Elevated expression of the genes encoding TNF-{alpha} and thromboxane synthase in leucocytes from patients with systemic sclerosis

V. Young1,*, M. Ho2, H. Vosper1, J. J. F. Belch2 and C. N. A. Palmer1,

1 Biomedical Research Centre and
2 Section of Vascular Medicine and Biology, Department of Medicine, Ninewells Hospital and Medical School, University of Dundee, Dundee DD1 9SY, UK


    Abstract
 Top
 Abstract
 Introduction
 Patients and methods
 Results
 Discussion
 References
 
Objective. To determine the expression of molecular markers of prostanoid/fatty acid signalling in leucocytes of patients with systemic sclerosis (SSc).

Methods. Gene expression in patient leucocytes was analysed using real-time fluorescence reverse transcriptase polymerase chain reaction for tumour necrosis factor {alpha} (TNF-{alpha}), thromboxane synthase (TXAS, CYP5A), prostacyclin synthase (CYP8A), monocyte chemoattractant protein-1 (MCP-1), peroxisome proliferator-activated receptors (PPAR) {alpha}, {delta} and {gamma}, low-density lipoprotein-associated lipoprotein lipase A2 (LDL-PLA2), apolipoprotein E (apoE) and cholesterol 27-hydroxylase (CYP27).

Results. Both TNF-{alpha} and TXAS showed an increase in mean expression in the diseased group (6.3-fold and 5.6-fold respectively, P<0.0001). These two markers, along with CYP27, PPAR{gamma} and apoE, provided predictive markers for the development of carotid artery disease within the SSc patient population.

Conclusion. The elevated levels of TNF-{alpha} and thromboxane seen in SSc patient sera are paralleled by increases in the expression of the appropriate genes in leucocytes. This method will allow us to screen for a large number of candidate markers of disease in order to increase our understanding of the processes underlying the pathology of SSc.

KEY WORDS: Transcriptional profiling, Autoimmune disease, Inflammation, Cardiovascular disease, Systemic sclerosis.


    Introduction
 Top
 Abstract
 Introduction
 Patients and methods
 Results
 Discussion
 References
 
Systemic sclerosis (SSc) is an autoimmune inflammatory disorder that mainly affects middle-aged and elderly women. It is usually described as a generalized disorder of connective tissue, characterized by fibrosis in the skin and various internal organs [1]. More recently the vascular component of SSc has been appreciated. Raynaud's phenomenon and other microcirculatory abnormalities are well recognized [2]. Characteristic features include endothelial dysfunction [3, 4] and haemorrheological abnormalities [5]. It is of interest to note that similar abnormalities are seen in patients with large-vessel disease secondary to atherosclerosis [6]. It may be considered that if these changes are pathogenic then atherosclerosis should occur in SSc. SSc is associated with premature death. The 5-yr survival rate is reported to vary between 34 and 73% [7], one-third of the deaths being thought to be due to cardiovascular causes [8]. We have reported large-vessel atherosclerosis in the lower limbs of patients with SSc [9]. Furthermore, we have observed that these patients develop premature carotid artery disease and that this is a major cause of mortality [10]. The causes of this disease are unknown but the pathology reflects an inflammatory burden that may provide the basis for the development of cardiovascular complications. Among the most effective treatments for the management of the cardiovascular complications of SSc are prostacyclin and its analogue, iloprost [11]. Increased levels of thromboxane and platelet aggregation have been reported in patients with SSc [5, 12]. This suggests that there is an imbalance in prostanoid/lipid signalling in the disease pathology. In this study, we used the real-time fluorescence-based reverse transcriptase polymerase chain reaction (RT-PCR) to determine the levels of expression of various markers of inflammation and vascular lipid signalling in leucocytes from patients with SSc and controls matched for age and sex. This study was performed using a total of 1.5 ml of each patient's blood, and has established a resource for the study of many more candidate genes.


