Correlation between cold and hot pattern in traditional Chinese medicine and gene expression profiles in rheumatoid arthritis

Abstract

Clinical manifestations of rheumatoid arthritis (RA) are diversified, and based on the manifestations, the patients with RA could be classified into different patterns under traditional Chinese medicine. These patterns decide the selection of herbal prescription, and thus they can help find a subset of rheumatoid arthritis patients for a type of therapy. In the present study, we combine genome-wide expression analysis with methods of systems biology to identify the functional gene networks for the sets of clinical symptoms that comprise the major information for pattern classification. Clinical manifestations in rheumatoid arthritis were clustered with factor analysis, and two factors (similar to cold and hot patterns in traditional Chinese medicine) were found. Microarray technology was used to reveal gene expression profiles in CD4⁺ T cells from 21 rheumatoid arthritis patients. Protein-protein interaction information for these genes from databases and literature data was searched. The highly-connected regions were detected to infer significant complexes or pathways in this protein-protein interaction network. The significant pathways and function were extracted from these subnetworks using the Biological Network Gene Ontology tool. The genes significantly related to hot and cold patterns were identified by correlations analysis. MAPK signalling pathway, Wnt signaling pathway, and insulin signaling pathway were found to be related to hot pattern. Purine metabolism was related to both hot and cold patterns. Alanine, aspartate, and tyrosine metabolism were related to cold pattern, and histindine metabolism and lysine degradation were related to hot pattern. The results suggest that cold and hot patterns in traditional Chinese medicine were related to different pathways, and the network analysis might be used for identifying the pattern classification in other diseases.

Keywords

gene expression profile / pathway / rheumatoid arthritis / traditional Chinese medicine / systems biology

Introduction

Rheumatoid arthritis (RA) is a systemic autoimmune disease affecting over 1% of the population [1]. The disease is characterized by synovial membrane hyperplasia and progressive destruction of arthritic joints, as well as inflammatory cell infiltration, including activated CD4⁺ T cells[2]. The CD4⁺ T cells play a crucial role in the pathogenesis of RA through their ability to stimulate the secretion of proinflammatory cytokines, such as tumor necrosis factor (TNF)-α and interleukin (IL)-1, inducing immunoglobulin production and matrix metalloproteinase secretion leading to osteoclastogenesis [3].

However, the clinical expressions of RA are rather heterogeneous, making it difficult to characterize biochemically [4]. Based on the clinical manifestations, the patients with RA could be classified into different patterns under traditional Chinese medicine (TCM); these TCM patterns can help find a subset of RA patients for a specific type of therapy [5,6]. The diversified clinical manifestations must be a consequence of biological networks comprising hundreds of thousands of gene expressions changed in various affected tissues and immune effector cells [4]. The gene profiles related to RA pathogenesis could partially explain the mechanism of joint symptoms related to RA diagnosis criteria [7]; however explorations on the correlation between clinical symptoms and gene profiles might lead to some new findings for RA pathogenesis that are yet to be clarified.

Microarray technology is a massively parallel method for assessing gene expression changes at the genome scale, and with this approach, sets of disease-specific changes in gene expression are readily identified, providing information on individual disease mediators [6]. The gene expression profiles in RA T cells bear evidence of the existence of multiple pathways of tissue destruction and repair [8]. A potential strategy of systems biology, using computational tools to predict biological networks arising from global, high-throughput data sets, can lead to a deeper understanding of the system [9]. In this paper, we combine genome-wide expression analysis with methods of systems biology to identify the functional gene networks for the sets of clinical symptoms that comprise the major information for TCM pattern classification.

Methods

Patients and CD4⁺ T-cell purification

In all, 21 patients with RA (all females; aged 42.8±9.9 years old) were enrolled in this study. The study was granted by the local Ethical Committee for Clinical Research, and each patient gave informed consent prior to the inclusion. Peripheral blood (about 15 ml) was collected in sodium-heparin vacutainer tubes. The CD4⁺ T cell population was purified by negative selection using a CD4⁺ T cells enrichment cocktail (StemCell, Canada) from blood.

