1 Department of Radiation and Cellular Oncology, University of Chicago, Chicago,
IL 60637, USA
2 Section of Neurosurgery, University of Chicago, Chicago, IL 60637, USA
3 Department of Neurosurgery, University of California San Francisco, San
Francisco, CA 94143, USA
* Author for correspondence (e-mail: rrw{at}rover.uchicago.edu)
Accepted 19 November 2002
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Summary |
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Key words: Angiogenesis, Expressional profiling, Intercellular communication and activation, Autocrine loops
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Introduction |
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Tumour cells are proposed to mediate tumour angiogenesis by secreting
soluble factors that enhance endothelial cell proliferation, migration and
tube formation as well as by direct cellular interactions with endothelial
cells. The interactions between tumour cells and endothelial cells are
complex, might be analogous to those observed during embryonic development and
have been hypothesized to be important in the process of tumour angiogenesis
(Darland and D'Amore,
1999).
In order to define intercellular interactions in detail, we modelled tumour angiogenesis by developing an in vitro co-culture system consisting of a human glioma cell line (U87MG) and human umbilical vein endothelial cells (HUVECs). Using this system, we investigated the following hypotheses.
In this report, we demonstrate that U87 cells promote phenotypic changes in HUVECs that include growth stimulation, activation of migration and morphogenetic changes including formation of net-like structures resembling neo-vasculature. We also detected expressional changes that parallel phenotypic changes of endothelial cells and show that these alterations are induced by U87-mediated soluble factors that might activate the formation of autocrine loops in the endothelial compartment. We were able to define several groups of genes that are consistent with observed phenotypic changes.
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Materials and Methods |
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Net-like formation assay
Six-well Transwell cell culture chamber (Costar) inserts with 0.4 µm
pores were used with HUVECs plated in the inserts and GFP-U87 in the chambers
(in duplicate) at a 1:10 HUVEC:GFP-U87 ratio. After 48 hours, the insert was
removed and stained with the PROTOCOL Hema 3® stain set (Biochemical
Sciences, Swedesboro, NJ) and photographed at 4x magnification.
Completely enclosed spaces were counted to quantify net-like structures.
Angiostatin and endostatin (100 ng ml1) and herbimycin A (1
µg ml1) were purchased from Calbiochem (La Jolla, CA).
Anti-VEGF antibody (100 ng ml1) was purchased from R&D
Systems (Minneapolis, MN).
Migration assay
24-well Transwell inserts with a 5 µm pore size were coated with a thin
layer of collagen (Type I, rat tail, BD Biosciences, Bedford, MA). HUVECs were
cultured in EGM-2 with 10% of the usual growth factors and serum + 0.1% bovine
serum albumin for 18 hours before switching to media with no growth factors or
serum for 4 hours before the start of the experiment. GFP-U87 cells or HUVECs
were washed twice with EBM-2 alone, trypsinized and plated into the lower
chambers at a ratio of one cell (in insert) to ten cells (in chamber cell
number). HUVECs subject to serum starvation were plated on inserts and
cultured for 18 hours before staining the membranes as described above. Cells
were photographed at 10x magnification, and four fields each from
duplicate samples were counted to quantify migration.
Transfer of activated HUVECs and induction of cytokines expression by
tumour conditioned medium
TransWell® chambers with 0.4 µm pores were loaded with GFP-U87 cells
or HUVECs in the inserts and HUVECs or EBM-2 only in the wells (see
Fig. 6A). After 24 hours in
culture, the inserts were removed and HUVECs in the wells were washed four
times with EBM-2. Then, inserts that contained HUVECs cultured with only EBM-2
in the wells (acceptor HUVECs) were transferred to the wells with HUVECs
cultured with U87 cells (activated HUVECs), HUVECs cultured with HUVECs (naive
HUVECs) and HUVECs cultured with medium only (negative control). After 24
hours, cells in the inserts were stained as for the net-like formation assay
and structures were counted and graphed as percent of negative control.
