TY - JOUR T1 - Differential Producibility Analysis (DPA) of Transcriptomic Data with Metabolic Networks: Deconstructing the Metabolic Response of M. tuberculosis A1 - Bonde, Bhushan K. A1 - Beste, Dany J. V. A1 - Laing, Emma A1 - Kierzek, Andrzej M. A1 - McFadden, Johnjoe Y1 - 2011/06/30 N2 - Author Summary Mycobacterium tuberculosis causes tuberculosis, leading to millions of deaths each year. Treatment takes 6 months or more, leading to lack of patient compliance and emergence of drug resistance. The pathogen takes so long to kill because it is able to enter a state of dormancy/latency/persistence where it is insensitive to drugs. There is an urgent unmet need to develop new antibiotics that target dormant/persistent/latent organisms. Most antibiotics target metabolic processes but it is difficult to examine the metabolism of the pathogen directly inside the host or host cells. It is of course possible to identify which genes are active by transcriptomics but there are no established and validated methods to use transcriptome data to predict metabolism. We here describe the development of such a method, called DPA. We validate the method with E. coli data and then use DPA to predict the metabolism of the TB pathogen growing inside host cells and from TB sputum samples. DPA demonstrates that the TB bacillus remodels its cells in response to the host environment, possibly to increase the pathogen's defenses against the host immune system. Discovering the metabolic details of this remodeling may identify vulnerable metabolic reactions that may be targeted with new TB drugs. JF - PLOS Computational Biology JA - PLOS Computational Biology VL - 7 IS - 6 UR - https://doi.org/10.1371/journal.pcbi.1002060 SP - e1002060 EP - PB - Public Library of Science M3 - doi:10.1371/journal.pcbi.1002060 ER -