extfieldsofstudy: Biology Medicine

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GRASP and IPCEF Promote ARF-to-Rac Signaling and Cell Migration by Coordinating the Association of ARNO/cytohesin 2 with Dock180

The ARF-GEF ARNO promotes motility by activating ARF6 and a subsequent downstream activation of Rac. ARNO is shown to associate with the Rac GEF Dock180 via its coiled-coil domain. Knockdown of scaffold proteins that bind ARNO disrupts the formation of this complex and disrupts ARF-to-Rac signaling.

INTRODUCTION
ARFs (ADP ribosylation factors) are members of the Ras superfamily of small GTPases. The six mammalian ARFs are divided into three classes based upon sequence similarity. ARFs 1-3 make up class I, ARFs 4 and 5 comprise class II, and ARF6 is the sole class III member. The class I and II ARFs recruit vesicle coats and promote vesicle budding in the secretory system. ARF6 is located predominantly in the cell periphery. ARF6 regulates trafficking between the plasma membrane and endosomal systems (Donaldson and Honda, 2005).
Like all GTPases, ARF6 cycles between an inactive GDPbound state and an active GTP-bound state. Interconversion between these two states requires the actions of accessory proteins. Guanine nucleotide exchange factors (GEFs) promote the binding of GTP and the activation of the GTPase. GTPase-activating proteins (GAPs), on the other hand, induce the hydrolysis of the bound GTP, thereby inactivating the GTPase. Although there are only six ARFs, the human genome encodes 15 ARF GEFs and 20 ARF GAPs. This discrepancy suggests that ARFs are regulated by different GEFs and GAPs at particular subcellular locations or during particular processes (Donaldson and Honda, 2005;Casanova, 2007).
There are five families of ARF-GEFs: the GBF/BIG family, cytohesins, EFA6s, BRAGs, and Fbox8. Three of these families, the cytohesins, EFA6s, and BRAGs, have been reported to act in the cell periphery and to regulate endocytosis, recycling, and cell shape (Casanova, 2007). The cytohesins in particular have been implicated in the regulation of the actin cytoskeleton and cell shape. Cytohesins are recruited to the plasma membrane in response to growth factor signaling and induce rearrangements in the cortical actin cytoskeleton (Klarlund et al., 1997(Klarlund et al., , 1998Frank et al., 1998a,b;Venkateswarlu et al., 1998a,b). We have previously shown that overexpression of ARNO/cytohesin 2 in Madin-Darby canine kidney (MDCK) cells promotes migration of these cells (Santy and Casanova, 2001). ARNO expression produces a scattering phenotype that resembles the action of the motility-promoting growth factor HGF.
Enhanced migration in response to ARNO expression requires ARF activation and the subsequent downstream activation of phospholipase D (PLD) and Rac (Santy and Casanova, 2001). ARNO-induced Rac activation can be blocked by coexpression of dominant negative mutants of Dock180 and Elmo (Santy et al., 2005). Dock180 and Elmo act together as a bipartite Rac-GEF that has been extensively implicated as an activator of Rac during migration and This article was published online ahead of print in MBC in Press (http://www.molbiolcell.org/cgi/doi/10.1091/mbc.E09 -03-0217) on December 16, 2009. phagocytosis (Hasegawa et al., 1996;Erickson et al., 1997;Nolan et al., 1998;Wu and Horvitz, 1998;Reddien and Horvitz, 2000;Gumienny et al., 2001;Brugnera et al., 2002). How ARNO and Dock180/Elmo coordinate to promote ARF-to-Rac cross-talk remains unclear.
Recent work on kinase signaling cascades has demonstrated that the output downstream of a particular kinase can be biased by protein-protein interactions. Scaffold proteins assemble the components of a particular signaling pathway into a complex and thereby promote signaling through that pathway (Morrison and Davis, 2003;Kolch, 2005;Dard and Peter, 2006;Pullikuth and Catling, 2007). We wondered whether similar processes bias ARF signaling downstream of ARNO toward Rac activation and motility.
We tested the hypothesis that protein-protein interactions direct ARF-dependent signaling downstream of ARNO toward Rac activation and motility. ARNO, like all the cytohesins, is made up of four distinct domains (see Figure 1). The sec7 domain is the catalytic ARF-GEF domain. The pleckstrin homology (PH) and polybasic domains mediate binding to membrane surfaces. The polybasic domain also functions as an intramolecular inhibitory domain (DiNitto et al., 2007), and the PH domain also interacts with other proteins. The coiled-coil domain promotes dimerization and interacts with a number of other proteins (Casanova, 2007). The coiled-coil domain of ARNO has previously been shown to interact with several small scaffold proteins that contain multiple protein-protein interacting domains. These include GRASP/Tamalin (Nevrivy et al., 2000;Kitano et al., 2002Kitano et al., , 2003, Pip3-E/IPCEF (Venkateswarlu, 2003), and CASP/ Cybr/CYTIP (Mansour et al., 2002;Tang et al., 2002;Boehm et al., 2003). These scaffold proteins can promote the recruitment of ARNO to the plasma membrane in response to growth factor or other signals (Venkateswarlu, 2003;Esteban et al., 2006). These proteins can control the subcellular location of ARNO and could therefore modulate ARNO signaling. We therefore investigated the role of the coiled-coil domain in ARNO-induced motility and Rac activation.

Expression Constructs
Recombinant adenoviruses for expressing full-length ARNO, ARNO E156K, or Dock180 under the control of the tetracycline responsive promoter have been previously described (Santy and Casanova, 2001;Santy et al., 2005). For the ⌬coiled-coil ARNO construct, PCR was used to amplify the region of human ARNO encoding amino acids 56 -400 and to add a myc-tag at the 5Ј end. For the ⌬PH ARNO a stop codon was introduced at position 269 and a myc-tag added at the 5Ј end by PCR. These products were cloned into pAdTet and recombinant adenoviruses produced as described (Hardy et al., 1997). GFP-GRASP was obtained from Mark Leid (Oregon State University). An IPCEF cDNA was isolated by PCR from Marathon ready human brain cDNA (Clontech, Mountain View, CA). The cDNA was sequenced and is identical to the human Pip3-E sequence (NM_015553). The HA-tag was added to the N-terminus by PCR. Both scaffold cDNAs were cloned into pAdlox and recombinant adenoviruses produced as described previously (Hardy et al., 1997). GFP-Elmo and GFP-Elmo-T625 were obtained from Kodi Ravichandran (University of Virginia). The GFP-Elmo constructs were subcloned into pAd-Tet and adenoviruses produced as described above. Truncated Dock180 constructs were created by PCR and cloned into pcDNA3.

Cell Culture
The T23 line of MDCKII cells that expresses the tetracycline responsive transactivator and Tet-off MCF-7 cells were obtained from James Casanova. MDCKs were maintained in DMEM with 10% FBS. MCF-7s were maintained in DMEM/F12 with 10% FBS and nonessential amino acids. Cells were maintained at 37°C and 5% CO 2 . Cell culture media was purchased from Mediatech (Manassas, VA), and FBS was purchased from Hyclone (Rockford, IL).

siRNA Knockdown
The siRNAs targeting human and dog GRASP (target sequence GCTTT-GAGATCCAGACTTA), human and dog Pip3-E/IPCEF (target sequence CA-CATCAGAAAGTGGATTT), human and dog CASP (target sequence CTG-GTGATGTCCTTGCAAA), firefly luciferase, and a scrambled nontargeting control (siControl 1) were obtained from Dharmacon (Lafayette, CO). Fluorescent Block-It Alexafluor Red control siRNA was obtained from Invitrogen (Carlsbad, CA). siRNAs were transfected into MCF-7 cells using LipoRNAi max, and into MDCK cells using Lipofectamine2000 (Invitrogen). Transfections were carried out using the manufacturer's suggested protocol for reverse transfection. For GTPase pulldown assays and immunofluorescence, 3 ϫ 10 5 MDCK cells were transfected with 100 pmol of siRNA in 35-mm dishes. After 48 h of knockdown the cells were trypsinized and replated onto two 60-mm dishes. Pulldown assays were performed 18 h after replating. For immunoprecipitations (IPs) 4 ϫ 10 6 MCF-7 cells were transfected with 300 pmol siRNA in 15-cm dishes. After 48 h of knockdown the cells were infected with adenoviruses encoding ARNO and Dock180. IPs were performed 18 h after infection.

Immunofluorescence
MDCK cells were grown on glass coverslips, infected, and fixed and stained as previously described (Santy and Casanova, 2001). Cells were observed and photographed using a Nikon Eclipse E800 digital camera (Melville, NY) equipped with a Diagnostic Instruments Spot II (Sterling Heights, MI) or a Zeiss Axioplan microscope (Thornwood, NY) equipped with a Diagnostic Instruments Spot RT3. The brightness and contrast of the entire image was adjusted, and scale bars were added using ImageJ (http://rsb.info.nih.gov/ ij/; Abramoff et al., 2004). Multipanel figures were assembled using Adobe Illustrator CS2 (San Jose, CA).

GTPase Pulldown Assays
Active ARF6 was isolated by binding to glutathione S-transferase (GST)-GGA3. Active Rac was isolated by binding to GST-PBD. Pulldown assays were performed as previously described (Santy and Casanova, 2001). Western blots of the pulldowns and saved samples of the starting lysate were analyzed by densitometry using ImageJ (Abramoff et al., 2004). Levels of active GTPase were first normalized to the amount of GTPase present in the starting lysate. Normalized levels of active GTPase in ARNO-expressing cells were divided by the normalized level of active GTPase in the control cells to give the fold activation of the GTPase induced by ARNO. Differences in GTPase activation were analyzed for significance using a paired t test on the indicated number of independent pulldown experiments.

Cross-linking and IP
Tet-off MCF-7s or MDCK cells were infected with adenoviruses encoding myc-ARNO and flag-Dock180 for 18 h (MCF-7s) or 3 h (MDCKs). Alternatively, expression constructs were transfected into MDCK cells using Lipofectamine LTX according to the manufacture's instructions, and cells were allowed to express for 18 h. Interacting proteins were cross-linked by treating the cells with the cell-permeable cross-linker DSP. Briefly, the cells were rinsed with PBS and then incubated with PBS, 150 M DSP for 30 min. Cells were subsequently lysed in 1% Triton X-100, 50 mM Tris, pH 7.5, 150 mM NaCl, 10 mM NaF, 1 mM Na 3 VO 4 , 10 mM sodium pyrophosphate, 2 mg/ml aprotinin, 1 mg/ml leupeptin, 1 mg/ml pepstatin, and 0.1 mM PMSF. Unsolubilized material was removed by centrifugation at 12,000 ϫ g for 10 min at 4°C. A small aliquot of the cleared lysate was saved, and the remainder of the lysate was incubated with M2 anti-flag resin for 2 h at 4°C. IPs were washed three times with lysis buffer and one time with TBS. Precipitated proteins were eluted into SDS-PAGE sample buffer. IP and lysate samples were boiled for 3 min to reverse the DSP cross-links and then analyzed by Western blot.