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cDNA array data analysis
HUVECs were grown in flasks to 80% confluence in complete medium (EGM-2)
and switched to EBM-2 at the time GFP-U87 cells were added. After
trypsinization, HUVECs were separated from GFP-U87 cells using magnetic beads
coated with CD31 (Dynal ASA, Oslo, Norway). Preparation of samples and
hybridization with cDNA arrays was done as previously described
(Khodarev et al., 1999;
Khodarev et al., 2002
). We
used ImageQuant® software (Amersham Biosciences, Piscataway, NJ) for data
acquisition with `peak volume' and local average background options as
previously described (Khodarev et al.,
1999
). Primary data were exported to Microsoft® Excel®
spreadsheets and intensities were assigned to each spot loaded on the cDNA
array. Subsequent analysis included data transformation, normalization,
filtration and clustering. All negative values produced by ImageQuant®
were transformed to zeroes with the aid of `AutoFilter' function in Excel. The
data were normalized across all compared arrays based on the average (mean)
values of signal intensities. This approach is known as `normalization by
global means' (Freeman et. al.,
2000
). Average intensity was calculated as
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The data were filtered in two steps. First, the data were filtered by
intensities using the receiver operating characteristic (ROC) method, which
allows the estimation and control of the levels of false-positive and
false-negative data based on the levels of signal detection
(Pepe, 2000). To the analysis
previously described, we applied cut-off levels of signal detection equal to
10% of average intensity Iav. Second, genes were selected
based on the ratios of response in co-culture related to monoculture. For the
present experiments, we used a twofold change as the cut-off level for ratios.
As an alternative approach, when genes were not expressed in monoculture but
were expressed in co-culture, we used subtraction of intensities in
monoculture from the same intensities in co-culture. Subtracted differences
were evaluated by Student's t test and statistically significant
differences (P<0.05) were included in the subsequent analysis. For
genes selected by P values, zero values were arbitrarily transformed
in 100 units of intensity to estimate the ratio of response.
For data clustering, we exported normalized ratio values to JMP software
(SAS Institute, Cary, NC) and performed multivariate analysis with
hierarchical clustering based on the estimation of the Euclidian distances by
Ward's method (Watson et al.,
2001). Data were visually presented using TreeView software
(http://rana.lbl.gov/EisenSoftware.htm)
(Eisen et al., 1998
). Out of
351 genes selected, 22 did not match current databases, 39 were not matched
with known functions and 290 were assigned to specific functions. For
functional classification, we used groups previously described by us
(Khodarev et al., 2001
) and
others (Stanton et al., 2000
)
with modifications according to the Proteome database
(http://www.proteome.com/databases/HumanPD/reports/385.html).
Experiments were performed with Research Genetics GeneFilters® GF211
arrays (Research Genetics/Invitrogen, Carlsbad, CA) and two independent
control co-cultures and monocultures, and were further confirmed by
quantitative real-time PCR and/or independent hybridizations with alternative
arrays Atlas® Human and Atlas® Human 1.2 [BD Biosciences/Clontech,
Palo Alto, CA; see supplementary Table 1
(http://jcs.biologists.org/supplemental)].
Quantitative PCR
cDNA was synthesized using Superscript II® reverse transcriptase
(Invitrogen Life Technologies, Carlsbad, CA) following the manufacturer's
recommendations, except that the addition of RNase inhibitor was omitted. cDNA
was diluted 1:10 in sterile water. Quantitative PCR was performed on an
ABI7700 (Applied Biosystems, Foster City, CA) using SYBR Green PCR reagents in
a 50 µl reaction mixture containing 5 µl 10x SYBR Green PCR
Buffer, 0.5 µl 10 mM primers, 4 µl dNTP mix, 6 µl 25 mM magnesium
chloride, 0.5 µl AmpErase, 0.25 µl Amplitaq Gold and 5 µl of the 1:10
diluted cDNA synthesis reaction product. PCR was performed for 40 cycles at
95°C for 15 seconds and 60°C for one minute after initial incubations
at 50°C for 2 minutes and 95°C for 10 minutes. PCR product specificity
and purity were evaluated by generating a dissociation curve following the
manufacturer's recommendations. Sample Ct values were normalized to
Ct values for 18S RNA, all of which were calculated from triplicate
reactions. Relative gene induction values were calculated following the
manufacturer's recommendations.
