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Christopher M. Sassetti

Researcher at University of Massachusetts Medical School

Publications -  149
Citations -  15611

Christopher M. Sassetti is an academic researcher from University of Massachusetts Medical School. The author has contributed to research in topics: Mycobacterium tuberculosis & Tuberculosis. The author has an hindex of 52, co-authored 138 publications receiving 13365 citations. Previous affiliations of Christopher M. Sassetti include University of Massachusetts Amherst & Harvard University.

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Genes required for mycobacterial growth defined by high density mutagenesis

TL;DR: The use of transposon site hybridization (TraSH) is described to comprehensively identify the genes required by the causative agent, Mycobacterium tuberculosis, for optimal growth, suggesting that the minimal gene set required for survival varies greatly between organisms with different evolutionary histories.
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Genetic requirements for mycobacterial survival during infection

TL;DR: A surprisingly large fraction of these genes are unique to mycobacteria and closely related species, indicating that many of the strategies used by this unusual group of organisms are fundamentally different from other pathogens.
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High-resolution Phenotypic Profiling Defines Genes Essential for Mycobacterial Growth and Cholesterol Catabolism

TL;DR: This work provides the most comprehensive genetic characterization of a sterol catabolic pathway to date, suggests putative roles for uncharacterized virulence genes, and precisely maps genes encoding potential drug targets.
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Mycobacterial persistence requires the utilization of host cholesterol

TL;DR: It is shown that mce4 encodes a cholesterol import system that enables M. tuberculosis to derive both carbon and energy from this ubiquitous component of host membranes.
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Comprehensive identification of conditionally essential genes in mycobacteria

TL;DR: A technique, transposon site hybridization (TraSH), which allows rapid functional characterization by identifying the complete set of genes required for growth under different conditions by combining high-density insertional mutagenesis with microarray mapping of pools of mutants.