GST Pulldown
The region of ARNO encoding the N-terminal 60 amino acids was amplified by PCR and inserted in frame into pGEX-2T to produce a construct encoding GST-coiled-coil. GST and GST-coiled-coil were purified as described for GST-GGA3. MDCK cells were infected with adenoviruses encoding Dock180 for 18 h. Cells were lysed as described above for IP. The postnuclear supernatant was incubated with glutathione Sepharose and 30 g GST or 30 g GSTcoiled-coil for 5 h at 4°C. Pulldowns were washed and blotted with goat anti-Dock180.

Migration Assays
Migration was tested using a transwell migration assay as previously described (Santy and Casanova, 2001).

Cell Fractionation
MDCK cells were infected with adenoviruses encoding the indicated proteins for 3 h. The cells were then scraped off the plate in 250 mM sucrose, 3 mM imidazole, pH 7.4, 1 mM EDTA, 1 mM DTT, 2 mg/ml aprotinin, 1 mg/ml leupeptin, 1 mg/ml pepstatin, and 0.1 mM PMSF. Cells were broken by eight passages through a 22-gauge needle. Unbroken cells and nuclei were removed by centrifugation for 2 ϫ 10 min at 12,000 ϫ g at 4°C. The postnuclear supernatant was separated into total membranes and cytosol by ultracentrifugation for 1 h at 100,000 ϫ g in a TLA 100.3 rotor at 4°C. Membrane and cytosol fractions were resuspended in equal volumes of SDS-PAGE sample buffer and analyzed by Western blot.

The Coiled-Coil Domain of ARNO Promotes ARF-to-Rac Cross-Talk
We hypothesize that protein-protein interactions bias ARF signaling downstream of ARNO toward modulating the actin cytoskeleton and promoting migration. ARNO contains one major protein-interacting domain, the coiled-coil domain ( Figure 1). Therefore we tested whether a truncation mutant of ARNO lacking this domain can induce epithelial motility. As we have previously shown, overexpression of wild-type (WT) ARNO produces the formation of large-fan shaped lamellipodia and a scattering phenotype in MDCK cells ( Figure 2A, rows 1 and 2; Santy and Casanova, 2001). Also as we have previously shown, this phenotype requires ARF activation, as a point mutant of ARNO, E156K, that cannot activate ARFs does not induce scattering ( Figure 2A, row 3). We have found that overexpressing a truncation of ARNO lacking the coiled-coil domain (⌬coiled-coil) also fails to produce scattering. MDCK cells expressing this truncation resemble the control cells or cells expressing the inactive ARNO point mutant ( Figure 2A, row 4). MDCK cells expressing ⌬coiled coil-ARNO do not produce large lamellipodia and remain anchored to their neighbors. The lack of a scattering phenotype suggests that unlike WT ARNO, the ⌬coiled coil-ARNO truncation does not promote epithelial migration. We tested the migration of these cells using a transwell migration assay. As can be seen in Figure 2B, although full-length ARNO significantly increases the number of cells that migrate through the filter, cells expressing ⌬coiled-coil ARNO show no increase in migration compared  with the control cells. Both full-length and ⌬coiled-coil ARNO show some perinuclear accumulation ( Figure 2C). Full-length ARNO is also localized to the plasma membrane at the leading edge of the lamellipodia ( Figure 2C, arrow), whereas ⌬coiled-coil ARNO can be seen at the plasma membrane between adjacent cells ( Figure 2C, arrowheads).
Overexpression of ARNO in MDCK cells not only activates ARF6, but also produces an increase in the level of active Rac (Santy and Casanova, 2001). This enhanced Rac activity is required for the scattering phenotype and increased motility seen in the ARNO-expressing cells (Santy and Casanova, 2001). The Dock180/Elmo complex is required for Rac activation downstream of ARNO and ARF6 in MDCK cells (Santy et al., 2005). We therefore tested the hypothesis that ARF-to-Rac signaling is impaired in the cells expressing ⌬coiled coil-ARNO compared with cells expressing full-length ARNO. The cells expressing ⌬coiled coil-ARNO activate endogeneous ARF6 to a level comparable to that seen in cells expressing full-length ARNO ( Figure 3A). This is expected because ⌬coiled coil-ARNO still has the ARF-activating Sec7 domain and the membrane-binding PH and polybasic domains. Significantly we find that cells expressing ⌬coiled coil-ARNO have levels of active Rac that are similar to those seen in control cells and that are significantly lower than those seen in cells expressing full-length ARNO ( Figure 3B). We conclude from this data that protein-protein interactions mediated by ARNO's coiled-coil domain are critical for promoting efficient ARF-to-Rac signaling.

The Coiled-Coil Domain Assembles ARNO and Dock180 into a Complex
We have previously demonstrated that the Dock180/Elmo complex is required for ARF-to-Rac signaling downstream of ARNO in MDCK cells (Santy et al., 2005). One possible mechanism for promoting efficient ARF-to-Rac signaling would be for both exchange factors to associate in a larger multiprotein complex. This would ensure that ARF activation occurs in the same area of the cell where Dock180 is available to respond and activate Rac. We investigated this possibility by determining whether Dock180 and ARNO could be coimmunoprecipitated. Cells expressing myc-ARNO and flag-tagged Dock180 were lysed, and the postnuclear supernatant was subjected to IP. Neither ARNO nor Dock180 is precipitated by an antibody against GFP (Supplemental Figure S1A). However, we found that when Flag-Dock180 is precipitated with M2 anti-Flag, a small amount of ARNO is coimmunoprecipitated (Supplemental Figure  S1A). The amount of ARNO coimmunoprecipitated with Dock180 could be enhanced by treating the cells with DSP, a cell-permeable cross-linker, before lysis (Supplemental Figure S1A,B). ARNO could be coimmunoprecipitated with Dock180 from both MCF-7 and MDCK cells (Supplemental Figure S1A,B), and ARNO could be coimmunoprecipitated with both overexpressed and endogenous Dock180 (Supplemental Figure S1, A and C). These data suggest that ARNO and Dock180 can associate in a larger multiprotein complex and that this association may be transient, weak, or mediated by intervening protein(s). We tested whether the coiledcoiled domain of ARNO is required for this association. We found that although full-length ARNO can be coimmunoprecipitated with Dock180, ⌬coiled-coil ARNO cannot (Figure 4A). Therefore we conclude that protein-protein interactions of ARNO's coiled-coil domain promote ARF-to-Rac signaling by bringing together ARNO and Dock180.
Protein-protein interactions of ARNO's coiled-coil domain have been reported to promote the recruitment of ARNO to the plasma membrane or to particular membrane subdomains (Venkateswarlu, 2003;Shmuel et al., 2006). Therefore it is possible that membrane binding is required for the interaction of ARNO and Dock180 and that the only role of the coiled-coil domain is to promote membrane association. To address this possibility, we used another truncation mutant of ARNO, ARNO ⌬PH. The ⌬PH mutant lacks the two membrane association domains: the PH and polybasic domains (Figure 1). We found that the ⌬PH ARNO was still able to bind to Dock180 ( Figure 4A). We also confirmed the subcellular location of these proteins by fractionation. MDCK cells expressing full-length, ⌬coiled-coil, or ⌬PH ARNO were lysed by passage through a 22-gauge needle. The postnuclear supernatant was fractionated into cytosolic and total membrane fractions by ultracentrifugation. The cell fractions were Western-blotted to determine the locations of E-cadherin (membrane), 〉-actin (cytosol), and the various ARNO constructs ( Figure 4B). Both fulllength and ⌬coiled-coil ARNO have a significant membranebound population. The ⌬PH ARNO, on the other hand, is entirely cytosolic ( Figure 4B). Therefore membrane association is not required for the interaction of ARNO and Dock180 and protein-protein interactions mediated by ARNO's coiled-coil domain are necessary. We were also able to isolate Dock180 by incubation of a Dock180-expressing cell lysate with a fusion of GST to the ARNO coiled-coil domain ( Figure 4C). These data suggest that the coiled-coil domain of ARNO is sufficient to mediate the interaction of ARNO with Dock180.
We next determined the region of Dock180 that is required for the interaction with ARNO. We cotransfected MDCK cells with ARNO and flag-tagged truncation mutants of Dock180 ( Figure 5A). We tested whether ARNO is coimmunoprecipitated with the Dock180 truncations. We first tested Dock DOHRS, a C-terminal truncation of Dock180, that lacks the Crk-binding domain (Grimsley et al., 2004). This mutant retained the ability to coimmunoprecipitate ARNO ( Figure 5B, top). Next we tried a series of truncation mutants including ⌬357-Dock, lacking the N-terminal 357 amino acids, ⌬900-Dock, lacking the N-terminal 900 amino acids, and Dock-N-1086 containing the first 1086 amino acids. Both the ⌬357 and ⌬900 mutants failed to coimmunoprecipitate ARNO, suggesting that the association of ARNO with Dock180 requires the first 357 amino acids. In support of this conclusion the Dock-N-1086 construct retains the ability to coimmunoprecipitate ARNO even though it is expressed at very low levels ( Figure 5B, bottom).
The N-terminal 357 amino acids of Dock180 is the region of Dock180 that is required for interaction with Elmo (Brugnera et al., 2002). Additionally this region contains an SH3 domain at its N-terminal end. Therefore we investigated the possibility that ARNO interacts with Dock180 via Elmo. If Elmo bridges the interaction between ARNO and Dock180, then a mutant of Elmo that cannot bind to Dock180 should disrupt this interaction. Elmo T625 is a truncation mutant that lacks the Dock180-binding domain (Brugnera et al., 2002). Expression of either WT Elmo or Elmo T625 did not prevent coIP of ARNO with Dock180 ( Figure 5C). Therefore we conclude that the N-terminus of Dock180 is required for the interaction of ARNO and Dock180 and that this association is independent of Elmo.

