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Int J Mol Sci. 2015 Apr 22;16(4):8949-67. doi: 10.3390/ijms16048949.

Novel systems modeling methodology in comparative microbial metabolomics: identifying key enzymes and metabolites implicated in autism spectrum disorders.

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Department of Biotechnology, Johns Hopkins University, Rockville, MD 20850, USA.
Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE 19716, USA.
Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE 19716, USA.


Autism spectrum disorders are a group of mental illnesses highly correlated with gastrointestinal dysfunction. Recent studies have shown that there may be one or more microbial "fingerprints" in terms of the composition characterizing individuals with autism, which could be used for diagnostic purposes. This paper proposes a computational approach whereby metagenomes characteristic of "healthy" and autistic individuals are artificially constructed via genomic information, analyzed for the enzymes coded within, and then these enzymes are compared in detail. This is a text mining application. A custom-designed online application was built and used for the comparative metabolomics study and made publically available. Several of the enzyme-catalyzing reactions involved with the amino acid glutamate were curiously missing from the "autism" microbiome and were coded within almost every organism included in the "control" microbiome. Interestingly, there exists a leading hypothesis regarding autism and glutamate involving a neurological excitation/inhibition imbalance; but the association with this study is unclear. The results included data on the transsulfuration and transmethylation pathways, involved with oxidative stress, also of importance to autism. The results from this study are in alignment with leading hypotheses in the field, which is impressive, considering the purely in silico nature of this study. The present study provides new insight into the complex metabolic interactions underlying autism, and this novel methodology has potential to be useful for developing new hypotheses. However, limitations include sparse genome data availability and conflicting literature experimental data. We believe our software tool and methodology has potential for having great utility as data become more available, comprehensive and reliable.

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