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(The FASEB Journal. 2004;18:1050-1059.)
© 2004 FASEB

Genetic loci that control the angiogenic response to basic fibroblast growth factor

MICHAEL S. ROGERS, RICHARD M. ROHAN, AMY E. BIRSNER and ROBERT J. D’AMATO1

Vascular Biology Program, Children’s Hospital, and Department of Ophthalmology, Harvard Medical School, Boston, Massachusetts, USA

1Correspondence: Department of Surgery, Children’s Hospital, 300 Longwood Ave., Boston, MA 02115, USA. E-mail: robert.damato{at}childrens.harvard.edu


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Angiogenesis is controlled by a balance between stimulatory growth factors and endogenous inhibitors. We propose that the balance of stimulators and inhibitors, as well as the general sensitivity of the endothelium to these factors, varies from individual to individual. Indeed, we have found that individual mouse strains have dramatically different responses to growth factor-induced neovascularization. Quantitative trait loci (QTLs), which influence the extent of angiogenesis induced by vascular endothelial growth factor (VEGF), were previously identified by our laboratory. Since genetic susceptibility may vary according to the angiogenic stimulator, we have undertaken a similar mapping approach to identify QTLs that influence basic fibroblast growth factor (FGF2) induced neovascularization in the BXD series of recombinant inbred mouse strains. Composite and multiple interval mapping identified areas of chromosomes 4, 13, 15, and 18. These new angiogenesis QTLs, named AngFq1 through AngFq4 (for angiogenesis due to FGF2), are different from previously identified VEGF QTLs. The mapped regions contain several genes involved in the angiogenic process including matrix metalloproteinase 16, eph receptor A7, angiopoetin 1, endothelial lipase, and autotaxin. Differences in these regions may influence individual susceptibility to angiogenesis related diseases such as cancer, macular degeneration, atherosclerosis, and arthritis.—Rogers, M. S., Rohan, R. M., D’Amato, R. J. Genetic loci that control the angiogenic response to basic fibroblast growth factor.


Key Words: quantitative trait locus • QTL • corneal neovascularization • recombinant inbred • BXD


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
THE IMPORTANCE OF ANGIOGENESIS in cancer is now firmly established, and recent studies have illustrated the importance of angiogenesis in other diseases including macular degeneration, diabetic retinopathy, heart disease, and obesity. A number of growth factors are important in angiogenesis, including FGF2 and VEGF. We initially reported strain-specific differences in responsiveness to these growth factors in mice (1 , 2) . Differential angiogenic responsiveness among strains has been confirmed in additional species and anatomical locations with FGF2 and VEGF (3 , 4) . Earlier we mapped the location of VEGF response loci in BXD recombinant inbred mice (1) . We have now mapped the genetic component of FGF2 responsiveness in the same recombinant inbred strain cross.

FGF2 is the founding member of the FGF family of growth factors. It is expressed by multiple cell types and is important in morphogenesis, development, hematopoiesis, and tumorogenesis. When expressed, it is found in the nucleus and the extracellular space. It lacks a signal sequence and is therefore translocated to the extracellular space in an ATP-dependant process that is independent of the ER-Golgi (5) . FGF2 requires heparin sulfate for activity and binds, together with heparin sulfate, to any of four FGF2 receptors. These receptors are tyrosine kinases with Ig superfamily extracellular domains. In addition to classical receptor tyrosine kinase signaling, FGF2 is involved in signaling in the nucleus as part of a complex with its receptor as well as independent of it. FGF2 is involved in proliferation, differentiation, and survival of numerous mesodermal and neuroectodermal cells including fibroblasts, endothelial cells, chondrocytes, smooth muscle cells, melanocytes, adipocytes, macrophages, astrocytes, and others (6) .

