Sequence data and the ecology of pathogens: phylogeny and beyond

This proposal aims to improve our ability to infer the ecological processes shaping a pathogen's evolution by understanding pathogen phylogenies in a novel way. It is important to understand the ecology of pathogen spread. Indeed, ecological ideas have much to offer in understanding pathogens in particular: ecologists are accustomed to complex datasets without the opportunity for truly controlled experiments, and ecological concepts such as competition and competitive exclusion, niche adaptation, and habitat filtering are increasingly the paradigm of choice for understanding pathogen evolution. An example of a question for which ecological ideas are particularly relevant is that of how and why some pathogens evolve widespread drug resistance rapidly while others maintain long-term coexistence of resistant and sensitive strains.

Pathogen phylogenies contain a lot of information, in principle, both about the specifics of where certain strains or sequences originate and about the general underlying processes shaping when, where, and which pathogen strains are able to spread. Mathematicians have developed tools to create maximum-likelihood phylogenetic trees representing the estimated ancestral patterns of a dataset, as well as tools to simultaneously infer a phylogeny and the population's demographic history. However, existing tools offer no systematic approaches to infer the ecological context shaping a pathogen's spread or evolution. In addition, current methodologies use very limited metrics to assess phylogenies, so to the extent that approaches do exist to use genetic data to understand epidemiological and ecological processes, these are based on very little of the rich information in genetic data.

The work proposed here aims to fill the gap between the rich datasets of pathogen genomes being gathered and our ability to analyse them. I will first develop a suite of ways to summarise phylogenetic trees, taking the topology of the tree into account. Existing methods identify the probability of a tree with the probabilities of its branching times, neglecting the tree's topology. Developing informative measures is likely to be challenging because of the other factors that affect trees (stochastic transmission and mutation, selection, and others). For each new summary I aim to find its distribution on random trees drawn uniformly from tree space, to determine how rare a given tree is. In the second stage of the work I aim to improve inference of the underlying ecological processes shaping pathogen evolution, by better understanding what features of phylogenetic trees (including the novel summary measures developed in the first stage) make them able to account for observed data. Inference of ecological processes from phylogenies will carry some of the same challenges that occur in the inference of population demographics, one of these being that the number of possible trees on n leaves is too high for summing over all such trees to be feasible. Yet such sums are at the heart of likelihood-based inference. I propose to use the features identified in the first stage of the proposal, together with an improved understanding of how the likelihood of the data D given a tree G, L(D|G), is distributed over tree space, to simplify the sum and improve inference.

The main immediate-term beneficiaries of this project are academic. However, I anticipate that the project will also benefit the government/public health sector, and ultimately the general public. The key government beneficiary is the Health Protection Agency (HPA). Internationally, groups analysing genomic data in public health settings, including the Center for Disease Control (USA) and the British Columbia Centre for Disease Control (BCCDC, in Canada, who recently released a high-profile paper on TB genomic data) are also likely beneficiaries. The HPA has indicated that over the next 5 years it is feasible for hospital settings to begin gathering genomic data in ``real time'', meaning as cases are detected; hospitals making use of genome sequencing for pathogens like methicillin-resistant Staphylococcus aureus (MRSA) will likely benefit from this work. I anticipate annual meetings with the Health Protection Agency to ensure that the new tools have impact in the public health domain. This work also has long-term implication for vaccine design, because of the role ecological competition can play in undermining the effects of vaccines through strain replacement. I am in contact with industrial and public health parties regarding vaccine effects and will disseminate results to them as appropriate.

There are additional benefits of this work for the broader public. This work combines mathematics with both systems biology (using a whole-genome analyses) and epidemiology. Clearly infectious diseases are of great relevance to the general public and are frequently in the media. Research Councils have agreed that is a priority to continue to engage the public regarding the ethical, legal and social issues (ELSI) associated particularly with synthetic biology but also broadly with systems biology. To ensure that the broader public has access to, and is engaged with, this work, I propose to make simulations of pathogen evolution available through intuitive web-based tools incorporating graphical representations of the transmission patterns, genomic data and phylogenetic trees. This will also aid academic beneficiaries. Because pathogens and drug resistance are frequently reported in the media, I anticipate that adding pathogens and evolution to this discussion will be an exciting aspect of this work from the public's point of view. For this reason I propose to hold public discussion events hosted at the Science Museum's Dana Centre. I have a track record of public engagement and look forward to continuing these activities.