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MMP13 mediates cell cycle progression in melanocytes and melanoma cells: in vitro studies of migration and proliferation

Background Melanoma cells are usually characterized by a strong proliferative potential and efficient invasive migration. Among the multiple molecular changes that are recorded during progression of this disease, aberrant activation of receptor tyrosine kinases (RTK) is often observed. Activation of matrix metalloproteases goes along with RTK activation and usually enhances RTK-driven migration. The purpose of this study was to examine RTK-driven three-dimensional migration of melanocytes and the pro-tumorigenic role of matrix metalloproteases for melanocytes and melanoma cells. Results Using experimental melanocyte dedifferentiation as a model for early melanomagenesis we show that an activated EGF receptor variant potentiates migration through three-dimensional fibrillar collagen. EGFR stimulation also resulted in a strong induction of matrix metalloproteases in a MAPK-dependent manner. However, neither MAPK nor MMP activity were required for migration, as the cells migrated in an entirely amoeboid mode. Instead, MMPs fulfilled a function in cell cycle regulation, as their inhibition resulted in strong growth inhibition of melanocytes. The same effect was observed in the human melanoma cell line A375 after stimulation with FCS. Using sh- and siRNA techniques, we could show that MMP13 is the protease responsible for this effect. Along with decreased proliferation, knockdown of MMP13 strongly enhanced pigmentation of melanocytes. Conclusions Our data show for the first time that growth stimuli are mediated via MMP13 in melanocytes and melanoma, suggesting an autocrine MMP13-driven loop. Given that MMP13-specific inhibitors are already developed, these results support the evaluation of these inhibitors in the treatment of melanoma.

Background
Growth factors control the fate of many cell types in the body and usually stimulate proliferation, survival and motility in cells that express the adequate receptor on their surface. Therefore, availability of growth factors and growth factor receptors must be tightly regulated on multiple levels to prevent aberrant growth. However, many tumors have developed mechanisms that render them independent of exogenous growth factors. One mechanism is the development of autocrine loops. Multiple tumors including melanoma produce high amounts of EGF, TGF-α, PDGF, or bFGF which accelerates tumor growth and goes along with a reduced patient survival [1][2][3][4][5]. Furthermore, mutations in growth factor receptors can generate continuous growth signals, e.g. in glioblastoma, breast, ovarian, prostate and lung squamous cell carcinomas, where the truncated epidermal growth factor receptor version vIII is expressed [6][7][8]. The oncogenic EGFR variant Xiphophorus melanoma receptor kinase (Xmrk) is also permanently active due to mutations that result in constitutive dimerization of this receptor tyrosine kinase [9,10]. Xmrk is the cause for highly aggressive melanoma in the Xiphophorus fish tumor model. It constitutes a very efficient oncogene that induces the steps necessary for melanoma formation in vivo in the fish model [11][12][13] and also in vitro in mammalian melanocytes [14,15].
Of the different steps required for tumor formation and progression, induction of cell motility and survival in the extracellular matrix are considered to be crucial prerequisites for a tumor cell to become metastatic. When a melanocyte succeeds to leave its natural epidermal environment and invades the dermis it has to face a new surrounding, consisting mainly of collagen. The lack of a proper cell-matrix attachment leads to an anoikis-like state and drives these cells into apoptosis [16,17]. Activation of growth factor receptors, however, can both protect the cells from apoptosis and induce migration in a threedimensional collagen environment [17][18][19]. Most migrating cells express either membrane-bound or secreted matrix metalloproteases (MMPs) at the cell front that digest the matrix and open space for the forward pushing cell body. MMPs are commonly upregulated after growth factor stimulation [20]. Although the best studied targets of these proteases are various matrix components, a growing body of evidence reveals the importance of MMPdependent cleavage of other extra-and intracellular substrates that have various cellular effects [21][22][23].
Here, we take advantage of the well defined transforming abilities of the oncogene xmrk and use it as model to analyze the cancer-inducing functions of receptor tyrosine kinases (RTK). In order to concentrate on RTKdriven effects alone without influences from secondary tumor-derived effects we are using Xmrk-expressing melanocytes (melan a) rather than melanoma cells. Activation of Xmrk leads to transformation of these cells and induces key features of the neoplastic phenotype of melanoma cells [14]. One of these key features is the occurrence of dedifferentiation, which can be directly visualized by decresed pigmentation and reduced tyrosine levels after Xmrk activation [15]. Besides dedifferentiation and unlimited proliferation, Xmrk has been previously reported to induce cellular migration of melanocytes in a two-dimensional migration assay [24] and mediate cell survival in three-dimensional collagen lattices [17]. In this study, we investigated the threedimensional migration behaviour. We found that Xmrk activation induced melanocyte migration in an amoeboid manner which is entirely independent of MMP activity. Instead, blocking MMPs with a broadband inhibitor mix stalled cell proliferation. The protease responsible for the proliferation effect was MMP13, as demonstrated by RNA knockdown experiments. Importantly, MMP13 was also found to be necessary for the proliferation of the human melanoma cell line A375.

EGF stimulation of melanocytes leads to MAPK-and PI3K-independent migration on collagen
To monitor the effects of signalling of the oncogenic RTK Xmrk we used HERmrk-transgenic melanocytes ("melan-a Hm") that transgenically express a chimeric protein consisting of an extracellular EGFR-and an intracellular Xmrk domain. It is important to note that these cells do not express endogenous EGFR [15]. The chimeric receptor displays the same intracellular signalling as Xmrk and in addition allows EGF induction instead of permanent activation. To find out which matrix components are suitable for migration of melana Hm we first performed a modified Boyden chamber assay on transwell inlays that were either left uncoated or were precoated with vitronectin, fibronectin, or collagen I. We used 100 ng/ml of EGF, which is the concentration that proved to be optimal for migration on uncoated transwell inlays [24]. The results demonstrate that only uncoated and collagen-coated membranes constitute a good migration substrate for the cells ( Figure  1A). However, significant EGF-induced migration on collagen I was only noted with reduced amounts of EGF as stimulus (1 ng/ml, Figure 1B).
For evaluating which downstream components are important for collagen-mediated cell migration, we performed migration experiments at 1 ng/ml EGF in the absence or presence of the following small molecule inhibitors: AG1478 (blocks kinase activity of EGFR and orthologues including Xmrk and HERmrk), LY294002 (blocks PI3K), PP2 (blocks SRC family kinases) and U0126 (blocks MEK). Inhibition of SRC kinases and HERmrk itself led to a reduction in cell motility ( Figure 1C), which is in accordance with previous observations monitoring two-dimensional migration in absence of collagen [24]. Single and combined inhibition of PI3K and MAPK pathways, however, revealed that both pathways are dispensable for 2D migration of HERmrk melanocytes ( Figure 1C). However, both inhibitors efficiently blocked the respective pathways at the applied concentration of 10 μM (Additional file 1, Figure S1A) The same observation was made when an independent MAPK inhibitor, namely PD184352, was combined with the PI3K inhibitor (Additional file 1, Figure S1B and, as inhibitor control: Additional file 2, Figure S2A, right lanes).

EGF stimulation induces several MMPs in a MAKPdependent manner
As interaction of cells with matrix components often induces both the secretion of matrix proteases and the secretion of extracellular matrix components, we screened for the expression of both groups of genes in response to EGF. EGF strongly upregulated the transcripts of matrix metalloproteases MMP1a, -1b, -3, -9 and -13, which are not or only slightly expressed in absence of EGF. The other investigated proteases or the matrix components collagen I, IV, laminin and fibronectin were not induced (Table 1). Inhibiting either Figure 1 EGF induces melanocyte motility on collagen I and the expression of several matrix metalloproteases. A-D: Hm cells were serum-starved for 24 h and seeded onto the upper chamber of transwell migration inlays. The transwell migration assays were stopped after 12 h. Cells were stained with crystal violet and the number of transmigrated cells was counted. A: Hm cells were seeded onto uncoated transwell migration inlays or inlays previously coated with vitronectin (Vn), fibronectin (Fn) or collagen I (Col I). Where indicated, EGF were applied to the lower chamber. B: Transwell migration assay of Hm cells seeded onto collagen I-coated inlays. The indicated concentrations of EGF were applied to the lower chamber. C-D: Transwell migration assay of Hm cells seeded onto collagen I-coated inlays and stimulated with 1 ng/ml EGF, applied to the lower chamber. Cells were additionally treated with AG1478 (AG), LY294002 (LY), PP2, U0126 (U0) (C) or a combination of LY294002 and U0126 (LY + U0) (D). **: p < 0.001 (Student's t test, paired, two-tailed). E: Hm cells were serum-starved for 24 h and subsequently left untreated or treated with 100 ng/ml EGF in presence or absence of AG1478 or U0126. After 8 h, cells were harvested and reverse transcription-PCR (in case of Mmp1a and Mmp1b) or realtime-PCR analysis (in case of Mmp3, -9 and -13) were performed. Realtime-PCR data were normalized to expression of actin. **: p < 0.001 (Student's t test, paired, two-tailed). F: Zymographic analysis of the supernatant of starved or EGF-treated Hm cells after two days of stimulation.
HERmrk or MEK with two independent inhibitors for each pathway totally abolished the induction of each of the MMPs (Figures 1E and Additional file 2, Figure  S2A-B). The fact that MMP induction depends on the MAPK pathway, which itself is not involved in the EGF induced migration (see above), indicates that MMPs are not required for EGF-induced migration on two-dimensional collagen substrate.
To confirm MMP activity, we performed a gelatin zymography, which is suitable for detecting active MMP2, MMP9 and MMP13. After EGF stimulation, the bands characteristic for active MMP9 and MMP13 were enhanced ( Figure 1F). To find out whether induction of these MMPs is a shared feature of HERmrk and human EGFR, we also treated melan-a cells transgenic for human EGFR (melan-a HER) with EGF in absence or presence of EGFR-and MEK inhibitors and monitored transcriptional induction of MMPs. We observed enhanced expression of Mmp1a, -1b, -3, and -13, but no induction of Mmp9. Again, EGFR-and MEK inhibition were effective (Additional file 2, Figure S2C) and prevented the expression of all Mmp's (Additional file 2, Figure S2D). The parental cell line melan-a did not respond to EGF stimulation (Additional file 2, Figure S2E).

