Advanced glycation end products cause increased CCN family and extracellular matrix gene expression in the diabetic rodent retina

Aims/hypothesis Referred to as CCN, the family of growth factors consisting of cystein-rich protein 61 (CYR61, also known as CCN1), connective tissue growth factor (CTGF, also known as CCN2), nephroblastoma overexpressed gene (NOV, also known as CCN3) and WNT1-inducible signalling pathway proteins 1, 2 and 3 (WISP1, −2 and −3; also known as CCN4, −5 and −6) affects cellular growth, differentiation, adhesion and locomotion in wound repair, fibrotic disorders, inflammation and angiogenesis. AGEs formed in the diabetic milieu affect the same processes, leading to diabetic complications including diabetic retinopathy. We hypothesised that pathological effects of AGEs in the diabetic retina are a consequence of AGE-induced alterations in CCN family expression.
Introduction
Diabetic retinopathy is a major complication of diabetes and a leading cause of blindness [1, 2]. Despite recent progress in understanding the pathogenesis of diabetic retinopathy, further research is warranted, as the disease remains neither preventable nor curable.
Diabetic retinopathy is preceded by an asymptomatic preclinical phase, in which a microangiopathy develops which is characterised by diffusely increased vascular permeability and capillary basement membrane thickening, resulting from excess accumulation of extracellular matrix components [3–5]. In later stages of preclinical diabetic retinopathy, endothelial cell and pericyte loss leads to vascular cell death and the development of acellular capillaries. Experimental prevention of basement membrane thickening has been shown to ameliorate these retinal vascular changes [6, 7]. Thus in galactose-fed rats, a model of diabetes, downregulation of fibronectin synthesis partly prevented retinal basement membrane thickening and also reduced pericyte and endothelial cell loss [6]. Combined downregulation of mRNA levels of the extracellular matrix components fibronectin, collagen type IV alpha 3 (Col4a3) and laminin beta 1 (Lamb1) not only prevented increases in their protein levels, but also reduced vascular leakage in the retina of rats with streptozotocin-induced diabetes [7]. These findings suggest that basement membrane thickening is not just an epiphenomenon of the diabetic state, but may be instrumental in the progression of sight-threatening diabetic retinopathy. Modulation of basement membrane thickening may therefore have a preventive effect on the development of diabetic retinopathy.
However, the mechanisms leading to diabetes-induced basement membrane thickening remain largely unknown. One of the postulated mechanisms is the formation of AGEs in the diabetic milieu. Inhibition of AGE formation by aminoguanidine has been shown to protect against retinal capillary basement membrane thickening [8]. AGE have also been shown to induce extracellular matrix synthesis in the diabetic rat kidney. A similar induction of extracellular matrix synthesis was also shown to be mediated by connective tissue growth factor (CTGF, also known as CCN2) [9, 10], a member of the family of proteins referred to as CCN (for cystein-rich protein 61 [CYR61, also known as CCN1], CTGF and nephroblastoma overexpressed gene [NOV, also known as CCN3]). CTGF leads to accumulation of extracellular matrix by induction of collagen, fibronectin and laminin synthesis, as well as decreased proteolysis of extracellular matrix components as a result of increased production of tissue inhibitors of metalloproteases (TIMP) [9, 11–18]. Recently, we observed that Ctgf+/− mice (lacking one functional allele for Ctgf) are protected from diabetes-induced basement membrane thickening of retinal and kidney glomerular capillaries ([19]; P. Roestenberg, F .A. Van Nieuwenhoven, R. Verheul et al., unpublished results).
We hypothesised in the present study that AGE-induced basement membrane thickening observed in the retina is at least partly mediated by CTGF. To establish the role of CTGF in the AGE-induced production of vascular basement membrane components and their mediators, we investigated the effects of aminoguanidine on the levels of CTGF, the other CCN family members (CYR61, NOV and WNT1-inducible signalling pathway protein 1, 2 and 3 [WISP1, −2, and −3, also known as CCN4, −5 and −6]) [20–22] and vascular basement membrane-related molecules in the retina of rats with streptozotocin-induced diabetes, as well as in the retina of mice infused with AGE.
Materials and methods
Animals All animal studies were carried out in accordance with the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. All experiments involving rats were reviewed and approved by the ethics committee for animal care and use of the Free University Medical Centre, Amsterdam, the Netherlands. All experiments involving mice were carried out in accordance with British Home Office regulations.
Streptozotocin-induced diabetic rat model Adult Wistar rats (Charles River, Maastricht, the Netherlands), weighing approximately 250 g, were randomly divided into three experimental groups: a control group (n = 14), a diabetic group (n = 16) and a diabetic group treated with aminoguanidine hydrogen carbonate (n = 14; Fluka, Buchs, Switzerland). Diabetes was induced by a single i.p. injection of 60 mg/kg streptozotocin (Sigma, St Louis, MO, USA). Immediately prior to use, streptozotocin was dissolved in cold 0.1 mol/l citrate buffer, pH 4.5. Control rats received a single i.p. injection of 0.1 mol/l citrate buffer only. Aminoguanidine was administered from day 1 at a dose of 1 g/l in drinking water. Serum glucose levels and body weight were monitored at the start and end of the experiment. Diabetes was verified by a serum glucose level >13.9 mmol/l. At 6 weeks, half of the rats were randomly selected from the three experimental groups and killed with a lethal dose of pentobarbital sodium (i.p.). At 12 weeks the remaining rats were killed. Eyes from each rat were rapidly enucleated, one being snap-frozen in liquid nitrogen and stored at −80°C, while the contralateral eye was fixed in 4% paraformaldehyde. Additionally, blood samples were collected and plasma levels of Nɛ-(carboxymethyl)lysine (CML) were measured by stable-isotope dilution tandem mass spectrometry [23].
In vivo administration of exogenous AGE Female C57BL/6 mice (10–12 weeks old) were randomly assigned to two groups of equal size and injected i.p. with either native mouse serum albumin (MSA) or glycoaldehyde-modified MSA (10 mg/kg) daily for seven consecutive days. At 3–4 h after the final injections, mice were killed, eyes enucleated and retinas dissected freshly, before being snap-frozen in liquid nitrogen.
Preparation of AGE-modified albumin The glycoaldehyde-modified MSA preparation was made according to Nagai et al. [24]. Following dialysis against PBS, endotoxin was removed using an endotoxin-removing column (Pierce, Rockford, IL, USA). Glycoaldehyde-MSA and native MSA were passed three times through separate columns to ensure that all contaminating endotoxin was removed. Analysis of the CML content of glycoaldehyde-MSA and native MSA was performed using gas chromatography–mass spectrometry. The lysine content of the samples was analysed by cation exchange chromatography and the values for CML were corrected for lysine loss and expressed as mmol CML/mol lysine as previously reported [25].
RNA isolation and mRNA quantification Snap-frozen rat and mouse eyes were allowed to thaw in ice-cold RNAlater (Ambion, Austin, TX, USA). The anterior chambers of the eyes were removed and the retinas were carefully dissected. Total RNA was extracted with TRIzol reagent (Invitrogen, Carlsbad, CA, USA). The amount of total retinal RNA isolated was approximately 12 μg per retina (spectrophotometric measurements at 260 nm), with no significant differences between the experimental groups. The integrity of the RNA samples was verified using an automated electrophoresis system (Experion; Bio-Rad, Hercules, CA, USA). All samples had sharp ribosomal RNA bands with no sign of degradation.A 2-μg aliquot of total RNA was treated with DNAse I (amplification grade; Invitrogen) and reverse-transcribed into first strand cDNA with Superscript II and oligo(dT)12–18 (Invitrogen).Details of the primers are given in the Electronic supplementary material (ESM) (ESM Table 1). Specificity of the primers was confirmed by a nucleotide–nucleotide BLAST (http://www.ncbi.nlm.nih.gov/blast/index.shtml) search. The presence of a single PCR product was verified by both the presence of a single melting temperature peak and detection of a single band of the expected size on a 3% agarose gel.Real-time quantitative PCR (qPCR) was performed in a sequence detection system (ABI Prism 5700; Applied Biosystems, Foster City, CA, USA). For each primer set a mastermix was prepared consisting of 1× SYBR Green PCR buffer (Eurogentec, Seraing, Belgium), 3 mmol/l MgCl2, 200 μmol/l each of dATP, dGTP and dCTP, 400 μmol/l dUTP, 0.5 U AmpliTaq Gold (Eurogentec) and 2 pmol primers. All cDNA samples were diluted 1:10 and amplified using the following PCR protocol: 10 min at 95°C, followed by 40 cycles of 15 s at 95°C and 60 s at 60°C and a melting program (60–95°C). Relative gene expression was calculated using the equation: R=E−Ct, where E is the mean efficiency of all samples for the gene being evaluated and Ct is the cycle threshold for the gene as determined during real-time PCR. All qPCR experiments were performed at least twice.Real-time qPCR data from the mouse experiments were normalised using 18S rRNA, which was determined to be stably expressed in all experimental groups. For the rat experiments, no suitable housekeeping genes that were not regulated by the diabetic background could be found. Therefore, the rat data were normalised using the relative starting amounts of cDNA, which was determined using a novel technique recently developed in our laboratory (J. M. Hughes, I. Klaassen, W. Kamphuis, C. J. F. Van Noorden and R. O. Schlingemann, unpublished results). In brief, reverse transcription reactions were carried out in duplicate with one set of reactions containing the normal dNTP mix and the parallel set of reactions containing a dNTP mix with α-32P-labelled dCTP. From each sample 4 μl of the α-32P-labelled dCTP-incorporated cDNA were pipetted on to separate nitrocellulose filters, which were allowed to air-dry. After washing with 0.1 mol/l phosphate buffer, radioactivity of the filters was measured using a scintillation counter (Beckman Coulter, Fullerton, CA, USA).
