By regfbec, on 16 March 2014
Jamie Heather, Katharine Best and I are on our way back from qImmunology, a great meeting dedicated specifically to the small but growing band of people determined to bring numbers into immunology. The meeting took place at L’Ecole de Physique, a remote “retreat” outsize the small village of Les Houches, originally established to host summer schools in physics. Set in the most beautiful alpine scenery imaginable, the meeting was small, lively , interactive and genuinely interdisciplinary (including, for example, the experimental physics of skiing, see fig inset
). I summarise below just a few of my favourite talks: but please feel free to comment, correct and add your own favourites to this page. I apologise to all those I don’t mention : there was just too much good science to cover here !
Anton Zilman discussed a mechanistic (mass action) model of interferon signalling. He discussed the slightly bizarre fact that there are over a dozen different type I interferons which all bind to the same receptor. The suggestion is that alpha and beta interferons have a different effect : certainly alpha is used to treat viral hepatitis, while beta is used to slow the progression of multiple sclerosis. Alpha and beta have different receptor affinities, and also interact in a complex way in inducing refractoriness to each other. The model proposes that a molecule of interferon binds either of two receptors, followed by binding to the other, and then inducing JAK/STAT phosphorylation and downstream signalling. Even with this simple scenario, the number of possible pathways, and hence rate parameters was too large (a common theme throughout the week) and had to be drastically simplified. An interesting property was that since agonist can bind either receptor chain, the dose response is modal, not saturating : at high concentrations two molecules of interferon can bind the two chains independently and therefore block chain association. This also affects the maximum binding that can be achieved.
A recurrent theme running though many presentations was the attempt to capture information about protein structure by looking at “coupling” between amino acids. The idea is that the statistical relationships between pairs (or more complex local patterns) of amino acids along a protein molecule (mostly antibody or TCR, as this was an immunology conference) may give useful information when comparing sets of related proteins (homologues, paralogues, or repertoires of antibodies and TcRs). These studies were motivated either by an attempt to bound the potential receptor repertoire by antigen-driven selection (e.g. Thierry Morra, Aleksandra Walzack, Yuval Elhanati) or by developing better ways of measuring “functional distance” between molecules that go beyond conventional alignments (Olivier Rivoire, Clement Nizak). Maybe these two motivations come to the same thing. Yeast display of antibodies, allowing capture by “avidity” was a particularly nice technological idea for gathering data for these approaches (Rhys Adams).
Co-operative behaviour between T cells was the focus of Gregoire Altan-Bonnet’s talk. He described an in vitro model where two T cells of different specificity could interact and “help” each other. For example a T cell which had a low peptide affinity (affinity turned out to be a key property) , and therefore produced little IL2, and little proliferation, could be “helped” by coculture with cells with a high affinity peptide. The molecular “helper pathway” was suggested to be via PI3 kinase that could be stimulated either via TCR or via IL2 receptor signalling. The signalling pathway was modelled (pathway modelling was another common theme) to include negative and positive feedback loops on the IL2 receptor alpha chain. Tregs could interrupt this IL2 cycle by grabbing and depleting limiting amounts of IL2.
Two novel theoretical frameworks particularly interested me. The first was presented by Vassili Soumelis and related to gene transcription data. The focus was on trying to capture the interaction between two stimuli acting on the same cell : for example, TLR agonists and cytokines on plasmacytoid dendritic cells or monocytes. The idea was to classify each gene according to the pattern of response to each signal alone, and to the two signals together. For example, stimulus A induced upregulation; stimulus B induced upregulation; but A and B together abrogated upregulation. All measured genes could then be classified semi-automatically according to 12(?) canonical patterns (a simplification from an original palette of 82). In general, responses showed multimodality : different groups of genes within the same cell showed different patterns. And, intriguingly, the ontology of the different sets of genes seemed to point to different pathways. This raised the interesting possibility that different cellular functions were being switched on in response to complex two-signal stimuli via different modalities. The approach seems to give insight into how cells might integrate complex mixtures of environmental signals.
A second was the modelling work on CD8 differentiation presented by Thomas Hoefer. The novelty here lay in the analysis of stochastic behaviour of single cells. He showed how higher moments of observed single cell data (covariances, variances etc.) could be used to enrich the dimensionality of the experimental measurements. Computationally, this could be implemented via the well-known (to physicists, apparently, although not to me) relationship between higher moments of distributions and the derivatives of master equation generating functions. Remarkably, this approach allowed excellent multiparameter inference and confidence interval estimation based on single in vivo time point data. Extracting useful data from single cell experiments are likely to have broad application as improved technologies for single cell tracking were a dominant feature of the meeting, ranging from sophisticated in vitro microfluidic/image analysis approaches (Michal Polonsky, Ira Zaretsky , Clement Nizak) to molecular barcoding in vivo (Leila Perie).
The last evening provided two of the highlights of the week. Rob De Boer provocatively set out to prove that the death rate of productively HIV-infected CD4 T cells was independent of cytotoxic T cell killing. He addressed the apparent paradox that viral set point and infected cell death rate are apparently unrelated. He developed an ODE model which with the novel feature of incorporating many coexisting cytotoxic T cells each with specificity for a different HIV epitope. Computationally, this was achieved by incorporating a resource-competition term in which an increased viral load was needed to maintain an increased T cell immune response. And emergent property of this model was that more epitopes could lead to a stronger immune response, but the contribution of each epitope becomes less significant. His elegant model beautifully predicted altered rates of viral escape during HIV progression, and the paradoxical observation that escape gave only a minor growth advantage during the metastable phase of disease.
Paul Thomas finished by discussing his remarkably rich data set from single cell CD8 TCR repertoire analysis. Focusing predominantly on mouse flu models, he showed data which counted the total number of epitope specific T cells (MHC multimer sorted) in a whole mouse (literaly !!) before and after immunisation. The ultimate quantitative immunology ! In this model, every naïve T cell carried a unique TCR clonotype. He ended with some data on “TCR revision”, the phenomenon of persistent TCR recombination in mature T cells, which left us all wondering whether the thymic selection paradigms would have to be rewritten !
I hope I haven’t butchered these presentations and ideas too much : but if I have got it wrong, apologies and do please correct me using the comments tab. And if you would like to receive notifications of new additions to this blog, please sign up with your email address as shown. And of course, forward the link to anyone you think may be interested !