EGF-stimulated melanocytes migrate in an amoeboid, MMP-and MAPK-independent manner in three-dimensional collagen gels
To monitor if MMP-independent migration only occurs if the melanocytes are migrating on a flat surface or whether it also takes place in three-dimensionally migrating cells, the melanocytes were analyzed by timelapse videomicroscopy in a 3D model. The migrative behaviour of melanocytes can be best observed when cells are kept under experimental conditions that reflect the composition of the dermis. Therefore Hm cells were embedded in a three-dimensional chamber filled with fibrillar collagen and overlayed with EGF-containing medium. Cells were then monitored for 48 h. Monitoring at high resolution revealed that migrating cells squeezed through the matrix and changed their shape to a rounded or ellipsoid appearance, seemingly without degrading the matrix. This is reminiscent of amoeboid migration in melanoma and other tumor cells in threedimensional migration model systems ( Figure 2A, arrows and Additional file 3, Movie S1) [25,26]. The concept of EGF-induced amoeboid migration in melanocytes was directly addressed using broad-spectrum MMP inhibition (Ilomastat and MMP9/13 inhibitor I). Inhibition of MMPs had no effect on migration speed ( Figure 2B), in agreement with the amoeboid migration which is a migration mode independent of matrix destruction by metalloproteases.
We also tested the effect of inhibiting the receptor itself and its downstream target responsible for Mmp upregulation, the ERK1/2 pathway. HERmrk signalling was abrogated using the EGFR inhibitor AG1478, while ERK1/2 inhibition was accomplished using the MEK inhibitor U0126. We first controlled the efficiency of both inhibitors in collagen gels. RT-PCR of all regulated Mmp genes demonstrated a successful inhibition of target gene induction by AG1478 and U0126 ( Figure 2C). As expected, inhibition of HERmrk resulted in strongly reduced cell migration ( Figure 2B). However, activation of ERK1/2 seemed to be dispensable for migration, as U0126 had no effect on cell speed ( Figure 2B). This was unexpected, as ERK1 and ERK2 do not only induce Mmp's, but reportedly play a role in cytoskeleton rearrangement, which is a prerequisite for motility of many cell types [27].

MMP inhibition results in a proliferation block of EGFtreated melanocytes
Besides their contribution to ECM remodelling and invasive migration, other functions of MMPs include the proteolytic release of matrix-bound growth factors or of transmembrane proteins. This would result in auto-or paracrine outside-in-signalling. Thus, we monitored apoptosis and cell cycle progression of EGF-stimulated ENSMUSG00000026193 - The highest induction of Mmp1a, -1b, -3, -9 and -13 was seen at 8 h after EGF induction. The symbols given in the table refer  HERmrk transgenic melanocytes in the absence or the presence of MMP inhibitors. To examine a possible effect on cell proliferation, we stimulated starved cells with EGF in absence or presence of the MMP inhibitor mix (containing MMP inhibitors Ilomastat and MMPinhibitor 9/13) and followed their proliferation for ten days. The inhibitors reduced cell proliferation to one third of the control ( Figure 3A). When we compared the effect of single MMP inhibitors with the MMP inhibitor mix, only MMP inhibitor 9/13 proved to be effective in blocking proliferation ( Figure 3B). Flow cytometry analyses demonstrated that while EGF Where indicated, cells were overlaid with EGF-containing medium. Graphs display the speed of single cells that was calculated from the time-lapse movies, and the mean speed for each condition is indicated. The following numbers of cells were examined: ctrl., EGF, EGF + U0126: n = 161 (for each); EGF + AG1478: n = 78; EGF + MMP inhibitors: n = 120. ***: p < 0.0001. C: Hm cells were embedded in collagen matrix and stimulated with EGF as described above. The supernatant contained DMSO, AG1478 or U0126, respectively. After 8 h, where maximal stimulation was seen before, whole collagen gels were used for RNA isolation and RT-PCR analysis of the indicated genes. In case of Mmp1a and -1b a lower, unspecific DNA band was seen, which was presumably due to oligonucleotide multimers that can form in the absence of specific template. The specific bands for MMP1a and -1b are indicated by an arrow. treatment of starved HERmrk melanocytes resulted in an increase of cells in S phase after 20-24 h, no cell cycle progression was seen in presence of the MMP inhibitor 9/13 ( Figure 3C). In addition, a slight increase of sub G1 cells seemed to occur in MMP inhibitor 9/13 treated cell populations, but this was not significant. Western blot analysis of cleaved caspase 3, the effector caspase downstream of intrinsic and extrinsic apoptosis stimuli, showed no apoptosis induction (Additional file 4, Figure S3A). Thus, the prevailing effect of blocking MMP9/MMP13 was the inhibition of cell cycle progression.
Cell cycle progression of the human melanoma cell line A375 is also blocked by MMP inhibition To address whether MMP-dependent cell cycle progression is also a feature of human melanoma cells, we tested the melanoma cell line A375. In contrast to starved melan-a Hm cells, starved A375 cells already expressed low amounts of MMP1, -3, -9, and -13 (data not shown). However, as we were interested in MMPs that are induced in response to growth stimulatory signals, we also analyzed the expression of these four genes in response to EGF and FCS. Under these conditions, an induction was only measured for MMP13 (Figure 4). Although EGFR stimulation of A375 results in protumorigenic cellular effects, such as enhanced survival [28], it is not sufficient to drive the cells into cell cycle (own observations). Thus, we performed the proliferation experiments using 10% FCS as stimulant.
The results mirrored the situation previously observed in melan-a Hm cells. Proliferation was blocked by the MMP inhibitor mix, and the only inhibitor responsible for this effect was MMP 9/13 ( Figures 5A and 5B). The progression of starved A375 cells into S phase, which is seen 20 and 24 h after FCS stimulation, was prevented in presence of MMP9/13 ( Figure 5C).
To validate this, we transfected melan-a Hm cells with a retroviral plasmid expressing Mmp13-specific shRNA, which resulted in a reduction of Mmp13 expression on RNA and protein level ( Figure 6A, left and middle panel). Melan-a Hm shMMP13 cells proliferated much slower than cells expressing a control plasmid ( Figure  6B). Interestingly, we also observed that Mmp13 downregulation went along with a strong increase in pigmentation, as visible by a 100% increase in melanin content ( Figure 6C, left and middle panel). This was accompanied by enhanced levels of tyrosinase RNA ( Figure 6C,  right).
A similar approach was done with the human melanoma cell line A375. As several tested shRNA constructs did not efficiently knock down the gene, we used commercial siRNA for this cell line, which reduced MMP13 transcript levels to approx. 33% ( Figure 6D, left). Western blot analysis also confirmed a reduction in the pro-and active forms of the protein, with 60 and 48 kDa, respectively ( Figure 6D, right). Instead of the previously conducted long-term proliferation assays, we performed a BrdU incorporation assay as a measure of DNA replication 72 h after transfection of the respective siRNA ( Figure 6E). Knockdown of MMP13 decreased BrdU incorporation to 60%. We also observed an increased fraction of siMMP13-transfected cells in the Figure 4 Upregulation of human MMP13 in response to FCS. A375 cells were starved for two days in presence of 0.5% dialyzed FCS and stimulated by the addition of 100 ng/ml EGF or 10% FCS. After 4 and 8 h, cells were harvested and monitored for the expression of MMP13 by realtime PCR. Human ribosomal S14 was used as reference gene. *: p < 0.05 (Student's t test, paired, two-tailed).  G0/G1 phase of the cell cycle when compared to control cells (Additional file 6, Figure S5). However, the effect was weaker than the effect seen in presence of the MMP 9/13 inhibitor displayed in figures 3C and 5C. Possibly, this is due to the incomplete MMP13 knockdown. It is also likely that the arrest is more enhanced in starved cells that are confronted with growth stimulus and MMP inhibitor at the same time. If MMP13 is knocked down in the normal growing cell culture, it may block cell cycle progression in general, irrespective of the cell cycle phase. This kind of behaviour is reminiscent of the effect of growth factor withdrawal, which can block the cell cycle in G1 and G2, and might point to the possibility that MMP13 releases an unidentified growth factor. To investigate whether the potential MMP13-dependent growth-promoting factor is secreted, we treated siMMP13-transfected A375 cells with conditioned supernatant from control siRNA-transfected cells. This could significantly restore BrdU incorporation to 80% of the control ( Figure 6E), indicating the presence of a soluble growth-promoting factor.
In summary, these data indicate that MMP13 plays an important role in the growth factor-induced proliferation of melanocytes and melanoma cells as well as in the dedifferentiation of melanocytes.

