Protein interaction for an interferon-inducible systemic lupus associated gene, IFIT1
S. Ye1,4,
H. Pang2,
Y.-Y. Gu1,
J. Hua1,
X.-G. Chen1,
C.-D. Bao1,
Y. Wang1,
W. Zhang1,
J. Qian1,
B. P. Tsao3,
B. H. Hahn3,
S.-L. Chen1,4,
Z.-H. Rao2 and
N. Shen1,4
1Shanghai Clinical Centre of Rheumatic Diseases and Institute of Rheumatology, Department of Rheumatology, Renji Hospital, Shanghai Second Medical University, Shanghai, 2Structural Biology Laboratory, Division of Biology, Tsinghua University, Beijing, China, 3Division of Rheumatology, Department of Medicine, University of California-Los Angeles, Los Angeles, CA 90095, USA and 4Health Science Centre, Institute for Biological Sciences, Chinese Academy of Sciences and Shanghai Second Medical University, Shanghai, China.
Correspondence to:
N. Shen, Department of Rheumatology, Renji Hospital, 145, Shandong Mid. Road, 200001, Shanghai, China. E-mail: shennand{at}online.sh.cn
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Abstract
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Objective. To identify disease-related genes and immune-regulatory pathways in the pathogenesis of systemic lupus erythematosus (SLE) by using gene expression profiling and proteinprotein interaction analysis.
Methods. Peripheral white blood cell gene expression profiles of 10 SLE patients were determined by oligonucleotide microarray analysis. Clustering of the gene expression profile was compared with the clinical immune phenotype. SLE-induced genes that were over- or under-expressed were determined and independently validated using a real-time polymerase chain reaction (PCR) method. To study their potential function and the possible pathways involved, a candidate gene was cloned and a GST (glutathione S-transferase) fusion protein was expressed in Escherichia coli. The fusion protein was further purified using the glutathione Sepharose 4B system, and was treated as bait to capture prey from SLE peripheral white blood cell lysate. MALDI-TOF (matrix-assisted laser desorption/ionizationtime-of-flight) mass spectrometry was then performed to determine the prey protein.
Results. Similarity was found between the gene expression profile and the immune phenotype clusters of the SLE patients. More than 20 disease-associated genes were identified, some of which have not been related to SLE previously. Of these genes, a cluster of interferon-induced genes were highly correlated. IFIT1 (interferon-induced with tetratricopeptide repeats 1) was one of these genes, and overexpression of its mRNA was confirmed independently by real-time PCR in a larger population (40 SLE patients and 29 normal controls). An IFIT1 protein protein interaction study showed that IFIT1 may interact with Rho/Rac guanine nucleotide exchange factor.
Conclusion. The gene expression profile seems to be the molecular basis of the diverse immune phenotype of SLE. On the basis of the SLE-related genes found in this study, we suggest that the interferon-related immune pathway is important in the pathogenesis of SLE. IFIT1 is the first gene described as a candidate gene for SLE, and may function by activating Rho proteins through interaction with Rho/Rac guanine nucleotide exchange factor. IFIT1 and the interferon-related pathway may provide potential targets for novel interventions in the treatment of SLE.
KEY WORDS: Systemic lupus erythematosus, Gene expression profile, Interferon, Interferon-induced protein with tetratricopeptide repeats 1, Proteinprotein interaction.
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Introduction
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Systemic lupus erythematosus (SLE) is a prototypical autoimmune disease and its aetiology remains undetermined. In this era of functional genomics, the microarray provides an appropriate high-throughput method of investigating gene regulation in SLE from an overall and relatively non-biased perspective [1]. Proteomics has also provided powerful tools to delineate disease pathways in terms of the network of proteinprotein interactions [2]. In this study, we used these strategies to identify disease-related genes and the possible pathways by which they function in the pathogenesis of SLE.