Scaffold Proteins Mediate the Association of ARNO with Dock180 and Promote ARF-to-Rac Cross-Talk
Given the small amount of ARNO that was coimmunoprecipitated with Dock180, we suspected that the interaction is not direct. Several small scaffold proteins have been identified that bind to ARNO's coiled-coil domain including GRASP, IPCEF, and CASP (Nevrivy et al., 2000;Mansour et al., 2002;Venkateswarlu, 2003). We therefore investigated the possibility that one of these scaffold proteins acts as a bridge linking ARNO and Dock180. The mRNA for these proteins can be almost completely eliminated by the transfection of siRNAs into MCF-7 cells (Supplemental Figure  S2). We transfected MCF-7 cells with siRNAs targeting one of the scaffold proteins to knock down expression of these proteins or with siRNA directed against firefly luciferase as a nontargeting siRNA control. After 48 h of knockdown these cells were infected with recombinant adenoviruses encoding myc-ARNO and flag-Dock180 and incubated for an additional 18 h. The cells were then treated with crosslinker and lysed, and the Dock180 was immunoprecipitated by incubation with M2 anti-flag resin. Western blotting of the immunoprecipitate with polyclonal antiserum directed against ARNO revealed that knockdown of either IPCEF or GRASP prevented association of ARNO with Dock180 (Figure 6A). Cells transfected with siRNA targeting CASP had lower expression levels of ARNO and Dock180; nevertheless ARNO could still be coimmunoprecipitated with Dock180 ( Figure 6A). CASP is specifically expressed in the immune system (Heufler et al., 2008). Using RT-PCR we could amplify a small region of this gene from MCF-7 RNA; however, we were unable to find any evidence for expression of this RNA in MDCK cells using multiple primer sets (data not shown). Therefore we conclude that CASP is not involved in promoting the interaction of ARNO and Dock180 or in promoting ARF6 to Rac cross-talk in epithelial cells.
If GRASP and IPCEF are involved in the assembly of a multi-GEF complex containing ARNO and Dock180, then they should also be present in a Dock180 IP. MDCK cells were infected with adenoviruses encoding flag-Dock180 and either GFP-GRASP or HA-IPCEF for 18 h. The cells were then treated with DSP cross-linker, and IP was performed with either mouse anti-myc as a negative control or M2 anti-Flag to precipitate Dock180. Western blotting revealed that none of the proteins was isolated with anti-myc antibody, whereas both GRASP and IPCEF could be coIPed with Dock180 ( Figure 6B).
We predicted that because knockdown of IPCEF or GRASP impairs the assembly of ARNO and Dock180 into a larger complex, knockdown of these proteins would also disrupt ARF-to-Rac signaling. We used the Rac pulldown assay to test this prediction in MDCK cells, because these cells show robust ARNO-induced Rac activation. Both  Figure S2). MDCK cells were transfected with siRNAs targeting IPCEF, GRASP, or a nontargeting siRNA. Two days later the cells were split onto duplicate plates and allowed to recover overnight. The cells were then infected with adenovirus that inducibly expresses ARNO for 3 h. ARNO expression was induced in one of the duplicate plates. The cells were then lysed, and active Rac was isolated by binding to GST-PBD. Levels of active Rac were normalized to the level of Rac in the starting whole-cell lysate. Multiple independent knockdown experiments were analyzed using a paired t test to determine whether reduced GRASP or IPCEF expression impairs ARNO-induced Rac activation. Knockdown of either IPCEF or GRASP significantly reduced Rac activation in the ARNO-expressing cells (Figure 7). Finally we confirmed that knockdown of IPCEF and GRASP impairs the ability of ARNO to produce fan-shaped lamellipodia and a scattering phenotype. MDCK cells were transfected with siRNAs targeting IPCEF, GRASP, CASP, or a nontargeting siRNA and treated as described for the Rac pulldown. After 3 h of ARNO expression the cells were fixed processed for indirect immunofluorescence. Control and CASP siRNA-treated cells show ARNO induced scattering, whereas ARNO-expressing cells transfected with siRNAs targeting IPCEF or GRASP are impaired in the production of lamellipodia and scattering (Figure 8). These data support the conclusion that protein-protein interactions mediated by the coiled-coil domain of ARNO promote ARF-to-Rac signaling. Additionally both IPCEF and GRASP are necessary to assemble a multiprotein complex containing ARNO and Dock180 and to produce efficient ARF-to-Rac cross-talk downstream of ARNO.

DISCUSSION
We have shown in this study that protein-protein interactions involving the coiled-coil domain of ARNO are critical for promoting ARF-to-Rac cross-talk downstream of ARNO. ARNO and Dock180 are present together in a larger multiprotein complex. Assembly of this complex requires the coiled-coil domain and two scaffold proteins, GRASP and All constructs are Flag-tagged at the N-terminus. (B) MDCK cells were cotransfected with the indicated Dock180 and pTRE-ARNO constructs in the presence of 2 ng/ml doxycycline to repress ARNO expression. After 18 h the doxycycline was removed, and ARNO allowed to express for 4 h. Cells were then treated with cross-linker, lysed, and incubated with M2 anti-flag as described in Materials and Methods. The immunoprecipitates were blotted with rabbit anti-ARNO and goat anti-Dock180 antibodies. A mixture of Dock180 antibodies directed against both N-and C-terminal epitopes was used to allow visualization of all truncations. (C) MDCK cells were infected with adenoviruses encoding Dock180, ARNO, and the indicated Elmo constructs for 4 h (WT Elmo) or for 18 h in the presence of 1 ng/ml doxycycline to moderate expression (T625). Cells were cross-linked, lysed, and immunoprecipitated as above. Immunoprecipitates and aliquots of the starting lysate were blotted with goat anti-Dock180, rabbit anti-ARNO, and rabbit anti-GFP.

The Role of ARF Activation in ARNO-induced Rac Activation
The experiments presented here define a scaffolding function for the coiled-coil domain of ARNO in assembling a complex of proteins containing the Rac-GEF Dock180. These data could be taken to indicate that ARNO-induced Rac activation is independent of ARNO's ARF-GEF activity. However, a point mutant of ARNO that lacks ARF-GEF function, E156K, fails to produce scattering when expressed in MDCK cells (Figure 2A, row 3; Santy and Casanova, 2001). Furthermore, we have previously demonstrated that this mutant does not enhance motility or lead to increased Rac activation in MDCK cells (Santy and Casanova, 2001). Therefore both the ARF-activating and scaffolding functions of ARNO are required for ARNO-induced Rac activation. Neither one alone is sufficient to produce robust activation of Rac.
The exact role of ARF activation in ARNO-induced Rac activation remains unclear but there are several possible actions of active ARFs that might be required. First, ARF6 activation has been shown to drive the recycling of lipid raft domains from an endosomal compartment to the plasma membrane (Balasubramanian et al., 2007). The recycling of these raft domains is required for Rac1 recruitment to the plasma membrane and for Rac1 activation (Balasubramanian et al., 2007). Additionally ARFs can directly modulate membrane lipid composition through activation of PLD and phosphoinositol-4-P-5 kinase to produce phosphatidic acid and phosphoinositol-(4,5)P 2 , respectively (Exton, 1997;Honda et al., 1999). We have previously shown that PLD is not required for ARNO-induced Rac activation (Santy and Casanova, 2001); however, the role of phosphoinositol-4-P-5 kinase in this process has not been investigated. The combined ARF-activating and -scaffolding functions of ARNO could ensure that membrane domains that recruit Rac and the Rac-GEF Dock180 are located in the same area of the plasma membrane.  The exact ARF that is required for ARNO-induced Rac activation is also ambiguous. The cytohesin family can activate all ARFs in vitro, but in cells ARNO is localized at the cell periphery along with ARF6 (Chardin et al., 1996;Frank et al., 1998a;Cohen et al., 2007). We have previously shown that MDCK cells expressing ARNO have a robust activation of endogenous ARF6 and no detectable activation of endogenous ARF1 (Santy and Casanova, 2001). However, given that the vast majority of ARF1 is located at the Golgi, the pulldown assay used in these experiments might be unable to distinguish the activation of a small pool of peripheral ARF1. Indeed null mutants of Steppke, the Drosophila cytohesin, have significant defects in growth and insulin signaling, whereas a Drosophila mutant of ARF6 shows more modest defects limited to defective cytokinesis during sperm formation (Fuss et al., 2006;Dyer et al., 2007). These data suggest that the cytohesins have functions that go beyond activation of ARF6. Knockdown experiments in cells have demonstrated that obvious trafficking defects are only distinguishable when pairs of ARFs are knocked down (Volpicelli-Daley et al., 2005). Activation of ARF1, ARF6, or both might therefore be required for ARNO-induced Rac activation.
Cell biological studies have identified important roles for ARF6 in endocytosis, recycling, cytokinesis, and regulation of the actin cytoskeleton. It is therefore somewhat surprising that an ARF6 mouse knockout model survives until midgestation, which suggests that other proteins can assume many ARF6 functions (Suzuki et al., 2006). A knockout of the cytohesins in mice has not been reported so it is not possible to compare cytohesin and ARF6 mutant phenotypes as can be done in Drosophila. In the ARF6 knockout mice, liver cells do not migrate properly after exiting the early hepatic epithelium. This leads to a failure of hepatic cord formation and hepatic apoptosis (Suzuki et al., 2006). ARF6-regulated cell migration therefore stands out as one of the critical functions of ARF6 during development. Intriguingly, this early migration requires HGF and overexpression of ARNO in MDCK cells mimics the effect of HGF on these cells (Santy and Casanova, 2001).
Recent experiments have demonstrated that active ARF6 can bind to cytohesin PH domains and recruit cytohesins to the plasma membrane (Cohen et al., 2007). These experiments led to the proposition of a model whereby ARF6 recruits a cytohesin that then activates ARF1 and ARF1 interacts with additional effectors to produce downstream actions (Cohen et al., 2007). This model is supported observations of the activation of ARF1 and ARF6 during phagocytosis (Beemiller et al., 2006). This study found that initially ARF6 is activated and that subsequently ARF1 is activated and that both ARFs are required for phagocytosis (Beemiller et al., 2006). In the context of ARNO-induced migration and Rac activation, this model suggests that ARF6 activation by ARNO could act as a positive feedback to stabilize ARNOcontaining complexes at the plasma membrane, whereas ARF1 activated by ARNO might interact with additional effectors to promote Rac activation. Further studies will be necessary to tease out the roles of the various ARFs during the regulation of motility.