Western blotting Protein was isolated from paraformaldehyde-fixed retinal tissue as described by Shi et al. [26]. In brief, retinas were dissected from the 4% paraformaldehyde-fixed rat eyes and pooled in 1.5 ml Eppendorf vials in antigen-retrieval buffer (20 mmol/l Tris, 2% SDS, pH 7). The pooled samples were then dissociated using a pestle and incubated at 100°C for 20 min followed by 2 h at 60°C. Supernatant fractions were collected after centrifugation at 4°C for 15 min at 10,000g. Protein concentrations were determined with a bicinchoninic acid protein assay kit (Perbio, Etten-Leur, the Netherlands) and adjusted to 2.5 μg/μl. For SDS-PAGE and western blots, proteins were separated using 13% mini gels under reducing conditions. Following gel electrophoresis, proteins were transferred to a nitrocellulose filter (Whatman Schleicher & Schuell, Brentford, Middlesex, UK) using a semi-dry transfer cell (Bio-Rad). At the end of the transfer, the filter was blocked in blocking buffer (1% non-fat skimmed milk powder, 1% BSA, 1 mmol/l NaN3 in Tris-buffered saline and 0.05% Tween) overnight at 4°C while being gently rocked. The filter was incubated in blocking solution for 2 h at room temperature, with the relevant antibodies as defined below. Following three washes in TBS/0.05% Tween-20, the blots were incubated in blocking solution with horseradish peroxidase-conjugated goat–anti-rabbit or goat–anti-mouse antibodies for 1 h at room temp. After extensive washing, blots were developed using a chemiluminescent kit (SuperSignal West Pico; Perbio). Filters were exposed to X-ray film (Kodak-Biomax, Herts, UK).Primary antibodies (ESM Table 2) were diluted with 0.3% skimmed milk powder in TBS/Tween and horseradish peroxidase-conjugated goat–anti-rabbit or goat–anti-mouse (Perbio) was diluted 1:20,000. Intensity of bands was quantified by densitometry using AlphaEase software (AlphaInnotech, San Leandro, CA, USA).
Immunohistochemistry Cryostat sections (10-μm thick) were stained using an indirect immunoperoxidase procedure as previously described [27]. Primary antibodies are listed in ESM Table 2. Primary antibody was omitted for negative controls. Indirect immunoperoxidase staining was performed using histostaining reagents (Powervision; ImmunoVision, Daly City, CA, USA) for all sections except those incubated with the TIMP1 antibody. The TIMP1 sections were indirectly stained using horseradish peroxidase-labelled rabbit–anti-goat antibody (P0160; Dako, Glostrup, Denmark).
Statistics CML data were log10 transformed to obtain a normal distribution. Significant differences (p < 0.05) in glucose and CML plasma levels, and in gene expression levels among groups were calculated with single ANOVA. The Bonferroni post hoc test was used to perform pairwise comparisons of groups.
Results
Glucose and CML levels in control and diabetic rats Induction of diabetes and the degree of hyperglycaemia in streptozotocin-treated rats was established by serum glucose levels (Fig. 1a). Streptozotocin treatment resulted in a three- to fourfold increase in serum glucose concentration after 6 and 12 weeks irrespective of aminoguanidine treatment.
Fig. 1Plasma glucose (a) and CML (b) levels in control and diabetic rats at 6 and 12 weeks of streptozotocin-induced diabetes. White bars, control rats; black bars, diabetic rats; cross-hatched bars, diabetic rats treated with aminoguanidine. *p < 0.05 and *** p < 0.001 for difference between experimental and control group. The aminoguanidine group was only significantly different from the groups with diabetes at 12 weeks (†p < 0.05). The error bars show the standard deviation for each groupPlasma levels of CML were measured to determine the efficacy of aminoguanidine treatment. CML plasma levels were elevated by twofold at 6 and 12 weeks after streptozotocin-treatment (Fig. 1b). Aminoguanidine treatment had no effect on CML levels at 6 weeks, but at 12 weeks the CML levels were decreased by approximately 25%.
CCN family gene expression in control and diabetic rats After 6 weeks of diabetes, Cyr61 mRNA levels in the diabetic retina were increased by threefold against control retina. Treatment with aminoguanidine reduced Cyr61 expression to levels that were not significantly different from control levels (Fig. 2). CYR61 protein was mainly localised in the ganglion cell layer (Fig. 3). No differences in staining patterns were found between experimental groups.
Fig. 2Gene expression of CCN family members. Fold change, compared with control values, in retinal mRNA levels of CCN family members in streptozotocin-induced diabetic rats at 6 (white bars) and 12 weeks (cross-hatched bars) after streptozotocin-induction and in aminoguanidine-treated, streptozotocin-induced diabetic rats at 6 (black bars) and 12 weeks (grey bars) of diabetes. *p < 0.05 for difference between experimental group and control group; †p < 0.05 for difference between aminoguanidine-treated diabetic group and diabetes-only groupFig. 3Immunohistochemical staining patterns of a CYR61, b CTGF and c TIMP1 in control rat retinas. Intense staining of CYR61 and CTGF was present in large cell bodies of the ganglion cell layer (GCL) and weak staining in the inner plexiform layer (IPL). Intense uniform immunostaining of TIMP1 was found in the GCL and weak staining in the IPL. INL inner nuclear layer, OPL outer plexiform layer, ONL outer nuclear layer, RCL rod and cones layer, RPE retinal pigment epithelium. Magnification: ×150Ctgf mRNA levels were elevated by twofold at 12 weeks of streptozotocin-induced diabetes. Aminoguanidine treatment almost completely prevented this increase (Fig. 2). Western blotting showed a 1.8-fold increase in CTGF protein levels in the retina of diabetic rats at 12 weeks, whereas aminoguanidine treatment also prevented this effect (Fig. 4). CTGF immunostaining was mainly found in the ganglion cell layer and was more diffuse throughout the outer plexiform layer, inner nuclear layer and inner plexifrom layer (Fig. 3). Differences in staining between experimental groups were not observed.
Fig. 4CTGF protein levels in retina of control and diabetic rats. a Western blots of CTGF and GAPDH as loading control. Samples were pooled for each group. A prominent band of CTGF protein is present in the 12-week diabetic group (12D; n = 8), whereas protein bands were similar in all other groups (control rats at 6 [6C; n = 6] and 12 [12C; n = 8] weeks, diabetic rats at 6 [6D; n = 8] weeks and diabetic rats treated with aminoguanidine at 6 [6AG; n = 7] and 12 [12AG; n = 7] weeks). b The blots were quantified by densitometry and expressed as a ratio of CTGF:GAPDHWisp1 and Wisp3 mRNA levels were low in retinas of all groups of rats. The levels never differed more than 1.4-fold between experimental and control groups. Due to small standard deviations, significant differences were found (Fig. 2), but it is doubtful whether these small differences are biologically meaningful. Nov and Wisp2 mRNA expression levels were too low to be detected in all groups of rats.
Expression of transforming growth factor beta 1 and 2 in control and diabetic rats To determine whether: (1) transforming growth factor beta (TGFB), an upstream regulator of CTGF production, is induced by diabetes; and (2) this induction is prevented by aminoguanidine in rat retina, we examined the mRNA levels of its two most common isoforms, Tgfb1 and Tgfb2. Tgfb1 mRNA expression was decreased by approximately 30% in retina of rats with diabetes for 6 weeks (Fig. 5). Whether this difference is biologically relevant remains to be determined. In all other experimental groups, Tgfb1 and Tgfb2 mRNA levels were similar to control levels (Fig. 5). Immunohistochemical analysis of TGFB1 revealed a vascular pattern of staining in all experimental groups (Fig. 6).