    Patients and methods
 Top
 Abstract
 Introduction
 Patients and methods
 Results
 Discussion
 References
 
Patient recruitment and phenotyping
Thirty-eight consecutive patients with SSc were recruited from our connective tissue disease clinics. All fulfilled the 1980 American Rheumatology Association criteria for SSc (diffuse and limited disease) [13]. Twenty-eight unaffected controls of similar age and sex were also recruited. These controls were derived from the general population in the area who were registered with their general practitioner. Any patient or control with evidence of another connective tissue disease or rheumatoid arthritis (which may also be associated with cardiovascular disease), as shown by a positive test for antibody to extractable nuclear antigens or DNA-binding antibody or rheumatoid factor, were excluded. Any individuals with symptoms of Raynaud's phenomenon were excluded from being a control subject.

Non-invasive vascular assessments
Carotid duplex scanning and ankle brachial blood pressure measurements (ABPI) were performed on all subjects as measures of peripheral artery disease.

Bilateral carotid duplex scanning
This was performed by a vascular technician using a Toshiba Powervision unit with a 7.5 MHz linear probe (Toshiba Medical Systems Ltd, Crawley, Sussex, UK). All the carotid vessels (common, internal and external) and the vertebral arteries were examined for stenosis using a combination of B-mode ultrasound and pulsed colour Doppler signals with audio and spectral analysis. The scans were recorded on video and later assessed by a single, experienced, blinded operator. Abnormalities were recorded according to the absence or presence of stenosis, i.e. normal or diseased carotid arteries.

Ankle Brachial Pressure Index (ABPI) determination
After a 10 min rest, bilateral ankle and brachial arterial systolic blood pressures were measured using a Sonicaid Doppler probe (Oxford Instruments, Oxon, UK) in conjunction with a Hawksley random zero sphygmomanometer (Hawksley Ltd, Lancing, UK). The ABPI was calculated as the ankle pressure in mmHg divided by the brachial pressure. The normal ABPI is >=1.0, and an APBI of <0.9 has 95% sensitivity and 100% specificity for detecting arterial disease using angiographically defined disease as the gold standard [14, 15].

RNA preparation and cDNA synthesis
Blood (1.5 ml) was taken from 38 patients and 28 age- and sex-matched controls. RNA was prepared using Qiagen RNeasy Blood Kits and the RNA was treated with DNase to remove residual genomic DNA contamination. The RNA yield from 1.5 ml of blood was generally around 7 µg. cDNA was synthesized from 500 ng of such RNA using You-Prime-Ready-To-Go beads from Pharmacia.

Real-time quantitative PCR analysis
TaqMan® real-time fluorescence PCR analysis was performed using primers and dual-labelled probes as described previously [16]. Each sample was assayed for the presence of mRNAs for tumour necrosis factor {alpha} (TNF-{alpha}), thromboxane synthase (TXAS, or CYP5A), prostacyclin synthase (CYP8A), monocyte chemoattractant protein-1 (MCP-1), peroxisome proliferator-activated receptors (PPAR{alpha}, PPAR{delta}, PPAR{gamma}), low-density lipoprotein-associated lipoprotein lipase A2 (LDL-PLA2), apolipoprotein E and cholesterol 27-hydroxylase (CYP27) using the probes and primer sets described in Table 1Go. These probe and primer sets were designed using Perkin Elmer Primer Express software and their specificity was checked against the Genbank database. One hundredth of the cDNA preparation was used per assay and this was performed in duplicate. We estimate that ~500 gene products may be analysed per 1.5 ml blood sample in this manner.


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TABLE 1.  Sequences of oligonucleotides used in this study

 

Data processing and statistical analysis
Fluorescence profiles from the Perkin Elmer 7700 sequence detection unit were analysed on an Apple Macintosh G3 using Perkin Elmer Sequence Detection software. Experimental reports of initial threshold (Ct) values were exported to Microsoft Excel 98. Ct values were transformed to linear, positive values using 2(x–Ct), where x is the Ct value obtained with non-reverse-transcribed RNA (negative control). Each value obtained was then divided by the value obtained with 18S rRNA for that patient sample and then multiplied by 10000, in order to bring it into a range that could be analysed by the statistical software.

Data were analysed using GraphPad Prism version 2.0 for the Macintosh. Population means and variances were determined and the groups were tested for Gaussian distribution. Comparison between populations of similar variance and Gaussian distribution were analysed with a standard t-test. Where the variances were not equal, Welch's correction was applied. In cases where a Gaussian population was not observed, a non-parametric Mann–Whitney test was applied. A P value of <0.05 was considered significant and a P value of <0.005 was considered highly significant. Correlation between variables was tested by standard linear regression analysis.


