Project Information

Mycobacterium tuberculosis TraCS

An ever-increasing body of evidence indicates that the design of novel and effective antitubercular drugs is limited by our ignorance of metabolic pathways critical for bacterial survival during infection. We know that the physiology of the bacterium is fundamentally changed in the in vivo environment, and we propose that the metabolic pathways altered can be effectively targeted by antibiotics.

We have defined a set of 214 genes required for metabolic adaptation of M. tuberculosis to the host environment, however, we are unable to predict the functions of most of these genes and have little idea what roles they play in the infection and how they interact to form functional pathways. If the interrelated functions of these genes could be elucidated, critical pathways or genes could be targeted for antibiotic development.

This proposal has two goals, to define the roles played by the genes we have found to be necessary for infection and to understand how they work together to form functional pathways. The most straightforward way to accomplish both tasks is to define groups of genes that act in a concerted fashion. The most comprehensive and least biased approaches to this problem are to directly identify the specific conditions under which each is essential and to delineate functional pathways by defining genetic interaction networks.

We propose to apply the Transposon capture and sequencing (TraCS) method developed at Broad with NIAID funding to accomplish both of these goals. We will define the specifc host environments under which each Mtb gene is essential, and establish complete and quantitative genome-wide genetic interaction networks for the genes determined to be critical for metabolic adaptation to the host. Of the critical genes, 50 have been identified as potentially most informative ?nodes? in metabolic networks. We will characterize genome-wide interactions in murine infection models for this core set.

The definition of these genetic interaction networks will provide three critical classes of information: First, we will assign functions to uncharacterized genes by associating them with specific host environments or to Mtb genes with known roles. Second, we will reconstruct the complete metabolic or regulatory pathways in an unbiased manner that requires no initial functional information. Finally, genetic interaction maps will identify critical interactions between pathways, providing a holistic picture of cellular physiology and a defined list of priority drug targets.

Samples are in the queue for sequencing, check back for data.