After we determined the crystal structure of the extracellular region of CD80 (1) which formed a plausible-looking dimer in the crystal lattice, one of our students, Andrea Iaboni was interested in seeing if it also formed a dimer at the cell surface.
Andrea was keen to try fluorescence resonance energy transfer (FRET), but this method in the hands of others at this institute didn’t seem that convincing owing to the limited signal to noise ratio for their experiments. At that time (year ~2000) bioluminescence resonance energy transfer (BRET) was becoming widely used and we decided to give this a go.
We were slightly surprised when both CD80 and CD86, which we expected to be a clear-cut monomer, both gave reasonably strong BRET signals, albeit with CD80 giving the stronger signal. John James, another student then found a series of theoretical treatments of RET previously implemented by Anne Kenworthy and Michael Edidin to test for the clustered organization of lipid-raft proteins (there wasn’t any). The theoretical analysis suggested that random or “background” interactions of monomers would give a substantial signal but this could be distinguished from the signal from authentic dimers if the acceptor/donor ratio is varied whilst keeping the overall expression level constant. The reason for this is that, for monomers, the acceptor environment of donors above a certain acceptor/donor ratio (~2) is effectively unaltered when the donors are replaced with acceptors (so transfer efficiency per donor stays constant), whereas for dimers energy transfer efficiency increases in a hyperbolic manner as more acceptor/donor pairs form. The key to thinking about this correctly is to focus on the transfer efficiency per donor, not the overall levels of transfer. Also, the mistake has been made of increasing the acceptor/donor ratio by holding the donor level constant and increasing the amount of acceptor present. However, this only increases the density and, therefore, the background levels of energy transfer making it difficult to distinguish between monomers and oligomers.
In our hands, most proteins expressed at the cell surface behave as monomers (2) (Fig. 1), but the contribution of random interactions to the BRET signal is much greater than previously believed. We proposed that the signals arising from random interactions are likely to have been mistakenly attributed to oligomerization in more than 40 studies of G protein-coupled receptors (GPCRs; the largest and most pharmacologically significant family of cell surface receptors), because in our hands two GPCRs, β2AR and mCannR2, gave monomer signatures in the new BRET assay (2) (Fig. 2). Not unexpectedly this conclusion has been very controversial. Our results indicate that the cell surface is a crowded microenvironment greatly favouring the cis-interactions of membrane proteins. Energy transfer between membrane-anchored and cytoplasmic proteins is negligible, an observation with important implications for the composition of signalling complexes that form at the cell surface. Very recently we expanded our analysis using an improved BRET competition assay designed by James Felce, to look at the stoichiometries of more than 70 GPCRs expressed by a single cell. We have also confirmed our findings for key examples from this analysis in single-molecule fluorescence-imaging experiments in collaboration with the Klenerman laboratory. Overall it seems as if the field has now shifted and there is no longer a widespread expectation that GPCRs invariably form dimers or higher-order oligomers.
Fig. 1: Type-1 experiments: varying the acceptor/donor ratio distinguishes between BRET arising from random versus oligomeric interactions.
(a) FACS analysis using PE-conjugated antibodies to the specified antigens and QuantiBRITE beads, demonstrating that GFP expression correlates with equivalent cell-surface staining. (b) Solid lines show fits of data obtained for the indicated BRET pairs to equation (1) in (2), that is, for dimers, and the dotted lines show the fit (for [GFP]/[Luc] > 2) to a constant value, as predicted for random interactions. Only the model that gave the best fit is shown. (c) Residual BRETeff values after nonlinear least-squares fitting to the indicated model, plotted as a moving average of the data. A good fit reduces residuals to zero. (d) Cells expressing CD80 or CD86 as a BRET pair were incubated with phosphate-buffered saline (– CTLA-4Fc) or 50 μg/ml CTLA-4Fc (+ CTLA-4Fc) before assaying for BRET.
Fig. 2: Two native class-A GPCRs are monomeric at the cell surface.