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Materials and methods
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Patients
Ten SLE patients (one male and nine female, age 28.8 ± 7.9 yr) at the Clinical Centre of Rheumatology at Shanghai Renji Hospital were enrolled in our study. The 10 patients were identified as A/A', B, C, D, E, F, G, H, S1 and S2; A' denotes a duplicated microarray test of patient A after treatment. Patients S1 and S2 were an SLE sib-pair. All patients except S1 were newly diagnosed and had received no therapeutic intervention (corticosteroids and cytotoxic agents). All clinical data were recorded according to the Systemic Lupus Activity Measure (SLAM) [3] (the scores of the 10 SLE patients ranged from 3 to 12 and averaged 6.8). Eleven items were added, viz. IFANA, double-stranded DNA (dsDNA) antibody, Sm, SSA, SSB, u1RNP, Rib P, anticardiolipin antibodies (ACL), ß2-glycoprotein I (ß2GP1), histone autoantibodies and hypocomplementaemia. This made a total of 43 items, which were together designated SLAM plus. In SLAM plus, dsDNA antibody was recorded as 0, 1, 2 according to the titre [<20, 2050 and >50 U/ml (Farr assay) respectively]. Other additional items were recorded as 0 or 1, which represented negative and positive respectively. Hep2 cells were used as substrates in the immunofluorescence antinuclear antibody (IFANA) test;
1:80 were considered positive (for clinical data see Table 1). Ten chips from age- and sex-matched healthy individuals served as normal controls. Another 40 SLE patients and 29 healthy individuals (matched for age and sex) were included for analysis by real-time polymerase chain reaction (PCR). Pooled white blood cell lysate was prepared from the blood of an additional 10 SLE patients (sonicated) in 1 mM PMSF/PBS (phenylmethylsulphonyl fluoride/phosphate-buffered saline) buffer for use in the proteinprotein interaction study. All SLE patients fulfilled at least four criteria for SLE (1997 revised criteria of the American College of Rheumatology). All patients and controls were from the Chinese Han population.

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FIG. 1. (A) Clustering based on the gene expression profile in SLE patients. (B) Clustering based on the clinical immune phenotype (the SLAM plus autoantibody spectrum) in the same SLE patients as those represented in (A). The 10 patients are identified as A/A', B, C, D, E, F, G, H, S1 and S2, where A' represents patient A after therapy, when the microarray analysis and clinical evaluation were repeated. Patients S1 and S2 are a SLE sib-pair. N represents normal controls. There is a correlation between the gene expression profile and immune phenotype.
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Microarray gene expression profiling [4]
A sample of 20 ml peripheral blood (anticoagulated with acid-citrate dextrose) was drawn from each patient and control subject, and was treated with red blood cell lysis buffer. Total RNA was extracted using TRIzol Reagent (Life Technologies, Carlsbad, CA, USA).
For the microarray analysis, we used the ExpressChipTM DNA Oligonucleotide Microarray System HO4 (Mergen, San Leandro, CA, USA). The chips contain 3360 spots, including 96 blank spots, eight spots for negative controls and 88 spots for housekeeping genes as positive controls. Genelist (http://www.mergen.com/HO4/HO4finder.asp) lists most of the immune-associated genes included. The experiments were performed according to the manufacturers protocol.
Total RNAs were retrotranscribed into cDNA using a cDNA synthesis kit (Roche, Basel, Switzerland). The primer was oligo[(dT)24T7 promoter]65, and ds-cDNAs were synthesized. After in vitro transcription from ds-cDNA (MEGAscriptTM transcription kit; Ambion, Austen, TX, USA), biotin-labelled cRNA probes (Biotin-CTP; Gibco BRL, Carlsbad, CA, USA) were obtained. The microarray was hybridized with the cRNA probe overnight at 30°C. After washing, blocking, applying streptavidin and first and second antibodies (labelled with Cy3), detection was performed with a ScanArray 5000 laser confocal microarray scanner (GSI Lumonics, Billerica, MA, USA). The data were exported by QuantArray microarray analysis software (GSI Lumonics). GeneSpringTM microarray analysis software version 4.2.1 (Silicon Genetics, Redwood City, CA, USA) was used to mine the gene expression data. After median normalization for each chip, the Welch t-test and Welch ANOVA (analysis of variance) were used to compare SLE patients with the control group. Differences were considered significant if P < 0.05. Subsequently, conventional two-fold change ratio analysis was performed separately, and the global error model was used (for the algorithm, see Fig. 2). The Spearman correlation was used in gene expression profiling and SLAM plus-based immune phenotype clustering.

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FIG. 2. Algorithm to detect significant differences in the gene expression profile between SLE patients (n = 10) and normal controls (n = 10). *The two-fold change cut-off and global error model methods show similar results, in that 28 gene spots obtained with the former method are included in the 29 gene spots obtained with the latter method. Three of these 28 gene spots on the HO4 chip are duplicated. Thus, we obtained 25 non-redundant genes.
