Yedluri Rupasree1, Shaik Mohammad Naushad2, Ravi Varshaa2, Govindaraj Swathika Mahalakshmi2, Konda Kumaraswami3, Liza Rajasekhar3, Vijay Kumar Kutala4. 1. Department of Clinical Pharmacology and Therapeutics, Nizam's Institute of Medical Sciences, Hyderabad, 500082, India. 2. School of Chemical and Biotechnology, SASTRA University, Tirumalaisamudram, Thanjavur, 613401, India. 3. Department of Rheumatology, Nizam's Institute of Medical Sciences, Panjagutta, Hyderabad, 500082, India. 4. Department of Clinical Pharmacology and Therapeutics, Nizam's Institute of Medical Sciences, Hyderabad, 500082, India. vijaykutala@gmail.com.
Abstract
INTRODUCTION: In view of our previous studies showing an independent association of genetic polymorphisms in folate, xenobiotic, and toll-like receptor (TLR) pathways with the risk for systemic lupus erythematosus (SLE), we have developed three statistical models to delineate complex gene-gene interactions between folate, xenobiotic, TLR, and signal transducer and activator of transcription 4 (STAT4) signaling pathways in association with the molecular pathophysiology of SLE. METHODS: We developed additive, multifactor dimensionality reduction (MDR), and artificial neural network (ANN) models. RESULTS: The additive model, although the simplest, suggested a moderate predictability of 30 polymorphisms of these four pathways (area under the curve [AUC] 0.66). MDR analysis revealed significant gene-gene interactions among glutathione-S-transferase (GST)T1 and STAT4 (rs3821236 and rs7574865) polymorphisms, which account for moderate predictability of SLE. The MDR model for specific auto-antibodies revealed the importance of gene-gene interactions among cytochrome P450, family1, subfamily A, polypeptide 1 (CYP1A1) m1, catechol-O-methyltransferase (COMT) H108L, solute carrier family 19 (folate transporter), member 1 (SLC19A1) G80A, estrogen receptor 1 (ESR1), TLR5, 5-methyltetrahydrofolate-homocysteine methyltransferase reductase (MTRR), thymidylate synthase (TYMS). and STAT4 polymorphisms. The ANN model for disease prediction showed reasonably good predictability of SLE risk with 30 polymorphisms (AUC 0.76). These polymorphisms contribute towards the production of SSB and anti-dsDNA antibodies to the extent of 48 and 40%, respectively, while their contribution for the production of antiRNP, SSA, and anti-cardiolipin antibodies varies between 20 and 30%. CONCLUSION: The current study highlighted the importance of genetic polymorphisms in folate, xenobiotic, TLR, and STAT4 signaling pathways as moderate predictors of SLE risk and delineates the molecular pathophysiology associated with these single nucleotide polymorphisms (SNPs) by demonstrating their association with specific auto-antibody production.
INTRODUCTION: In view of our previous studies showing an independent association of genetic polymorphisms in folate, xenobiotic, and toll-like receptor (TLR) pathways with the risk for systemic lupus erythematosus (SLE), we have developed three statistical models to delineate complex gene-gene interactions between folate, xenobiotic, TLR, and signal transducer and activator of transcription 4 (STAT4) signaling pathways in association with the molecular pathophysiology of SLE. METHODS: We developed additive, multifactor dimensionality reduction (MDR), and artificial neural network (ANN) models. RESULTS: The additive model, although the simplest, suggested a moderate predictability of 30 polymorphisms of these four pathways (area under the curve [AUC] 0.66). MDR analysis revealed significant gene-gene interactions among glutathione-S-transferase (GST)T1 and STAT4 (rs3821236 and rs7574865) polymorphisms, which account for moderate predictability of SLE. The MDR model for specific auto-antibodies revealed the importance of gene-gene interactions among cytochrome P450, family1, subfamily A, polypeptide 1 (CYP1A1) m1, catechol-O-methyltransferase (COMT) H108L, solute carrier family 19 (folate transporter), member 1 (SLC19A1) G80A, estrogen receptor 1 (ESR1), TLR5, 5-methyltetrahydrofolate-homocysteine methyltransferase reductase (MTRR), thymidylate synthase (TYMS). and STAT4 polymorphisms. The ANN model for disease prediction showed reasonably good predictability of SLE risk with 30 polymorphisms (AUC 0.76). These polymorphisms contribute towards the production of SSB and anti-dsDNA antibodies to the extent of 48 and 40%, respectively, while their contribution for the production of antiRNP, SSA, and anti-cardiolipin antibodies varies between 20 and 30%. CONCLUSION: The current study highlighted the importance of genetic polymorphisms in folate, xenobiotic, TLR, and STAT4 signaling pathways as moderate predictors of SLE risk and delineates the molecular pathophysiology associated with these single nucleotide polymorphisms (SNPs) by demonstrating their association with specific auto-antibody production.
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