Immunohistochemistry
U87 tumours were excised 17 days after implantation in the right hind limb
of female nude mice (Frederick Cancer Research Institute, Frederick, MD). The
care and use of experimental animals was in accordance with institutional
guidelines. Excised tumours were placed in 10% neutral-buffered formalin,
embedded in paraffin and sectioned at 6 µm thickness. Sections were
deparaffinized and rehydrated through xylene and serial dilutions of ethanol
to distilled water. Slides were incubated in antigen retrieval buffer (DAKO,
Carpinteria, CA) pH 6.0 and heated in a microwave oven at 95°C for 15
minutes. After rinsing, slides were incubated in 3% hydrogen peroxide for 5
minutes and then 10% normal horse serum in 0.025% Triton X-100/PBS for 30
minutes. CD31 (1 µg ml1, #1506, Santa Cruz Biotechnology,
Santa Cruz, CA) and Tie-2 (5 µg ml1, #AF313, R&D
Systems, Minneapolis, MN) were applied on the slides for 1 hour at room
temperature in a humidity chamber. Following TBS washing, slides were
incubated with biotinylated secondary antibody (Vector Laboratories,
Burlingame, CA) followed by ABC reagents (Vector Laboratories).
Antigen-antibody complexes were detected using DAB substrate chromogen system
with TBS-Tween 20 (DAKO) wash buffer. The entire procedure was performed using
an automated staining chamber (DAKO). Slides were briefly immersed in
haematoxylin for counterstaining and evaluated using light microscopy.
Quantification of Tie-2 receptor and CD31
For the quantification of the immunohistochemical staining, a colorimetric
processing approach was employed (R. Yassari et al., unpublished). Briefly,
multiple representative images from each slide were acquired using a Nikon
Coolpix 955 camera with fixed optical parameters, light intensity and
magnification. Acquired images were stored as TIFF files and evaluated using
Image Processing Tool Kit 4.0 (Reindeer Games, Asheville, NC) and Adobe
Photoshop 6. Areas of positive staining were selected using a fixed colour
range across all images. The hue and saturation of the selected regions were
then discarded and luminance used to represent staining intensity ranging from
0 to 255. A similar procedure was employed to perform background subtraction.
We used CD31 immunostaining in membranes and tissues as controls. Significance
(P<0.05) was determined with unpaired two-tail Student's
t test.
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Results |
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Growth stimulation of HUVECs was also examined in co-cultures in which a permeable membrane was placed between HUVECs and U87 cells (1:5 HUVEC:U87 ratio). At 48 hours, the number of HUVECs in co-culture was 2.0 times greater than the number of HUVECs in monoculture (10.7±2.2x104 vs 5.3±0.3x104 cells; Fig. 1B). Data from these experiments suggest that activation of HUVEC growth is associated with soluble factor(s) produced by U87 tumour cells.
Co-cultivation of HUVECs with U87 cells also induced sequential morphological changes in both cell types. U87 cells began to aggregate and form a net-like pattern 5 hours after the initiation of co-culture (Fig. 1C,D). Simultaneously, the morphology of HUVECs began to change. The `teardrop-like' HUVECs observed in monoculture assumed a narrower extended shape as they began to align themselves with the U87 cells (Fig. 1D, inset, white arrow). By 12 hours, the U87 network was complete and spaces began to form between the boundaries of aligned HUVECs and U87 cells (Fig. 1E). At 24 hours, HUVECs formed net-like structures resembling a vascular network (Fig. 1F).
Co-culture of HUVECs with U87 cells activates HUVEC migration and
net-like formation, which are suppressed by angiogenesis inhibitors
To investigate the effects of U87 cells on HUVEC motility and
differentiation, we employed TransWell® cell culture chamber inserts to
restrict interactions between cell types to soluble factors. At a 10:1
U87:HUVEC ratio, we observed an 80% increase in HUVEC migration in co-culture
(347.3±6.1) compared with HUVEC monoculture (195.2±3.2,
P<0.001; Fig.
2A).
Using TransWell® inserts without collagen coating and a pore size of 0.4 µm, HUVECs were cultured in the inserts with U87 cells in the chamber beneath at a U87:HUVEC ratio of 10:1. The presence of U87 cells increased the formation of net-like structures in the HUVEC compartment (Fig. 2C,D) similar to that described for Matrigel® migration assays (Donovan et al., 2001). The difference between co-culture (21±4) and monoculture (4±1) was statistically significant (P=0.027, Fig. 2B). We noted that the morphology of the endothelial cells involved in the formation of net-like structures also changed (Fig. 2E,F) In these regions, endothelial cells appeared elongated, with protruded cytoplasm and aligned themselves along the perimeter of the enclosed spaces (Fig. 2F, white arrow). These data suggest that tumour cells can induce endothelial cells to differentiate into structures that resemble in vivo neo-vascularization.