The Role of Multiple Scaffold Proteins during ARNO-induced Rac Activation
Rac activation in ARNO-expressing cells is impaired by knockdown of either GRASP or IPCEF, suggesting that both of these proteins are necessary for this signaling cascade. Although both proteins bind to ARNO's coiled-coil domain, the precise binding sites of these two proteins are unknown so it is unclear if they overlap. Additionally ARNO exists in cells as a dimer (DiNitto et al., 2007). Therefore even if the binding sites for the two proteins overlap, the ARNO dimer could be bound to both proteins at the same time. One possible model for the activation of ARF6 and Rac by ARNO is depicted in Figure 9. In this scenario one of the scaffolds would recruit and anchor ARNO at a particular subcellular location so that it is available to interact with Dock180. The other scaffold would recruit ARNO into the Dock180 complex to promote ARF-to-Rac cross-talk. One or both of these interactions could be regulated by upstream signals.
IPCEF contains a PH domain and the cytohesin-interacting domain. When IPCEF and ARNO are cooverexpressed MDCK cells were transfected with 80 pmol of the indicated siRNA plus 20 pmol of Block-It Alexafluor red, fluorescent control siRNA, as a transfection marker. Forty-eight hours after transfection cells were replated onto coverslips and infected with adenovirus encoding ARNO in the presence of doxycycline to prevent transgene expression. After 18 h of recovery the doxycycline was removed, and the cells were allowed to express ARNO for 3 h. Cells were then fixed and stained with mouse anti-ARNO followed by Alexa-488 -conjugated anti-mouse secondary antibody and Alexa-647conjugated phalloidin. In the merge ARNO is psuedocolored green, f-actin is psuedocolored red, and the fluorescent siRNA is psuedocolored blue. Bar, 25 M.
Scaffolds Promote ARF-to-Rac Crosstalk Vol. 21, February 15, 2010 they are coordinately recruited to the plasma membrane in a PI-3-kinase-dependent manner after treatment of the cells with growth factors. Additionally binding of IPCEF to the coiled-coil domain of ARNO enhances ARNO's GEF activity (Venkateswarlu, 2003). A number of growth factors, including HGF, PDGF, NGF, EGF, CSF, and insulin have been shown to enhance membrane ruffling and migration in an ARF6-dependent manner (Venkateswarlu et al., 1998a,b;Zhang et al., 1999;Palacios and D'Souza-Schorey, 2003;Hall et al., 2008). These growth factors act via receptor tyrosine kinases and activate PI-3-kinase. Therefore IPCEF is well suited to recruit ARNO to a particular subcellular location and anchor it there in response to growth factor signaling. GRASP/Tamalin contains multiple protein-protein interaction domains. In neuronal tissues Tamalin is present in several large multiprotein complexes (Kitano et al., 2002(Kitano et al., , 2003. Tamalin complex formation regulates trafficking of the group 1 metabotropic glutamate receptors (Kitano et al., 2002(Kitano et al., , 2003. Although GRASP/Tamalin is highly expressed in the nervous system, it is also present at lower levels in a variety of tissues (Nevrivy et al., 2000). Significantly Tamalin is required for ARF6-dependent Rac activation downstream of the TrkC receptor (Esteban et al., 2006). These data suggest that GRASP/Tamalin is the likely candidate for building a larger complex that contains both ARNO and Dock180 and promotes ARF-to-Rac signaling.
Interestingly a different Rac GEF, Kalirin, has been reported to promote ARF6-dependent Rac activation downstream of EFA6, another ARF6-GEF (Koo et al., 2007). Therefore different ARF-GEFs might not only regulate ARFs at different subcellular locations, they might also couple ARF to different signaling pathways to achieve similar outputs. Clearly much work remains to delineate signaling networks involving ARF6.
The data presented here demonstrate that IPCEF and GRASP promote ARF-to-Rac signaling downstream of ARNO by assembling a multiprotein complex containing ARNO and Dock180. Scaffold proteins assemble complexes containing multiple members of a signaling cascade. The ability of scaffold proteins to regulate and modulate signaling pathways is well recognized for other signaling cascades, particularly for the MAPK cascade. A variety of scaffolds including, KSR, MORG1, MP1, and JIPs can alter the kinetics, location, and output of MAPK modules (Morrison and Davis, 2003;Kolch, 2005;Dard and Peter, 2006;Pullikuth and Catling, 2007). Our data suggest that similar principles could apply to signaling pathways involving ARF6.
Although there are six mammalian ARFs, there are 15 Sec7 domain GEFs. These GEFs can be divided into five families, and at least three of these families are able to activate ARF6. These data strongly suggest that different GEFs are optimized to regulate ARFs during different processes and at different subcellular locations. Scaffold proteins are likely to play a central role in localizing these GEFs and biasing downstream outputs toward particular pathways. Grimsley


s2fieldsofstudy: Biology Medicine

text:
Differential survival following trastuzumab treatment based on quantitative HER2 expression and HER2 homodimers in a clinic-based cohort of patients with metastatic breast cancer

Background We have recently described the correlation between quantitative measures of HER2 expression or HER2 homodimers by the HERmark assay and objective response (RR), time-to progression (TTP), and overall survival (OS) in an expanded access cohort of trastuzumab-treated HER2-positive patients with metastatic breast cancer (MBC) who were stringently selected by fluorescence in situ hybridization (FISH). Multivariate analyses suggested a continuum of HER2 expression that correlated with outcome following trastuzumab. Here we investigate the relationship between HER2 expression or HER2 homodimers and OS in a clinic-based population of patients with MBC selected primarily by IHC. Methods HERmark, a proximity-based assay designed to detect and quantitate protein expression and dimerization in formalin-fixed paraffin-embedded (FFPE) tissues, was used to measure HER2 expression and HER2 homodimers in FFPE samples from patients with MBC. Assay results were correlated with OS using univariate Kaplan-Meier, hazard function plots, and multivariate Cox regression analyses. Results Initial analyses revealed a parabolic relationship between continuous measures of HER2 expression and risk of death, suggesting that the assumption of linearity for the HER2 expression measurements may be inappropriate in subsequent multivariate analyses. Cox regression analyses using the categorized variable of HER2 expression level demonstrated that higher HER2 levels predicted better survival outcomes following trastuzumab treatment in the high HER2-expressing group. Conclusions These data suggest that the quantitative amount of HER2 expression measured by Hermark may be a new useful marker to identify a more relevant target population for trastuzumab treatment in patients with MBC.

Background
Over-expression of HER2 has been linked to both adverse prognosis and improved responsiveness to treatment with trastuzumab (Herceptin, Genentech) in a sub-population of metastatic breast cancers [1][2][3][4][5]. Trastuzumab offers significant disease-free and overall survival advantages in both the metastatic and adjuvant settings in patients with HER2 over-expressing tumors [6][7][8][9][10][11]. Currently, the selection of patients with breast cancer for treatment with trastuzumab is based on the measurement of HER2 receptor protein expression by immunohistochemistry (IHC), or by the presence of HER2 gene amplification as detected by FISH. Despite the success of trastuzumab, recent data from NCCTG 9831, NSABP B-31, and CALGB 150002 have called into question the accuracy of the current methods used to identify those patients who are most likely to benefit from treatment with trastuzumab [12][13][14]. New approaches to the accurate quantitation of HER2 expression and HER2 dimerization in FFPE breast tumor specimens may have the potential to identify responders more precisely.
We have previously reported that quantitative measures of HER2 expression or HER2 homodimers in FFPE specimens from MBC patients enrolled in a trastuzumab expanded access program identified sub-populations of patients with different clinical outcomes, such that patients whose tumors expressed higher levels of HER2 or HER2 homodimers lived longer [15]. Those patients had been stringently selected for trastuzumab treatment primarily by FISH (90%). Here we describe the correlation of quantitative measurements of HER2 expression (H2T) or HER2 homodimers (H2D) with OS following trastuzumab exposure in a cohort of patients with MBC who were selected for treatment by IHC (88%) or FISH (12%) in a routine clinic setting. Quantitative HER2 measurements were made by the HERmark assay (Monogram Biosciences) using FFPE breast tumor specimens. HER2 homodimers are defined as HER2:HER2 associations that are sufficient to lead to HER2 phosphorylation and subsequent activation of downstream signaling pathways. These could include HER2:HER2 dimers or HER2 monomers that are in close proximity (< 80 nm) as a result of pathological over-expression.

The VeraTag technology
The VeraTag technology is a proximity-based method designed to accurately and reproducibly quantitate protein expression and protein-protein complexes, including cell surface-expressed protein dimers, in FFPE specimens. The first assays developed for clinical testing using the VeraTag platform measure HER2 protein expression (H2T) and HER2 homodimer (H2D) levels accurately and reproducibly, and are referred to as HERmark. These assays have undergone a formal validation at Monogram Biosciences and are regulated by the College of American Pathologists under the specifications established by CLIA (Clinical Laboratory Improvement Amendment) [16]. IHC (Herceptest, DAKO) and FISH assays were performed according to standard protocols at the local institutions where the patients were treated.

Description of the clinical cohort
This cohort is composed of 75 patients drawn from six oncology clinics in Japan. Patients were included if they had metastatic breast cancer, had been treated with at least one chemotherapy regimen prior to receiving trastuzumab either as single agent or in combination with chemotherapy, and had FFPE specimens available for testing by the HERmark assay. Assessments of HER2 over-expression by IHC or FISH were performed by the pathologist at the local hospital. All clinical follow-up was performed at the local hospital, and clinical outcome data were drawn from medical chart review. This study protocol was approved by the ethical committee of each institution and informed consent from the patients was taken

Statistical methods
Measurements of H2T and H2D were tested for association with OS, defined as the time from start of trastuzumab treatment to cancer-associated death or the end of the follow-up period. The log-transformed values of HER2 expression and HER2 homodimers were used for analyses. Deaths not attributed to breast cancer were considered as censored at the time of death. One patient was excluded from the analyses because she died merely 20 days after her first dose of trastuzumab, and the duration of treatment was considered insufficient to include the patient as "trastuzumab-treated." Univariate analyses were performed using the Kaplan and Meier (KM) method, hazard function plots were estimated using the life-table method, and multivariate models were generated using Cox proportional hazards regression analysis. Cox proportional hazards models were fitted to examine the associations of H2T, H2D, or their ratio H2T/H2D with outcome, with adjustment for other potential prognostic factors. The adjustment factors included treatment group (trastuzumab-only or trastuzumab plus chemotherapy), estrogen and progesterone receptor status, and a composite of them, hormone receptor status, screening Herceptest score performed at the treating hospital, Herceptest score repeated at Monogram, Histoscore performed on repeat IHC at Monogram, tumor stage, and presence of metastases in the liver, lung, skin, bone, lymph nodes, brain, chest wall, or other viscera. Metastatic sites occurring in less than 10% of the total cohort were not considered as individual variables. The stepwise variable selection method was used to select the "best" models.
Statistical analyses were performed at Yokohama City University Medical Center using the statistical software packages SAS and R.

Results and Discussion
The median duration of patient follow-up period was 18.2 months. The median duration of trastuzumab treatment was 14.6 months. A description of the clinical characteristics of the cohort is provided in Table 1. Of the 75 patients with HER2 test +2 or +3 assessed at the local hospitals, 25 had IHC 0 or +1 by the central pathological review. Nine patients had advanced disease, while 66 had recurrence. The number of pre-chemotherapy before trastuzumab treatment was also shown in Table 1. For the 66 patients with recurrence, the number of pre-chemotherapy received after recurrence was analyzed.

Correlation of HERmark measurements with IHC
We have previously shown the relationship between HERmark measures of HER2 expression and IHC performed under standard conditions by a single pathologist in 170 FFPE breast tumors [16]. The results demonstrated a general correlation between the two assays, however the HERmark assay provided a more quantitative assessment of HER2 expression distributed over a 3-log dynamic range compared to the semiquantitative readout of IHC (0-3+ scale) (see Additional file 1: Figure S1). We attempted to make the IHC measurement more quantitative by considering the histo-score (H score), where staining intensity is corrected for percent tumor involvement. The HERmark assay still demonstrated a greater ability to quantitate HER2 expression over a wider dynamic range.
In this cohort, we initially compared H2T to the screening IHC calls provided by the treating hospitals. The distribution of H2T measurements ranged over a 3-log dynamic range, and there was no significant difference between the H2T distributions of those samples scored as 2+ or 3+ by the screening IHC assays (Figure 1a). We repeated the IHC assays on all specimens. The repeat assays were performed centrally and read by a single pathologist. Approximately one-third of the specimens were scored as 0 or 1+ upon repeat. Comparison of the repeat IHC results with the HER2 expression levels by HERmark yielded a stair-step correlation (Figure 1b) reminiscent of previous comparisons with centralized IHC results (see Additional file 1, Figure S1).

