ABSTRACT: We have used microarray technology to identify the transcriptional targets of Rho subfamily GTPases. This analysis indicated that murine fibroblasts transformed by these proteins show similar transcriptomal profiles. Functional annotation of the regulated genes indicate that Rho subfamily GTPases target a wide spectrum of biological functions, although loci encoding proteins linked to proliferation and DNA synthesis/transcription are up-regulated preferentially. Rho proteins promote four main networks of interacting proteins nucleated around E2F, c-Jun, c-Myc, and p53. Of those, E2F, c-Jun and c-Myc are essential for the maintenance of cell transformation. Inhibition of Rock, one of the main Rho GTPase targets, leads to small changes in the transcriptome of Rho-transformed cells. Rock inhibition decreases c-myc gene expression without affecting the E2F and c-Jun pathways. Loss-of-function studies demonstrate that c-Myc is important for the blockage of cell-contact inhibition rather than for promoting the proliferation of Rho-transformed cells. However, c-Myc overexpression does not bypass the inhibition of cell transformation induced by Rock blockage, indicating that c-Myc is essential, but not sufficient, for Rock-dependent transformation. These results reveal the complexity of the genetic program orchestrated by the Rho subfamily and pinpoint protein networks that mediate different aspects of the malignant phenotype of Rho-transformed cells Experiment Overall Design: In order to generate the cell clones used in this study, we took advantage of the oncogenic properties of the constitutively active versions (Q63L mutants) of Rho subfamily proteins when overexpressed in rodent fibroblasts (Schuebel et al., 1998). Based on this property, we transfected NIH3T3 cells with plasmids encoding the indicated versions of Rho subfamily proteins to obtain foci of transformed cells. Selected foci were picked, pooled, and used for the subsequent microarray experiments. Experiment Overall Design: To avoid the activation of genetic programs related to serum withdrawal or contact inhibition that may confound the detection of Rho-specific transcriptomal changes (Coller et al., 2006), we cultured the chosen cell lines and the parental NIH3T3 cells in the presence of serum and maintained them at confluency levels lower than 70% prior to RNA extraction. In addition, we isolated total RNAs from eight (in the case of NIH3T3 cells), seven (in the case of RhoAQ63L-transformed cells) and six (in the case of RhoBQ63L- and RhoCQ63L-transformed cells) independent cell cultures in order to make it possible a robust statistical treatment of the data obtained. Experiment Overall Design: three 10-cm diameter plates containing exponentially growing cultures of IMB11-1P (expressing RhoAQ63L) IMB11-2P (expressing RhoBQ63L), or IMB11-3P (expressing RhoCQ63L) cells were washed with PBS and their total cellular RNAs isolated using the RNeasy kit (Qiagen) according to the supplierâs specifications. The quantity and quality of the total RNAs obtained was determined using 6000 Nano Chips (Agilent Technologies). Total RNA samples (4 ug) were then processed for hybridization on MGU75Av2 microarrays (Affymetrix) using standard Affymetrix protocols at the CIC Genomics and Proteomics Facility (www.cicancer.org). Normalization, filtering and analysis of the raw data obtained from the microarrays was carried out with the Bioconductor software (www.bioconductor.com) using de ReadAffy package and the RMA application. The RMA algorithm was selected over the standard Affymetrix software because it provides a better precision in signal detection to achieve adequate normalization of multiple microarray hybridizations, especially in cases of low levels of gene expression (Bolstad et al., 2004; Gautier et al., 2004; Gentleman et al., 2004; Parrish & Spencer, 2004). We considered a gene to be differentially expressed when exhibiting a signal ⥠100 and met the following criteria: in the case of the characterization of the transcriptome of Rho-transformed cells, we regarded a gene as common to all GTPases when: i) It showed a fold change ⥠1.5 in at least two of the cell lines used. ii) The fold change in the third cell line was ⥠1.0 and displayed a similar variation trend when compared to the other two cell lines (i.e., similar up-regulation or down-modulation in the three cell lines). iii) The fold change values in the three cell lines had always P values ⤠0.01. We regarded a gene as common to only two GTPases when: i) It showed a fold change ⥠1.5 in two the cell lines with P values ⤠0.01. ii) The fold change in the third cell line was non-existent or, alternatively, had P values ⥠0.01. We considered a gene as uniquely-regulated by a GTPase when: i) The fold change in the expression levels of is transcript in the cell line transformed by that GTPase was ⥠1.5 with P value ⤠0.01. ii) The fold change, if any, obtained in the other cell lines had P values ⥠0.01. Statistical analyses were performed using F-statistics.