TCM pattern classification and correlation analysis

Based on TCM, the clinical manifestations related to TCM pattern classification were collected from all RA patients prior to blood collection. The clinical manifestations were clustered into the TCM pattern with factor analysis. The TCM patterns related genes were selected with correlation analysis (coefficient>0.5 or<-0.5, and P<0.05 as significant). All data were analyzed on a SAS9.1.3 statistical package (Order No. 195557).

Microarray and data analysis

Total RNA was extracted from CD4⁺ T cells using TRIZol reagent according to the instructions of the manufacturer. Probes were verified for amplification yield and incorporation efficiency by measuring the DNA concentration at 280 nm, Cy3 incorporation at 550 nm, and Cy5 incorporation at 650 nm. For each color, 10 pmol incorporated dye was fragmented and resuspended in a 500 μl hybridization solution. The samples were then hybridized to dual-color human Whole Genome Microarray (University of British Columbia, Canada) that contained 4 arrays of probes representing around 23 232 well-characterized transcripts. The arrays were hybridized in microarray hybridization chambers overnight at 42°C. After washing, the slides were scanned with GenePix 4000B scanner.

All nonflagged array elements, for which the fluorescent intensity in each channel was 1.5 times greater than the local background, were considered well measured. The ratio values were log-transformed (base 2) and stored in a table (rows, individual cDNA clones; columns, single mRNA species). cDNA spots that fulfilled the intensity criteria on at least 80% of the microarrays were analyzed. Data for the remaining genes were centered by subtracting (in log space) the median observed value in order to remove any effect of the amount of mRNA in the common reference pool.

Protein–protein interaction network

The information on human protein-protein interactions was obtained from Databases, including BIND (Biomolecular Interaction Network Database), BioGRID (the General Repository for Interaction Datasets), DIP (Database of Interacting Proteins), HPRD (Human Protein Reference Database), IntAct (Database System and Analysis Tools for Protein Interaction Data) and MINT (Molecular Interactions Database); the data were complemented with curated relationships parsed from literature using Agilent Literature Search [10]. These datasets are mostly based on experimental evidence, and we did not include data that were deemed to be of lower quality. The protein-protein interaction network was visualized using Cytoscape [11].

Highly-connected clusters of the integrated network

We integrated the database and the literature data mining networks and then used Incremental Principal Component Analysis (IPCA) to analyze the characteristics of our network. The IPCA algorithm can detect densely connected regions in the interactome network [12]. Interactomes with a score greater than 2.0 and at least 4 nodes were taken as significant predictions in this study.

Gene ontology analysis

To identify the function of each cluster generated by IPCA individually, GO clustering analysis was performed with the proteins described in all subnetworks. For this purpose, the latest version of Biological Network Gene Ontology (BiNGO) tool [13] was used to statistically evaluate groups of proteins with respect to the existing annotations of the Gene Ontology Consortium. The degree of functional enrichment for a given cluster was assessed quantitatively (P value) by hypergeometric distribution as implemented in BiNGO tool. We selected the 10 GO biological categories with the smallest P values as significant.

Results

The cold and hot classifications in TCM patterns

The clinical manifestations were clustered into two sets (factors in Table 1) with factor analysis. Joint pain relieved with warming, cold feeling in joints and joint pain aggravated by cooling were in set 1; these were categorized in TCM cold pattern. Meanwhile, hot feeling in joints, red colored joints, and joint pain relieved with cooling were in set 2, which were categorized in TCM hot pattern.

TCM cold pattern and its functional gene networks

The TCM cold pattern related genes were selected using correlation analysis. All related genes are listed in Table 2.

To refine further the functional properties of these genes, the official symbols of the genes in Table 2 were used to search protein interaction information from protein interaction databases and literature data. Cytoscape, a network visualization tool, converts a list of genes (with or without accompanying expression information) into a relevant network (Fig. 1). The network contains 153 nodes and 1205 edges; here, the nodes represent proteins, and the edges represent interactions between the proteins. Four significantly highly-connected regions were proposed by IPCA, and these subnetworks of highly-connected regions were visualized using Cytoscape (Fig. 2).