Fig. 5Fold change of Tgfb1 and Tgfb2 expression in diabetic rats at 6 (white bars) and 12 weeks (cross-hatched bars) after streptozotocin-induction and in aminoguanidine-treated diabetic rats at 6 (black bars) and 12 weeks (grey bars) after streptozotocin-induction. Tgfb1 was significantly decreased in the diabetic rats after 6 weeks (*p < 0.05 vs control). This difference was not observed in the aminoguanidine-treated group at 6 weeks. Tgfb2 expression was not significantly altered at either time pointFig. 6Immunohistochemical staining patterns of TGFB1 (a, d), laminin (b, e) and fibronectin (c, f) in retina of control rats (a–c) and rats 12 weeks after streptozotocin-induced diabetes (STZ) (d– f). Immunostaining of TGFB1, laminin and fibronectin was confined to the retinal microvasculature and did not notably differ between control and streptozotocin sections. Magnification: × 150
Expression of extracellular matrix molecules in control and diabetic rats CTGF and CYR61 are known modifiers of the extracellular matrix. Therefore, we investigated expression patterns of various extracellular matrix components. Col4a3 mRNA levels in the rat retina were elevated by threefold after 6 weeks of diabetes. Aminoguanidine treatment inhibited this induction of Col4a3 mRNA levels by 30% (Fig. 7). Lamb1 mRNA levels showed a 1.5-fold increase in 12-week diabetic rats, which was virtually unaffected by aminoguanidine treatment. Fibronectin mRNA levels were not affected by streptozotocin-induced diabetes. Timp2 mRNA levels were not affected either, but Timp1 mRNA levels were elevated by 2.5-fold in retina of 12-week diabetic rats, this increase being completely prevented by aminoguanidine treatment (Fig. 7). Laminin and fibronectin were localised immunohistochemically in microvessels in rat retina (Fig. 6). This staining pattern was similar in all groups. TIMP1 immunostaining was restricted to the ganglion cell layer in all groups of rats (Fig. 3).
Fig. 7Gene expression of extracellular matrix components. Fold change of extracellular matrix gene expression as indicated in diabetic rats at 6 (white bars) and 12 weeks (cross-hatched bars) after streptozotocin-induction and in aminoguanidine-treated diabetic rats at 6 (black bars) and 12 weeks (grey bars) after streptozotocin-induction. *p < 0.05 for difference between experimental and control group; †p < 0.05 for difference between aminoguanidine-treated diabetic group and diabetes-only group
CCN family mRNA expression in control and AGE-treated mice AGE-treated MSA infusion of mice induced an increased retinal expression of Cyr61 and Ctgf mRNA by 3.7-fold and twofold, respectively, compared with control mice (Fig. 8). Wisp1 and Wisp3 mRNA expression was not affected by AGE-treated MSA infusion (data not shown).
Fig. 8Relative mRNA levels of Cyr61 and Ctgf in retinas of control (white bars) and AGE-treated (black bars) mice, depicted as fold change in comparison with control mice. *p < 0.05 for effect of AGE treatment on Cyr61 and Ctgf mRNA levels
Summary of results In the retina of rats with streptozotocin-induced diabetes, mRNA levels of the CCN family members Cyr61 and Ctgf were increased threefold at 6 weeks and twofold at 12 weeks, respectively, whereas expression of all other CCN family members was not notably affected. CTGF protein levels in retina were also elevated twofold at 12 weeks of diabetes. In the aminoguanidine-treated diabetic rats these increases were partly counteracted by treatment with this AGE inhibitor. In line with these findings, treatment of mice with exogenous AGE induced elevated retina mRNA levels of Cyr61 and Ctgf, but not of the other CCN family members. In parallel, mRNA levels of some extracellular matrix components were also increased in the retina of diabetic rats, an effect also prevented by aminoguanidine treatment.
Discussion
We present here a comprehensive expression analysis of the CCN family of fibrosis-inducing cytokines in the retina of rats with streptozotocin-induced diabetes. Messenger RNA and protein levels of CYR61 and CTGF, both known to be capable of modulating the extracellular matrix, were increased in diabetic rats, whilst the AGE inhibitor aminoguanidine attenuated these effects of diabetes. We also found that exogenously administered AGEs are capable of inducing Cyr61 and Ctgf expression in the adult mouse retina in vivo. Taken together, these data present evidence that AGEs are both necessary and sufficient to cause increased levels of CYR61 and CTGF in the diabetic retina.
Expression of CTGF at the mRNA or protein level has previously been demonstrated in vivo in normal and diabetic rat [28] and human retina [29], as well as in cultured retinal microvascular cells [30] and astrocytes [31]. Our data on CTGF are in agreement with a previously reported twofold increase in Ctgf expression in the diabetic rat retina [28]. In our study, immunostaining with a polyclonal anti-CTGF antibody showed staining in the ganglion cell layer [28] and diffusely throughout the larger part of the inner rat retina. In contrast, in a previous study of the human diabetic retina employing a monoclonal anti-CTGF antibody, CTGF was detected in microglia and pericytes in the microvasculature of the inner retina [29]. These varying patterns of CTGF protein distribution may be species-related or due to differences in specificity of the antibodies used.
Gene expression analysis was also performed on the other five known members of the CCN family. We found Cyr61 expression in the normal adult rat retina and upregulation in the diabetic retina. In addition to CTGF, this suggests a possible role for CYR61 in the development of diabetes-related retinal sequelae. The lack of detectable Nov and Wisp2 expression argues against a role for these two proteins in normal or diabetic retina. This is in agreement with a previous study, which demonstrated that Wisp2 mRNA was not present in tissues of the adult rat [32]. Expression of Wisp1, which is much less studied but known to suppress cancer cell growth in vivo [33], was slightly decreased in diabetic retina, whereas Wisp3, a cell growth suppressor and inhibitor of angiogenesis [34], was increased. The significance of these findings remains unclear, as the functions of the CCN family members are complex. Still, such opposing actions may be reflective of a regulatory balance or may indicate that some CCN family members are redundant, although this is currently not considered to be the case in other tissues [35].
The role of CTGF in diabetic renal pathology has been clearly established. CTGF is responsible for mesangial expansion [36] and increased extracellular matrix deposition [37, 38] as observed in early stages of diabetic nephropathy. Therefore, CTGF and/or CYR61 may have a similar role in diabetic retina and be responsible for the thickening of microvascular basement membranes observed in early stages of diabetic retinopathy.
We also therefore examined expression patterns of the genes encoding several extracellular matrix-related molecules. Expression of the basement membrane component Col4a3 was found to be increased concomitantly with Cyr61 expression after 6 weeks of diabetes, an increase significantly reduced in aminoguanidine-treated diabetic rats. At 12 weeks of diabetes, Timp1 and Lamb1 expression demonstrated a concomitant increase with Ctgf, which was significantly attenuated in the aminoguanidine-treated group (not statistically significant for Lamb1). These findings suggest a causal role of AGEs in the diabetes-induced production of CTGF, CYR61, COL4A3 and TIMP1 in the retina. Whether increased Lamb1 expression is also mediated by AGEs remains to be determined. However, this possibility is supported by a previous study showing that protein and mRNA levels of the ribosomal protein SA (previously known as laminin receptor 1 [67 kD, ribosomal protein SA]) are upregulated by AGEs in cultured retinal microvascular endothelial cells [39].
AGE CML plasma levels, used in our study as a marker of AGE formation in the rat diabetes model, were not altered at 6 weeks in diabetic rats, making an effect of AGEs at this time point questionable. However, it should be noted that CML is merely one of many types of AGE known to be generated under hyperglycaemic conditions [40] and that aminoguanidine has been shown to decrease serum AGE levels in diabetic rats as early as 6 weeks after diabetes induction [41].
In previous studies in the diabetic rat kidney, Ctgf and fibronectin gene expression both increased after 32 weeks of streptozotocin-induced diabetes [10]. These changes were prevented by aminoguanidine treatment. As AGE accumulation in the diabetic kidney was prevented by aminoguanidine, those authors surmised that the anti-fibrotic effects of aminoguanidine could be at least partially mediated by a decrease in CTGF expression [10]. Our study may have been too short to observe an increase of fibronectin in the diabetic retina, but otherwise our results are in line with these findings in kidney. Additional studies will be necessary to further elucidate the ability of CYR61 and CTGF to directly modulate these extracellular matrix molecules in retinal vascular cells.
TGFB, considered to be the most important fibrotic factor, has been shown to upregulate CTGF in many cell types in vitro [42–44] and in vivo [45, 46]. Although our findings indicate that TGFB production is not increased in the diabetic retina, a role for TGFB in the observed upregulation of CTGF cannot be ruled out, as the regulation of TGFB bioavailability is complicated and not solely dependent on the level of TGFB production [47].
We have recently demonstrated that vascular endothelial growth factor (VEGF) increases expression of Ctgf and Cyr61 in the rat retina in vivo as well as in retinal vascular endothelial cells in vitro (E. J. Kuiper, J. M. Hughes, I. M. C. Vogels et al., unpublished results). As AGEs are known inducers of VEGF in retinal cells [48, 49], it is possible that the increases in CTGF and CYR61 observed in our animal models are the result of AGE-induced VEGF.