Discussion
In most melanomas, MMPs are aberrantly expressed. All MMPs upregulated in Hm cells were previously reported to be produced in melanoma, in particular MMP1 and -9 [29]. The cause of MMP expression in melanoma is largely unknown, but continuous ERK signalling, e.g. by autocrine FGF-or B-Raf V600E -signalling is responsible for their expression in some melanoma cell lines [30,31].
The generally favoured function of MMPs in melanoma progression is the remodelling of the extracellular matrix that enables both the transition of radial to vertical growth phase and angiogenesis in more advanced stages of the disease [30,32,33]. However, although tumor cells commonly express ample amounts of MMPs, MMP-independent migration was reported for melanoma, fibrosarcoma and breast cancer cells [25,26,[34][35][36].
Consistent with the concept of MMP-independent migration, our data show that the EGF-induced upregulation of MMP13 in melanocytes supports cell cycle progression instead of invasive migration.
MMP13, also called collagenase-3, is expressed in a very restricted manner in the human body, but is often upregulated under pathological conditions, such as cancer and arthritis [37]. Under physiological conditions, it is mainly expressed in bone and cartilage, where it helps to remodel the growing tissue. Consequently, MMP13-/-mice show defects in growth plate cartilage and disturbed ossification, which is at least partly the result from interstitial collagen accumulation [38]. Hence, collagens, such as collagen II and IV, are the best investigated MMP13 targets [39,40]. However, the role of MMP13 in mediating melanocyte and melanoma cell proliferation as described in this manuscript is in line with emerging non-classical MMP functions in outsidein signalling and cell cycle control. The subsequent signal transduction events responsible for this process are unclear so far, but matrix-or cell surface proteins, either activated or made accessible by MMP13 dependent cleavage, may be involved. Generally, MMPs can release growth factors such as HB-EGF and TGF-α, but also secreted factors or proteins that can regulate growth factor availability, such as IGFBP1-, -3 and -5 and FGF receptor [41][42][43][44][45][46][47]. In squamous cell carcinoma, MMPs generate autocrine loops that are able to stimulate several receptors of the EGFR family [48]. It is well possible that a similar effect occurs MMP13-dependently in Hm and A375 cells. For MMP13, a wide substrate specificity was described, and several non-matrix substrates are already known, such as TGF-β, CTGF, fibrinogen and ICAM-1 [37,40,[49][50][51]. Furthermore, MMP9 is a confirmed target of MMP13, and it is also involved in the cleavage of numerous substrates, including integrin precursors and LIF [52,53]. Whether these or yet unknown targets are responsible for proliferation in melanoma will be investigated in the future.
Interestingly, protein expression of MMP13 is absent from nevi, but was noted in almost 50% of cutaneous melanoma [54]. A functional role for stromal MMP13 in melanoma development was recently described in a MMP13-/-mouse model [55]. In these mice, B16F1 melanoma grafts displayed reduced tumor growth and strongly decreased metastasis and angiogenesis compared to wildtype mice. Together with our data, it appears that tumor cell -or stroma-derived MMP13 plays a role in several processes of melanoma development. This makes it a potentially attractive drug target. Selective MMP13-specific protease inhibitors are already developed [56,57] and are currently used in mouse models for arthritis [58]. In future studies, we will investigate the effect of specific MMP13 inhibitors in animal melanoma models.

Conclusions
Our data demonstrate that MMP13 links growth-stimulatory signals such as EGF and FCS to cell cycle progression in melanocytes and melanoma cells and to dedifferentiation in melanocytes. The data indicate that the protease is important for migration-independent processes of melanoma formation, possibly by releasing a yet unidentified growth factor. As MMP13 also plays a role for melanoma progression and specific inhibitors are already developed, it might be considered as a target for the treatment of MMP13-sensitive melanoma.

Knockdown methods
For cloning of the shMMP13 knockdown vector, the oligonucleotides gatccccGAGCACTACTTGAAATCATtt-caagagaATGATTTCAAGTAGTGCTCtttttggaaa and agcttttccaaaaaGAGCACTACTTGAAATCATtctctt-gaaATGATTTCAAGTAGTGCTCggg, containing the target sequence of murine Mmp13 [ENSMU SG00000050578], were annealed and cloned into pRe-troSuper previously digested with BglII and HindIII. The resulting plasmid was retrovirally delivered into melan-a Hm cells and selected by puromycin treatment to obtain stable cellular expression. For human melanoma cells, commercially available control siRNA and siRNA against human MMP13 (Smart Pool On Target Plus, Thermo Scientific, [ENSG00000137745]) were used. siRNA was transfected using X-treme gene transfection reagent (Roche), according to the manufacturer's recommendations. Downregulation was monitored by realtime-PCR.

Cell proliferation assay
Cells were starved for three days in DMEM containing 1.5% dialyzed FCS (Invitrogen) and seeded at 3 × 10 4 cells per well of a 6-well plate. Hm cells were treated with 100 ng/ml EGF, and A375 cells were treated with 10% FCS in absence or presence of 10 μM Ilomastat, 10 μM MMP9/13 inhibitor 1, or both. The controls were treated with the corresponding amount of DMSO. Cells were harvested by trypsinization after 2, 4, 6, 8, and 10 days, pelleted, resolved in PBS and counted under the microscope.

BrdU incorporation assay
72 h after siRNA treatment, cells were incubated with 10 μM BrdU for 24 h. The following day, BrdU incorporation was quantified using a colorimetric BrdU cell proliferation ELISA, as recommended by the manufacturer (Roche). were used as reference genes for murine and human genes, respectively. Relative expression levels were calculated applying REST software [59]. For all genes indicated, realtime analysis was performed at least three times independently from three different cDNA templates. The respective oligonucleotide sequences are available on request.

Melanin quantification
Melan-a Hm cells from EGF-treated cell culture were trypsinized, and 5 × 10 5 cells were spun down in an Eppendorf centrifuge. The supernatant was discarded and the pellet was dissolved in 1 N NaOH. Melanin concentration was determined by measurement of optical density at 475 nm and compared to a standard curve obtained using synthetic melanin (Sigma). Pigment determination was performed three times independently.

Zymographic analysis
FCS-free culture media of melan-a Hm cells, untreated or pretreated with EGF for two days, were harvested, adjusted according to the cell number and concentrated using Amicon Ultracel 10 k columns unless indicated otherwise. Samples were mixed with 2× loading buffer (50 mM  Where indicated, transwell inlays were pre-coated with 3 μg/ml vitronectin (Biosource), 10 μg/ml collagen I (Upstate Biotechnology) or 10 μg/ml fibronectin (BD Biosciences), yielding fibrillar layers. The indicated concentrations of EGF were applied to the lower chamber, and inhibitors were applied in the given concentration to the upper and lower chamber. After 12 h, the transwell assay was stopped. The cells on the upper side of the membrane were removed with a cell scraper, before the membrane was fixed for 5 minutes in methanol and stained for 20 minutes with 2% crystal violet dissolved in 2% ethanol. The membranes were then washed with PBS and the number of cells on the lower side of the membrane was counted. The migration rate was determined in absolute numbers. At all conditions, the assay was performed at least three times independently.

Collagen matrix migration assay and cell tracking
Cells were embedded within a 3D fibrillar collagen matrix (final collagen concentration: 1.67 mg/ml) and either overlaid with starving medium (DMEM containing 2.5% dialyzed serum (Invitrogen)) or starving medium containing 500 nM EGF, which was the optimal concentration for migration of Hm cells under these conditions. For the inhibition experiments, MEK inhibitor U0126, MMP inhibitors Ilomastat and MMP9/13 inhibitor I, alone or in combination, AG1478 or the respective amount of DMSO were added to the matrix and the starving medium. The collagen matrix component in the chamber was approximately 2/3 of the total volume, the medium supernatant was 1/3. The chamber was hermetically sealed with paraffine, incubated at 37°C for 48 h and migration was monitored by timelapse videomicroscopy. Locomotor parameters were obtained by computer-assisted cell tracking and reconstruction of the xy coordinates of cell paths for a step interval of 4 minutes. For each condition, three independent samples were measured, and the speed was calculated for 40 randomly chosen cells per sample. The viability of the cells was >95% and did not change in presence of EGF or inhibitors.

