Supplementary methods
Image acquisition for protein quantification
Images were scanned at a minimum resolution of 300 dpi. The protein levels were quantified by
densitometry of raw TIFF images using the NIH Image-based software Scion Image (Scion
Corporation) as described (Venturini et al., 1997; Rossi et al., 2005).
. The densitometry values obtained for each protein were then normalized by using the densitometry
values of the corresponding vinculin. Different exposure were recorded for each blot, in order to avoid
to analyze over-saturated films (Katsuno et al., 2003; Rossi et al., 2005; Tergaonkar et al., 2005;
Venturini et al., 1997).
Affymetrix Analysis
We present our data restricting the analysis of differentially expressed genes to the ones that are called
Increased or Decreased and, moreover, with a Fold Change greater or equal to 2.0 in each comparison
in both replicates. This custom-made criteria was applied to the 3 comparisons, i.e. normal vs
transformed, normal vs reverted or transformed vs reverted, and 750 differentially expressed genes
were selected among the 12.000 ones analyzed.
Analysis of differentially expressed genes was obtained by hierarchical clustering with the J-Express
software. The best fit was obtained with an hierarchical clustering algorithm based on Average Linkage
cluster method and Euclidean Distance as distance matrix. The target of this algorithm is to compute a
dendrogram that assembles all elements into a single tree. The dendrogram is then represented
graphically by colouring each gene's cell on the basis of their Fold Change: genes with Fold Change of
0 (genes unchanged) are coloured black, increasingly positive Fold Change (up-regulated genes) are
coloured red of increasing intensity and increasingly negative Fold Change (down-regulate genes) are
coloured green of increasing intensity.
Genes are grouped by their Fold Change and different areas of the dendrogram can be identified as a
fingerprint of the three different cell lines transcriptional profile.
Legend of supplementary figures
Figure S1 Comparison between RNA and protein levels of cell cycle regulatory proteins (A-B)
Relative change in mRNA (detected by micro array hybridization) and protein (detected by
immunoblot followed by quantification and normalization of densitometry tracings of the blots) are
shown.
Figure S2 The expression level of Cyclin D1 (panel A), Cyclin E (panel B) and p21cip1 (panel C) in
normal ( ), transformed ( ) and reverted ( )cell lines growing in media supplemented with different
initial glucose concentration are shown. Plotted data are mean ± standard deviation computed from at
least three independent experiments.
Cdk4 (D) and cdk2 (E) activity for normal ( ), transformed ( ) and reverted ( ) cell lines growing in
media supplemented with different initial glucose concentration are shown. Plotted data are mean ±
standard deviation of cdk’s activity computed from four independent experiments
Figure S3 RNA, protein and activity analysis of PI3K/Akt pathway. (A) In each column is showed
the type of regulation (I: increased; D: decreased; NC: not change) of the considered genes. (B-C)
Schematic representation of PI3K/Akt pathway regulated genes in our Affymetrix screening for the
comparisons between transformed vs. normal (panel B) and transformed vs. reverted (panel C). Some
regulators and targets of this pathway are also indicated. Within each panel, proteins are color-coded
according to change in expression of their encoding mRNAs as detected by Affymetrix analysis as
follows: Red: Increase; Green: Decrease; Yellow: No change. (D) Analysis of Akt expression and
activity in normal (N), transformed (T) and reverted cells (R) along the time course. The cells were
lysed and protein extract were subjected to SDS-PAGE followed by Western blotting with antibodies
recognizing Akt and Phospho-Akt protein.
References
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