Quantitative exploration of the REF52 protein database: cluster analysis reveals the major protein expression profiles in responses to growth regulation, serum stimulation, and viral transformation.
Quantitative protein databases reveal the response of cells to experimental variables, such as exposure to growth factors or transfection with a transforming gene. The nature of the response depends on the type of cell and its internal state at the time of the stimulus. By constructing a protein database to study a given cell line, we can better understand the differentiated state of the cell, the growth regulatory mechanisms it employs, the particular mechanisms it uses to cope with its environment, and the ways these mechanisms may have been compromised through mutation or transformation. The REF52 database is a quantitative database designed to study growth control and transformation in a well-defined family of normal and transformed rat cell lines. The database, which has been described and analyzed elsewhere (J. I. Garrels and B. R. Franza, J. Biol. Chem. 1989, 264, 5283-5298 and J. I. Garrels and B. R. Franza, J. Biol. Chem. 1989, 264, 5299-5312) is further explored here using cluster analysis. This method reveals the most common protein expression profiles for each series of two-dimensional gels without requiring any prior hypothesis or queries on the part of the investigator. This study reveals, for each experiment, large and small clusters of protein expression profiles, most of which have readily apparent biological meaning. For example, large clusters of proteins induced or repressed during growth to confluence have been revealed, and several clusters of transformation-sensitive proteins reveal differential effects of transformation by DNA- and RNA-tumor viruses. This analysis extends our earlier quantitative explorations of the REF52 protein database and helps to show how such a database can be used to provide context and guidance for molecular studies of regulation in a given cell system.
Garrels
J I
JI
Cold Spring Harbor Laboratory, NY 11724.
Franza
B R
BR
Chang
C
C
Latter
G
G
D000465:Algorithms; D001798:Blood Proteins / Q000235:genetics; D002472:Cell Transformation, Viral / Q000235:genetics; D016000:Cluster Analysis; D003198:Computer Simulation; D016208:Databases, Factual; D015180:Electrophoresis, Gel, Two-Dimensional; D006801:Humans; D009857:Oncogenes; D010449:Peptide Mapping; D011506:Proteins
D001798:Blood Proteins; D011506:Proteins