Additional material
Additional file 1: Figure S1 -Inhibition of MEK and PI3K in EGFtreated melan-a Hm cells. A: Starved Hm cells were pretreated for 1 h with the indicated concentrations of U0126 or LY294002 before 100 ng/ ml EGF was added for 10 minutes. Cells were lysed, and 50 μg was then subjected to Western blot analysis and probed with the indicated antibodies. ERK2 was used as control. B: Transwell migration assay of Hm cells seeded onto collagen I-coated inlays and stimulated with 1 ng/ml EGF, applied to the lower chamber. Cells were additionally treated with 10 μM LY294002 (LY), 2 μM PD184352 (PD) or a combination of LY294002 and PD184352 (LY + PD) (D). **: p < 0.001 (Student's t test, paired, two-tailed).
Additional file 2: Figure S2 -MAPK-dependent MMP expression in melan-a Hm and melan-a HER cells. A: Starved Hm cells were pretreated for 1 h with 20 μM AG1478, 10 μM EGFR-I (both inhibitors targeting EGFR and HERmrk), 10 μM U0126 or 2 μM PD184352 (both MEK inhibitors) before 100 ng/ml EGF was added for 10 minutes. Cells were lysed, and 50 μg was then subjected to Western blot analysis and probed with the indicated antibodies. ERK2 was used as control. B: Hm cells were serum-starved for 24 h and subsequently left untreated or treated with 100 ng/ml EGF in presence or absence of 10 μM EGFR-I or 2 μM PD184352. After 8 h, cells were harvested and reverse transcription-PCR was performed for the indicated genes (35 cycles). C: as in A, but performed with melan-a HER cells. D: as in B, but performed with melana HER cells, and with additional treatment with the inhibitors AG1478 (20 μM) and U0126 (10 μM). E: The starved parental cell line melan-a was treated with EGF, and a reverse transcription PCR was performed for the indicated genes (35 cycles). Pc = positive control (EGF-treated Hm cells); nc = negative control (H 2 O).
Additional file 3: Movie S1 -Time-lapse movie of EGF-treated melan-a Hm cells. Cells were visualized with an inverted microscope at 200-fold magnification and were photographed every 4 min for a total of 48 h.
Additional file 4: Figure S3 -Inhibition of MMP9 and MMP13 does not affect apoptosis in Hm and A375 cells. A: Starved Hm cells were left untreated or were treated with 100 ng/ml EGF in the absence or presence of the MMP9/13 inhibitor for 24 and 48 h. Cells were then lysed and 50 μg of cell lysate was analyzed by Western blot. Apoptosis induction was investigated using an antibody against cleaved caspase 3. As apoptosis control, UV-irradiated A375 cells were used. ERK2 was used as loading control. B: as in A, but performed with A375 cells and 10% FCS as growth stimulus.
Additional file 5: Figure S4 -Inhibition of cell cycle progression by two additional MMP inhibitors. A: Starved Hm cells were treated with EGF in the absence or presence of the general MMP inhibitor CP 471474 (100 μM) or the MMP13-selective inhibitor CL 82198 (75 μM) and incubated for 24 h. Their cell cycle state was analyzed by flow cytometry. B: as in A, but performed with A375 cells and FCS as growth stimulus.
Additional file 6: Figure S5 -Knockdown of MMP13 delays cell cycle progression in A375 cells. A375 cells were transfected with control-or MMP13-specific siRNA for 72 h, and the cell cycle state was monitored by flow cytometry. Starved A375 cells served as control.


extfieldsofstudy: Biology Medicine

text:
Gene Expression Patterns in Bone Following Mechanical Loading

The advent of high-throughput measurements of gene expression and bioinformatics analysis methods offers new ways to study gene expression patterns. The primary goal of this study was to determine the time sequence for gene expression in a bone subjected to mechanical loading during key periods of the bone-formation process, including expression of matrix-related genes, the appearance of active osteoblasts, and bone desensitization. A standard model for bone loading was employed in which the right forelimb was loaded axially for 3 minutes per day, whereas the left forearm served as a nonloaded contralateral control. We evaluated loading-induced gene expression over a time course of 4 hours to 32 days after the first loading session. Six distinct time-dependent patterns of gene expression were identified over the time course and were categorized into three primary clusters: genes upregulated early in the time course, genes upregulated during matrix formation, and genes downregulated during matrix formation. Genes then were grouped based on function and/or signaling pathways. Many gene groups known to be important in loading-induced bone formation were identified within the clusters, including AP-1-related genes in the early-response cluster, matrix-related genes in the upregulated gene clusters, and Wnt/β-catenin signaling pathway inhibitors in the downregulated gene clusters. Several novel gene groups were identified as well, including chemokine-related genes, which were upregulated early but downregulated later in the time course; solute carrier genes, which were both upregulated and downregulated; and muscle-related genes, which were primarily downregulated. © 2011 American Society for Bone and Mineral Research.

Introduction
B one responds in an anabolic manner to physiologic dynamic loading. For example, the midshaft humerus in the throwing arm of baseball pitchers and catchers showed enhanced bone mass, structure, estimated strength, and resistance to torsion compared with the nonthrowing control arm. (1) In contrast, bone mineral density (BMD) in astronauts decreased 1.0% and 1.5% in the spine and hip, respectively, per month of spaceflight. (2,3) Mechanical loading is a potent anabolic stimulus that substantially strengthens bones. Using the rat forelimb loading model, we showed that loading for only 3 minutes/day over 16 weeks doubled the energy necessary to break the ulna (4) and increased the fatigue life of the bone by 100-fold. (5) Mechanical loading uses pathways currently being investigated for new drug development, such as low density lipoprotein receptor-related protein 5 (LRP5) and sclerostin. (6) Others have proposed that the mechanical loading response is fundamental to bone biology, and many drugs work by modulating the responsiveness of bone to loading. (7) Hence, we believe that mechanical loading can be used as a paradigm for bone anabolism. This approach has the advantage of producing a local response, so the contralateral limb can be used as a control to compare with loading effects on the loaded limb.
signaling pathways and gene expression in loaded bone, (12,13) so we hypothesize that the gene expression patterns are timedependent, with early activities stimulating a chain reaction of events that directly affects bone cell behavior and ultimately leads to bone formation.
The primary goal of this study was to determine the time sequence for gene expression in a bone subjected to mechanical loading. We evaluated loading-induced gene expression over a time course of 4 hours to 32 days. We then used bioinformatics tools to cluster genes into similar expression patterns and group genes within common signaling pathways.
Mechanical loading A standard model for bone loading was employed in which the right forelimb was loaded axially for 3 minutes per day while the left forearm served as a nonloaded contralateral control. (4,14,15) Prior to loading, animals were anesthetized with 3.0% isoflurane administered at a flow rate of 1.5 L/min. Compressive load was applied as an oscillating Haversine waveform for 360 cycles at a frequency of 2 Hz using a Bose ElectroForce 3200 Series electromechanical actuator (EnduraTEC, Eden Prairie, MN, USA). The peak load achieved during loading was 13 N, which has been shown previously to be anabolic. (14) Rats were subjected to loading sessions every day with 24 hours between sessions. The study groups listed earlier are referenced to the number of days (or hours) after the first bout of bone loading was applied. At the appropriate time point, animals were anesthetized with isoflurane and euthanized by cervical dislocation.

Histology
Nine additional adult female Lewis rats were subjected to the loading protocol for histologic analysis. These rats were euthanized 1 and 4 days after beginning loading. The shafts of the right and left forearms with intact ulnae and radii were dissected, freed of excess muscle, and fixed in 10% neutral buffered formalin (NBF) for 48 hours. The fixed forearms were decalcified in a 70:30 solution of 10% ethylenediamine tetraacetic acid (EDTA) and 4% phosphate-buffered formalin (PBF) for 4 weeks. After decalcification, forearms were embedded in paraffin and sectioned at the ulnar midshaft at 4 mm. Sections were stained with hematoxylin and eosin (H&E) and used to identify active osteoblasts on the periosteal surfaces of loaded ulnae. Active osteoblasts were counted and defined as osteoblast cell bodies that were plump and present in the layer of cells immediately adjacent to newly formed osteoid on the bone surface. The sections were photographed on a Nikon Optiphot-2 microscope (Nikon, Inc., Melville, NY, USA) using 5Â and 40Â objectives and imported into Image-Pro Plus 6.3 (MediaCybernetics, Inc., Bethesda, MD, USA) analysis software for quantification. Total perimeter of the periosteal surface was measured, and average osteoblast number per total bone perimeter (mm, Ob.N/B.Pm) was reported.

RNA isolation
The shafts of the right and left ulnae were dissected, freed of all soft tissue, and snap frozen in liquid nitrogen. The ulnae were stored at -808C until RNA isolation. RNA was extracted using Trizol (Invitrogen, Carlsbad, CA, USA) and RNeasy Mini Kits (Qiagen, Inc., Valencia, CA, USA). Frozen ulnae were placed into a mortar containing liquid nitrogen and crushed with a pestle. The crushed bone was homogenized in Trizol, incubated, and centrifuged. The supernatant was removed, and RNeasy Mini Kits were used to isolate RNA. RNA was treated with a DNA-free kit (Ambion, Austin, TX, USA) to remove any residual DNA. RNA quality and quantity were determined using a spectrophotometer (NanoDrop, Wilmington, DE, USA). A paired t test was used to compare average total RNA quantity obtained from loaded and control bones for each time group. Average RNA quantity and standard errors were reported, and a p value < .05 was considered statistically significant.
Quantitative polymerase chain reaction (qPCR) Three matched RNA samples from loaded and control ulnae for each time group were used for quantitative real-time PCR (qPCR) experiments. RNA was reverse transcribed using the SuperScript III kit with oligo(dT) primers (Invitrogen). cDNA was diluted to a concentration of 2.5 ng/mL and used in qPCR reactions. A portion of the rat collagen type 1a1 (Col1a1) gene sequence was amplified using a Taqman gene expression assay (assay ID: Rn01463848_m1, Applied Biosystems, Inc., Carlsbad, CA, USA). Serial dilutions of a single sample were amplified to calculate relative expression levels, which then were standardized to bactin expression to facilitate comparison among samples. The reactions were performed on an ABI 7900HT Fast Real-Time PCR System (Applied Biosystems). A paired t test was used to compare Col1a1 expression in loaded and control conditions. Average fold change and standard errors were reported, and p < .05 was considered statistically significant.