The subnetworks of highly connected regions and functions of the nodes were found to be mainly involved in alanine and aspartate metabolism, alkaloid biosynthesis II, benzoate degradation via CoA ligation, limonene and pinene degradation, methylnaphthalene degradation, phenylalanine metabolism, purine metabolism, and tyrosine metabolism.

Particularly, the articular manifestations in cluster 1 were related to tyrosine metabolism (Fig. 2A), and purine metabolism (Fig. 2C).

TCM hot pattern and its functional gene networks

The TCM hot pattern related genes were selected using correlation analysis. All related genes are listed in Table 3.

To refine further the functional properties of these genes, the official symbols of the genes in Table 3 were used to search protein interaction information from protein interaction databases and literature data. The network contains 1080 nodes and 17 892 edges (Fig. 3). In addition, 15 significantly highly-connected regions were proposed by IPCA, and these subnetworks of highly-connected regions were visualized using Cytoscape (Fig. 4).

The subnetworks of highly connected regions and functions of the nodes are involved mainly in ribosomal protein, NADH dehydrogenase, eukaryotic translation initiation factor, ATP synthase, glutathione peroxidase, phosphodiesterase, glutamate metabolism. In particular, the following related pathways in each cluster were included: translation, regulation of translation initiation in cluster 1 (Fig. 4A); oxidative phosphorylation in cluster 2 (Fig. 4B); MAPK signaling pathway in cluster 3 (Fig. 4C); complement and coagulation cascades, Wnt signaling pathway, MAPK signaling pathway, VEGF signaling pathway and insulin signaling pathway in cluster 4 (Fig. 4D); glutathione metabolism in cluster 5 (Fig. 4E); purine metabolism and pyrimidine metabolism in cluster 6 (Fig. 4F); urea cycle and metabolism of amino groups in cluster 7 (Fig. 4G); purine metabolism and caffeine metabolism in cluster 8 (Fig. 4H); pphingolipid metabolism in cluster 9 (Fig. 4I); cell adhesion molecules, antigen processing and presentation in cluster 10 (Fig. 4J); glycosylphosphatidylinositol (GPI)-anchor biosynthesis in cluster 11 (Fig. 4K); citrate cycle in cluster 12 (Fig. 4M); tryptophan metabolism, urea cycle and metabolism of amino groups in cluster 13 (Fig. 4N); and insulin signaling pathway in cluster 14 (Fig. 4O).

Discussion

Articular manifestations could be classified into 3 types (i.e., pain, swelling and deformation), which are included in RA diagnosis in biomedicine; cold feeling in joints and pain relieved with warming, which are important for TCM cold pattern differentiation; and hot feeling and pain relieved with cooling, which are important for TCM hot pattern differentiation [6].

The TCM cold and hot patterns based on clinical articular manifestaions were related to different pathways in this study. Cold feeling of joint and joint pain relieved with warming included in TCM cold pattern were more specifically related to alanine, aspartate, tyrosine metabolism; on the other hand, hot feeling of joint and joint pain relieved with cooling included in TCM hot pattern were more specifically related to TGF-beta signaling pathway, calcium signaling pathway, tumors, cell cycling, histidine metabolism, and lysine degradation (Table 4).