As shown in Fig. 2G,
angiostatin (O'Reilly, 1997)
and endostatin (O'Reilly et al.,
1997
), two reported inhibitors of in vivo angiogenesis, inhibit
the formation of net-like structures. Inhibition of net-like formation in
co-culture was also observed following exposure to Herbimycin A and anti-VEGF
antibody, whereas no inhibition was detected in HUVEC monocultures (data not
shown). These data demonstrate that angiogenesis inhibitors suppress the
tumour-induced endothelial phenotype and that tumour cell/endothelial cell
interactions are required for specific types of growth inhibition to
occur.
Expressional profiling of tumour-activated endothelial cells reveals
transcriptional reprogramming coordinated with phenotypic switch
An advantage of our in vitro co-culture system of U87 cells and HUVECs is
the ability to evaluate molecular changes that are otherwise too complex to
model in vivo. Genes associated with the phenotype of `tumour-activated'
endothelial cells might include the expression of the tumour-specific/induced
markers of neo-vascularization. To identify the genes associated with the
`tumour-activated' phenotype, we compared the gene expression profiles of
HUVECs co-cultured with U87 cells with HUVECs grown in monoculture. The
complete dataset included 290 genes; 91 genes with consistent changes in at
least two consecutive time points are presented in supplementary Table 1
(http://jcs.biologists.org/supplemental).
Of these, 16 genes were selected for confirmation by quantitative real-time
PCR. 14 of these genes (87.5%) were consistently upregulated by both DNA
arrays and quantitative PCR. The functional group corresponding to cell
structure/motility/extracellular matrix (ECM)-related genes represented 1.81
times more genes than random samples from the array with significant
difference (P<0.0001). Three other functional groups directly
correlated with observed phenotypic changes. They are genes for growth
response or cell proliferation, for receptors, and for the soluble growth
factors, cytokines and chemokines (listed as `ligands' in
Fig. 3). In the cell
shape/motility/ECM group, we observed significant transcriptional response in
several members of collagen, keratin, tubulin and integrin gene families
(Fig. 3). Among the receptor
and ligand groups, several genes previously described as being involved in in
vivo angiogenesis were detected. These genes include the receptors Tie-2,
Flt-1 (VEGF RI) and FGFRII (Fig.
3). These findings are consistent with observed morphological
changes in endothelial cells induced by tumour cells as reported above.
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Tie-2 expression is increased in co-culture compared with
monoculture
We validated our expression-profiling data by examining the expression of
the Tie-2 receptor. Tie-2 is important for the stabilization and maturation of
vessels, and has been identified as a marker of tumour angiogenesis
(Yancopoulos et al., 2000). We
found that, on the transcriptional level, the Tie-2 receptor was not expressed
in endothelial cells grown in monoculture, but was expressed by endothelial
cells grown in co-culture with increasing levels at 12 and 24 hours
(Fig. 3).
To detect protein changes, we stained HUVECs attached to membranes from TransWell® chambers using antibodies to CD31 and Tie-2. Data were quantified as described in Materials and Methods. HUVECs grown in both monoculture and co-culture expressed high levels of CD31 membrane staining and did not show significant differences between mono- and co-cultures. Tie-2 staining was not detected in HUVECs grown in monoculture but was present in HUVECs grown in co-culture with U87 cells (Fig. 4A). Significant Tie-2 staining of endothelial cells was also observed in U87 xenografts (186.05±2.53) compared with endothelial cells in normal mouse brain (176.65±1.67, P<0.03) (Fig. 4B). No significant difference in CD31 staining was observed between U87 xenografts (189.28±0.72) and normal mouse brain (186.11±2.01, P=0.22). These data demonstrate that expressional changes detected in co-culture might correlate with tumours growing in vivo.
|
Tumour cells induce in endothelial cells the accumulation of mRNAs
for receptors and ligands, and the expression of cytokines potentially
involving autocrine loops
Our analysis of cDNA array data indicated increased expression of matching
pairs of receptors and ligands in HUVECs during co-cultivation with U87 cells
(Fig. 3). We evaluated changes
in gene expression using quantitative PCR for various receptor-ligand pairs,
including members of the FGF family of receptors and ligands (FGF-7/FGFRII and
FGF12/CFR1), small inducible cytokine receptors and ligands (RANTES/CCR1,CCR3
and CCR5), CALCRL (CRGP type 1)/adrenomedullin and transforming growth factor
(TGF) family members (TGFßRII/TGFß3). Quantitative PCR data were
consistent with cDNA array data (Fig.