Kaplan-Meier, hazard function plots, and multivariate Cox analyses
As a whole, two-year survival rate of the cohort was 60.8% (95% C.I., 48.4 -73.2%). In the univariate analyses, patients were classified into four subgroups defined by the 25 th , 50 th , and 75 th percentiles for each of the three variables, H2T, H2D, and their ratio H2D/ H2T, in order to examine the assumption of linearity for the continuous variables used in the subsequent Cox regression analysis. A significant difference in OS was found between the four subgroups with respect to H2T (Log rank test, p = 0.003; Figure 2), but not for the others (p = 0.371 and p = 0.573). The 25 th , 50 th , and 75 th percentiles for the log-transformed values of H2T were 1.41, 1.80, and 2.25, respectively. The patients' characteristics by H2T subgroup (< the median value of H2T: n = 37 v.s. • the median value of H2T: n = 37) is summarized in Table 1. Higher proportion of patients with greater H2T value than its median value had IHC 2+ or 3+ (p < 0.001) and higher HER2 score on repeat testing (p < 0.001). No significant difference in OS was found between the IHC subgroups (log-rank test: p = 0.610). Table 2 summarizes the other univariate analyses for OS. Figure 3 shows the hazard function plots estimated in the four H2T subgroups. The subgroups with the 25% highest and lowest H2T values had substantially lower risk of death than the middle two subgroups. These univariate analyses revealed a parabolic relationship between continuous measures of HER2 expression and risk of death, suggesting that the assumption of linearity for the HER2 expression measurements may be also shows that hormone receptor status (HR = 0.18, p = 0.014, 95%CI: 0.04-0.71) and brain metastases (HR = 0.07, p < 0.001, 95%CI: 0.02-0.24) were significant prognostic factors. Thus, the best multivariate model contained three variables, H2T, progesterone receptor status, and brain metastases. Dividing the cohort into high HER2-expressing (≥ the median value of H2T) and low HER2-expressing (< the  median value of H2T) sub-groups and using the Cox regression analysis with the continuous H2T value in each of the two subgroups, we found that those patients with higher values for HER2 expression live longer than those with lower values for H2T in the high HER2expressing group (HR = 0.06, p = 0.010, 95%CI: 0.01-0.51) ( Table 4). In contrast, in the low HER2-expressing group, the opposite trend (those with lower H2T values are favored) was observed (HR = 16.0, p = 0.017, 95%CI: 1.64-155.9). These data suggest that there are two sub-populations of patients in this cohort that behave differently with respect to HER2 expression and OS.
Trastuzumab is a remarkably effective drug for patients with breast cancer who over-express HER2, or have evidence of HER2 gene amplification. However, not all patients with MBC respond to trastuzumab, perhaps because not all patients who receive the drug are truly over-expressing HER2, or perhaps because some proportion of those that do over-express HER2 also signal through other pathways that are not antagonized   effectively by trastuzumab. Such patients may be expected to experience fleeting responses when treated with trastuzumab, or even to show no evidence of clinical response at all. As the ability to accurately assess such escape pathways gradually evolves, we should be able to identify the mechanisms of resistance to trastuzumab, so that effective strategies for combination therapy can be developed and tested in prospective clinical trials. That process will take time, but perhaps a first step could be to improve our ability to accurately quantitate HER2 protein expression and try to gain a better understanding of how differences in expression levels among individual breast tumors affect the natural history of HER2-positive breast cancer and, more importantly, response to HER2-targeted agents like trastuzumab.
Recently presented data at the 2007 ASCO annual meeting have raised questions about the accuracy of current testing methods [12][13][14]. In particular, a reanalysis of the central testing data from the NSABP B-31 trial demonstrated that nearly 10% of the trastuzumab-treated population in the trial were IHC < 3+, FISH-negative on repeat testing, but benefited from trastuzumab therapy just as the IHC 3+ and FISHpositive groups did [14]. Perhaps this observation reflects fundamental differences between the pathophysiology of breast cancer in the adjuvant setting compared with the metastatic setting, or perhaps we need better tests for HER2. Whatever the case, there appears to be a genuine need to re-examine the issue of patient selection for trastuzumab, with an eye toward new technologies and new approaches that have the potential not only to improve current standards, but also to begin identifying alternative signaling complexes that can be targeted by combination therapy regimens including trastuzumab.
In this study, we describe the relationship between quantitative measures of HER2 expression or HER2 homodimers and overall survival in a cohort of patients with MBC who were treated with trastuzumab. The assay system used to make these measurements is CLIA validated for accuracy and reproducibility, and by virtue of its ability to quantitate proteins and protein dimers, provides a more granular view of the HER2 landscape than either IHC or FISH [16]. The assay has previously been correlated with clinical outcomes following trastuzumab therapy in a cohort of MBC patients who were very carefully selected for treatment using FISH at the Jules Bordet Institute in Belgium [15].
The current study represents the second clinical experience with the HERmark assay, involving a group of patients who were selected for trastuzumab treatment primarily by IHC (88%) performed in the course of routine clinical care. The initial KM analyses and hazard function plots of the entire cohort suggested that two distinct populations were present, and the observed relationships between HER2 expression and OS for the two sub-groups were going in opposite directions. Using a cutoff of the median value of log 10 HER2 expression = 1.80, we then re-analyzed the patient sub-group that had HER2 expression values greater than the cutoff to see whether this sub-population showed any evidence that higher levels of HER2 expression yielded longer OS on trastuzumab. Data from the Bordet cohort described previously had shown such an association, and we wanted to see if we could confirm this relationship in the current study. Because the Bordet cohort was so stringently selected for HER2 over-expression or gene amplification, patients with low HER2-expression, such as those that represented approximately one-third of the current cohort, may have been excluded. Given the differences in the methodology used to select patients in the two cohorts (90% FISH in Bordet, 88% IHC in the current study), as well as the stringency applied (expanded access cohort with mandatory central confirmation of HER2 status in Bordet, routine clinical care in the current study), it is perhaps not so surprising that we might observe this difference. These data appear to confirm the relationships that were seen in the Bordet cohort and also suggest that, within a population of patients that are identified as HER2-positive by the commonly used methods currently available to clinical oncologists, precise quantitation of HER2 expression can define multiple sub-populations of patients that experience different clinical outcomes in response to trastuzumab treatment. These data suggest that patients can be characterized according to the degree of HER2 protein expression that their tumors exhibit, and that quantitative expression levels matter clinically. It is important to note that these data do not address the question of whether some patients treated with trastuzumab may not benefit clinically. Because we tested only patients who were treated with trastuzumab, the finding that some did not benefit as much as others does not equate to some did not benefit at all. Prospective studies or retrospective analyses of prospectively designed clinical trials where HER2-positive patients were randomized to receive trastuzumab or not will need to be tested in order to answer that question. Nonetheless, data from the current study demonstrate that trastuzumab-treated patients with low HER2 expression levels appear to behave differently with respect to OS than those with high HER2 expression levels. Whether this subset of patients behaves differently because they exhibit normal rather than elevated but low levels of HER2 is a matter for future study. In addition, we should have carried out the analyses to examine the relationship between HER2 expression levels and OS in patients with IHC 2+ or 3+. We could not obtain statistically reliable results, however, because the number of patients included in the subgroup was small (n = 50) and, in particular, the number of patients with brain metastases was only 5. This is also a matter for future study.
What do these data imply about the impact of trastuzumab on HER2-positive tumors that express different levels of the target? Previous data have shown that the amount of HER2 protein expressed correlates with disease progression in MBC patients not treated with trastuzumab [3]. Data from the current study are restricted to patients that have been treated with trastuzumab, but nonetheless support the general conclusion that the amount of HER2 expression matters clinically. One potential explanation for such a finding is that the amount of HER2 expression is a marker for the degree of HER2 dependency that exists in the tumor. Tumors that massively over-express HER2 may be "addicted" to signaling through HER2, potentially explaining their association with rapid progression in the absence of trastuzumab as well as enhanced susceptibility to antagonism by trastuzumab. Tumors which express lower levels of HER2 may be less vulnerable to trastuzumab because they are less dependent on HER2 alone, but rather derive at least some of their proliferative drive from protein-protein associations (HER1:HER2 or HER2:HER3 heterodimers) or variants (p95/HER2) that are not as susceptible to antagonism by trastuzumab [17][18][19][20][21][22][23]. Other explanations involving mutations in downstream components (PTEN) that affect PI3-kinase signaling activity as well as involvement of alternative signaling pathways (eg. IGF-1R) are equally plausible [24,25]. Alternatively, the association between higher levels of HER2 expression and better outcomes on trastuzumab may reflect enhanced targeting of HER2 overexpressing cells by immune mechanisms triggered by trastuzumab binding, like antibody dependent cellular cytotoxicity (ADCC). Perhaps tumors at the lowest end of the HER2-expression distribution, that appear to have favored outcomes on trastuzumab, are simply expressing normal levels of HER2, and thus have a fundamentally different prognosis than their counterparts expressing significantly higher levels of HER2. These questions, as well as the general finding that, among tumors already classified as HER2 over-expressing or gene-amplified by standard assays, precise assessments of HER2 expression levels correlate with clinical outcomes following trastuzumab treatment, demand further scrutiny in larger, well-controlled trials of patients with metastatic breast cancer.
Although the technology enabling quantitative assessments of potential escape pathways for trastuzumab is still evolving, the opportunity to begin to sub-divide populations of patients into their constituent groups based on precise measurements of HER2 and its family members holds tremendous promise at a time when numerous targeted drugs are becoming available for inclusion in combination therapy regimens for patients with breast cancer. It is our hope that observations derived from the current study represent an early step in that direction.

Conclusion
It is concluded that the quantitive HER2 measurement by Hermark assay may be useful for identifying a more relevant target population to trastuzumab treatment in patients with MBC. It is warranted to study the significance of the quantitive HER2 measurement in the adjuvant situation with anti-HER2 therapy for primary breast cancer patients.
Additional file 1: Figure S1. Correlation of HER2 expression Vera Tag and IHC in 170 breast tumors (FFPE). The correlation between the Vera Tag measure of HER2 expression and IHC in 170 FFPE breast tumor specimens. This is a reproduction of figure 6a from Shi, et al. (footnote 2) provided here for convenience. Click here for file [ http://www.biomedcentral.com/content/supplementary/1471-2407-10-56-S1.DOC ] We are grateful to Jodi Weidler, Yolanda Lie, Ahmed Chenna, Ellen Paxinos, Agnes Pacquet, Rajiv Dua, Elicia Penuel, Jeannette Whitcomb and Gordon Parry for critical input and helpful discussions. We would like to thank members of the Monogram Biosciences operations team as well as the clinical laboratory personnel for their contributions to the development of the VeraTag assay system. We would also like to thank and acknowledge the patients who participated in this study, without whom new insights into the biology of breast cancer would not be possible.


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RNA Genes: Retroelements and Virally Retroposable microRNAs in Human Embryonic Stem Cells

Embryonic stem cells (ESCs) are capable of undergoing self-renewal, and their developmental ability is known as the stemness. Recently, microRNAs (miRNAs) as regulators have been isolated from ESCs. Although Dicer and DiGeorge syndrome critical region gene 8 (DGCR8) are essential factors for the biogeneration of miRNA, Dicer-knockout (KO) ESCs have showed to fail to express differentiation markers and DGCR8-KO ESCs have showed to be arrest in the G1 phase. Furthermore, Dicer-KO ESCs lost the ability to epigenetically silence retroelemtns (REs). REs are expressed and transposed in ESCs, whose transcripts control expression of miRNAs, and their transposable retroelement (TE) expression is, therefore related to ESC proliferation and differentiation, suggesting that the interplay between miRNAs and REs may have a deep responsibility for the stemness including a short G1/S transition and for RE regulation in ESCs.