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IFIT1 mRNA expression in SLE using real-time PCR [5]
To re-evaluate the expression pattern of the interferon-induced with tetratricopeptide repeats 1 (IFIT1) gene independently, total RNA from another 40 SLE patients was reverse-transcribed into cDNA using the Superscript II RT kit (Invitrogen Life Technologies, Carlsbad, CA, USA). The control group comprised 29 healthy individuals. Real-time PCR (ABI 7900, Applied Biosystems, Foster City, CA, USA) was performed according to the manufacturers protocol. The primers and Taqman probe for IFIT1 were as follows: IFIT1-F: 5'-GCCTCCTTGGGTTCGTCTATAA-3', IFIT1-R: 5'-TCAAAGTCAGCAGCCAGTCTCA-3'. IFIT1-TaqMan probe: FAM-AGCCCTGGAGTACTATG AGCGGGCC-TAMRA. GAPDH (glyceraldehyde 3-phosphate dehydrogenase) is treated as internal control. GAPDH-F: 5'-GAAGGTGAAGGTCGGAGTC-3', GAPDH-R: 5'-GAAGATGGTGATGGGATTTC-3'. GAPDH-TaqMan Probe: FAM-CAAGCTTCCCGTTCTCAGCC-TAMRA. In each cycle, fluorescent signals of both IFIT1 and GAPDH were collected during the PCR in order to determine their Ct values. The value of Ct (IFIT1 Ct minus GAPDH Ct), which was inversely correlated with the number of copies of the IFIT1 mRNA, was then calculated and compared between the two groups. SSPS 9.0 statistics software (SPSS, Chicago, IL, USA) was used to perform the t-test, and P < 0.05 was considered significant.
Molecular cloning of IFIT1 and expression of GSTIFIT1 fusion protein [67]
The full-length coding sequence of IFIT1 (1437bp) was amplified with the primer (forward) 5'-CGCGGATCCATGAGTACAAATGGTGAT-3', (reverse) 5'-TCCGCTCGA GCTAAGGACCTTGTCTCAC-3'. After purification of the PCR product and BamHI/XhoI digestion, IFIT1 was inserted into plasmid pGEX-6P-1. The vector was transfected into the cloning host Escherichia coli XL-1-Blue. The recombinant plasmid was screened by PCR and 1% agarose gel electrophoresis, and was confirmed by automated DNA sequencing (service provided by Genecore, Shanghai, China).
pGEX-IFIT1 was transfected into the expression clone host, E. coli BL21(DE3) cells. Isopropylthiogalactoside (IPTG) (0.1 mmol/l) was added to induce protein expression. pGEX-6P-1 vector-transfected BL21 cells [which express glutathione S-transferase (GST) only] and non-transfected BL21 cells were treated as controls. Escherichia coli organisms were then harvested and lysed in PBS pH 7.3/1 mM PMSF by sonication. The supernatant was added to a prepacked glutathione Sepharose 4B column (Amersham Pharmacia Biosciences, Piscataway, NJ, USA). After thorough rinsing with PBS (pH 7.3) to baseline, the fusion protein was eluted with 5 mmol/l reduced glutathione/PBS. Then 12% SDSPAGE (sodium dodecyl sulphatepolyacrylamide gel electrophoresis) was performed.
GSTIFIT1 as bait to capture prey protein [89] and MALDI-TOF (matrix-assisted laser desorption/ionizationtime-of-flight) mass spectrometry analysis
GSTIFIT1 and GST were bound to two glutathione Sepharose 4B columns separately. Pooled peripheral white blood cell lysate from SLE patients was made to flow through the GST column in order to remove non-specific proteins which bind to GST or matrix. The flow-through was then collected and added to the GSTIFIT1 column to interact with the GSTIFIT1. After thorough rinsing with PBS to baseline, PBSsaline gradient washing was performed, using 5 volumes of column-bed PBSNaCl with saline concentrations of 150, 200, 400, 600, 800, 1000 and 2000 mM; these were added sequentially to the column to elute the protein(s) captured by IFIT1. A concentrator tube with 5000 Da molecular weight cut-off (Millipore, Billerica, MA, USA) was used to reduce the collection volume of each gradient to 50 µl. SDSPAGE (12%) was then performed. After Coomassie Blue staining and depigmentation, the most significant new protein band was cut out from the gel and subjected to digestion and MALDI-TOF mass spectrometry (MS). The definition of the most significant new protein band (presumably with a higher specificity) was as follows: (i) it was a new band other than the fusion protein or GST; (ii) it was obtained at saline concentration of
400 mM [8]; and (iii) there was enough protein present for MS identification to be possible. MALDI-TOF MS was performed by National Centre of Biomedical Analysis, Beijing.