MAPK signalling pathway, Wnt signaling pathway, and insulin signaling pathway were found to be related to hot feeling of joint and joint pain relieved when cooling in TCM hot pattern. In fact, hot feeling in joint was always shown together with the red colored joint, indicating inflammatory response in the joint. Purine metabolism was found to be related to both TCM cold and hot patterns in this study. MAPK signaling pathway, the transforming growth factor beta (TGF-β) signaling pathway, calcium signaling pathway, and vascular endothelial growth factor (VEGF) signaling pathway have wide bioactivities, including inflammation and immune responses. The Mitogen-Activated Protein Kinase (MAPK) pathways transduce a various external signals, leading to a wide range of cellular responses, including growth, differentiation, inflammation, and apoptosis. Although MAPK kinase 3 (MKK3) and MAPK kinase 6 (MKK6) activate the p38 pathway, they regulate distinct subsets of proinflammatory cytokines [14]. The TGF-β signaling pathway is involved in many cellular processes in both the adult organism and the developing embryo, including cell growth, cell differentiation, apoptosis, cellular homeostasis, and other cellular functions. Many cytokines, including TGF-β1, have been implicated in the progressive growth and invasion of the synovial pannus into the surrounding cartilage and bone in RA. TGF-β exerts its growth and antiapoptotic effects on fibroblasts, at least in part, by activation of the PI 3-kinase/Akt pathway [15]. TGF-β1 may trigger a significant IL-16 response in synovial fibroblasts in RA, and this mode of IL-16 induction represents a novel pathway leading to IL-16 production in synovial fibroblastss operating independently of NFκB signaling [16]. Furthermore, CD4⁺CD25⁺ regulatory T cells mediated immune suppression; this could limit immunopathogenesis associated with chronic inflammation, which is contact-dependent, antigen-nonspecific, and involves a nonredundant contribution from the cytokine TGF-β[17]. Calcium is a common signaling mechanism because it exerts allosteric regulatory effects on many enzymes and proteins once it enters the cytoplasm. Calcium can act in signal transduction after influx, resulting from the activation of ion channels; it can also act as a second messenger caused by indirect signal transduction pathways, such as G protein-coupled receptors. Thus, calcium signaling pathway is involved in diversified bioactivities. Neutrophils comprise key cells involved in inflammatory reaction. Changes in cytosolic free Ca²⁺ play a central role in triggering neutrophil responses [18]. In addition, controlled variation in intracellular calcium concentration is a key component of the immune response signaling pathway in lymphocytes [19]. Great evidence shows that VEGFR-2 is the major mediator of VEGF-driven responses in endothelial cells, and it is considered to be a crucial signal transducer in both physiologic and pathologic angiogenesis. There is also considerable evidence in various autoimmune diseases, such as systemic lupus erythematosus, RA and multiple sclerosis, and an interrelationship between the VEGF system and these disorders [20]. The binding of VEGF to VEGFR-2 leads to a cascade of different signaling pathways, resulting in the up-regulation of genes involved in mediating the proliferation and migration of endothelial cells and in promoting their survival and vascular permeability. Emerging evidence indicates that VEGF plays a critical role in host inflammatory responses in several disease states, including atherosclerosis, sepsis, and RA [21]. Aberrant VEGF signaling is a feature of several other pathologic conditions, such as RA [22]. In our study, MAPK signaling pathway, TGF-β signaling pathway, calcium signaling pathway, and VEGF signaling pathway were found to be related to TCM hot pattern. Thus, those pathways are more likely involved in the inflammatory and immune responses in RA pathogenesis.

In this study, we also found that TCM hot pattern was related to tumors and Wnt signaling pathway, suggesting that RA pathogenesis might have some relations with tumor development. The risk of malignancies in patients with RA has raised some concern, particularly with immunosuppressive approaches to disease management. A review of the literature and meta-analysis characterizing the associated risk of overall malignancy and 4 site-specific malignancies (i.e, lymphoma, lung, colorectal, and breast cancer) in patients with RA was conducted, and the results showed that patients with RA appear to be at higher risk of lymphoma and lung cancer and had potentially decreased risk for colorectal and breast cancer compared with the general population [23]. Leukemia inhibitory factor (LIF) produced by joint tissue cells overexpressed in arthritis could contribute to the pathogenesis of arthritis [24]. The Wnt signaling pathway describes a complex network of proteins well known for their roles in embryogenesis and cancer [25]. The Wnt signaling pathway has a crucial role in regulating cell growth and differentiation and is required for tissue homeostasis and repair. Although constitutive activation of the Wnt pathway can lead to abnormal cell growth and cancer, modulation of Wnt signaling might have a therapeutic benefit for tissue regeneration in numerous diseases. Recently, preclinical studies have demonstrated that treatments with antibodies against the Wnt inhibitor, Dickkopf1 (DKK1), and with the positive Wnt modulator, R-Spondin1 (RSpo1), were sufficient in repairing the bone lesions in multiple myeloma and RA and in restoring the damaged mucosa in experimental colitis, respectively. Given that physiological Wnt response is essential for the regeneration of damaged tissues, the modulation of the Wnt pathway might be beneficial for the treatment of multiple human diseases [26,27]. Furthermore, VEGF, which is involved in RA immune and inflammatory responses, could be also released by tumor cells and induce tumor neovascularization [28]. Our results on the relations between tumor pathogenesis and TCM hot pattern in RA suggest that RA pathogenesis could be further clarified from the Wnt signaling pathway, cell cycle, and other tumor-related pathways.

