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

References

[1] Hochberg MC, Spector TD. Epidemiology of rheumatoid arthritis: update. Epidemiol Rev 1990; 12(1): 247–252

[2] Seemayer CA, Distler O, Kuchen S, Müller-Ladner U, Michel BA, Neidhart M, Gay RE, Gay S. Die Rheumatoide Arthritis: Neuentwicklungen in der Pathogenese unter besonderer Berücksichtigung der synovialen Fibroblasten. Z Rheumatol 2001; 60(5): 309–318(Rheumatoid arthritis: new developments in the pathogenesis with special reference to synovial fibroblasts)Müller-Ladner UMichel BANeidhart MGay REGay S

[3] Schurigt U, Pfirschke C, Irmler IM, Hückel M, Gajda M, Janik T, Baumgrass R, Bernhagen J, Bräuer R. Interactions of T helper cells with fibroblast-like synoviocytes: up-regulation of matrix metalloproteinases by macrophage migration inhibitory factor from both Th1 and Th2 cells. Arthritis Rheum 2008; 58(10): 3030–3040Hückel MGajda MJanik TBaumgrass RBernhagen JBräuer R

[4] Centola M, Frank MB, Bolstad AI, Alex P, Szanto A, Zeher M, Hjelmervik TO, Jonsson R, Nakken B, Szegedi G, Szodoray P. Genome-scale assessment of molecular pathology in systemic autoimmune diseases using microarray technology: a potential breakthrough diagnostic and individualized therapy-design tool. Scand J Immunol 2006; 64(3): 236–242

[5] He Y, Lu A, Zha Y, Tsang I. Differential effect on symptoms treated with traditional Chinese medicine and western combination therapy in RA patients. Complement Ther Med 2008; 16(4): 206–211

[6] Lu C, Zha Q, Chang A, He Y, Lu A. Pattern differentiation in Traditional Chinese Medicine can help define specific indications for biomedical therapy in the treatment of rheumatoid arthritis. J Altern Complement Med 2009; 15(9): 1021–1025

[7] van der Pouw Kraan TC, van Gaalen FA, Huizinga TW, Pieterman E, Breedveld FC, Verweij CL. Discovery of distinctive gene expression profiles in rheumatoid synovium using cDNA microarray technology: evidence for the existence of multiple pathways of tissue destruction and repair. Genes Immun 2003; 4(3): 187–196

[8] Butte A. The use and analysis of microarray data. Nat Rev Drug Discov 2002; 1(12): 951–960

[9] Gilchrist M, Thorsson V, Li B, Rust AG, Korb M, Roach JC, Kennedy K, Hai T, Bolouri H, Aderem A. Systems biology approaches identify ATF3 as a negative regulator of Toll-like receptor 4. Nature 2006; 441(7090): 173–178

[10] Vailaya A, Bluvas P, Kincaid R, Kuchinsky A, Creech M, Adler A. An architecture for biological information extraction and representation. Bioinformatics 2005; 21(4): 430–438

[11] Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 2003; 13(11): 2498–2504

[12] Li M, Chen JE, Wang JX, Hu B, Chen G. Modifying the DPClus algorithm for identifying protein complexes based on new topological structures. BMC Bioinformatics 2008; 9(1): 398

[13] Maere S, Heymans K, Kuiper M. BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics 2005; 21(16): 3448–3449

[14] Yoshizawa T, Hammaker D, Boyle DL, Corr M, Flavell R, Davis R, Schett G, Firestein GS. Role of MAPK kinase 6 in arthritis: distinct mechanism of action in inflammation and cytokine expression. J Immunol 2009; 183(2): 1360–1367

[15] Yoshizawa T, Hammaker D, Boyle DL, Corr M, Flavell R, Davis R, Schett G, Firestein GS. Role of MAPK kinase 6 in arthritis: distinct mechanism of action in inflammation and cytokine expression. J Immunol 2009; 183(2): 1360–1367

[16] Hoberg M, Rudert M, Pap T, Klein G, Gay S, Aicher WK. Attachment to laminin-111 facilitates transforming growth factor beta-induced expression of matrix metalloproteinase-3 in synovial fibroblasts. Ann Rheum Dis 2007; 66(4): 446–451

[17] Wahl SM, Chen W. Transforming growth factor-beta-induced regulatory T cells referee inflammatory and autoimmune diseases. Arthritis Res Ther 2005; 7(2): 62–68

