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|Title:||Comparative network-based recovery analysis and proteomic profiling of neurological changes in valproic acid-treated mice||Authors:||Goh, W.W.B.
Functional Class Scoring (FCS)
Proteomics Expansion Pipeline (PEP)
valproic acid (VPA)
|Issue Date:||5-Mar-2013||Citation:||Goh, W.W.B., Sergot, M.J., Sng, J.C., Wong, L. (2013-03-05). Comparative network-based recovery analysis and proteomic profiling of neurological changes in valproic acid-treated mice. Journal of Proteome Research 12 (5) : 2116-2127. ScholarBank@NUS Repository. https://doi.org/10.1021/pr301127f||Abstract:||Despite its prominence for characterization of complex mixtures, LC-MS/MS frequently fails to identify many proteins. Network-based analysis methods, based on protein-protein interaction networks (PPINs), biological pathways, and protein complexes, are useful for recovering non-detected proteins, thereby enhancing analytical resolution. However, network-based analysis methods do come in varied flavors for which the respective efficacies are largely unknown. We compare the recovery performance and functional insights from three distinct instances of PPIN-based approaches, viz., Proteomics Expansion Pipeline (PEP), Functional Class Scoring (FCS), and Maxlink, in a test scenario of valproic acid (VPA)-treated mice. We find that the most comprehensive functional insights, as well as best non-detected protein recovery performance, are derived from FCS utilizing real biological complexes. This outstrips other network-based methods such as Maxlink or Proteomics Expansion Pipeline (PEP). From FCS, we identified known biological complexes involved in epigenetic modifications, neuronal system development, and cytoskeletal rearrangements. This is congruent with the observed phenotype where adult mice showed an increase in dendritic branching to allow the rewiring of visual cortical circuitry and an improvement in their visual acuity when tested behaviorally. In addition, PEP also identified a novel complex, comprising YWHAB, NR1, NR2B, ACTB, and TJP1, which is functionally related to the observed phenotype. Although our results suggest different network analysis methods can produce different results, on the whole, the findings are mutually supportive. More critically, the non-overlapping information each provides can provide greater holistic understanding of complex phenotypes. © 2013 American Chemical Society.||Source Title:||Journal of Proteome Research||URI:||http://scholarbank.nus.edu.sg/handle/10635/77832||ISSN:||15353893||DOI:||10.1021/pr301127f|
|Appears in Collections:||Staff Publications|
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