We chose the technique of interval mapping to identify genetic regions responsible for the differential angiogenic responsiveness to FGF2. Interval mapping calculates the probability of a QTL’s position and its effect (7) . There are three variations of interval mapping, each with its own advantages and disadvantages. Simple interval mapping is the computationally least complex but is inadequate for situations in which multiple loci, particularly those that are linked, contribute to the phenotypic effect (8) . Composite interval mapping extends simple mapping to consider the effect of multiple loci (9 , 10) . However, since this model treats loci independently, epistasis between QTLs is not easily considered. Multiple interval mapping (11) utilizes multiple regression to test for independent and interactive loci. The value of this technique, however, is complicated by the uncertainty of appropriate significance testing (11) . Thus, we used both composite and multiple interval mapping to test linkage.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
Mouse strains and corneal micropocket assay
The corneal micropocket assay was performed as described (12) using pellets containing 10 ng of FGF2 or 180 ng carrier-free human recombinant VEGF 165 (R&D Systems, Minneapolis, MN, USA). The area of vascular response was assessed on the fifth (FGF2) or sixth (VEGF) postoperative day using a slit lamp. Vessel area was calculated using the equation 0.2{pi} x VL x CH, where VL is vessel length from the limbus in millimeters and CH is clock hours around the cornea. Attempts were made to make comparable measurements by holding mice until several strains could be assayed at once, thus reducing lot-to-lot variability in the pellets and reducing pellet age differences. This resulted in mice of several different ages being assayed, but when the same strain was assayed at different ages there was no significant difference in results. Five mice (10 eyes) per BXD strain were analyzed and similar numbers of control C57BL/6J and DBA/2J were included in each assay to confirm consistency. All mouse strains were obtained from Jackson Laboratories (Bar Harbor, ME, USA) and housed in Children’s Hospital’s animal facility on standard diet and bedding until the assay was performed. All animal studies were conducted according to protocols approved by the Institutional Animal Care and Use Committee of Children’s Hospital.

Multiple linear regression was performed on right eye vessel area using the formula A = {alpha} + ß1a + ß2e + ß3x + ß4g + {varepsilon}, where a is the deviation of the animals age from the mean animal age, e is the deviation of left vessel area from the strain and experiment mean, x is the deviation of the C57BL/6J mean in that experiment from the C57BL/6J mean in all experiments, g is the deviation of the strain mean from the B6D2F1/J mean. Regression was also explored using the model A = ({alpha}+ß1a+ß2e+ß4g)(C+ß3x) + {varepsilon} (where C is the C57BL/6J strain mean value) under the hypothesis that differences in C57BL/6J control values resulted from differences in FGF2 availability in different pellet batches (response to growth factor amount in this assay is generally linear in this range), but important differences in the results of the two models were not observed. Significance in regression was determined using an F test.

QTL analysis software
Windows QTL Cartographer version 2.0 (13) was used for linkage analysis. Default settings were used throughout unless otherwise noted. Significance levels for interval mapping were determined by permutation using 10,000 iterations. Likelihood ratio (LR) scores for various P values are as follows: P < 0.05 = 17.8, P < 0.01 = 22.0, P < 0.001 = 25.7. Composite interval mapping was performed using both forward regression with 5 and 6 control markers (default) and forward regression with backward elimination to select control markers. No appreciable difference was observed in the data resulting from the two methods of control marker selection. Multiple interval mapping was performed using forward regression on markers to select an initial model, with software defaults used otherwise. QTLs were added until strain averages were no longer significantly better predictors of vessel length than model predicted strain values (P>0.8 by F test).

Genotyping and mapping
Genotypes for BXD strains and linkage map data were obtained from the Mouse Genome Database version 2.8, Nov. 8, 2002 (MGD, http://www.jax.org) (14) . This database was used to identify additional markers that are polymorphic between C57BL/6J and DBA/2J mice. Genotyping of BXD strains was performed by PCR using primers annotated in the MGD and DNA purchased from Jackson Labs or purified directly from the animals assayed as reported (1) . Cycling profiles were determined empirically and performed in a PTC-200 thermal cycler (MJ Research, Reno, NV, USA). Taq polymerase and other PCR reagents were from Qiagen (Valencia, CA, USA).