5). Co-cultivation of HUVECs with U87 cells resulted in the
accumulation of mRNAs for matching receptor and ligand genes. These
receptor-ligand gene pairs might be involved in the formation of autocrine
loops in tumour-stimulated endothelial cells, which has previously only been
shown to occur in glioma cells (Westphal
et al., 1997; Tada et al.,
1994
).
|
To investigate the functional significance of these observations we first hypothesized that, if tumour-activated endothelial cells express cytokines and growth factors with autocrine potential, such `activated' endothelial cells will (after primary contact with tumour cells) retain an ability to activate naive endothelial cells that have never been exposed to tumour cells. We used HUVECs plated in inserts of Transwell® chambers and cultivated in EBM as `acceptor' cells and negative controls (Fig. 6A). HUVECs loaded in the wells and co-cultivated with U87 were designated `activated' or donor cells and HUVECs co-cultivated with HUVECs only were designated `naive' cells. Transfer of acceptor HUVECs to `activated' HUVECs and subsequent co-culture for 24 hours led to a significant increase in the number of net-like structures compared with transfer to naive HUVECs (Fig. 6B). These data suggest that co-culture leads to the induction of HUVEC self-activating factors by HUVECs, which are released some time after the removal of tumour cells. Our second hypothesis proposes that ligands involved in the formation of tumour-induced autocrine loops will be expressed by HUVECs only after exposure to tumour cells or tumour-conditioned medium, and will not be expressed by HUVECs in monoculture. We investigated this possibility employing two ligands (RANTES and FGF7) based on DNA array data (Fig. 3). Fig. 6C shows that both of these ligands are significantly expressed in HUVEC monocultures only after stimulation by tumour-conditioned medium. These findings are consistent with expression profiling as described above. Taken together, these data suggest the presence of endothelial self-activating autocrine loops, which are induced following stimulation by tumour cells.
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Discussion |
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We described in this report an in vitro co-culture system that simulates direct and indirect interactions between tumour cells and endothelial cells. Our data show that our co-culture system recapitulates components of the angiogenic phenotype, including endothelial cell proliferation, migration and differentiation into the net-like structures. We also demonstrate that anti-angiogenic compounds inhibit the formation of net-like structures by endothelial cells grown in co-culture with tumour cells. Further investigations might detect new potential targets for therapeutic intervention and provide an approach for screening of compounds with anti-angiogenic potential.
Tumour-induced phenotypic changes coordinate with transcriptional
reprogramming of endothelial cells, which can be detected by expression
profiling
One of the major advantages of our co-culture system is the ability to
investigate the molecular changes that occur as a result of tumour
cell/endothelial cell interactions. Although the `cross-talk' between cell
types might be bidirectional, in this study, we limited our investigations to
tumour-cell-induced changes in endothelial cell gene expression. Our data
demonstrate significant differences in the expression of endothelial genes,
which coordinate with phenotypic changes observed in HUVECs co-cultured with
U87 cells, with the cell structure/motility/ECM, receptors, ligands and cell
proliferation genes as major response groups
(Fig. 3; supplemental Table 1).
These observations on the molecular level are consistent with the phenotypic
features we observed.
Several independent laboratories have reported differences in endothelial
cell gene expression during Matrigel-induced differentiation into the net-like
structures in vitro. Differential gene expression has been assessed using
suppression subtraction hybridization (SSH)
(Glienke et al., 2000) or
gene-calling techniques (Kahn et al.,
2000
). Also, SAGE was applied for investigation of gene expression
in endothelium (St Croix, 2000; Lal, et
al., 1999
). These methods and models differ from our approach.
However, in spite of the differences in techniques, model systems, and
methods, some commonalities exist between our data and the data reported by
others. For example, we detected changes in the transcriptional response of
bone morphogenetic proteins (BMP) 6 and 8, as well as BMP receptor II. BMP6
was found to be a marker of tube formation
(Glienke et al., 2000
),
whereas BMP1 was identified as a possible tumour endothelial marker (TEM) in
vivo (St Croix et al., 2000
).