INTRODUCTION
Today we know that the human genome encodes noncoding RNA (ncRNA) from its non-coding DNA (ncDNA) sections. It is known that ncDNA is predominantly composed of multiple segments of transposable retroelements (TEs) such as, long interspersed elements (LINEs) and, short interspersed elements (SINEs, human Alu), retrotransposons, DNA transposons and satellites (Fig.  1A) [1]. The ncRNAs contain ribosomal RNA (rRNA), transfer RNA (tRNA), small nuclear RNA (snRNA), small nucleolar RNA (snoRNA), short interfering RNA (siRNA), microRNA (miRNA), other classes of small RNAs and longer RNA intermediates including complex patterns of interacting and overlapping sense and antisense transcripts from both coding and non-coding regions of genomic DNA [2,3]. The small RNAs are functional and their sizes fall within a range of 19 to 31 nucleotides (nts). The most common small RNAs are miRNAs, which consist of genomic and resident miRNA genes, which are subsequently produced by digestions on two kinds of the nuclease III, Drosha and Dicer, and then directly target messenger RNAs (mRNAs) or possibly epigenetic machinery in human cells [4].
Embryonic stem cells (ESCs) including somatic and germline stem cells (GSCs), employ their capacity for unlimited self-renewal and differentiation for tissue development and homeostasis. In mammalian embryo, the first stage of this differentiation involves differentiation into either the cells which form the inner cell mass and those which form the trophoblast [5][6][7]. The cells of the inner cell mass, which are known as ESCs, are pluripotent, meaning that they can give rise to all the cells of the animal. This is *Address correspondence to this author at the Retroviral Genetics Group, Nagoya City University, Nagoya, 467-8603, Japan; Tel: +81-52-836-3430; E-mail: fatfuji@hotmail.co.jp quality known as stemness and this stemness retained by the ESCs themselves. Thus, the stem cells can produce differentiated daughter cells, as well as replicate themselves.
The key transcriptional regulators Oct4, Sox2 and Nanog, etc. have been identified as the self-renewal and differentiation-related factors in ESCs [8][9][10][11][12]. Artificial pluripotent stem (iPS) cells demonstrate the roles played by these transcriptional regulator genes [13], but these cells developed into tumors (due to the operation of myc oncogene and oncoviral vectors). Recent cancer research indicates that alteration of miRNA profiles, such as those involved in oncogenic miRNA (oncomirs) upregulation or tumour suppressor miRNA downregulation could induce tumors even without an oncovirus or oncogenes [14]. This suggests that tumourigenesis of iPS cells might be due to dysregulation of miRNAs induced by transfection of transcriptional factors rather than transduction of the oncogene myc or oncovirus vectors themselves [15][16][17] as evidenced by tumourigenesis in iPS cells without oncovirus vectors and myc oncogenes [18]. Since miRNA genes are mobile and functional genetic elements [19], the miRNA profile of ESCs is likely to be different in depending on the niche cells in which they are located when it is self-renewal or differentiation conditions. The character of ESCs may be very different from that of cancer such as embryonic carcinoma and carcinoma-like cells including iPS cells [20]. Based on research with skin cell iPS cells and ESCs, it has become clear that miRNAs play an important role in the precise mechanisms regulating self-renewal and differentiation in these cells [21].
It appears that miRNAs may be the master regulators of stemness. Further, it also appears that tumourigenesis may be rooted in something other than the transcriptional and cellular signaling factors of oncoproteins. The ESC transcripts, including those of miRNAs, contain many retrovirus-like retrotransposon-derived repeats (about 50%)

Human
Murine Canine Alu-pri-miRNA [22,23]. De novo LINE-1 (L1) retrotransposition events occurred in the human brain [24]. Further, L1 retrotransposition events have been shown to occur at early stages in human embryogenesis [25] and L1 transcripts are related to the mobilization of human SINE Alu [26]. The miRNA genes are located within Alu and L1, and about 20% of known human miRNAs are derived from these repetitiveelement sequences [27]. It has been reported that over 1,000 genes of the human genome may be controlled by an Aluassociated promoter and that a dual relationship exists between the miRNA gene clusters and the miRNA targets in Alu regions that may have evolved together [28,29]. Furthermore, epigenetic chromatin remodeling of Alu sequences plays an important role in the regulation of miRNA expression [30]. Taken together, these findings suggest that miRNAs may be the master regulator of ESC self-renewal and differentiation and that the miRNA genes may control both abilities through expression of TEs.

R t l t
Therefore, it appears that the activity of Alu and L1 is more complex than previously documented in artificially-induced transcriptional factors because ESCs specific transcriptional factors do not control the epigenetic state except for that of polycomb-group proteins [31].
The seed sequences of 2-to 7 or 8-nts in the 5'-end of miRNAs recognize not only the 3' untranslated regions (3'UTRs) of its mRNAs but also the coding regions of Oct4, Sox2 and Nanog [32] through an imperfect match to repress target mRNA and probable stability of the mRNA target. Computer analysis has shown that miRNAs target the 3'UTR as well as the 5'UTR and protein coding regions (CDS) [33]. Further, experimental evidence indicates that the miRNA genes could target the CDS and/or 3'UTR of OCT4, Sox2  Clusters of miRNAs in human chromosome 19 (Chr19) and murine chromosome 7 are represented. These genomic miRNA genes are closely or overlapping localized in TEs, such as Alu and B1. The direction of TEs may not matter. TE could promote the expression of pri-miRNA (Alu-pri-miRNA) and genomic miRNAs from the pri-miRNA can be silence on translation of target mRNAs in ESCs. (C) ESC specific resident miRNAs for self-renewal and differentiation. Human ESCs express self-renewal specific set of miRNAs. The cluster of miR-371, 372 and 373 is related to the self-renewal of ESCs. The cluster of miR-290 in murine is also specific for ESC self-renewal and the murine miR-290 cluster genes are transferred from ESCs to neighboring cells by ESC microvesicles (ESMVs). For differentiation of ESCs, other resident miRNAs are presented and then the profile of miRNAs in differentiated cells, such as granulocytes and monocytes is greatly different from that of ESCs.

ES cell specific
and Nanog [34] and could regulate all multi-gene expression. The miRNA-induced silencing complex (miRISC) appears to regulate this process. The centre of miRISC is an Argonaute protein (Ago) and the seed region of miRNAs in the miRISC targets the sequences of mRNA with incomplete paring. However, a miRNA gene superposes to multiple-targets and a mRNA is targeted by multiple-miRNAs. Therefore, elucidation about how the targets of miRNA selected and how the miRNA genes switched on and off requires additional insights from theoretical ciphering of RNA codes even if suppose a quantum computing model independent of the nucleic acid pairing [19].
In this paper, we explore the significant expression of ESC miRNAs induced by TEs and we propose a model involving TEs forming 'RNA wave' in ESCs. To further understand an importance role of fine-tuning by the miRNA genes in ESC epigenetic silencing, we also explore an extended model of quantum theory for RNA coding operating without the operon involvement.

ALU TEs AS A SWITCH FOR STEMNESS
TEs in the human genome have been shown to occupy ~46% of its genomic sequences (Fig. 1A) [1]. Since TEs transcribe non-coding RNA and this RNA is reversetranscribed and re-integrated into its own genome, if miRNA is encoded in TEs, the miRNA genes in TEs could also be duplicated according to the same process as TEs. It has been reported that human miRNAs may be derived from retroelements (REs) [29,35,36]. It has been suggested that around half of all human genomic information is derived from RNA. Further, human MADE1, miniature invertedrepeat TE (MITE) involves total 550 paralogs upon miR-548a-1 (Chromosome 6: Chr6), miR-548a-2 (Chr6), miR-548a-3 (Chr8), miR-548b (Chr6), miR-548c (Chr12), miR-548d-1 (Chr8) and miR-548d-2 (Chr17) as numbers of 24, 81, 82, 23, 124, 71 and 145 paralogs in individual genome of humans, respectively. MADE1 emerged along the primate evolutionary lineage and the miR-548 gene is an orthologs in the human, chimpanzee and rhesus macaque genomes [35]. Further, miR-548 targets MITEs of the human genome. In turn, MITEs including MADE1 are widely distributed among eukaryotes and miRNA and endogenous siRNA genes may exist due to the palindrome-like RNA structures form a hairpin, which can be catalyzed by Dicer. Thus, a self-regulation system may be involved in MADE1 and the miRNA genes. This system may be necessary for the maintenance of the small RNA-derived human genome. Subsequently, transposition of TEs seems to be deleterious to the genome. However, the genome itself may be constructed of and evolved by small RNAs in TEs. Previously, we proposed "RNA wave model," which holds that retroviruses control their own amplification rate in order to minimize their deleterious effects on their hosts, as exemplified by the latent state of human immunodeficiency virus type 1 (HIV-1) [37]. According to RNA wave, RNA tags can transport miRNA genes, which are a mobile genetic element that allows cross-talk among cells, organs and individuals from vertical to horizontal. Further, it has been reported that human TE Alu evolved symbiotically with miRNAs [29,38].
The relationship between Alu retroelements, one of the SINE TEs, and a miRNA cluster on chromosome 19 (C19MC) in humans is compatible with RNA wave [28,39]. As mentioned above, members of the C19MC-gene family, the miR-515-1, miR-517a and miR-519a-1 genes, are transcribed by Pol III from the upstream section of Alu TE as miRNA promoters, suggesting that some interplay between miRNAs and TEs may act to the circuit on and off. The human miRNA gene cluster, including SINEs, miR-371, miR-372 and miR-373 in the down stream of C19MC, and the murine miR-290, miR-291a, miR-291b, miR-292, miR-293, miR-294 and miR-295 cluster are similar to ESCs in their self-renewal capability (Fig. 1B, C). Furthermore, murine miR-290 cluster genes are transferred from ESCs to neighbouring cells by microvesicles (Fig. 1C) [40,41]. Recently, synthetic miRNAs, viral miRNAs and endogenous miRNAs have been reported to be transferred between cells and to be able to spread their functionally silencing signals across cell boundaries [42,43]. In addition, the vault particles implicated in intracellular transport contain small RNAs and these small RNAs may be related with the multidrug resistance mechanisms [44]. Thus, the direction of self-renewal or differentiation in ESCs and their neighbors may be commonly mediated through the information stored in the retroposable miRNA genes. Additionally, TEs is not a tag in this case, TE itself may be a RNA gene.
Alu transposition is required for LINE TEs but that is not dependent on LINE-encoded protein [26]. Two miRNAs, miR-95 and miR-151 in LINE2 target its LINE2 sequences [45]. The transcripts including miRNAs in ESCs contains many TE-derived repeats (~50%) [22,23]. LINE1 retrotransposition events occur at early stages in human embryogenesis and human ESCs in vitro [24,25]. Thus, the miRNA genes in TEs are completely disregarded in ESC investigations for human embryogenesis; this is a mistake at all and TEs may be a switch of self-renewal and differentiation of ESCs via miRNAs. Therefore, nobody has asked what is the code of RNA intermediates, such as miRNA, to elucidate the switch operation for ESC selfrenewal and differentiation much shorter nanoRNA and TEs, furthermore intron and protein coding RNAs.