Physical map positions were obtained, where possible, from the UCSC annotation of the mouse genome (Feb. 2002 assembly of the mouse genome at http://genome.cse.ucsc.edu/). Where a marker had no UCSC annotated position on the MGSCv3 assembly, the NCBI annotated position (http://www.ncbi.nlm.nih.gov/mapview/static/MVstart.html) was used. No attempt was made to correct positions in the NCBI assembly since <2% of our marker positions annotated in both assemblies differ by >250 bp. For linkage analysis, data was converted to a pseudocentimorgan scale by dividing the position of the marker in Mbp by 2.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
We previously demonstrated a substantial difference in angiogenic responsiveness to FGF2 between various inbred mouse strains (2) . The assay used in these studies and the current paper has been described (12) . Briefly, a slow-release pellet containing an angiogenic growth factor is implanted in the cornea, which is normally avascular. Vessels then grow into the cornea from the surrounding limbal vessels, which demarcate the cornea from the adjacent conjunctiva. The length of these new vessels (in mm) and the distance around the cornea in which neovessels form (in clock hours, where 1 clock hour equals 1/12 the circumference) are then measured, and these values are used to calculate the total area on the cornea covered by vessels. We chose C57BL/6J as the background strain for genetic mapping and for analysis of sequence variation. The rationale for using C57BL/6J lies in the observation that, of the mouse strains tested, the responsiveness of the C57BL/6J strain is near the mode. The C57BL/6J genome is also almost fully sequenced (15) .

Recombinant inbred (RI) strains allow multiple phenotypic measurements for a given genotype, increasing the rigor of QTL mapping. Thus, RI strains derived from C57BL/6J were chosen for this study. Of these RI strains, the two largest commercially available sets are AXB/BXA (derived from A/J crosses) and BXD (derived from crosses with DBA/2J). To maximize the chance of detecting differences, the latter set was chosen since the difference in responsiveness is larger between DBA/2J and C57BL/6J than between A/J and C57BL/6J. The set of BXD RI strains is also the largest.

Initial analysis of the data from the BXD strains showed non-normality that was nearly significant (P=0.07 Shapiro-Wilk’s test). This could result from one or two large QTLs, as observed in the case of VEGF (1) . However, initial mapping did not reveal a small number of linked markers and so rescaling was attempted. A square root transformation of the vessel area resulted in data that were approximately normally distributed. Since vessel area results from the product of clock hours and vessel length, this suggested that the two components of vessel area may not be independent. Indeed, a plot of clock hours vs. vessel area demonstrated a strong correlation between these measures of corneal angiogenesis. As vessel length is the most precise measurement of the two values and these data are normally distributed among BXD strains, we decided to use vessel length alone as the measure of angiogenic response for further analysis. Results of the corneal neovascularization assay with pellets of FGF2 are shown in Fig. 1 .



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  		Figure 1. Distribution of BXD strain corneal neovascular response to FGF2. Mean response to VEGF by BXD strains is plotted in the bar graph. Mean and standard deviation for parental strains, F1 strains, and BXDs are represented as ideal gaussian curves. BXD strain means are: BXD-1, 0.33; BXD-2, 0.49; BXD-5, 0.17; BXD-6, 0.52; BXD-8, 0.18; BXD-9, 0.51; BXD-11, 0.32; BXD-12, 0.34; BXD-13, 0.25; BXD-14, 0.48; BXD-15, 0.51; BXD-16, 0.63; BXD-18, 0.61; BXD-19, 0.68; BXD-20, 0.46; BXD-21, 0.56; BXD-22, 0.53; BXD-23, 0.24; BXD-24, 0.77; BXD-25, 0.43; BXD-27?, 0.40; BXD-28, 0.56; BXD-29, 0.06; BXD-30, 0.73; BXD-31, 0.41; BXD-32, 0.52; BXD-33, 0.43; BXD-34, 0.64; BXD-35, 0.55; BXD-36, 0.48; BXD-38, 0.78; BXD-39, 0.56; BXD-40, 0.53; BXD-42, 0.51; C57BL/6J, 0.47; DBA/2J, 0.61; and B6D2F1/J, 0.57.