Exon arrays
The array methodology is summarized in a flow diagram (Fig. 1). Quality-control measures were employed to ensure that highquality RNA would be hybridized to exon arrays. A high-quality RNA sample was defined as having a minimum 260:280 ratio of 2.00. Four RNA samples had a 260:280 ratio of slightly less than 2.00, and these samples were chosen to optimize the quantity of total RNA as well as the quality. The range of 260:280 ratios of all samples used was 1.96 to 2.31.
Five matched (control and loaded) ulna RNA samples from each time group were used for exon array analysis. One exception to this was the 12-day group, where only four matched samples were used. RNA from the control and loaded ulnae from 54 animals were analyzed on separate arrays. The exon array hybridizations were carried out using the facilities of the Center for Medical Genomics (CMG) at Indiana University School of Medicine. One microgram of each sample was labeled and hybridized using the Affymetrix WT protocol [GeneChip Whole Transcript (WT) Sense Target Labeling Assay Manual, Version 4, Affymetrix, Santa Clara, CA, USA]. All processing was done in balanced batches. The exon arrays were scanned using the GeneChip Scanner 3000 using the Affymetrix GeneChip Operating System (GCOS). Data were exported for analysis in the Partek Genomics Suite (Partek, Inc., St. Louis, MO, USA).
The robust multiarray average (RMA) algorithm (16)(17)(18) was used to import raw data from the core probe sets, which represented over 8000 genes. A two-way ANOVA model [variables were loading condition (loaded or control) and animal] was used to identify differentially expressed genes, which were defined as having p < .01 and fold change beyond AE1.4. Probe sets with a signal value of less than 3 were not reliable and were excluded from analysis. Raw data and analyzed data were MIAMEcompliant (19) and were deposited in the Gene Expression Omnibus database (series accession number GSE22286).

Clustering analysis
Differentially expressed genes were further analyzed with a kmeans (ie, partitioning) clustering algorithm. The inclusion criterion for a gene to qualify for clustering analysis was that the gene must be differentially expressed at a minimum of one of the 11 time points.

Functional characterization
Groups of genes then were defined based on gene and associated protein function using Ingenuity Pathways Analysis (IPA, Ingenuity Systems, Redwood City, CA, USA, www.ingenuity. com). IPA uses information about gene relationships from the literature to characterize gene sets, create gene networks, and identify important signaling pathways in gene expression data.

Results
Histology H&E-stained sections through the ulnar midshaft were used to count osteoblasts and look for evidence of bone formation (Fig. 2). There were no osteoblasts on the periosteal surface of control ulnae at either 1 or 4 days after loading. Some osteoblasts were present on the periosteal surface of a single loaded bone on 1 day, but no active osteoblasts were observed in any other loaded bones on 1 day. The average Ob.N/B.Pm was 2.22 AE 4.97 for the loaded bones 1 day after loading. The most remarkable results were in loaded bones in the 4-day group. Osteoid was observed on the periosteal surface of loaded bones, which indicated that new bone was being formed. In addition, active osteoblasts were present and the average Ob.N/B.Pm was 33.1 AE 6.6 for loaded bones at 4 days. Osteoclasts were not observed in any of the bone sections.

Total RNA
The total amount of RNA isolated from loaded bones was significantly greater than the total amount of RNA isolated from control bones for all time points except 2 days (Fig. 3). Total RNA increased substantially from 4 to 8 days in the loaded bones, and then the amount declined from 12 to 32 days.
Col1a1 expression qPCR was used to measure Col1a1, a major bone matrix gene, to characterize matrix synthesis. (20)(21)(22) Figure 4 shows that Col1a1 gene expression began to increase in loaded ulnae at 4 days, when active osteoblasts were present on the periosteal bone surface (Fig. 2). Col1a1 expression peaked at 12 days in loaded bones and declined toward baseline levels at later time points. Col1a1 expression was not changed in control ulnae. Since Col1a1 expression corresponded with the observation of active osteoblasts, we presume that osteoblasts were the primary contributors to the qPCR signal for Col1a1. Furthermore, the Col1a1 expression time course was very similar to the time course of osteoblast recruitment and bone formation observed by others. (8)(9)(10) Clustering analysis The primary goal of the clustering analysis was to determine the patterns of gene expression in bones subjected to mechanical loading over a time sequence of 4 hours to 32 days. We identified 1051 genes that were differentially expressed at a minimum of one of the 11 time points. This gene list was analyzed using k-means clustering. Six distinct patterns of differential gene expression were identified over the time course (Fig. 5): an earlyresponse cluster (blue line) in which the genes were upregulated early but not late in the time course, three matrix-formation (up) clusters (red lines) that followed the pattern of matrix synthesis illustrated by expression of the Col1a1 gene (Fig. 4), and two matrix-formation (down) clusters (green lines) that were downregulated during matrix formation. There were 88 genes in the early-response cluster, and genes in this cluster were upregulated early on, primarily at 4 hours. There were 23 genes in the matrix-formation (up) high-magnitude cluster, 182 genes in the matrix-formation (up) medium-magnitude cluster, and 308 genes in the matrix-formation (up) low-magnitude cluster. Genes in these three clusters were upregulated during matrix formation, and expression reached peak levels between 12 and 16 days. There were 124 genes in the matrix-formation (down) high-magnitude cluster and 326 genes in the matrix-formation (down) low-magnitude cluster. Genes in these two clusters were downregulated during matrix formation, and differential expression was greatest at 16 days. Within each cluster of genes, we identified groups of genes that have common functions or are part of common pathways using IPA. The complete list of 1051 genes is available in Supplemental Table S1.
Early-response cluster Selected gene groups from the early-response cluster are listed in Table 1 and included AP-1, calcium signaling, chemokine, cytokine, and matrix. Of these, the chemokine group that was upregulated within 12 hours of loading was novel for bone formation. Genes in the chemokine group included a binding

Matrix-formation (up) clusters
Expression of genes in the matrix-formation (up) clusters peaked during the synthetic phase of bone formation. Several important gene groups were identified and include apoptosis, calcium signaling, cytokine, growth factor, ion channel, matrix, muscle, neurotransmitter, solute carrier, transforming growth factor b (TGF-b) signaling, and Wnt/b-catenin signaling ( Table 2). The matrix group included many of the expected genes associated with bone formation, such as Alpl, Bglap, and Col1a2. Probes targeting the Col1a1 gene were not detected on the exon array and therefore Col1a1 is not reported in Table 2, but using qPCR, we found that its expression followed a similar pattern (Fig. 4). The solute carrier group represents a novel finding with respect to bone mechanotransduction. This group includes transporters for amino acids and many ions. Presumably, such transport is necessary to facilitate the synthetic activity of osteoblasts.

Matrix-formation (down) clusters
Genes in the matrix-formation (down) clusters were downregulated during the synthetic phase of bone formation. Several important gene groups were identified in these clusters and include calcium signaling, cell cycle, chemokine, cytokine, growth factor, ion channel, matrix, neurotransmitter, TGF-b signaling, ubiquitin, and inhibitors of Wnt/b-catenin signaling ( Table 3). In particular, there were large groups of muscle and solute carrier genes that were downregulated. Another important finding in this cluster was that genes encoding inhibitors of both TGF-b (Grem1 and Chrdl1) and Wnt/b-catenin signaling (Sost and Sfrp4) were downregulated, supporting observations that negative regulation of inhibitors was important to enhance signaling through these pathways. (21)

Discussion
We have identified six distinct patterns of gene expression, and these were categorized into three primary clusters of genes: Genes in the early-response cluster (n ¼ 88) were upregulated primarily at 4 hours. Expression of genes in the matrix-formation (up) clusters peaked between 12 and 16 days, and this cluster was subdivided into three magnitudes: high (n ¼ 23), medium (n ¼ 182), and low (n ¼ 308). Expression of genes in the matrix-formation (down) clusters peaked at 16 days, and this cluster was subdivided into two magnitudes: high (n ¼ 124) and low (n ¼ 326). Annexin A2 S100a4 S100 calcium-binding protein A4 S100a10 S100 calcium-binding protein A10 Chemokine Ccbp2 Chemokine-binding protein 2 Ccl2 Chemokine (C-C motif) ligand 2 Ccl7 Chemokine (C-C motif) ligand 7 Cxcl1 Chemokine (C-X-C motif) ligand 1 Cxcl13 Chemokine (C-X-C motif) ligand 13 Cytokine Il1rl1 Interleukin 1 receptor-like 1 Il1rl2 Interleukin 1 receptor-like 2 Osm Oncostatin M Osmr Oncostatin M receptor Socs3 Suppressor of cytokine signaling 3 Stat3 Signal transducer and activator of transcription 3 Tnfrsf12a Tumor necrosis factor receptor superfamily, member 12A Matrix Adamts1 ADAM metallopeptidase with thrombospondin type 1 motif Ecm1 Extracellular matrix protein 1 Serpina3n Serine (or cysteine) peptidase inhibitor, clade A, member 3N Serpine1 Serpin peptidase inhibitor, clade E, member 1 Tfpi2 Tissue factor pathway inhibitor 2 Other Ccnd2 Cyclin d2 Clic1 Chloride intracellular channel 1 Gpr1 G protein-coupled receptor 1 Kcne4 Potassium voltage-gated channel, Isk-related subfamily, gene 4 Lep Leptin Sdc4 Syndecan 4  early-response, matrix-formation (up), and matrix-formation (down). Genes within these clusters were grouped based on function and/or signaling pathways using IPA. Many gene groups known to be important in loading-induced bone formation were identified, as were several novel gene groups whose function with respect to bone formation is not known.

