Objective Despite considerable work on defining disease pathways, several aspects of collagen-induced arthritis (CIA) remain poorly defined, in particular those contributing to the initiation phase of the disease. It is thought that in CIA the activation of circulating leucocytes, their interaction with the endothelial lining followed by subsequent transendothelial migration and infiltration into tissue represents the first and determining step in a complex sequence of processes mediating tissue injury. In this study we attempted to define the genetic basis of this stage of disease using genetic linkage studies, in-vivo imaging and expression profiling.
Methods A genome scan with 132 informative markers was performed on 155 (DBA/1J×FVB/N) F2 mice. Linkage analysis was performed by combining genotyping data from the genome scan and the phenotypic data of leucocyte adherence, leucocyte rolling fraction, functional capillary density, centre line red blood cell velocity and capillary width as well as the expression level of the selected genes Cd44, Il13rα1, Ccr3, Defb3, Sele, Sell, Selp, Xcl1, Il1β, Tnfα and Ifnγ as traits.
Results Multiple classic quantitative trail loci (QTL) controlling leucocyte–endothelial cell interactions were identified on chromosomes 8 and 17 as well as expression QTL controlling the expression of several differentially expressed adhesion molecules and cytokines on chromosomes 1, 2, 5, 6, 7, 8, 12, 15, 16 and 17.
Conclusion The study describes for the first time QTL controlling the CIA initiating leucocyte–endothelial cell interaction.
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Collagen-induced arthritis (CIA) is the classic animal model of rheumatoid arthritis.1 2 CIA is induced in susceptible strains of mice (eg, DBA/1J, C57Bl/10.q) by immunisation with collagen type II in complete Freund's adjuvant (CFA). The development of CIA is thought to depend on autoreactive lymphocytes. The activated lymphocytes migrate to the joint where an inflammatory cascade involving T cells, macrophages, B cells and activated synoviocytes is triggered. This cellular infiltration together with the production of a complex array of cytokines and other soluble mediators contribute to synovial proliferation, pannus formation, cartilage and bone destruction.3 4 The susceptibility and severity of CIA vary significantly among inbred strains indicating that both major histocompatibility complex (MHC)and non-MHC genes are associated with the susceptibility to CIA.5 6 The conservative estimate for the non-MHC susceptibility genes is suggested to be more than 40. Recently, we and others have localised quantitative trail loci (QTL) that regulate CIA in mice mostly in crosses involving the DBA/1J strain.7 Despite that effort additional QTL contributing to the susceptibility for CIA and to additional disease traits are yet to be identified.
As the inflammatory process within joint tissues represents a key feature of arthritis, understanding of the mechanisms inducing and sustaining this aspect of disease pathology would allow the development of new and powerful therapeutic strategies. This can be achieved by direct on-line visualisation, for example, using the technique of intravital fluorescence microscopy, which allows us to dissect the complex scenario of cell inflammatory response with differentiation between cellular subtypes and their distinct adhesion molecule-dependent interaction within the microcirculation. The approach of in-vivo microscopy has successfully been applied in joints of mice with antigen-induced arthritis,8 and by our group to the CIA model induced in the DBA/1J and FVB/N mice.9
In numerous organ pathologies, including arthritis, the activation of circulating leucocytes, their interaction with the endothelial lining followed by subsequent transendothelial migration and infiltration into tissue represents the first and determining step in a complex sequence of processes mediating tissue injury.10 11 Indeed, the expression of leucocyte–endothelial cell interaction-relevant adhesion molecules such as E-selectin and β1/β2-integrins, P-selectin, ICAM-1, VCAM-1, PECAM-1, VLA-4 and Mac-1 in synovial tissue from patients with rheumatoid arthritis were extensively documented.12 13 Veihelmann et al14 demonstrated high numbers of adherent leucocytes upon clinical manifestation of antigen-induced inflammatory arthritisin mice regardless of the acute, intermediate or chronic phase of disease. In addition, our group showed that leucocyte adhesion is apparent even if clinical symptoms are still missing, underscoring leucocyte–endothelial interaction as an integral part of propagation and perpetuation and initiation of disease.9 The predominantly upregulated adhesion molecule in our earlier study was CD44, a molecule thought to be involved in the development of arthritis, although its exact role remains controversial.15 16
In this study we attempted to define the genetic basis of the initiation phase of CIA using genetic linkage studies, in-vivo imaging and expression profiling. Thereby, we focused on the leucocyte–endothelial cell adhesion.