Interestingly, insulin signaling pathway was found to be related to TCM patterns of RA in our study. Insulin is a hormone released by pancreatic beta cells in response to elevated levels of nutrients in the blood. Insulin triggers the uptake of glucose, fatty acids, and amino acids into the adipose tissue, muscle and the liver, promoting the storage of these nutrients in the form of glycogen, lipids and proteins, respectively. Obesity and insulin resistance are strongly associated with systemic markers of inflammation and endoplasmic reticulum stress. c-Jun N-terminal kinases (JNK) are activated by inflammatory cytokines and have key roles in beta-cell apoptosis and in negative regulation of insulin signaling [29]. TNF- α plays an important role in obesity-linked insulin resistance and diabetes mellitus by activating at least two serine kinases that are capable of promoting negative regulation of key elements of the insulin signaling pathway. Pharmacological inhibition of TNF-α is currently in use for the treatment of rheumatoid, and some case reports have shown clinical improvement of diabetes in patients treated with the TNF-α blocking monoclonal antibody infliximab [30]. The results on insulin signaling pathway in this study suggest that insulin signaling pathway might be directly or indirectly involved in RA pathogenesis, and that it could be a potential target for RA therapy.

Amino acid metabolism has a wide range of bioactivities as a basic metabolism. In our study, tyrosine, alanine, and aspartate metabolism were found to be related to TCM cold pattern. Tyrosine kinases play a central role in the activation of signal transduction pathways and cellular responses that mediate the pathogenesis of RA; additionally, tyrosine kinase inhibitor may be powerful in treating RA and other inflammatory diseases [31]. In patients with rheumatic diseases, serum creatine kinase (CK) level was significantly correlated with lactate dehydrogenase (LDH), aspartate aminotransferase (AST), alanine aminotransferase (ALT), ESR, and C-reactive protein (CRP) [32]. Recent findings on antibodies against L-asparaginyl-tRNA synthetase (Asn) confirm the role of aspartate in RA [33]. Although purine metabolism was found to be related to all sets of TCM patterns in this study, the involvement of purine metabolism in RA pathogenesis remained unclear; as such, further exploration on this topic must be conducted. Adenosine receptors are known to modulate the release of some inflammatory mediators in RA patients [34]. The anti-inflammatory activities of methotrexate and sulphasalazine may be mediated by corresponding increases in endogenous adenosine levels [35]. The results on amino acid metabolism suggest that it might be important for further stratification of RA given that joint cold feeling in the TCM cold pattern and hot feeling in the TCM hot pattern should be treated with different approaches in TCM [5]. The same results also suggest that more explorations should be conducted on amino acid metabolism in RA pathogenesis.

In conclusion, the articular manifestations have been clustered into TCM cold and hot patterns using factor analysis. MAPK signalling pathway, Wnt signaling pathway, and insulin signaling pathway have been found to be related to TCM hot pattern. Purine metabolism has been found to be related to both of the TCM cold and hot patterns, while alanine, aspartate, and tyrosine metabolism are related to TCM hot pattern. The results suggest that TCM cold and hot patterns are related to different pathways, and that the amino acid metabolism might be responsible for TCM cold and hot pattern classification in RA.

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