Genomewide Assessment of Mycobacterium tuberculosis Conditionally Essential Metabolic Pathways

Abstract

A better understanding of essential cellular functions in pathogenic bacteria is important for the development of more effective antimicrobial agents. We performed a comprehensive identification of essential genes in Mycobacterium tuberculosis, the major causative agent of tuberculosis, using a combination of transposon insertion sequencing (Tn-seq) and comparative genomic analysis. To identify conditionally essential genes by Tn-seq, we used media with different nutrient compositions. Although many conditional gene essentialities were affected by the presence of relevant nutrient sources, we also found that the essentiality of genes in a subset of metabolic pathways was unaffected by metabolite availability. Comparative genomic analysis revealed that not all essential genes identified by Tn-seq were fully conserved within the M. tuberculosis complex, including some existing antitubercular drug target genes. In addition, we utilized an available M. tuberculosis genome-scale metabolic model, iSM810, to predict M. tuberculosis gene essentiality in silico. Comparing the sets of essential genes experimentally identified by Tn-seq to those predicted in silico reveals the capabilities and limitations of gene essentiality predictions, highlighting the complexity of M. tuberculosis essential metabolic functions. This study provides a promising platform to study essential cellular functions in M. tuberculosis. IMPORTANCE Mycobacterium tuberculosis causes 10 million cases of tuberculosis (TB), resulting in over 1 million deaths each year. TB therapy is challenging because it requires a minimum of 6 months of treatment with multiple drugs. Protracted treatment times and the emergent spread of drug-resistant M. tuberculosis necessitate the identification of novel targets for drug discovery to curb this global health threat. Essential functions, defined as those indispensable for growth and/or survival, are potential targets for new antimicrobial drugs. In this study, we aimed to define gene essentialities of M. tuberculosis on a genomewide scale to comprehensively identify potential targets for drug discovery. We utilized a combination of experimental (functional genomics) and in silico approaches (comparative genomics and flux balance analysis). Our functional genomics approach identified sets of genes whose essentiality was affected by nutrient availability. Comparative genomics revealed that not all essential genes were fully conserved within the M. tuberculosis complex. Comparing sets of essential genes identified by functional genomics to those predicted by flux balance analysis highlighted gaps in current knowledge regarding M. tuberculosis metabolic capabilities. Thus, our study identifies numerous potential antitubercular drug targets and provides a comprehensive picture of the complexity of M. tuberculosis essential cellular functions.

Genome-wide identification of essential genes in Mycobacterium intracellulare by transposon sequencing — Implication for metabolic remodeling

“…TnSeq analysis has been used to study various genera of bacteria 7 . In mycobacteria, essential genes for growth were determined globally in M. tuberculosis H37Rv [10][11][12] and M. marinum E11 15 . We determined the essential genes of one of the human pathogenic NTM M. intracellulare.…”

“…tnSeq. The TnSeq libraries were constructed as previously described 12 . The resultant TnSeq libraries were sequenced using a HiSeq.…”

“…2500HO, 125 bp PE run using v4 chemistry (Illumina). Sequence reads were analyzed as previously described 12 . Briefly, we first trimmed sequence reads for transposon and adaptor sequences and then discarded the sequence reads that were shorter than 18 bp.…”

“…The trimmed sequence reads were mapped (allowing 1 bp mismatch) to the M. intracellulare ATCC13950 genome (GenBank: NC_016946.1) and converted output to SAM format using Bowtie2 68 . The numbers of sequence reads at each TA site were counted and converted to the wig format, the input file format for TRNSIT 23 using the custom Python script 12 . After averaging the obtained read counts between the three replicates in each experimental setting, statistical analysis for determination of essential genes and fitness for hypoxic growth was performed by the Hidden Markov Model (HMM) and resampling analysis on TRANSIT, respectively.…”

“…The resulting lists of essential genes can be explored for possible drug targets because inhibition of the corresponding pathways theoretically leads to the suppression of bacterial growth 8 . In M. tuberculosis, TnSeq has revealed genes required for fitness in optimal medium 9,10 , in minimal medium 11,12 , in response to CD4 T cell immunity 13 , and has elucidated genes associated with in vivo persistence 13 and different susceptibilities to antibiotics 14 . In NTM, essential genes have been determined in M. marinum, the main host of which is poikilothermic animals such as fish, frogs and reptiles 15 .…”