Novel insights from computational approaches to infection and immunity
By regfbec, on 4 July 2014
The UCL Infection and Immunity Computational Hub was officially launched with a one day meeting entitled “Novel Insights from Computational Approaches to Infection and Immunity”, which took place at the Royal Free Hospital, London. The meeting certainly highlighted the diversity of computational approaches which can be used to better understand host/pathogen interactions. But themes encapsulated the day’s presentations for me. The first was the use of genomics, and specifically high throughput sequencing, to map both geographical and evolutionary change (whether of viruses, bacteria, T cells or humans !) over space and time. A second theme was that simplified but intelligently designed mechanistic models (whether peptide/MHC binding, T cell homeostasis, or cytotoxic T cell killing) could provide real insight into understanding extremely complex biological systems.
The meeting was opened by Hans Stauss, the Director of the new research Institute of Immunity and Transplantation , who emphasized the central role for computational and bioinformatics analysis in maximizing the potential of the new Institute to deliver advances in biomedical research which would have a real impact on patient care. And, on cue, personalized medicine was the theme for the first speaker , Peter Coveney (Chemistry, UCL). He emphasized that large multiscale mechanistic models of biological processes would be key to using patient-specific data to inform clinical care, and hence deliver truly personalized medicine. He briefly outlined two examples, the processing of HIV polyproteins, and subsequent loading of individual peptides onto MHC molecules. The molecular dynamic simulations required for the latter were computationally intensive, and needed to be replicated many times in order to have confidence in the outcome. Access to high-performance parallel grid computing, potentially using tens of thousands of cores, was an absolute requirement for such approaches. In a similar vein, Robin Callard (Institute of Child Health) integrated data-driven statistical regression, with mechanistic ODE models of T cell homeostasis to probe the recovery of the CD4 cell compartment in HIV children following antiretroviral therapy. Strikingly, delay in treatment led to a more rapid rate of recovery, but a long term deficit in CD4 T cell counts. The models clearly predicted that earlier intervention led to better long term reconstitution, putting into question the current clinical practice of delaying anti-retroviral treatment.
Richard Goldstein (Division of Infection and Immunity, UCL and my co-organiser of the meeting) addressed the question of whether HIV viral sequencing can be used to identify transmission events and hence map the parameters of the HIV epidemic at global or individual scale. Using a large data set linking viral sequence to clinical information, he developed evolutionary Bayesian models which could be used to infer infectivity values (man-to-man, man-to-woman etc.) , and predict the most probable infection pathways within a specific outbreak. Richard argued the large data set made the inferred model parameters robust at a population levels, although stochasticity and uncertainty limited the predictive accuracy in individual cases. The results suggested that over reliance on patient derived information introduced significant systematic bias into estimates of infectivity rates in different patient groups. Vincent Plagnol explored the topical area of expression quantitative trait loci (eQTL), describing improved more statistically rigorous methods for linking known single base pair polymorphisms (SNPs) to heterogeneous levels of specific gene transcription. The strategy offers a powerful new approach for identifying functional gene modules, thus gaining insight into the mechanisms regulating the host pathogen interaction.
After lunch, I spoke about computational challenges in analysis of T cell receptor sequence sets obtained by high throughput sequencing. The enormous diversity (up to 10^14) of possible alpha and beta chains means that even genetically identical individuals will end up with a very distinct set of receptors. The challenge is to recognize antigen-dependent changes in repertoire against this enormous amount of “molecular noise”. I discussed the analogous problem of automatically identifying objects in images or sentences in texts, and outlined an approach which deconstructs TCR CDR3 sequences into a series of overlapping amino acid triplets, and then counts the frequency of clusters of similar triplets in immunized or unimmunized mice. The approach, borrowed from the “bag-of-words” machine learning algorithm showed promise in distinguishing repertoires of T cells isolated from mice at different times post-immunization.
I was followed by Nick Thomson (Sanger Institute and London school of Tropical Medicine and Hygiene). He discussed the application of bacterial genomics to map both the global distribution and local transmission routes of Shigella and Cholera. The enormous current political and media interest in the spread of antibiotic-resistance in human bacterial pathogens made this a particularly timely topic. Francois Balloux (Institute of Human Genetics) returned to evolutionary questions, discussing the challenge of reconstructing either human migration patterns or the spread of microbial pathogens from genomic sequence data. Despite showing some beautiful dynamic images modeling the global spread of the human population through the ages, François ended with a note of caution, emphasizing that temporal as well as spatial data series were required to make robust inferences of migratory or transmission patterns. A lively discussion between Richard and Francois ensued and was eventually adjourned for continuation at a later date over a drink ! The final UCL talk was given by Alexei Zaikin (Mathematics and Women’s Health, UCL) who asked whether intracellular regulatory circuits could give rise to intelligent behavior. He outlined several examples of such molecular intelligences, including a fascinating genetic implementation of Pavlovian conditioning. He then went on to show how, counter intuitively, the introduction of “noise” (stochastic variation in the signal) could under certain circumstances improve the reliability of the decision making process. For those of us who don’t like noise, its worth mentioning that in all cases too much noise utterly destroyed intelligent behavior !
The final key note talk was given by Rob de Boer, Director of the Institute for Biodynamics and Biocomplexity, Utrecht University, The Netherlands. His elegant talk addressed the surprising observation that, as HIV infection progresses the rate of mutational escape decreases, and the apparent benefit in terms of increased viral replication diminishes. This data had led some to the provocative suggestion that cytotoxic T cell immune control of HIV becomes unimportant as infection progresses. Using a simplified but beautiful model of a multi-epitope immune response, Rob showed that the observed changes arose naturally from the fact that the more epitopes are involved in a response, the less important is each epitope individually. Far from implying that CTL responses were unimportant, the breadth of the response as well as the magnitude turn out to be key to long term control of this virus.
The meeting was closed by Judy Breuer, head of the Department of Infection, UCL, who thanked all the speakers and reiterated the commitment of the Division of Infection and Immunity to strengthening the research in the computational arena. It was a long but fascinating day, and reinforced the extraordinary breadth of high quality computational biology already going on at UCL and the LSTHM. The signs are this area is going to be one of continued growth for some time to come !