GSMN-ML- a genome scale metabolic network reconstruction of the obligate human pathogenMycobacterium leprae

Leprosy, caused byMycobacterium leprae, has plagued humanity for thousands of years and continues to cause morbidity, disability and stigmatization in two to three million people today. Although effective treatment is available, the disease incidence has remained approximately constant for decades so new approaches, such as vaccine or new drugs, are urgently needed for control. Research is however hampered by the pathogen's obligate intracellular lifestyle and the fact that it has never been grownin vitro. Consequently, despite the availability of its complete genome sequence, fundamental questions regarding the biology of the pathogen, such as its metabolism, remain largely unexplored. In order to explore the metabolism of the leprosy bacillus with a long-term aim of developing a medium to grow the pathogenin vitro, we reconstructed anin silicogenome scale metabolic model of the bacillus, GSMN-ML. The model was used to explore the growth and biomass production capabilities of the pathogen with a range of nutrient sources, such as amino acids, glucose, glycerol and metabolic intermediates. We also used the model to analyze RNA-seq data fromM.lepraegrown in mouse foot pads, and performed Differential Producibility Analysis to identify metabolic pathways that appear to be active during intracellular growth of the pathogen, which included pathways for central carbon metabolism, co-factor, lipids, amino acids, nucleotides and cell wall synthesis. The GSMN-ML model is thereby a usefulin silicotool that can be used to explore the metabolism of the leprosy bacillus, analyze functional genomic experimental data, generate predictions of nutrients required for growth of the bacillusin vitroand identify novel drug targets. Author summary Mycobacterium leprae, the obligate human pathogen is uncultivable in axenic growth medium, and this hinders research on this pathogen, and the pathogenesis of leprosy. The development of novel therapeutics relies on the understanding of growth, survival and metabolism of this bacterium in the host, the knowledge of which is currently very limited. Here we reconstructed a metabolic network ofM.leprae- GSMN-ML, a powerfulin silicotool to study growth and metabolism of the leprosy bacillus. We demonstrate the application of GSMN-ML to identify the metabolic pathways, and metabolite classes thatM.lepraeutilizes during intracellular growth.