[18] Davies EV, Hallett MB. Cytosolic Ca2+ signalling in inflammatory neutrophils: implications for rheumatoid arthritis (Review). Int J Mol Med 1998; 1(2): 485–490 (Review)

[19] Sweeney ZK, Minatti A, Button DC, Patrick S. Small-molecule inhibitors of store-operated calcium entry. ChemMedChem 2009; 4(5): 706–718

[20] Carvalho JF, Blank M, Shoenfeld Y. Vascular endothelial growth factor (VEGF) in autoimmune diseases. J Clin Immunol 2007; 27(3): 246–256

[21] Hao Q, Wang L, Tang H. Vascular endothelial growth factor induces protein kinase D-dependent production of proinflammatory cytokines in endothelial cells. Am J Physiol Cell Physiol 2009; 296(4): C821–C827

[22] Kowanetz M, Ferrara N. Vascular endothelial growth factor signaling pathways: therapeutic perspective. Clin Cancer Res 2006; 12(17): 5018–5022

[23] Smitten AL, Simon TA, Hochberg MC, Suissa S. A meta-analysis of the incidence of malignancy in adult patients with rheumatoid arthritis. Arthritis Res Ther 2008; 10(2): R45

[24] Lotz M, Moats T, Villiger PM. Leukemia inhibitory factor is expressed in cartilage and synovium and can contribute to the pathogenesis of arthritis. J Clin Invest 1992; 90(3): 888–896

[25] Lie DC, Colamarino SA, Song HJ, Désiré L, Mira H, Consiglio A, Lein ES, Jessberger S, Lansford H, Dearie AR, Gage FH. Wnt signalling regulates adult hippocampal neurogenesis. Nature 2005; 437(7063): 1370–1375

[26] Zhao J, Kim KA, Abo A. Tipping the balance: modulating the Wnt pathway for tissue repair. Trends Biotechnol 2009; 27(3): 131–136

[27] Katoh M, Katoh M. STAT3-induced WNT5A signaling loop in embryonic stem cells, adult normal tissues, chronic persistent inflammation, rheumatoid arthritis and cancer (Review). Int J Mol Med 2007; 19(2): 273–278

[28] Kowanetz M, Ferrara N. Vascular endothelial growth factor signaling pathways: therapeutic perspective. Clin Cancer Res 2006; 12(17): 5018–5022

[29] Liu G, Rondinone CM. JNK: bridging the insulin signaling and inflammatory pathway. Curr Opin Investig Drugs 2005; 6(10): 979–987

[30] Araújo EP, De Souza CT, Ueno M, Cintra DE, Bertolo MB, Carvalheira JB, Saad MJ, Velloso LA. Infliximab restores glucose homeostasis in an animal model of diet-induced obesity and diabetes. Endocrinology 2007; 148(12): 5991–5997

[31] Page TH, Smolinska M, Gillespie J, Urbaniak AM, Foxwell BM. Tyrosine kinases and inflammatory signalling. Curr Mol Med 2009; 9(1): 69–85

[32] Lee YH, Choi SJ, Ji JD, Song GG. Serum creatine kinase in patients with rheumatic diseases. Clin Rheumatol 2000; 19(4): 296–300

[33] Suderman M, Hallett M. Tools for visually exploring biological networks. Bioinformatics 2007; 23(20): 2651–2659

[34] Forrest CM, Harman G, McMillan RB, Stoy N, Stone TW, Darlington LG. Modulation of cytokine release by purine receptors in patients with rheumatoid arthritis. Clin Exp Rheumatol 2005; 23(1): 89–92

[35] Morgan SL, Oster RA, Lee JY, Alarcón GS, Baggott JE. The effect of folic acid and folinic acid supplements on purine metabolism in methotrexate-treated rheumatoid arthritis. Arthritis Rheum 2004; 50(10): 3104–3111

Acknowledgements:

This research is supported in part by the projects from the Ministry of Sciences and Technology (International Collaboration Project) (No. 2006DFA31731), the National Science Foundation of China (No. 90709007 and No. 30825047), and by the E-institutes of Shanghai Municipal Education Commission (No. E03008).

RIGHTS & PERMISSIONS

2014 Higher Education Press and Springer-Verlag Berlin Heidelberg