A major finding of our study was the ability of aminoguanidine to attenuate the increase in Ctgf expression observed in the diabetic rat retina. Tikellis et al. [28] have reported that perindopril, an ACE inhibitor, prevents increased Ctgf expression in diabetic rat retina [28]. This suggests that both interventions may affect a common molecular mechanism leading to the upregulation of CTGF. As ACE inhibition has been shown to prevent AGE accumulation in diabetic tissues [50], it is feasible that inhibition of AGE formation could be a common molecular mechanism allowing both ACE inhibitors and aminoguanidine to inhibit the increase of retinal CTGF.
In summary, this study provides the first evidence that, in addition to CTGF, the CCN family molecules CYR61, WISP1 and WISP3 play possible roles in the development of early stages of experimental diabetic retinopathy. At the very least, these results warrant further study into the functional aspects of these molecules in the eye, and of how these aspects pertain to the development of diabetic retinopathy. Additionally, we demonstrate for the first time that AGEs directly upregulate both CTGF and CYR61 levels in the retina in vivo and that aminoguanidine inhibits these diabetes-induced increases. This provides the first evidence that CTGF and CYR61 are downstream effectors of AGEs in the diabetic retina and implicates them as possible targets for future intervention strategies.
Electronic supplementary material
Below is the link to the electronic supplementary material.

advanced glycation end products
extracellular matrix
cystein-rich protein 61
connective tissue growth factor
diabetic retinopathy
basement membrane
experimental
aminoguanidine
diabetes mellitus
gene expression regulation


Structure and function of mammalian cilia

In the past half century, beginning with electron microscopic studies of 9 + 2 motile and 9 + 0 primary cilia, novel insights have been obtained regarding the structure and function of mammalian cilia. All cilia can now be viewed as sensory cellular antennae that coordinate a large number of cellular signaling pathways, sometimes coupling the signaling to ciliary motility or alternatively to cell division and differentiation. This view has had unanticipated consequences for our understanding of developmental processes and human disease.
Introduction
Although in 1835, Purkinje and Valentin (1835) published the first treatise to include studies of mammalian cilia, the history of what may be considered modern research on mammalian cilia does not extend back more than a few years longer than the history of this journal. Motile mammalian cilia attracted the attention of microscopists before the advent of electron microscopy, because they moved and were obviously similar in this motion to protozoan cilia. Nevertheless, several light microscope structures whose later fine structure appearance (and molecular biology) had little in common with cilia were nevertheless labeled cilia, including the stereocilia of the hair cells, which are in fact modified microvilli. In 1954, the electron microscope observations of Fawcett and Porter (1954) definitively characterized the 9 + 2 pattern of the cytoskeleton, the axoneme, of motile mammalian and other cilia. They showed that this structure was enclosed by an extension of the cell membrane, the ciliary membrane. The axoneme of true cilia was always an extension of a basal body and related to the nine-fold construction of the centriole. By 1956, Porter (1957) and De Robertis (1956) realized that cilia were found as sensory structures in, for example mammalian photoreceptors, where the central pair of microtubules in the axoneme was absent, and the cilia were nonmotile, hence 9 + 0. Finally, in the early 1960s, a number of electron microscopy studies showed that solitary 9 + 0 cilia, now termed primary cilia, are present on many differentiated cells of tissues of the mammalian and vertebrate body, including kidney cells, fibroblasts, neurons and even Schwann cells (Barnes 1961; Sorokin 1962; Grillo and Palay 1963).
Although specific antibodies against detyrosinated and acetylated tubulin became available to detect primary cilia in immunofluorescence microscopy (Piperno et al. 1987; Cambray-Deakin and Burgoyne 1987), primary cilia were generally neglected as interesting cell organelles. The biggest problem was that primary cilia had no demonstrable function, even though considerable circumstantial evidence suggested that they were some form of chemical or mechanical sensor. On this basis, Poole et al. (1985) proposed that primary cilia of connective tissue cells functioned as a cellular cybernetic probe, or in current terminology, as a cellular global positioning system. Roth et al. (1988) then demonstrated that primary cilia on cultured kidney epithelial cells could be bent by flow, and Wheatley (1995) suggested that if primary cilia in renal epithelia did not function properly in sensing flow, there would be pathophysiological consequences. Nevertheless, many cell biologists dismissed the primary cilium as vestigial.
While early studies of mammalian cilia focused on showing that the mechanism and biochemistry of motility was virtually identical to that discovered in other cilia, more recent studies have examined the way that cilia are built. These studies have led to novel insights for both motile and nonmotile, including primary, cilia. All cilia can now be viewed as sensory cellular antennae that coordinate a plethora of cellular signaling pathways, and this view has had unanticipated consequences for our understanding of developmental processes and disease. A recent review of mammalian cilia structure and function covering many of the topics presented here, in further detail, is found in Satir and Christensen (2007).
The mechanism of ciliary motility and primary ciliary dyskinesia
The basic mechanism of ciliary motility is now reasonably well understood, although many details of how waveform is generated and propagated remain obscure. While much of the information about this mechanism was derived from protistan and invertebrate organisms (Satir 1985), the basic principles clearly apply to mammalian cilia of the respiratory and reproductive tract, and to brain ependymal cilia. An electron micrograph of cilia of the mouse oviduct is shown in Fig. 1.
Fig. 1Classic transmission electron micrograph of mouse oviduct cilia. Cross-sections show the 9 + 2 axoneme of motile cilia (asterisk). The axoneme grows from a basal body, with a basal foot (arrowhead) pointing in the direction of the effective stroke. The transition zone between basal body and axoneme contains the ciliary necklace (arrow). (From Dirksen and Satir 1972, unpublished, with permission)
Ciliary motility is caused by the relative sliding of the nine outer axonemal doublets, operating as two opposing sets, one (doublets 1–4) to produce the effective stroke (or principal bend in flagella), one (doublets 6–9) to produce the recovery stroke (or reverse bend), powered by ATP and a set of molecular motors, the axonemal dyneins. The axonemal dyneins are arranged in two sets of arms: the outer dynein arms (ODAs) consisting of two heavy-chain dynein isoforms in mammalian cilia, packaged with intermediate and light chains, with four identical ODAs, each aligned along 96-nm long doublet microtubule (Nicastro et al. 2006), and the more complex inner dynein arms (IDAs). The IDAs, more centrally located, consist of at least seven heterodimeric and monomeric heavy-chain dynein isoforms per 96 nm. Generally, the ODAs and IDAs function together, but the ODAs principally regulate beat frequency, in part by cAMP-dependent phosphorylation of an ODA regulatory light chain, while the IDAs control beat form. Both frequency and form change by changing the rate of sliding and the switching of activity between the doublet sets. Sliding is converted into propagated bending, most efficiently by control of IDA activity via phosphorylation and mechanical interaction involving the radial spoke, central pair complex.
In effect, the 9 + 2 axoneme is a nanomachine composed of perhaps over 600 proteins in molecular complexes, many of which also function independently as nanomachines. The enzymes that control axonemal response, for example protein kinase A, are structurally part of the complexes. Strong support for this mechanism of ciliary motility comes from studies of paralyzed and other mutants of protistan cilia, where a structural defect and a corresponding genetic mutation can directly be correlated with a change in ciliary beat. Similarly, strong support for the applicability of the mechanism to human cilia comes from studies, mainly of ciliary structural defects and genetic mutations associated with impaired mucociliary function in the respiratory system, which produces sinusitis and bronchiectasis. The genetic diseases are now called primary ciliary dyskinesia (PCD). Clinical features of PCD, which include chronic rhinitis/sinusitis, otitis media, male infertility and an increased incidence of hydrocephalus, point to physiological processes where ciliary motility is essential (Afzelius 2004). As might be anticipated, prominent causes of PCD are mutations that affect the assembly or function of the dynein arms—most commonly a mutation in DNAH5, a gene encoding a dynein heavy chain of the ODAs (Olbrich et al. 2002).
Any mutation affecting the ciliary beat mechanism will potentially produce PCD, depending on the severity of beat impairment. An instructive example is hydin, a component of the central pair projection complex that interacts with the radial spokes in the switching mechanism between doublet sets. Hydin-deficient flagella of Chlamydomonas become intermittently paralyzed, stopping for long times at the end of the effective or recovery strokes, where the direction of beat is reversed (Lechtreck and Witman 2007). Mice defective in Hydin develop hydrocephalus and die shortly after birth. Lechtreck et al. (2008) analyzed ciliary structure and motility in these mice. The central pair projection assigned to the hydin complex was missing. Mutant cilia were unable to bend properly and frequently stalled, implying that because central pair structure was defective, interactions between the central pair and the radial spokes that affected dynein arm switching were abnormal, and consequently cilia-generated flow was impaired. Motility of tracheal cilia was also impaired. One would predict that human mutations in HYDIN are also likely to result in hydrocephalus or a form of PCD.