BMP receptor II was reported to be upregulated during endothelial cell
differentiation in Matrigel (Glienke et
al., 2000
). SAGE databases identified overexpression of
angiogenesis-related genes including VEGF, fibronectin and adrenomedulin in
glioblastoma, compared with normal brain tissue
(Lal et al., 1999
). SAGE
analysis was also used to evaluate transcriptional responses to MCP1 in
endothelial cells treated with macrophage-conditioned media
(de Waard et al., 1999
). SSH
was used to compare differences in expressional profiles between freshly
isolated high endothelial venule endothelial cells and high endothelial venule
endothelial cells cultivated in vitro
(Girard et al., 1999
).
Differential gene expression was detected for thrombospondin-1, proteases
ADAMTS1 and ADAMTS4, fibronectin, integrins
2,
5 and
V,
and receptors Tie-2, Flt-1 and BMPRII. In cDNA experiments, temporal
co-clustering of serum-responsive genes revealed co-clustering of VEGF with
FGF2, ICAM-1, MCP1 and FGF7 (Iyer et al.,
1999
). Collagen IV
2 was identified as a pan-endothelial
marker in vivo using SAGE analysis (St
Croix et al., 2000
) while collagen IV was detected in
differentiating endothelial cells in collagen gels using GeneCalling
(Kahn et al., 2000
). Another
isoform, collagen V, was detected in Matrigel-induced endothelial cells using
the SSH technique (Glienke et al.,
2000
). Keratin K7 and integrin
2 were reported to be
upregulated in differentiating endothelial cells
(Kahn et al., 2000
) as was
integrin
5 (Glienke et al.,
2000
). Therefore, comparison of our dataset with those published
by other investigators indicates the presence of several gene groups that are
common to all models. These include components of ECM and ECM-related
signalling molecules (integrins, collagens, keratins, proteases, cellular
adhesion molecules), several cytokines and growth factors (VEGF, FGF, BMPs,
MCP1), receptors and signalling molecules. Expansion of endothelial cell
expression databases and meta-analysis will elucidate the gene groups, gene
families and pathways associated with general angiogenesis and tumour-specific
angiogenesis.
Tumour-induced activation involves the induction of autocrine loops
in endothelial cells
Our data suggest that tumour-induced transcriptional reprogramming of
endothelial cells might involve the establishment of autocrine loops in
endothelial cells. Examples include coordinated transcriptional expression of
FGF receptors and FGF proteins, CCR receptors 1, 3 and 5 and their putative
ligand RANTES, TGFßRII receptor and TGFß3, and CALCRL (CGRP1)
receptor and adrenomedullin (Fig.
5). Consistent with these DNA array data, we found that tumour
cells can induce the release of self-activating factors by endothelial cells.
Examples of such factors include RANTES and FGF7, which are not expressed by
HUVECs in monoculture but are significantly expressed following exposure to
tumour-conditioned medium. Additional experiments are required to evaluate
different receptors and ligands potentially involved in tumour-induced
endothelial autocrine loops. However, our data indicate the existence of such
loops and provide a system for further investigations. It has been recently
suggested that FGF2 transfected murine aortic endothelial (MAE) cells, which
constitutively express FGFRII, might be stimulated through formation of
autocrine loops (Dell'Era et al.,
2001). It has also been proposed that the Streptococcus
pneumoniae cell wall (PCW) can induce autocrine loops involving tumour
necrosis factor
in cerebral endothelial cells
(Freyer et al., 1999
).
TGFß3 has been shown to regulate the differentiation of embryonic cardiac
endothelial cells (Potts et al.,
1991
). Interestingly, transcriptional upregulation of TGFß3
was found in Kaposi's sarcoma associated herpes virus (KSHV)-infected dermal
microvascular endothelial cells during the transformation from
cobblestone-shaped cells to spindle-shaped cells, which are characteristic for
Kaposi's sarcoma lesions (Poole et al.,
2002
). Adrenomedullin has been demonstrated to be an autocrine
regulator of proliferation in human endometrial endothelial cells during
endometrial angiogenesis (Nikitenko et
al., 2000
). Adrenomedullin signalling is induced via its
interaction with CALCRL (CGRP1) receptor and downstream activation of
adenylate-cyclase/protein-kinase-A-dependent cascades
(Belloni et al., 2001
).
Therefore, some pathways of autocrine regulation of endothelial cells are
described in the literature, but induction of endothelial autocrine loops by
tumour cells has yet to be described and characterized.
In summary, the experimental system reported here, may provide useful tools for further detailed examination of the interactions between tumour and endothelial cells.
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Acknowledgments |
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