Our study provides the very first data concerning the role of GAM, a previously uncharacterized factor, in human cells, in addition to shedding a new light on the regulation and functions of miR-17-92 miRNAs. Altogether, our results show that GAM is central to a gene regulatory network, illustrated on the working model of , through which: (i) miR-17, -20a, -92a-1 and let-7 miRNAs reduce GAM expression by targeting GAM transcripts; (ii) GAM downregulates miR-17-92 miRNAs both by impairing the transcriptional activation of the miR-17-92 cluster by c-Myc and by differentially affecting the maturation of miR-17-92 miRNAs; (iii) GAM also impairs the upregulation of miR-17-92 miRNAs by TGFβ effectors, possibly by decreasing the transcriptional activity of SMADs following the activation of the TGFβ canonical pathway; (iv) GAM increases cell apoptosis while reducing cell proliferation, and modulates, directly or indirectly, the levels of E2F1 and Ras; and (v) GAM decreases the levels of and interacts in a RNase-dependent manner with Drosha, the main effector of miRNA maturation in the nucleus.
Figure 8. Working model depicting the intricate interaction loops between GAM, TGFβ effectors, Drosha, miR-17-92 and let-7 miRNAs. For clarity, only the main interactions have been represented. Arrows indicate activating effects, and T-barred arrows inhibitory (more ...)
Using synthetic pre-miRNAs as well as antisense inhibitory RNAs for luciferase assays, qRT–PCRs and western blotting analyses, we provided strong evidence that endogenous miR-17-92 and let-7 miRNAs target GAM transcripts. Importantly, our experiments also showed that, while miR-17, miR-20a and miR-92a-1 work together to downregulate GAM transcripts, miR-92a-1 upregulates E2F1 in contrast to miR-17 and miR-20a. As miR-92a-1 is the only miRNA of the cluster with orthologs in non-vertebrates, it is possible that, following gene duplications in vertebrate lineage, evolution has kept the different miR-17-92 miRNAs in cluster to establish a yet poorly understood mechanism of differential maturation, thus allowing for the fine tuning of their effects on their hundreds of respective target genes.
Our promoter studies confirmed the previously established activating effects of c-Myc on the miR-17-92
), and showed that GAM limits, directly or indirectly, c-Myc trancriptional activation of the miR-17-92
cluster through TSS1 without affecting the levels of endogenous c-Myc, although the mechanistic bases of GAM/c-Myc interference remains to be elucidated. They revealed that the miR-17-92
cluster constitutes a very complex transcriptional unit with regulatory sequences spanning over several thousands base pairs, from the site bound by c-Myc just upstream of the TSS2 (10
) to an upstream 123-bp fragment containing E2F1
previously shown to allow the transcriptional activation of the cluster by E2F factors (12
) to sequences located upstream of the TSS1 and the 5′-end of Clu-2
(this article). In any case, it can be expected that the transcription of a cluster containing miRNAs targeting key regulators of cell proliferation would be regulated in a number of different ways. Of note, we have previously shown that chicken GAM interacts with the heterogenous nuclear ribonucleoprotein U (hnRNPU/SAF-A) (17
), which is known to interact with different transcription factors, including the glucocorticoid receptor, a member of the superfamily of hormone nuclear receptors. Through this interaction, hnRNPU represses glucocorticoid-induced activation by sequestrating the glucocorticoid receptors on the nuclear matrix (31
). Further experiments would be required to determine whether interacting with hnRNPU might allow GAM to block the accession of c-Myc or other transcriptional regulators to the miR-17-92
However, transcriptional regulation cannot explain why each of the miR-17-92
miRNAs is processed differentially within a certain type of cells based on qRT–PCRs and RNase-protection assays. This form of differential expression of miR-17-92
miRNAs seems similar to a certain degree in the different cell lines studied, which suggests that it may represent a more general phenomenon. Namely, it was previously shown that miR-17-92
miRNA levels in mouse embryos are not uniform and change differentially according to the stage of development (32
). Therefore, there is not much doubt that these miRNAs are processed with a differential efficiency from the miR-17-92
primary transcripts. Interestingly, the relative levels of miR-20a
and even more of miR-92a
remained consistently higher than those of the miRNAs in a more 5′-position, whether cells were submitted to the different treatments, or not. One can hypothesize that the differential processing of miR-17-92
miRNAs depends on their relative position in the cluster. Addressing this question will require to determine the levels of the different miRNAs after shuffling their respective pre-miRNA sequences within the expression constructs.
We further show that GAM and TGFβ respectively downregulates and upregulates miR-17-92
miRNAs in HEK-293, HepG2 and MCF7 cells, and that GAM also downregulates them in K562 lymphoid cells. In each case, the above effects were miRNA-specific. This suggests that GAM as well as TGFβ effectors can modulate the levels of the factors which control the specific processing of each of miR-17-92
miRNAs. To date, nothing is known about these regulators, except that hnRNPA1 is required to allow the specific processing of miR-18a
). Nevertheless, the fact that miR-18a
levels remained consistently low in the different cell lines, with or without treatment, indicate that these cells contain factors able to specifically bias the processing of miR-17-92
primary RNAs toward the production of miRNAs such as miR-17
. Furthermore, overexpressing the whole miR-17-92
cluster using a high expression pCMV
vector did not change miR-17
expression, while increasing the levels of miR-20a
by no more than about 30 and 60%, respectively. This suggests that the factors controlling the processing of miR-17-92
primary transcripts by Drosha are present in limiting amounts, thus precluding the in vitro
analysis of the consequences of GAM-Drosha interaction on the activity of a purified Microprocessor complex following GAM
overexpression. Interestingly, GAM was previously shown to interact not only with hnRNPU/SAF-A, but also with hnRNP-M and Matrin 3, a component of the nuclear matrix (17
). As hnRNPU and hnRNP-M4 have been found in the Microprocessor (34–36
), and as hnRNPU has also been shown to interact with Matrin 3 (37
), this raises the question of whether GAM might impair the access of Drosha to some of its target pri-miRNAs by relocating it in specific domains of the nuclear matrix.
