Metadata of the Book that will be visualized online Book Title Systems Biology of Tuberculosis Book SubTitle Copyright Year 2013 Copyright Holder Springer Science+Business Media, LLC Editor Family Name McFadden Particle Given Name Johnjoe Suffix Division Microbial and Cellular Sciences Organization University of Surrey Address Guildford District, GU2 5XH, Guildford, Surrey, United Kingdom Email [email protected] Editor Family Name Beste Particle Given Name Dany J.V. Suffix Division School of Biomedical and Molecular Scien Organization University of Surrey Address Stag Hill, GU2 7XH, Guildford, Surrey, United Kingdom Email [email protected] Editor Family Name Kierzek Particle Given Name Andrzej M. Suffix Division Faculty of Health and Medical Sciences Organization University of Surrey Address Stag Hill, GU2 5XH, Guildford, Surrey, United Kingdom Email [email protected] Systems Biology of Tuberculosis 1 Johnjoe McFadden • Dany J.V. Beste 2 Andrzej M. Kierzek 3 Editors 4 Systems Biology 5 of Tuberculosis 6 7 Editors Johnjoe McFadden Dany J.V. Beste Microbial and Cellular Sciences School of Biomedical and Molecular Science University of Surrey University of Surrey Guildford, Surrey, UK Guildford, Surrey, UK Andrzej M. Kierzek Faculty of Health and Medical Sciences University of Surrey Guildford, Surrey, UK ISBN 978-1-4614-4965-2 ISBN 978-1-4614-4966-9 (eBook) DOI 10.1007/978-1-4614-4966-9 Springer New York Heidelberg Dordrecht London Library of Congress Control Number: 2012951628 © Springer Science+Business Media, LLC 2013 This work is subject to copyright. 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Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com) Introduction 8 Reconstruction of evolutionary history by Bayesian analysis of extant genome 9 sequences suggests that the Mycobacterium tuberculosis complex emerged as an 10 infection of anatomically modern humans carrying the L3 mitochondrial haplotype 11 around one hundred thousand years ago. Since then, M. tuberculosis has demon- 12 strated a remarkable ability to persist amongst small, highly vulnerable populations 13 of early humans migrating out of Africa and to thrive in response to changing 14 demography and recent massive population expansion. The Global Plan to Stop TB 15 proposes its elimination—defined as fewer than one case per million individuals— 16 by 2050 [1]. Given our history of intimate companionship, how can we envisage a 17 strategy to drive this microbe towards extinction? 18 While we can readily collect data on tuberculosis epidemiology, attempts to 19 explain patterns of disease—and hence to design rational control strategies—reveal 20 contributions from multiple variables. It is clear that disease results from some com- 21 bination of host, microbial, and environmental factors, but it is hard to generate 22 mechanistic models that tease these apart. This is an example of an “inverse prob- 23 lem”, in which we have to try and infer the parts by examination of the whole. This 24 is anathema to reductionist biology, and it is the territory to which systems biology 25 aspires. 26 Sydney Brenner highlights the attempt to address ill-posed inverse questions as 27 a lethal flaw in the systems approach, predicting a spiral into low-input, high- 28 throughput, no-output biology [2]. Systems biologists counter by proposing an 29 iterative process of modelling and forward testing of derived hypotheses. This 30 involves an interesting mix of logical reasoning. Wikipedia pithily describes deduc- 31 tion (in which a conclusion is determined by a precondition) as the province of 32 mathematicians, induction (in which a conclusion is a probable outcome of a pre- 33 condition) as the province of scientists, and abduction (in which a precondition is 34 inferred from a conclusion) as the province of detectives. While Sherlock Holmes 35 may dispute elements of this categorisation, the systems biology agenda strives 36 towards some clear-sighted integration of all three methods. 37 A systems approach to tuberculosis can be envisaged as a series of nested prob- 38 lems (often inverse problems) ranging in scale from the level of populations, to 39 vi Introduction 40 individuals, to the cellular and molecular that can conveniently be branded as 41 “­systems epidemiology” [3], “systems vaccinology” [4], and so on. The present 42 volume comprises a series of contributions from groups who are at the forefront of 43 applying systems biology approaches to understand tuberculosis, working primarily 44 at the cell biology end of this spectrum. 45 Beste and McFadden [5] and Jamshidi et al. [6] review advances in metabolic 46 modelling. Metabolomics is particularly appropriate for network modelling. 47 Connectivity between components in the network is direct—one metabolite changes 48 into another—in contrast to the indirect spatial and temporal interactions used to 49 build networks of genes and proteins. Jamshidi et al. describe fundamental pro- 50 cesses of network modelling, while Beste and McFadden stress the importance of a 51 robust experimental system to generate data for modelling, outlining the advantages 52 of growth in a chemostat as a means to optimise relative homogeneity of the bacte- 53 rial populations under study. Both groups stress the dependence of metabolic mod- 54 elling on the availability of an accurately annotated genome. Contributions from a 55 series of outstanding researchers established the foundations of mycobacterial bio- 56 chemistry and metabolism during the first half of the twentieth century, but a rela- 57 tive neglect of tuberculosis research in the 1960s and 1970s left multiple gaps in our 58 knowledge. In the genome era, there is a tendency to fill these gaps using sequence 59 homologies with other organisms. Both papers stress the importance of caution in 60 this, favouring an iterative process in which metabolic models are used both to 61 ­formulate and test forward hypotheses as well as to track back and correct misan- 62 notations. Even core textbook metabolic pathways are found to differ between 63 M. tuberculosis and “canonical” E. coli. 64 Lack of information about the physiological state of M. tuberculosis within infected 65 humans presents a major challenge for modelling tuberculosis pathogenesis, and 66 models derived from microbial culture systems have been usefully extended to metab- 67 olomics of intracellular M. tuberculosis in macrophages. With the exception of a lim- 68 ited range of microbe-specific metabolites, it is difficult to derive direct experimental 69 data on mycobacterial metabolism in infected tissues; in fact it probably makes sense 70 to view host and microbe as a single, integrated metabolic system. It is likely that the 71 bacteria sample a highly diverse range of intracellular and extracellular microenviron- 72 ments during infection, with availability of nutrients and oxygen varying widely over 73 space and time [7]. A systems biology challenge will be to infer microbial physiologi- 74 cal states on the basis of measurements of host metabolism. 75 Both of the metabolic modelling papers stress the importance of integrating 76 metabolomics with transcriptional profiling and functional genomics. Waddell et al. 77 [8] and Rao et al. [9] take up the story from a transcriptome perspective. Traditional 78 transcriptional profiling is also contingent on accurate genome annotation to select 79 genes that are interrogated in microarray platforms. More recent high-density tiling 80 arrays and RNA sequencing approaches remove this limitation and are starting to 81 uncover a considerable repertoire of non-coding RNA outside of annotated open 82 reading frames [10]. This includes small intergenic RNAs, antisense transcripts, and 83 cis-encoded untranslated regions that are likely to regulate the stability of mRNA 84 transcripts and the efficiency of their