The Microbial Ecology of Human-Associated Bacterial Communities

UNIVERSITY COLLEGE LONDON The microbial ecology of human-associated bacterial communities Liam Shaw January 2018 A thesis submitted to University College London for the degree of DOCTOR OF PHILOSOPHY Declaration I confirm that the work presented in this thesis is my own. Where information has been derived from other sources, I confirm that this has been indicated in the thesis. Liam Shaw London, April 2018 2 Abstract The bacterial communities within the human body have important associations with health and disease. Understanding their complexity requires ecological approaches. In this thesis, I apply ecological techniques and models to explore the microbial ecology of human-associated bacterial communities at multiple scales. In the first half of this thesis, I explore the oral microbiome using 16S rRNA gene sequencing data to characterise the effect of various factors on its diversity. Multiple factors apart from disease can also affect the oral microbiome, but their relative importance remains a matter of debate. In Chapter 2, I use a dataset of saliva samples from a family of related Ashkenazi Jewish individuals to show that host genetics plays much less of a role than shared household in explaining bacterial community composition. In Chapter 3, I use a large dataset of plaque samples from women in Malawi to investigate associations between bacterial taxa and periodontal disease. I show that the signals from gingivitis and periodontitis can be distinguished, and use correlation networks to identify important taxa for the develop- ment of disease. The second half of this thesis deals with the effect of antibiotics on the human microbiome. I demonstrate new approaches at two extremes of scale: abstracting the gut microbiome to a single metric, and also investigating the worldwide distribution and diversity of a single resistance gene. In Chapter 4, I develop a new and simple mathematical model of the gut microbiome’s response to antibiotic perturbation and fit it to empirical data, showing that in some individuals the gut microbiome appears to return to an alternative stable state, raising questions about the long-term impact of antibiotics on previously healthy bacterial communities. Antibiotic use also selects for resistance, which is a growing concern, particularly as resistance can be transmitted horizontally on mobile genetic elements. In Chapter 5, I describe a global dataset of isolates con- taining the mobilized colistin resistance gene mcr-1 and use the diversity present within a composite transposon alignment to explore its distribution and spread across multiple bacterial communities. 3 Impact statement The last decade has seen great interest in understanding the role of the bacterial communities within the human body for health and disease, but their complex ecology is still far from satisfactorily understood. In this thesis, I present a number of different approaches to the sequencing datasets that are now increasingly available about these communities. These approaches represent distinct contributions, but have in common an ecological perspective that attempts to infer ecological units and interactions from the data at different scales. My findings have several implications. My work on the oral microbiome provides important examples of how to integrate detailed host genetic and demographic data into a microbiome analysis to avoid confounding. The effects of antibiotics on our bacterial communities and therefore our health are a global concern, both in terms of the develop- ment and spread of resistance, but also possible long-term detrimental effects on healthy individuals. The new mathematical model I develop for the perturbation response of the gut microbiome could form a common basis for comparing different antibiotic treatments and assessing their effects, something that is urgently needed to rationally decide lengths of antibiotic courses. For the spread of resistance, I show how a phylogenetic analysis of a mobile genetic element carrying an antibiotic resistance gene is possible by using a combined approach to search all publicly available sequence data. Future work may rou- tinely use this approach, including integrating the analysis for multiple resistance genes to understand how resistance spreads globally. 