Materials and methods
Mice and immunisation
All animals used in this study were obtained from the Jackson Laboratory and were housed at the central animal care facility at the University of Rostock. All procedures and assays were approved by the state's Animal Care Committee and followed the guidelines for the care and use of laboratory animals.
CIA was induced in animals according to established protocols previously described by our group.6 In brief, 8–12-week-old (DBA/1J×FVB/N) F2 progeny were immunised with 125 µg of bovine collagen II (Chondrex) in CFA (DIFCO Laboratories, Detroit, USA). Three weeks after immunisation in-vivo multifluorescence microscopy was performed.
In-vivo multifluorescence microscopy
For quantitative offline analysis a computer-assisted microcirculation image analysis system was used (CapImage V.7.4; Zeintl, Heidelberg, Germany). We measured functional capillary density (FCD) as well as leucocyte–endothelial cell interaction in postcapillary venules. The flow behaviour of leucocytes was analysed with respect to free floating, rolling and adherent leucocytes (see supplemental file 1, available online only). In postcapillary venules, centre line RBC-velocity (VRBC) was determined using the line shift method (CapImage; Zeintl).
Gene selection and quantitative PCR
Based on our previously published and unpublished experimental data,9 we selected 11 genes (Cd44, Il13rα1, Ccr3, Defb3, Sele, Sell, Selp, Xcl1, Il1β, Tnfα and Ifnγ), which were differently expressed at the early stage of arthritis and/or are known to be involved in cell adhesion. Paws and lymph nodes were removed after in-vivo multifluorescence microscopy and snap frozen. RNA was then extracted from snap frozen tissues with the RNeasy Mini Kit (Qiagen, Germany) according to the manufacturer's instructions. For reverse transcription, we used 300 U Superscript RNase H− Reverse Transcriptase, 20 U RNasin, 3 µM random hexamers (Amersham Pharmacia Biotech, Freiburg, Germany). Gene quantification was performed on the ABI Prism 7700 sequence detection system (Perkin-Elmer Applied Biosystems, Weiterstadt, Germany). TaqMan primers and probes were purchased from Perkin-Elmer Applied Biosystems. For each RNA isolation measurements of gene expression were taken two times and the means of these values were used for further analysis. The comparative Ct method and the internal control (GAPDH) were used to normalise the expression levels of target genes.
For the genetic analysis 132 informative microsatellite markers covering the genome to the extent of 94% with average intermarker distance of 11.22 centi-Morgan (cM) were used. The accuracy of our loci order and interval maps was verified by comparing the genetic map calculated by our data with the mouse genome informatics map. A complete list of the informative markers we used is available upon request. Genomic DNA was isolated from a 1 cm tail tip using standard isolation protocols17 and all mice were genotyped for 132 microsatellite loci reactions run on. The genotyping was performed on a CEQ 8800 Beckman Coulter (Krefeld, Germany) sequencer and genotypes were scored using the program Fragment analysis supplied by the manufacturer.
All linkage analyses have been made with QTX Map Manager software. The order of the loci was obtained from the mouse genome informatics map. Rolling and adherent leucocytes, functional capillary density, VRBC, capillary width and the gene expression level data were taken as phenotypes. Continuous trait values were checked for normal distribution and logarithmic values were used when it was necessary. Outliers were detected using Grubb's test at the 95% significance level.
As the significant and suggestive linkage threshold values, we have followed the guidelines from the permutation test of data (n=1000). The association between markers and phenotypes was also tested by F statistic (analysis of variance) (KaleidaGraph software).
Synovial microcirculation at the onset of arthritis
To determine synovial microcirculation in the knee joint, in-vivo fluorescence microscopy was performed. None of the animals exhibited clinical symptoms of arthritic disease at the time point of analysis (3 weeks after immunisation). A total of 155 collagen-immunised (DBA/1J×FVB/N) F2 mice was used to assess microvascular perfusion (functional capillary density, capillary width and VRBC) and leucocyte–endothelial cell interaction (rolling and adherent leucocytes) in synovial tissue. Increased fractions of rolling leucocytes along and numbers of attaching cells to the venular endothelium are characteristic of an inflammatory response in synovial tissue. The results of the measurement performed by in-vivo fluorescence microscopy are as follows. The values of FCD ranged from 427 to 801 cm/cm2 (mean±SD 596±88 cm/cm2). The capillary width was 4.11±0.3 µm. The VRBC amounted to 0.72±0.28 mm/s. The rolling fraction as a marker of leucocyte activation ranged from 5 to 85% (mean±SD 34.7±16.9%). The adherent leucocytes (sticker) varied from 40 to 2621 cells/mm2 (mean±SD 839±549 cells/mm2).