Furthermore, while GAM downregulated both Drosha and miR-17-92
miRNAs, TGFβ signaling upregulated the same miRNAs without changing Drosha levels. Of note, the upregulation of miR-17-92
miRNAs by TGFβ may possibly help to understand how TGFβ signaling can promote tumor growth and metastasis (25
), given that miR-17-92
miRNAs tend to increase cell proliferation and are usually considered as oncomiRs (5
). Furthermore, the effects of both GAM and TGFβ effectors were miRNA-specific. For example, TGFβ upregulated let-7e
but did not change let-7d
levels in HepG2 cells. On the other hand, while overexpressing GAM
or transfecting cells with siGAM
did not affect the levels of let-7e
slightly but significantly decreased let-7d
levels, an effect unexpectedly impaired by TGFβ. Accordingly, qRT–PCR experiments in SW480 cells as well as miRNA microarrays in THP-1 monocytic cells showed that the levels of a number of miRNAs remain unchanged following transfection of these cells with either siGAM
or a construct overexpressing GAM
, while other miRNAs are either upregulated or downregulated (Tili et al.
, unpublished results). Therefore, neither the downregulation of Drosha by GAM nor GAM-Drosha interaction leads to a general decrease of miRNA levels. Furthermore, neither GAM nor TGFβ exert global effects on the activity of the miRNA processing machinery. In contrast, it is more likely that the miRNA-specific effects of both GAM and TGFβ effectors may arise from their respective effects on the expression and/or the activity of factors controlling the processing of specific pri-miRNAs, especially that of miR-17-92
primary transcripts. Of note, it has been previously shown that, depending on BMP or TGFβ signaling, SMAD1, SMAD5 or SMAD3 can control Drosha-mediated miRNA maturation through their binding to DEAD box RNA helicase p68 (27
). It would thus be very interesting to determine if SMAD2 and/or SMAD3 might similarly interfere with the processing of miR-17-92
pri-miRNAs by Drosha under TGFβ signaling, and, if this is the case, if GAM would be able to impair this interaction between SMAD2 or SMAD3 and Drosha. However, while we were able to confirm the previously established GAM–SMAD1 interaction (24
), we did not found any GAM-SMAD2 or GAM–SMAD3 interaction (Tili et al.
, unpublished results). Ultimately, elucidating how the differential processing of miR-17-92
miRNAs is controlled and identifying their key target transcripts may be critical to understand the role of the miR-17-92
cluster in tumor formation.
Importantly, in addition to opposing the upregulation of miR-17-92 miRNAs by TGFβ, GAM impaired the activation of TGFβ responsive genes, while TGFβ in turn downregulated GAM and GAM transcripts levels. This suggests that GAM may be a bona fide TGFβ effector, potentially implicated in many aspects of TGFβ signaling. It will thus be interesting to check whether GAM might differentially affect the cytostatic and the pro-metastatic activities of TGFβ.
We also show that GAM modulates the levels of E2F1 and Ras at least in part through its repressing effects on endogenous miR-92a-1
, while increasing cell apoptosis and reducing cell proliferation. The precise effects of GAM on such complex phenotypes will not be easy to determine. As GAM seems to regulate the levels of many miRNAs, and in particular miRNAs which potentially target hundreds of transcripts (e.g. ~1.000 for miR-17
, and ~700 for miR-92a-1
), GAM activity is likely to affect cell homeostasis in a number of different ways. Of note, it has also been shown that TGFβ1 can overcome Ras mitogenic effects (38–40
), and that Ras can counteract TGFβ signaling by altering the expression of TGFβ type II receptor (41
). It would thus also be interesting to look whether GAM may play a role in these reciprocal interactions. Finally, the available public data show that GAM
expression is higher in brain, kidney and lung tumors but lower in breast, colon, pancreas and prostate cancers, and that the expression of the miR-17-92
cluster negatively correlates with that of GAM
except in lung cells (http://www.cgl.ucsf.edu/Research/genentech/gepis/index.html
). Given the above results, one can speculate that, while GAM
has not yet been connected to a particular pathology, GAM
misexpression may impair the proper balance between pro- and anti-proliferation factors, thus increasing the probability of tumor formation.
As a last remark, the emergence of the vertebrate lineage coincided with successive gene duplications, the apparition of new transcriptional regulators and new signaling molecules, and the acquisition of new functionalities such as a complex and more performing brain or adaptative immunity. It is thus probable that the new level of molecular complexity reached by vertebrates has been made possible by the development of robust genetic circuitries required to maintain cell homeostasis while allowing the organism to dynamically respond to an ever changing environment. Like GAM, TGFβ as well as the miR-17-92 cluster also appeared in vertebrates. It is therefore not surprising that they may be implicated in the control of cell homostasis and interact with each other in so many different ways. As GAM transcripts are potentially targeted by ~150 miRNAs, GAM is likely to participate in feedback regulatory loops with many other miRNAs.