Many people diagnosed with PCD have situs inversus totalis that is reversal of left–right asymmetry of body organs such as the heart. This is now known to be produced, because fluid flow produced by motile cilia at the embryonic node is defective (Hirokawa et al. 2006). The usual flow pattern produced by the cilia is unidirectional right-to-left, and this activates a left-side-only gene cascade, probably by impinging on primary cilia at the left side of the node (McGrath et al. 2003). The nodal cilia are mainly missing the central pair of axonemal microtubules, hence, although motile, are often 9 + 0, but with some unique ODA dynein isoforms (Supp et al. 1999). It is not known whether the response of the primary cilia at the node is to mechanical displacement (flow sensing) or if it involves morphogens produced in membrane parcels in the node (Tanaka et al. 2005). Morphogen-containing multimembrane vesicles are a feature of nodal response that could also apply respiratory and other ciliated epithelia, where flow occurs.
While mouse mutants or knockouts that are missing cilia develop situs inversus, hydin mutants do not show this phenotype, presumably because their motility is sufficient to generate appropriate nodal flow. In a similar way, by examining the PCD phenotype of mutations in various ciliary proteins, identified by proteomics, it should be possible to dissect details of the function of the protein in the mechanism of beat generation (Avidor-Reiss et al. 2004; Badano et al. 2006; Blacque et al. 2005).
Principles of ciliogenesis
A major breakthrough in the understanding of the importance of cilia in the body has come from the study of Chlamydomonas ciliogenesis. In that cell, the cilia are traditionally called ‘flagella’. Ciliogenesis involves transport of materials into, along and out of the cilium, directly visualized as ‘intraflagellar transport’ or IFT (reviewed by Rosenbaum and Witman 2002). IFT requires molecular motors, several kinesins for anterograde transport and a specialized cytoplasmic dynein for retrograde transport. Coupling the transported cargo to the molecular motors that move along the outer doublets of the axoneme are two protein complexes (Cole 2003) comprising about 19 IFT proteins. Remarkably, this machinery is conserved almost universally wherever cilia are built, and orthologs of the motors and IFT proteins are found in sensory cilia and their derivatives, such as the mammalian photoreceptor (Baker et al. 2003) and in primary cilia, as well as in protistan motile cilia. However, motile cilia of mammalian tissues often assemble in mass (Gaillard et al. 1989; Dirksen 1991). Centriologenesis of hundreds of basal bodies occurs from a fibrogranular mass in the cytoplasm and cilia sprout from the cell surface. It is unclear whether classical IFT is the major mechanism of ciliogenesis in respiratory epithelium, or what modifications of the process occur.
Wheatley (1969), Fonte et al. (1971) and Archer and Wheatley (1971) studied ciliogenesis of primary cilia of established cell lines in vitro. Primary cilia grow when the cells become confluent and reach stationary phase. Cells resorb their cilia shortly before entering mitosis. Postmitotic cells reassemble primary cilia in G1 and maintain the cilium as the cells enter growth arrest (G0) and undergo differentiation. In contrast to motile cilia, the primary cilium emanates uniquely from the distal end of the existing mother centriole of the centrosome, which migrates to the cell surface during growth arrest. Ciliogenesis by IFT is initiated while the centrosome is positioned at the Golgi apparatus near the nucleus, while extensive IFT and elongation of the cilium take place after docking of the nascent cilium at the cell surface, where microtubule pairs are quickly assembled to form the mature axoneme. Two recent and extensive reviews of the complex interactions between IFT particles, molecular motors, centrosomal proteins and other microtubule-associated proteins in ciliogenesis are presented in Pedersen et al. (2008) and Blacque et al. (2008).
Ciliopathies
The first human illness that could be linked to primary cilia in epithelial cells was polycystic kidney disease (PKD). One of the orthologs of IFT88 is the mammalian protein polaris mutated in a transgenic mouse Tg737orpk, which was a model for the study of PKD. Chlamydomonas IFT88 mutants are defective in ciliogenesis, and Pazour et al. (2000) demonstrated that, similarly, cilia of the mouse kidney were abnormally short or missing, which suggested that PKD might be a ciliary disease. This conclusion was strengthened by two succeeding discoveries. Mechanical deflection of kidney tubule epithelial cells induces an increase in intracellular calcium (Praetorius and Spring 2001), and the PKD proteins, polycystins 1 and 2 in Ca2+-signaling, localize to the ciliary membrane (Pazour et al. 2002; Yoder et al. 2002). Many other proteins whose functions are disrupted in cystic diseases, e.g. nephrocystins, have now been localized to the cilium or at the ciliary basal body, supporting the conclusion that the primary cilium in kidney tubules partly functions as a mechanosensor that, upon bending, activates a series of signaling pathways in the cilium to control development and homeostasis of the tissue (for reviews, see Yoder et al. 2007; Hildebrandt and Zhou 2007), as originally envisaged by Roth et al. (1988) and Wheatley (1995). Thus, even if primary cilia are present, unless these proteins are targeted appropriately to the cilia or the ciliary basal body, pathology develops.
It is now clear that the ciliary membrane is a privileged and specialized compartment for receptor signaling and that the polycystins are only one type of many membrane proteins that must be targeted to the primary cilium to function properly. Other carefully documented examples include the receptor tyrosine kinase PDGFRα (Schneider et al. 2005) and the patched receptors for hedgehog signaling (Rohatgi et al. 2007) that control cell growth and differentiation processes. Further, essential downstream components in PDGFRα and hedgehog signaling as well as signaling in neurotransmission, Wnt pathways, extracellular matrix interaction and osmolyte transportation uniquely localize to the cilium and/or to the ciliary centrosome (Gerdes et al. 2007; Corbit et al. 2008; for reviews, see Singla and Reiter 2006; Michaud and Yoder 2006; Christensen et al. 2007; Christensen and Ott 2007). In many of these signaling systems, the cellular response is initiated by receptor activation in the primary cilium. Deficiencies in placement of receptors and their immediate downstream signaling components in the cilium or at the ciliary base results in ciliopathy, including cystic kidney, pancreatic and liver diseases, retinitis pigmentosa, cancer, defective neurogenesis, Bardet-Biedl syndrome, polydactyly, anosmia and other developmental defects. In the Tg737 mouse, these defects lead to early perinatal death. As shown in Fig. 2, primary cilia with hedgehog receptor pathway signaling are found on human embryonic stem cells (Kiprilov et al. 2008), which suggests that ciliary signaling is involved in differentiation from the beginning of embryogenesis. In embryogenesis, signaling through the primary cilium is necessary for normal development, probably because such signaling regulates the balance between cell division, polarity, migration, differentiation and apoptosis for many tissues.
Fig. 2Primary cilia of human embryonic stem cells. Immunofluoresence microscopy using acetylated α tubulin antibody (tb) reveals the presence of primary cilia (arrows) on human embryonic stem cells. In the absence of stimulation, the hedgehog receptor ‘patched’ (Ptc) colocalizes with the acetylated α tubulin all along the ciliary membrane. Red and green channels are displaced in the images to define colocalization more clearly. Nuclei are stained with DAPI (blue). Upon stimulation, as part of the signaling cascade, Ptc leaves the cilium and the smoothened receptor (Smo) enters to activate the hedgehog signaling cascade. Asterisk marks the ciliary base. (From Kiprilov et al. 2008, with permission, courtesy of The Journal of Cell Biology)
Primary cilia in adult tissues
Primary cilia persist in many differentiated cells, including kidney tubule epithelial cells, fibroblasts and neurons, after organogenesis is complete and cell division rates fall. Presumably, as semipermanent structures, the cilia function as mechano- or chemosensors and as a cellular global positioning system to detect changes in the surrounding environment, to initiate cellular replacement after damage, for example. To test this hypothesis, techniques are being used to knock out primary cilia or ciliary proteins in specific tissues of adult organisms.
Davenport et al. (2007) have completed one of the first of these studies, using an inducible system in adult mice to disrupt IFT in several different ways, causing loss of primary cilia. Respiratory motile cilia are probably more stable and relatively unaffected. Surprisingly, when primary cilia are lost from all adult tissues, the devastating abnormalities and lethality seen after embryonic loss of cilia are not observed. PKD eventually develops a year after induction. The same delay has been reported by Piontek et al. (2007) after adult-specific knockout of polycystin-1. This delay correlates well with a greatly reduced rate of cell division and a different pattern of gene expression in the mature kidney and could explain the increase in human PKD with age. Such changes in cell proliferation rate may relegate the primary cilium of adult tissues to a less immediate role, involving long-term homeostasis of the tissue. Because signaling through primary cilia is coupled to cell cycle events, for example in PDGFRαα signaling in fibroblasts, long-term disruption of ciliary signaling could be a factor in oncogenesis.
One adult tissue responds immediately to ciliary knockout however—that is nervous tissue, most specifically neurons in the hypothalamus. Knockout of all adult primary cilia in the mouse, or specifically only of primary cilia on POMC neurons, leads to hyperphagia—compulsive and excessive eating—leading to obesity. Obesity then causes numerous secondary defects resembling type II diabetes. These defects do not occur if the knockout mice are kept on a restricted diet. Eating behavior is regulated by the hormone leptin. These fndings suggest that the leptin receptor might be located in the membrane of the primary cilia of the POMC neurons.
