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OMICS. 2019 Jan;23(1):1-16. doi: 10.1089/omi.2018.0116. Epub 2018 Sep 12.

Rise of Clinical Microbial Proteogenomics: A Multiomics Approach to Nontuberculous Mycobacterium-The Case of Mycobacterium abscessus UC22.

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1 Institute of Bioinformatics , International Technology Park, Bangalore, India .
2 Manipal Academy of Higher Education , Manipal, India .
3 Center for Systems Biology and Molecular Medicine, Yenepoya Research Centre, Yenepoya (Deemed to be University) , Mangalore, India .
4 School of Biotechnology , Amrita Vishwa Vidyapeetham, Kollam, India .
5 Department of Biotechnology, M.S. Ramaiah Institute of Technology , Bangalore, India .
6 Department of Microbiology and Molecular Biology, ICMR-National JALMA Institute for Leprosy & Other Mycobacterial Diseases , Agra, India .


Nontuberculous mycobacterial (NTM) species present a major challenge for global health with serious clinical manifestations ranging from pulmonary to skin infections. Multiomics research and its applications toward clinical microbial proteogenomics offer veritable potentials in this context. For example, the Mycobacterium abscessus, a highly pathogenic NTM, causes bronchopulmonary infection and chronic pulmonary disease. The rough variant of the M. abscessus UC22 strain is extremely virulent and causes lung upper lobe fibrocavitary disease. Although several whole-genome next-generation sequencing studies have characterized the genes in the smooth variant of M. abscessus, a reference genome sequence for the rough variant was generated only recently and calls for further clinical applications. We carried out whole-genome sequencing and proteomic analysis for a clinical isolate of M. abscessus UC22 strain obtained from a pulmonary tuberculosis patient. We identified 5506 single-nucleotide variations (SNVs), 63 insertions, and 76 deletions compared with the reference genome. Using a high-resolution LC-MS/MS-based approach (liquid chromatography tandem mass spectrometry), we obtained protein coding evidence for 3601 proteins, representing 71% of the total predicted genes in this genome. Application of proteogenomic approach further revealed seven novel protein-coding genes and enabled refinement of six computationally derived gene models. We also identified 30 variant peptides corresponding to 16 SNVs known to be associated with drug resistance. These new observations offer promise for clinical applications of microbial proteogenomics and next-generation sequencing, and provide a resource for future global health applications for NTM species.


acquired resistance; global health; multiomics; next-generation sequencing; nontuberculous mycobacterial species; proteogenomics

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