TRANSFORMATION AND REPROGRAMMING IN STEMNESS
Over 10 years ago, embryonic clone cells were produced from the nuclei of cultured cells [46] and then the first mammalian clone was produced [47]. DOLLY, the cloned ovine, proved that genome modification has been considered as irreversible event, but the genome can be reversible to able to reprogram by factors in the oocytes to make them pluripotent once again. The question is what is the factor(s). Recent research indicates that it may be miRNA genes [48,49]. The initial profile of cloned bovine embryos was changed after somatic cell nuclear transfer as the reprogramming [49]. Further, the nuclear transfer with minimal contamination by the donor cytoplasm was accomplished by metaphase II G1-arrested G0 quiescent state cells [47]. This technological finding as reprogramming has forced us to accept that two factors may be in play here: resident miRNA genes and genomic miRNA genes.
'Reprogramming' was also used to create artificially induced adult somatic iPS cells. Recent papers by Werning et al. [50], Hanna et al. [51] or Takahashi et al. [52] gave the results that iPS cells from skin fibroblasts were isolated upon transformation with a combination of the transcription factor genes, Nanog, Sox2, Oct4 and/or Myc. These iPS cells were problematic in that Myc or vector retroviruses induced tumour formation, and iPS cells can essentially allow for isografting only, while ESCs can be considered for use of variable allografting [51][52][53][54]. Together with above nuclear transfer, experiments with iPS have caused biologist to completely reassess their thinking regarding the role of transcriptional factors, miRNA genes and other small nucleic acids (miRNA assessment).
It was originally thought that tumourigenesis might be avoided in iPS cells because it was believed that tumourigenesis was mediated by the oncogene Myc and other oncovirus vectors. However, it is now clear that it is not oncoviruses but oncomirs that can induce tumours and that oncogenes are controlled by the oncomirs or tumour suppressor miRNAs [17]. Further, iPS and ESCs up-regulate the expression of the miR-302 and 17-92 cluster, which is the oncomir in leukemia and lung cancer, and iPS cells show lower miR-371-cluster expression than ESCs, in which cluster expression is involved in self-renewal [55]. MiR-222 promotes continuous proliferation of human carcinoma cells [56][57][58]. In contrast, miR-222 is involved in helping human endometrial stromal cells to exit the cell cycle and enter the differentiation [59]. Therefore, at least miR-222 and likely other miRNA sets play an important role in reprogramming cells. Although the miRNA profiles represent clear difference from iPS cells to ESCs, the ability to reprogram cells throws the existing understanding of stemness into disarray.
Since transcriptional and post-transcriptional regulatory network could be determined in silico [60,61], miR-145 controls Nanog, Sox2 and Oct4 in ESCs as well as in tumourigenesis [62,63]. Murine miR-134, miR-296 and miR-470 target the protein-coding sequence of Nanog, Oct4 and Sox2 in various combinations, leading to transcriptional and morphological changes in differentiating murine ESCs [32,64]. Although it has been shown that Nanog, Sox2 and Oct4 transcriptional regulators collaborate to form regulatory circuitry consisting of autoregulatory and feed-forward loops, which contribute to pluripotency and self-renewal of human ESCs [65], the miRNA findings discussed above suggest that reprogramming of differentiated adult cells by transduction may be a "phantom of the operon." Later to further distinguish between transformation and reprogramming, the DGCR6 and Dicer gene were knocked out in ESCs.

DGCR8 OR DICER-DEFICIENT ESCs
DCGR8 cooperates with the RNase III enzyme Drosha in the biogenesis of pre-miRNA from pri-miRNA transcribed from non-coding regions of the human genome. As shown in Fig. (2), ESCs in which the DCGR8 gene had been knockout (KO) are arrested in the G1 phase of the cell cycle, but these DGCR8-null ESCs remain alive [66]. It has been suggested that resident miRNAs are present independently in the cytoplasm, and these miRNAs may be able to self-proliferate the division of ESCs. Expression of markers of primitive ectodermal-FGF5, and epithelial-KRT18 was delayed in embryoid bodies (EBs), and DGCR8-KO EBs did not form a cyst. Therefore, DGCR8-null ESCs have a defect in their differentiation ability. The pluripotent markers of Oct4, Rex1, Sox2 and Nanog were not expressed [66]. It has been suggested that the DGCR8 gene is essential for the selfrenewal silencing that normally occurs with the induction of differentiation. Surprisingly, it has been reported that by aberration of another RNase III enzyme Dicer ESCs are viable and retain their colony-forming ability and the ability to express Bmp4, HINF4A, GATA1, Brachyury and HPRT ESC-specific genes [67]. The Dicer-null ESCs show a prolonged G1 phase of the cell cycle and differentiation defects [68]. These results strongly support two elements of the RNA wave model: 1) the presence of resident miRNAs that might play an important role the self-renewal function of ESCs, and 2) the existence of genomic miRNAs, which are necessary for ESC differentiation (Fig. 2).
By the high through-put analysis of the miRNA, ESCspecific miRNAs have been cloned and isolated. When human ESCs-specific miRNAs grouped by 'AAAGUGCU' seed sequence were shown, miR-17-5p, miR-18a, miR-18b, miR-20a, miR-20b, miR-93, miR-100a, miR-106b, miR-302a, miR-302b, miR-302c, miR-302d, miR-373, miR-519b, miR-519c, miR-519d, miR-520b and miR-520e including the same seed region were upregulated. These prominent upregulated miRNAs were clustered in human Chr 4, 13, 19 and X [69]. Comparison between human and mouse ESCs revealed that human miR-302b, 302c and miR-302d on Chr 4 are related to miR-302 on murine Chr 3. Another cluster of miRNAs, miR-371, miR-372, miR-373*, and miR-373 on human Chr 19 is the murine homologues of miR-290, miR-291a, miR-291b, miR-292, miR-293, miR-295 on Chr 7 [70,71]. These miRNAs belong to a miRNA gene family common to human and murine (miRBase 13.0 see Fig. 1A). The miR-302 and miR-371 genes are regulated by KLF4, OCT4 and SOX2 transcriptional factors and miR-145 suppresses these three transcriptional factors in human ESCs [63,70]. In the case of murine, miR-470 and miR-134 target the coding region of Oct4 and Sox2 mRNAs, respectively. Further, miR-470, miR-296 and miR-134 target to 3'UTR of Nanog mRNA [64]. According to miRBase, Nanog and SOX2 are targeted by 12 and 26 kinds of multi-miRNAs in human ESCs, and 17 and 26 kinds of multi-miRNAs in mouse, respectively (miRBase 13.0). In these miRNAs, miR-21 of both human and murine has binding sites in the 3'UTR of the mRNA that codes for SOX2, and probably OCT4 [72]. Like polycomb [73], the RE1-silencing transcriptional factor (REST) interacts with cis-elements in promoter of the miR-21 gene, REST represses expression of miR-21. Not only REST, but also the ESC-related transcriptional factors OCT4/SOX2/Nanog/TCF3 bind to the promoter of ESCspecific miRNAs [74]. For example, expression of pri-let-7g is dependent on OCT4 and let-7g is abundant in ESCs [75]. Although Lin28 inhibits expression of let-7g, ectopical expression of Lin28 promotes the reprogramming of human somatic cells into iPS cells [76] and is associated with human multiple cancer types [77]. Further, miR-302 reprogrammed human skin cancer cells into iPS cells [78]. Although quite recently it has been reported that downregulation of the p53 guardian by siRNAs of p53 can increase the emergence rate of iPS cells produced by skin cells [79][80][81][82][83], we well know that the dominantly inherited Li-Fraumeni multi-carcinogenesis syndrome (MIM151623) as a case of mutations of p53 in human [84,85]. Since the p53 was regulated by miR-34a and miR-125b [86,87], these data suggest that production and proliferation of iPS cells may closely be controlled by miRNAs, and that ESCs are maintained not just by ESC-specific transcriptional factors but also by ESC-specific miRNAs as multiple factors. However, it is still not clear how the miRNA genes switch stemness on or off for a change and tuning of cell phenotypes.
One possible answer is an epigenetic regulation mechanism such as that involving miR-290. MiR-290-295 cluster in murine and are indirectly involved in epigenetic gene regulation. This cluster repressed DNA methyltransferase (DNMT), retinoblastoma like-2 (RBL2) and CpG methylation of the promoter by suppression of the DNMT regulated expression of OCT4 [88,89]. Further, Lin28 regulates the production of the histone, H2a [63], suggesting that miRNAs may be indirectly involved in epigenetic gene regulation in ESCs. But there is a further question: why was only Lin28 finally suppressed via regulation of histone?