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Results
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SLE microarray gene expression profiling
Results of gene expression profiling and the clinical immune phenotype data showed that when clustering was found before treatment it also tended to be found after treatment. In addition, sib-pairs were also clustered together, i.e. patients A/A', S1 and S2 were close neighbours in the cluster tree (Fig. 1). Moreover, there seemed to be an association between the gene expression profile and the clinical immune phenotype. Patients A and D, who had only cutaneousmucosal lesions and serositis, were clustered together at one end of the gene expression or clinical manifestation spectrum. Patient C, who had predominantly gastrointestinal vasculitis, was located at the other end of spectrum. The remaining patients had mainly haematological involvement and/or glomerulonephritis.
Identification of SLE-related genes
Testing the reproducibility of our oligonucleotide microarray system showed that when probes from the same RNA sample were hybridized to two chips separately, the correlation coefficient (r2) for all signals was 0.93; however, 4.8% of the genes showed a difference of more than 2-fold. To increase the reliability of our microarray data and to optimize the experimental procedure and validation method, we performed a combination of the Welch t-test with conventional two-fold change filtering analysis. The results were similar to those obtained with a method using the global error model. To some extent, statistical significance is more important than an artificial two-fold cut-off [10]. Ultimately, we obtained 25 non-redundant, over- or under-expressed, statistically significant SLE genes (Table 2). The clustering of these SLE-induced genes shown in the array results indicated that there was a strongly correlated gene cluster that contained genes that were all interferon-inducible. These genes were IFIT1, IFIT4, OAS2, OAS1, OASL and Ly6E (Fig. 3). By using real-time PCR in a larger independent population, we validated the microarray expression data for IFIT1, IFIT4, OAS2, Ly6E and C/EBPD (data not provided except for IFIT1). Real-time PCR confirmed that the level of expression of IFIT1 mRNA was significantly up-regulated in SLE (n = 40) compared with normal controls (n = 29; P < 0.001) (Fig. 4). This reinforces the reliability of our microarray data and algorithm.

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FIG. 3. Clustering of 25 SLE-associated genes from the microarray analysis. A cluster of IFN-induced genes, including the candidate gene IFIT1, is highlighted.
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FIG. 4. Level of expression of IFIT1 mRNA in SLE patients (n = 40) and normal controls (NC; n = 29), determined by real time-PCR. Ct is inversely correlated with the number of gene copies. IFIT1 is up-regulated in SLE (P < 0.001). The lines are the standard deviation.
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Next we focused on the candidate gene IFIT1, whose function is not clear and has not been related to SLE in the literature.
Molecular cloning of IFIT1 and expression and purification of the GSTIFIT1 fusion protein
The recombinant pGEX-IFIT1 plasmid was constructed, and automated DNA sequencing of the positive clone showed that the inserted DNA was identical to the human IFIT1 gene sequence, with no shift in the reading frame.
After IPTG induction, SDSPAGE showed that vector pGEX-6P-1 transfection led to expression of a 26 kDa GST fragment. Recombinant pGEX-IFIT1 plasmid transfection led to expression of a 82 kDa protein that matched the predicted size of the GSTIFIT1 fusion protein (the molecular weight of IFIT1 is 56 kDa, and adding 26 kDa for GST gives 82 kDa). The GSTIFIT1 fusion protein was purified with a glutathione Sepharose 4B column (Fig. 5).

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FIG. 5. GST-IFIT1 fusion protein purification. Lane 1, protein marker; lane 2, pre-IPTG induction; lane 3, post-IPTG induction; lane 4, supernatant; lane 5, precipitants; lane 6, flow-through; lane 7, purified GSTIFIT1 fusion protein.