(a) Both β2AR and mCannR2, expressed separately as BRET pairs or together as a BRET pair, and β2AR and CD2 expressed as a BRET pair, give similar low BRETeff values exhibiting [GFP]/[Luc] ratio independence beyond a [GFP]/[Luc] ratio of 2, with lines representing the best fit to the data, as described in (2). (b) Variation of the acceptor/donor ratio for β2AR shows no evidence for oligomerization at three different levels of surface expression, with the highest level being ~2 × 106 molecules/cell. (c) BRETeff values for a heterodimerizing class-C GPCR, GABAβR, consisting of GABAβR1 and GABAβR2 subunits, exhibit the hyperbolic relationship with acceptor/donor ratio expected for a dimer, as do BRETeff values obtained for a fusion protein consisting of β2AR and the heterodimerizing coiled-coil domains of the GABAβR complex (β2ARcoil1 and β2ARcoil2). β2ARcoil2Δ is a truncated version of β2ARcoil2 with only half the GABAβR2 coiled-coil. (d) The decrease in interaction when β2AR is expressed as a BRET pair with GABAβR2 is due to the homodimerization of GABAβR2 at the cell surface, which decreases by half the effective density of molecules present.
- Ikemizu S, Gilbert RJ, Fennelly JA, Collins AV, Harlos K, Jones EY, Stuart DI, Davis SJ. (2000) Structure and dimerization of a soluble form of B7-1. Immunity. 12, 51-60.
- James JR, Oliveira MI, Carmo AM, Iaboni A, Davis SJ. (2006) A rigorous experimental framework for detecting protein oligomerization using bioluminescence resonance energy transfer. Nat Methods. 3, 1001-6.
BRET and GPCRs Papers
Felce JH, Knox RG, Davis SJ. (2014), Biophys J. 106, L41-3Details
Zinselmeyer BH, Heydari S, Sacristán C, Nayak D, Cammer M, Herz J, Cheng X, Davis SJ, Dustin ML, McGavern DB. (2013), J Exp Med. 210, 757-74Details
Weimann L, Ganzinger KA, McColl J, Irvine KL, Davis SJ, Gay NJ, Bryant CE, Klenerman D. (2013), PLoS One. 8, e64287Details
Gregory AP, Dendrou CA, Attfield KE, Haghikia A, Xifara DK, Butter F, Poschmann G, Kaur G, Lambert L, Leach OA, Prömel S, Punwani D, Felce JH, Davis SJ, Gold R, Nielsen FC, Siegel RM, Mann M, Bell JI, McVean G, Fugger L. (2012), Nature. 488, 508-11Details
Klenerman D, Shevchuk A, Novak P, Korchev YE, Davis SJ (2012), Philos Trans R Soc Lond B Biol Sci. 368, 20120027Details
Ikemizu S, Chirifu M, Davis SJ (2012), Nat Immunol. 13, 1141-2Details
Klenerman D, Korchev YE, Davis SJ. (2011), Curr Opin Chem Biol. 15, 696-703Details
James JR, McColl J, Oliveira MI, Dunne PD, Huang E, Jansson A, Nilsson P, Sleep DL, Gonçalves CM, Morgan SH, Felce JH, Mahen R, Fernandes RA, Carmo AM, Klenerman D, Davis SJ. (2011), J Biol Chem. 286, 31993-2001Details
Dunne PD, Fernandes RA, McColl J, Yoon JW, James JR, Davis SJ, Klenerman D. (2009), Biophys J. 97, L5-7Details
James JR, Davis SJ. (2007), Nat Methods. 4, 4Details
James JR, Davis SJ. (2007), Nat Methods. 4, 601Details
Berlanga O, Bori-Sanz T, James JR, Frampton J, Davis SJ, Tomlinson MG, Watson SP. (2007), J Thromb Haemost. 5, 1026-33Details
James JR, White SS, Clarke RW, Johansen AM, Dunne PD, Sleep DL, Fitzgerald WJ, Davis SJ, Klenerman D. (2007), Proc Natl Acad Sci U S A. 104, 17662-7Details
A rigorous experimental framework for detecting protein oligomerization using bioluminescence resonance energy transfer
James JR, Oliveira MI, Carmo AM, Iaboni A, Davis SJ. (2006), Nat Methods. 3, 1001-6Details