    Results
 Top
 Abstract
 Introduction
 Patients and methods
 Results
 Discussion
 References
 
The performance of the TaqMan assays was tested using the TXAS probe and primer pairs on increasing amounts of cDNA prepared from one individual. Figure 1Go shows a representative amplification profile from duplicate serial dilutions. The assay is highly sensitive, with TXAS mRNA easily detected in less than 1 ng of total RNA, and highly quantitative, with a linear response over several orders of magnitude of signal (r2=0.99, P=0.0001). RNA was prepared from the blood of patients and controls and assayed for TNF-{alpha}, TXAS, CYP8A, MCP-1, PPAR{alpha} PPAR{delta}, PPAR{gamma}, LDL-PLA2, apolipoprotein E and CYP27. Distribution plots for these results are shown in Fig. 2Go. The levels of expression of TNF-{alpha} and TXAS were markedly elevated, with a 6.3-fold increase for TNF-{alpha} and a 5.6-fold increase for TXAS levels (Table 2Go). All of the other targets were expressed at similar levels between control and patient populations. The expression of CYP8A (prostacyclin synthase) was below the level of detection in all samples and is therefore not included in the data analysis. A significant correlation between TNF-{alpha} and TXAS expression was observed, and this was more pronounced in the control group (Fig. 3Go). It was immediately obvious that the control populations did not express TNF-{alpha} and TXAS with a normal distribution and eight individuals lay well above the means in this control population, suggesting a subclinical ‘activated’ subgroup. We then analysed the influence of these quantitative traits on the vascular pathology of the disease. Specifically, we compared the levels of these markers and examined their role in the development of carotid artery disease (CAD) or lower limb peripheral arterial disease within the SSc population (Fig. 4Go). Significant alterations were seen in the individuals with CAD (Table 3Go), but none of the markers appeared to associate with lower limb vascular disease (data not shown). Both TXAS and TNF-{alpha} showed higher means in the CAD-positive group (2.1-fold and 1.7-fold respectively); however, the difference in the mean for TNF-{alpha} was not significant in this group (P=0.104). The level of CYP27 mRNA was significantly increased in the CAD-positive population (1.4-fold, P=0.0477). The association of the means in the subgroups was much weaker than that seen between the SSc and control populations; however, highly significant differences in the population variances were seen with these markers and also for PPAR{gamma} and apolipoprotein E. Indeed, the CAD-positive group had a greater number of ‘high expressers’ for each gene. These high expressers with CAD were, however, different individuals for each gene product. This suggests that the development of CAD in this population is heterogeneous and that these markers have different contributions to disease development in individual patients. With this in mind, we looked at outlier expression data for all these variables to determine if we could predict the development of CAD in the SSc group (Table 4Go). Using the scoring strategy outlined in Table 4Go, we were able to predict patients with CAD with high levels of sensitivity (76%), specificity (80%) and predictive ability (84%). It is quite clear that we cannot do this using any single marker used in the study. This finding demonstrates that transcriptional profiling data can provide a sophisticated analysis of the heterogeneity of cardiovascular disease in SSc patients. However, our current findings are rather preliminary and are not in themselves clinically useful. We are currently exploring an expanded range of molecular markers and genetic traits that may provide further insight into the pathology of CAD in patients with SSc.



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FIG. 1.  Quantification of TXAS mRNA levels by real-time fluorescence RT-PCR (TaqMan) analysis. Small amounts of total RNA are sufficient to detect TXAS mRNA. Shown is the fluorescence trace obtained from a TaqMan reaction using the Perkin Elmer Sequence Detection software. Various amounts of total RNA (50 ng, 5 ng and 500 pg) were used in duplicate for the amplifications shown. The Ct value is scored as the cycle number where the fluorescence level crosses the threshold value (thick horizontal line). The threshold value is defined by the upper 95% confidence limit on a baseline collected between cycles 3 and 15.