THE KEY FOR STEMNESS MAY BE FOUND IN THE LATENCY OF HIV-1
DNA and histone methylation is one of the epigenetic modifications of chromatin, which may be related to ESC programming [88,89]. In ESCs, programming of cells results in loss of competency for the abbreviated G1 phase, such as independence of the release of RB protein from E2F transcriptional factor [90]. Since complete silencing of the typical cell cycle gene expression is necessary for the programming of ESCs, it is of great interest that miRNAs could be related to the epigenome. The heterochromatin domains are highly methylated and are also show histone modifications characteristic of silenced chromatin, such as trimethylated histone H3 lysine 9 (H3K9me ) [91]. Further, Dicer is essential for the formation of heterochromatin [92], suggesting that miRNAs control the methylation mechanisms of chromatin [93,94]. In order to change from Fig. (2). DGCR8-or Dicer-null ESCs. DGCR8-null ESCs lack differentiation ability, and, in turn DGCR8 is essential for the silencing of self-renewal. Dicer-null ESCs also lack differentiation ability. But both null cells remain alive and pluripotent markers still continue to be expressed. In Fig. (1B), there is self-renewal set of human miRNAs (miR-371 cluster in C19MC), suggesting that the resident miRNAs are present independently and the resident miRNAs might play important role in the ESCs' self-renewal program. These data are based on the criteria of the RNA wave. Retained self-renewal self-renewal to differentiation, self-renewal set of miRNAs derived from Alu-miRNAs must be switched off and differentiation set of miRNAs from Alu-miRNAs should be switched on. Recently, human miRNA-offset RNAs (moRNA) have been discovered [95]. Although chordate moRNAs are a relative small fraction of the miRNA family, moRNAs are produced from pri-miRNAs probably in Alu or L1 and could target to miRNAs and mRNAs, suggesting that in the case of human, TEs may play an important role in the epigenetic silencing of miRNA and/or mRNA expression. As in the above-described MADE1, human siRNA candidates from MADE1 have been revealed by computer analysis. Therefore, these data suggest that human moRNAs or siRNAs in TEs sought to be related to epigenetic silencing. Thus, the resident miRNAs, moRNAs, siRNAs and unusually small RNAs (usRNAs), which is much smaller than canonical miRNA [96] and the so-called nanoRNAs (nnRNAs) source from TEs are speculated as precursors of transcripts from retrovirus in ncDNA regions (Fig. 3A), suggesting that the function of HIV-1 proviruses may be similar to that of TEs in epigenetic regulation.
The typical model of complete and epigenetic silencing in TEs is like to be the latency of HIV-1 infection [97]. The integrated provirus corresponding to MADE1 could produce miRNAs from the 3'UTR (Fig. 3B). The targeting the 5'UTR by miRNAs can lead to histone 3 protein H3K9 methylation (H3K9me+) (Fig. 3A) and/or DNA methylation. Subsequently, epigenetic LTR-miRNA (Alu-miRNA) switch-off is occurred following histone deacetylation by histone deacetylase (HDAC) and the profile of the resident miRNAs changes in the cells. This is how the latent state is induced and the latent state is in control of the TEs. In the case of ESCs, programming should be initiated from the TE silencing like the latency.
On the other hand, started up reprogramming in iPS cells, it is resembles the reactivation state of HIV-1. Activation of LTRlike Alu promoter and/or environmental stimulation such as that with IL-2 and TNF-induce transcriptional factor HIV-1 Tat corresponding to OCT4, SOX2 and Nanog in iPS cells (Fig.  3B). High concentration of transcriptional factor in the cells inhibits the activity of normal expression of genomic miRNA genes via suppression of Dicer activities, like Tat as an activator of RAS [37]. The H3K9me+ is reversed [98] and H3K4 plus H3K36me+ as well as CpG DNA methylation induce silencing of the LTR-miRNA (Alu-miRNA) (Fig. 3A). Therefore, transduction of OCT4, SOX2 and Nanog might induce an aberrant TE-expression state in differentiated skin cells and iPS cells may be occasionally turned into the tumourigenic cells by an aberrant miRNA gene-expression in TEs because Dicer loss promotes tumourigenesis [99]. To maintain the self-renewal state, the Alu-miR371 family is needed to switch on of selfrenewal and to control TEs. In transcriptional factors dominant state, the Alu-miR371 cannot work at all. Consequently, it is clear that reprogramming could never be achieved only by transformation such as that in iPS cells because even ESCs decrease their pluripotency by epigenetic alteration of the expression of TEs in murine cells [100]. The human genome in ESCs always represents latent states between miRNAs and TEs. In turn, ESC genome robustness may be based on a circuit of fragile miRNAs in TEs, in turn based upon the capacious memory of retrotransposons, which may be built on the RNA wave including the niches' mobile miRNA information.

MOBILE miRNA CODE
Observations of the expression of tens of thousands of miRNAs-TEs suggest that the classical promotertranscription-protein view is contradicted because the miRNAs in TEs identified have been studied in relative depth and the functions have gradually been investigated in genomic in vivo and in silico studies. In light of the low number of validated miRNAs, bioinformatics may play a key role albeit in small ncRNAs [101], not obeying simple Watson-Crick DNA base pairing. A subset of the Watson-Crick model in the seed sequences is currently used to explain the incomplete target pairing of miRNA. A rather broader set of additional rules may be useful to explain how some small RNAs to develop bioinformatics approaches. The seed sequences of miRNAs target the 3'UTR as well as the 5'UTR and protein coding regions (CDS) based on computer analysis [98]. The miRNA genes could target CDS and/or 3'UTR of OCT4, Sox2 and Nanog [34]. Further, a miRNA, such as miR-24 regulates expression of E2F2 by recognizing out of the seed rule [102]. The resident miR-24 gene with miR-155 and miR-17 is specifically involved in the differentiation of granulocytic, monocytic megakaryocytic, and pro-B lymphoid as somatic stem cells [103]. In zebrafish, miR-214 without canonical seed pairing can effectively target a mRNA for silencing [104]. These data suggest that tuning by miRNAs is probably a highspeed event. Therefore, except for complete and partial seed base-pairing, we should add a neo-mechanism for the regulation by miRNA.
Recently, Fabian et al. [105] have reported that miRNAmediated translational repression precedes target mRNA deadenylation. However, their model still depends on the seed sequences of miRNA-the target ones of mRNA. Further, there is no specificity of miRISC to bind to a specific miRNA. This is different from aminoacyl-tRNA synthetase, which recognizes a specific amino acid and the tRNAs corresponding to that amino acid. This yields the hypothesis that circular shaped mRNA produced by the capbinding factor eIF4E plus eIF4G and poly(A)-binding protein PABP could make miRISC into a linear strand of mRNA. Since it is well known that episomal HIV-1 DNA forms the ring, if miRNA is also circular and miRISC including helicase activities links a miRNA ring, as shown in Fig. (4A), the miRNA rings may enter into the mRNA strand, resulting in probable inhibition of mRNA circularization [106].
Quite recently, it has been reported that HIV-1 replication and reactivation in patients with chronic HIV-1 infection are inhibited by shRNA (shN366) including HIV-1 miRNA MIRN367 [107]. This report suggests that TE-miRNA autoregulating system is involved in the latency model in humans. Therefore, for example of the miRNA gene ring, we represent here the miRNA rings in HIV-1 viral RNA [37], additionally in differentiation and in self-renewal specific sets into ESC (Fig. 4A). In Fig. (4A), according to electrostatic potential of each atom of the RNA bases with methyl residue instead of ribose 5-phosphate by the fragment molecular orbit methods (FMO) based on quantum theory [108], at first total electrostatic potential of A (approximate -0.05 milli-Hartree), U (-0.03), G (0.258) and C (-0,002) Fig. (3). A model of epigetic on/off switching mechanism of transposable miRNAs. (A) Epigenetic silencing off by the resident small RNAs is shown. The function of nnRNA is similar to that of siRNA. Alu-pri-miRNAs as the TE could be targeted by nnRNA during transcription epigenetically. This process induces complete silencing off, that starts translation of mRNAs for differentiation of ESCs. The Ago/nnRNA complex circulized as a Buddhist rosary ('Jyuzu' in Japanease) and the ring of nnRNA may epigentically lock Alu-pri-miRNA transcription by methylation of histone by Clr4-like human methyltransferase (HMT) enzyme, Suv39 associated with Swi6/HP1 and chromo domaincontaining protein 1 (Chp1) plus RNA-induced transcriptional silencing complex protein 3 (Tas3) binding to H3K9me+ histone following CpG methylation by DNA methyltransferase (DNMT) [111]. (B) The HIV-1 latency model. The Weinberg and Morris model [97] is shown with minor modifications. The HIV-1 3'LTR U3 region contains at least three genomic miRNA genes: MIR#4, MIRN367 and MIRH1. The 5'LTR/gag/pol/env region may be corresponding to Alu TE. These genomic miRNAs yeild silencing of HIV-1 proviral expression (Switch OFF). Environmental factors, such as the resident miRNA in ESMV, IL-2, X-ray, dietary food etc. affect the profile of miRNAs in cells, nnRNA from digestion of the resident miRNAs, retroposable miRNAs or mobile miRNAs completely stopped expression of Alu-pri-miRNA described in (A). This is the mechanism of latency. Dominant transcriptional factor induces HIV-1 mRNA transcription (Switch ON). In this state, iPS cells may be produced; however, since there is no step of complete silencing on, iPS cells may occasionally change into tumour cells. Therefore, the differentiation set of the resident miRNAs should be completely switched off to reprogram the somatic cell.    base were calculated. Although in the original code, hypothesized long ago, the archaic code (G, C) would expand toward the complete variants (C, G, A, U) in the early evolution of mRNA [109,110], only G had a positive potential. The calculated potencies were represented as a radar shape (Fig. 4A). We focused on the neighbouring bases and checked G multiplication in sequences of each miRNA (see black dots in Fig. 4A). In the seed sequences (see shaded diamonds in Fig. 4A), there was no specific relation between the G multiplication and miRNA specific sets in ESCs. But except for MIRH1 and MIR29b-1, black dots were not involved in the seed sequences of other miRNAs. Further, we found unbalanced and balanced electron potencies of the G base cluster in some miRNAs. In such miRNAs, electrons in the circular miRNAs move from one base to a neighboring base according to electrostatic potential of each atom of the RNA bases with FMO, whereas electrons may spin round. Depended on the direction of rotating electron, the direction of the magnetic field would go up or down. The superposing of the magnetic direction of electrons according to quantum theory may be the code of RNA (Fig. 4B), like quantum computing [19]. The pulse waves of the electrons in the organ hydrogen (H) atoms of water in magnetic resonance image (MRI), could, in part, be derived from a source of the magnetic waves produced by miRNA superposing for silencing gene expression, such as aberrant presence of miRNAs in tumor [17]. By measure miRNA electron spin, it may be available for investigation of the relationship between stemness and physicochemical characters of tuning by miRNAs and furthermore therapy of RNA diseases.

CONCLUSIONS
The RNA wave consists of four alternative concepts for crosstalk among cells: 1) mobile miRNAs induce transcriptional and post-transcriptional silencing processes; 2) the induced silencing information expands to intracellular, inter-cellular and intra-organ, inter-organ communication by free mobile miRNAs; 3) the mobile miRNAs can self-proliferate; and 4) cells contain both resident and genomic miRNAs. Thus, the non-selfish miRNAs are regarded as the basic unit of the evolution to move horizontally in genomic space. The miRNAs would horizontally transpose in TEs by feeding of miRNAscontaining foods, viruses, etc. The retroposable miRNAs might create phenotype by changing and tuning of the gene expression in the genomic space, in which its genotype can be vertically inherited under pressures of the environment corresponding to Darwininsm.
To clarify the capacity of ESCs to renew themselves and to differentiate into various cell lineages, miRNAs and TEs as RNA genes have an important role. The highlight of the importance of miRNAs in TEs has been shown in controlling ESC growth and differentiation by miRNAs. Although the significance of miRNA function has not yet been evaluated, retroposable miRNAs in TEs are expressed in ESCs, suggesting that TEs may switch on or off the alteration of ESCs from self-renewal, differentiation and reprogramming including the epigenetic regulation. This paper will aid in our understanding of the relationship between oncoviral miRNA genes and TEs. Understanding the seed theory for miRNA targets and further breaking the RNA code under the RNA wave will shed light on the quality of stemness in ESCs.

(-) Retrovirus
An RNA virus. The genetic composition and copying process of viruses are the same as that of retroelements. HIV-1 is a typical retrovirus. A particle of virus contains about 10 kilobases of two singlestranded (+) RNA.

Chr Chromosome
The coiled coil DNA and histone proteins that is believed to be as the basic conformation. The number of chromosome in a human somatic cell is twice 22 plus X and X (female) or X and Y (male). The DNA of all chromosomes is composed of a genome.
ncRNA Non-coding RNA About 98% DNA of 3.2 gigabases of genome is protein non-coding region that therefore has been believed to be nonfunctional region as junk. The region encodes non-coding RNA containing retroelement and microRNA.

SUPPLEMENTARY MATERIAL
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