Early-response cluster
The early-response cluster was characterized primarily by genes that showed increased expression 4 hours after a single loading session. It is known that gene expression of components in the AP-1 transcription factor complex increases with mechanical loading. Fosl1 and Junb genes were both upregulated shortly after loading in our study and those of others, (23,24) and Fosb gene expression increased after loading and may be associated with osteoblast differentiation and bone formation. (25) Others have observed increased protein expression of FOS and JUN family members after loading in association with osteoblast proliferation. (26) JUND is one of the primary AP-1 signals when osteoblasts are fully differentiated during matrix production and mineralization, and JUND inhibits the S phase of the cell cycle. (26) Our results are consistent with these findings because JunD gene expression increased during the matrix-formation phase, which corresponded to the observation of differentiated osteoblasts on the bone surface. A group of chemokines was identified in the early-response cluster and presents a potentially novel finding. Chemokines act as chemotactic molecules for a number of different cell types. Chemokines are known to be important in many biologic functions, including development, immune function, and wound repair. (27) Many cytokines and chemokines that function in the immune system regulate osteoblasts and osteoclasts, (28) and osteoblasts are known to produce both chemokines and their receptors. (29) The chemokine-related genes identified in our study included a binding protein (Ccbp2), C-C motif ligands (Ccl2 and Ccl7), and C-X-C motif ligands (Cxcl1 and Cxcl13). CXCL13 upregulates alkaline phosphatase activity and also induces release of b-N-acetylhexosaminidase, which is involved in bone remodeling. (30) There also was a group of chemokine-related genes that were downregulated during matrix formation, which included a C-C motif ligand (Ccl11), C-C motif receptors (Ccr1, Ccr2, and Ccr3), a C-X-C motif ligand (Cxcl12), and a C-X-C motif receptor (Cxcr5). Ligands that bind CCR1 are chemoattractants that promote recruitment of marrow cells that can develop into osteoclasts. (31) CCR2 is expressed on osteoclasts, (32) and CCR2 knockout mice have high bone mass, impaired osteoclast function, and are resistant to ovariectomy-induced bone loss. (33) The receptor for CXCL13, CXCR5, is expressed in osteoblasts. (30) We postulate that chemokines in the early-response cluster may be active shortly after a mechanical loading event to recruit osteoblasts and/or osteoblast precursors to a site of bone formation. However, later in the synthetic phase, additional osteoblasts are not necessary, so chemokines may be downregulated. It is also possible that differential expression of chemokines is related to the interaction of the bone cells and immune cells. CXCL12 plays a role in fetal bone marrow colonization, (34,35) and interaction of CXCL12 and its receptor, CXCR4, is necessary for retention of hematopoietic stem cells (HSCs) in adult bone marrow. (36,37) CXCL12/CXCR4 signaling modulates bone resorption, (38) osteoblast proliferation, (39) and priming of hematopoietic progenitor cells. (40,41) In addition, CXCL13/CXCR5 signaling is known to affect several aspects of Bcell functionality. (42,43) A group of cytokines was present in the early-response cluster as well. In bone, cytokines are usually associated with osteoclasts and increased bone resorption. However, osteoclasts were not observed in any of the histologic sections we analyzed. Oncostatin M (OSM) is a cytokine that was shown recently to inhibit Sost gene expression in primary calvarial osteoblasts, and OSM treatment of calvariae in vivo enhanced bone formation. (44) In our study, Osm expression was highest at 4 hours and could have contributed to decreased Sost expression during the synthetic phase.

Matrix-formation (up) and matrix-formation (down) clusters
Most genes exhibited differential expression during the synthetic phase of bone formation and belonged to the matrix-formation (up) and matrix-formation (down) clusters. The matrix-formation (up) clusters were comprised of three groups that exhibited different degrees of upregulation in response to loading, and the matrix-formation (down) clusters were comprised of two groups that were downregulated in response to loading. Matrix genes dominated the upregulated clusters, whereas muscle-related genes dominated the downregulated clusters. Several gene groups were expressed in both up-and downregulated clusters, including solute carriers, Wnt/b-catenin signaling, and TGF-b signaling.
The largest group of upregulated genes identified in the synthetic phase was associated with extracellular matrix and had 31 members, including proteoglycans, collagens, genes related to collagen synthesis, osteoblast markers, matrix-related proteins, and matrix metallopeptidases (MMPs). These groups of genes are well known to be upregulated with mechanical loading and bone formation. Probes targeting the Col1a1 gene were not detected on the exon array platform, so Col1a1 expression was measured using qPCR. Col1a1 expression in loaded ulnae followed a similar pattern as the genes in the matrix-formation (up) clusters. A few matrix genes were part of the matrix-formation (up) high-magnitude cluster, which had 23 total members and reached peak upregulation of approximately 4-fold at 12 days. Lox is important for collagen cross-linking, and Cthrc1 and Lepre1 are associated with collagen synthesis. In addition, this cluster included two proteoglycans, Acan and Vcan; a metallopeptidase inhibitor, Timp1; and two minor collagens, Col5a1 and Col11a2.
The second most abundant gene group in the matrixformation (up) clusters was comprised of solute carriers, which have not been previously associated with mechanical loading. Twenty solute carriers were upregulated by loading, and examples of the types of molecules transported include amino acids, glucose, and various ions. In addition, 15 solute carriers were downregulated during matrix formation. Solute carriers may be involved in supporting matrix synthesis by adjusting ion L Chemokine (C-C motif) receptor 1 Ccr2 L Chemokine (C-C motif) receptor 2 Ccr3 L Chemokine (C-C motif) receptor 3 Cxcl12 L Chemokine (C-X-C motif) ligand 12 Cxcl14 H Chemokine (C-X-C motif) ligand 14 Cxcr5 L Chemokine (C-X-C motif) receptor 5 L Radical S-adenosyl methionine domain containing 2 levels and bringing amino acids and other necessary ingredients into the osteoblasts to facilitate matrix protein production. In addition to gene groups, we identified signaling pathways important in loading-induced bone formation. The Wnt/bcatenin signaling pathway is known to play an integral role in mechanotransduction and enhances the sensitivity of osteoblasts and osteocytes to loading. (45) The matrix-formation (up) clusters included several genes from the canonical Wnt pathway and also included two inhibitors, Kremen1 and Wif1. Additionally, two Wnt inhibitors, Sost and Sfrp4, were downregulated during matrix formation. It was shown recently that Sost gene expression decreased after in vivo bone loading and increased with hind limb unloading. (15) Thus, decreased expression of Sost is associated with increased Wnt signaling and bone formation. SFRP4 has not been as well studied. The Dkk1 gene encodes another Wnt antagonist that has been shown previously to decrease with loading, (15) but probes targeting the Dkk1 gene were not detected on the exon array platform used in our study. We also observed increased Osm expression at early time points, and this has been shown to contribute to decreased Sost expression in bone. (34) LRP5 is a Wnt coreceptor, and Wnt signaling through LRP5 is required for mechanically induced bone formation. (45) In addition, it has been suggested that LRP5 may inhibit gut-derived serotonin synthesis, providing an endocrine regulatory mechanism to increase bone mass. (46) Muscle genes were the largest group of downregulated genes during matrix formation. This group was comprised mainly of genes encoding muscle structural components and included members of the actin, myosin, and troponin gene families. It is important to note that all muscle was stripped from the ulnae during dissection, so RNA was isolated from bone and marrow only with no contamination from muscle. Therefore, these genes were expressed in cells within the bone. Others have identified muscle genes that were differentially expressed between osteoblasts and osteocytes. (47) Interestingly, 36 muscle-related genes were downregulated in osteoblasts with respect to osteocytes, including many of the same genes that were downregulated with loading in our study (Acta1, Dmd, Myocd, Myl1, Myplf, Tnni2, Tnnt3, and Tpm2). (47) We speculate that our finding that muscle-related genes are downregulated during bone formation is congruent with the finding that musclerelated genes are downregulated in osteoblasts compared with osteocytes. Although the involvement of muscle-related genes and proteins in bone biology is not well understood, it is clear that they are highly regulated in bone cells.
The TGF-b pathway is important in loading-induced bone formation, (48,49) and many genes in the TGF-b superfamily were upregulated during matrix formation. Two TGF-b superfamily inhibitor genes (Chrdl1 and Grem1) were downregulated. Overall, it seems that signaling through the TGF-b pathway was enhanced mostly by increasing expression of TGF-b inducers and decreasing expression of TGF-b inhibitors of bone formation.
A limitation of our approach is that because we isolated RNA from the entire ulnar shaft, we cannot discern regional changes in bone formation in response to loading. It is well documented that mechanical loading elicits a regional bone-formation response in the ulna loading model. (4,14,50) Most of the bone forms in the ulna midshaft on the periosteal surface, and greater bone formation is observed on the medial surface than on the lateral surface. (14) In addition, since bone is deposited primarily on the medial and lateral periosteal surfaces in the ulnar midshaft, the geometric properties I max and I min change with loading and are location-dependent. I min increased significantly in the ulnar midshaft, whereas I max was moderately increased with loading distally. (4) In addition, we have completed a computational model that was programmed to form bone in regions that had greatest strain energy density. (50) This computational model almost perfectly mimicked the locations of new bone formation measured in the ulna, thus showing that there is a predictable pattern of bone formation in response to loading.
In conclusion, we determined the time sequence of gene expression in a bone subjected to mechanical loading, identified groups of genes that shared a time-dependent gene expression pattern, and determined which functions or pathways the genes had in common. We identified six clusters of genes that exhibited unique expression patterns in response to loading over a time course that captured key periods of the bone-formation process, including expression of matrix-related genes, the appearance of active osteoblasts, and bone desensitization. Many gene groups known to be important in loading-induced bone formation were identified within the clusters, including matrix, Wnt/b-catenin, and TGF-b. Several novel gene groups were identified as well, including chemokine, solute carrier, and muscle, whose functions with respect to bone formation are not known.