BXD strains, which represent recombinant mixtures of the parental C57BL/6J and DBA/2J strains bred to homozygosity, exhibited a broad range of vessel lengths from the BXD-29 value of 0.06 to the BXD-38 value of 0.78. Both are significant deviations from values of the parental strains. The mean (mode) of the vessel lengths for the 34 strains was 0.48 (0.51), very near that of the C57BL/6J value of 0.47 mm (Fig. 1) . As would be expected with data bounded at the lower and upper end of the range (lower by zero, upper by the fixed distance from the limbus to the pellet), the data are not gaussian but do reasonably approximate a truncated gaussian curve (Fig. 1) . The broad unimodal nature of the RI data distribution suggested the existence of multiple QTLs governing the responsiveness to FGF2. Phenotyping of F1 progeny of C57BL/6J and DBA/2J matings revealed a mean vessel length of 0.57 mm, very near the DBA/2J strain mean of 0.61 mm (Fig. 1) . Since all alleles in RI strains are homozygous, the design of these experiments does not allow for the determination of dominance. Nevertheless, the approximation of the F1 animal mean to the DBA mean suggests that one or more of the QTLs identified in this report may exhibit dominance.

Because we noted some differences among different eyes within a given strain, we have made an effort to ascertain whether environmental variables might alter the responsiveness of these mice to FGF2. To determine sources of variability in the assay, we performed multiple regression using left eye vessel area (to control for environmental contribution), age, C57BL/6J control animal vessel area (to control for differences in pellet batch composition and age), and strain mean (to control for genetic differences) to predict right eye vessel area. Only the genetic contribution to vessel area proved statistically significant in this analysis (P<10–36), and its effects could explain 66% of the variance in vessel length. We attribute the remaining differences to variability in experimental procedure (exact pellet size and composition, location, and size of surgical pocket etc.) or environmentally induced variability not shared by both eyes (minor abrasion, minor infection, irritation, etc.).

We observed that FGF2 responsiveness correlated generally to VEGF responsiveness in BXD strains (Fig. 2 ), with ~40% of the variance in the FGF2 responsiveness explicable by VEGF responsiveness. This is consistent with reports that a significant fraction of FGF2 responsiveness is mediated by local production of VEGF, as evidenced by the ability of anti-VEGF antibodies to reduce the density of corneal neovascularization induced by FGF2 (16) . To determine whether this effect could explain the correlation we observed, we used adenovirally expressed soluble VEGF receptors to block VEGF signaling (17) . We have previously shown that these constructs are able to inhibit 75–80% of VEGF-induced corneal neovascularization (17) . When expressed in mice implanted with 80 ng FGF2 pellets, we observed 50–65% inhibition of corneal neovascularization (Fig. 3 ). This indicates that the fraction of FGF2-induced corneal neovascularization that is VEGF dependent is sufficient to explain the correlation between VEGF and FGF2 responsiveness in our assay.



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  		Figure 2. Corneal neovascular response in BXD mouse strains to 10 ng FGF2 is partially predicted by response to 180 ng VEGF (in a slow release pellet). Actual FGF2-induced vessel area is as follows (mm2): BXD-01, 0.43; BXD-02, 0.76; BXD-05, 0.15; BXD-06, 0.79; BXD-08, 0.17; BXD-09, 0.73; BXD-11, 0.31; BXD-12, 0.31; BXD-13, 0.24; BXD-14, 0.56; BXD-15, 0.61; BXD-16, 1.80; BXD-18, 0.75; BXD-19, 1.03; BXD-20, 0.50; BXD-21, 0.79; BXD-22, 0.83; BXD-23, 0.24; BXD-24, 1.45; BXD-25, 0.41; BXD-27, 0.44; BXD-28, 0.68; BXD-29, 0.61; BXD-30, 1.20; BXD-31, 0.57; BXD-32, 0.73; BXD-33, 0.45; BXD-34, 0.97; BXD-35, 0.70; BXD-36, 0.58; BXD-38, 1.15; BXD-39, 0.62; BXD-40, 0.76; BXD-42, 0.70. VEGF values are from our previous work (1) .



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  		Figure 3. Inhibition of FGF2 response by soluble VEGF receptors. Differences between FcFlk-expressing animals and control (Fc-expressing and untreated) animals were highly significant (P<10–14 for FGF2 and <10–18 for VEGF). Increased Flk expression resulted in significant differences only in animals assayed for FGF2 response (P<0.005). Differences between untreated and control Fc-expressing animals were not significant for either cytokine.