The hypothalamic hormone somatostatin is a negative regulator of leptin. While leptin leads to reduced eating, somatostatin increases eating behavior. The somatostatin receptor sst 3 is localized to primary cilia in hypothalamic neurons (Stepanyan et al. 2007). The results of Davenport et al. (2007) suggest that, much like the patched and smoothened receptors work in hedgehog signaling, the leptin receptor and a somatostatin receptor could work in a Yin-Yang relationship within the POMC primary cilium (Satir 2007).
Whatever the precise cell biological explanation of the relationship between ciliary knockout and hyperphagia, the effect of knockout is behavioral. We might expect that mutations in other proteins of neuronal primary cilia could lead to other behavioral responses. There are reports that the rages that fueled the famous Hatfield-McCoy feud of Appalachia probably were driven by a family suffering from Von Hippel-Landau (VHL) syndrome. VHL protein controls ciliogenesis and is localized to cilia (Schermer et al. 2006). Perhaps a more ciliocentric view of neuronal activity affecting recurrent or addictive actions is warranted.
Conclusions
Beginning with important electron microscopic studies and culminating in immunolocalization combined with molecular genetic technology, much has been learned about motile, sensory and primary cilia in mammals. Defects in building the primary cilium or mutations in ciliary membrane or axonemal proteins lead to ciliopathies, important human diseases. The cilium has moved to a prominent place in studies of embryogenesis and tissue differentiation and maintenance. There are hints that the fundamental cell biology of cilia will also be important in oncogenesis, aging diseases and human behavioral disorders. The strides of the past half century in understanding this organelle have been impressive, and the promise of discovery in the next half century is compelling.

motility
primary cilia
signaling
motile cilia
ciliopathies
sensory organelles


Joint effects of HLA, INS, PTPN22 and CTLA4 genes on the risk of type 1 diabetes

Background/hypothesis HLA, INS, PTPN22 and CTLA4 are considered to be confirmed type 1 diabetes susceptibility genes. HLA, PTPN22 and CTLA4 are known to be involved in immune regulation. Few studies have systematically investigated the joint effect of multiple genetic variants. We evaluated joint effects of the four established genes on the risk of childhood-onset type 1 diabetes.
Introduction
The risk of complex diseases such as type 1 diabetes is generally thought to be influenced by multiple genetic and non-genetic factors, and it has been hypothesised that interactions between genes, or epistasis, are very common for such diseases [1]. The presence of interactions could be one of the reasons why searching for susceptibility loci for many diseases has been less successful than expected [2]. When moving from monogenic diseases to complex diseases, it seems reasonable to assess more than one locus at a time, although models become increasingly complex as the number of loci increases [3].
Whereas few or no common genetic variants have been firmly established for most common diseases [4], there are now at least four genetic loci that are established as causally involved in the aetiology of type 1 diabetes. They give us a unique possibility to evaluate gene–gene interactions among established susceptibility genes. Specific allelic combinations of DRB1, DQA1 and DQB1 in the human leucocyte antigen (HLA) complex, variants in the insulin gene (INS), the cytotoxic T lymphocyte antigen-4 gene (CTLA4) and the protein tyrosine phosphatase, non-receptor type 22 gene (PTPN22) have been repeatedly associated with type 1 diabetes susceptibility [5–8] using different approaches. All established loci are thought to be involved somehow in immune regulation, but details of the mechanisms relating the polymorphisms to risk of type 1 diabetes are in most cases poorly understood.
Evaluating the joint effects of genes contributes important information for risk prediction, and is also thought to provide information about biological interactions, although the latter is controversial and more complex than commonly thought [2, 3, 9].
Previous studies have assessed interaction between HLA and INS and reported divergent results [10–18]. The reported results of the joint effect of HLA and INS are confusing not only because they have shown diverging results but also because the definitions and terminology of interactions are not consistent [19]. The interpretation of statistical interaction depends on the choice of scale used to measure the effects [2]. Although additivity of risks is often taken as independence [20], multiplicativity of risk is sometimes also taken as independence [21]. The joint action of HLA and INS has variously been described as being multiplicative [14, 15], additive [13], providing evidence of interaction [15], and non-interacting [13, 14, 17].
Few studies have investigated the more recently established susceptibility loci PTPN22 and CTLA4 in the context of joint effects on the risk of type 1 diabetes. The studies that have been done have mainly concluded that there is a non-interaction [22, 23] effect, but here also the results have diverged [24–27].
The aim of our study was to assess the joint effects of the four established susceptibility loci HLA, INS, CTLA4 and PTPN22 in type 1 diabetes, using a consistent approach with both population-based case-control and family trio designs and with large sample sizes.
Methods
Participants We analysed two independent type 1 diabetes data sets. One nuclear family set consisted of 421 trios with mother, father and one child diagnosed in Norway with type 1 diabetes before age 15 years (225 [53.4%] of the affected children were boys). The families were collected between 1993 and 1997. In families with more than one affected sibling, only the proband was included in the analyses. The case-control data set consisted of 1,331 type 1 diabetes patients (51.9% boys and 48.1% girls) and 1,625 (51.4% boys and 48.6% girls) control participants aged <15 years. In analyses involving age of disease onset, we divided the data sets according to age of disease onset of the affected child into three groups (0–4.9, 5–9.9 and ≥10 years). The controls were randomly selected from the official population registry among children born between 1985 and 1999 and recruited in 2001, as previously described [28]. The patients in the case-control material were from the Norwegian Childhood Diabetes Registry consecutively recruited between 1997 and 2000 [29] and between 2002 and 2005. The type 1 diabetes patients and their family members were recruited by the Norwegian Childhood Diabetes Study Group, including all paediatric departments in Norway. All type 1 diabetes patients were diagnosed according to EURODIAB criteria [30]. The study was approved by the local ethics committee, and informed consent was obtained from all participants or their parents.
Genomic DNA extraction and genotyping In the majority of the type 1 diabetes case-control samples we used DNA extracted from buccal cells [31]. In all remaining samples, DNA was extracted from peripheral whole blood using a salting-out protocol. Genotyping of HLA-DRB1, −DQA1 and −DQB1 was performed using PCR-SSOP (sequence-specific oligonucleotide probing) mainly following published methods [32], or PCR-SSP (sequence-specific primer) [33, 34], or using time-resolved fluorescence technology in the Delfia assay (Perkin-Elmer Life Sciences, Turku, Finland). HLA genotypes were grouped into four risk categories based on DQB1, DQA1 and DRB1 genotypes, including DRB1*04 subtyping. The majority of DRB1*04-DQA1*0301-DQB1*0302 haplotypes in Norway are DRB1*0401 or −0404 (constituting >94% of the haplotypes) [35]. In the present study the other rare subtypes are referred to as DRB1*04XX. Because of the almost complete linkage disequilibrium between INS-VNTR allele classes and the −23 HphI polymorphism, we genotyped −23 HphI (rs689) as a marker for the INS-VNTR. The −23 HphI A allele corresponds to the VNTR class I and the −23 HphI T allele correspond to the VNTR class III. In PTPN22 we genotyped the single nucleotide polymorphism (SNP) Arg620Trp (rs2476601). The SNP JO27_1 (rs11571297) was genotyped in CTLA4. SNP genotyping was performed by TaqMan allelic discrimination assays on an ABI 7900HT DNA Analyzer (Applied Biosystems, Foster City, CA USA). Primer and probe sequences are shown in Electronic Supplementary Material (ESM) Table 1. The PCR conditions are available on request.