Identification of QTL controlling the CIA initiating leucocyte–endothelial cell interaction
To identify QTL genetically linked to the CIA-initiating leucocyte–endothelial cell interaction, a genome scan with 132 informative markers was performed on 155 (DBA/1J×FVB/N) F2 mice. Linkage analysis by combining the genotype data from the genome scan and the phenotype data of leucocyte adherence, rolling fraction, FCD, VRBC and capillary width revealed novel QTL on chromosomes 8 and 17 (table 1).
One locus controlling FCD was detected on chromosome 8. QTL had significant evidence of linkage to marker D8Mit92 (logarithm of the odds (LOD) score of 4.75). Interestingly, the sticker phenotype was also affected by the same locus on chromosome 8 (LOD score 3.75). In addition to these loci, we identified a second locus on chromosome 17 controlling the sticker phenotype. It had a suggestive evidence of linkage to marker D17Mit197 (LOD score 3.67). No QTL could be identified for the capillary width phenotype VRBC and the leucocyte rolling fraction phenotype.
Expression of genes potentially involved in leucocyte–endothelial cell interaction
Differently expressed genes at the early stage of arthritis identified in our previously published and unpublished studies as well as genes known to be involved in leucocyte–endothelial cell interaction were selected for the analysis: Cd44, Il13rα1, Ccr3, Defb3, Sele, Sell, Selp, Xcl1, Il1β, Tnfα and Ifnγ. The expression levels of the selected genes were determined in joints and/or in lymph nodes, depending on the respective gene expression pattern, by using TaqMan analysis and normalised to that with GAPDH (table 2). In the paws all analysed genes could be detected, whereas in the lymph nodes Defb3 was not detectable (table 2).
Identification of QTL controlling gene expression
To identify candidate loci controlling transcriptional genes involved in leucocyte–endothelial cell interaction at the early stage of arthritis, we performed linkage analysis on 155 (DBA/1J×FVB/N) F2 animals with the expression level of the selected genes as traits.
In the joint, expression levels of six genes (Ccr3, Il13rα1, Dfb3, Selp, Xcl1 and Tnfα) were controlled by multiple QTL (table 3). The expression levels of Cd44, Sell and Sele did not show linkage to any marker, although Cd44 and Sell showed high relative expression (467.1 and 140.4; table 2). In contrast, the expression levels of those genes that showed linkage to markers were relatively low (1.2–68.9; table 2). Loci controlling the expression of Selp mapped to chromosome 2 with a significant LOD score of 4.01 and chromosome 16 with a highly significant LOD score of 6.01. The expression level of Ccr3, a chemokine (C-C motif) receptor on chromosome 9 was controlled by loci on chromosome 1 (LOD score 3.02) and chromosome 7 with a highly significant LOD score of 5.29. The expression of Defb3 was controlled by loci on chromosome 15 (LOD score 2.54) and chromosome 16 (LOD score 3.34). The chromosome 15 locus maps to a region where several QTL (Cia26, Cia30, Cia31, Cia32 and Cia38) are known to be involved in CIA. Suggestive loci on chromosome 17, 15 and 6 controlled the expression level of Il13rα, Tnfα and Xcl1, respectively.
In lymph nodes, the expression of Cd44, Il13rα1, Ccr3, Sell and Il1β was controlled by multiple QTL (table 4). The expression of Cd44 and Il13rα1 was respectively controlled by loci on chromosome 5 with a LOD score of 3.02 and chromosome 15 with a LOD score of 2.60.
Interestingly and in contrast to joints, the expression of Ccr3 in lymph nodes was controlled by loci on chromosome 8 (LOD score 3.15) and on chromosome 12 (LOD score 2.97). The expression level of Sell showed linkage to marker D1Mit424 with a LOD score of 2.91. Loci controlling the expression of Il1β mapped to chromosome 2 with a LOD score of 2.82. When comparing the expression QTL identified in the lymph nodes with QTL described in the literature, we observed an overlap of the expression QTL of Cd44 with Cia14, the expression QTL of Il13rα1 with Cia30 and Cia34 and the expression QTL of Sell with Cia9. All are QTL controlling CIA severity.