4 Acknowledgements My first and principal thanks go to my supervisors. From the start, Nigel Klein success- fully passed on to me his excitement and enthusiasm for bacterial communities. He has generously allowed my exploration of this developing field in multiple directions while remaining supportive. Robin Callard’s attitude to mathematical modelling was a strong influence. His support and advice early on in my PhD was invaluable, and I am very glad to have had him as a supervisor. Sarah Walker never failed to make time to provide robust advice on statistics and to frame problems in a characteristically incisive way. I always looked forward to leaving our meetings with a new set of questions to address. Finally, Francois Balloux has been perhaps my closest scientific mentor throughout the three years, and I am extremely thankful for his encouragement and scepticism in roughly equal measures. His diverse scientific interests and democratic attitude to collaboration have provided me with an ideal environment in which to develop as a researcher. The work I have completed required many collaborators. In rough chronological order and at the risk of omission: Ronan Doyle introduced me to the world of 16S and much more, and generously allowed me to analyse the data he had spent so much effort collecting together with Ulla Harjunmaa and others (Chapter 3). Andre Ribeiro, Adam Roberts, and Andrew Smith were a friendly and relaxed team to work with, and Adam Levine was extremely patient with my near-endless enquiries about the dataset (Chapter 2). Developing the antibiotic perturbation model was much easier with Chris Barnes on hand to offer a fresh perspective (Chapter 4). Working closely with Lucy van Dorp to understand the story of mcr-1 was one of my most rewarding scientific experiences to date (Chapter 5). Our common CoMPLEX background made it a particular pleasure to investigate the complex world of bacterial genetics together. I would also like to thank the anonymous peer reviewers whose comments considerably improved all of the work in this thesis. Thanks to all those who indirectly made this thesis a very enjoyable experience. Camilo Chacón-Duque, Florent Lassalle, Stephen Price, Matteo Fumagalli, Javier Men- doza, and the wider GEE department were a wonderful group of people to share office space and science with. Vania de Toledo was an indefatigable source of administrative support. My friends and family were uniformly great, but particular thanks to my parents and to all inhabitants of Senrab Street and Ebbisham Drive (permanent or otherwise). I would like to highlight for a special mention those who I’ve talked to about my thesis: my scientific peers Joel Hellewell, John Lees, Rox Middleton, and Tim Russell, and my non-scientific peer Claire Hall. Finally, I am grateful to the Engineering and Physical Sciences Research Council for supporting my research. It is a privilege to have been provided with public money to work in such a fascinating field. 5 Contents List of Figures 9 List of Tables 11 Acronyms 12 1 Introduction 15 1.1 The human microbiome ........................... 15 1.1.1 History ............................... 15 1.1.2 Role in health and disease ..................... 17 1.1.3 Sequencing ............................. 18 1.1.4 Summary .............................. 23 1.2 The oral microbiome ............................ 23 1.2.1 Structure and characterization ................... 23 1.2.2 Factors shaping the oral microbiome ................ 26 1.2.3 Associations with disease ..................... 29 1.2.4 Summary .............................. 31 1.3 Antibiotics and the microbiome ....................... 31 1.3.1 Antibiotics and the individual ................... 32 1.3.2 Antimicrobial resistance ...................... 35 1.3.3 Summary .............................. 36 1.4 Ecological approaches ........................... 36 1.4.1 Diversity and composition ..................... 37 1.4.2 Associations and interaction networks ............... 38 1.4.3 Stability and variability ....................... 38 1.4.4 Nested genetic diversity and horizontal gene transfer ....... 39 1.5 Conclusions ................................. 41 2 Genetics and the environment in the salivary microbiome 43 2.1 Introduction ................................. 44 2.2 Materials and Methods ........................... 46 2.2.1 Cohort ............................... 46 2.2.2 Sampling and extraction ...................... 47 6 2.2.3 Inclusion of host genetics ...................... 49 2.2.4 Statistical analysis ......................... 51 2.3 Results .................................... 51 2.3.1 Description of cohort ........................ 51 2.3.2 Host genetic similarity and microbiome similarity ......... 52 2.3.3 Shared household affects salivary microbiome composition .... 55 2.3.4 Spouses share taxa at the sub-genus level ............. 55 2.3.5 Persistence of household effects after co-habiting ......... 59 2.3.6 Relying on pedigree kinships produces a genetic signal ...... 60 2.4 Conclusions ................................. 60 2.4.1 Discussion ............................. 60 2.4.2 Limitations ............................. 62 2.4.3 Summary .............................. 63 3 Periodontal disease and the

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