Metabolomic Signatures in Guinea Pigs Infected with Epidemic-Associated W-Beijing Strains of Mycobacterium tuberculosis
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- 作者：Bagganahalli S. Somashekar ; Anita G. Amin ; Pratima Tripathi ; Neil MacKinnon ; Christopher D. Rithner ; Crystal A Shanley ; Randall Basaraba ; Marcela Henao-Tamayo ; Midori Kato-Maeda ; Ayyalusamy Ramamoorthy ; Ian M. Orme ; Diane J. Ordway ; Delphi Chatterjee
- 刊名：Journal of Proteome Research
- 出版年：2012
- 出版时间：October 5, 2012
- 年：2012
- 卷：11
- 期：10
- 页码：4873-4884
- 全文大小：850K
- 年卷期：v.11,no.10(October 5, 2012)
- ISSN：1535-3907
文摘
With the understanding that the laboratory propagated strain of Mycobacterium tuberculosis H37Rv is of modest virulence and is drug susceptible, in the present study, we performed a nuclear magnetic resonance-based metabolomic analysis of lung tissues and serum obtained from guinea pigs infected by low dose aerosol exposure to clinical isolates of Mycobacterium tuberculosis. High Resolution Magic Angle Spinning NMR coupled with multivariate statistical analysis of 159 lung tissues obtained from multiple locations of age-matched na茂ve and 30 and 60 days of infected guinea pig lungs revealed a wide dispersal of metabolic patterns, but within these, distinct clusters of signatures could be seen that differentiated between naive control and infected animals. Several metabolites were identified that changed in concert with the progression of each infection. Major metabolites that could be interpreted as indicating host glutaminolysis were consistent with activated host immune cells encountering increasingly hypoxic conditions in the necrotic lung lesions. Moreover, glutathione levels were constantly elevated, probably in response to oxygen radical production in these lesions. Additional distinct signatures were also seen in infected serum, with altered levels of several metabolites. Multivariate statistical analysis clearly differentiated the infected from the uninfected sera; in addition, Receiver Operator Characteristic curve generated with principal component 1 scores showed an area under the curve of 0.908. These data raise optimism that discrete metabolomic signatures can be defined that can predict the progression of the tuberculosis disease process, and form the basis of an innovative and rapid diagnostic process.