Comprehensive mapping of essential-gene interactions in M. tuberculosis
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Updated: Nov 6, 2026

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays
Published on: November 12, 2012
Isaac J Kresse1, Natalie Y Sayegh1, Vanisha Munsamy-Govender1
1Laboratory of Host-Pathogen Biology, The Rockefeller University, New York, NY, USA.
Abstract:
Mycobacterium tuberculosis, the causative agent of tuberculosis, remains the world's deadliest pathogen1. Despite intensive study, many of its genes remain uncharacterized, which limits the insight that can be gained into tuberculosis biology and constrains drug development. Genetic interaction (GI) mapping is a powerful strategy to define functional relationships between genes by quantifying how the phenotypic effects of combined perturbations deviate from expectations based on single-gene perturbations2,3. Here we develop a dual-sgRNA CRISPR-interference platform that enables high-throughput GI mapping in mycobacteria. We address key technical challenges in library construction and data interpretation by incorporating unique barcodes for each sgRNA pair and implementing a robust statistical pipeline for GI detection. Focusing on essential genes, we generate comprehensive GI maps in the model mycobacterium Mycobacterium smegmatis and the pathogen M. tuberculosis. These maps recapitulate known biology, uncover thousands of previously unrecognized interactions and identify functional connections between essential genes and drug targets. By generating AlphaFold3-based protein-protein interaction predictions4 for all gene pairs in our GI maps, we show that a subset of GIs is likely to reflect underlying physical associations. Validation of selected GIs confirms both novel biological relationships and opportunities for synergistic targeting. Overall, this work establishes a generalizable framework for dual-gene perturbation and defines the architecture of essential-gene networks in mycobacteria, advancing both our understanding of tuberculosis biology and the rational design of combination therapies.
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