Data analysis The HLA haplotypes were grouped as high risk, intermediate risk, neutral risk and low risk according to the following criteria: high-risk category, DRB1*0401/04XX-DQA1*03-DQB1*0302/DRB1*03-DQA1*05-DQB1*0201 (DR4-DQ8/DR3-DQ2); low-risk category, all genotypes with at least one DQB1*0602 allele; intermediate-risk category, DRB1*0404-DQ8/DR3-DQ2, DR3-DQ2/DR3-DQ2, DR4-DQ8/DR4-DQ8 (with the exception of DRB1 0404-DQ8 homozygotes, which were grouped as neutral), DRB1*0401 or 040XX-DQ8/X (X≠DQB1*0602 or DR3-DQ2). The remaining haplotypes were grouped in the neutral-risk category. For assessment of two-locus joint effects, we pooled genotypes of INS, PTPN22 and CTLA4 as follows: INS class I/I genotypes were compared with I/III together with III/III genotypes. The PTPN22 TT and CT genotypes were compared with CC. CTLA4 (JO27_1) TT genotypes were compared with TC and CC genotypes. Not all individuals were genotyped for non-HLA polymorphisms because of lack of DNA. We did not exclude individuals with some missing genotypes to prevent the loss of important information when looking at joint effects between polymorphisms. The available numbers of individuals for each analysis are seen in the tables. Data were presented using stratified 2 × 2 tables and analysed using logistic regression models including interaction (product) terms, in SPSS for Windows (version 14.0; SPSS, Chicago, IL, USA). In addition to formal analyses treating HLA categories as categorical in the logistic regression, we also tested for interactions when treating HLA category as a continuous variable coded 1, 2, 3, 4, thus maximising the power under alternative models where the effect of a non-HLA locus (as measured by the odds ratio [OR]) was assumed to decrease (or increase) (logit) linearly over the four HLA risk categories (test for interaction with one degree of freedom). Case-only analyses were used to estimate interaction parameters and to test for deviation from multiplicative effects using logistic regression [36]. In addition to the increased power obtained by utilising all cases (from case-control and trio materials) simultaneously, case-only analyses have increased power by making the implicit assumption that there is no association between the two loci in the population; i.e. the OR for their association is 1.0 in the population. Thus, under reasonable assumptions the case-only analysis makes the most efficient use of data to assess deviation from multiplicative models. In the case-control analysis, likelihood ratio tests comparing nested logistic regression models were used as global tests for interaction. The transmission disequilibrium test [37] was performed using the UNPHASED application implemented in the UNPHASED software version 2.4 [38]. For the trio data, 95% confidence intervals for the relative risk were estimated using conditional logistic regression in UNPHASED. Receiver operating curve (ROC) and confidence bounds for the area under the curve were estimated assuming a non-parametric distribution and analyses were done using SPSS version 14.0. Genotypes were added sequentially in order of likelihood ratio (or equivalently by the absolute risk conferred by a given genotype combination estimated using Bayes’ formula). Two four-locus genotype combinations were absent among cases in our material, and a very low value for the estimated absolute risk was imputed for these to allow inclusion in the ROC curve estimation with all four loci simultaneously. A p value <0.05 was considered to be statistically significant.
Results
The single-locus main effects are shown in ESM Table 2 (case-control data) and ESM Table 3 (trios). Compared with the neutral HLA risk category, the high-risk category showed a strong association with type 1 diabetes, with OR 20.6; for the intermediate-risk category the OR was 5.7 and for the low-risk category it was 0.09. INS, PTPN22 and CTLA4 also showed an association with type 1 diabetes, as expected. The transmission of the risk allele in the nuclear families confirmed the associations in INS, PTPN22 and CTLA4 (JO27_1), although with borderline significance for JO27_1 (ESM Table 3).
Joint effect of HLA and PTPN22 ORs for the effect of PTPN22 varied across the HLA risk categories and were significant in some of the subgroups. The ORs were smaller for the risk-conferring HLA genotypes, indicating negative deviation from a multiplicative model. A global test of interaction (with 3 df) between HLA and PTPN22 in the logistic regression model confirmed a significant interaction (p = 0.024). In the trio data, the relative risk conferred by the PTPN22 T allele was similar in the strata defined by HLA group, with no evidence for deviation from a multiplicative model (ESM Table 4). A case-only analysis among all cases from the case-control and family materials (ESM Table 5) supported a significant negative deviation from multiplicative effects, with weaker ORs conferred by PTPN22 in the HLA risk categories (3 df test for interaction; p = 0.028). When treating HLA-encoded risk as a continuous variable in the analysis (1 df), the interaction was even more statistically significant (p = 0.003). There was no association between HLA and PTPN22 among the controls (3 df test; p = 0.19). We tried to fit the case-control data to an additive odds model using generalised linear models in STATA (version 9), as described by Skrondal [39]. However, convergence was not obtained, suggesting that the data did not fit well to an additive model.
Joint effect of HLA and INS The 3 df test for interaction between INS and HLA was not statistically significant (p = 0.67). There was also no statistically significant deviation from a multiplicative model in the trio data (test for interaction, p = 0.5) (ESM Table 4) or in the case-only analysis (3 df test; p = 0.49) (ESM Table 5); even when treating HLA-encoded risk as a continuous variable in the analysis there was no significance (1 df test, p = 0.12). There was also no association between INS and HLA among controls, as expected (3 df test; p = 0.41).
Joint effect of HLA and CTLA4 The ORs for CTLA4 in the different HLA categories (Table 1) indicated no deviation from a two-locus multiplicative model (3 df test; p = 0.53). This was also the case in the trio data set (ESM Table 4) and was supported by the case-only analysis (ESM Table 5; 3 df test; p = 0.57). Again, there was no association between the two loci among controls (3 df test; p = 0.21).
Table 1Interaction between HLA-INS (-23HphI), HLA-PTPN22 (Arg620Trp) and HLA-CTLA4 (JO27_1) in the case–control data set using logistic regressionHLA categoryaNon-HLA genotypesCasesControlsOR95% CITest for interaction (p value)n (%)n (%)INSb0.67High riskI-I218 (25.2)20 (2.8)1.560.76–3.21III+98 (28.6)14 (2.5)1Intermediate riskI-I412 (47.6)122 (17.1)2.101.52–2.89III+160 (46.8)99 (17.4)1Neutral riskI-I221 (25.5)346 (48.5)2.341.74–3.15III+81 (23.7)299 (52.6)1Low riskI-I14 (1.6)225 (31.6)3.260.92–11.54III+3 (0.9)156 (27.5)1Total12071281PTPN220.024High riskTT + TC89 (23.2)8 (3.1)1.260.55–2.88CC237 (27.8)27 (2.6)1Intermediate riskTT + TC177 (46.1)55 (21.2)1.310.91–1.86CC406 (47.7)164 (15.6)1Neutral riskTT + TC111 (28.9)121 (46.7)2.441.80–3.31CC199 (23.4)536 (51.0)1Low riskTT + TC7 (1.8)75 (29.0)3.271.18–9.06CC9 (1.1)323 (30.8)1Total12351309CTLA40.53High riskTT122 (24.9)14 (3.6)0.900.45–1.84TC-CC210 (27.9)22 (2.5)1Intermediate riskTT241 (49.2)65 (16.5)1.591.14–2.23TC-CC346 (46.0)148 (16.7)1Neutral riskTT121 (24.7)212 (53.8)1.341.01–1.77TC-CC187 (24.9)439 (49.7)1Low riskTT6 (1.2)103 (26.1)1.800.63–5.18TC-CC9 (1.2)275 (31.1)1Total12421278n, Number of cases/controlsaHLA risk categories: high risk, DQA1*03-DQB1*0302/DQA1*05-DQB1*0201 (DQ8/DQ2), where DRB1≠0404; low risk, at least one DQB1*0602 allele independent of genotype on the other allele; intermediate risk, DRB1*0404-DQ8/DR3-DQ2, DR3-DQ2/DR3-DQ2, DR4-DQ8/DR4-DQ8 (excluding homozygous DRB1 0404), DRB1*0401 or 040x-DQ8/X (X≠DQB1*0602 or DR3). The remaining haplotypes were grouped in the neutral risk category (see Methods)bRepresents genotypes III/III and I/III
Joint effects of non-HLA loci There was also no indication of deviation from multiplicative two-locus joint effects of PTPN22-INS, INS-CTLA4 or PTPN22-CTLA4 in the case-control data (Table 2) or in the case-only analysis (ESM Table 5) (all p > 0.39). For the trios, the test for interaction between PTPN22 and CTLA4 showed p = 0.046 (ESM Table 6). Taken together with the analysis of the case-control and the case-only data, this weighs against any deviation from a multiplicative two-locus joint effect also of CTLA4 and PTPN22.
Table 2Interaction between INS-PTPN22, INS-CTLA4 and PTPN22-CTLA4 in the case-control data set using logistic regressionNon-HLA genotypesCasesControlsOR95% CITest for interactiona (p value)n (%)n (%)INSPTPN    0.67I-ITT+TC274 (72.9)156 (58.4)1.741.38–2.18I-ICC594 (71.6)587 (54.8)1 III+TT+TC102 (27.1)111 (41.6)1.891.38–2.58III+CC236 (28.4)485 (45.2)1 Total (n) 1,2061,339  INSCTLA4    0.42I-ITT354 (73.1)222 (53.9)1.511.23–1.86I-ITC-CC520 (71.0)492 (54.8)1 III+TT130 (26.9)190 (46.1)1.310.99–1.73III+TC-CC212 (29.0)405 (45.2)1 Total (n) 1,2161,309  PTPNCTLA4    0.78TT+TCTT143 (29.7)75 (17.7)1.461.04–2.05TT+TCTC-CC239 (31.7)183 (20.1)1 CCTT339 (70.3)348 (82.3)1.41.15–1.67CCTC-CC514 (68.3)729 (79.9)1 Total (n) 1,2351,335  The low-risk genotypes were used as reference (CC, TC-CC, TC-CC)aLikelihood ratio tests of whether the OR conferred by one locus is significantly different over strata defined by genotypes in the other locusn, Number of cases/controls
Joint effects of more than two susceptibility loci We also tested models with all three-way and four-way interactions involving the four susceptibility loci using logistic regression (categorising all loci in two groups: increased risk genotypes or not), but none of the multi-way interactions were statistically significant (all p > 0.29). The simultaneous distribution of risk genotypes at all four loci among cases and controls is shown in ESM Table 7. The results show that the more risk loci an individual carries, the higher the relative risk, but the presence or absence of HLA risk loci influences the relative risk much more than the other loci, as expected. For instance, carrying risk genotypes at all three non-HLA loci but not at HLA is associated with a much lower risk than HLA risk genotypes together with low-risk genotypes at all three other loci. The relative risk (OR) conferred by simultaneously carrying high- or moderate-risk HLA and risk genotypes at all the three other loci compared with non-risk-associated genotypes at all four loci was 61. The expected relative risk under a strict multiplicative model involving all four loci was 123 (multiplying all four single-locus effects by each other). The relatively small number of individuals simultaneously carrying all risk genotypes indicates that the observed negative deviation from a four-way multiplicative model was not statistically significant, in accordance with the formal test cited above.