Disclosures
All the authors state that they have no conflicts of interest.


Temporal polyethism, life expectancy, and entropy of workers of the ant Ectatomma vizottoi Almeida, 1987 (Formicidae: Ectatomminae)

We investigated the changes in the behavioral repertoire over the course of life and determined the life expectancy and entropy of workers of the ant Ectatomma vizottoi. Newly emerged ants were individually marked with model airplane paint for observation of behaviors and determination of the age and life expectancy. Ants were divided into two groups: young and old workers. The 36 behaviors observed were divided into eight categories. Workers exhibit a clear division of tasks throughout their lives, with young workers performing more tasks inside the colony and old workers, outside, unlike species that have small colonies. This species also exhibits an intermediate life expectancy compared to workers of other species that are also intermediary in size. This supports the hypothesis of a relationship between size and maximum life expectancy, but it also suggests that other factors may also be acting in concert. Entropy value shows a high mortality rate during the first life intervals.

Introduction
The reproductive success of social insects is largely attributed to the division of labor between reproductive and sterile individuals (Gronenberg 1996). This has been an important subject to understand the evolution of social behavior (Oster and Wilson 1978;Bourke and Franks 1995). Division of labor may or may not be associated with morphology and consequently to worker size, known as caste polyethism, in which larger workers forage while smaller workers perform within-nest tasks (Oster and Wilson 1978). When this is not the case, division of labor may be associated with a temporary caste, where groups of individuals are specialized in particular tasks based on age for a given period of time, termed age polyethism (Wilson 1971;Fresneau et al. 1982;Fresneau 1984;Oster and Wilson 1978).
Age polyethism is currently referred to, more neutrally, temporal polyethism, which describes the process of task allocation over time (Bourke and Franks 1995). This change of emphasis occurred because most of the existing data on age polyethism only show a correlation between age and task (e.g., Calabi and Rosengaus 1988, Corbara et al. 1989, Dejean and Lachaud 1991. Temporal polyethism is very common among social insects, particularly among ants (Wilson 1971;Calabi 1989), with younger workers performing within-nest tasks and older workers foraging for the colony (Caetano et al. 2002). According to MacDonald and Topoff (1988), temporal polyethism is usually associated with physiological changes, when the period of developing ovaries corresponds with the age when workers care for the brood and queen. Reabsorption of ovaries occurs in the next stage, and the development of the poison gland corresponds with a period when workers perform activities associated with defense and foraging.
The level of temporal polyethism varies widely among social insects and appears to be correlated with colony size (Wilson 1971;Jeanne 1999;Thomas and Elgar 2003). Inter-specific comparative studies suggest that task specialization and polyethism are influenced by colony size (Bourke 1999, Anderson andMcShea 2001). Species with larger colonies typically have greater spatial organization of work, caste polymorphism, and shorter queuing delays in task partitioning (Wilson 1971). In colonies of ants, bees, wasps, and termites composed of thousands to millions of workers, tasks in the colony are divided among highly specialized workers (Möglich and Hölldobler 1974;Hölldobler and Wilson 1990;Crosland et al. 1997). In contrast, in small colonies with less than one hundred workers, individuals tend to be more generalists and perform multiple types of daily tasks, such as in Bombus bees studied by Cameron and Robinson (1990), Polistes wasps by Post et al. (1988), and small ant colonies of Rhytidoponera metallica by Thomas and Elgar (2003). Thus, variations in temporal polyethism may reflect different levels of social organization of insects based on their phylogenetic profile, with highly eusocial species tending to be ergonomically more specialized compared to primitive species. (Hölldobler and Wilson 1990;Jaisson et al. 1992).
Division of labor may influence the life expectancy of workers of social insects. Antonialli-Junior et al. (2007) reported that the life expectancy of E. planidens workers gradually decreases after the onset of foraging. However, few studies have examined the life expectancy of workers. In ants, little information is available on queens (Hölldobler and Wilson 1990). Studies on younger workers that perform intranidal tasks and older workers that perform extranidal activities have investigated the relationship between age and behavior: Cammaerts-Tricot and Verhaeghe (1974) correlate age and the distance ants follow a of poison gland extract; Cammaerts-Tricot (1975) describes the effect of age on the response to an ant of another species; Higashi (1974) describes mandibular wear in intranidal vs. extranidal nest construction; Rosengren (1977) compares behavior of younger and older foragers; Weir (1958a) compares nest construction activities in older and younger workers. Gordon (1984), Wilson and Brown (1984) and Miguel and Del-Claro (2005) examined whether younger ants perform intranidal tasks while older workers, outside the nest.
Given the importance of division of labor for the understanding of the level of social organization of insects, this study investigated whether temporal polyethism occurs in E. vizottoi workers, and its relationship with life expectancy and entropy under laboratory conditions.

Materials and methods
Two E. vizottoi colonies were collected at the campus of the State University of Mato Grosso do Sul/UEMS, in Dourados-MS (22º13′16′′; 54º48′20′′). The underground nests were excavated according to Antonialli-Junior and Giannotti (2001), transferred to artificial nests (Antonialli-Junior and Giannotti 2002;2003), and maintained at a constant temperature of 26°C and natural light cycle.
Temporal polyethism Wilson (1971) and Miguel and Del-Claro (2005) observed that newly emerged workers can be identified by their pale coloration compared to older workers. Thus, each newly emerged ant was individually marked on the thorax with model airplane paint (Nakata 1996), using five different colors in paired combinations, objective of which is to monitor the age of the ant. This procedure allowed the observation of behaviors ants before and after foraging during their life, resulting into two age groups: young workers, defined as those that do not forage, and old workers, which perform this task (Hölldobler and Wilson 1990).
Thirty-five workers were used in the study. To establish the behavioral repertoire of the two classes of workers 93 h of observation were conducted following the scanning method described by Altmann (1974), totaling 10,131 records. The behavioral repertoire of these two groups was analyzed with Cluster grouping by Morisita-Horn tests (multivariate analysis), proper to show percentage data (Wolda 1981).

Determination of life expectancy and entropy
The relative frequency that each one of the two groups (young and old workers) performed each behavior of the eight categories was calculated by the expression: F r =F i /n, where F i =absolute frequency of each behavior and, n=total number of observations of all behaviors.
A classic method of life table to calculate the life expectancy of insects has been developed by Krebs (1994) and Silveira Neto et al. (1976). However, some authors including Fernandes et al. (2003), Tofolo and Giannotti (2005), Krüger and Erthal (2006) and  used an equivalent method developed by Carey (1993).
For the construction of the life table, 102 individually marked workers as previously described were used, and the parameter described by Carey (1993) was estimated by: where T x =total number of days at age x of survivors until the last possible day of life; x=age interval of 1 day; w= maximum age in days reached by the last surviving individual; L x =average of probability of survivors between successive ages, calculated as L x =l x −(d x )/2; l x =proportion of survivors among newly emerged workers in relation to age interval; d x =proportion of individuals that died between age x and x+1, calculated as d x =l x -l x+1 ; The remaining life expectancy (e x ) of individuals at the beginning of each age interval was calculated as e x =T x /l x . The entropy (H), a measurement of the heterogeneity in the survival pattern defined as the ratio between the number of days lost by the population due to deaths and the number of days that workers lived was calculated by the equation: where e x =life expectancy at age x; d x =proportion of individuals that died between the ages x and x+1; e 0 =life expectancy at the initial age, and w=maximum age reached by the last surviving individual.