 


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FIG. 2.  Comparison of the expression of nine gene products between SSc patients and matched controls. The column plots show the individual data points obtained for each patient. The values are arbitrary values relative to the 18S rRNA control. The means of the SSc patients and the matched controls are represented by a horizontal bar. ***Highly significant difference in the mean between the two populations.

 

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TABLE 2.  Patient vs control comparisons

 


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FIG. 3.  TNF-{alpha} and TXAS are co-ordinately regulated. Scatterplots of TXAS vs TNF-{alpha} in the patients and controls. Linear regression was used to fit the lines to the data. In the control and patient populations, TNF-{alpha} and TXAS were significantly correlated (r2=0.905 and r2=0.4988 respectively). No other variables correlated significantly, e.g. MCP-1 vs TNF-{alpha}, r2=0.07; MCP-1 vs PPAR{delta}, r2=0.08.

 


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FIG. 4.  Comparison of markers in patients with and without coronary artery disease. The column plots show the individual values obtained for each patient. The values are arbitrary, relative to the 18S rRNA control. The means of the CAD-free patients (CAD-) and the patients with carotid artery disease (CAD+) are represented by a horizontal bar. *Significant difference between the means of the two groups (P<0.05).

 

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TABLE 3.  SSC patients with and without CAD

 

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TABLE 4.  Multiple markers are required to predict CAD

 


    Discussion
 Top
 Abstract
 Introduction
 Patients and methods
 Results
 Discussion
 References
 
Transcriptional profiling of large numbers of candidate disease genes is becoming increasingly popular as awareness of the power of new genomic information and technologies increases. Chip-based technologies are one part of the future of such profiling, for snapshot analysis of expression between two populations. However, this is an approach that is more qualitative than quantitative. Fluorescence-based real time RT-PCR, however, is a highly sensitive and quantitative method for the transcriptional profiling of individual samples, thus allowing statistical analysis of the distribution of expression and comparison with phenotype. We have estimated that this method can be used to document the levels of expression of more than 500 genes from 1.5 ml of blood. This equals the power of proposed disease-specific chips and allows a degree of quantitation and flexibility not available with chip-based technologies.

We have shown that this technology is suitable for comparing gene expression levels in case–control studies and disease progression. Previous reports using immunological assays have shown increases in both TNF-{alpha} and thromboxane levels in the serum of SSc patients, but the studies have not been large enough or the assays sensitive enough to provide useful data to compare with phenotypic data [12, 1719]. The definition of such molecular markers for disease allows the monitoring of pharmacological intervention in the population. As with a number of diseases with a vascular component, increased platelet aggregation has been implicated in the vascular disease of SSc. There is an increase in the level of thromboxane (TXA2), measured as the stable metabolite TXB2 [20] and altered platelet sensitivity to prostacyclin [21]. Furthermore, white blood cell activation occurs [5]. Increased TXAS in these activated white bloods cells will promote platelet aggregation, endothelial dysfunction [4] and vascular damage [3]. Attenuating such a cascade may improve vascular behaviour in these patients. Indeed thromboxane receptor blockade has a profound effect on platelet behaviour and on digital skin blood flow in patients with SSc [22]. The increases in TNF-{alpha} seen in the SSc patients may result in higher monocytic and endothelial activation. This would be expected to contribute to the greater burden of atherosclerosis observed in these patients [9].

We propose that TaqMan-based transcriptional profiling is an excellent technology to follow the molecular events during the pathogenesis of SSc and will be useful for monitoring gene expression during pharmacological intervention with novel anti-inflammatory agents.


    Acknowledgments
 
We would like to thank Anne Bancroft for her help in the collection of samples for this study. We would also like to thank Lillian Yengi, Colin MacPhee, Lisa Patel and Gary Moore for designing and providing TaqMan reagents. This work was supported by a grant from the Raynaud's and Scleroderma Association, UK. HV is supported by a grant from SmithKline Beecham, Harlow, UK.


    Notes
 
Correspondence to: C. N. A. Palmer. Back

*Present address: The Scottish Crop Research Institute, Invergowrie, UK. Back


    References
 Top
 Abstract
 Introduction
 Patients and methods
 Results
 Discussion
 References
 

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Submitted 29 January 2001; Accepted 28 February 2002