Leucocyte migration into the joint has been implicated in the onset of arthritis and in the regulation of disease activity. Therefore, identifying the master genes controlling this pathogenic event could be a critical step towards the generation of more effective therapies.
In this study, we mapped both phenotypic QTL and expression QTL controlling the disease-initiating leucocyte–endothelial cell interaction. We first identified several QTL affecting microvascular perfusion and inflammatory cell response in synovial tissue and the identified QTL controlling the expression of genes involved in leucocyte–endothelial cell interaction at the early stage of arthritis. Our analysis was carried out in (DBA/1J×FVB/N) F2 progeny immunised with CFA and collagen II. In a previous microcirculatory study we showed that the inflammatory cell response within the microcirculation of the knee joint was increased in DBA/1J compared with FVB/N mice at the onset phase of arthritis.9 The inflammatory cell response is indicated by the enhanced activation and interaction of leucocytes with the microvascular endothelium. We found two loci controlling the adhesion of leucocytes, one of the regulating steps in the cascade of inflammatory cell response. The loci were located on chromosomes 8 and 17. Surprisingly, no known adhesion molecule previously implicated in leucocyte–endothelial cell interaction, for example, selectins, CD44, etc, map to those loci. This suggests that there are novel candidate genes involved in this process. Interestingly, the locus on chromosome 8 also controlled the functional capillary density, which is a measure of the number of capillaries with red blood cell flow in a defined region. It is an important indicator of microcirculatory function as it reflects the surface available for exchange of oxygen, fluid and solutes. This finding suggests the existence of a gene(s) controlling both phenotypes. Several potential candidate genes map to this locus, for example, members of the cadherin family genes: Cdh11 (E-cadherin, 46.5 cM), Cdh13 (T-cadherin, 64 cM) and Cdh15 (M-cadherin, 67 cM). Cadherins are integral membrane adhesion molecules that typically mediate calcium-dependent adhesion between cells of the same type within a tissue.18,–,23
The expression QTL analyses were performed for 11 genes that were shown to be differentially expressed in the knee joint and in lymph nodes of CIA mice at the onset of disease9 24 (unpublished data). Seven genes were trans-controlled, two cis-controlled (Sell and Il1β) and for two gene no controlling locus could be mapped (Sele and Ifnγ). Some had two expression QTL.
Two QTL controlling the expression of P-selectin in paw joints overlap with known loci previously linked to the CD4:CD8 T-cell ratio and disease onset/severity phenotypes located on chromosome 2 (Trmq3) and lymphocyte cell proliferation located on chromosome 16 (Lp1).6 P-selectin is expressed on activated vascular endothelium. Selectin-mediated cell–cell interaction is a prerequisite for subsequent firm attachment, transmigration of leucocytes and is probably involved in the selective migration of CD4 and CD8-positive cells.25,–,31 It is tempting to speculate that different migratory properties of various T-cell subsets are important for the efficient regulation of the immune responses for example, regulatory T-cell differentiation and development.32
A further adhesion molecule expressed by leucocytes and implicated in arthritis is CD44. CD44 has been shown to function primarily by supporting leucocyte rolling at inflammatory sites,33 34 but observations in Cd44 knockout (KO) mice suggest that the loss of Cd44 might facilitate lymphocyte homing to peripheral lymph nodes during inflammation.35 36 The expression of Cd44 was controlled by a locus on chromosome 5. The locus Cia14 has been identified in this area,24 37 suggesting the possible existence of a quantitative trait gene controlling disease through the expression of Cd44 in lymph nodes.
In conclusion, this is to our knowledge the first study presenting loci controlling CIA-initiating leucocyte–endothelial cell interaction. We also identified unique QTL affecting the expression of genes involved in leucocyte–endothelial cell interaction. The most identified expression QLT were trans-regulated, suggesting that polymorphic genes (transcription factors or others) may have an impact on larger transcriptional networks involved in disease pathogenesis.
Funding The study was supported by a grant from the Medical Faculty of the University of Rostock (FORUN).
Provenance and peer review Not commissioned; externally peer reviewed.