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Identifying IFIT1-interacting proteins by MALDI-TOF MS peptide mass fingerprinting
GSTIFIT1 was treated as a bait to capture prey protein from a pooled lysate of peripheral white blood cells from SLE patients. Pretreatment removed proteins that may have interacted with GST or the matrix. Because protein interaction occurs mainly through hydrophobic bonds, the specificity of the IFIT1 protein partner would be expected to increase as the saline concentration of elution was increased. SDSPAGE showed that there were no visible protein bands when the saline concentration reached 1000 mM. We obtained a significant new band in the 4050 kDa range at the concentration of 800 mM. (white arrow in Fig. 6).

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FIG. 6. Using a GST tag to capture proteins that interact with IFIT1. Lane 1, protein marker; lane 2, rinsing with PBS plus NaCl 400 mM; lane 3, 600 mM NaCl; lane 4, 800 mM NaCl; lane 5, 1000 mM NaCl; lane 6, 2000 mM NaCl. In order to undergo further MS identification, a new protein band (other than fusion protein or GST) had to have high specificity, i.e. it had to appear at an NaCl concentration of 400 mM [8], and it had to be sufficiently intense to indicate that there was enough protein for analysis. The white arrow indicates a protein band (about 45 kDa) captured at 800 mM NaCl. Black arrows indicate proteins present in insufficient quantity (band at about 35 kDa) or GST contamination (band at about 26 kDa).
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In the identification of protein(s) interacting with IFIT1 by MALDXI-TOF MS, candidate proteins had to fulfil the following conditions: (i) it must not be a fragment of the GSTIFIT1 fusion protein; (ii) it must not be a protein from E. coli; and (iii) common contaminants in MS analysis, such as keratoprotein, immunoglobulin G, heat-shock proteins and ribosomal proteins, had to be excluded [11]. MALDI-TOF MS peptide mass fingerprinting and database searching (Mascot, http://www.matrixscience.com) identified the IFIT1 partner as a set of highly similar Rho/Rac guanine nucleotide exchange factors (GEFs) (Fig. 7). The quality of MALDI-TOF data is quite good, but the database search score was not very high, ranging from 42 to 43 (this score is a measure of the statistical significance of a match). There were two possibilities: (i) that our captured protein band was a mixture of several GEF homology proteins; and (ii) that the protein was a novel protein of the GEF family.

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FIG. 7. MALDI-TOF MS peptide mass fingerprinting of proteins interacting with IFIT1. A group of Rho/Rac GEF proteins were identified.
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Discussion
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Attempts have been made to classify disease subtypes by microarray gene expression profiling in leukaemia and lymphoma [12, 13]. In the present study, we found a correlation between the gene expression profile and the clinical immune phenotype in SLE patients. As our sample of patients was small, the data we obtained do not cover the variation of lupus adequately. An extensive discussion of disease subtype classification at the molecular level is not possible here. This can be considered a pilot study, and our results merely suggest one possible direction for future research in rheumatology.
Our results indicate that interferon and its associated immune regulatory pathway may play an important role in SLE. Evidence has already been obtained that the interferon family, in particular interferons of type I, is crucial in the pathogenesis in SLE. Blanco et al. [14], obtained promising data indicating serum from SLE patients can induce normal monocytes to be transformed into a dendritic cell phenotype. The effector in the serum was interferon
(IFN-
). By adding anti-IFN-
antibody, this effect will be diminished. As a strong form of antigen-presenting cell, the dendritic-like cell can capture apoptotic cells and nucleosomes effectively, and then present self-nuclear antigen to lymphocytes. This mechanism may result in the breakdown of self-tolerance and autoimmunity in SLE. On the other hand, nuclear antigen and the anti-DNA/nucleosome antibody complex are strong inducers of interferon [15], and this forms a feedback circle including the antigen-presenting cell, interferon and nuclear antigen. Our microarray expression profiling revealed a related cluster of genes that are inducible by interferon. These SLE-related genes include IFIT1, IFIT4, OAS1, OAS2 and OASL. The 2'-5'-oligoadenylate synthetases (OASs) are induced mainly by interferon type I. The function of OASs is considered to be antiviral, and these enzymes have been found to be up-regulated in the serum of SLE patients [16]. We are particularly interested in IFIT1, the function of which is not clear and has not been related to SLE previously. We confirmed independently that the expression of IFIT1 mRNA is significantly up-regulated in a larger SLE population using real-time PCR. Rozzo et al. [17], using microarrays to study the congenic lupus murine model (NZB x B6 F1), demonstrated that the interferon-induced gene ifi202 may be a susceptibility gene in lupus, and argued that ifi202 could be a target for intervention in SLE. Basic local alignment search tool (BLAST) between human IFIT1 and mouse ifi202 shows no homology between the two genes.
