May 2006
Volume 47, Issue 13
Free
ARVO Annual Meeting Abstract  |   May 2006
Molecular Pathways in Developmental Mouse Retina Revealed by Proteomic Trajectory Mapping and Data Mining
Author Affiliations & Notes
  • H. Matsumoto
    Biochemistry and Molecular Biology, University of Oklahoma Health Sciences Center, Oklahoma City, OK
  • H. Haniu
    Biochemistry and Molecular Biology, University of Oklahoma Health Sciences Center, Oklahoma City, OK
  • N. Takemori
    Biochemistry and Molecular Biology, University of Oklahoma Health Sciences Center, Oklahoma City, OK
  • A. Singh
    Biochemistry and Molecular Biology, University of Oklahoma Health Sciences Center, Oklahoma City, OK
  • N. Komori
    Biochemistry and Molecular Biology, University of Oklahoma Health Sciences Center, Oklahoma City, OK
  • Footnotes
    Commercial Relationships  H. Matsumoto, None; H. Haniu, None; N. Takemori, None; A. Singh, None; N. Komori, None.
  • Footnotes
    Support  NIH Grants EY13877 & EY12190
Investigative Ophthalmology & Visual Science May 2006, Vol.47, 5512. doi:
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      H. Matsumoto, H. Haniu, N. Takemori, A. Singh, N. Komori; Molecular Pathways in Developmental Mouse Retina Revealed by Proteomic Trajectory Mapping and Data Mining . Invest. Ophthalmol. Vis. Sci. 2006;47(13):5512.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose: : In order to understand the molecular events underlying the postnatal retinal development we have been mapping the proteomic trajectories of mouse retinal proteins. In this work we plan to elucidate the molecular pathways underlying the retinal development by proteomics data mining.

Methods: : The retinas of C57BL/6 mice were collected at P1, P3, P5, P7, P9, P14, P21, and adult (>P28) stages. The proteins were extracted and analyzed on two–dimensional (2–D) gels. The 2–D gel spots were quantified by densitometric scanning followed by image analysis by Progenesis Workstation (Nonlinear). Four sets of experiments (n=4) were averaged to obtain 174 proteomic trajectories. We clustered the 174 trajectories into four types (Juvenile–type or J–type, Adult–type or A–type, Transient–type or T–type, and Constant–type or T–type) by self–organizing maps (SOM). We determined the identities of these proteins by peptide mass fingerprinting and fragment ion fingerprinting (MS/MS). We conducted proteomic data mining by Pathway Analysis 3.0 (Ingenuity) in two modes. The first analysis inquired the difference in protein function between the four trajectory types. The second inquired protein interaction in the total set of identified (ca 500) retinal proteins inferred by the known pathways.

Results: : The first analysis characterized each trajectory type by the dominance of particular sets of protein expression; for example, RNA–post–transcriptional modification (J–type) and small molecule and carbohydrate metabolism (A–type). The second analysis suggested the involvement of 14 pathways which have central nodes including MYC, HRAS, AKT1, TNF, TGFB1, and IKBKB.

Conclusions: : These results demonstrate that proteomic trajectory mapping and data mining give us detailed information on the dynamic relationships of retinal proteins during development and will be a powerful tool for the systematic understanding of the molecular pathways underlying the developmental process of retina. For example, proteomic trajectory mapping and data mining would aid us to understand a pathological state and recovery from it by treatment by drugs.

Keywords: retinal development • proteomics • gene/expression 
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