Exploring metabolic pathway disruption in the subchronic phencyclidine model of schizophrenia with the generalized singular value decomposition

Xiaolin Xiao, Neil Dawson, Lynsey MacIntyre, Brian J. Morris, Judith A. Pratt, David G. Watson, Desmond J Higham

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Abstract

The quantification of experimentally-induced alterations in biological pathways remains a major challenge in systems biology. One example of this is the quantitative characterization of alterations in defined, established metabolic pathways from complex metabolomic data. At present, the disruption of a given metabolic pathway is inferred from metabolomic data by observing an alteration in the level of one or more individual metabolites present within that pathway. Not only is this approach open to subjectivity, as metabolites participate in multiple pathways, but it also ignores useful information available through the pairwise correlations between metabolites. This extra information may be incorporated using a higher-level approach that looks for alterations between a pair of correlation networks. In this way experimentally-induced alterations in metabolic pathways can be quantitatively defined by characterizing group differences in metabolite clustering. Taking this approach increases the objectivity of interpreting alterations in metabolic pathways from metabolomic data.

We present and justify a new technique for comparing pairs of networks - in our case these networks are based on the same set of nodes and there are two distinct types of weighted edges. The algorithm is based on the Generalized Singular Value Decomposition (GSVD), which may be regarded as an extension of Principle Components Analysis to the case of two data sets. We show how the GSVD can be interpreted as a technique for reordering the two networks in order to reveal clusters that are exclusive to only one. Here we apply this algorithm to a new set of metabolomic data from the prefrontal cortex (PFC) of a translational model relevant to schizophrenia, rats treated subchronically with the N-methyl-D-Aspartic acid (NMDA) receptor antagonist phencyclidine (PCP). This provides us with a means to quantify which predefined metabolic pathways (Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolite pathway database) were altered in the PFC of PCP-treated rats. Several significant changes were discovered, notably: 1) neuroactive ligands active at glutamate and GABA receptors are disrupted in the PFC of PCP-treated animals, 2) glutamate dysfunction in these animals was not limited to compromised glutamatergic neurotransmission but also involves the disruption of metabolic pathways linked to glutamate; and 3) a specific series of purine reactions Xanthine <- Hypoxyanthine <-> Inosine <- IMP -> adenylosuccinate is also disrupted in the PFC of PCP-treated animals.

Network reordering via the GSVD provides a means to discover statistically validated differences in clustering between a pair of networks. In practice this analytical approach, when applied to metabolomic data, allows us to quantify the alterations in metabolic pathways between two experimental groups. With this new computational technique we identified metabolic pathway alterations that are consistent with known results. Furthermore, we discovered disruption in a novel series of purine reactions that may contribute to the PFC dysfunction and cognitive deficits seen in schizophrenia.

Original languageEnglish
Article number72
Number of pages20
JournalBMC Systems Biology
Volume5
DOIs
Publication statusPublished - 16 May 2011

Fingerprint

Generalized Singular Value Decomposition
Phencyclidine
Singular value decomposition
Metabolic Networks and Pathways
Pathway
Schizophrenia
Metabolomics
Metabolites
Prefrontal Cortex
Cortex
Animals
Model
Cluster Analysis
Rats
Glutamic Acid
Genes
Reordering
Encyclopedias
Inosine
Receptor

Keywords

  • biological pathways
  • systems biology
  • metabolomic data
  • metabolites
  • networks
  • generalized singular value decomposition
  • GSVD
  • schizophonia
  • metabolic pathways
  • phencyclidine

Cite this

Xiao, Xiaolin ; Dawson, Neil ; MacIntyre, Lynsey ; Morris, Brian J. ; Pratt, Judith A. ; Watson, David G. ; Higham, Desmond J. / Exploring metabolic pathway disruption in the subchronic phencyclidine model of schizophrenia with the generalized singular value decomposition. In: BMC Systems Biology. 2011 ; Vol. 5.
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Exploring metabolic pathway disruption in the subchronic phencyclidine model of schizophrenia with the generalized singular value decomposition. / Xiao, Xiaolin; Dawson, Neil; MacIntyre, Lynsey; Morris, Brian J.; Pratt, Judith A.; Watson, David G.; Higham, Desmond J.

In: BMC Systems Biology, Vol. 5, 72, 16.05.2011.

Research output: Contribution to journalArticle

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AU - Dawson, Neil

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