To map the regions of the genome responsible for the variation in FGF2 responsiveness, genotypes of the BXD strains at marker positions throughout the genome and the map positions of those markers were required. Marker genotypes were obtained from two sources. The majority of marker genotypes were obtained from the Mouse Genome Database version 2.8 (MGD) (14) . In addition, selected markers known to be polymorphic between C57BL/6J and DBA/2J mice were genotyped in our laboratory (1) . Recombination map positions were obtained from the MGD. Physical map positions were obtained from their annotated position on the public assembly of the mouse genome (15) . Since marker density can affect significance tests, we used a subset of these markers selected to be 3–7 cM apart and maximally informative for all BXD animals. Where it was possible to interpolate missing marker genotypes from surrounding genotypes, this was done as long as marker genotype estimations based on the physical and recombination maps agreed. Because significant differences were not observed when physical positions were used, because fewer double and triple crossovers were observed in the physical map order, and because results can be directly transferred to the mouse genome when looking at candidates, we present the mapping information in terms of the physical map. The data were analyzed using the QTL Cartographer software package (13) .

Single marker regression analysis yielded associations between FGF2-induced vessel length and regions on chromosomes 4, 12, and 15 (P<0.01). Markers on chromosomes 5, 9, 10, 11, and 17 showed somewhat weaker association with FGF2-induced vessel length (P<0.05, Table 1 ). Simple interval mapping (7) revealed a region of near-significant linkage with the vessel length on chromosome 15.


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  		Table 1. Markers showing significant association with FGF2 induced vessel lengtha

Given that marker association suggests the possibility of multiple linked QTLs and simple interval mapping is inappropriate in such situations (8) , we performed composite interval mapping (9 , 10) (CIM; Fig. 4 ) with significance levels determined by permutation. This reduced the number of linked areas to four exhibiting significant linkage (P<0.05), one of which exhibited highly significant linkage (P<0.001). Presumably this reduction in number is a result of nonsyntenic association between linked areas and regions initially linked by single marker regression.



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  		Figure 4. Composite and simple interval mapping. A) Whole genome composite interval map. Top: likelihood ratio statistic or likelihood of a region being linked to FGF2-induced corneal neovascularization. Bottom: predicted additive effect of a region on FGF2-induced corneal neovascularization. B–D) Composite interval map (CIM, solid line) and simple interval map (SIM, dashed line) likelihood ratios for each locus as indicated. Empty association symbols indicate statistical significance (P<0.05). Significance levels for CIM and marker identities are indicated. B) Chromosome 4 map. C) Chromosome 13 map. D) Chromosome 15 map. E) Chromosome 18 map.

Multiple interval mapping (MIM) analysis (11) was then performed (Table 2 ). This revealed the same areas of linkage as well as suggesting linkage on chromosome 6. The additional QTL on chromosome 6 predicted by MIM modeling may be the result of the sensitivity of this approach, however the region was not significant to the P = 0.05 level in composite interval mapping. In addition to location and effect, MIM allows the prediction of epistasis among QTLs in the model. These tests were performed and no epistatic interactions were revealed. The final model R2 value was 0.887, indicating a good fit between model and data (see Fig. 5 ). We conclude therefore that there are FGF2 response-modifying polymorphisms between C57BL/6J and DBA/2J mice on chromosome 4 between 7.7 and 45.2 Mbp, on chromosome 13 between 16.4 and 35.6 Mbp, on chromosome 15 between 32.0 and 62.3 Mbp, and on chromosome 18 between 74.8 and 84.8 Mbp. We have designated these QTLs AngFq1, AngFq2, AngFq3, and AngFq4 (on chromosomes 4, 13, 15, and 18, respectively) for angiogenesis due to FGF2.


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  		Table 2. QTLs predicted by multiple interval mapping together with the expected effect and the fraction of variance in vessel length explained by eacha



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  		Figure 5. Vessel lengths predicted by multiple interval mapping (horizontal axis) correspond closely to strain mean vessel lengths (vertical axis), indicating that the MIM model in Table 3 can substantially explain the variance observed in mean vessel length among BXD strains.