ROC curve Another way to assess the predictive utility of combinations of genetic risk markers is the ROC curve [40]. This utilises the genotypes of all included individuals and assesses the combination of sensitivity and specificity of different combinations of genotypes. ROC curves for HLA alone, pairwise combinations of HLA and non-HLA loci, and multiple genotypes (Fig. 1) showed an area under the curve of 0.82 for HLA alone, which was only marginally increased by adding non-HLA loci.
Fig. 1ROC curve for HLA genotypes in four categories and for combinations of genotypes defined by HLA and non-HLA susceptibility loci. The area under the curve (95% confidence interval) was 0.820 (0.803–0.836) for HLA (dark blue line), 0.828 (0.811–0.844) for HLA+CTLA4 (purple line), 0.835 (0.819–0.851) for HLA+PTPN22 (grey line), 0.840 (0.824–0.855) for HLA+INS (green line), 0.848 (0.833–0.863) for HLA+INS+PTPN22 (yellow line) and 0.852 (0.837–0.867) for HLA+INS+PTPN22+CTLA4 (red line). Turquoise dashed line, reference line
Age of disease onset and sex We found no significant deviation from a multiplicative model concerning age–locus and sex–locus interaction for any of the genes. This was confirmed in the trio families and case-only analysis (ESM Tables 8 and 9).
Discussion
The present study is a comprehensive evaluation of joint effects of the four most well established type 1 diabetes susceptibility genes in both a large case-control series and family material. The relative risk conferred by PTPN22 was stronger in the lower-risk HLA categories than in the high-risk HLA category, while all other two-locus combinations (HLA-INS, HLA-CTLA4, INS-CTLA4, INS-PTPN22 and PTPN22-CTLA4) were consistent with multiplicative models. Although model-free methods have been developed for gene–gene interaction studies, such as multifactor dimensionality reduction (see [1] and references therein), these methods are designed for the detection of novel susceptibility loci, which was not the goal of our investigation.
Two of the three previous studies of the joint effect of PTPN22 and HLA were in accordance with our results [25, 27] while the other study found no deviation from a multiplicative model [22]. It should be noted that the interaction between PTPN22 and HLA found in the case-control material and case-only analysis was not replicated in our trio data. One of the reasons for this could be lower statistical power in the trio data. Using the Quanto program ([41]; http://hydra.usc.edu/gxe), we found that we had more than 80% power to detect significant two-way gene–gene interaction if the true interaction parameter was 0.5. For the trio design we would need as many trios as we had cases in the case-control study to obtain a similar power. Our number of trios was only about a third of the number of cases in the case-control study, with consequently lower power. The case-only design is known to be the most efficient to detect interaction under certain assumptions. For instance, we had >99% power to detect interaction if the true interaction parameter was 0.5.
The few studies concerning two-locus interaction effects between HLA and CTLA4 and among non-HLA genes [22] have generally indicated multiplicative effects, which is in accordance with our results. Some previous studies have found that the relative risk conferred by INS was similar in subgroups defined by HLA susceptibility genes [11, 12], while three studies have indicated that the effect was stronger in the low risk-HLA categories [16–18], a finding that was only partially supported by our data. On the other hand, one relatively small study has found that the effect of INS was confined to the high-risk HLA-DR4 group [10].
The reason for diverging results of the joint effect of established type 1 diabetes susceptibility genes in the literature could be that the studies have been performed with varying sample sizes and with different study designs. Linkage studies [14, 15], case-control association studies [10, 12, 13, 16] and family trio designs [16, 18, 22, 25] have all been used in these studies. Smaller studies might be inadequate to reveal significant interactions. Different criteria for categorising the HLA risk groups could potentially influence the result of the joint effect of HLA and other type 1 diabetes susceptibility genes. However, our conclusions were not affected by alternative classification of HLA risk groups, such as into DR4-DQ8 vs DR3-DQ2 carriers (data not shown). Studies in different populations and ethnic groups have indicated some heterogeneity in HLA-associated risk of type 1 diabetes and it is also possible that gene–gene interactions may vary across populations. However, despite the observed variations in population risk of type 1 diabetes and in HLA haplotype frequencies across populations, the relative predisposing effects of HLA haplotypes seem to be consistent across populations [42]. In our study all the patients were diagnosed before 15 years of age. The fact that the relative risks associated with both risk genotypes and low-risk genotypes seem to diminish with age above 15 years [43] raises the question whether gene–gene interactions may also differ in different age-groups.
Although no preventive intervention is available for type 1 diabetes today, prediction of disease is an important part of strategies for prevention, both for recruitment of participants for research studies and for identification of target populations for future preventive interventions. Understanding the interacting effect of the established type 1 diabetes susceptibility genes will increase this possibility. In a multiplicative model the relative risk (RR) for a person holding a high-risk genotype at both loci compared with a person with low risk at both loci will be RRlocus1 × RRlocus2. In terms of absolute risk differences, a doubling or tripling of risk due to INS or PTPN22 would be greater for a person with a high-risk HLA genotype than it would for someone with a low-risk HLA genotype. The absolute risk for persons with a given genotype can be estimated by multiplying the average cumulative incidence in the population (0.42% cumulative risk up to age 15 years in Norway [44]) by the ratio of genotype frequency in patients and genotype frequency in controls [18], or using Bayes’ formula. For instance, for a person with a low-risk HLA genotype, a high- or low-risk INS genotype will define whether the estimated absolute risk is approximately 0.01% or 0.028% (absolute risk difference, 0.027%), whereas for those with the high-risk HLA genotype INS will define an estimated risk of approximately 3.0% or 4.7% (absolute risk difference 1.7%).
As discussed in the general setting by Janssens et al. [40], increasing the number of susceptibility loci considered simultaneously generally increases the predictive value for disease. The downside is that the proportion of the population simultaneously carrying multiple risk alleles becomes minute even with a moderate number of susceptibility polymorphisms, and that even with relatively large data sets, as in our study, the absolute risk estimate becomes imprecise. The high-risk HLA genotype is carried by fewer than 3% of population controls, but confers a very high risk of disease. Several practical and scientific aspects of prediction should be considered when evaluating the utility of different prediction regimes. The ROC curve analysis confirms that, despite the higher absolute risk for those few with combinations of several risk markers, adding non-HLA genetic markers only marginally increases the utility of the prediction over that of HLA alone. While up to six susceptibility loci in addition to those studied here have recently been established in type 1 diabetes [45], the magnitude of the effect for each additional locus is very much smaller than that of HLA and even smaller than that of INS and PTPN22, suggesting that they are likely to add only marginally to the prediction of disease in individuals. Furthermore, an informal assessment of the number needed to be genetically screened in order to obtain a cohort of high-risk individuals, which will give rise to a given number of cases of type 1 diabetes, and the costs connected to the genotyping also suggest limited cost-effectiveness in adding non-HLA genetic markers to the prediction regime (data not shown).
In conclusion, in this comprehensive study of interactions among established type 1 diabetes susceptibility genes, we found that the joint effect of HLA and PTPN22 was significantly less than multiplicative in the case-control material, while a multiplicative model could not be rejected for HLA-INS, HLA-CTLA, PTPN-INS, INS-CTLA4 and PTPN-CTLA4. Despite near-multiplicative effects for most loci, and the fact that groups with very high relative risk of type 1 diabetes can be identified by testing for multiple susceptibility genes, only a small proportion of the population (and cases with type 1 diabetes) simultaneously carry HLA and multiple non-HLA susceptibility genotypes.
Electronic supplementary material
Below is the link to the electronic supplementary material.
ESM Table 1
(PDF 14.3 kb)
ESM Table 2
(PDF 110 kb)
ESM Table 3
(PDF 69.2 kb)
ESM Table 4
(PDF 51.2 kb)
ESM Table 5
(PDF 31.9 kb)
ESM Table 6
(PDF 48.8 kb)
ESM Table 7
(PDF 25.9 kb)
ESM Table 8
(PDF 68.0 kb)
ESM Table 9
(PDF 25.1 kb)

ins
hla
ptpn22
ctla4
genes
type 1 diabetes
interaction