How IFIT1 protein functions in SLE has been studied using a proteomics strategy. Protein function and disease pathways are all ultimately founded on proteinprotein interaction. Extensive study of proteinprotein interaction networks is the focus of the latest developments in proteomics. The new strategy is to clone the target protein in order to capture protein complexes by using affinity-purification tags. Gavin et al. [11] used a tandem affinity-purification (TAP) tag to purify and identify yeast protein complexes. Marcotte et al. [2] published a similar study, using the FLAG-Tag approach [FLAG is a hydrophilic octapeptide (DYKDDDDL)]. Sophisticated two-dimensional gel techniques and yeast two-hybrid systems were not used, and MS has been used to identify protein interactions, and to help find novel proteins. MALDI-TOF MS has high resolution and mass accuracy that can meet the technical demands of protein interaction research, and it has therefore become one of the most important tools in proteomics. The GST pull-down assay has been used to analyse interactions between known candidate proteins for a number of years [8]. We have modified the GST pull-down assay to find unknown partner(s) of our candidate molecules. Structure prediction shows that IFIT1 has six tetratricopeptide repeats (TPR domains) (www.smart.embl-heidelberg.de/smart/show_motifs.pl) (Fig. 8). The tetratricopeptide repeat is known to be the basis of proteinprotein interaction, and this could be the structural basis of our findings.

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FIG. 8. Structure basis of IFIT1, which contains six TPR domains (determined using www.smart.embl-heidelberg.de/smart/show_motifs.pl).
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MALDI-TOF MS peptide mass fingerprinting has shown that IFIT1 may interact with Rho/Rac GEF. GEFs play an important role in the regulation of Rho protein activation and the downstream pathway. The Rho proteins are small G proteins, or GTPases, and there are several members of the family, including Rho A, Rho C, Rac 1 and Cdc42. By interacting with GEFs, Rho proteins switch from an inactivated GDP-binding form to an activated GTP-binding state. Rho proteins are important components of intracellular signal transduction, and are involved in cytoskeletal rearrangement, cell cycle regulation, migration, phagocytosis and stress responses [18]. After activation by GEFs, Rho proteins can activate the JNK (Jun-NH2-terminal kinase) and p38 MAPK (mitogen-activated protein kinase) pathways (Rho proteins can also activate NF
B (induce apoptosis) and precipitate Fcg receptor-mediated phagocytosis [1920]. IFIT1 may cross-react with Rho/Rac GEF, thereby becoming involved in SLE immune responses. Recently, on the basis of the common regulatory pathway shared by T- and B-cell receptors and other immune receptors, some researchers have proposed the concept of multi-subunit immune-recognition receptors (MIRRs) [21], and have claimed that a thoroughly studied member of the GEF family, vav, may play a critical role in the integration of immune signalling. However, the detailed characteristics of GEFs are still undetermined.
Our study has highlighted the interferon-related pathway and an interferon-induced gene, IFIT1, and the results we obtained may be relevant to the pathogenesis of SLE. Our data show only that IFIT1 may interact with Rho/Rac GEFs. When this IFIT1 partner protein has been characterized by protein sequencing, it will be possible to use the standard GST pull-down assay to validate our results. Also, further in vitro and in vivo studies are needed to determine the exact role of IFIT1 in the interferon-related pathway of SLE. Our approach is not suitable for the study of protein interactions that depend on post-translational modification, or for the detection of weak, transientbut perhaps importantprotein interactions. However, our study does provide some interesting clues about the immune regulatory pathway in SLE, and may help in the identification of targets for therapeutic intervention.
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Acknowledgments
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This work was supported by the Chinese National Natural Science Foundation (grant numbers 30000154, 30271224), Shanghai Science and Technology Department Fund (01JC14029, 02QMB1404) and Fok Ying Tung Education Foundation Young Teacher Award (81030). We also thank the Chinese National Centre of Biomedical Analysis (NCBA), Beijing, for MS analysis. Our colleagues at UCLA are funded by the USA Public Health Service grants and by donations from the Dorough Foundation, the Paxson Family and the Arthritis Foundation.
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Submitted 4 November 2002;
Accepted 12 February 2003