Comparative Network-Based Recovery Analysis and Proteomic Profiling of Neurological Changes in Valproic Acid-Treated Mice

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.