theories.
These nuclear bodies contain many other proteins too. For example, in the picture below the protein SUMO-1 is coloured in green and PML is in red. When the green of SUMO-1 and the red of PML overlap, they produce a yellow colour, indicating that they are in the same place in the cell.
During my PhD, I photographed many proteins overlapping with PML, and in the time that has elapsed since I finished my PhD, hundreds of papers have been published on these nuclear bodies.
Building a network map
Oddly enough, no one seems to have sat down and made a list of all the proteins known to be in these nuclear bodies — until now. A team from Belgium has now produced the first ‘interactome’ — a map of all the 166 proteins that are known to interact with PML in these nuclear bodies.
They used information from protein interaction databases and they carried out a large literature search. Using the software Cytoscape they produced a network map:
But what can we actually learn from this network?
It highlights the 70 interactions that are available in the literature but have not been included in standard protein interaction databases.
All the proteins required to add SUMO to a protein and remove it again are present in these bodies.
38% of all the PML interaction partners have been reported to be modified by SUMO. And database search by the authors suggest that this will rise to at least 56%.
It allows a proteins to be sorted on the basis of their function (based on UniProt function keywords).
Overwhelmingly the function that dominates is transcriptional regulation. Other functions are also prominent, including apoptosis (cell death), viral infection and post-translational modification proteins. These are not necessarily mutually exclusive functions but it does give some interesting pointers to follow up.
More than anything else, this network indicates the complexity of these bodies. At the moment, this network does not even capture the dynamic nature of these bodies: proteins move in and out of these bodies all the time. The only one that is always there is PML. (And yet, odder still, mice that don’t have any PML still seem to be perfectly normal so this doesn’t really give us a clue to their function.)
And what if you think there is a protein missing? The authors invite you to email them at ellen.vandamme‹at›ua.ac.be and they’ll add the information as soon as possible. The network itself can be downloaded.
I think this is a really interesting and potentially very useful way of visualising these mysterious PML nuclear bodies.
Van Damme E, Laukens K, Dang TH, & Van Ostade X (2010). A manually curated network of the PML nuclear body interactome reveals an important role for PML-NBs in SUMOylation dynamics. International journal of biological sciences, 6 (1), 51-67 PMID: 20087442
Note: Intro added 23/3/10