ARTIST: High-Resolution Genome-Wide Assessment of Fitness Using Transposon-Insertion Sequencing

摘要

Transposon-insertion sequencing (TIS) is a powerful approach for deciphering genetic requirements for bacterial growth in different conditions, as it enables simultaneous genome-wide analysis of the fitness of thousands of mutants. However, current methods for comparative analysis of TIS data do not adjust for stochastic experimental variation between datasets and are limited to interrogation of annotated genomic elements. Here, we present ARTIST, an accessible TIS analysis pipeline for identifying essential regions that are required for growth under optimal conditions as well as conditionally essential loci that participate in survival only under specific conditions. ARTIST uses simulation-based normalization to model and compensate for experimental noise, and thereby enhances the statistical power in conditional TIS analyses. ARTIST also employs a novel adaptation of the hidden Markov model to generate statistically robust, high-resolution, annotation-independent maps of fitness-linked loci across the entire genome. Using ARTIST, we sensitively and comprehensively define Mycobacterium tuberculosis and Vibrio cholerae loci required for host infection while limiting inclusion of false positive loci. ARTIST is applicable to a broad range of organisms and will facilitate TIS-based dissection of pathways required for microbial growth and survival under a multitude of conditions. Author Summary Transposon insertion sequencing (TIS) is a powerful method that couples high-density transposon mutagenesis with next-generation sequencing to comprehensively assess the fitness of thousands of transposon mutants across a genome. TIS is an extremely flexible technique that has been used to define genomic loci required for bacterial growth and survival in a variety of species and in many different growth conditions, including during host infection. However, there remain several important limitations to current TIS analysis methods. First, TIS data are not routinely normalized for the impact of experimental variability; second, most analyses are restricted to annotated loci and do not completely exploit the richness of TIS datasets; finally, TIS analysis methods are not easily accessible to most biologists. Here we present a pipeline—ARTIST—that addresses these issues and will transform TIS-based studies. We used ARTIST to conduct robust analyses of Mycobacterium tuberculosis and Vibrio cholerae in vivo TIS datasets and comprehensively defined the genetic requirements of these pathogens for host infection. The ARTIST pipeline will make TIS analysis accessible to many researchers and greatly enhance the rigor of and insights gained from TIS studies in a wide range of microorganisms.