Elucidating missing links of the TCR signaling network

Just published:
Phosphorylation site dynamics of early T-cell receptor signaling. LA Chylek, V Akimov, J Dengjel,  KTG Rigbolt, WS Hlavacek, B Blagoev. PLOS ONE 9, e104240

Stimulation of the T-cell receptor (TCR) can trigger a cascade of biochemical signaling events with far-reaching consequences for the T cell, including changes in gene regulation and remodeling of the actin cytoskeleton. A driving force in the initiation of signaling is phosphorylation and dephosphorylation of signaling proteins. This process has been difficult to characterize in detail because phosphorylation takes place rapidly, on the timescale of seconds, which can confound efforts to decode the order in which events occur. In addition, multiple residues in a protein may be phosphorylated, each involved in distinct regulatory mechanisms, necessitating analysis of individual sites.

To characterize the dynamics of site-specific phosphorylation in the first 60 seconds of TCR signaling, we stimulated cells for precise lengths of time using a quench-flow system and quantified changes in phosphorylation using mass spectrometry-based phosphoproteomics. We developed a computational model that reproduced experimental measurements and generated predictions that were validated experimentally. We found that the phosphatase SHP-1, previously characterized primarily as a negative regulator, plays a positive role in signal initiation by dephosphorylating negative regulatory sites in other proteins. We also found that the actin regulator WASP is rapidly activated via a shortcut pathway, distinct from the longer pathway previously considered to be the main route for WASP recruitment. Through iterative experimentation and model-based analysis, we have found that early signaling may be driven by transient mechanisms that are likely to be overlooked if only later timepoints are considered.

Pre-game announcements!

Greetings, loyal readers! You might remember that a few months ago we showed you the q-bingo game card that we brought to the last q-bio conference, and asked for your ideas on what terms are popular (or perhaps overused) in systems biology so that we could use them in future games. I can now announce that we have used your ideas in the set of playing cards for this year's conference!

If you're at the conference and want to play, come to Poster Session 1 tomorrow (Thursday) and stop by poster #11 (hint: it's very violet) to pick up your card AND to learn about some exciting research that will be coming out in just a few days. See you there!

If you're not coming to the conference (or even if you are), you can still join the fun by following us on (our new) Twitter: @qbiology.

Thanks, but no thanks

I am posting from the q-bio Summer School, where we are enjoying many discussions about modeling. Several lecturers have advised the junior modelers attending the school, who are mostly graduate students and postdocs, to find an experimental collaborator. I appreciate the advice and the benefits of having an experimental collaborator, but I am usually quite irked by the reasons stated for seeking out opportunities to collaborate with an experimentalist. One reason I've heard many times is that modelers need an experimentalist to explain the biology to them and to help them read papers critically. It certainly could be useful to have a more experienced researcher aid in formulating a model, but that person might as well be a modeler familiar with the relevant biology. I don't subscribe to the idea that modelers need a collaborator to evaluate the soundness of a paper. To suggest so seems insulting to me. Modelers do need to consult experts from time to time to understand the nuances of an unfamiliar experimental technique, for example, but so do experimentalists. I am probably more annoyed by the popular sentiment that a collaborator is essential for getting predictions tested. If I were an experimentalist, I might be insulted by this idea. It's unrealistic to think that experimentalists are lacking for ideas about which experiment to do next. If your prediction is only appealing to your experimental collaborator, then maybe it's not such an interesting prediction? Modelers should be more willing to report their predictions and let the scientific community follow up however they may, partly because it's unlikely that your collaborator is going to be the most qualified experimentalist to test each and every prediction you will ever make. I think the real reason to collaborate with an experimentalist is shared goals and interests and complementary expertise. Finding such a colleague is wonderful, but it shouldn't be forced, and the absence of a collaborator shouldn't be an impediment to progress. If you have a good prediction, you should report it, and if you want to model a system, you should pursue that. Eventually, you will know the system as well as the experimentalists studying it, if not better. After all, it's your role as a modeler to integrate data and insights, to elucidate the logical consequences of accepted understanding and plausible assumptions, and to suggest compelling experiments. Finally, I want to speak to the notion that modelers should do their own experiments. I think that's a good idea if you want to be an experimentalist. If you want to be a modeler, be a modeler.