Candidate genes known to be located near the AngFq1 peak on chromosome 4 include matrix metalloproteinase 16 (Mmp16), a membrane-bound activator of MMP2 that may be involved in blood vessel matrix remodeling (18) ; Eph receptor A7 (Epha7), a member of the ephrin receptor family of guidance molecules expressed in the vasculature (19) ; TGF-ß-activated kinase 1 (Map3k7), a signaling molecule downstream of the angiogenesis regulator TGF-ß (20) ; and the endostatin binding protein tropomyosin ß (Tpm2) (21) .

The AngFq2 peak on chromosome 13 contains secreted frizzled-related protein 4 (Sfrp4), a regulator of wnt signaling (22) , as well as clusters of two angiogenesis inhibitor-related molecules: prolactin-related molecules (23) at 27 Mbp and serpins (24) at 32.5 Mbp.

The AngFq3 region on chromosome 15 includes genes for multiple angiogenesis regulatory proteins such as semaphorin 5a (Sema5a) (25) , frizzled 6 (Fzd6) (22) , osteoprotegerin (Tnfrsf11b) (26) , and angiogenic factors angiopoietin 1 (Agpt) (27) and autotaxin (Enpp2) (28) . Also in the region are hyaluronic acid synthase 2 (Has2) (29) , which provides a hyaluronic acid framework for ingrowing blood vessels, and the myc proto-oncogene, a major regulator of the anti-angiogenic protein thrombospondin (30) .

Finally, a few of the candidate genes known to be located in the chromosome 18 AngFq4 region include endothelial lipase (Lipg), a major determinant of the concentration of the angiogenesis inducer HDL (31) ; the putative angiostatin receptor mitochondrial F1 ATP synthase (Atp5a1) (32) ; and proline-serine-threonine phosphatase interacting protein 2 (Pstpip2), a regulator both of WAVE2, which is required for effective capillary migration, and (via the abl proto-oncogene) of VEGF concentration (33) .


   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
REFERENCES
 
In recent years it has become evident that the host contributes significantly to the progression of diseases such as cancer by providing the tumor’s blood supply. One implication of this study is that underlying genetic polymorphisms may contribute to the progression of angiogenic diseases. Indeed, such associations have already been demonstrated. VEGF, IL-8, and IL-10 polymorphisms have been implicated in melanoma and prostate cancer development (34 35 36) . Nucleotide differences in the promoter and 5' UTR of the VEGF gene are correlated with increased gene expression (37) and sarcoidosis (38) . Individuals carrying a particular polymorphism (D104N) in the angiogenesis inhibitor endostatin have a 2.5-fold increased risk for developing prostrate cancer (39) . The increased serum endostatin level in Down’s syndrome patients (a result of the additional copy of the collagen XVIII gene they carry) may account for the decreased prevalence of solid tumors in these patients (40) . Polymorphisms in some other genes that regulate angiogenesis and tumor growth have also been shown to affect tumor incidence and growth rates (41) .

Several QTLs whose phenotypic effect could be due to altered FGF2 responsiveness coincide with the AngFq loci we have identified (Table 3 ). These include several for overall body size, such as Bwtq6 (42) , which affects 10 week body weight in a C57BL/6J-DBA/2J cross, and Bwq1, which determines body weight at 60 days in a KK-C57BL/6J cross. In the latter, the non-agouti-associated QTLs also correlate with angiogenesis response regions. Genes for wound healing such as Stheal3 (43) , Heal4, Heal7, and Heal8 (44) are coincident with FGF2 response genes. In all these cases, the requirement for new vasculature to support additional tissue mass may explain the correlation between these traits and FGF2-induced vessel length.


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  		Table 3. Published QTLs coincident with FGF2-induced angiogenesis QTLs

The same requirement may explain the correspondence between some tumor-related QTLs and our FGF2-induced angiogenesis QTLs. For example, several lung tumor susceptibility genes (e.g., Sluc, Pas7; refs 45 , 46 ) are coincident with FGF2 response genes. Even more interesting is the coincidence between AngFq1 and Mmtg1, a locus associated with increased tumor burden in polyoma middle-T antigen transgenic mice. Tumors in these mice show increased microvessel density associated with increased tumor burden, strongly suggesting that an angiogenesis-related effect is responsible for increased tumor burden (47) . In the same strain cross, a tumor latency-related gene (Apmt1) colocalizes with AngFq3, indicating that angiogenic responsiveness likely plays an important role in overall tumor growth in this model.