Temporal polyethism
The average age of the onset of activities outside the nest by workers was 95.774±35.674 days. Thirty-five behaviors were performed by young workers and 32, by old workers. The method of Cluster grouping pointed differences between the repertoires of the two groups: old and young workers (Fig. 1).
Over the course of their lives, workers performed activities inside and outside the nest that were grouped into eight behavioral categories: body grooming, brood care, feeding, nest defense, colony maintenance, communication, immobility, and other behaviors ( Fig. 2 and Table 1). Young workers spent more time tending the brood, followed by body grooming and feeding. Old workers spent more time in activities related to colony maintenance and defense, and immobility ( Fig. 2 and Table 1): young workers spent 26% of the time immobile, while old workers 40%.

Life expectancy
The average life expectancy of workers was 212.00± 122.253 days, and the maximum life expectancy was 423 days. The life expectancy in the first life interval was e x =138.89, reaching 244.12 at the 31st day of life, followed by relative stabilization until the 53rd day and then a gradual decrease (Fig. 3). A high mortality rate (46.6%) occurred during the first 53 days of life, and only 36.3% of the individuals survived after reaching the average life expectancy.

Entropy
The entropy found for E. vizottoi workers was H=0.920 (Fig. 4), indicating a high mortality rate during the first life intervals. The survival curve remained between 0.5 and 1.0 until the last life interval (Fig. 4). Because of the high number of deaths during the first 53 days of life, the survival curve for this species has a convex aspect, stabilizing between 54 and 151 days of life, without deaths. After this age, there is a gradual increase in deaths (Fig. 4).

Discussion
The average age when workers begin to perform activities outside the nest was similar to that observed in E. edentatum workers (Antonialli-Junior and Giannotti Fig. 1 Dendrogram similarity (Morisita-Horn) among the two workers groups of Ectatomma vizottoi. Young workers 1 young workers colony 1, Young workers 2 young workers colony 2, Old workers 1 old workers colony 1, Old workers 2 old workers colony 2 2003), which start foraging with an average age of 97 days. In contrast, E. tuberculatum workers (Champalbert and Lachaud 1990) begin performing activities outside the nest with less than 45 days of age. Similarly, the ponerine Ponera pennsylvannica begins to perform these activities between one and five days of age, although older workers are more frequently observed ). In P. caffraria colonies, 6 days after emergence, some newly emerged workers perform within-nest tasks, while others forage outside (Agbogba 1994). However, this is probably influenced by colony size, since in small colonies, younger workers tend to forage at an earlier age (Hölldobler and Wilson 1990).
According to Gordon et al. (2005), the transition from inside to outside work is extremely variable and it seems to be typical of ants that exhibit temporal polyethism. Hölldobler and Wilson (1990) reviewed studies on temporal castes and only 16 species exhibited "typical age polyethism," characterized by younger workers tending the brood and queen, and older workers transitioning to nest work, followed by foraging (Hölldobler and Wilson 1990). Other studies reported that younger ants perform tasks inside and older ones work outside the nest (McDonald and Topoff 1985;Wilson and Hölldobler 1986). Gordon et al. (2005), studying the red harvester ant Pogonomyrmex barbatus (Myrmicinae), reported the sequence of tasks performed by marked individuals in three laboratory colonies and observed that ants often move from inside to outside tasks. However, there was a wide variation in this sequence.
The social organization of highly eusocial species of bees and wasps share several convergent characteristics, including prominent temporal polyethism, and division and specialization of tasks (O'Donnell and Jeanne, 1993). Workers of Polybia occidentalis, a species considered highly eusocial among Vespidae, perform more specialized tasks than workers of "primitively eusocial wasps" (Jeanne 1991).
In addition, age-correlated division of labor may be influenced by the number of individuals in the colony. In large colonies, age-correlated division of labor may be more specialized, whereas in smaller colonies, the behavioral repertoire of individuals appears to be more flexible, such as in the ant R. metallica (Thomas and Elgar 2003).
In fact, Thomas and Elgar (2003) found differences in task specialization between large colonies (>450 workers), and small colonies (<80 workers). These authors also observed temporal polyethism in large but not small colonies. In large colonies, old workers spent significantly more time foraging than young workers, while young Fig. 2 Relative frequency of the eight categories of behaviors of Ectatomma vizottoi workers workers spent more time caring for brood. In small colonies, young and old workers spent similar amounts of time foraging and tending the brood. In the present study, the colonies of E. vizottoi were small with approximately 60 to 90 workers (Vieira et al. 2007). However, the tasks performed by young workers significantly differ from those performed by older workers, although with similarities between the two groups. Ant colonies of primitive lineages, such as Amblyoponinae and poneromorph ants, vary in size from dozens (see A. pallipes Traniello 1978; Dinoponera quadriceps Dantas-de-Araújo and  to hundreds of individuals (see E. tuberculatum Hora et al. 2005; Paraponera clavata Breed and Harrison 1988). Thus, prominent polyethism would not be expected in primitive groups, especially in smaller colonies, since workers may start foraging earlier, due to the needs of the colony.
Possible reasons for the onset of foraging might be the need of resources or simply an intrinsic characteristic of the species. This is probably influenced by colony size, since in small colonies, younger workers tend to forage at an earlier age. Old workers from large colonies of R. metallica spent significantly longer periods of time foraging than individuals from small colonies, who typically have shorter foraging bouts (Thomas 2002). However, larger colonies may require more food to sustain their larvae and adults (Thomas and Elgar 2003).
The activities associated with brood care are also primarily performed by young workers, as also observed in E. planidens colonies (Antonialli-Junior and Giannotti 2003). Champalbert and Lachaud (1990) studying E. tuberculatum, concluded that behavioral specializations progressively appear with age, and there is a sensitive period when social stimulus affects the establishment of behaviors associated with brood care. The fact that workers at this age spend more time caring for the brood explains the higher frequency of cannibalism of immature individuals, as they have more contact with eggs, larvae, and pupae and are more likely to detect any type of physical or physiological problems during these different immature stages (Wilson 1971;Antonialli-Junior and Giannotti 2003). Also, Tofolo and Giannotti (2005), studying E. brunneum, hypothesized that in the laboratory, due to space limits, workers regulate the size of the colony with cannibalism. According to Gordon et al. (2005), temporal polyethism and/or the progression from one task to another is the product of interacting factors, such as spatial distribution. For other authors, this is a consequence of physiological changes that occur as individuals age (Macdonald and Topoff 1988, Billen 1982, Fénéron et al. 1996, Robinson et al. 1994. Fénéron et al. (1996) found a relationship between behavior and physiological maturation in E. tuberculatum workers, where the behavioral ontogeny from brood care to foraging activity appears to be closely related to glandular changes. Another factor is the current needs of the colony and the environment. When workers are removed from the colony, other workers change tasks (McDonald and Topoff 1985). Also, some individuals are consistently more active throughout their lives, than others, and these more active individuals are more likely to perform tasks outside the nest (e.g., Retana and Cerdá 1991).
The average life expectancy of E. vizottoi workers was longer than the 188.23 days reported for E. planidens workers , although E. brunneum workers (Tofolo and Giannotti 2005) have an average life expectancy of 1.6 years. According to Hölldobler and Wilson (1990), variations in life expectancy might reflect differences in body size, which would explain the differences described above, as E. planidens is smaller than E. vizottoi, than in turn is similar in size to E. brunneum. In contrast, Pachycondyla striatula workers (Melo 2008) are larger than those of Ectatomma, but have shorter life expectancy. However, life expectancy seems to be determined by the sum of several factors.
In this study we observed that when workers begin to forage, life expectancy gradually decreases as described by Antonialli-Junior et al. (2007) for E. planidens workers. These authors attribute the decrease in life expectancy to greater exposure to certain factors such as greater energy or exposure to sunlight or ultraviolet rays. According to Gordon et al. (2005), foraging activity is low under laboratory conditions, and studies on life expectancy described a greater average longevity for the workers of E. planidens Giannotti 2003, Antonialli-Junior et al. 2007) .The entropy value was H= 0.920, between H=0.5 and H=1.0, which indicates that many workers die in the first intervals of life. This high mortality rate during the first days of life might be associated with the stress caused by laboratory conditions, as suggested by Antonialli-Junior et al. (2007), or the paint used to mark newly emerged ants. According to Carey (1993), low entropy (H close to 0) indicates that individuals tend to die later, while entropy closer to 1 indicates a high mortality of young individuals. The entropy found for E. vizottoi explains the high mortality during the first 53 days. This same pattern of entropy was observed in E. planidens workers ). In contrast, E. brunneum workers die on average near the 70th day (Tofolo and Giannotti 2005), which is a consequence of longer life expectancy. Although a pattern is likely to exist, individuals live longer under laboratory conditions than in the field, as a result of lower risk and energy expenditures (Hölldobler and Wilson 1990).
Our findings demonstrate that E. vizottoi workers exhibit a clear division of tasks throughout their lives, with young workers performing more tasks inside the colony and old workers, outside, although some tasks are performed by both groups. In spite of the number of deaths during the first intervals of life, which could induce foraging activity, workers of this species begin their activities outside the nest at an age similar to that of other species already studied. This species also exhibits an intermediary life expectancy compared to those of workers of other species that are also intermediary in size. This supports the hypothesis of a relationship between size and maximum life expectancy.