Wikoff WR, Pendyala G, Siuzdak G, Fox HS.
Metabolomic analysis of the cerebrospinal fluid reveals changes in phospholipase expression in the CNS of SIV-infected macaques.
J Clin Invest. 2008 Jun 2;
PubMed.
In this study, Wikoff et al. utilized non-biased metabolomics approaches to investigate CSF metabolome changes in SIV-infected monkeys. The authors reported that the levels of several specific carnitines, fatty acids, and lysophosphatidylcholines were increased in the infected monkeys with encephalitis, possibly due to the upregulation of PLAs and the breakdown of BBB. This report is an exemplar of applying the methodology developed in Dr. Siuzdak’s lab (i.e., XCMS) to neuroscience. Certainly, the current study elegantly demonstrates the enormous potential of metabolomics for the discovery of novel biomarkers and pathogenic pathways in neurodegenerative disorders.
However, the study also reveals the limitation of current technologies. Although hundreds of distinct features were observed, only 12 metabolites were identified and the identities of 11 metabolites were confirmed by using Q-TOF. To overcome such limitation, high resolution FT-MS/MS or high sensitivity capillary NMR studies are warranted to identify other unknown metabolites, which may lead to the discovery of novel biological pathways. In the future, approaches that couple genomics, proteomics, and metabolomics tools may enable us to understand the system from a holistic perspective, as opposed to the reductionist approach.
Comments
Indiana University School of Medicine
In this study, Wikoff et al. utilized non-biased metabolomics approaches to investigate CSF metabolome changes in SIV-infected monkeys. The authors reported that the levels of several specific carnitines, fatty acids, and lysophosphatidylcholines were increased in the infected monkeys with encephalitis, possibly due to the upregulation of PLAs and the breakdown of BBB. This report is an exemplar of applying the methodology developed in Dr. Siuzdak’s lab (i.e., XCMS) to neuroscience. Certainly, the current study elegantly demonstrates the enormous potential of metabolomics for the discovery of novel biomarkers and pathogenic pathways in neurodegenerative disorders.
However, the study also reveals the limitation of current technologies. Although hundreds of distinct features were observed, only 12 metabolites were identified and the identities of 11 metabolites were confirmed by using Q-TOF. To overcome such limitation, high resolution FT-MS/MS or high sensitivity capillary NMR studies are warranted to identify other unknown metabolites, which may lead to the discovery of novel biological pathways. In the future, approaches that couple genomics, proteomics, and metabolomics tools may enable us to understand the system from a holistic perspective, as opposed to the reductionist approach.
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