No area of significant linkage for FGF2-induced angiogenesis overlaps with those for VEGF-induced angiogenesis. This may reflect the fact that we used different measures for VEGF and FGF2 linkage (area and length, respectively). However, when we repeat the VEGF mapping using vessel length as the measure, the VEGF QTLs AngVq1 and AngVq2 are still identified as the major QTLs segregating in the cross (data not shown). Therefore, if our FGF2 screen had identified all of the QTLs that contribute to the variance in vessel length as much as AngFq4 does, we would have expected to detect AngVq1. The fact that we did not suggests there are more regions linked to FGF2-induced angiogenesis than were detected in this screen and that these regions dilute the overall contribution of AngVq1 and AngVq2. When less stringent stopping rules are used in MIM (i.e., when the algorithm is allowed to identify more regions of association than the bare minimum required to explain the variance in the data), a region corresponding to AngVq2 (1) is linked to FGF2-induced angiogenesis. Also a region on chromosome 10 that associates with FGF2-induced angiogenesis (Table 1) corresponds to a region predicted by MIM to be involved in VEGF-induced angiogenesis (ref 1 ; MIM 4). That we noted a common locus when we relaxed stopping rules in MIM suggests that additional commonalities would have been observed if more BXD strains had been available to allow us to perform a more thorough screen.

Angiogenesis has been demonstrated many normal and pathologic processes. Examples of the former include organ development and regeneration (48) , fat deposition (49) , hair growth (50) , and cyclic changes in the female reproductive tract (51) . The latter group includes macular degeneration, diabetic retinopathy, psoriasis, atherosclerosis, arthritis (52) , and endometriosis (53) . Further studies are needed to test the relevance of polymorphisms controlling FGF2 responsiveness in these processes. While it is possible that genetic loci that influence corneal neovascularization may not alter blood vessel development in other tissues, we believe the effect is systemic. We have already demonstrated a correlation between corneal neovascularization and endothelial cell migration from aortic rings among different strains of mice (2) . Thus, characterization of novel angiogenesis-regulating genes detected by the QTL mapping of the corneal angiogenic phenotype may broadly identify additional therapeutic targets for anti-angiogenic agents.

Besides angiogenesis, FGF2 plays a role in the growth and development of several cell and tissue types. As a result, FGF2 therapy has been suggested in several other diseases, including the healing and strengthening of vessel wall aneurisms (54) , replacement of neurons (55) , and regeneration of osteoporetic bone (56) . The loci we have identified may be relevant in the development of these diseases as well as the response of patients to FGF2 therapy.

Along with our previous work, these experiments reveal the important effect that host genetics has on angiogenesis, and therefore on angiogenesis-dependent pathologies (Fig. 6 ). We expect that identification of the specific genes that underlie differential growth factor response will have prognostic value, as the functional relevance of individual polymorphisms is determined, and therapeutic value, as compounds that modify their activity are discovered. The possibility that specific and novel anti-angiogenic therapy can be developed and implemented at early stages of disease development may render affected individuals "low angiogenic," resulting in increased health and survival.



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  		Figure 6. DBA/2J alleles of AngFq and AngVq loci can alter the angiogenic response to FGF2 compared with mice carrying the C57BL/6J alleles at these loci.


   ACKNOWLEDGMENTS
 
We wish to thank Bill Dietrich for guidance and assistance in carrying out these studies. We thank Filip A. Farnebo for assistance with the adenovirus experiments. We also thank Xingyu Wang for useful suggestions. This research was funded by a grant from the National Institutes of Health (R01-EY12726).

Received for publication December 5, 2003. Accepted for publication March 15, 2004.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

  1. Rogers, M. S., Rohan, R. M., Birsner, A. E., D’Amato, R. J. (2003) Genetic loci that control vascular endothelial growth factor-induced angiogenesis. FASEB J. 17,2112-2114[Abstract/Free Full Text]
  2. Rohan, R. M., Fernandez, A., Udagawa, T., Yuan, J., D’Amato, R. J. (2000) Genetic heterogeneity of angiogenesis in mice. FASEB J. 14,871-876[Abstract/Free Full Text]
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